The paper proposes a Recursive Autonomous Agent (RAA) framework for intelligent manufacturing, combining goal-driven autonomous agents with a recursive organizational structure. It identifies individual autonomy and collective coordination as coupled mechanisms that enable local decision-making and alignment with shared goals. The framework uses goal and capability contracts to define local objectives and facilitate the composition of production units, allowing coordinated groups to function as units within larger organizations.
By Yuning Chen, Hongda Zhang, Linqiang Hu, Ziqing Zhou, Yi Liu, Zhao Huang, Ding Zhou, Haining Xu
The paper "Corralling AI Risks with Grassroots Contracts" identifies three major AI risks affecting digital platforms: (1) AI control of global platforms, (2) unaccountable AI agents operating alongside humans, and (3) unattributed deepfakes. It proposes grassroots contracts—digital agreements inspired by Pierre‑Joseph Proudhon—to mitigate these risks by establishing accountability for AI agents in social graphs and ensuring digital provenance in social networks. The authors also present smartphone applications that implement these contracts and demonstrate how they address the identified risks.
By Ehud Shapiro
EviSeek is a training‑free framework that explicitly links question‑derived evidence demands to observation‑conditioned actions and selective evidence retention. By making the informational purpose of interaction explicit, it guides agents to acquire only the evidence needed to answer a question. On the ESI‑Bench dataset, EviSeek improves accuracy by 7.1 percentage points over a single‑model agent with Qwen3‑VL‑32B and by 3.7 points with Gemini 3.1 Pro, while also boosting visual‑support‑weighted accuracy and reducing token usage.
By Jintao Li, Zhiyong Cao, Baihua Xiao, Yu Bai, B\"orje F. Karlsson
The paper introduces Coca-Cola, a continuous latent context compression model that transforms raw tool outputs into latent tokens for LLM agents, bypassing the need for explicit text representation. By embedding these latent tokens into the agent’s input space and employing Action‑Preserving Training, the method ensures that decision‑critical information is retained while reducing token consumption by 70.01%–89.24%. Experiments on web‑search agents across multiple QA benchmarks demonstrate significant reductions in runtime (24.45%–55.97%) and consistent improvements in task performance.
By Yuetong Liu, Zhehao Zhou, Yitong Zhang, Haorui Chen, Jia Li
RAGAM-AI is a hybrid framework that merges reinforcement learning (RL) with Generative AI to manage resource allocation in Multi-access Edge Computing (MEC) environments. It addresses reward generalization challenges by employing a Large Language Model (LLM) for feedback and uses a Multi-Agent Soft-Actor Critic (MASAC) with a surrogate reward model to guide resource orchestration across distributed edge servers. Compared to single-agent SAC, REINFORCE, and random policies, RAGAM-AI achieves lower deviations in CPU, memory, and bandwidth allocation while reducing latency and data rate costs.
By Shreya K. Chari, John S. Vardakas, Kostas Ramantas, Christos Verikoukis
The paper "CompressionBench: How can your coding agents compress LLMs?" investigates whether autonomous coding agents can compress large language models (LLMs) while staying within a specified storage budget. The study frames compression as a terminal task where agents receive a source checkpoint, storage limit, inference environment, and time budget, then generate compressed weights and executable inference code, guided by perplexity feedback and verified by independent checks. Across six agents and six model-size settings, 29 of 36 submissions passed verification, achieving an average 3.4% increase in perplexity over BF16 and 77.33% downstream accuracy, with detailed experiments revealing strengths and failure points in agent-driven compression.
By Xinrui Zhong, Zhifeng Li, Zhiming Wang, Yongqi Liu, Shanglin Yang, Yang Zhou, Zikun Li, Zhuoming Chen, Beidi Chen
The paper introduces ZETA, an architecture that separates routing policy from action execution in scientific vision agents. By resolving scientific intent before invoking specialized visual tools, ZETA treats measurement as a deterministic operation grounded in explicit geometry, enabling independent evaluation of routing correctness and downstream execution. Experiments on a zero‑shot benchmark of 51 scientific‑intent queries demonstrate that explicit routing semantics can dramatically improve routing accuracy, especially for models like Qwen 3.8 where segmentation routing accuracy jumps from 6.2% to 75.0% and detection routing accuracy from 60.0% to 100%.
