arXiv AI
Jun 19
Exit-and-Join Dynamics for Decentralized Coalition Formation
arXiv:2606. 19683v1 Announce Type: new Abstract: This paper studies coalition formation as a decentralized dynamical process driven by unilateral exit-and-join decisions.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Jun 19
Playful Agentic Robot Learning
arXiv:2606. 19419v1 Announce Type: cross Abstract: Current agentic robot systems can write executable Code-as-Policy programs, observe feedback, and revise behavior across multiple attempts, but they remain largely task-driven: reusable skills are acquired only after explicit instructions.
arXiv AI
Jun 19
MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learning
arXiv:2506. 14990v3 Announce Type: replace Abstract: Benchmarks play a central role in reinforcement learning (RL) research, yet their computational constraints often shape what is studied.
arXiv AI
Jun 19
UniMM: A Unified Mixture Model Framework for Multi-Agent Simulation
arXiv:2501. 17015v2 Announce Type: replace Abstract: Simulation plays a crucial role in assessing autonomous driving systems, where the generation of realistic multi-agent behaviors is a key aspect.
arXiv AI
Jun 19
Advancing DialNav through Automatic Embodied Dialog Augmentation
arXiv:2606. 19948v1 Announce Type: new Abstract: For embodied agents capable of physical interaction, the capability to create and understand dialog is crucial to ensure both safety and effectiveness.
arXiv AI
Jun 19
CRAX: Fast Safe Reinforcement Learning Benchmarking
arXiv:2606. 20376v1 Announce Type: cross Abstract: Safety is a core concern for deploying reinforcement learning (RL) agents in real-world domains such as robotics and autonomous driving.
arXiv AI
Jun 19
VitalAgent: A Tool-Augmented Agent for Reactive and Proactive Physiological Monitoring over Wearable Health Data
arXiv:2605. 29483v2 Announce Type: replace Abstract: Wearable devices enable continuous monitoring of physiological signals such as ECG and PPG, but existing mHealth systems are largely limited to task-specific prediction pipelines or reactive question answering over static summaries.
arXiv AI
Jun 19
SoftSkill: Behavioral Compression for Contextual Adaptation
arXiv:2606. 20333v1 Announce Type: new Abstract: Agent skills are commonly deployed as natural-language Markdown files that encode answer policies, evidence-use habits, and task procedures.
arXiv AI
Jun 19
TelcoAgent: A Scalable 5G Multi-KPM Forecasting With 3GPP-Grounded Explainability
arXiv:2606. 19821v1 Announce Type: new Abstract: Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks.
arXiv AI
Jun 19
Contagion Networks: Evaluator Bias Propagation in Multi-Agent LLM Systems
arXiv:2606. 20493v1 Announce Type: cross Abstract: When large language models serve as evaluators in multi-agent systems, their systematic evaluation biases propagate through the agent network.
arXiv AI
Jun 19
The Tao of Agency: Autotelic AI, Embedded Agency and Dissolution of the Self
arXiv:2606. 19924v1 Announce Type: new Abstract: Most artificial intelligence systems are built on the assumption that goals are exogenous and specified by the designer.
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).
arXiv Machine Learning
Jun 19
Matching Markets meet Cumulative Prospect Theory: Towards Optimal and Adversarially Robust Learning
arXiv:2606. 19883v1 Announce Type: new Abstract: We study a multi-agent multi-armed bandit problem in the competitive setup with two-sided matching markets under a human centric decision making model.
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.
arXiv Machine Learning
Jun 19
Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation
arXiv:2510. 08807v2 Announce Type: replace-cross Abstract: From loco-motion to dextrous manipulation, humanoid robots have made remarkable strides in demonstrating complex full-body capabilities.
arXiv AI
Jun 19
Bistable by Construction: Wall-Clock-Calibrated State Monitors Have No Moment-Detection Regime at Agent Cadence
arXiv:2606. 19386v1 Announce Type: cross Abstract: Runtime monitors for autonomous agents commonly threshold an accumulated internal state - a behavioural baseline, a drift statistic, or, in our prior work, a modelled affective state.
arXiv AI
Jun 19
Efficient and Sound Probabilistic Verification for AI Agents
arXiv:2606. 20510v1 Announce Type: cross Abstract: Securing AI agents that operate in complex digital environments has become a critical need, and runtime monitoring approaches that formulate and enforce policies expressed in a formal language like Datalog offer a promising solution.
arXiv AI
Jun 19
Measuring Biological Capabilities and Risks of AI Agents
arXiv:2606. 19899v1 Announce Type: cross Abstract: This paper addresses a rapidly emerging policy challenge: how to generate and interpret credible evidence about the biological capabilities and risks of AI scientists, or agentic AI systems capable of autonomously or collaboratively performing multi-step scientific tasks.
arXiv AI
Jun 19
Class-Incremental Motion Forecasting
arXiv:2603. 09420v3 Announce Type: replace-cross Abstract: Motion forecasting enables autonomous vehicles to anticipate scene evolution by predicting the future trajectories of dynamic agents.
arXiv AI
Jun 19
The Autonomy Tax: Defense Training Breaks LLM Agents
arXiv:2603. 19423v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents increasingly rely on external tools (file operations, API calls, database transactions) to autonomously complete complex multi-step tasks.