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Terminal-Bench 2.1

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Headlines · 4

  1. Gear open-source framework targets recursive AI self-improvement

    Gear developers introduced an open-source, meta-learning-inspired framework in which a task agent produces execution traces and a meta-agent uses those traces and evaluation feedback to improve prompts, tools, workflows, and context management. The framework separates search decisions from execution and evaluation and includes a working GEPA variant with shared failure diagnosis and staged evaluation. Its stated goal is to make methods such as GEPA, RRSI, and Harness² easier to implement and compare. Developers reported higher task pass rates with Gear-optimized configurations using GPT 5.6 Luna at medium reasoning effort, but supplied no independent measurements.

    r/MachineLearning · 🔥 5
  2. Nanbeige releases Nanbeige4.3-33B open-weight teacher model

    Nanbeige released Nanbeige4.3-33B under the Apache 2.0 license, positioning it as a teacher model for smaller agents, including a planned Nanbeige4.3-3B. The model card lists a 512K context window, while Nanbeige reported 80.9 on Terminal-Bench 2.1 versus 73.0 for Qwen3.8-27B under the same settings. The results were not independently replicated, and the model scored 60.3 versus Qwen3.8-27B’s 61.7 on SWE-Bench Pro.

    r/MachineLearning · 🔥 2
  3. 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
  4. REMORY Uses Soft Memory Tokens for Agent Context Compaction

    The REMORY paper introduces a neural memory network that appends bounded soft memory tokens after a textual summary, helping a frozen LLM approximate continuation from the full history. Its authors report that the approach approaches the full-context joint score on SummHay using 5.2% of input positions, while Qwen3.8-27B and GLM-5.3-Flash show fewer repeated tool outputs and tool errors on long-horizon agent benchmarks.

    r/LocalLLaMA · 🔥 0

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