arXiv:2608.30952v1 Announce Type: new
Abstract: Chemistry questions often demand exact computation and database lookups that a language model cannot supply from its parameters, so it must reach for e...
By Armin Dariani, Sifan Wu, Bang Liu, Entao Yang
The study investigates whether large language models (LLMs) rely solely on their initial task context or adapt based on experimental feedback when selecting fine‑tuning configurations for neural operators. Experiments across partial differential equation families show that LLMs outperform random search and Bayesian optimisation, with early configurations already near the top of a random‑search pool and subsequent proposals changing in response to validation scores. Controlled interventions confirm that the LLM combines a task‑dependent prior with sensitivity to observed outcomes.
By Julian Chan, Javier Mora Jimenez
The paper introduces Conformal Interval-Driven Self-Evolution (CISE), a method that uses conditional conformal inference and online density-ratio estimation to create candidate‑specific reward intervals for self‑evolving search in materials science. CISE applies conservative interval‑based rewards, ensuring that only candidates whose required property intervals lie entirely within feasible regions are returned. Experiments on three self‑evolving search tasks show that all candidates returned by CISE are true positives under high‑fidelity evaluation, whereas baseline methods produce more candidates but include false positives.
By Kangjun Noh, Soyu Kim, Kyungwoo Song
Chemistry questions often demand exact computation and database lookups that a language model cannot supply from its parameters, so it must reach for external tools. Tool use here is a three-part prob...
arXiv:2607. 00531v1 Announce Type: cross Abstract: Scientific reasoning is an increasingly important capability of large language models, yet improving the robustness and efficiency of training such reasoning remains a key open challenge.
By Xuefeng Liu, Mingxuan Cao, Qinan Huang, Thomas Brettin, Rick Stevens, Le Cong
The paper proposes ANCHOR, a composition policy that decouples crystal structure prediction (CSP) from de novo crystal generation (DNG) by using a frozen CSP model as a fixed evaluator and training the policy with multi‑objective rewards, including adaptive novelty. ANCHOR significantly improves metrics such as MSUN and SUN compared to traditional DNG approaches, and demonstrates transferability across different CSP backbones and distillation into Crystalite‑CSP. The study shows that fine‑tuning DNG models directly on these rewards yields limited gains, whereas the ANCHOR framework better leverages the CSP prior for more efficient crystal discovery.
By Emma Lei Hovmand, Jonas Elsborg, Melih Kandemir, Arghya Bhowmik