arXiv:2606. 09774v2 Announce Type: replace Abstract: Configuring an advanced scientific simulator, translating a modeling goal into a valid, runnable input deck, is a persistent bottleneck that costs domain scientists hours to days.
By Matthew Ho, Brian Liu, Jixuan Chen, Audrey Wang, Lianhui Qin
arXiv:2607. 00436v1 Announce Type: new Abstract: Large language model agents are increasingly connected to scientific software, yet it remains unclear when tool access makes scientific computation more reliable rather than merely more complex.
By Ke Zhang, Sahchit Chundur, Mohammad Javad Qomi, Maziar Raissi
SPADE (Self-Play in Adaptive Synthetic Executable Environments) is a reinforcement‑learning framework where a single large language model acts as both an Environment Designer—creating executable, long‑horizon training environments—and a Reasoning Agent—learning to act within those environments. The framework uses a regret signal based on the difference between rewarded performance with and without privileged hints to guide the Designer toward environments that are challenging yet solvable. Experiments show that, when scaled to 30‑billion‑parameter models, SPADE outperforms fixed‑environment baselines by significant margins across math, science, code, and reasoning benchmarks, and improves tool‑use performance on BFCL‑v4 and ACEBench‑Agent.
whyItMatters":"By making environment design a learnable component, SPADE enables continuous self‑improvement and demonstrates that adaptive, self‑generated training environments can substantially boost language‑model performance across diverse tasks."
By Bo Liu, Simon Yu, Yiding Jiang, Ao Qu, Andrew Zhao, Zichen Liu, Junsu Kim, Zijian Zhou, Seungone Kim, Tongzheng Ren, Mickel Liu, Hanfei Yu, Zhaorun Chen, Weiyan Shi, Paul Pu Liang, Luke Zettlemoyer, Yejin Choi, Natasha Jaques
arXiv:2609.12533v1 Announce Type: cross
Abstract: Real-world Earth observation (EO) agents must translate high-level scientific questions into executable workflows to acquire observations, prepare da...
By Zhutao Lv, Chenhao Dang, Yi Feng, Yanpei Gong, Xiaolei Wang, Junyan Ye, Conghui He, Weijia Li
arXiv:2606. 13148v1 Announce Type: new Abstract: Climate and environmental decision-making increasingly requires reasoning across heterogeneous inputs, including gridded physical data, satellite imagery, geospatial context, and simulator outputs.
By Dat Tien Nguyen, Thao Nguyen, Fadillah Adamsyah Maani, Huy M. Le, Muhammad Umer Sheikh, Numan Saeed, Muhammad Haris Khan, Salman Khan
La Agente ’Optima is an agentic framework that builds and manages Bayesian optimization campaigns for self‑driving laboratories, separating large language model reasoning from campaign execution. It maintains a persistent optimization state, allowing consistent repetitive loops and auditable decisions, and only returns control to the agent when interpretation or revision is needed. In tests on digital discovery tasks and physical platforms, it corrected measurement failures, improved yields, and recommended formulation changes, outperforming human‑directed campaigns in cost and material usage.
By Marcel M\"uller, Jiaru Bai, Willi Gottstein, Abhijoy Mandal, Mohammad Nazeri, Elia Savino, Yanlin Fang, Sujoy Das, Sergio Pablo Garc\'ia Carrillo, Yeonghun Kang, Juan B. P\'erez-S\'anchez, Simone Pilon, Martin Fitzner, Timothy No\"el, Frank Gu, Varinia Bernales, Al\'an Aspuru-Guzik