AI agents

Tool use, function calling, orchestration and the protocols that let models act rather than only answer.

16,003 stories · RSS feed

arXiv AI

Jun 19

Optimal Order of Multi-Agent and General Many-Body Systems

arXiv:2606. 20485v1 Announce Type: cross Abstract: This paper develops a general framework for analyzing multi-agent systems with feedback loops between agents actions and collective observations.

By Jake J. Xia

agents

arXiv Machine Learning

Jun 19

DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning

arXiv:2606. 19656v1 Announce Type: cross Abstract: A natural recipe for intelligent robotic decision-making is initializing from pretrained generative control policies, which have summarized offline experience, and adapting them to self-collected online experience.

By Calvin Luo, Chen Sun, Shuran Song

agents diffusion reinforcement-learning robotics fine-tuning multimodal

arXiv Machine Learning

Jun 19

A Differentiable Composite Approximation Framework for Autonomous Underwater Vehicle Maneuvering Modeling from Sea-Trial Data

arXiv:2606. 19711v1 Announce Type: cross Abstract: Field-based modeling from onboard measurements can produce autonomous underwater vehicle (AUV) maneuvering models that reflect real operating characteristics.

By Aobo Wang, Aifei Xia, Zihao Wang, Lizhu Hao

agents

arXiv Machine Learning

Jun 19

Execution-State Capsules: Graph-Bound Execution-State Checkpoint and Restore for Low-Latency, Small-Batch, On-Device Physical-AI Serving

arXiv:2606. 20537v1 Announce Type: new Abstract: Mainstream LLM serving systems reuse prefix work mainly through paged or radix key-value (KV) caches.

By Liang Su

llms agents robotics efficiency benchmarks data-engineering

arXiv AI

Jun 19

DynAMO:Dynamic Asset Management Orchestration via Topological Multi-Agent Scheduling

arXiv:2606. 19382v1 Announce Type: cross Abstract: While LLM-powered agents offer end-to-end automation for industrial asset lifecycles, real-world Industry 4.

By Kanishk Kushwaha, Vikrant Vinod Bansode, Harsh Vardhan, Dhaval C. Patel

llms agents efficiency benchmarks safety

arXiv AI

Jun 19

CogniFold: Always-On Proactive Memory via Cognitive Folding

arXiv:2605. 13438v3 Announce Type: replace Abstract: Existing agent memory remains predominantly reactive and retrieval-based, lacking the capacity to autonomously organize experience into persistent cognitive structure.

By Suli Wang, Yiqun Duan, Yu Deng, Rundong Zhao, Dai Shi, Minghua Deng, Chen Chen, Xinliang Zhou

agents benchmarks

arXiv Machine Learning

Jun 19

Agentic Symbolic Search: Characterizing PDEs Beyond Hand-crafted Expressions, Meshes, and Neural Networks

arXiv:2606. 20467v1 Announce Type: new Abstract: Mathematicians understand a PDE solution through mathematical structures rather than tables of computed values.

By Zongmin Yu, Liu Yang

agents safety

arXiv Machine Learning

Jun 19

A Survey of On-Policy Distillation for Large Language Models

arXiv:2604. 00626v4 Announce Type: replace Abstract: As Large Language Models continue to grow in both capability and cost, transferring frontier capabilities into smaller, deployable students has become an important engineering problem, and knowledge distillation remains a common technique for this transfer.

By Mingyang Song, Mao Zheng

llms agents reinforcement-learning efficiency safety

arXiv AI

Jun 19

Augmenting Game AI with Deep Reinforcement Learning

arXiv:2606. 20210v1 Announce Type: new Abstract: Immersion in video games depends not only on graphics, audio, and game mechanics, but also on the quality of in-game characters.

By Alessandro Sestini, Joakim Bergdahl, Amir Baghi, Jean-Philippe Barrette-LaPierre, Florian Fuchs, Linus Gissl\'en

agents reinforcement-learning

arXiv AI

Jun 19

A Multi-Agent system for Multi-Objective constrained optimization

arXiv:2606. 20236v1 Announce Type: new Abstract: Many decision-making problems in computing and networking systems can be naturally formulated as cost-minimization problems under performance constraints.

By Federica Filippini

rag agents reinforcement-learning

arXiv AI

Jun 19

Multi-Agent Transactive Memory

arXiv:2606. 19911v1 Announce Type: new Abstract: The decentralized deployment of LLM agents with diverse capabilities across diverse tasks motivates infrastructure for knowledge sharing across heterogeneous agent populations.

By To Eun Kim, Xuhong He, Dishank Jain, Ambuj Agrawal, Negar Arabzadeh, Fernando Diaz

llms rag agents

arXiv AI

Jun 19

Dual-Agent Framework for Cross-Model Verified Translation of Natural-Language Protocols into Robotic Laboratory Platform

arXiv:2606. 20120v1 Announce Type: cross Abstract: Biological experiment protocols are written in natural language, whereas automation systems rely on predefined control commands, creating a semantic gap that limits autonomous execution.

