mlsanityData to 14 Oct 2026

Offline Preference-Based Trajectory Evaluation

arXiv 2606.17541 ↗First posted 16 Jun 2026cs.LG cs.AIcited by 2

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Offline evaluation of agentic systems often collapses trajectories to terminal success, discarding information about partial progress and inducing widespread ties, creating substantial statistical inefficiency by reducing effective sample size and weakening the ability to distinguish systems. We propose preference-based trajectory evaluation, which compares trajectories directly through temporal preferences over progress and time-to-return profiles. We find that, across diverse agentic and interactive benchmarks, standard success-based metrics produce tied comparisons on roughly 75% of instances, whereas trajectory-aware preferences reduce ties to roughly 35%, improving discriminative power, ranking stability, and data efficiency. Our results suggest that benchmark saturation, often attributed to poor data collection or problem difficulty, may also be explained by the choice of evaluation measure.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 30 of its 46 references that are in the map. First posted on arXiv 16 Jun 2026.

Builds on

30 of 46 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation13 Oct
  2. 2024TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks18 Dec
  3. 2024AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents24 Jan

Other references · 27

  1. 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,184
  2. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 807
  3. 2017Deep Reinforcement Learning that Matterscited by 754
  4. 2018On Evaluation of Embodied Navigation Agentscited by 547
  5. 2021Dynabench: Rethinking Benchmarking in NLPcited by 459
  6. 2024tinyBenchmarks: evaluating LLMs with fewer examplescited by 310
  7. 2024Reinforcement learningcited by 247
  8. 2023Nash Learning from Human Feedbackcited by 224
  9. 2024A Minimaximalist Approach to Reinforcement Learning from Human Feedbackcited by 144
  10. 2021Active Testing: Sample-Efficient Model Evaluationcited by 82
  11. 2022Mapping global dynamics of benchmark creation and saturation in artificial intelligencecited by 71
  12. 2025Position: Evaluating Generative AI Systems Is a Social Science Measurement Challengecited by 63
15 more
  1. 2021Comparing Test Sets with Item Response Theorycited by 57
  2. 2021Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarkingcited by 53
  3. 2024Active Evaluation Acquisition for Efficient LLM Benchmarkingcited by 35
  4. 2026When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturationcited by 33
  5. 2025Reliable and Efficient Amortized Model-based Evaluationcited by 31
  6. 2021Better than Average: Paired Evaluation of NLP Systemscited by 30
  7. 2024Label-Efficient Model Selection for Text Generationcited by 19
  8. 2024AcTracer: Active Testing of Large Language Model via Multi-Stage Samplingcited by 17
  9. 2026Comparison requires valid measurement: Rethinking attack success rate comparisons in AI red teamingcited by 15
  10. 2022Active Evaluation: Efficient NLG Evaluation with Few Pairwise Comparisonscited by 10
  11. 2025Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigorcited by 7
  12. 2025SimBA: Simplifying Benchmark Analysis Using Performance Matrices Alonecited by 6
  13. 2022Offline Retrieval Evaluation Without Evaluation Metricscited by 2
  14. 2023Recall, Robustness, and Lexicographic Evaluationcited by 1
  15. 2023How Much Freedom Does An Effectiveness Metric Really Have?cited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Multi-Agent Transactive Memorycited by 4
  2. 2026Correct Choices, Failed Sequences: Choice2Seq Benchmarks Correctness Retention Across Output Interfacescited by 0

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