AI agents

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

8,550 stories · RSS feed

arXiv Machine Learning

Jun 25

AeroCast: Probabilistic 3D Trajectory Prediction for Non-Cooperative Aerial Obstacles via Transformer-MDN Architecture

arXiv:2606. 25122v1 Announce Type: cross Abstract: Autonomous aerial vehicles operating in shared airspace must predict the future positions of non-cooperative obstacles to plan evasive maneuvers before a collision becomes unavoidable.

By Syed Izzat Ullah, Jose Baca

llms agents multimodal benchmarks

arXiv Machine Learning

Jun 25

ClayBuddy: A Framework, Evaluation, & Mitigation of Coding Agent Failures

arXiv:2606. 19380v3 Announce Type: replace-cross Abstract: Software engineering and deployment are increasingly delegated to AI coding agents.

By Kenneth Ge, Andre Assis

agents safety

arXiv Machine Learning

Jun 25

Swarm-Inspired Generation of Collective Behaviors in Graph Dynamical Systems

arXiv:2606. 24958v1 Announce Type: new Abstract: Collective behavior arises when locally interacting units produce coordinated global organization, from synchronization in dynamical systems to task-relevant information flow on graphs.

By Ji Chen, Song Chen, Chengzhang Gong, Li Fan, Chao Xu

agents robotics benchmarks

arXiv Machine Learning

Jun 25

Project Auto-World: Towards Automated Benchmarking of Neural Relational Reasoners

arXiv:2606. 24965v1 Announce Type: cross Abstract: Reasoning about relational structures remains a significant challenge for neural models, particularly when they must systematically apply learned knowledge to problem instances that are harder than those seen in training.

By Anirban Das, Joanne Boisson, Irtaza Khalid, Sumita Garai, Steven Schockaert

llms agents benchmarks

arXiv Machine Learning

Jun 25

Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents

arXiv:2606. 26080v1 Announce Type: new Abstract: Process reward models enable fine-grained, step-level evaluation of LLMs, yet building them for agentic settings remains prohibitively difficult: long-horizon interactions, irreversible actions, and stochastic environment feedback make both human annotation and Monte Carlo estimation infeasible at scale.

By Changdae Oh, Wendi Li, Seongheon Park, Samuel Yeh, Tanwi Mallick, Sharon Li

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 25

Forget to Improve: On-Device LLM-Agent Continual Learning via Budget-Curated Memory

arXiv:2606. 25115v1 Announce Type: new Abstract: On-device language-model agents improve by accumulating experience in retrieved memory rather than by updating weights.

By Beining Wu, Zihao Ding, Jun Huang, Yanxiao Zhao

llms agents robotics benchmarks

arXiv Machine Learning

Jun 25

Reward-Conditioned Attention: How Reward Design Shapes What Autonomous Driving Agents See

arXiv:2606. 25127v1 Announce Type: new Abstract: We investigate how reward design shapes the internal attention patterns of reinforcement learning agents trained for autonomous driving.

By Mohamed Benabdelouahad, Ahmed Djalal Hacini, Nadir Farhi, Aissa Boulmerka

agents reinforcement-learning robotics safety

arXiv Machine Learning

Jun 25

Agentic evolution of physically constrained foundation models

arXiv:2606. 25532v1 Announce Type: cross Abstract: Artificial intelligence increasingly drives automated scientific discovery, yet contemporary generalist agents lack physical grounding, frequently hallucinating hardware-incompatible designs.

By Jiangwei Zhang, Wen Sun, Chong Wang, Shiyao Li, Cheng Che, Chunjing Han, Dan Meng, Jian Yang, Yu Wang, Rui Hou

agents benchmarks

arXiv Machine Learning

Jun 25

Bias-Controlled Primal-Dual Natural Actor-Critic: Optimal Rates for Constrained Multi-Objective Average-Reward RL

arXiv:2606. 25012v1 Announce Type: new Abstract: Many reinforcement learning (RL) problems in the infinite-horizon average-reward setting require optimizing multiple conflicting objectives while satisfying multiple safety constraints.

By Ankur Naskar, Swetha Ganesh, Vaneet Aggarwal

agents reinforcement-learning safety

arXiv Machine Learning

Jun 25

BiPACE: Bisimulation-Guided Policy Optimization with Action Counterfactual Estimation for LLM Agents

arXiv:2606. 25556v1 Announce Type: cross Abstract: Stepwise group-based RL is an attractive way to train long-horizon LLM agents without a learned critic: it reuses multiple sampled rollouts to estimate local advantages.

