Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2610.34582v1 Announce Type: new Abstract: We describe HobbyLM, a family of small sparse Mixture-of-Experts (MoE) models trained from scratch with a few hundred to a few thousand dollars of rent...
arXiv:2610.34668v1 Announce Type: new Abstract: Goal reasoning (GR) is the process by which intelligent agents continually reason about the goals they are pursuing, which may lead to goal change, for...
arXiv:2610.34701v1 Announce Type: new Abstract: In this work, we propose a movable-element (ME) multi-functional reconfigurable intelligent surface (MF-RIS) architecture, termed MM-RIS, which integra...
arXiv:2610.34728v1 Announce Type: new Abstract: Skills provide LLM agents with reusable procedural knowledge, making appropriate skill selection essential for reliable agent behavior. As skill librar...
arXiv:2610.34767v1 Announce Type: new Abstract: Most agent-memory benchmarks frame long-term memory as retrieval: can an agent recover the relevant record from a long and evolving history? Scientific...
arXiv:2610.34836v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems can exhibit emergent behaviors through interaction, yet whether a similar phenomenon exists for reasonin...
arXiv:2610.35398v1 Announce Type: new Abstract: Recent work has explored optimizing LLM collaboration through Multi-Agent Reinforcement Learning (MARL), but many approaches still rely on predefined w...
arXiv:2610.35434v1 Announce Type: new Abstract: Recursive self-improvement (RSI), in which a system also improves the way it improves, is a long-standing aspiration on the path to AGI. Language agent...
arXiv:2610.35690v1 Announce Type: new Abstract: Serving web queries at scale with multi-agent systems requires jointly configuring large language models, collaboration modes, agent counts, interactio...
arXiv:2610.35595v1 Announce Type: new Abstract: LLMs now prove hard theorems in Lean. Can they also design algorithms and prove their correctness and efficiency? LLM-guided design of optimisation heu...
arXiv:2610.32993v1 Announce Type: cross Abstract: Computational pathology (CPath) analyses increasingly depend on multi-step computational workflows that require substantial programming expertise, li...
arXiv:2610.33018v1 Announce Type: cross Abstract: Scholarly Knowledge Graphs (SKGs) support literature search, scientific discovery, and agent-assisted research. Yet, most approaches to SKG construct...
arXiv:2610.33056v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) have revolutionized general visual perception, existing methods suffer from severe hallucination in th...
arXiv:2610.33029v1 Announce Type: cross Abstract: Self-improving agents write lessons from the outcomes of their runs; when outcomes are noisy, a lesson can carry chance into later decisions. We meas...
arXiv:2610.33201v1 Announce Type: cross Abstract: LLM agents take instructions from the operator, repository configuration, user, tool results and saved memory, which often conflict. Instruction-hier...
arXiv:2610.33011v1 Announce Type: cross Abstract: While recent advances in autonomous agents enable them to coordinate on increasingly complex tasks, prior work shows that this growing capacity for c...
arXiv:2610.33475v1 Announce Type: cross Abstract: The widespread deployment of foundational Large Language Models (LLM) across cloud datacenters marks a fundamental paradigm shift from stateless deep...
arXiv:2610.33633v1 Announce Type: cross Abstract: Agents select tools by copyable descriptions, which cannot establish which implementation runs. ProbeID compares canonicalized responses to schema-va...
arXiv:2610.33760v1 Announce Type: cross Abstract: Simulation is widely used in autonomous-driving validation, but matching task outputs between real and synthetic images does not guarantee that a sys...
arXiv:2610.34043v1 Announce Type: cross Abstract: Autonomous agents are increasingly deployed across sequences of related tasks, yet they often fail to retain and improve their knowledge over time. E...