By Hyeonna Choi, Jung Yup Kim, Hyuneui Lim, Seunggyu Jeon

llms agents robotics

arXiv AI

Jun 19

Human-on-the-Loop Orchestration for AI-Assisted Legal Discovery

arXiv:2606. 19812v1 Announce Type: new Abstract: Autonomous Large Language Model (LLM) agents are increasingly deployed in electronic discovery (e-discovery), where compounding errors across multi-step reasoning chains can constitute legal malpractice.

By Anushree Sinha, Srivaths Ranganathan, Abhishek Dharmaratnakar, Debanshu Das

llms agents

arXiv AI

Jun 19

Beyond Static Leaderboards: Predictive Validity for the Evaluation of LLM Agents

arXiv:2606. 19704v1 Announce Type: new Abstract: Agent benchmarks are growing fast, but no single benchmark touches more than four or five of the dimensions that deployment exposes.

By Dhaval C. Patel, Kaoutar El Maghraoui, Shuxin Lin, Yusheng Li, Tianjun Feng, Chun-Yi Tsai, Yihan Sun, Wei Alexander Xin, Akshat Bhandari, Tanisha Rathod, Aaron Fan, Sanskruti Vijay Shejwal, Tomas Pasiecznik, Sagar Chethan Kumar, Tanmay Agarwal, Rohith Kanathur, Sam Colman, Amaan Sheikh, Dev Bahl, Ann Li, Krish Veera, Alimurtaza Mustafa Merchant, Shambhawi Baswaraj Bhure, Sajal Kumar Goyla, Chengrui Li, Kirthana Natarajan, Rui Li, Thomas Ajai, Rujing Li, Vivek G. Iyer, Sanjaii Vijayakumar, Yitong Bai, Ayal Yakobe, Darief Maes, Yassine Jebbouri, Tianyang Xu, Thai Quoc On, Vera Mazeeva, Winston Li, Yuval Shemla, Yeshitha Bhuvanesh, Rushin Bhatt, Siddharth Chethan Gowda, Alisha Vinod, Caroline Cahill, Shriya Aishani Rachakonda, Yunfeng Chen, Aryaman Agrawal, Aman Upganlawar, Mao Le Jonathan Ang, Yubin Sally Go, Madhav Rajkondawar, Yang-Jung Chen, Trisha Maturi, Ananya Kapoor, Andrew Li, Shrey Arora, Mana Abbaszadeh, Shen Li, Charles Xu, Byeolah Kwon

llms agents multimodal benchmarks

arXiv AI

Jun 19

FAPO: Fully Autonomous Prompt Optimization of Multi-Step LLM Pipelines

arXiv:2606. 19605v1 Announce Type: cross Abstract: Multi-step LLM pipelines fail through interactions among retrieval, reasoning, and formatting steps, so prompt-only optimization can miss bottlenecks in the chain.

By Paul Kassianik, Baturay Saglam, Huaibo Zhao, Blaine Nelson, Supriti Vijay, Aman Priyanshu, Amin Karbasi

llms agents benchmarks

arXiv AI

Jun 19

Analyzing Defensive Misdirection Against Model-Guided Automated Attacks on Agentic AI Systems

arXiv:2606. 20470v1 Announce Type: cross Abstract: Agentic AI systems increasingly rely on language-model components to interpret instructions, process external data, invoke tools, and coordinate with other agents.

By Reza Soosahabi, Vivek Namsani

agents multimodal benchmarks safety

arXiv AI

Jun 19

StaminaBench: Stress-Testing Coding Agents over 100 Interaction Turns

arXiv:2606. 19613v1 Announce Type: cross Abstract: We introduce StaminaBench, a benchmark that measures the stamina of coding agents: how many consecutive interaction turns (change requests) they can handle before failing.

By Vlad Sobal, Shuo Yang, Yuting Zhang, Wei Xia, Stefano Soatto

llms agents benchmarks

arXiv AI

Jun 19

HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-trainin

arXiv:2606. 20189v1 Announce Type: cross Abstract: Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD).

By Maciej Wozniak, Jesper Ericsson, Hariprasath Govindarajan, Truls Nyberg, Thomas Gustafsson, Patric Jensfelt, Olov Andersson

agents diffusion computer-vision robotics efficiency multimodal benchmarks safety

arXiv AI

Jun 19

Multi-Head Attention-Based Feature Extractor Integration with Soft Actor-Critic for Porosity Prediction and Process Parameter Optimization in Additive Manufacturing

arXiv:2606. 20087v1 Announce Type: new Abstract: Additive manufacturing process optimization requires precise parameter control to minimize defects such as porosity.

By Kianoush Aqabakee, Leonardo Stella

agents reinforcement-learning

arXiv AI

Jun 19

UltraQuant: 4-bit KV Caching for Context-Heavy Agents

arXiv:2606. 20474v1 Announce Type: cross Abstract: Context-heavy agents place unusual pressure on the key-value (KV) cache: long prefixes are reused across many short turns, while concurrency determines whether the serving system can keep GPUs utilized.

By Inesh Chakrabarti (Advanced Micro Devices, University of California, Los Angeles), David Limpus (Advanced Micro Devices, Purdue University), Aditi Ghai Rana (Advanced Micro Devices), Bowen Bao (Advanced Micro Devices), Spandan Tiwari (Advanced Micro Devices), Thiago Crepaldi (Advanced Micro Devices), Ashish Sirasao (Advanced Micro Devices)

agents efficiency

Get the daily briefing