arXiv:2602. 07075v5 Announce Type: replace-cross Abstract: Current chemical large language models (LLMs) predominantly rely on explicit Chain-of-Thought (CoT) to solve complex reasoning problems.
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Large language models offer a promising foundation for chemical reasoning, bringing together chemical knowledge and multistep problem solving. Chemical intuition can provide an initial sense of plausi...
Latent JEPA is a new framework that trains continuous latent thoughts to anticipate informative aspects of future solutions in chemical reasoning, without verbalizing every intermediate step. It combines autoregressive learning with joint-embedding prediction of one or more future views, using textual and molecular prediction objectives that link latent thoughts to subsequent reasoning and molecular outcomes. Experiments on ChemCoTBench demonstrate improvements in molecular optimization, editing, and reaction metrics, and representation analyses show that future prediction makes latent thoughts more informative about molecular outcomes and better aligned with chemical structure.
arXiv:2505.16782v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT...
arXiv:2606. 16360v1 Announce Type: cross Abstract: Chain-of-thought (CoT) prompting improves reasoning in large language models (LLMs) by externalizing intermediate computation as discrete text tokens, but this textual interface also introduces redundancy and inference overhead.
Chemical reasoning language models are expected to produce faithful chain-of-thought (CoT) explanations when answering chemistry tasks, but across four model families and twelve tasks, hallucinations are widespread and largely independent of answer correctness. Attribution analyses reveal that these models use a shared scratchpad function: Chem‑R and ether‑0 rely on fragmented SMILES drafts, while ChemDFM‑R emphasizes scaffold, positional, and naming cues. Perturbing Chem‑R’s SMILES sketches degrades generation, indicating that structural drafts can be causally load‑bearing even when verbal structural claims are largely inert.
The paper investigates the effectiveness of activation steering in latent chain-of-thought (CoT) reasoning compared to explicit CoT. It finds that steering continuous latent thoughts yields weaker impacts on language generation, even when hidden representations are shifted similarly. The authors propose a latent-to-language transition gap, supported by evidence of abrupt output distribution changes at the transition boundary and weaker bidirectional control in latent CoT.