arXiv AI
RITE: Evaluating End-to-End Resilience to Silent Tool Errors in E-Commerce Agentic Systems
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arXiv AI
Sep 1
AgentRx: Diagnosing AI Agent Failures from Execution Trajectories
arXiv:2602.02475v2 Announce Type: replace Abstract: AI agents often fail in ways that are difficult to localize because executions are probabilistic, long-horizon, multi-agent, and mediated by noisy...
arXiv AI
Jun 2
Early Diagnosis of Wasted Computation in Multi-Agent LLM Systems via Failure-Aware Observability
arXiv:2606. 01365v1 Announce Type: new Abstract: Tool-using multi-agent large language model (LLM) systems spend computation through model tokens, tool calls, retries, and code execution before producing an answer.
arXiv AI
Jun 30
Entity Binding Failures in Tool-Augmented Agents
arXiv:2606. 30531v1 Announce Type: new Abstract: Tool-augmented language-model agents are often evaluated by whether they select the correct tool, produce valid API arguments, and complete the requested task.
arXiv Machine Learning
3d ago
Benchmarking and Error Analysis of Deep Search and Deep Research LLM Agents
The paper introduces a new methodology and analytical framework to diagnose errors in the intermediate execution traces of autonomous Deep Search and Deep Research Large Language Model agents. It presents a taxonomy that classifies these errors into System‑Level and Task‑Level categories, and uses systematic examination of step‑by‑step traces to show how they impact overall performance. The authors also provide empirical experiments and design guidelines aimed at improving the robustness, reasoning, and reliability of these agents.
arXiv AI
Sep 24
Silent Failures in Agent-Tool Interaction: An Audit of ToolUniverse
The paper audits silent failures in agent-to-tool interactions within the ToolUniverse environment, focusing on 15 scientific tools. It identifies 91 failures—primarily missing data or inconsistent search/filtering—occurring mainly in the API and wrapper layers, and shows how these silent failures can propagate downstream into seemingly valid outputs. The authors propose contextual reliability and recommend testing, disclosure, monitoring, and measurement strategies for the agent-tool pipeline.
arXiv AI
4d ago
Lost in Recovery: Demystifying Recovery Behavior of Computer-Use Agents via Goal-Oriented Action Replay
arXiv:2610.27954v1 Announce Type: new Abstract: Recent advances in large (vision) language models have enabled computer-use agents that automate digital workflows via fine-grained keyboard and mouse...