Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2609.39131v1 Announce Type: new Abstract: Large language model (LLM) serving requires substantial memory to store model weights and KV caches. As models grow larger and contexts become longer,...
arXiv:2609.39547v1 Announce Type: new Abstract: GUI agents built on large language and vision-language models still struggle on unseen applications and complex multi-step tasks, as completing real GU...
arXiv:2609.39777v1 Announce Type: new Abstract: LLM-based multi-agent systems coordinate specialized reasoning through aggregation, interaction, and adaptive control, yet their potential for graph le...
arXiv:2609.40143v1 Announce Type: new Abstract: Compact regulatory DNA can free up space in vector payloads, reduce synthesis and assay burden, and expose which sequence features drive predicted acti...
arXiv:2609.39584v1 Announce Type: cross Abstract: Edge computing has emerged as a critical computing paradigm in modern distributed systems by migrating data processing closer to end users and Intern...
arXiv:2604.19146v2 Announce Type: replace Abstract: Particle accelerator beamline optimization is a high-dimensional control problem traditionally requiring significant expert intervention. We presen...
arXiv:2608.13888v2 Announce Type: replace Abstract: Fashion Outfit Composition (FOC) requires sequentially assembling fashion items into a stylistically cohesive ensemble. Existing works struggle to...
arXiv:2602.23934v2 Announce Type: replace-cross Abstract: This paper presents a novel autonomous robotic assembly framework for constructing stable structures without relying on predefined architectu...
arXiv:2610.00097v1 Announce Type: new Abstract: Recent diffusion and autoregressive models have substantially improved text-to-video generation, yet producing coherent long-form story videos with con...
arXiv:2610.00319v1 Announce Type: new Abstract: Collaborative perception enables connected agents to share complementary observations for 3D object detection, extending sensing range and mitigating o...
arXiv:2610.00960v1 Announce Type: new Abstract: A video benchmark should reward the capability it claims to measure, yet models can exploit answer options, question text, or partial visual evidence....
arXiv:2610.01052v1 Announce Type: new Abstract: We argue that as video generation extends to longer durations, subject consistency should be evaluated over \textit{dynamic subject sets}. We therefore...
arXiv:2610.01215v1 Announce Type: new Abstract: GUI agents require high-quality interaction trajectories to learn how software environments respond to actions, maintain state, and support multi-step...
arXiv:2610.01750v1 Announce Type: new Abstract: Cooperative 3D tracking must integrate complementary observations across agents and time while maintaining consistent identities. When evidence integra...
arXiv:2610.01870v1 Announce Type: new Abstract: Urban environments are shaped by design choices with long-term implications for health, safety, and quality of life, yet evaluating proposed interventi...
arXiv:2610.01939v1 Announce Type: new Abstract: Vision language model (VLM) agents can control robots through visual feedback and action primitives, but repeated model invocations and redundant obser...
arXiv:2610.01999v1 Announce Type: new Abstract: Spatial reasoning benchmarks evaluate vision-language models across diverse tasks, but task-level scores do not reveal which underlying capabilities ac...
arXiv:2610.01742v1 Announce Type: cross Abstract: Equipping artificial agents with spatial intelligence requires a comprehensive generative prior over the dynamic 3D world. We propose World Motion Mo...
arXiv:2610.02196v1 Announce Type: cross Abstract: We study test-time evolution for humanoid loco-manipulation: solving tasks that a controller was never trained for by repurposing its existing skills...
SkillLens introduces a hierarchical skill-evolution framework that organizes skills into a four-layer graph of policies, strategies, procedures, and primitives, allowing retrieval at mixed granularity. The system first retrieves semantically relevant skill seeds, expands them via a degree‑corrected random walk, and uses a verifier to decide whether to accept, decompose, rewrite, or skip each visited unit. This approach enables agents to reuse compatible subskills while locally adapting mismatched components, and theoretical analysis shows sublinear cost under sparse mismatch assumptions, with empirical results on MuLocbench and ALFWorld demonstrating consistent improvements over strong baselines.