AI Coding Live News

Terminal agents frequently struggle to convert partial command-line progress into complete end-to-end task resolution. Researchers from Tencent AI Lab and NUS introduce Terminal-RSI, alternating Recursive Self-Rewrite (RSR) of verified trajectories with critic-inheriting Recursive Policy-Refine (RPR) alongside Turn-Boundary Skip-Observation GAE (TB-GAE). Evolving Qwen3.8-27B across five stages yields Terminal-RSI-27B, achieving 86.52% on Terminal-Bench 2.1 and 79.00% on Terminal-Bench Hard.

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Microsoft Research and Stanford University have unveiled AutoEnvScaling, an automated data flywheel that frames RL environment creation itself as a terminal execution task. A proposer agent builds diverse environments directly from solver rollouts, slashing cost per valid environment by 75% while achieving a 25x higher valid environment yield over prompt-only baselines. Crucially, unifying environment design and solving within a single model enables Recursive Self-Improvement (RSI), yielding +12.2 points on Terminal-Bench 2.1 and doubling scores on Terminal-Bench 4.0.

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As autonomous software engineering agents and command-line assistants evolve from single-turn tool invocation toward complex, multi-stage delivery, the scarcity of high-quality, long-horizon terminal interaction data has emerged as a fundamental bottleneck. Researchers from USTC, Alibaba Group (AMAP), and SUSTech introduce TerminalHorizon (arXiv:2610.13166), a fully automated data synthesis engine for training long-horizon terminal agents. Addressing standard pitfalls where synthetic environments oversimplify core challenges and eliminate the need for exploration, TerminalHorizon extracts workflows from real-world tasks, pinpoints omitted steps through independent execution loops, dynamically reconstructs environments based on observed evidence, and organically scales task complexity across multiple stages under unified objectives. Synthesizing 1.5K environments and 2,852 trajectories averaging 52.8 turns, the authors train TerminalHorizon-27B (fine-tuned on Qwen3.5-27B). The model achieves 55.8% on Terminal-Bench 2.0 (+14.2 points over base Qwen3.5-27B), sweeping 12 benchmarks across coding agents, production environments, and reasoning with project datasets open-sourced.

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Terminal coding agents are expanding from standard software engineering into scientific domains like bioinformatics, materials science, and physics. However, building training sandboxes requires executable reference behavior and rigorous verifiers capable of distinguishing true semantic correctness from superficial syntactic mimicry—a process that currently requires tedious manual engineering per task. Researchers from Tsinghua University and Tencent introduce Software-in-the-Loop Reconstruction (SWR, arXiv:2610.02710), a self-supervised environment scaling framework. SWR extracts ground-truth outputs and verifiers from existing executable scientific software workflows by perturbing input configurations into public observations and hidden evaluations. Agents synthesize complete, editable programs from input-output schemas without viewing source code, evaluated by hierarchical verifiers testing semantic fidelity, structural integrity, and anti-shortcut compliance. Implemented across 500 workflows and 46 software suites spanning six scientific domains, SWR generates 1,422 verified trajectories. Supervised fine-tuning of Qwen3.8-27B elevates Terminal-Bench 2 average success from 47.94% to 53.56%, establishing a scalable paradigm for scientific terminal agent training.

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Terminal agents execute stochastic model outputs directly in real bash environments, where an erroneous command (e.g., misconfigured dependency install, file deletion) irreversibly corrupts system state and derails downstream progress. Researchers introduce Mid-Harness, a paradigm that allocates test-time compute precisely at the boundary between model generation and the evaluation harness. By sampling and verifying candidate actions before sending them to the terminal—rather than expensively generating multiple full rollout trajectories—Mid-Harness elevates Pass@1 on TerminalBench-Lite from 50.00% to 68.03% under action scaling, achieving vastly higher reliability at substantially lower token cost.

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Terminal agents operate through stochastic model generations, but a single errant command can irreversibly degrade sandbox environments. Mid-Harness introduces test-time compute scaling at the boundary between model and harness. By sampling and verifying candidate actions before terminal dispatch without modifying the underlying generator or harness, Mid-Harness boosts Pass@1 on TerminalBench-Lite from 50.00% to 68.03% under a GPT-5.6 Sol verifier, outperforming whole-trajectory scaling at lower token expenditure.

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