Linear self-attention transformers provably implement in-context SARSA and actor-critic via explicit constructions, with gradient flow converging exponentially to the target parameter manifold under rich training MDPs.
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Omni-MATH supplies 4428 human-verified Olympiad math problems that expose top LLMs achieving only 52.55% to 60.54% accuracy on the most difficult items.
Current audio-language models fail to use clinical multimodal context for dysarthric speech recognition, but context-aware LoRA fine-tuning delivers large accuracy gains on the SAP dataset.
citing papers explorer
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Transformers Provably Implement In-Context Reinforcement Learning with Policy Improvement
Linear self-attention transformers provably implement in-context SARSA and actor-critic via explicit constructions, with gradient flow converging exponentially to the target parameter manifold under rich training MDPs.
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Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models
Omni-MATH supplies 4428 human-verified Olympiad math problems that expose top LLMs achieving only 52.55% to 60.54% accuracy on the most difficult items.
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When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition
Current audio-language models fail to use clinical multimodal context for dysarthric speech recognition, but context-aware LoRA fine-tuning delivers large accuracy gains on the SAP dataset.