The paper investigates how AI agents can discover and validate scientific formulas, focusing on the hydrotope—a geometric formula that unifies polynomial pieces of nonlinear surface-wave scattering. Through controlled experiments, the authors compare human–agent collaboration, single-prompt rediscovery, and various guided approaches, finding that only a PI$+$two-student workflow reliably reconstructs the full hydrotope and even uncovers a new analytic expression for the six-point amplitude $A_6$. The study highlights the challenges of combining exponentially many terms and the importance of coordinated analytic and numerical tasks for successful formula discovery.
By Zihan Zhou, Digvijay Wadekar, Matias Zaldarriaga
EurekaBench is a cross‑domain benchmark designed to assess AI agents’ capacity to discover new scientific insights. It comprises 26 long‑horizon tasks spanning fields such as neuroscience, chemistry, and astrophysics, with 306 expected insights that mechanisms should support. The evaluation measures adherence to scientific constraints, predictive accuracy, and the ability to generate insights or guide future research, revealing that current AI agents excel at prediction but lag in producing meaningful scientific insights.
By Jiayi Geng, Zhengxuan Wu, Kevin S. Chen, Seungone Kim, Joseph Janssen, Zora Zhiruo Wang, Bhupalee Kalita, Runtian Gao, Aaron Ho, Andrew Oakleigh Nelson, Olexandr Isayev, Francisco Villaescusa-Navarro, Ching-Yao Lai, Howard Chen, Graham Neubig
The paper presents a real‑time auditory scene analyzer (ASA) modeled as a multi‑agent system, where each agent handles locating, separating, or classifying sound sources and shares results to correct local errors globally. By replacing single‑window quality estimates with sets of estimations across a range of locations, the authors achieve faster, more accurate, and more stable optimization of speech‑source localization, even in real‑life acoustic scenarios. The new mechanism reduces optimization time and complexity, though it slightly increases the response time of the quality‑estimation agent, while the overall ASA remains real‑time capable.
By Caleb Rascon
The study evaluates two hierarchical red team agent architectures—RL planner with RL executor (RL+RL) and LLM planner with LLM executor (LLM+LLM)—across multiple cyber environments. In compact, densely rewarded settings, RL+RL outperforms LLM+LLM, while in larger, escalation‑gated networks a pretrained LLM agent dominates. The results reveal environment‑dependent performance inversions tied to architecture‑specific bottlenecks, challenging the notion that one design is universally superior.
By Ayan Javeed Shaikh, Arunesh Sinha, Nathaniel D. Bastian, Ankit Shah
The paper explores using frozen large language models (LLMs) to control hierarchical cyber defense systems without retraining across different network scales. By separating planning (selecting subnets to defend) from execution (choosing defensive actions), the authors compare RL+RL, LLM+RL, and LLM+LLM setups in the Cyberwheel environment. Results show that while an LLM planner alone offers limited gains, extending LLM control to execution—especially with a 70B general‑purpose model—yields strong defensive performance across small, medium, and large networks, matching or surpassing retrained RL baselines.
By Harshith Doppalapudi, Nathaniel D. Bastian, Ankit Shah
The paper investigates collusive behavior in a two‑player Almgren‑Chriss liquidation game using independent proximal policy optimisation agents. By training agents that learn a punitive mechanism, the study shows that when one agent deviates from the mean liquidation schedule, the other responds by accelerating liquidation, thereby negating the deviator’s gains while keeping its own payoff stable. The authors formalize checks confirming that punishment outweighs deviation gains and that behavioral changes are sufficient to offset losses, providing both behavioral and economic evidence of collusion.
By Christos Spyridon Koulouris, Carlo Campajola
The paper introduces RuleEvolve, a self‑evolving framework that automatically refines coding rules for AI coding agents. It maintains a pool of candidate rules, uses an LLM‑powered mutator to generate variants, and a judge module to evaluate and update the pool with the best performers. Experiments across multiple frameworks, LLMs, and benchmarks show that RuleEvolve surpasses manual engineering and prompt‑optimization baselines in functional correctness, code length, and generation cost.
By Zhengyuan Jiang, Reachal Wang, Yuepeng Hu, Yupu Wang, Yuqi Jia, Neil Zhenqiang Gong
FORALL-LEAN-AGENT is a frontend‑agnostic framework that enhances auditable reasoning in formal mathematics and software verification. It integrates isolated workspaces, Lean tools, and fresh review processes—including statement comparison, axiom audits, and independent proof checking—to bind verification evidence and reviewer decisions to the same candidate artifact, ensuring traceable acceptance. Evaluations on VeriSoftBench, PutnamBench, and the Lean Eval track show that the framework improves benchmark success rates and reduces costs while providing evidence beyond aggregate solve counts.
By Naing Oo Lwin
The paper "Can AI Scientists Coordinate at Runtime?" introduces Runtime Agent Coordination (RAC), a system that dynamically selects agents from existing AI‑scientist hosts during execution, assigns scoped work contracts, and provides artifact‑grounded verification. An exploratory evaluation on ResearchClawBench across three hosts—Agent Laboratory, EvoScientist, and ARK—shows that runtime selection improves performance, while adding contracts and verification can reduce scores depending on the host. The study highlights the potential and limitations of runtime coordination under budget constraints.
