mlsanityData to 7 Oct 2026

FACET: Preserving Source Intent and Executable State in Terminal Task Synthesis

arXiv 2608.18580 ↗First posted 19 Aug 2026cs.AI cs.PLcited by 3

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Abstract (arXiv)

Training terminal agents requires scalable executable supervision, yet synthesizing high-quality terminal tasks remains challenging. Each task couples an instruction, an initialized environment, a reference solution, and an executable verifier; if these artifacts are generated from inconsistent assumptions, the resulting task may be unsolvable or incorrectly evaluated. Meanwhile, multi-stage synthesis can discard the goals, dependencies, state transitions, and procedural constraints encoded in the original sources. We present FACET (Fine-grained Agentic Construction of Executable Tasks), a framework that addresses both information preservation and cross-artifact consistency. FACET reconstructs related agent skills into coherent, information-rich scenarios, then realizes and repairs the execution environment before generating the final task artifacts. The resulting container state serves as shared grounding for the instruction, solution, and verifier, while execution-based validation and targeted repair correct artifact-specific failures without unnecessarily regenerating valid components. FACET produces complex terminal tasks with dense executable checks, and successful trajectories collected from these tasks provide effective, data-efficient supervision. Fine-tuning models across multiple scales consistently improves performance on Terminal-Bench 2.1, while analyses of alternative generation schemes support the importance of environment-grounded construction for task validity and solution-verifier alignment. These results establish source-intent preservation and shared executable-state grounding as key principles for scalable terminal-task synthesis.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 13 of its 33 references that are in the map. First posted on arXiv 19 Aug 2026.

Builds on

13 of 33 references.

References · 13

  1. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,442
  2. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 877
  3. 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 515
  4. 2025Measuring AI Ability to Complete Long Software Taskscited by 142
  5. 2026Endless Terminals: Scaling RL Environments for Terminal Agentscited by 43
  6. 2026On Data Engineering for Scaling LLM Terminal Capabilitiescited by 34
  7. 2026Tmax: A simple recipe for terminal agentscited by 24
  8. 2026Toward Scalable Terminal Task Synthesis via Skill Graphscited by 16
  9. 2026TerminalWorld: Benchmarking Agents on Real-World Terminal Taskscited by 10
  10. 2026LiteCoder-Terminal: Scaling Long-Horizon Terminal Environments for Learning Language Agentscited by 7
  11. 2026What Makes Interaction Trajectories Effective for Training Terminal Agents?cited by 6
  12. 2026NexForge: Scaling Agent Capabilities through Requirement-Driven Task Synthesis for LLMscited by 4
1 more
  1. 2026Intern-S2-Preview: Scientific Agentic Foundation Modelcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

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  1. 2026Meta-Task: Turning Terminal Task Synthesis into a Terminal Task for Scalable Agent Training
  2. 2026Terminal-World: Scaling Terminal-Agent Environments via Agent Skills
  3. 2026Recursive Synthesis for Long-Horizon Terminal Tasks
  4. 2026CLI-Universe: Towards Verifiable Task Synthesis Engine for Terminal Agents
  5. 2026Terminal Agents: A Survey of AI Agents in Command-Line Environments
  6. 2026A Self-Evolving Framework for Efficient Terminal Agents via Observational Context Compression
  7. 2026Evidence-Calibrated Runtime Reconstruction for Agent Skills Across Heterogeneous Coding Agents
  8. 2026Grounded Skill Synthesis from Code at Scale for Agentic Intelligence