Daniel Kaiser retweeted
Dario Amodei was so worried about Y2K that his high school teacher still remembers it decades later.
He’s been a doomer for much longer than I thought!
Long before they were soon-to-be billionaires, Dario and Daniela Amodei were students at San Francisco’s most elite public high school. sfchronicle.com/tech/article…
Daniel Kaiser retweeted
In any good zombie movie, the biggest problem is not necessarily the zombie themselves. It's how humans reveal their worst tendencies in a world with zombies.
(This is a post about AI.)
Daniel Kaiser retweeted
Want to know who's the most cited person in ICLR 2027, and which submissions cite your paper?
I downloaded all 43,013 public ICLR 2027 submissions through the OpenReview API and asked my good friends Codex and Claude to build a site that ranks the most-cited papers and authors.
#1 paper is not Attention Is All You Need 👀
Link: modal-labs-leon-dev--iclr202…
Daniel Kaiser retweeted
‼️百余名 AI 学者联合发起“学生慢科研”倡议,呼吁本科生别急着凑论文、赶顶会,多花时间打基础,把问题研究明白。
根据公布的首批名单,署名者来自 MIT、CMU、哈佛、清华、北大、香港中文大学、Google DeepMind 等高校和研究机构。
倡议提到了一个现象:越来越多的本科生急于进入实验室,希望短期内做出一篇顶会论文;一些研究生申请者,简历上已经列了好几篇文章,面试时却讲不清技术细节、研究动机,以及自己在研究中经历过哪些失败。
签署学者们表示;招生时,他们更看重学生对工作的理解,包括每个设计决策的原因、方法的局限,以及后续可以怎么改进。一篇真正吃透、经得起追问的论文,比挂名多篇论文更有说服力。
短时间内灌水式地产出多篇文章,在评估中还会成为减分项。
他们希望学生愿意花时间读透经典工作,反复验证结果,学会自己发现问题、分析问题,也能接受一段时间里没有论文产出。
研究过程中形成的判断力、独立思考能力,是这份倡议强调的培养目标。
同时,签署教师也作出了具体承诺:在研究生招生中,不将论文数量作为筛选标准,重点考察理解深度、思维品质和科研潜力;面试时深入追问研究过程和论文细节;在自己的实验室里,把培养独立思考能力放在产出论文前面。
我的态度是:支持,但是这倡议真能有用,我把键盘吃了。
因为学生最关心的问题是:
我认真研究了一年,暂时没有论文,申请时能过简历筛选吗?
进组之后,一个值得研究但迟迟没有结果的问题,老师愿意让我继续做吗?
再往上,老师自己的聘期考核,学校愿意给多少时间?
这些事情都得有实际的安排。
申请有截止日期,毕业有时间要求,学生很难拿自己的前途去赌一句“慢慢来”。
学生会看老师们签了什么,更会看老师们最后录取了谁。
抽象的是签署的很多人都已经26年发了几十篇论文了….
Daniel Kaiser retweeted
Should Europe leave frontier AI to the Americans and the Chinese?
The idea has gained ground in Europe. It is a fundamental mistake.
Build or surrender: for now, the choice is still ours.
Full article below.
Daniel Kaiser retweeted
Coming from a Pakistani on Reddit.
Even now uk is heading towards in becoming majority Islamic country.
New paper from the team at Sakana AI:
Recursive Self-Improvement through Multi-Agent Self-Supervision (MASS)
How do we keep improving AI when the tasks become too complex for humans to reliably evaluate?
For open-ended research, we eventually hit a "supervision bottleneck." If we use the model itself as the solver, optimizer, and evaluator, it gets trapped in a homogeneous loop, reinforcing its own blind spots rather than correcting them.
To break this loop, we drew inspiration from Marvin Minsky’s Society of Mind. What if you prompt a single model to organize copies of itself into a structured team, then distill what the team learned back into the base model?
MASS alternates between two loops:
1/ Workflow Optimization: the model iteratively evolves its own multi-agent workflows, figuring out how subagents should communicate and route information to each other.
2/ Workflow Internalization: fine-tune the shared model on the successful multi-agent trajectories so the coordination behavior gets baked into the weights.