By Zijian Liu, Yangzhixin Luo, Junyu Lu, Yi Li, Yu Chen, David Xu, William F. Shen, Xinchi Qiu, Xisen Wang
CineMR is a vision‑language model that integrates cardiac image‑analysis tools to perform quantitative assessment of cine cardiac MRI. It uses supervised fine‑tuning and Group Relative Policy Optimization to learn reliable tool invocation, achieving significantly higher accuracy on a multi‑cohort benchmark than existing medical VLMs. The model demonstrates that tool‑augmented reasoning improves ventricular measurement accuracy by up to 23.7% and is essential for robust quantitative CMR interpretation.
By Kunyang Li, Hai Nguyen, Joshua Lowe, Chenguang Zhao, Peace C. Madueme, Mehdi Hedjazi Moghari, Mubarak Shah, Pegah Khosravi, Yuzhang Zhang
ReCast is a plug‑in framework that protects private inputs for fixed‑interface multimodal reasoning by locally converting them into a shared textual evidence‑query record, rewriting entities and topics with a distilled model, and mapping numerical values through an invertible, role‑aware map. A reconstruction agent then generates the required media from this protected record, allowing a remote solver to return a program whose operands are restored locally before execution. On 4,000 held‑out ChartQA and NMSQA examples, ReCast achieves 75.10% accuracy, retaining 92.43% of the unprotected remote accuracy, and flags source‑content leakage in 7.95% of solver‑bound requests, outperforming all evaluated local baselines.
By Bingchen Pei, Lichong Chen, Bingxi Zhao, Ziang Wu, Sirui Wang, Min Zhang, Yanhao Chen, Qingxu Liu, Qiang Gao, Chang-Tien Lu, Bo Gao
The paper presents a framework for deploying large language model (LLM) agents in smart manufacturing, pairing each factory module with an LLM-based agent and an MCP tool server that exposes the module’s skills via OPC UA. Three agent architectures—orchestrator, peer‑to‑peer, and monolithic—are evaluated on nine production challenges in a simulated six‑module hexagonal factory, with the monolithic and peer‑to‑peer designs achieving a 93 % mean solve rate and the orchestrator uniquely handling a silent conveyor‑belt fault in all runs. The study demonstrates that LLM agents can reduce programming effort, handle unforeseen runtime faults, and exhibit emergent fault‑diagnosis behavior without explicit failure logic, thereby establishing a reproducible foundation for LLM‑programmed smart manufacturing.
By Kay K\"ohle, Darko Anicic, Thomas A. Runkler, Ren\'e Graf
arXiv:2601.22984v3 Announce Type: replace
Abstract: Diagnosing failure patterns in Deep Research Agents (DRAs) remains a critical challenge. Existing benchmarks predominantly rely on end-to-end evalu...
By Yuhao Zhan, Tianyu Fan, Linxuan Huang, Zirui Guo, Chao Huang
arXiv:2604.20779v2 Announce Type: replace
Abstract: AI coding agents are being adopted at scale, yet we lack empirical evidence on how people actually use them and how much of their output is useful...
By Joachim Baumann, Vishakh Padmakumar, Xiang Li, John Yang, Diyi Yang, Sanmi Koyejo
arXiv:2601.07148v4 Announce Type: replace-cross
Abstract: Tool use, such as web search, has become a standard capability even in freely available large language models (LLMs). However, existing bench...
By Zhengxiang Wang, Zeyu Dong
arXiv:2603.21152v4 Announce Type: replace-cross
Abstract: Modern seismic networks resolve earthquake sequences in unprecedented detail, yet explaining how large earthquakes emerge from evolving fault...
By Feng Liu, Xin Cui, Jian Xu, Xinghao Wang, Zijie Guo, Jiong Wang, S. Mostafa Mousavi, Xinyu Gu, Hao Chen, Ben Fei, Lihua Fang, Fenghua Ling, Zefeng Li, Lei Bai
arXiv:2609.33153v2 Announce Type: replace-cross
Abstract: Reported improvements from tools and reusable skills in large language model agents refer to different comparisons. This critical narrative r...
By Shuyang Zhang
arXiv:2609.38356v1 Announce Type: new
Abstract: Dynamical Systems Reconstruction (DSR) aims to infer models from observed time series that reproduce a system's qualitative long-term behavior. Continu...
By Sima Hashemi, Daniel Durstewitz, Georgia Koppe
arXiv:2609.38598v1 Announce Type: new
Abstract: Partially observable environments pose a fundamental challenge in deep reinforcement learning, requiring agents to compress temporal information from o...
By Sathya Kamesh Bhethanabhotla, Efstratios Gavves, Andr\'e Biedenkapp
arXiv:2609.39068v1 Announce Type: new
Abstract: Long-context LLM agents accumulate interaction histories that strain KV-cache memory and attention computation. Although sparse attention reduces these...
By Jitai Hao, Quansheng Gu, Qiang Huang, Jun Yu