arXiv:2603. 15952v2 Announce Type: replace Abstract: Large language models (LLMs) are capable of emulating reasoning and using tools, creating opportunities for autonomous agents that execute complex scientific tasks.
By Jacopo Teneggi, S. M. Bargeen A. Turzo, Tanya Marwah, Alberto Bietti, P. Douglas Renfrew, Vikram Khipple Mulligan, Siavash Golkar
arXiv:2606. 31229v1 Announce Type: new Abstract: Ideation plays a pivotal role in scientific discovery.
By Keyu Zhao, Lingyan Kong, Fengli Xu, Yong Li
M3OS is a multi‑agent large‑language‑model system that separates molecular‑design reasoning from optimization‑state management using a Monte Carlo graph search. The system maintains a persistent graph of evaluated candidates, transformations, and evidence, while LLM agents use role‑specific contexts to generate and edit molecules with tool‑driven and knowledge‑guided approaches. Across three benchmarks, M3OS outperforms baselines, demonstrating the benefit of persistent search state, specialized agents, and controlled execution for multi‑constraint molecular optimization.
The paper introduces a text-based, multi-agent version of the board game Clue to test multi-step deductive reasoning in large language models (LLMs). Six LLM-based agents (GPT‑4o‑mini and Gemini‑2.5‑Flash) play turn‑based games, and a tool‑augmented approach uses a structured possibility matrix to convert implicit game state into explicit remaining possibilities, thereby offloading memory and deductive constraints from the agents. The study compares this tool‑augmented method against a baseline to assess its impact on reasoning quality and task success in a strategic reasoning environment.
By Rebecca Ansell, Autumn Toney-Wails
arXiv:2606. 04751v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in scientific tasks.
By Leonardo Bertolazzi, Katya Tentori, Raffaella Bernardi
The paper introduces a multitask large reasoning model for molecular science that incorporates chemical knowledge via a multispecialist architecture, chain-of-thought supervision, and molecule-informed reinforcement learning. It coordinates prediction and inference specialists across ten molecular tasks—including description, generation, nomenclature translation, property prediction, and reaction prediction—using task-conditioned routing. The model surpasses more than 20 general-purpose and molecular large language models, improving aggregate performance by 50.3% and outperforming leading multitask baselines on most tasks, while maintaining interpretable chemical inference and demonstrating a workflow for CNS candidate generation and retrosynthetic planning.
By Pengfei Liu, Shuang Ge, Xiaobo Wang, Xin Liu, Jun Tao, Yan Li, Chao Liu, Ling Chen, Zhixiang Ren