By Hanyang Wang, Weijieying Ren, Yuxiang Zhang, Ding Cao, Zhizhao Zeng, Ke Zeng, Tianxiang Zhao

llms agents reinforcement-learning

arXiv Machine Learning

Jun 25

RevengeBench: Reverse Engineering Code-Space Policies from Behavioral Experiments

arXiv:2606. 26094v1 Announce Type: new Abstract: For most of scientific history, researchers studying behavior could only infer hidden mechanisms from outward actions: an inverse problem that becomes more tractable when observation is augmented by targeted intervention.

By Babak Rahmani, Sebastian Dziadzio, Joschka Str\"uber, Sergio Hern\'andez-Guti\'errez, Matthias Bethge

llms agents benchmarks safety

arXiv Machine Learning

Jun 25

ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments

arXiv:2606. 25207v1 Announce Type: new Abstract: Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding strong configurations within a limited budget.

By Taicheng Guo, Haomin Zhuang, Kehan Guo, Yujun Zhou, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

llms agents safety

MIT News AI

Jun 25

Improving the speed and energy-efficiency of AI agents

A new system, known as Murakkab, optimizes the design and deployment of multistep workflows that power AI applications.

By Adam Zewe | MIT News

agents

arXiv Machine Learning

Jun 25

Uncertainty Quantification for Computer-Use Agents: A Benchmark across Vision-Language Models and GUI Grounding Datasets

arXiv:2606. 25760v1 Announce Type: new Abstract: Computer-use agents turn vision-language model (VLM) predictions into executable GUI clicks, so reliable uncertainty estimates are essential for rejection, calibration, miss-severity ranking, and spatial safety regions.

By Divake Kumar, Sina Tayebati, Devashri Naik, Amanda Sofie Rios, Nilesh Ahuja, Omesh Tickoo, Ranganath Krishnan, Amit Ranjan Trivedi

llms agents multimodal benchmarks safety

arXiv Machine Learning

Jun 25

Closed-Loop Graph Algorithm Execution with Small Language Models: Step Accuracy and Rollout Reliability

arXiv:2606. 24980v1 Announce Type: new Abstract: Small language models offer an efficient alternative to large-scale systems, but their ability to execute structured algorithms over multiple dependent decisions remains poorly understood.

By Michal Podstawski

llms agents

arXiv Machine Learning

Jun 25

Minimax PAC Bounds for Learning in Exogenous Contextual MDPs

arXiv:2606. 25170v1 Announce Type: cross Abstract: We study PAC learning in tabular discounted Markov decision processes with exogenous i.

By Corentin Pla, Hugo Richard, Marc Abeille, Vianney Perchet

agents reinforcement-learning

arXiv Machine Learning

Jun 25

Lifelong In-Context Learning with Transformers Requires Parametric Forms of Attention

arXiv:2606. 25342v1 Announce Type: new Abstract: Lifelong continual learning remains an obstacle on the path to human-like intelligence.

By Luke McDermott, Robert W. Heath jr., Rahul Parhi

llms agents efficiency

arXiv Machine Learning

Jun 25

Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization

arXiv:2606. 26002v1 Announce Type: new Abstract: We present HiReLC, a hierarchical ensemble-reinforcement learning framework for automated joint quantization and structured pruning of deep neural networks.

By Kamar Hibatallah Baghdadi, Kawther Guoual Belhamidi, Sara Belhadj, Aissa Boulmerka, Nadir Farhi

llms agents computer-vision reinforcement-learning fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 25

The Interplay of Harness Design and Post-Training in LLM Agents

arXiv:2606. 25447v1 Announce Type: new Abstract: Tool-integrated LLM agents are often wrapped within a harness: the scaffolding that determines which tools are exposed, how they are described, and what auxiliary information accompanies each per-step observation.

By Kyungmin Kim, Youngbin Choi, Seoyeon Lee, Suhyeon Jun, Dongwoo Kim, Sangdon Park

llms agents

arXiv Machine Learning

Jun 25

Semantic Consistency Policy Optimization for Reinforcement Learning of LLM Agents

arXiv:2606. 25852v1 Announce Type: new Abstract: Group-based reinforcement learning effectively post-trains LLM agents for long-horizon, sparse-reward tasks by deriving step-level credit from trajectory outcomes.

By Peng Xu, Sijia Chen, Junzhuo Li, Xuming Hu

llms agents reinforcement-learning

Get the daily briefing