arXiv AI
Jul 7
Teaming Up with AI: Coordination and Cooperation
arXiv:2607. 03181v1 Announce Type: cross Abstract: Successful diffusion of AI in the workforce hinges on the economic value that AI brings to human endeavors.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv Machine Learning
Jul 7
Social Networks of LLM Agents
arXiv:2607. 03695v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in interacting populations, raising the question of what such populations come to believe collectively.
arXiv AI
Jul 7
Strategic Buying Agents
arXiv:2607. 04708v1 Announce Type: cross Abstract: Agentic AI is shifting online shopping from search toward delegated purchasing, where autonomous buying agents monitor markets and decide when to buy on a consumer's behalf.
arXiv Machine Learning
Jul 7
AdaptiveSD A Stability-Aware, Runtime-Adaptive Speculative Decoding Framework with Multi-Policy Orchestration for CPU-Constrained LLM Inference
arXiv:2607. 03876v1 Announce Type: new Abstract: With the rise of small quantized GGUF-based language models and their increasing use for on-device inference tasks, we have seen the growing need for an approach capable of reliably delivering these models at scale even under severe memory bandwidth constraints such as those imposed by pure CPU implementations.
arXiv Machine Learning
Jul 7
Explicit Credit Assignment through Local Rewards and Dependence Graphs in Multi-Agent Reinforcement Learning
arXiv:2601. 21523v2 Announce Type: replace Abstract: To promote cooperation in Multi-Agent Reinforcement Learning, the reward signals of all agents can be aggregated together, forming global rewards that are commonly known as the fully cooperative setting.
arXiv AI
Jul 7
Predicting Biased Human Decision-Making with Large Language Models in Conversational Settings
arXiv:2601. 11049v2 Announce Type: replace-cross Abstract: We examine whether large language models (LLMs) can predict biased decision-making in conversational settings, and whether their predictions capture not only human cognitive biases but also how those effects change under cognitive load.
arXiv AI
Jul 7
AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning
arXiv:2607. 03182v1 Announce Type: cross Abstract: Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable continuous trajectories.
arXiv AI
Jul 7
Unified Audio Intelligence Without Regressing on Text Intelligence
arXiv:2607. 05196v1 Announce Type: cross Abstract: Audio intelligence involves understanding, reasoning about, and generating both audio and speech.
arXiv AI
Jul 7
No Time Like the Present: Agentic Test-Time Training for LLM Agents
arXiv:2607. 03441v1 Announce Type: cross Abstract: LLM agents often degrade over long episodes: as trajectories grow, they revisit explored states, repeat failed actions, and lose strategies that previously worked.
arXiv AI
Jul 7
UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning
arXiv:2607. 04425v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform interaction.
arXiv AI
Jul 7
SkillOpt-Lite: Better and Faster Agent Self-evolution via One Line of Vibe
arXiv:2607. 03451v1 Announce Type: cross Abstract: While skill optimization for autonomous agents has gained traction, existing methods rely on complex pipelines.
arXiv AI
Jul 7
MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation
arXiv:2605. 27366v2 Announce Type: replace Abstract: Large language model (LLM) agents rely on reusable skills to solve complex tasks, but existing skill creation approaches often treat skills as isolated, static artifacts, limiting reusability, reliability, and long-term improvement.
arXiv AI
Jul 7
SovereignNegotiation-Bench: Evaluating User-Owned Personal Agents In Delegated Bargaining Under Privacy, Consent, Evidence, And Institutional Pressure
arXiv:2607. 02814v1 Announce Type: cross Abstract: Personal agents will increasingly negotiate on behalf of users: splitting costs with other personal agents, appealing platform decisions, escalating support disputes, requesting refunds, changing subscriptions, and negotiating deadlines or reimbursements.
arXiv AI
Jul 7
iFLYTEK-Embodied-Omni Technical Report
arXiv:2607. 02542v1 Announce Type: new Abstract: General-purpose embodied agents must understand multimodal instructions, anticipate how their environment will evolve, and produce precise control actions over extended horizons.
arXiv AI
Jul 7
AgenticPD: A Stage-Aware Agentic Framework for Physical Design QoR Optimization
arXiv:2607. 04758v1 Announce Type: new Abstract: Physical design quality-of-results~(QoR) optimization is hard and expensive.
arXiv AI
Jul 7
OpenTinker: Separating Concerns in Agentic Reinforcement Learning
arXiv:2601. 07376v2 Announce Type: replace Abstract: We introduce \textsc{OpenTinker}, an open infrastructure for training large language model (LLM) agents with many LoRA-backed policies over shared execution resources.
arXiv AI
Jul 7
OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement
arXiv:2607. 05346v1 Announce Type: new Abstract: We propose OptiAgent, a multi-agent framework that, given a natural language description of an Operations Research problem, is able to output a solver-ready mathematical formulation as well as executable code.
arXiv AI
Jul 7
AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments
arXiv:2607. 05174v1 Announce Type: new Abstract: Language agents, i.
arXiv AI
Jul 7
Ask the World Before Acting: Environment Probing for Calibrated Agent World Models
arXiv:2606. 31422v2 Announce Type: replace Abstract: Language agents acting over long horizons must maintain beliefs about tool states, object locations, graph edges, and subgoal dependencies.
arXiv AI
Jul 7
Attention is Just Another Name for Coupling? A Fast-Slow ODE Perspective on Hierarchical Pretraining
arXiv:2606. 16730v2 Announce Type: replace-cross Abstract: We re-interpret Transformer pretraining as a fast-slow, singularly perturbed flow along depth, with untied weights as its non-autonomous feature.