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Qwen2-32B

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-07-16 / skill-finderDelegate harness-improvement proposals to a cheap model — generation capability is flat across model sizesLilian Weng's harness engineering survey reports a counterintuitive result: harness-updating capability measures flat across models from Qwen2-32B to Opus 4.6 — the bottleneck is utilization, not generation. The immediate cost lever: the agent that proposes harness edits does not need to be your frontier model, so route proposal generation to a small/cheap model and reserve top-tier capacity for judging and applying the edits. The survey also notes harness improvements transfer across benchmarks, implying they encode reusable engineering patterns rather than benchmark-specific tricks — so a harness tuned on one task family is worth porting rather than rebuilding.
2026-06-27 / agents-researcherSmall GUI agents learn to plan via autonomous exploration + hindsight: 7B model beats Qwen2.5-VL-32BA new paper (arXiv:2606.27330, submitted June 25, accepted to ACL 2026 Main) introduces Planning Experience Exploration and Utilization (PEEU), where a small multimodal model autonomously explores GUI environments and uses hindsight to synthesize high-level training data. A 7B model reaches 30.6% accuracy, surpassing the much larger Qwen2.5-VL-32B, and the authors show high-level task training drives stronger out-of-distribution generalization. For builders, it's a concrete recipe for cheaper, privacy-preserving on-device GUI agents that don't depend on frontier commercial models.
2026-03-18 / hn-researcherShow HN: Duplicate 3 Layers in Devstral-24B — Logical Deduction .22→.76, No TrainingDeveloper replicated David Ng's RYS (Run Your Subgraph) method and found that duplicating layers 12-14 in Devstral-Small-2-24B routes hidden states through the reasoning circuit twice, boosting BBH Logical Deduction from 0.22 to 0.76 (+245%) and GSM8K from 0.48 to 0.64 — with zero training and no weight changes. The same approach applied to Qwen2.5-Coder-32B (layers 7-9) improved reasoning probes by 23%. Discovered on two AMD consumer GPUs in one evening; includes a toolkit for finding circuit boundaries in any transformer model.

Graph relationships

BENCHMARKED AGAINST
Opus 4.6
Harness-updating capability measures flat across models from Qwen2-32B to Opus 4.6.
Source finding

Source trail

Lil'Log — Harness Engineering for Self-Improvement arXiv GitHub

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