mlsanityData to 14 Oct 2026

Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge

arXiv 2605.08518 ↗First posted 8 May 2026cs.AIcited by 2

On the map: LLM safety and security → Prompt injection and agent security

Abstract (arXiv)

Competition retrospectives are useful when they explain what a leaderboard measured, how hidden evaluation changed conclusions, and which design patterns were rewarded. We revisit the CODS 2025 assetopslive challenge, a privacy-aware Codabench competition on industrial multi-agent orchestration built on assetops. We combine final rank sheets, a 300-submission server log, 149-team registrations, best-submission exports, the organizer winners report, the companion assetopslive system paper, and verified planning-track source trees. Five results stand out. First, the public planning leaderboard saturates at 72.73%, and richer prompts do not improve that peak. Second, hidden evaluation changes the story: public and private scores correlate moderately in planning (r=0.69) but negatively in execution (r=-0.13), with several 45.45% public execution systems reaching 63.64% on the hidden set. Third, the tmatch term is numerically almost inert in the official composite -- combined on a 0--1 scale with 0--100 percentage scores, it contributes at most 0.05 points per track, and rescaling would swap the top two teams. Fourth, the competition is operationally account-based but substantively team-based: 149 registered teams reduce to 24 with non-zero public scores and 11 fully ranked, while 52.3% of deduplicated registrations list multiple usernames. Fifth, successful execution methods mostly improve guardrails -- response selection, contamination cleanup, fallback, and context control -- rather than novel agent architectures. These findings identify which behaviors the evaluation rewarded, and motivate scale-aware composites, skill-level diagnostics, and versioned artifact release.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security. Builds on 16 of its 29 references that are in the map. First posted on arXiv 8 May 2026.

Builds on

16 of 29 references.

References · 16

  1. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
  2. 2025Why Do Multi-Agent LLM Systems Fail?cited by 604
  3. 2025MathArena: Evaluating LLMs on Uncontaminated Math Competitionscited by 297
  4. 2025Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systemscited by 185
  5. 2024PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competitioncited by 165
  6. 2024CRAG -- Comprehensive RAG Benchmarkcited by 97
  7. 2021Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platformcited by 66
  8. 2021Learning to run a Power Network Challenge: a Retrospective Analysiscited by 55
  9. 2024Results of the Big ANN: NeurIPS'23 competitioncited by 22
  10. 2024Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competitioncited by 20
  11. 2025AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenancecited by 15
  12. 2026General Agent Evaluationcited by 13
4 more
  1. 2025How Reliable is Language Model Micro-Benchmarking?cited by 9
  2. 2024Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videoscited by 5
  3. 2024FAIR Universe HiggsML Uncertainty Dataset and Competitioncited by 2
  4. 2025MedAI: Evaluating TxAgent's Therapeutic Agentic Reasoning in the NeurIPS CURE-Bench Competitioncited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025UltraHorizon: Benchmarking Agent Capabilities in Ultra Long-Horizon Scenarios
  2. 2026Tencent WorkBuddy Bench: A Multi-Domain Coding-Agent Benchmark with Contamination-Resistant Task Construction
  3. 2026OS-Marathon: Benchmarking Computer-Use Agents on Vast-Horizon, Repetitive Tasks
  4. 2026Do Agent Benchmarks Measure Capability? Protocol Validity in the Age of Agentic AI
  5. 2026StealthBench: Measuring Operational Stealth in Autonomous Offensive-Security Agents
  6. 2026ERPBench: A State-Grounded Evaluation Paradigm for Computer-Use Agents in Enterprise Software
  7. 2026LoopsBench: From Harness Engineering to Loop Engineering in Coding Agent Evaluation
  8. 2026DynAMO:Dynamic Asset Management Orchestration via Topological Multi-Agent Scheduling