arXiv:2607. 17417v1 Announce Type: new Abstract: Large language models confabulate chemical objects (molecular formulas, space groups, formation energies) in fluent reasoning traces, concentrated on long-tail entities where confidence is least trustworthy.
By Can Polat, Mustafa Kurban, Erchin Serpedin, Hasan Kurban
arXiv:2606. 03660v1 Announce Type: new Abstract: Large language models are increasingly used as chemistry assistants, yet most chemistry benchmarks still score only final answers.
By Hongyu Guo, Hao Li, He Cao, Gongbo Zhang, Li Yuan
arXiv:2607. 09999v1 Announce Type: cross Abstract: We show that post-training quantization can silently alter how large language models reason even when task accuracy is preserved.
By Renuka Oladri, Mohan Vamsi Varadaraju Priya, Jerry Wu
arXiv:2606. 27383v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as research assistants, yet it remains unclear whether they can calibrate research takeaways to the strength and scope of the supporting evidence.
By Yu Fu, Yongqi Kang, Yong Zhao
arXiv:2607. 22962v1 Announce Type: new Abstract: LLM agents that operate over many turns accumulate facts in an external memory store and reuse them as premises for downstream reasoning.
By Yan Zhang, Shibo Li
CORE is a search controller that uses a verifier to obtain a certified conflict core, backjumps to the latest decision in that core, and caches the conflict to prevent repetition. In experiments on 2,000 graph‑coloring instances, CORE cuts median verifier calls by up to 39.8% compared to chronological repair, and improves success rates on five reasoning tasks, achieving 75.9% with Qwen2.5‑7B‑Instruct and 84.2% with Qwen3‑8B versus 72.5% and 81.8% for Tree of Thoughts. The approach also reduces verifier calls and generated tokens on both language‑model backbones.
By Siyu Song, Rui Xu, Jia Lin, Kai Liu, Weifang Wang