After two cycles with a 27B open-weights model, performance improved 1.2-1.6x per output token on tough research benchmarks (MLR-Bench, ScienceAgentBench, DSBench).
Multi-agent traces were also much better training data than single-agent ones. The model learns faster from jointly learning high-level orchestration and bounded subagent execution. The results showing that the model can bootstrap its own coordination skills by organizing copies of itself into teams feels promising as a mechanism towards self-improvement.
Blog: pub.sakana.ai/mass/
Paper: arxiv.org/abs/2610.12176
Incredible work by Hyunin Lee, Jinglue Xu, Jeffrey Seely, Yujin Tang, and our collaborators from UC Berkeley (Somayeh Sojoudi, Matei Zaharia, Donghyun Lee)!
Introducing Multi-Agent Self-Supervision (MASS): Recursive self-improvement through collective intelligence
Blog: pub.sakana.ai/mass/
As AI systems tackle increasingly open-ended research problems, human experts may struggle to assess results and guide further progress. The model itself may be the best available optimizer and evaluator. The challenge is obtaining useful self-supervision within that homogeneous loop, where a model risks reinforcing its own blind spots rather than correcting them.
We ask whether a model can learn from the collective intelligence of its own team.
We introduce Multi-Agent Self-Supervision (MASS). One shared model solves tasks through a team of virtual subagents, searches for better multi-agent workflows, then uses its own judgments to select the best ones.
Training on the best team’s executions provides self-supervision by distilling the team’s collective experience into the shared model.
We ran two cycles using a 27B open-weights model on synthetic open-ended research tasks. After the second cycle, score per output token reached 1.2-1.6x the base model's level across four research benchmarks.
Three key results:
First, MASS enables role generalization within an RSI loop: training only on task-solving data from the optimizee naturally improves the capabilities of both the optimizer and the evaluator.
Second, multi-agent trajectories are more efficient training data than single-agent trajectories for RSI.
Third, optimizer capacity, rather than evaluator capacity, can be the bottleneck in the speed of improvement.
We believe the community needs to investigate homogeneous RSI carefully to support AI safety. When a model is also its own evaluator, shared blind spots could allow mistakes to be accepted as progress and reinforced through training. For AI to improve beyond human expertise safely, we need to understand these failures and preserve effective ways to detect them and intervene.
Paper: arxiv.org/abs/2610.12176
Code: github.com/SakanaAI/mass
This work was a collaboration with researchers at UC Berkeley.
Daniel Kaiser retweeted
Check out this loser!
>uploads a draft to Chat on Sept. 8th
>gets scooped by Chat on Oct. 6th
"The result released by OpenAI (#311) on October 6, 2026... identical to a preliminary version of the present paper that was uploaded to ChatGPT on September 8, 2026"
LMAO get rekt
Daniel Kaiser retweeted
You have to be a real jerk to admit you've been operating subscription accounts at half speed and that all you had to do was flip a switch to fix it.
Day 1/
We have optimized the default speed to be ~50% faster across GPT-6 Astra and GPT-6.1 Sol through the subscription across all our products and partners using Sign in With ChatGPT (including OpenCode, Pi, Amp, Devin, ...).
No changes needed on your end and this should be felt within the next two hours.
Daniel Kaiser retweeted
Replying to @Pontifex
Really? 2008: "Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes" arxiv.org/abs/0812.4360 Tweet: xitter.cf/SchmidhuberAI/status/1…
congratulations to 🇩🇪 for reaching the LLM Pareto frontier, by narrowly Pareto-dominating… *checks report*… gpt-oss-120b, from… *checks history books*… 13 months ago
Daniel Kaiser retweeted
Artists: but I don't want to lose my job. I love it.
Mathematicians: but I don't want to lose my job. I love it.
Economists: LONG HAVE I AWAITED THIS HOUR! DESTROY ME, EFFICIENCY, AND LET UTILIZATION-ADJUSTED TOTAL FACTOR PRODUCTIVITY RISE ON THE WIND OF MY ASHES!
The point of a PhD is for the student to train to become an independent researcher. Part of this is coming up with their own research ideas, executed from start to finish. I don't see much value in massive projects with each person contributing little-they can do that in industry
Daniel Kaiser retweeted
Breaking News!
Code FUBAR!!!
