The results show that chemical CoT is neither a faithful explanation nor merely a post-hoc rationalization, but a hallucination-prone molecular scratchpad, which cautions against treating CoT as direct evidence of faithful reasoning and motivates process-level supervision beyond answer-only evaluation.
Abstract
Chemical reasoning language models are expected to derive molecular answers through faithful chain-of-thought (CoT). However, across four reasoning model families and twelve chemistry tasks, hallucination is widespread and largely decoupled from answer correctness: correct answers often coexist with fabricated structural claims absent from the relevant molecules. Yet this does not make the reasoning trace computationally irrelevant. Attribution analyses suggest a shared scratchpad function expressed in model-specific forms: Chem-R and ether-0 rely on fragmented SMILES drafts, whereas ChemDFM-R emphasizes scaffold, positional, and naming cues. Notably, perturbing Chem-R's SMILES sketches degrades generation, showing that structural drafts can be causally load-bearing even when verbal structural claims are largely inert. Together, these results show that chemical CoT is neither a faithful explanation nor merely a post-hoc rationalization, but a hallucination-prone molecular scratchpad. This finding cautions against treating CoT as direct evidence of faithful reasoning and motivates process-level supervision beyond answer-only evaluation.
This paper studies hallucinated CoT as a discourse-structural phenomenon, not only a factual one, and suggests that discourse structure provides an interpretable signal for detecting reasoning hallucinations and complements existing factuality and uncertainty-based hallucination detectors.
A novel, large-scale reasoning dataset of reaction mechanisms, and the FukuyamaBench, a difficult benchmark derived from Fukuyama's Advanced Organic Reaction Mechanism book, to rigorously evaluate model performance on hierarchical mechanism reasoning, demonstrate that mechanism-aware training substantially enhances che...
Xingyu Dang, Haocheng Tang, Junmei Wang et al.· arXiv.org· 0 citations
HALO (Hallucination-Aware Layered Oversight) is presented, an assurance architecture which treats hallucination as a containable failure mode rather than an eliminable one and detail each layer, give particular attention to evidence-based confidence (which verifies extractions against the source document rather than tr...
Bogdan Raduta, Horia Velicu, Alexandru Preda et al.· arXiv.org· 0 citations
A concise two-axis framework that integrates an “intrinsic-extrinsic” distinction in source attribution introduced by Ji et al. with a “faithfulness-factuality” distinction in contextual grounding surveyed is presented, yielding four clearly defined hallucination types applicable across tasks, modalities and architectu...
Misbah Khan, Preston Billion-Polak, T. Khoshgoftaar· IEEE Access· 0 citations
A unified symbolic, behavioral, and mechanistic framework that connects symbolic triggers with internal failure dynamics in transformer architectures and provides an interpretable basis for diagnosing and stabilizing symbolic reasoning in LLMs is introduced.
This work presents ACT-Eval, an evaluation framework that decomposes chess commentary into atomic claims and routes them to engine-supported tools and expert-annotated gold references to assess factual correctness, conceptual coverage, and move-quality judgment.
S. A. Hebbar, Pei-Yao Sheng, Sewoong Oh et al.· 0 citations
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