Long-Horizon Agent Trajectory Attribution: A Unified Benchmark and Fine-Grained Annotation Framework

arXiv:2608.06909 · cs.AI · Submitted 2026-08-07 · Read on arXiv

Jing Chen, Yang Sun, Li Zhang, Lin Xu, Jie Shi

cs.AI

Submitted: 2026-08-07

Updated: 2026-08-10

Comments: 17 pages, 4 figures, 7 tables

Code: https://github.com/chenjing-2024/agent-trajectory-attribution

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 118× in this paper · explained in 2 episodes
• Trace — 9× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Large Language Model (LLM) — 3× in this paper · explained in 3 episodes
• Multi-Agent System — 3× in this paper · explained in 6 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Reasoning Traces — 2× in this paper · explained in 3 episodes
• Annotation Ambiguity — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Multi-agent Collaboration — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• AgentRx: Diagnosing AI Agent Failures from Execution Trajectories
• Seeing the Whole Elephant: A Benchmark for Failure Attribution in LLM-based Multi-Agent Systems
• AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
• AttriGuard: Defeating Indirect Prompt Injection in LLM Agents via Causal Attribution of Tool Invocations
• Rethinking Failure Attribution in Multi-Agent Systems: A Multi-Perspective Benchmark and Evaluation
• CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution
• AgentCanary: A Security Evaluation Framework for Autonomous AI Agents in Real Executable Environments
• ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis
• AgentBench: Evaluating LLMs as Agents
• ASEval: Automated Trajectory-Level Security Testing for Autonomous Agents
• The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution
• Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failures
• HINTBench: Horizon-agent Intrinsic Non-attack Trajectory Benchmark
• Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems
• MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents

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