Inside our approach to the Model Spec - OpenAI
<a href="https://news.google.com/rss/articles/CBMiZ0FVX3lxTE9ReXBhMS1ONTNtWWtRVDN0aVRkMThHS3NjV1dNRFNaR0J2TDdHdWFLRkw5ZGdNcGR0OEpJV05QSGJONjBXVncyZ3RURzg1WFpmblBtb21JMDBPWk9rRmNGOTk0bTlYTzQ?oc=5" target="_blank">Inside our approach to the Model Spec</a> <font color="#6f6f6f">OpenAI</font>
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Paper close reading: "Why Language Models Hallucinate"
People often talk about paper reading as a skill, but there aren’t that many examples of people walking through how they do it. Part of this is a problem of supply: it’s expensive to document one’s thought process for any significant length of time, and there’s the additional cost of probably looking quite foolish when doing so. Part of this is simply a question of demand: far more people will read a short paragraph or tweet thread summarizing a paper and offering some pithy comments, than a thousand-word post of someone’s train of thought as they look through a paper. Thankfully, I’m willing to risk looking a bit foolish, and I’m pretty unresponsive to demand at this present moment, so I’ll try and write down my thought processes as I read through as much of a a paper I can in 1-2 hours.
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Paper close reading: "Why Language Models Hallucinate"
People often talk about paper reading as a skill, but there aren’t that many examples of people walking through how they do it. Part of this is a problem of supply: it’s expensive to document one’s thought process for any significant length of time, and there’s the additional cost of probably looking quite foolish when doing so. Part of this is simply a question of demand: far more people will read a short paragraph or tweet thread summarizing a paper and offering some pithy comments, than a thousand-word post of someone’s train of thought as they look through a paper. Thankfully, I’m willing to risk looking a bit foolish, and I’m pretty unresponsive to demand at this present moment, so I’ll try and write down my thought processes as I read through as much of a a paper I can in 1-2 hours.
Qwen3.5-4B GGUF quants comparison (KLD vs speed) - Lunar Lake
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Goal-Conditioned Neural ODEs with Guaranteed Safety and Stability for Learning-Based All-Pairs Motion Planning
arXiv:2604.02821v1 Announce Type: new Abstract: This paper presents a learning-based approach for all-pairs motion planning, where the initial and goal states are allowed to be arbitrary points in a safe set. We construct smooth goal-conditioned neural ordinary differential equations (neural ODEs) via bi-Lipschitz diffeomorphisms. Theoretical results show that the proposed model can provide guarantees of global exponential stability and safety (safe set forward invariance) regardless of goal location. Moreover, explicit bounds on convergence rate, tracking error, and vector field magnitude are established. Our approach admits a tractable learning implementation using bi-Lipschitz neural networks and can incorporate demonstration data. We illustrate the effectiveness of the proposed method
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