mlsanityData to 7 Oct 2026

FrontierChallenge: Evaluating Scientific Workflow Completion

arXiv 2608.24979 ↗First posted 25 Aug 2026cs.AI cs.CL cs.SEcited by 1

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Abstract (arXiv)

Scientific agents increasingly analyze data, execute code, and produce research artifacts, yet most benchmarks emphasize final answers, isolated programs, or a single domain. We introduce FrontierChallenge, a cross-domain benchmark comprising 300 end-to-end scientific workflows. In this paper, we release and evaluate 97 of these tasks, spanning quantum chemistry, molecular dynamics, materials characterization, analytical chemistry, life science, and electrochemistry/environment. Each task provides fixed inputs and specifies a bundle of required scientific deliverables. We evaluate twelve frontier models with three agent scaffolds. Pass Rate measures the fraction of tasks satisfying the full-completion criterion, while Avg. Score captures partial progress. Each of the best-performing configurations completed only 20 of the 97 released tasks, yielding a Pass Rate of 20.6%. Partial progress translated especially poorly into complete delivery in analytical chemistry and electrochemistry/environment: Avg. Scores reached 87.6 and 94.9, but the highest Pass Rates were only 4% and 0%. Among non-passing Claude Code trajectories, 75.5% still ended with language claiming completion. Complementary HDS6 process scores correlate strongly with task outcomes, supporting FrontierChallenge as a benchmark of Heavy Duty Solver capabilities. These findings show that neither high partial scores nor confident claims of completion reliably indicate that a scientific task has been fully delivered, highlighting the need to evaluate end-to-end workflow execution and the completeness of scientific deliverables together.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 25 Aug 2026.

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22 of 34 references.

References · 22

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  2. 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
  3. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
  4. 2023GAIA: a benchmark for General AI Assistantscited by 1,084
  5. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,002
  6. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 877
  7. 2025PaperBench: Evaluating AI's Ability to Replicate AI Researchcited by 286
  8. 2024ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discoverycited by 208
  9. 2024LAB-Bench: Measuring Capabilities of Language Models for Biology Researchcited by 138
  10. 2024CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmarkcited by 115
  11. 2025BixBench: a Comprehensive Benchmark for LLM-based Agents in Computational Biologycited by 75
  12. 2024BLADE: Benchmarking Language Model Agents for Data-Driven Sciencecited by 72
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  1. 2025ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflowscited by 47
  2. 2025AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research Suitecited by 45
  3. 2025Scaling Agents via Continual Pre-trainingcited by 39
  4. 2026Agents' Last Examcited by 25
  5. 2026SciAgentGym: Benchmarking Multi-Step Scientific Tool-use in LLM Agentscited by 12
  6. 2026Benchmarking AI Agents for Addressing Scientific Challenges Across Scalescited by 10
  7. 2026NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers?cited by 9
  8. 2026BioAgent Bench: An AI Agent Evaluation Suite for Bioinformaticscited by 8
  9. 2026Apodex 1.1: Scaling Agentic Intelligence for Complex Workcited by 2
  10. 2026Apodex Discovery: Reality Benchmarks and Environments for Evaluating and Building Discoverative Artificial Intelligencecited by 1

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