Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2603.06859v4 Announce Type: replace Abstract: Credit assignment for a team of LLM agents, what each message was worth, has mostly been treated as prediction: a learned critic, a trajectory-leve...
arXiv:2610.06083v2 Announce Type: replace Abstract: Most adversarial attacks on deep reinforcement learning (DRL) assume white-box access to the victim policy, which rarely holds in practice. This pa...
arXiv:2605.19352v2 Announce Type: replace-cross Abstract: Understanding how humans and artificial intelligence systems predict and plan by interacting with their environment is a fundamental challeng...
arXiv:2610.09163v1 Announce Type: new Abstract: Task-oriented conversational agents remain fragile under real world conversation scenarios as they rarely follow a predictable script, especially when...
arXiv:2610.09772v1 Announce Type: new Abstract: Small language-model agents on edge devices must hold a persona and reason correctly at once, inside one context window that fills with conversational...
arXiv:2610.09788v1 Announce Type: new Abstract: Reward modeling often requires jointly representing and reasoning over multiple evaluation criteria, yet verbalizing this process token by token can in...
arXiv:2610.09769v1 Announce Type: cross Abstract: Large language models are increasingly contributing to mathematical research, where progress often depends on efficient proof search, incremental imp...
arXiv:2610.10088v1 Announce Type: cross Abstract: Self-evolving agents distill task-solving experience into skills for future reuse, but these skills can encode incorrect procedures or non-transferab...
arXiv:2610.09444v1 Announce Type: new Abstract: Forecasting future 3D occupancy from surround-view cameras is essential for autonomous driving, yet existing approaches rely on dense voxel or bird's-e...
arXiv:2610.10133v1 Announce Type: new Abstract: Real-world low-quality images suffer from complex mixed degradations, including but not limited to noise, blur, atmospheric effects, etc. Recent agenti...
arXiv:2610.10090v1 Announce Type: new Abstract: Collaborative perception extends the sensing range of autonomous vehicles, but its performance degrades when shared features arrive stale or incomplete...
arXiv:2610.10438v1 Announce Type: new Abstract: Embodied and assistive agents must do more than recognize objects: they must reason about where an object belongs given the layout of an environment an...
arXiv:2610.10125v1 Announce Type: cross Abstract: Household robots need to continually navigate to different objects in the same environment, many of which are small and portable, such as tools and t...
arXiv:2604.19710v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models bring rich world knowledge and reasoning capabilities to autonomous driving. However, most driving VLAs are tra...
arXiv:2610.10388v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models have made them capable generalist physical agents for a range of manipulation tasks. However, success...
arXiv:2610.04432v2 Announce Type: replace Abstract: Building interactive simulators from real-world observations is a promising way to scale embodied data, but current pipelines still rely heavily on...
arXiv:2610.09890v1 Announce Type: cross Abstract: Inverse optimization estimates the weights of an objective function that explain observed decisions as optimal solutions, and is used in a variety of...
The paper introduces DAC (Divide and Cooperate), a role‑decomposed training framework that separates evidence acquisition (searcher) and response generation (generator) in agentic search systems. DAC uses task‑specific external verification signals to provide cross‑verification rewards, allowing the generator to abstain when evidence is insufficient and to assign credit appropriately to each role. Experiments on seven QA benchmarks and two model backbones show that DAC consistently outperforms single‑agent and multi‑agent baselines, with gains attributed to coordinated improvements in both search and generation.
The paper introduces TRACE, an algorithm that learns from natural‑language feedback by treating user intent as latent constraints and exploring feasible action regions organized in a tree. TRACE tests candidate constraints by generating compliant actions and only commits to a refinement when repeated feedback does not contradict it, offering two modes: falsification and identification. The authors provide theoretical coverage guarantees and demonstrate that TRACE‑Identification outperforms prompting baselines on six language‑feedback tasks, achieving higher success rates and robustness to identity corruption.
FREEEVOLVE automates the optimization loop used by agent evolvers, allowing the system to decide what to test, how much evidence to gather, which candidates to pursue, and when to stop, all within specified resource limits. By treating these decisions as an editable evolution skill and improving them through meta‑evolution, the method learns a capability that replaces hand‑crafted search loops. Experiments on several benchmarks show that FREEEVOLVE improves primary metrics by an average of 13.6 points and that meta‑evolved skills add further gains, indicating transferability across environments.