LatentChem：从文本思维链到化学推理的潜在思考

摘要

化学领域的大型语言模型（LLM）主要依赖自然语言的显式思维链（CoT）进行复杂推理。然而化学推理本质上具有连续性和结构性，强行将其压缩为离散的语言标记会导致根本性的表征失配，从而限制效率与性能。我们提出LatentChem——一种潜在推理接口，它将化学计算与文本生成解耦，使模型能在连续潜在空间中直接执行多步推理，仅对最终输出生成语言。值得注意的是，我们观察到一致的涌现行为：当仅针对任务成功率进行优化时，模型会自发内化推理过程，逐步摒弃冗长的文本推导，转向隐式的潜在空间计算。这种转变不仅是风格性的，更具计算优势。在多项化学推理基准测试中，LatentChem在ChemCoTBench上相较基于CoT的强基线实现了59.88%的非平局胜率，同时推理速度平均提升10.84倍。我们的研究结果实证表明：化学推理作为连续潜在动态的实现方式，比离散化语言轨迹更自然且更有效。

English

Chemical large language models (LLMs) predominantly rely on explicit Chain-of-Thought (CoT) in natural language to perform complex reasoning. However, chemical reasoning is inherently continuous and structural, and forcing it into discrete linguistic tokens introduces a fundamental representation mismatch that constrains both efficiency and performance. We introduce LatentChem, a latent reasoning interface that decouples chemical computation from textual generation, enabling models to perform multi-step reasoning directly in continuous latent space while emitting language only for final outputs. Remarkably, we observe a consistent emergent behavior: when optimized solely for task success, models spontaneously internalize reasoning, progressively abandoning verbose textual derivations in favor of implicit latent computation. This shift is not merely stylistic but computationally advantageous. Across diverse chemical reasoning benchmarks, LatentChem achieves a 59.88\% non-tie win rate over strong CoT-based baselines on ChemCoTBench, while delivering a 10.84times average inference speedup. Our results provide empirical evidence that chemical reasoning is more naturally and effectively realized as continuous latent dynamics rather than discretized linguistic trajectories.

LatentChem：从文本思维链到化学推理的潜在思考

LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning

摘要

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