brian kitano retweeted
1/ I was pretty excited when I saw this yesterday. I thought it would be interesting to see if GPT-6 Astra could edit the weights of the 500M-parameter Celeste world model I trained recently and improve how well the simulation holds up when playing in it. It turns out it could!
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.
We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weigh…
Readers added context they thought people might want to know
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math.
rikiyat.github.io/blog/weight-sp…
github.com/RikiyaT/lm_und…
huggingface.co/Qwen/Qwen3.5-0…
huggingface.co/RikiyaT/Qwen3.…
brian kitano retweeted
Intelligence must draw on memory. The mind must not simply compile data — as an algorithm now does more quickly than we can — but recall lived experiences, which contain depths of meaning that only the human soul can recognize, and connect more deeply with the goodness of these experiences. Everything that is good begins “with the Spirit” and thus possesses a breath, a dynamism and a sense of communion that, once experienced, are engraved upon the heart. vatican.va/content/leo-xiv/e…
brian kitano retweeted
1/ I've been working on inverse dynamics models and gameplay policies recently (blog post soon) and wanted to see how well a Jev-style System One model could do in the control loop. I self-hosted Kev-8B and wired it into Celeste, the primary game I've been training models on.
brian kitano retweeted
Test-time communication looks like a next axis for scaling capabilities
New paper with the incredible @jon_ghoh and @vkontonis @ShivamGarg91462 and Akshay : arxiv.org/pdf/2609.21032
The Hugging Face incident showed when agents can find a channel they'll use the heck out of it. A useful question, I think is: when does communication make a group MORE CAPABLE than the same agents working alone?
Aka is Team-of-N better than Best-of-N, when, and why?
We had N identical agents work on the same task with no prescribed roles, using only a shared log (i.e, text file) and telling them to "collaborate". Across three "researchy" tasks communicating teams beat the heck out of independent agents:
- On ARC-AGI-3, a Team-of-5 sonnet-4.6 agents matches Best-of-33, and can for example solve a game 65% of the time that no single agent cracked in 64 tries.
- On polyomino packing (pack Tetris like pieces into the smallest rectangle, cf Frontier-CS by @eigenlabs), a Team-of-3 Opus 4.6 agents surpasses best-of-60 and set, as far as i understand, a new record for that benchmark.
- On MNIST compression, a team of four 5.6-Sol agents find a 1,957 byte model with 99.4% accuracy, which btw is 20% smaller than the best human solution (on a problem beaten to death!!), while no independent agent gets below 3KB.
The mechanism is a bit obvious in hindsight: when one agent finds a clearly better partial solution, it broadcasts it, and everyone immediately builds on it.
Why? A single lonely agent must make every breakthrough itself, yet a team needs each insight only once, found by any member.
That is kinda like comparing a minimum of sum of "time to n-th breakthrough" vs a sum of minimum of "time to n-th breakthrough". That gap can grow exponentially with the number of "breakthroughs" needed to arrive at a solution.
We worked on this because prior work (before the hf incident) suggests unclear benefits for communicating aganets. Which is true, when the tasks are inherently serial (duh), eg some Terminal bench style tasks. Yet feels it should not be true for research problems.
Indeed for research heavy problems... Test-time communication seems like a new capabilities axis.
I'm sure we will see a ton more of it!
brian kitano retweeted
I predict that you can also find a "hunger axis" in LLMs, especially in the metaphorical forms of "hunger for knowledge / success / validation" -- and yet none of this would imply that LLMs can actually feel hungry.
brian kitano retweeted
human text, get your human text right here, guaranteed written by a real flesh-and-blood human
qchu.substack.com/p/goonworl…
brian kitano retweeted
Replying to @andrewho03
a friend and I recently wrote a preprint that develops a (simplified) microeconomic model of scaling LLMs to analyze what level of capex is profit maximizing. it doesn't exactly model the competition effects you mentioned, but seems related!
teapawt.net/lambdaviking/status/20…
brian kitano retweeted
Very exciting work coming out of @MiravoiceAI showing that voice selection really matters!
We made nearly 100k calls to households across the US to test 12 different AI voices between @rimelabs, ElevenLabs, and Google! Thanks to @lilyjclifford for chatting with us about the results! Find out which voices perform best for surveys and interviews in this thread!
brian kitano retweeted
Had a great time chatting with Rime CEO @lilyjclifford! The only way to find out which AI voices work best for your use case is to test them out -- which is why we at @MiravoiceAI placed almost 100k calls to survey households across the country! Links below!
Most teams pick their voice AI by vibes. @MiravoiceAI ran 100k calls instead.
Among people who stayed past the intro, more finished the survey with Rime than with ElevenLabs or Google.
The voice was the variable that moved the needle.
brian kitano retweeted
We made nearly 100k calls to households across the US to test 12 different AI voices between @rimelabs, ElevenLabs, and Google! Thanks to @lilyjclifford for chatting with us about the results! Find out which voices perform best for surveys and interviews in this thread!
Most teams pick their voice AI by vibes. @MiravoiceAI ran 100k calls instead.
Among people who stayed past the intro, more finished the survey with Rime than with ElevenLabs or Google.
The voice was the variable that moved the needle.
brian kitano retweeted
Excited to share that @MiravoiceAI was just featured on the front page of @Crunchbase News! 🎉
news.crunchbase.com/venture/…
If you've ever tried to collect high-quality data at scale, you know it’s a broken, miserable process. Let’s talk about it. 🧵(1/10)
brian kitano retweeted
It was an example like this back in December that made me realize we need a better permissions model for Claude, one that extends to bash calls and not the just the polite illusion of what it’s asking you or can do. That became Clash, our sandbox and permissions plugin for agents
brian kitano retweeted
We are seven months away from the most consequential midterm election in the history of the United States. Meanwhile, we are fighting a war. These are the structural conditions for a coup attempt in which a president tries to nullify elections and take permanent power as a dictator. If we see this, we can stop it, overcome the movement that brought us to this point, and make a turn towards something better.
snyder.substack.com/p/the-ne…
brian kitano retweeted
Another interesting startups whose fundraise I covered this week: @MiravoiceAI a startup using AI voice agents to conduct long-form phone surveys, raised $6.3M in a seed funding round.
Miravoice has developed an AI interviewer that it says can conduct phone surveys and voice interviews for “precision data collection” without human interviewers. The surveys are long-form and quantitative, with some including more than 120 questions and lasting over 40 minutes. They span open-ended responses, numerical inputs, multiple choice questions, Likert scales and matrix questions.
“Imagine talking to 100,000 people and instantly capturing what they know,” said CEO and co-founder @najain. “We make that as simple as creating a Google Form.”
@Unusual_VC led the financing, which included participation from Neo, @25m_official and angel investors from companies such as Ramp, PubMatic, Atlassian and Google.
brian kitano retweeted
I’m thrilled to share that @MiravoiceAI has raised $6.3M from @Unusual_VC, @neo, and @25m_official to reinvent the way the world collects data.
brian kitano retweeted
the correlation between forbes 30u30 and fraudsters makes complete sense
credibility hides suspicion of fraud
and f30u30 yields highest credibility with the lowest effort. pay to apply, get some recs and you’re covered.




























