LiteTrajEval pairs budget-bounded rule profiles with a single rubric-guided LLM judge, improving failure-localization alignment with human annotations by roughly 20–35 percentage points while cutting cost about 6x and evaluation time more than 8x
Related research and updates
Synopsis
The work presents LiteTrajEval, a lightweight architecture for budget-bounded trajectory evaluation that derives compact domain-specific rule profiles offline, then preprocesses each trajectory online, marks heuristic failure signals, serializes it under a fixed global budget, and invokes a single rubric-guided LLM judge to produce structured diagnostic reports; on public Magentic-One-style and tau-bench-style trajectory datasets it improves failure-localization alignment with human annotations by roughly 20–35 percentage points on Magentic-One and up to 23 percentage points on tau-retail compared with AgentRx, while reducing cost by about 6x and evaluation time by more than 8x, and it has also been deployed in the authors' enterprise agentic platform.
Fig. 1: Overview of the LiteTrajEval pipeline. An offline step (dash line) generates domain-specific rule profiles from accumulated trajectory outputs. The online path processes heterogeneous agent execution logs, serializes trajectories under a fixed budget, and evaluates them with rubric-guided LLM judge.
arXivInterpretation
It introduces LiteTrajEval, a budget-bounded trajectory evaluation architecture for production settings that separates offline derivation of domain-specific rule profiles from an online pipeline of trajectory preprocessing, heuristic failure-signal marking, serialization under a fixed global budget, and a single rubric-guided LLM judge producing structured diagnostic reports. Relative to repeatedly running full trajectory evaluation, the architecture moves construction of domain-specific rule profiles to an offline stage and compresses trajectories online under a fixed global budget, bringing evaluation cost and time into a controlled range. The abstract reports evaluation on public Magentic-One-style and tau-bench-style trajectory datasets and deployment in an enterprise agentic platform; implementation details and experimental configuration are not given in the abstract.
On failure-localization alignment with human annotations, LiteTrajEval improves over AgentRx by roughly 20–35 percentage points on Magentic-One and up to 23 percentage points on tau-retail. The result anchors evaluation quality in a comparison against AgentRx on public trajectory datasets with human annotations, rather than reporting only a single system's absolute performance. The evidence is the public-dataset comparison reported in the abstract; the abstract does not give sample sizes, annotation protocol, or statistical testing details.
Alongside the alignment gains, LiteTrajEval reduces cost by about 6x and evaluation time by more than 8x. This indicates that budget-bounded preprocessing plus a single judge call can improve both cost and latency, two key production constraints, without giving up failure-localization alignment. The evidence is the cost and time multipliers reported in the abstract; the abstract does not specify the cost basis, hardware environment, or timing methodology.
Perspective
The work targets production LLM agent trajectories that need repeated evaluation, in settings where traces contain tool calls, observations, retries, and external outputs and where diagnostic value is unevenly distributed across raw tokens. Because it is designed to run under a fixed global budget, it suits teams with constrained budget and latency that still need structured diagnostic reports, such as engineers and operators running an enterprise agentic platform. The offline-derived rule profiles are domain-specific, implying that moving to a new domain requires rebuilding the profiles. The abstract notes deployment in an enterprise agentic platform, indicating the aim extends beyond research benchmarks.
At the abstract level, sample sizes, human annotation protocol, statistical significance testing, and the cost basis and timing methodology are not given, so the robustness of the alignment gains and the cost and time multipliers still needs confirmation in the full text. The domain-specific nature of the rule profiles leaves cross-domain transfer unclear. The abstract also mentions deployment in an enterprise agentic platform but provides no deployment scale, online metrics, or consistency evidence with offline evaluation. Because this reading is based on the abstract only, figures and experimental details are not included, and these points should be treated as open questions for the full text.