BullshitBench: GPT-5.5 and 5.5-Pro update! They did NOT do well – 5.5 about the same level as GPT-5.4 (around 30-35 rank, 45% pushback). GPT-5.5-Pro did WORSE – only about 35% pushback. I must say the Pro result kind of shocked me. This is actually interesting, what this tells
RESEARCH
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DeepSeek V4 Pro Flash Day-0 vLLM Support Long-Context
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Day-0 support for @deepseek_ai V4 Pro and Flash on vLLM — a new generation of DeepSeek model, purpose-built for tasks up to 1M tokens. Alongside the release, we're publishing a first-principles walkthrough of the new long-context attention and how we implemented it in vLLM. x.com/deepseek_ai/st…
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DeepSeek V4 Launch with SGLang Optimizations and RL Pipeline
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DeepSeek V4 by @deepseek_ai just dropped! SGLang is ready on Day 0 with a full stack of optimizations from architectures to low-level kernels. We also deliver a verified RL training pipeline in Miles (by @radixark) for V4 at launch: Native "ShadowRadix" Design: DeepSeek V4's
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Research advances in healthcare and medical science applications
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2 support pieces https://
thelancet.com/journals/lance
t/article/PIIS0140-6736(26)00659-8/abstract
… https://
nature.com/articles/s4159
1-026-04389-4
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Claude Plays Pokémon Becomes Legitimate Agent Benchmark
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Claude Plays Pokémon turning into a legit agent benchmark is awesome
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Naming failure modes matters in nondeterministic AI systems
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Agree on the pattern, disagree on it being pointless… putting names on the failure modes matters more when the system is this nondeterministic.
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AI Training Loop Creates Data Feedback Cycle Concerns
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Whatever people build around the model today becomes training data for the next one, that loop just keeps tightening to, TBH, a worrying point haha. Quite excited/scared to see where this "slopacopalypse" will lead us.
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Semantic Correctness Over Exact Match for Agent Evaluation
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Semantic correctness over exact match is the right call for agent work, exact match penalizes paraphrases the downstream agent doesn't even care about.
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Medical imaging AI proves effective but LLMs lack real world evidence
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Superhuman interpretation AI for medical images, such as mammography and endoscopy, has been proven to improve diagnostic accuracy in multiple randomized trials but mostly not implemented. But LLMs for clinical decision support have little real world medicine proof, but are
