arXiv:2607. 22591v1 Announce Type: new Abstract: Populations of autonomous agents deployed in unknown environments (e.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2601. 21754v3 Announce Type: replace Abstract: While Large Language Models (LLMs) excel in language-based agentic tasks, their applicability to unseen, nonlinguistic environments (e.
arXiv:2601. 19792v4 Announce Type: replace-cross Abstract: For generative AI agents to partner effectively with human users, the ability to accurately predict human intent is critical.
arXiv:2609.00474v1 Announce Type: cross Abstract: LLMs are increasingly deployed as orchestrators that coordinate specialized subagents to solve complex tasks through natural language. However, in ma...
ViSMoE introduces a visual‑aware sparse Mixture‑of‑Experts framework for embodied referring expression grounding, enabling an agent to navigate real environments and localize a target object from natural language instructions. By routing visual information through specialized experts, the method produces discriminative representations for both navigation views and candidate objects, unlike prior approaches that use a single vision encoder. Experiments on the REVERIE and SOON datasets show that ViSMoE surpasses existing state‑of‑the‑art methods.
arXiv:2602. 00471v2 Announce Type: replace Abstract: While Visual Multi-Agent Systems (VMAS) promise to enhance comprehensive abilities through inter-agent collaboration, empirical evidence reveals a counter-intuitive "scaling wall": increasing agent turns often degrades performance while exponentially inflating token costs.
arXiv:2606. 18271v1 Announce Type: new Abstract: As Earth Observation data generation outpaces downlink bandwidth and human-in-the-loop processing, a widening gap has emerged between onboard collection and actionable ground intelligence.