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Structured pruning of LLMs is often beaten by training a smaller dense model from scratch with the same token budget, suggesting pruning is closer to expensive architecture search than knowledge transfer.
Sakana AI and UC Berkeley propose recursive harness iteration, where an agent's external harness self-improves over a few rounds, matching higher reasoning tiers while cutting costs by up to 60%.
Researchers introduce Continual Harness, a system that lets embodied agents iteratively refine their own prompts, tools and memory mid-task without resetting the environment, building on work that made AI the first to complete Pokémon Blue and Crystal.
Researchers developed Meta-Harness, an automated system that optimizes LLM application code to improve accuracy while cutting token usage, achieving 7.7-point gains on text classification and 4.7-point improvements on math reasoning tasks.
Google Research finds that enabling reasoning in LLMs helps them recall simple facts through two mechanisms: extra tokens provide computational buffer time, and intermediate facts prime the correct answer—but hallucinated intermediate facts sharply…
Researchers show the Platonic Representation Hypothesis—the claim that scaled AI models converge on shared internal representations—is largely a statistical artifact of measurement bias, with the apparent convergence disappearing once properly calibrated.
Researchers introduce Recursive Language Models, which let LLMs decompose and recursively call themselves to process inputs up to 100 times longer than their native context windows, outperforming GPT-5 by 26% on long-context tasks.
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