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Human-written solutions can actually *hurt* model performance on math problems, highlighting a critical gap between strategy usage and executability that Selective Strategy Retrieval (SSR) effectively bridges.
Stop struggling with ad-hoc codebases: dLLM offers a unified, open-source framework to reproduce, fine-tune, and build diffusion language models, even from BERT-style encoders.
Now you can audit black-box LLM APIs for cheating (model substitution, overbilling) with <1% overhead, using verifiable computation.
LLMs can now autonomously design and build better-performing agents using OpenSage, an agent development kit that lets them self-generate agent topology, toolsets, and memory structures.
