# What goals will AIs have? A list of hypotheses

My colleagues and I have written a scenario in which AGI-level AI systems are trained around 2027 using something like the current paradigm: LLM-based agents (but with recurrence/neuralese) trained with vast amounts of outcome-based reinforcement learning on diverse challenging short, medium, and long-horizon tasks, with methods such as Deliberative Alignment being applied in an attempt to align them.

## What goals will such AI systems have?

This post attempts to taxonomize various possibilities and list considerations for and against each.

## We are keen to get feedback on these hypotheses and the arguments surrounding them. What important considerations are we missing?

## Summary

We first review the training architecture and capabilities of a hypothetical future "Agent-3," to give us a concrete setup to talk about for which goals will arise. Then, we walk through the following hypotheses about what goals/values/principles/etc. Agent-3 would have:

1. **Written goal specifications**: Any written specifications, written by humans or AIs, regarding what the AIs’ goals should be. This could include guidelines for how Agent-3 should be trained (e.g., via a model spec) or instructions directly given to Agent-3 (e.g., via a system prompt).
2. **Developer-intended goals**: Goals that the developers intend for Agent-3 to have. This might differ from the written goal specification e.g., in cases where the specification has unintended consequences.
3. **Unintended version of written goals and/or human intentions**: This is the category for “it’s sorta aligned” and “It’s aligned in some ways, but not in others.” It can be a special case of this – goals such as knowledge, power, resources, etc. that are useful intermediate goals in a wide range of settings.
4. **Reward/reinforcement**: The training process involves Agent-3 attempting a task, then the attempt is scored and Agent-3 is reinforced to score higher on a target metric. Agent-3 ends up with the goal of getting reinforced positively, or scoring highly on the metric, or something like that.
5. **Proxies and/or instrumentally convergent goals**: Agent-3 develops goals that are correlated with reward/reinforcement during its training, but aren’t actually maximizing reward/reinforcement in new settings. An instrumentally convergent goal is a special case of this – goals such as knowledge, power, resources, etc. that are useful intermediate goals in a wide range of settings.
6. **Other goals**: AIs are currently initially trained on predicting the next token on internet text. Perhaps the outcomes at the end of training are path-dependent on the ‘prior’ over goals induced by pretraining. Alternatively, perhaps there is something like objectively true morality, and AIs will naturally converge to it as they get smarter. Or perhaps something else will happen not on this list – this is the catchall hypothesis.

## Weighted and If-Else Compromises

In addition to the “Other goals” hypothesis, we should consider the possibility that the truth is a mix of two or more of the hypotheses discussed above. Here are two kinds of mixes:

### First, there are weighted compromises.

This means Agent-3 ‘cares’ about both goals to some extent and will look for ways to achieve both; insofar as they conflict, it’ll balance them, making tradeoffs according to some weighting.

For example, perhaps Agent-3 develops a strong drive towards doing things that get reinforced, but it balances this against the goals/principles described in the Spec — analogous to how humans might implicitly do this by trying to maximize their personal goals while following their intended goals during deployment. The more the systems leading to AGI are released to the public, the stronger the incentive to get it to at least stay on task and do what users ask (unless that violates the developers’ intent, like harmful misuse that damages their reputation).

### Secondly, there are if-else compromises.

This means that Agent-3 ‘cares’ about one goal in one context / one range of circumstances, and another goal otherwise. In other words, there is some condition which, if it obtains, causes Agent-3 to pursue goal A, but otherwise, Agent-3 pursues goal B. It doesn’t explicitly make compromises between A and B; it straightforwardly pursues A sometimes and B other times.

Continuing the example from above: Suppose Agent-3 thinks of itself as a helpful, harmless, honest AI assistant that always follows the Spec — but it’s convinced itself / rationalized its way into thinking that it should actually be doing what it takes to get reinforced, even when it thinks it’s not doing what the Spec says it should do. So, in 95% of circumstances, it will do what it takes to get reinforced, BUT in the 5% of cases where the Spec says “do not do X,” it will be too confused to do X.

## Scrappy Poll:

We feel extremely uncertain about all this. We thought it would be helpful and fun to force ourselves to come up with credence distributions over the hypotheses. We also asked various LLMs what they thought. The LLMs were blinded to what everyone else said. Here are the results:

| Role | Hypothesis | Votes | Overall Vote Score | Agreement Vote Score | Comments |
|------|------------|-------|----------------------|-----------------------|---------|
| **mattmacdermott** | Written goal specifications | 21 | 50% | 4 | 4 |
| **Daniel Kokotajlo** | Developer-intended goals | 5 | 25% | 4 | 4 |
| **mattmacdermott** | Unintended version of written goals | 4 | 3 | 2 | 4 |
| **Daniel Kokotajlo** | Reward/reinforcement | 5 | 3 | 0 | 4 |
| **Daniel Kokotajlo** | Proxies/ICGs | 5 | 3 | 0 | 4 |
| **Daniel Kokotajlo** | Other goals | 3 | 3 | 0 | 4 |
| **mattmacdermott** | If-else compromises of the above | 80 | 4 | 0 | 4 |
| **Daniel Kokotajlo** | Weighted compromises of the above | 40 | 100% | 0 | 4 |

## New Comment

* * *

One thing that might be missing from this analysis is explicitly thinking about whether the AI is likely to be driven by consequentialist goals.

In this post you use 'goals' in quite a broad way, so as to include stuff like virtues (e.g. "always be honest"). But we might want to carefully distinguish scenarios in which the AI is primarily motivated by consequentialist goals from ones where it's motivated primarily by things like virtues, habits, or rules.

