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Dysco cuts training loss by up to 9 times and boosts federated learning performance by dynamically aligning client-specific subspaces, tackling a critical source of instability in LoRA aggregation.
LLM agents can now autonomously generate complex skills with multi-file dependencies, rivaling human-authored skills, thanks to a co-evolutionary verification process that doesn't need ground truth labels.
Squeeze your LLM's KV cache by 82% without significant performance loss using VQKV's novel vector quantization approach.
