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DeepWeaver transforms the way LLMs synthesize evidence, leading to answers that are not only more comprehensive but also better grounded in citations.
TrajDebug uncovers the root causes of failures in long-horizon agent trajectories, enabling targeted improvements that could significantly boost agent performance.
Forget external signals – unlock better LLM post-training by mining model internals with sparse autoencoders to reveal data diversity, difficulty, and quality.
Current reward models are surprisingly bad at judging story quality, achieving only 66% accuracy in selecting human-preferred narratives – a gap closed by a new, purpose-built reward model.
