Do you need Jev? Typed decisions from an ordinary LLM with one token and logprobs
You can get Jev-style typed decisions from an ordinary LLM by forcing one output token and reading its logprobs. Where that matches Jev, and where it loses.
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You can get Jev-style typed decisions from an ordinary LLM by forcing one output token and reading its logprobs. Where that matches Jev, and where it loses.
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We catch up with RLM first author Alex Zhang, MIT PhD, on Jev, PhD masxing, and the future of harnesses.
Article URL: https://blog.cloudflare.com/clef-decision-models/ Comments URL: https://news.ycombinator.com/item?id=49923692 Points: 218 # Comments: 78
Concurrent autoregressive inference creates a fundamental interference problem: prefilling a newly arrived long prompt can delay tokens for requests that are already decoding.
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The free models that actually work with OpenClaw, tested: Gemini Flash, Qwen on Groq, local Ollama models, OpenRouter free tier and what to skip.
Cohere released Embed 5 on Wednesday, giving teams the option to index data with Embed 5 Pro and query those The post Cohere’s faster query model barely dents retrieval quality in its tests appeared first on The New Stack.
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TL;DR In only a few weeks, PyTorch Conference North America 2026 will begin in San Jose, bringing together the open source AI community to share ideas and collaborate. If you’ve...
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Three machines, one model, one agent. A field report on what actually changed, and what I’d keep if I started over.
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