Beyond Context Limits: Subconscious Threads for Long-Horizon Reasoning
To break the context limits of large language models (LLMs) that bottleneck
reasoning accuracy and efficiency, we propose the Thread Inference Model (TIM),
a family of LLMs trained for recursive and decompositional problem solving, and
TIMRUN, an inference runtime enabling long-horizon structured reasoning beyond
context limits. Together, TIM hosted on TIMRUN supports virtually unlimited
working memory and multi-hop tool calls within a single language model
inference, overcoming output limits, positional-embedding constraints, and
GPU-memory bottlenecks. Performance is achieved by modeling natural language as
reasoning trees measured by both length and depth instead of linear sequences.
The reasoning trees consist of tasks with thoughts, recursive subtasks, and
conclusions based on the concept we proposed in Schroeder et al, 2025. During
generation, we maintain a working memory that retains only the key-value states
of the most relevant context tokens, selected by a rule-based subtask-pruning
mechanism, enabling reuse of positional embeddings and GPU memory pages
throughout reasoning. Experimental results show that our system sustains high
inference throughput, even when manipulating up to 90% of the KV cache in GPU
memory. It also delivers accurate reasoning on mathematical tasks and handles
information retrieval challenges that require long-horizon reasoning and
multi-hop tool use.
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Tina Li
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Ai Vy Ngo
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Philip Schroeder
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Assaf Ben-Kish
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Assaf Ben-Kish
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Jack O'Brien
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Jack O'Brien
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James Glass
Beyond Context Limits: Subconscious Threads for Long-Horizon Reasoning
To break the context limits of large language models (LLMs) that bottleneck
reasoning accuracy and efficiency, we propose the Thread Inference Model (TIM),
a family of LLMs trained for recursive and decompositional problem solving, and
TIMRUN, an inference runtime enabling long-horizon structured reasoning beyond
context limits. Together, TIM hosted on TIMRUN supports virtually unlimited
working memory and multi-hop tool calls within a single language model
inference, overcoming output limits, positional-embedding constraints, and
GPU-memory bottlenecks. Performance is achieved by modeling natural language as
reasoning trees measured by both length and depth instead of linear sequences.
The reasoning trees consist of tasks with thoughts, recursive subtasks, and
conclusions based on the concept we proposed in Schroeder et al, 2025. During
generation, we maintain a working memory that retains only the key-value states
of the most relevant context tokens, selected by a rule-based subtask-pruning
mechanism, enabling reuse of positional embeddings and GPU memory pages
throughout reasoning. Experimental results show that our system sustains high
inference throughput, even when manipulating up to 90% of the KV cache in GPU
memory. It also delivers accurate reasoning on mathematical tasks and handles
information retrieval challenges that require long-horizon reasoning and
multi-hop tool use.
The Thread Inference Model (TIM) and TIMRUN enable long-horizon reasoning in large language models by using reasoning trees and a rule-based subtask-pruning mechanism to manage working memory and GPU resources efficiently.
Thread Inference Model TIM TIMRUN recursive problem solving decompositional problem solving long-horizon structured reasoning reasoning trees subtask-pruning mechanism working memory key-value states positional embeddings GPU-memory bottlenecks inference throughput mathematical tasks information retrieval