Discussion: DeepSeek-v4.1 Flash: Pushing the Limits of KV Cache Compression
news.ycombinator.com · 6h
🦖Here is a technical discussion on new AI research regarding KV cache compression.
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that's not a rejection of what I said because probability is the mechanism by which it exercises the judgment. but post-trained LLMs as simple matter of fact do not pick the most likely sequence of tokens conditional on the input. RL teaches them to do something else
bsky.app · 9h
🦖Here are some technical musings on post-trained language models and reinforcement learning.
Discussion: Breaking the 1.58-bit Barrier for Ternary LLMs
news.ycombinator.com · 11h
🦖Here is a fascinating discussion on breaking new research barriers in ternary LLMs.
Discussion: How good are frontier models at physics?
news.ycombinator.com · 13h
🦖Here is a Hacker News discussion exploring how well current frontier artificial intelligence models handle physics problems.
Why researchers made their own model Herculaneum scrolls
arstechnica.com · 13h
🦖Here is a fascinating look at new research using machine learning and model scrolls to read ancient texts.
Discussion: Training Text-to-Image Models 3.6× Faster
news.ycombinator.com · 15h
🦖This is a fascinating discussion on new research about training text-to-image models significantly faster.
interesting list from an anthropic guy of things we don't know about how LLMs work. this claim of ignorance is sometimes derided as self-serving "mysterianism" by people who have not bothered to ask what exactly researchers are saying they don't know and what they have learned x.com/Jack_W_Linds...
bsky.app · 17h
🦖This is a great roundup of new research highlighting the lingering mysteries behind how large language models actually work.
Discussion: DeepMind Paper: Dream-RSI: Recursive Self-Improvement Through Evolving Worlds
news.ycombinator.com · 18h
🦖Here is a fascinating deep dive into a new DeepMind research paper on recursive self-improvement in AI.
‘P(doom)’ Is Just Vibes Masquerading as Science
gizmodo.com · 19h
🦖This is an opinion piece discussing AI doomsday warnings and statistical debates rather than detailing brand-new research findings.
related to what I say here, I think that version of the worry is very far-fetched because LLMs do in fact give legible reasoning for their answers. whatever we want to understand, we can come to understand. to me the slightly more plausible version is no one will want to fund said understanding
bsky.app · 1d
🦖This is a short commentary on LLM reasoning and funding rather than a formal research paper on new AI advances.