Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2609.25569v1 Announce Type: new Abstract: Soccer tactics are interactive: an attacking action changes the opponent's defensive problem, and the observed response depends on the multi-agent matc...
arXiv:2609.25701v1 Announce Type: new Abstract: We study distributed Byzantine-resilient actor-critic multi-agent reinforcement learning (AC-MARL), where agents collectively learn policies through lo...
arXiv:2609.26146v1 Announce Type: new Abstract: Risk monitoring in multi-agent systems is commonly built on a per-state primitive that scores each state independently and selects the top K. Under cro...
arXiv:2609.26384v1 Announce Type: new Abstract: Medical image interpretation is high-volume and time-consuming, and while AI interpretation can reduce workload, fully autonomous deployment carries po...
arXiv:2609.26219v1 Announce Type: cross Abstract: Long-running LLM agent workflows often revise interior context spans while retaining long suffixes. Although suffix tokens remain unchanged, altered...
arXiv:2609.26642v1 Announce Type: cross Abstract: Successful agent execution need not identify which future product improvement its user would value. We present a decision-specific audit that maps a...
arXiv:2406.04814v4 Announce Type: replace-cross Abstract: Video diffusion models can enable embodied agents to anticipate plausible futures from the recent past, but they are typically trained offlin...
arXiv:2605.01047v2 Announce Type: replace-cross Abstract: Hallucinations remain an unsolved problem for LLMs, and package hallucinations are a particularly dangerous instance of this phenomenon. Pack...
arXiv:2609.25047v1 Announce Type: new Abstract: Autonomous agents that automatically build artificial intelligence (AI) models could broaden access to AI across science and engineering. A popular lin...
arXiv:2609.25054v1 Announce Type: new Abstract: For a long-horizon LLM agent, the memory question is not what was once recorded but what \emph{currently holds}. Most designs answer it only indirectly...
arXiv:2609.26035v1 Announce Type: new Abstract: Conversational agents often express answers in a uniformly confident register. We test whether expressed uncertainty, provenance-aware assertion, and e...
arXiv:2609.26086v1 Announce Type: new Abstract: An agentic retrieval system issues a sequence of search queries and must decide, at each step, whether the evidence collected so far is enough to stop....
arXiv:2609.25578v1 Announce Type: new Abstract: Urban-scale 3D reconstruction from satellite imagery supports disaster response, city monitoring, and geospatial digital twins, yet neural rendering me...
arXiv:2609.25271v1 Announce Type: cross Abstract: Sidescan sonar is a common sensor for both manned and autonomous marine exploration and mapping, yet very few methods build upon or exploit the geome...
arXiv:2606.27729v2 Announce Type: replace Abstract: 6-DoF LiDAR-based localization is a fundamental capability for autonomous systems operating in large-scale outdoor environments. Many deep-learning...
The paper introduces an adequacy‑aware calibration protocol for generative social simulators that integrates amortized posterior estimation, synthetic identifiability assessment, matched‑sample‑size adequacy checks, diagnosis‑guided repair, and held‑out audits. Applied to a second‑hand luxury resale market, the protocol reveals that behavioural parameters are recoverable but calibration is approximate and overconfident for one parameter, and that the simulator’s reachability reference is violated in every cell, particularly in mean purchased tier. The repair improves two of four cells but fails to restore full adequacy, and a held‑out audit uncovers a buyer‑breadth‑dispersion miss not detected earlier; profile‑source ablation shows language‑model‑derived persona profiles outperform a flat rule baseline, though within‑category brand relabelling has no consistent effect. whyItMatters":"The study demonstrates that without an adequacy check, generative social models may appear valid descriptively yet fail to capture key emergent network structures, highlighting the need for rigorous calibration protocols in social simulation research."
The paper discusses how large language model agents now act as privileged principals with kernel‑grade authority, yet lack the trusted mediation traditionally required for operating‑system security. It introduces a taxonomy that distinguishes between provenance‑based deterministic checks and content‑semantic checks, identifying a central mediation gap in distinguishing data from instruction and authorized from unauthorized actions. The authors argue that this gap creates an irreducible risk of undetected attacks whenever inputs and actions are not pre‑enumerated, and they propose defenses across runtime monitoring, architectural separation, and authorization while critiquing current evaluation practices. They extend the analysis to AI‑native operating systems where the model itself serves as the arbitration core, outlining design constraints, challenges, and a research agenda.
The paper discusses the need to adapt incident reporting frameworks for AI agents, which are rapidly deployed and face unique security challenges. By comparing AI systems and agents and consulting 23 experts, the authors identify key reporting elements such as agent memory, autonomy levels, and tool usage. They also highlight open research questions, potential reporting weaknesses like data leakage, and outline privacy requirements for secure AI agent deployment.
WeightBridge is a lightweight library that streamlines weight transfer between trainers and rollout generators in reinforcement learning systems, particularly for large language models. It automatically maps trainer and rollout weight layouts, then performs redundancy‑free, load‑balanced transfers while supporting various synchronization modes. Experiments show that WeightBridge can cut GPU stall time by up to 42× compared to leading open‑source RL frameworks, and it was easily integrated into two different frameworks by a coding agent.
arXiv:2609.24631v1 Announce Type: cross Abstract: Autonomous parking in nonconvex and narrow environments remains challenging. Although optimal-control methods can explicitly enforce vehicle dynamics...