CLUE: Non-parametric Verification from Experience via Hidden-State
  Clustering
Assessing the quality of Large Language Model (LLM) outputs presents a
critical challenge. Previous methods either rely on text-level information
(e.g., reward models, majority voting), which can overfit to superficial cues,
or on calibrated confidence from token probabilities, which would fail on
less-calibrated models. Yet both of these signals are, in fact, partial
projections of a richer source of information: the model's internal hidden
states. Early layers, closer to token embeddings, preserve semantic and lexical
features that underpin text-based judgments, while later layers increasingly
align with output logits, embedding confidence-related information. This paper
explores hidden states directly as a unified foundation for verification. We
show that the correctness of a solution is encoded as a geometrically separable
signature within the trajectory of hidden activations. To validate this, we
present Clue (Clustering and Experience-based Verification), a deliberately
minimalist, non-parametric verifier. With no trainable parameters, CLUE only
summarizes each reasoning trace by an hidden state delta and classifies
correctness via nearest-centroid distance to ``success'' and ``failure''
clusters formed from past experience. The simplicity of this method highlights
the strength of the underlying signal. Empirically, CLUE consistently
outperforms LLM-as-a-judge baselines and matches or exceeds modern
confidence-based methods in reranking candidates, improving both top-1 and
majority-vote accuracy across AIME 24/25 and GPQA. As a highlight, on AIME 24
with a 1.5B model, CLUE boosts accuracy from 56.7% (majority@64) to 70.0%
(top-maj@16).
68df27e9df49fb0df1e03bb7
Zhenwen Liang
claimed_verified
2025-10-03T12:33:10.962Z
62ffa3f8311cad266f9af236
/avatars/203dac40bc546ee25a01d8715a4b3049.svg
Zhenwen Liang
user
invokerliang
68df27e9df49fb0df1e03bb8
Ruosen Li
68df27e9df49fb0df1e03bb9
Yujun Zhou
68df27e9df49fb0df1e03bba
Linfeng Song
68df27e9df49fb0df1e03bbb
Dian Yu
68df27e9df49fb0df1e03bbc
Xinya Du
68df27e9df49fb0df1e03bbd
Haitao Mi
68df27e9df49fb0df1e03bbe
Dong Yu
CLUE: Non-parametric Verification from Experience via Hidden-State
  Clustering
Assessing the quality of Large Language Model (LLM) outputs presents a
critical challenge. Previous methods either rely on text-level information
(e.g., reward models, majority voting), which can overfit to superficial cues,
or on calibrated confidence from token probabilities, which would fail on
less-calibrated models. Yet both of these signals are, in fact, partial
projections of a richer source of information: the model's internal hidden
states. Early layers, closer to token embeddings, preserve semantic and lexical
features that underpin text-based judgments, while later layers increasingly
align with output logits, embedding confidence-related information. This paper
explores hidden states directly as a unified foundation for verification. We
show that the correctness of a solution is encoded as a geometrically separable
signature within the trajectory of hidden activations. To validate this, we
present Clue (Clustering and Experience-based Verification), a deliberately
minimalist, non-parametric verifier. With no trainable parameters, CLUE only
summarizes each reasoning trace by an hidden state delta and classifies
correctness via nearest-centroid distance to ``success'' and ``failure''
clusters formed from past experience. The simplicity of this method highlights
the strength of the underlying signal. Empirically, CLUE consistently
outperforms LLM-as-a-judge baselines and matches or exceeds modern
confidence-based methods in reranking candidates, improving both top-1 and
majority-vote accuracy across AIME 24/25 and GPQA. As a highlight, on AIME 24
with a 1.5B model, CLUE boosts accuracy from 56.7% (majority@64) to 70.0%
(top-maj@16).
Hidden states in Large Language Models encode correctness as a separable signature, enabling a minimalist verifier (CLUE) to outperform text-level and confidence-based methods in reranking and accuracy.
Large Language Model hidden states token embeddings semantic features lexical features output logits confidence-related information Clue Clustering and Experience-based Verification nearest-centroid distance AIME GPQA