When to Trust the Cheap Check: Weak and Strong Verification for Reasoning

• URL: http://arxiv.org/abs/2602.17633v1
• Date: Thu, 19 Feb 2026 18:47:38 GMT
• Title: When to Trust the Cheap Check: Weak and Strong Verification for Reasoning
• Authors: Shayan Kiyani, Sima Noorani, George Pappas, Hamed Hassani,
• Abstract summary: We formalize the tension between strong and weak verification.<n>We show that optimal policies admit a two-threshold structure and that calibration and sharpness govern the value of weak verifiers.<n>We develop an online algorithm that provably controls acceptance and rejection errors without assumptions on the query stream, the language model, or the weak verifier.
• Score: 26.38833436936642
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reasoning with LLMs increasingly unfolds inside a broader verification loop. Internally, systems use cheap checks, such as self-consistency or proxy rewards, which we call weak verification. Externally, users inspect outputs and steer the model through feedback until results are trustworthy, which we call strong verification. These signals differ sharply in cost and reliability: strong verification can establish trust but is resource-intensive, while weak verification is fast and scalable but noisy and imperfect. We formalize this tension through weak--strong verification policies, which decide when to accept or reject based on weak verification and when to defer to strong verification. We introduce metrics capturing incorrect acceptance, incorrect rejection, and strong-verification frequency. Over population, we show that optimal policies admit a two-threshold structure and that calibration and sharpness govern the value of weak verifiers. Building on this, we develop an online algorithm that provably controls acceptance and rejection errors without assumptions on the query stream, the language model, or the weak verifier.

Related papers

• IMMACULATE: A Practical LLM Auditing Framework via Verifiable Computation [49.796717294455796]
  We present IMMACULATE, a practical auditing framework that detects economically motivated deviations.<n>IMMACULATE selectively audits a small fraction of requests using verifiable computation, achieving strong detection guarantees while amortizing cryptographic overhead.
  arXiv  Detail & Related papers  (2026-02-26T07:21:02Z)
• Preventing the Collapse of Peer Review Requires Verification-First AI [49.995126139461085]
  We propose truth-coupling, i.e. how tightly venue scores track latent scientific truth.<n>We formalize two forces that drive a phase transition toward proxy-sovereign evaluation.
  arXiv  Detail & Related papers  (2026-01-23T17:17:32Z)
• MARS: Unleashing the Power of Speculative Decoding via Margin-Aware Verification [7.935725883885573]
  Speculative Decoding (SD) accelerates autoregressive large language model (LLM) inference by decoupling generation and verification.<n>We propose Margin-Aware Speculative Verification, a training-free and domain-agnostic verification strategy that adapts to the target model's local decisiveness.<n>Our method conditions verification on decision stability measured directly from the target logits and relaxes rejection only when strict verification provides minimal benefit.
  arXiv  Detail & Related papers  (2026-01-21T22:03:06Z)
• All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection [67.89888669159899]
  RFC Bench is a benchmark for evaluating large language models on financial misinformation under realistic news.<n>The benchmark defines two complementary tasks: reference free misinformation detection and comparison based diagnosis.
  arXiv  Detail & Related papers  (2026-01-07T18:18:28Z)
• Fact-Checking with Large Language Models via Probabilistic Certainty and Consistency [7.806516365113592]
  Large language models (LLMs) are increasingly used in applications requiring factual accuracy.<n>While fact-checking can mitigate these errors, existing methods typically retrieve external evidence indiscriminately.<n>We introduce Probabilistic Certainty and Consistency (PCC), a framework that estimates factual confidence.
  arXiv  Detail & Related papers  (2026-01-05T21:57:41Z)
• Critical or Compliant? The Double-Edged Sword of Reasoning in Chain-of-Thought Explanations [60.27156500679296]
  We study the role of Chain-of-Thought (CoT) explanations in moral scenarios by systematically perturbing reasoning chains and manipulating delivery tones.<n>Our findings reveal two key effects: (1) users often trust with outcome agreement, sustaining reliance even when reasoning is flawed.<n>These results highlight how CoT explanations can simultaneously clarify and mislead, underscoring the need for NLP systems to provide explanations that encourage scrutiny and critical thinking rather than blind trust.
  arXiv  Detail & Related papers  (2025-11-15T02:38:49Z)
• Validating Solidity Code Defects using Symbolic and Concrete Execution powered by Large Language Models [0.0]
  This paper introduces a novel detection pipeline that integrates custom Slither-based detectors, Large Language Models (LLMs), Kontrol, and Forge.<n>Our approach is designed to reliably detect defects and generate proofs.
  arXiv  Detail & Related papers  (2025-09-16T12:46:11Z)
• Say It Another Way: Auditing LLMs with a User-Grounded Automated Paraphrasing Framework [17.91981142492207]
  We introduce AUGMENT, a framework for generating controlled paraphrases grounded in user behaviors.<n>AUGMENT leverages linguistically informed rules and enforces quality through checks on instruction adherence, semantic similarity, and realism.<n>Case studies show that controlled paraphrases uncover systematic weaknesses that remain obscured under unconstrained variation.
  arXiv  Detail & Related papers  (2025-05-06T14:17:30Z)
• TrustLoRA: Low-Rank Adaptation for Failure Detection under Out-of-distribution Data [62.22804234013273]
  We propose a simple failure detection framework to unify and facilitate classification with rejection under both covariate and semantic shifts.<n>Our key insight is that by separating and consolidating failure-specific reliability knowledge with low-rank adapters, we can enhance the failure detection ability effectively and flexibly.
  arXiv  Detail & Related papers  (2025-04-20T09:20:55Z)
• OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs [59.836774258359945]
  OpenFactCheck is a framework for building customized automatic fact-checking systems.<n>It allows users to easily customize an automatic fact-checker and verify the factual correctness of documents and claims.<n>CheckerEVAL is a solution for gauging the reliability of automatic fact-checkers' verification results using human-annotated datasets.
  arXiv  Detail & Related papers  (2024-05-09T07:15:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.