arXiv — cs.AI preprintsInternational15 October 2026
Long-Horizon-Terminal-Bench 2.0: Automating Diverse Task Construction and Measuring Sustained Agent Progress
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.14146v1 Announce Type: new Abstract: Long-horizon terminal benchmarks measure whether an agent can sustain progress through extended workflows with process rewards, not only whether its final state passes a binary check. We present Long-Horizon-Terminal-Bench 2.0 (LHTB 2.0), an extension of Long-Horizon-Terminal-Bench that preserves its 46 original tasks while correcting task and evaluation-infrastructure defects and extending 31 tasks. LHTB 2.0 contains 77 tasks across 13 domains and eight task types. The extended tasks are produced through a systematic reconstruction-task pipeline
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Artificiality's First Exam: An LLM-Referenced C-test for Supraintelligence15 October 2026
- The Silent Failures in Agentic Data Science15 October 2026
- ClawLoop: Self-Improving Agents via Harness-Native Training Loop15 October 2026
- Correction Training for Diffusion Models15 October 2026
- VETNET: Assembling Benchmark Datasets for AI-Driven Veterinary Innovation15 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.