VeryTrace: Verifying Reasoning Traces through Compilable Formalism and Structured Verification
- URL: http://arxiv.org/abs/2606.24124v1
- Date: Tue, 23 Jun 2026 04:15:48 GMT
- Title: VeryTrace: Verifying Reasoning Traces through Compilable Formalism and Structured Verification
- Abstract summary: Multi-step reasoning with Chain-of-Thought (CoT) prompting remains fragile.<n>This paper presents VeryTrace, a zero-shot verification-and-repair framework.
- Score: 3.5959223920188776
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multi-step reasoning with Chain-of-Thought (CoT) prompting remains fragile: logical errors or hallucinations in early steps silently propagate, producing confident but incorrect conclusions. This paper presents VeryTrace, a zero-shot verification-and-repair framework that formalizes natural-language reasoning traces into a structured, compilable representation. VeryTrace introduces a Domain-Specific Language (DSL) that (i) makes step dependencies explicit, (ii) mechanizes quantitative content as executable expressions, and (iii) structures semantic inferences via deduction schemas. Our hybrid verifier combines deterministic checks for computational correctness, dependency resolution, and constraint satisfaction with targeted LLM audits for non-mechanizable semantic judgments, enabling step-level error localization and repair. Across three diverse domains-competition mathematics (AIME 2025), robotics planning (LLM-BabyBench), and kinship reasoning (CLUTRR), VeryTrace improves accuracy over zero-shot baselines on state-of-the-art LLMs without requiring domain-specific training or in-context examples, demonstrating that formalized trace verification achieves both precision and generalization.
Related papers
- The Tell-Tale Trace: Detecting Reasoning Failures in LLMs Using Chain-of-Thought Dynamics [0.9668407688201359]
Chain-of-thought (CoT) reasoning improves large language model (LLM) performance.<n>Existing approaches that leverage verbalized CoTs to monitor reasoning correctness largely evaluate the semantic correctness or consistency of individual intermediate steps.<n>We show that capability failures can manifest as distributed, task-dependent changes in the structure of visible reasoning.
arXiv Detail & Related papers (2026-08-04T08:04:36Z) - PRISM: A Dual View of LLM Reasoning through Semantic Flow and Latent Computation [15.91920027845529]
PRISM (Probabilistic Reasoning Inspection through Semantic and Implicit Modeling) is a framework and diagnostic tool for jointly analyzing both levels.<n>It uncovers systematic patterns in the reasoning process, showing that failed trajectories are more likely to become trapped in unproductive verification loops.<n> PRISM makes these behaviors observable and analyzable rather than relying solely on final-task accuracy.
arXiv Detail & Related papers (2026-03-24T03:31:53Z) - VERGE: Formal Refinement and Guidance Engine for Verifiable LLM Reasoning [4.3414302048068745]
We present a neurosymbolic framework that combines Large Language Models with SMT solvers to produce verification-guided answers.<n>We introduce three key innovations: (1) multi-model consensus via formal semantic equivalence checking, (2) semantic routing that directs different claim types to appropriate verification strategies, and (3) precise logical error localization via Minimal Correction Subsets.<n>With the GPT-OSS-120B model, VERGE demonstrates an average performance uplift of 18.7% at convergence across a set of reasoning benchmarks compared to single-pass approaches.
arXiv Detail & Related papers (2026-01-27T20:59:11Z) - Veri-Sure: A Contract-Aware Multi-Agent Framework with Temporal Tracing and Formal Verification for Correct RTL Code Generation [4.723302382132762]
silicon-grade correctness remains bottlenecked by: (i) limited test coverage and reliability of simulation-centric evaluation, (ii) regressions and repair hallucinations, and (iii) semantic drift as intent is reinterpreted across agent handoffs.<n>We propose Veri-Sure, a multi-agent framework that establishes a design contract to align agents' intent and uses a patching mechanism guided by static dependency slicing to perform precise, localized repairs.
arXiv Detail & Related papers (2026-01-27T16:10:23Z) - Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement [66.51979814832332]
model formulates procedural graph extraction as a multi-round reasoning process with dedicated structural and logical refinement.<n>Experiments demonstrate that model achieves substantial improvements in both structural correctness and logical consistency over strong baselines.
arXiv Detail & Related papers (2026-01-27T04:00:48Z) - CoT-Seg: Rethinking Segmentation with Chain-of-Thought Reasoning and Self-Correction [50.67483317563736]
This paper aims to explore a system that can think step-by-step, look up information if needed, generate results, self-evaluate its own results, and refine the results.<n>We introduce CoT-Seg, a training-free framework that rethinks reasoning segmentation by combining chain-of-thought reasoning with self-correction.
arXiv Detail & Related papers (2026-01-24T11:41:54Z) - VIRO: Robust and Efficient Neuro-Symbolic Reasoning with Verification for Referring Expression Comprehension [51.76841625486355]
Referring Expression (REC) aims to localize the image region corresponding to a natural-language query.<n>Recent neuro-symbolic REC approaches leverage large language models (LLMs) and vision-language models (VLMs) to perform compositional reasoning.<n>We introduce VIRO, a neuro-symbolic framework that embeds lightweight operator-level verifiers within reasoning steps.
arXiv Detail & Related papers (2026-01-19T07:21:19Z) - Implicit Reasoning in Large Language Models: A Comprehensive Survey [67.53966514728383]
Large Language Models (LLMs) have demonstrated strong generalization across a wide range of tasks.<n>Recent studies have shifted attention from explicit chain-of-thought prompting toward implicit reasoning.<n>This survey introduces a taxonomy centered on execution paradigms, shifting the focus from representational forms to computational strategies.
arXiv Detail & Related papers (2025-09-02T14:16:02Z) - CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection [60.98964268961243]
We propose that guiding models to perform a systematic and comprehensive reasoning process allows models to execute much finer-grained and accurate entailment decisions.<n>We define a 3-step reasoning process, consisting of (i) claim decomposition, (ii) sub-claim attribution and entailment classification, and (iii) aggregated classification, showing that such guided reasoning indeed yields improved hallucination detection.
arXiv Detail & Related papers (2025-06-05T17:02:52Z) - Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study [40.143148197878354]
We introduce FineLogic, a fine-grained evaluation framework that assesses logical reasoning across three dimensions.<n>We study how different supervision formats in fine-tuning shape reasoning abilities.<n>We find a key trade-off: natural language supervision excels at generalization, whereas symbolic supervision is superior at instilling structurally sound, atomic reasoning steps.
arXiv Detail & Related papers (2025-06-05T09:34:12Z) - Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification [52.095460362197336]
Large language models (LLMs) struggle with consistent and accurate reasoning.
LLMs are trained primarily on correct solutions, reducing their ability to detect and learn from errors.
We propose a novel collaborative method integrating Chain-of-Thought (CoT) and Program-of-Thought (PoT) solutions for verification.
arXiv Detail & Related papers (2024-10-05T05:21:48Z)
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.