Yesterday's Nino 3.4 sea-surface temperature anomaly (preliminary) was a record 3.047°C above the 1991-2020 baseline, beating the previous record of 3.034°C set on November 19, 2015.
It will take about 2 weeks to verify. Stay tuned!
Daniel Kaiser retweeted
We are in a massive anthropomorphic AI delusion cultivated by bad actors who want market share / capture & control. AI cannot and will not ever have its own volition. The only thing that needs to be regulated are the humans using it for control.
Daniel Kaiser retweeted
Replying to @kevinnbass
@kevinnbass mapped this machine for Anthropic and METR in the US.
This is the EU map.
All information is fully sourced and available through public links.
1/ I mapped the money behind the EU's AI rulebook.
The organizations that demanded the rules, co-wrote the rules, and are now paid to evaluate the rules are funded by the same handful of fortunes.
2/ The EU AI Office is a ~37-person unit enforcing the world's first comprehensive AI law.
It outsourced its evaluation muscle through a €9M tender. Four of the six named consortium members are EA-ecosystem organizations: FAR.AI, GovAI, Epoch AI, METR.
The referee and the rule-writers share the same funders
3/ Follow the money: Coefficient Giving (Moskovitz) put >$30M into the orgs in this map. Jaan Tallinn's Survival and Flourishing Fund added millions more. Steven Schuurman seeded the biggest Brussels EA lobby shop.
Vitalik Buterin gave the Future of Life Institute ~$665M. This month, German investor Jan Beckers committed €50M more through his GTR Foundation.
4/ The part nobody says out loud: the funders of the warning are shareholders of the warned-about.
Tallinn led Anthropic's 2021 Series A. Moskovitz invested in the same round (est. 0.8-2.5%; $500M of it moved to a nonprofit in 2025). Beckers said on record in March that he is privately invested in both OpenAI and Anthropic.
The safety movement and the boom share the same owners.
5/ The rulebook's central concept, "systemic risk", comes from papers whose acknowledgments thank GovAI and Future of Life Institute staff.
The Code of Practice was vice-chaired by GovAI, KIRA and a Tallinn-funded Oxford institute.
At least 8 of the 60 Scientific Panel members carry ecosystem affiliations.
6/ There is a paid pipeline. The Talos Network, funded by the same donors, trains EU AI policy fellows and says its alumni "work across the EU AI Office". One confirmed placement joined the Office in January 2025.
An EA Forum strategy post urged the movement to join the Office as it was being set up, noting members were already writing letters of interest to Heads of Unit.
7/ They sell the problem and the solution from the same money pile.
The biorisk studies used to argue against open-source AI trace to an MIT lab funded with ~$6M by this ecosystem.
The contracts to evaluate those very risks now flow to ecosystem organizations. Same money, both sides of the ledger.
8/ The newest move: this month's "Transformative AI Strategy for Europe" states it received no funding or direction from any company, government or EU institution.
True, and incomplete. Its editorial lead is KIRA's Daniel Privitera. KIRA received donations from an NGO that is linked to an Anthropic Investor. Its research leads include KIRA's Philip Fox, GovAI's Noemi Dreksler and Anton Leicht, whom KIRA lists as an affiliate. Roughly 20 of ~45 named contributors come from the mapped ecosystem, fronted by Vestager, Varadkar and a Nobel laureate.
KIRA's main institutional funder is the GTR Foundation of Jan Beckers (see 4).
9/ Proportionality, because it matters: Big Tech declares €73M+ per year lobbying the EU. This ecosystem spends a fraction of that.
That is the story. Brussels was influenced cheaply: through expertise, staffing, fellowships and evaluation contracts, not ad campaigns.
10/ To be precise: I am not claiming illegality. The Commission ran an open tender. The panel was appointed openly.
12/ Disclosure, because transparency cuts both ways: I build open-source AI for healthcare. I have a side in this debate, and now you know which one.
Everything above is documented from public sources and claims no illegality. If any organization named here disputes a fact, my replies are open: correct me, and I will correct the record publicly.
IMPORTANT: Shared funders do not prove coordination.
But when the demanders, drafters, evaluators and strategists all draw from the same money, independence is a question Europe gets to ask.







