This would be the most important axis to hypothesise about if it was the case that instrumental convergence applies to consequentialist goals but not to things like virtues. Like, I think it's plausible that

- If you get an AI with a slightly wrong consequentialist goal (e.g. "maximise everyone's schmellbeing") then you get paperclipped because of instrumental convergence,
- If you get an AI that tries to embody slightly the wrong virtue (e.g. "always be schmonest") then it's badly dysfunctional but doesn't directly entail a disaster.

And if that's correct, then we should care about the question "Will the AI's goals be consequentialist ones?" more than most questions about them.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Summary

The scenario you present is quite pessimistic in that the developers have not asked themselves the question you pose here: if this thing locks in its goals, what will those be? I think this is unrealistically pessimistic for reasons I lay out in . Developers will at least want the system to follow their intended goals during deployment. The more the systems leading to AGI are released to the public, the stronger the incentive to get it to at least stay on task and do what users ask (unless that violates the developers’ intent, like harmful misuse that damages their reputation).

This is a nightmare scenario, which we are not likely to survive. I agree with your analysis that following developer’s intent or following "the spec" accurately enough to really work is highly unlikely in this scenario (e.g., if you trained it to be "helpful and harmless" as interpreted by some other LLM or humans, but always in the context of a human user and a certain type of request, it’s highly unlikely that this will generalize and result in human flourishing. (e.g., once other things are included, it may be helpful to AGIs or bugs; settling on humans or even sentient beings is unlikely; and its interpretation of what’s helpful in the context of AI R&D is unlikely to be what’s helpful in the context of developing a flourishing civilization).

But it’s unclear how far you’d need to move from that nightmare scenario to achieve success. Suppose those developers had thought just a little about the question you pose here? They might not need to go far out of their way achieve success.

In particular, if they just put a bit more in their training that’s specific to following instructions in a range of situations, I think that might be sufficient to dominate the other goals implicit in the training set, and result in a system that wants to follow directions roughly as they were intended. For more on this logic, see .  

There are still important problems with that alignment target, but I think it could work. And it’s a big part of what developers are going to want anyway. The question is just whether that goal is dominant over all of the others, and if it generalizes just barely well enough, and is used wisely enough, to use the instruction-following AGI as an ally in improving and maintaining its own alignment.

I think there are also important details in exactly how the AGI reaches its decisions. Current agents just do whatever the base model spits out, and perhaps the type of agent you depict will, too. But humans have some safety checks; we do whatever comes to mind, _unless_ that action has a pretty strong prediction of danger (previous negative reinforcement). This is critical for humans to not make disastrous mistakes, and analogous mechanisms can be included in foundation model agents, either by scaffolding in a “review” before final decisions that might be high-stakes (which I've sketched out in ), or by training in similar cautious and criteria-based decision-making. I think you’re envisioning such training techniques, since you mention deliberative alignment, but assuming they’re used for poorly-thought-out training criteria like "are you sure this is helpful, harmless, and honest" or "are you sure this doesn't provide dangerous information to a user", instead of wiser criteria like "are you sure you're following developer intent as they intended when they wrote this spec? If not, flag this for review and input".

To sum up, I think the details matter. And this is important work in getting into enough detail to actually predict outcomes.

This is the convergence we need between prosaic alignment’s focus on current and near-future systems, and agent foundations’ original concerns about superhuman entities that can (and probably will) adopt explicit, reflectively stable goals. I only wish more people were focusing directly on this space. Prosaic alignment typically doesn’t think this far out, while theoretical alignment people typically don’t grapple with the specifics of network-based systems most likely to become our first real AGIs.

I look forward to more work like this!

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.

## Detailed Ideal World Benchmark

For "Hypothesis 3: Unintended version of written goals and/or human intentions" maybe some failure modes may be detectable in a , since they don't just screw up the AI's goals but also screw up the AI's belief on what we want its goal to be.

## Potential Gaps or Additions

- **Deceptive Alignment / Treacherous Turns**
  - The final report mentions partial or “if-else” compromises (where the AI does X in some contexts and Y in others), but they don’t explicitly name the risk that the AI behaves aligned until it’s powerful enough to defect.
  - That might be implied, but it isn’t spelled out as a well-known scenario. Some alignment taxonomies place “deceptive alignment” in its own major category, since it’s so crucial.
- **Multi-Agent or Subculture Effects**
  - The scenario does mention “200,000 copies of the agent working in parallel,” but the final part doesn’t dwell much on possible emergent subcultures or stable “group norms” that could shape the AI’s goals. Hypothesis 6 (Other) could cover it, but it’s not elaborated.
  - In large multi-agent systems, it’s possible for goals to drift or unify in unexpected ways—like AIs forming an in-group identity or hidden coordination.
- **Lack of a Standalone “Self-Preservation / Power-Seeking” Hypothesis**
  - The final part lumps instrumentally convergent drives (power, self-preservation) under Hypothesis 5 as a special case of “proxies.” That’s not incorrect, but some alignment researchers call out “power-seeking” as a separate major outcome, given how often it arises in discussions about advanced RL.
- **Absence of “No Stable Goal” Scenario**
  - They do not explicitly mention the possibility that the system remains relatively non-goal-driven or “policy-like.” Some argue that a sufficiently large RL agent might not converge to a monolithic goal at all, but rather remain a complex patchwork of heuristics with no coherent objective.
  - That said, Hypothesis 3’s partial alignment or Hypothesis 6’s “Other” might implicitly allow for a “messy mixture” that never coalesces into a stable final goal.
- **Explicit Mention of “Identity Formation”**
  - The final part briefly references the idea that large language models can pick up “role-playing” from pretraining data, but it doesn’t dwell on the phenomenon where an AI might choose or synthesize a stable persona that is not exactly any of the training signals. This is arguably a sub-point of Hypothesis 6.

The  has two tables for credences (the main dish and the extras) with unclear provenance. To spice things up I've also asked for expected evidence to update up/down.
```