Consilience for Verifier-Free Test-Time Scaling

ID: b8c369c6
Original: https://arxiv.org/abs/2608.09898
PDF: https://arxiv.org/pdf/2608.09898v1
Authors: Lecheng Kong, Like Hui, Haitao Mao, Jun Huan
Published: 2026-08-10
Updated: 2026-08-10
arXiv Categories: cs.CL, cs.LG
AAIF Category: models
Source Type: paper
Tags: test-time-scaling, reasoning, confidence, llm
Quality Score: 4/5
Fetched At: 2026-08-12T04:29:18.480369+00:00

Chinese Guide

论文揭示 confidence-based 无验证器测试时扩展（VF-TTS）在复杂任务上灾难性失效：均匀的高置信度往往意味着没有真正探索，反而偏爱"自信的错误答案"，据此提出 consilience 框架通过组合式指标显式评估置信度的时间不对称性，主动惩罚早期高置信而强制要求终局确定性，在研究生级数学与自由格式代码生成上均显著优于现有 baseline

Why It Matters

论文揭示 confidence-based 无验证器测试时扩展（VF-TTS）在复杂任务上灾难性失效：均匀的高置信度往往意味着没有真正探索，反而偏爱"自信的错误答案"，据此提出 consilience 框架通过组合式指标显式评估置信度的时间不对称性...

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Key Metadata

Paper title: Consilience for Verifier-Free Test-Time Scaling
Authors: Lecheng Kong, Like Hui, Haitao Mao, Jun Huan
arXiv: https://arxiv.org/abs/2608.09898
PDF: https://arxiv.org/pdf/2608.09898v1
Published: 2026-08-10
Updated: 2026-08-10
arXiv categories: cs.CL, cs.LG
Related AAIF tags: test-time-scaling, reasoning, confidence, llm

English Abstract

Test-time scaling often uses an external verifier, such as compilers and test cases in coding or trained value functions in robotics applications, to obtain high-quality rollouts. Verifier-free test-time scaling (or VF-TTS) is gaining extensive attention as a mechanism to enhance Large Language Model (LLM) reasoning, primarily because we do not have access to such high-quality verifiers in many real-world applications. Among existing VF-TTS methods, confidence-based VF-TTS methods, which compute and rank rollouts solely by confidence, are particularly promising. Such methods introduce near-zero overhead for sample evaluation and require minimal access to internal model states, making the methods highly flexible across models and tasks. In this paper, we demonstrate a critical limitation of existing confidence-based VF-TTS methods by showing that such methods catastrophically break down on complex tasks. We observe a very interesting phenomenon: uniformly high confidence frequently indicates a failure to explore, favoring confidently wrong answers. To address this, our core insight is that robust cognitive search requires a specific confidence trajectory pattern: such methods perform exploratory branching at the beginning, as manifested by low initial confidence, and converge to a high final confidence solution. To implement this insight, we introduce consilience, a novel selection framework that explicitly evaluates the temporal asymmetry of confidence in reasoning. We operationalize this via a combinatorial metric that actively penalizes high initial confidence while strictly demanding final certainty. Extensive experiments covering both graduate-level mathematics problems and free-form code generation demonstrate that consilience effectively outperforms existing baselines, validating our novel perspective on completion confidence.

English Summary

Demonstrates a critical limitation of confidence-based verifier-free test-time scaling (VF-TTS): uniformly high confidence often signals a failure to explore rather than correctness, biasing selection toward confidently wrong answers. The proposed consilience framework operationalizes a "specific confidence trajectory pattern" exploratory branching (low initial confidence) followed by converging certainty (high final confidence) via a combinatorial metric that penalizes high initial confidence while strictly demanding final certainty. Across graduate-level mathematics and free-form code generation, consilience outperforms existing confidence-based VF-TTS baselines.

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