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Research2026-07-10 · source-backed
Prior work found individual parameters whose removal collapses LLM performance by orders of magnitude. arXiv 2607.08733 shows the effect isn't universal across models, then tests the obvious corollary that Super Weight-aware training should work. It doesn't. Training 100 to 8,192 Super Weights in isolation drops OLMo-1B and OLMo-7B to random-guessing accuracy, and expanding to local neighborhoods doesn't rescue it. A clean negative result against a seductive intuition, and negative results this direct are rare enough to be worth reading.
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Simon Willison released LLM / Shared entity: LLM / Earlier coverage / Tension
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; earlier LLM coverage from 2026-06-19.
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; earlier LLM coverage from 2026-06-18.
Simon Willison released LLM / Shared entity: LLM / Earlier coverage
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; earlier LLM coverage from 2026-06-19.