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Topic: "kv-cache-management"
not much happened today
gpt-5-pro gemini-2.5 vllm deepseek-v3.1 openai google-deepmind microsoft epoch-ai-research togethercompute nvidia mila reasoning reinforcement-learning inference speculative-decoding sparse-attention kv-cache-management throughput-optimization compute-efficiency tokenization epochairesearch yitayml _philschmid jiqizhixin cvenhoff00 neelnanda5 lateinteraction mgoin_ blackhc teortaxestex
FrontierMath Tier 4 results show GPT-5 Pro narrowly outperforming Gemini 2.5 Deep Think in reasoning accuracy, with concerns about problem leakage clarified by Epoch AI Research. Mila and Microsoft propose Markovian Thinking to improve reasoning efficiency, enabling models to reason over 24K tokens with less compute. New research suggests base models inherently contain reasoning mechanisms, with "thinking models" learning to invoke them effectively. In systems, NVIDIA Blackwell combined with vLLM wins InferenceMAX with significant throughput gains, while Together AI's ATLAS adaptive speculative decoding achieves 4× speed improvements and reduces RL training time by over 60%. SparseServe introduces dynamic sparse attention with KV tiering, drastically improving throughput and latency in GPU memory management.