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LLM performance hinges on the code around the model, and Meta-Harness proves that automating the design of this "harness" can significantly boost results across diverse tasks.
Get faster long-context LLM inference without sacrificing accuracy: LookaheadKV predicts KV cache importance, outperforming costly draft generation methods by 14.5x.
Fine-tuning LLMs can kill their in-context learning abilities, but this work identifies a simple fix: only update the value matrix.
Language agents can now navigate complex, constrained environments with significantly improved success rates thanks to a new framework that combines multi-plan aggregation with constrained decoding and adaptive re-planning.
