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Dipankar Sarkar
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dipankarsarkar
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dipankarsarkar
dipankar
dipankarsarkar
AI & ML interests
Building the AI-native stack. Agents as infrastructure, safety as architecture, performance as plumbing. I publish the receipts: papers, datasets, demos.
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We just open-sourced THX-01, a 322M decision model. Most AI decisions in production are classification in disguise: which team gets this ticket, is this email phishing, what's the total on this invoice. Calling a large LLM for each one is slow, expensive, and its confidence means nothing. THX-01 is built for exactly these decisions. You send it a message or document plus typed questions, and in one forward pass (~10 ms) it returns: - an answer to every question, with a calibrated probability for each option - exact numbers stated in the document, or null if the number isn't there - verbatim quotes and the passages that support each answer On our 4-language support-ticket benchmark it matches Claude Sonnet 5.5 (98.4 vs 98.5) and is about 150x faster. Because its confidence is calibrated, it handled about 75% of tickets automatically with zero errors. pip install thx01 https://huggingface.co/doofz/THX-01
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oro-ai/orobench-trajectories
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AQ-MedAI/MedMemoryBench
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dipankarsarkar
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dipankarsarkar/llm-evaluation-self-audit
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dipankarsarkar/grite-corpus
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Jun 30
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dipankarsarkar/gpuemu-corpus
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