By Shubhabrata Mukherjee, Jeffrey Donatelli, Daniela Ushizima
The paper studies repeated principal-agent games in a multi-armed bandit setting where the principal offers arm-specific incentives, the agent chooses an arm to maximize its own reward plus incentive, and the principal observes only the chosen arm and its own reward. Because the principal cannot see the agent’s reward, traditional estimation fails, so the authors develop an estimator based on the sequence of incentives and chosen arms, analogous to online inverse optimization. Using this estimator, they design a policy that achieves low (square‑root) regret and validate the approach with simulations for personalized incentive design in long‑term statin treatment.
By Ilgin Dogan, Zuo-Jun Max Shen, Anil Aswani
arXiv:2602.01493v3 Announce Type: replace-cross
Abstract: Solving diverse partial differential equations (PDEs) is fundamental in science and engineering. Large language models (LLMs) have demonstrat...
By Zhuoyuan Wang, Hanjiang Hu, Xiyu Deng, Saviz Mowlavi, Yorie Nakahira
arXiv:2605.18991v3 Announce Type: replace-cross
Abstract: We take the position that agent security must be approached as a systems problem: the AI model powering the agent must be treated as an untru...
By Mihai Christodorescu, Earlence Fernandes, Ashish Hooda, Somesh Jha, Johann Rehberger, Kamalika Chaudhuri, Xiaohan Fu, Khawaja Shams, Guy Amir, Jihye Choi, Sarthak Choudhary, Nils Palumbo, Andrey Labunets, Nishit V. Pandya
arXiv:2609.12097v2 Announce Type: replace-cross
Abstract: In bespoke art commissions, laypeople know what they feel but lack the words to specify it: one participant wanted a laid-off truck driver de...
By Yu-Chao Wang, Yanhong Lu, Yingjie Victor Chen, Tim McGraw
arXiv:2611.05601v1 Announce Type: new
Abstract: A deep RL agent trained on a fixed persona reward plays one way: an Explorer keeps exploring while starving, a Survivor hoards on an empty map. Since e...
By AL Muqshith Mohammed Shifan, Kevin Christopher Chua, Ali Neshati, Loutfouz Zaman, Cristiano Politowski
arXiv:2611.05609v1 Announce Type: new
Abstract: Multi-teacher on-policy distillation (MOPD) extends on-policy distillation (OPD) to consolidate capabilities from multiple teachers. Although prior wor...
By Yichen Wang, Yufa Zhou, Siqi Zhu, Haozheng Luo, Jingzhi Bao, Zhichao Liu, Zijun Wei
arXiv:2611.06084v1 Announce Type: new
Abstract: Hybrid language models interleave a few softmax-attention layers with many linear-attention or state-space layers, so every layer keeps a memory of ear...
By Mingyuan Ma, Yangmin Li, Harris Nover, Zheyu Fu, Po-Han Huang, Alex Settle, Ashwin Nanjappa
arXiv:2611.06704v1 Announce Type: new
Abstract: Training an agent to play Pac-Man with only up, down, left and right actions raises a basic question: when performance is poor, what should be improved...
By Xinglu Wang, Edward Wang, Yong Zhang, Zhenan Fan
arXiv:2611.05580v1 Announce Type: cross
Abstract: We study repeated general games over $T$ rounds between a utility-maximizing optimizer and a learning agent, where budget constraints couple the play...
By Giannis Fikioris, Balasubramanian Sivan, \'Eva Tardos
arXiv:2611.05991v1 Announce Type: cross
Abstract: Multi-agent LLM applications repeat most of their prompt tokens across requests, and serving engines reuse the cached state of shared prefixes. vLLM...
By Muhammad Faizan Anwar, Sina Keshvadi
arXiv:2611.06257v1 Announce Type: cross
Abstract: Open-weight language models that fit on a single consumer GPU are becoming capable on a growing range of tasks, and they matter because they can run...
By Jorge Garc\'ia-Carrasco, Javier Sanchis, \'Alvaro Navarro, Alejandro Mat\'e, Juan Trujillo
arXiv:2509.02861v2 Announce Type: replace
Abstract: The necessary integration of renewable energy sources, combined with the expanding scale of power networks, presents significant challenges in cont...
By Carlo Fabrizio, Gianvito Losapio, Marco Mussi, Alberto Maria Metelli, Marcello Restelli
arXiv:2605.11221v2 Announce Type: replace-cross
Abstract: Predictive models in medicinal chemistry depend on structured assay data locked in primary publications. This bottleneck is acute in targeted...
By Yaochen Rao, Farzaneh Jalalypour, N. M. Anoop Krishnan, Roc\'io Mercado