Back to #artificial intelligence
#artificial intelligence Preprint Open access

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

Kou Shi Zun Wang Qisheng Su Shiting Huang Ziao Zhang Zhen Fang Qingnan Ren Jin Liu Yu Zeng Yiming Zhao Lin Chen Zehui Chen Feng Zhao
Aug 2026
Artificial Intelligence

Abstract

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.

View source

Similar papers

#artificial intelligence Review Dec 2025

Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025

Investigating how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments finds that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes.

Ruanqianqian Huang, Avery Reyna, Sorin Lerner et al. · 19 citations · ⚡1
#artificial intelligence Review Open access Jan 2026

A systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunities

This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning techniques to ASD, highlighting key challenges and opportunities, particularly the need for models that can integrate complex data to improve diagnostic accuracy and treatment outcomes.

Rafael Muñoz-Terol, Jesús Peral, Sandra Amador et al. · 4 citations · ⚡1

Convergent Evolution: How Different Language Models Learn Similar Number Representations

This paper identifies two different routes through which models can acquire geometrically separable features: they can learn them from complementary co-occurrence signals in general language data, including text-number co-occurrence and cross-number interaction, or from multi-token addition problems.

Deqing Fu, Tianyi Zhou, Mikhail Belkin et al. · 3 citations
#artificial intelligence Review Jun 2026

Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective

This survey model agent state as a dynamic graph, where memories, tools, skills, workflows, and inter-agent relations are represented as typed nodes, edges, and subgraphs updated through schema-constrained rewrites to provide a compact structural lens for designing and governing self-evolving agents.

Yuanyuan Xu, Wenjie Zhang, Yin Chen et al. · 2 citations
#artificial intelligence Open access May 2025

TabularQGAN: a quantum generative model for tabular data synthesis

A novel quantum generative model for synthesizing tabular data by proposing a quantum generative adversarial network architecture with flexible data encoding and a novel quantum circuit ansatz for effectively modeling tabular data is introduced.

P. Bhardwaj, Caitlin Jones, Lasse Dierich et al. · 2 citations

Related blog posts