arXiv AI
AgentSelect: Benchmark for Narrative Query-to-Agent Recommendation
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arXiv AI
Jun 9
SearchSwarm: Towards Delegation Intelligence in Agentic LLMs for Long-Horizon Deep Research
arXiv:2606. 09730v1 Announce Type: new Abstract: Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose context demands can grow without bound, yet model context windows remain inherently finite.
arXiv AI
Oct 7
WorkflowOps: Learning Agent Collaboration Priors for Multi-Agent Workflow Orchestration
WorkflowOps is a multi‑agent workflow orchestration framework that learns collaboration priors from historical workflows. It uses a transition probability matrix to guide DAG construction, a sufficiency‑driven loop to create new agents on demand via an LLM, and a layered semantic matching strategy to reduce LLM calls. Experiments show that WorkflowOps improves end‑to‑end pass rates, especially on structured, decomposable tasks.
arXiv AI
Jun 16
SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution
arXiv:2605. 18401v2 Announce Type: replace-cross Abstract: Long-horizon LLM agents generate traces that could become reusable experience, but raw trajectories are noisy, local, and hard to govern.
arXiv AI
Sep 28
Agentick: A Unified Benchmark for General Sequential Decision-Making Agents
Agentick is a unified benchmark for sequential decision‑making agents that evaluates RL, LLM, VLM, hybrid, and human agents on 37 procedurally generated tasks across six capability categories, four difficulty levels, and five observation modalities via a single Gymnasium‑compatible interface. It includes a Coding API, oracle reference policies, pre‑built SFT datasets, a composable agent harness, and a live leaderboard. An evaluation of 27 configurations and over 90,000 episodes shows no single approach dominates, with GPT‑5 mini leading overall, PPO excelling in planning and multi‑agent tasks, and the reasoning harness boosting LLM performance by 3‑10×, while ASCII observations outperform natural language.
arXiv Computation and Language
3d ago
AgentHop: A Diagnostic Benchmark for Agentic Multi-Hop Question Answering over Scientific Citation Graphs
AgentHop is a diagnostic benchmark comprising 1,011 multiple‑choice questions that test agentic multi‑hop question answering over scientific citation graphs. It provides a controlled seven‑tool sandbox with fixed token, turn, and tool‑call limits, allowing researchers to dissect model performance across four operational axes: retrieval, synthesis, tool‑call, and resource management. Analysis of 19 models shows distinct family‑specific behaviors, such as GPT models committing early, Anthropic and GLM checkpoints verifying before committing, DeepSeek and Kimi over‑searching, and Gemini‑3 Pro maintaining balance, while within‑family differences are revealed in retrieval versus synthesis strategies.
arXiv AI
Aug 5
HyperAgent: Planning and Acting over Tool-Schema Hypergraphs for Tool-Use LLM Agents
arXiv:2608. 02650v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external tools to complete complex real-world tasks.