Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2610.27870v1 Announce Type: cross Abstract: Hamilton-Jacobi (HJ) reachability analysis offers formal safety guarantees for control systems, but training accurate neural certificates remains dif...
arXiv:2607.08925v2 Announce Type: replace Abstract: Training agents directly on the task they will be deployed on often leads to the best policy performance, but training on physical robots can cause...
arXiv:2609.33848v2 Announce Type: replace Abstract: Large language model (LLM) agents increasingly undertake extreme-long (xlong) horizon tasks, where a single execution can span hours, hundreds of m...
arXiv:2610.26770v2 Announce Type: replace Abstract: Harness development plays a crucial role in building capable AI agents. Recent approaches improve harnesses through recursive self-improvement (RSI...
arXiv:2610.26926v1 Announce Type: new Abstract: Large language models (LLMs) possess broad factual knowledge and strong reasoning capabilities, but solving complex tasks also requires procedural know...
arXiv:2610.27227v1 Announce Type: new Abstract: Cooperative language-model agents can improve without weight updates by revising reusable skills, but only if a shared team reward is traced to the res...
arXiv:2610.27109v1 Announce Type: cross Abstract: Spoken interaction is moving from alternating turns to full-duplex conversation, where an agent listens while speaking. Learning this coordination re...
arXiv:2610.28381v1 Announce Type: cross Abstract: Reasoning about safety is essential for embodied agents powered by large language models (LLMs). We introduce the Contextual Action and Plan Safety b...
arXiv:2601.16400v2 Announce Type: replace Abstract: Real-world visual question answering (VQA) may be context-dependent: an image-question pair could be under-specified, such that the correct answer...
arXiv:2610.27104v1 Announce Type: new Abstract: Reliable language-guided perception is essential for embodied agents, where questions about objects, events, and their locations must be answered from...
arXiv:2610.27110v1 Announce Type: new Abstract: Autonomous Unmanned Aerial Vehicle (UAV) navigation in urban environments requires precise pose estimation. Because Global Navigation Satellite System...
AutoRedClaw is an automated red‑teaming framework designed for claw‑style agents such as OpenClaw, which maintain persistent configuration, memory, and reusable skills. It generates end‑to‑end attacks and the necessary environments, using a strict quality‑checking protocol and execution‑based validation to ensure realistic, effective samples. The framework produces a 330‑case benchmark, RedClaw‑Bench, achieving over 30% effective attack rate at less than $0.10 per successful sample and demonstrating higher attack success rates than existing benchmarks across multiple agent systems.
The article discusses how glass-box deep reinforcement learning (DRL) can discover actuation strategies for high‑speed, nonlinear, unsteady flows without explicit models, potentially revealing mechanisms beyond classical control. It highlights the challenges of applying DRL to real high‑speed flows, including simulation cost, data efficiency, sensing in extreme environments, reward design, explainability, and generalization across Mach and Reynolds numbers. The authors argue that overcoming these constraints will require physics‑aware learning, robust real‑time operation, and extensive validation to translate DRL capabilities into reliable, certified technology.
Self-Credit is an agentic reinforcement learning method that transforms sparse outcome rewards into dense supervision signals through self-generated credit assignment and intra-group rescaling. The approach involves the policy model summarizing key evidence with citations, refining these citations by comparing actions across successful and failed rollouts, and reweighting advantages based on the rescaled credit. Experiments on BrowseComp-Plus, GAIA, and xBench-DeepSearch show significant performance gains over baseline models and other agentic RL methods.
arXiv:2610.27690v1 Announce Type: new Abstract: Simulation-ready world reconstruction demands physical interactivity beyond visual realism, which existing black-box 3D generation fails to deliver. We...
arXiv:2610.27999v1 Announce Type: new Abstract: Moving from controllable videos to genuinely playable worlds requires persistent world state, executable game rules, and low-latency interaction. We ou...
arXiv:2610.28382v1 Announce Type: new Abstract: Training multimodal search agents requires tasks that make visually grounded search necessary for determining the answer. Textual identity clues and un...
arXiv:2610.28410v1 Announce Type: new Abstract: Deep learning has become the default tool for medical image analysis, driven by the open-source movement. However, achieving the best performance on a...
arXiv:2610.28530v1 Announce Type: new Abstract: Collaborative perception enables agents to retrieve the scene semantics and geometry beyond the field of view of any individual agent. This broader sce...
arXiv:2610.28554v1 Announce Type: new Abstract: Despite the success of video world models, extending them to multiple agents is difficult because it requires generating several agent-specific video s...