Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2607. 12650v1 Announce Type: cross Abstract: Tool access alone does not make LLM empirical reasoning governable: accepted outputs need not descend from attested evidence, and accepted deductions need not hold up under formal scrutiny.
arXiv:2607. 12893v1 Announce Type: new Abstract: Long-term memory has become a foundational capability for LLM-based agents that accompany users across extended, multi-session interactions.
arXiv:2607. 12227v1 Announce Type: new Abstract: We revisit the evaluation of automatic harness evolution for LLM agents.
arXiv:2607. 12112v1 Announce Type: cross Abstract: Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obstacle prevents robust deployment in dynamic environments: catastrophic forgetting, wherein sequential task updates erase previously acquired knowledge across visual, linguistic, and cross-modal representations.
arXiv:2607. 12172v1 Announce Type: cross Abstract: Decentralized gradient descent (DGD) is widely used for solving distributed optimization problems over networks of agents.
arXiv:2605. 10834v2 Announce Type: replace Abstract: AI pentesting agents are increasingly credible as offensive security systems, but current benchmarks still provide limited guidance on which will perform best in real-world targets.
arXiv:2607. 12463v1 Announce Type: new Abstract: Coding agents must integrate external tool returns into ongoing reasoning - a capability that standard left-to-right pretraining on code exposes only in its forward direction.
arXiv:2607. 13027v1 Announce Type: cross Abstract: Large Language Model (LLM) agents have moved beyond generating responses to executing multi-step tasks by calling tools, observing the results, and iteratively deciding the next action.
arXiv:2607. 11913v1 Announce Type: cross Abstract: Recent advancements in agentic AI have increasingly moved toward graph-based methods, driven by the demand for explainable, human-centered, and non-linear reasoning workflows.
arXiv:2607. 12257v1 Announce Type: new Abstract: On-device research agents search a corpus, read sources, and write a cited brief on a personal laptop.
arXiv:2607. 09759v2 Announce Type: replace-cross Abstract: Building assistants that can continually watch the world, remember what they see, and reason over their accumulated experience is a long-standing goal, and recently multimodal agents equipped with long-term memory over video streams have attracted increasing interest.
arXiv:2607. 12619v1 Announce Type: new Abstract: The rapid emergence of LLM-powered autonomous and semi-autonomous agents is reshaping software systems from static, request-response components into goal-directed, adaptive, and tool-using computational actors.
arXiv:2607. 11953v1 Announce Type: new Abstract: Does a reinforcement-learning agent that earns high reward represent its task's latent state, or only a reward-correlated shortcut?
arXiv:2607. 11948v1 Announce Type: new Abstract: Regulated financial institutions operating under data-residency rules need tenant-owned language models that can run inside the institution's perimeter.
arXiv:2607. 12085v1 Announce Type: new Abstract: Evaluating retail conversational agents requires methods beyond lexical-overlap metrics to assess intent alignment, factuality, helpfulness, clarity, tone, and overall response quality.
arXiv:2603. 08862v2 Announce Type: replace-cross Abstract: Autonomous navigation in highly constrained environments remains challenging for mobile robots.
arXiv:2607. 11985v1 Announce Type: cross Abstract: Hybrid quantum-classical machine learning workflows repeatedly evaluate many small parametrized circuits during training and model exploration.
arXiv:2607. 12747v1 Announce Type: new Abstract: Failure attribution for LLM-based agentic systems, i.
arXiv:2607. 12397v1 Announce Type: new Abstract: LLM agents act in external environments where each action changes the state that later decisions condition on, and where a single wrong step can waste interaction budget or trigger irreversible side effects long before the final failure is observed.
arXiv:2607. 12180v1 Announce Type: cross Abstract: An AI teammate's design properties (personality, communication style, when it speaks) can shape a team's trust, coordination, and decisions.