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AI Agent Papers·2026
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FACET: Preserving Source Intent and Executable State in Terminal Task Synthesis

Kou Shi, Zun Wang +11·Affiliation: Shanghai AI Laboratory, Affiliation: Fudan University[0.25em] +2

Proposes FACET, a framework that synthesizes verifiable terminal tasks by reconstructing scenario intent and grounding instruction, solution, and verifier in a shared executable container state. Key features include environment-first generation, execution-based validation, and targeted repair to preserve source intent and cross-artifact consistency.

#terminal#agent-skills#ai-agent#evaluation#benchmark+4
AI Agent Papers·2026
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Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments

Jie Wu, Zhenru Zhang +12·Qwen Team, Alibaba Group, Tsinghua University +2

Reconstructs executable terminal workspaces from recorded agent trajectories and synthesizes verifiable single- and multi-round coding tasks for agent training; it replays file operations, uses an LLM completion agent to fill missing files/dependencies, and verifies tasks with autogenerated test suites.

#terminal#coding-agents#qwen#sft#benchmarks+3
AI Agent Papers·2026
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An Empirical Study of Harness Design for Coding Agents

Run-Ze Fan, Zihao Zhang +7

Measures how individual harness components—planning, action space, and context management—affect coding agents' success, cost, and behavior. Uses a modular harness across 176 matched settings on SWE-Bench Verified and Terminal-Bench 2.1 to isolate component effects and surface model- and budget-dependent trade-offs.

#coding-agents#evaluation#benchmarks#long-horizon#software-engineering+4
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