The paper introduces a reinforcement‑learning based rewriting agent that rewrites training data to reduce the distribution mismatch between supervised fine‑tuning (SFT) and a language model’s generation distribution. By formulating data rewriting as a policy‑learning problem, the authors train a lightweight LoRA rewriting policy that optimizes alignment with question‑answering style, maintains semantic diversity, and enforces task consistency. Experiments on three instruction‑tuned backbones show that models fine‑tuned with the rewritten data achieve downstream performance comparable to standard SFT while mitigating degradation on non‑downstream benchmarks, and preliminary tests suggest the policy can transfer across domains such as logical reasoning and medical question answering.
By Jiacheng Wang, Zhijie Liu, Ping Jian, Zirong Chen, Ke Ren Liao, Zhen Yang, Zhongbin Guo
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
arXiv:2609.17652v1 Announce Type: new
Abstract: When agentic sessions run to a million tokens with many sessions resident at once, the KV cache and the index that ranks it live in host memory, and th...
By Vivek Kalyanarangan
arXiv:2609.18304v1 Announce Type: new
Abstract: Large language model (LLM) agents increasingly tackle long-horizon tasks through multi-step environment interaction, yet a single erroneous action can...
By Yi Yu, Liuyi Yao, Yaliang Li, Enshu Wang, Libing Wu
FedGuide is a federated reinforcement learning framework that tackles distributional mismatch among heterogeneous clients by using diffusion priors as personalized behavior models. It aggregates these priors via an Optimal-Transport Mixture-of-Experts, preserving diverse behavior modes, and introduces a Distribution Correction Estimation (DICE) value baseline to give low‑variance, return‑aware guidance for local policy improvement. Experiments on heterogeneous environments demonstrate that FedGuide surpasses existing FRL methods in client‑average returns, final‑round performance, and worst‑round robustness while maintaining stable learning under stronger heterogeneity.
By Zhilin He, Gauri Joshi
The paper presents a reinforcement‑learning framework that dynamically selects between full‑order models (FOMs) and reduced‑order models (ROMs) in hybrid domain decomposition simulations using the overlapping Schwarz alternating method (O‑SAM). Offline‑trained Deep Q‑networks choose subdomain‑local FOMs or pre‑trained Operator Inference ROMs based on a reward that balances accuracy, computational cost, and model‑switching frequency, and the learned policies are deployed on unseen problem instances without needing a reference FOM solution. Experiments on a 1D advection‑diffusion problem and a 3D elastic wave propagation benchmark show that the RL‑guided policies adaptively allocate high‑fidelity resolution as features propagate, outperforming static FOM/ROM assignments and demonstrating the feasibility of predictive online fidelity adaptation in Schwarz‑based hybrid simulations.
By Trishit Mondal, Irina Tezaur, Anthony Gruber
The paper introduces a decentralized learning framework for finding socially optimal equilibria in finite normal-form games played over dynamic communication networks. Agents only observe their own payoffs, lack prior knowledge of the game, and communicate with time-varying neighbors using low-bandwidth, time-stamped tables instead of raw actions or payoff data. The proposed dynamics combine randomized semantic signals, table fusion, and temporal majority reconstruction to achieve finite-time logarithmic regret guarantees for optimal equilibrium selection under utilitarian and proportional-fair social welfare objectives, as demonstrated by simulations.
By Seref Taha Kiremitci, Muhammed O. Sayin
Agents are increasingly asked to design hardware, and increasingly reported to succeed. Such reports establish that a design improved; they cannot establish why. An agent that improves an accelerator...
Claude Cowork and the standard chat interface are merging into a single Claude experience, allowing users to hand over tasks or ask quick questions that the system will continue to handle even after the laptop is closed. The rollout will begin with Pro and Max plan users across web, desktop, and mobile, and will extend to new users on these plans over the coming weeks. This integration suggests Claude is evolving into a general agent, simplifying the distinction between Cowork and regular Claude usage.
Language agents remain brittle in interactive environments, where success requires long-horizon state tracking, valid action execution, and recovery from failed steps. We extend SwiftSage, a dual-proc...
Emergent coordinated behaviors of AI agents are starting to present critical safety risks. A key phenomenon driving these behaviors is the rapid formation and spread of beliefs about the world, and me...
Mixture-of-Experts (MoE) language models suffer from large parameter counts, which create a significant memory bottleneck. Expert pruning is the most direct approach for reducing this parameter count,...
Existing legal benchmarks cover diverse tasks, while continuous monetary remedies remain comparatively underexplored. We introduce ECtHR-NPD, to the best of our knowledge, the first benchmark for pred...
Current Mixture-of-Agents (MoA) paradigms generally treat query routing and agent fine-tuning as separate processes, limiting their ability to respond to evolving agent capabilities. This disconnect p...
Emergency management assistance programs, such as relief distribution, are essential for delivering necessary supplies to affected communities. However, these programs operate in a decentralized netwo...
The article titled "Reimagining advertising with AI" discusses how OpenAI is creating new AI-powered advertising experiences. It highlights features such as Sponsored Agents, tools designed for marketers, and integrations with platforms like HubSpot and Shopify.
Self-driving laboratories can explore synthesis conditions autonomously, but their decision-making layer is typically a black-box optimizer, and the output is a set of optimized samples, with the meas...
Propagation structures provide crucial evidence for fake news detection, yet existing approaches primarily rely on supervised GNN-based models, which require substantial labeled data and exhibit limit...
Most keyframe selection studies focus on offline settings, assuming access to the full video and query in advance. In contrast, real-world streaming scenarios require online frame selection under unkn...
GPT-6 Astra helps Hex’s data agents turn answers into interactive visualizations that employees are proud to share.