Long-Horizon-Terminal-Bench 2.0: Automating Diverse Task Construction and Measuring Sustained Agent Progress
cs.AI
Submitted: 2026-10-09
Updated: 2026-10-09
Project page: https://zli12321.github.io/LHTB
Sources
- TerminalWorld: Benchmarking Agents on Real-World Terminal Tasks
- DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
- Kimi K3: Open Frontier Intelligence
- Measuring AI Ability to Complete Long Software Tasks
- Beyond Final Scores: A Systematic Evaluation of Agents for Long-Horizon AI Research and Development
- Recursive Synthesis for Long-Horizon Terminal Tasks
- Self-Supervised Scaling of Terminal Environments for Scientific Domains
- Long-Horizon-Terminal-Bench: Testing the Limits of Agents on Long-Horizon Terminal Tasks with Dense Reward-Based Grading
- Scaling Trajectories for Complex Tasks through Recursive Self-Rewrite
- Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
- MiniMax Sparse Attention
- EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis
- APEX-Agents
- T1: Terminal Agent Reinforcement Learning for Long-Horizon Tasks
- Before the Rollout Ends: Early Terminal Reward Prediction for Long-horizon Coding Agents
- Harness-Bench: Evaluating Agent Harnesses across Models and Realistic Workflows
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