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Claim-Level Reliability Assessment for Efficient Te... | AI Research

Key Takeaways

  • Claim-Level Reliability Assessment (CLR) is a training-free framework designed to improve the reasoning accuracy of large language models (LLMs) by reallocat...
  • Furthermore, recognizing the inherent difficulty of generating entirely correct solutions under fixed model capabilities, CLR shifts the focus to semantic falsification.
  • This approach exploits a fundamental asymmetry between solution construction and claim refutation.
  • Constructing a valid solution requires a flawless reasoning path, whereas refuting an incorrect claim requires identifying only a single decisive flaw.
  • This targeted search for negative evidence systematically compresses the survival space of high-confidence incorrect traces, effectively suppressing erroneous consensus via nonlinear reliability scoring.
Paper AbstractExpand

We propose claim-level falsification as a principle for test-time scaling and instantiate it through Claim-Level Reliability Assessment (CLR), a training-free framework that reallocates test-time compute from additional solution sampling to targeted verification. Since whole-trace evaluation often obscures decisive errors due to signal dilution from routine tokens, CLR condenses each reasoning trace into a compact set of decision-critical claims, thereby isolating its logical anchors. Furthermore, recognizing the inherent difficulty of generating entirely correct solutions under fixed model capabilities, CLR shifts the focus to semantic falsification. This approach exploits a fundamental asymmetry between solution construction and claim refutation. Constructing a valid solution requires a flawless reasoning path, whereas refuting an incorrect claim requires identifying only a single decisive flaw. This targeted search for negative evidence systematically compresses the survival space of high-confidence incorrect traces, effectively suppressing erroneous consensus via nonlinear reliability scoring. Across four LLMs and four reasoning benchmarks under matched budgets, CLR generally improves upon pass@1 and self-consistency. On GPT-OSS-20B/CMIMC25, for instance, CLR exceeds pass@1 by 27.15 percentage-points and raises self-consistency accuracy from 77.50\% to 82.19\% with 37.0\% fewer tokens.

Claim-Level Reliability Assessment (CLR) is a training-free framework designed to improve the reasoning accuracy of large language models (LLMs) by reallocating test-time compute from generating more solutions to verifying the logical consistency of existing ones. Instead of relying on whole-trace evaluation, which the authors argue is often diluted by routine tokens, CLR identifies and tests the specific logical anchors that determine whether a solution is correct.

The Problem with Whole-Trace Evaluation

Current test-time scaling methods often struggle because they treat statistical confidence as a proxy for logical reliability. The authors note that high statistical confidence does not guarantee that a reasoning trace is sound. Furthermore, evaluating an entire reasoning trace is difficult because most tokens are routine, creating a "weakly discriminative background" that hides fatal errors. While step-by-step verification can catch these errors, it is computationally expensive and often requires specialized supervision or separate verifiers.

How CLR Works

CLR operates in a two-stage pipeline that treats reasoning as a falsifiable process:

  1. Claim Extraction: For every sampled reasoning trace, the model generates a compact set of "decision-critical claims"—the specific logical steps or constraints that must be true for the final answer to be correct. 2. Falsification-Based Verification: The model is prompted to search for a single decisive flaw in these claims. The authors exploit an asymmetry in reasoning: while constructing a perfect solution is difficult, refuting an incorrect one only requires finding one error. 3. Nonlinear Reliability Scoring: The framework assigns a reliability score to each trace based on how many claims survive this falsification check. It uses a nonlinear penalty, meaning that if a trace has multiple decision-critical claims, the failure of even one significantly reduces the trace's influence on the final consensus.

Performance and Efficiency

The authors evaluated CLR across four LLMs (Gemma-4-12B-it, GPT-OSS-20B, GPT-OSS-120B, and Qwen3.5-27B) and four reasoning benchmarks. By reallocating compute, CLR often outperformed standard self-consistency methods. For example, on the CMIMC25 benchmark, CLR improved the accuracy of GPT-OSS-20B from 77.50% to 82.19% while using 37.0% fewer tokens. In other cases, such as with Gemma-4-12B-it on HMMT25, CLR achieved significant accuracy gains (from 76.67% to 88.75%) under a matched-request budget.

Limitations and Considerations

The authors clarify that CLR is not a guarantee of correctness, but rather a method to improve the reliability of answer aggregation. Because the framework only reweights existing candidates, it cannot recover a correct answer if that answer was not generated during the initial sampling stage. Additionally, the effectiveness of CLR can vary based on the base model's capabilities; for models that are already highly accurate, such as Qwen3.5-27B, the performance gains are more modest because there is less "headroom" for improvement. The authors also note that the choice of the number of claims (M) involves a trade-off between semantic coverage and computational cost.

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