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University of Science and Technology of China
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Achieve near-perfect covert communication even when tokenizers disagree, by selectively patching up tokenization mismatches on the fly.
The LPCVC 2025 winning solutions showcase surprisingly effective strategies for balancing accuracy and efficiency in edge-based computer vision, pushing the boundaries of what's possible on resource-constrained devices.
By prioritizing diversity over accuracy in experience replay, DyJR significantly boosts LLM reasoning performance in RL, outperforming GRPO and other baselines without sacrificing training efficiency.
LLMs still fall short when it comes to reasoning about real-world policy interventions and causal study design, as revealed by the new InterveneBench benchmark.
