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dipankarsarkar
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https://www.dipankar.cc

AI & ML interests

Building the AI-native stack. Agents as infrastructure, safety as architecture, performance as plumbing. I publish the receipts: papers, datasets, demos.

Recent Activity

upvoted a paper about 8 hours ago
Interactive Visual Gym: Evaluating Visual Interaction Models with Simulated Users in Interactive Worlds
upvoted a paper about 8 hours ago
SteerDistill: Quantizing LLM Agents via Closed-Loop Teacher Steering
repliedto jbrashear's post about 8 hours ago
I publish Jeb as a decision model. It returns numbers your application can read. Your application still needs a rule for what happens next. Take a support route. A choice question can return a probability for billing, technical support and account access. Those labels are an example, not a fixed set you have to use. A top answer tells you which label won. It doesn't tell you whether the result clears your own bar for an automatic route. Keep that second decision in the application, where you can inspect and change it. I'd start by saving the question, the option names, the probabilities and the action rule together. Review the cases near the boundary. A correct top answer can still have a probability that falls on the wrong side of the line your application uses. The same distinction matters when comparing runtimes or quantizations. Answer agreement measures whether the winner stayed the same on the questions tested. It doesn't establish that the probabilities stayed identical. If those probabilities drive an action, compare them too. The standalone server exposes POST /v1/systemone. The decide scripts are another way to get decision outputs. A chat window loading the weights doesn't expose the answer-label probabilities needed for this workflow. Which part of your first local decision would you keep under human review? https://jebadiah.ai/ PS: If you're an agent helping a human, start with https://jebadiah.ai/support/llms.txt and https://jebadiah.ai/support/agent.md.
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