SPIN Unprocessed July 8, 2026 ai_technology research
Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents
View original on arxiv.orgOverview
arXiv:2607.05775v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and operate over extended horizons. Reported benchmark gains often obscure recurring failure modes documented across otherwise unrelated evaluation efforts. This paper synthesizes 27 benchmark, taxonomy, and audit papers (2023-2026), spanning 19 distinct benchmarks, into a cross-cutting taxonomy of agent l
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from arXiv Artificial Intelligence
View all →- SearchEyes: Towards Frontier Multimodal Deep Search Intelligence via Search World Simulation
- PCBWorld: A Benchmark Environment for Engine-Grounded PCB Design Automation
- Uncovering Latent Depression Severity for Binary Depression Detection via Advantage-weighting Ranking
- StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems
- Onnes: A Physics-Grounded Multi-Agent LLM Simulator for Cryogenic Fault Diagnosis in Quantum Computing Infrastructure
- TurnOPD: Making On-Policy Distillation Turn-Aware for Efficient Long-Horizon Agent Training
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO