arXiv AI
Jul 7
SkillFab: An Agent-Native Skill Production Platform
arXiv:2607. 03780v1 Announce Type: cross Abstract: SkillFab is an agent-native platform for turning missing capabilities into reviewed, reusable Agent Skills.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Jul 7
TRACE: Capability-Targeted Agentic Training
arXiv:2604. 05336v2 Announce Type: replace Abstract: Models often fail to complete agentic tasks because they lack core capabilities required by the target environment.
arXiv AI
Jul 7
Automated Data Readiness for Scientific AI
arXiv:2607. 02771v1 Announce Type: new Abstract: Leadership computing facilities steward large-scale scientific datasets that routinely require substantial transformation before serving as AI training data.
arXiv AI
Jul 7
VERITAS: Towards a General-Purpose Replication Tool for Scientific Research
arXiv:2607. 02931v1 Announce Type: new Abstract: AI tools are accelerating scientific publication while the systems that review it struggle to keep up, and independent verification of published research has become both harder and more important.
arXiv AI
Jul 7
VideoSearcher: Empowering Video Deep Research with Multi-Tool Agentic Reasoning via Reinforcement Learning
arXiv:2607. 02927v1 Announce Type: cross Abstract: Video understanding is moving beyond closed-context perception toward open-world evidence exploration, a paradigm formalized as Video Deep Research (VDR).
arXiv AI
Jul 7
Policy Improvement with Style-Specific Demonstrations
arXiv:2506. 16995v4 Announce Type: replace Abstract: Proficient game agents with diverse play styles enrich the gaming experience and enhance the replay value of games.
arXiv Machine Learning
Jul 7
LLM-Guided Transportation Hub Capacity Planning with Textual Business Inputs
arXiv:2607. 03651v1 Announce Type: new Abstract: While traditional hub capacity planning models optimize effectively for quantitative inputs, they often fail to digest qualitative business context.
arXiv AI
Jul 7
When Outcome Looks Right But Discipline Fails: Trace-Based Evaluation Under Hidden Competitor State
arXiv:2605. 18580v2 Announce Type: replace Abstract: Outcome-only evaluation can certify economically unsafe agents: a policy can hit a business KPI while violating deployable behavioral discipline.
arXiv Machine Learning
Jul 7
Governing Generative AI Across Financial Institutions: An SR 26-2-Compatible Framework for Generative AI Risk Control
arXiv:2607. 04103v1 Announce Type: cross Abstract: The release of SR 26-2 marks a significant modernization of U.
arXiv AI
Jul 7
PLACEMEM: Toward a Compute-Aware Memory Plane for Lifelong Agents
arXiv:2607. 04089v1 Announce Type: new Abstract: Lifelong agents need more than larger context windows and better retrieval.
arXiv AI
Jul 7
Conflict-Based Lazy Search for Fast Multi-Manipulator Planning
arXiv:2607. 04124v1 Announce Type: cross Abstract: Employing multiple manipulators can boost efficiency and accomplish tasks that a single manipulator cannot do.
arXiv AI
Jul 7
Multiplayer Interactive World Models with Representation Autoencoders
arXiv:2607. 05352v1 Announce Type: cross Abstract: We introduce the first multiplayer world model for highly dynamic environments governed by complex physical interactions.
arXiv AI
Jul 7
Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents
arXiv:2606. 27806v3 Announce Type: replace Abstract: Language agents plan by generating not only actions but also implicit predictions of how the world will change.
arXiv Machine Learning
Jul 7
Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence
arXiv:2602. 24115v2 Announce Type: replace Abstract: Open RAN (O-RAN) exposes rich control and telemetry interfaces across the Non-RT RIC, Near-RT RIC, and distributed units, but also makes it harder to operate multi-tenant, multi-objective RANs in a safe and auditable manner.
arXiv AI
Jul 7
CAP-CoT: Cycle Adversarial Prompt for Improving Chain of Thoughts in LLM Reasoning
arXiv:2604. 23270v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) prompting has emerged as a simple and effective way to elicit step-by-step solutions from large language models (LLMs).
arXiv AI
Jul 7
Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration
arXiv:2509. 10656v2 Announce Type: replace-cross Abstract: For groups of autonomous agents to achieve a particular goal, they must engage in coordination and long-horizon reasoning.
arXiv AI
Jul 7
TACTIC-KG: Toward Small Agent Teams for Cyber Threat Intelligence Knowledge Graph Construction
arXiv:2607. 05001v1 Announce Type: cross Abstract: Cyber Threat Intelligence (CTI) reports are predominantly unstructured, heterogeneous, and noisy, which limits their direct usability for automated analysis and reasoning.
arXiv AI
Jul 7
AgentLTL: A Trace-Verification Framework for Measuring, Enforcing, and Training Procedural Compliance in Tool-Using LLM Agents
arXiv:2607. 02599v1 Announce Type: cross Abstract: Tool-using LLM agents are usually evaluated by final-answer correctness or LLM judges.
arXiv AI
Jul 7
Efficient Perception in Automotive Detection and Tracking Using Neuromorphic Computing
arXiv:2607. 04921v1 Announce Type: cross Abstract: Deep learning algorithms are notorious for their high carbon footprint and computational demands that limit their deployment on edge devices and raise concerns about their long-term sustainability.
arXiv AI
Jul 7
Beyond Forecasting: The Belief-to-Trade Layer in Prediction-Market Agents
arXiv:2607. 03015v1 Announce Type: new Abstract: Forecasting future events has attracted growing attention as a testbed for general-purpose AI.