mini-SWE-agent

Headlines · 4

1. 1d
   The Bitter Lesson and the AI Factory

   An opinion article argues that improving models can make elaborate workflows, domain rules, prompts, and harnesses less useful than simpler, model-led approaches. The author cites reported comparisons in which minimal instructions performed well on Zstandard tasks, test-driven development instructions worsened performance, and a near-bare mini-swe-agent matched or outperformed Claude Code and Codex CLI on DeepSWE. The author also claims that a concise prompt to Claude Code with Opus 5.5 can beat an “AI factory.” The supplied report does not provide the underlying benchmark data or independently verify these comparisons and broader conclusions.

   r/ClaudeAI · 🔥 2
2. Oct 24
   Coding-Agent Harness Changes Raise Costs Without Reliable Gains

   Shandong University researchers Liu and Han compared Claude Code, OpenCode, and mini-SWE-agent while holding the language model constant. Most performance differences were similar to rerun noise, while the harnesses consumed two to three times as many tokens as the leanest option; OpenCode showed a significant deficit on the larger task pool. A small cross-check on Terminal-Bench 2.1 showed the same pattern.

   The Decoder · 🔥 0
3. Oct 23
   SWE-sweep Tests AI Agents Finding Hidden Bugs Without Issue Hints

   Researchers from Meta Super Intelligence Lab, Harvard University, Stanford and other institutions introduced SWE-sweep, a software-engineering benchmark built from 100 real open-source repositories containing 4,100 historical vulnerabilities. Unlike conventional issue-driven benchmarks, it gives coding agents only complete repositories and requires them to discover and repair hidden defects without descriptions, error messages or vulnerability locations. DoNews reported that successful repairs must turn a hidden test from failing to passing while preserving the repository’s full original test suite, with the initial leaderboard run uniformly on mini-SWE-agent.

   DoNews · 🔥 0
4. Oct 10
   Xiaomi's MiMo-V2.6 report details $2.6M single-run RL post-training

   Xiaomi's MiMo team published a technical report, "MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement," covering two sparse MoE models: MiMo-V2.6-Pro (1.02T total parameters, 42B activated) and MiMo-V2.6-Flash (310B total, 15B activated). The report says each RL step samples 1,568 prompts and roughly 25,000 agent trajectories totalling 2.7–3.7B tokens, with RL post-training costs of about $2.6M for Pro and $900K for Flash. The team also open-sourced more than 7,000 RL environments plus an end-to-end RL framework.

   InfoQ 中文 AI&大模型 · 🔥 0

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