arXiv:2504.06188v2 Announce Type: replace-cross
Abstract: AI agents can extend their capabilities at inference time by loading reusable skills into context, yet equipping an agent with too many skills, particularly irrelevant ones, degrades performanc…
arXiv:2603.27539v1 Announce Type: cross
Abstract: Multi-agent systems based on large language models (LLMs) for financial trading have grown rapidly since 2023, yet the field lacks a shared framework for understanding what drives performance or for ev…
arXiv:2511.14043v3 Announce Type: replace-cross
Abstract: AI Scientific Assistant Core (AISAC) is a transparent, modular multi-agent runtime developed at Argonne National Laboratory to support long-horizon, evidence-grounded scientific reasoning. Rath…
arXiv:2601.00809v2 Announce Type: replace-cross
Abstract: Agentic workflows driven by large language models (LLMs) are increasingly applied to Building Information Modelling (BIM), enabling natural-language retrieval, modification and generation of IF…
arXiv:2603.27404v1 Announce Type: cross
Abstract: Large Language Models (LLMs) are being increasingly used as autonomous agents in complex reasoning tasks, opening the niche for dialectical interactions. However, Multi-Agent systems implemented with s…
arXiv:2601.15109v3 Announce Type: replace-cross
Abstract: Interoperable data and intelligence flows among allied partners and operational end-users remain essential to NATO’s collective defense across both conventional and hybrid threat environments. …
arXiv:2603.27771v1 Announce Type: cross
Abstract: Multi-agent systems composed of large generative models are rapidly moving from laboratory prototypes to real-world deployments, where they jointly plan, negotiate, and allocate shared resources to sol…
arXiv:2603.11560v3 Announce Type: replace-cross
Abstract: This paper develops a dynamical framework for adaptive coordination in systems of interacting agents referred to here as Feedback-Coupled Memory Systems (FCMS). Instead of framing coordination …
arXiv:2603.28082v1 Announce Type: new
Abstract: Generating coherent and communicative visual sequences, such as image sequences and videos, remains a significant challenge for current multimodal systems. Despite advances in visual quality and the inte…
arXiv:2603.26684v1 Announce Type: cross
Abstract: Multi-Agent Path Finding (MAPF) requires collision-free trajectories for multiple agents on a shared graph, often with the objective of minimizing the sum-of-costs (SOC). Many optimal and bounded-subop…