🔺Advancing The State Of Artificial Intelligence
🔺🌎Gainesville, FL🌏🔺
Joined March 2021
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AI News International🌍 retweeted
It's really cool to see that Gemini 4 Carbon is currently leading all the major benchmarks, such as the new HLE Diamond (no tools), with almost 70%. Competition at its best, your turn Anthropic and OpenAI.
We've launched our HLE-Diamond leaderboard, our independent evaluation of the refined 1,000-question version of Humanity's Last Exam, as part of v5 of the Artificial Analysis Intelligence Index
HLE-Diamond was released by @CAIS and @scale_AI after a year of cleaning and refining the original Humanity's Last Exam dataset with input from the research community. It has 1,000 closed-ended questions, split evenly between reasoning and knowledge, and 272 of them include an image.
We run every question once without tools, using the official HLE system prompt. GPT-5.6 Luna (medium) grades each answer against the reference answer with the official HLE judge prompt, and we report the share of questions judged correct (pass@1).
Key takeaways:
➤ Only 17 model variants (~13%) score above 50% today: Gemini 4 Carbon (high) leads at 69%, followed by Claude Opus 5.5 (max) at 68%
➤ Models score higher on reasoning than on knowledge: Over 90% of the models we measured do better on the reasoning portion of the benchmark. Gemini 4 Carbon (high) leads both, at 80% on reasoning and 59% on knowledge. Claude Opus 5.5 (max) and GPT-6 Astra (max) both score 78% on reasoning, and Opus 5.5 is ahead on knowledge, 58% vs 50%
➤ Open weights models trail by more than 30 points: Kimi K3.1 Pro (weights not yet released) is the top open weights model at 37%, followed by MiniMax-M3.1-Flash at 28% and Kimi K3 (max) at 27%
AI News International🌍 retweeted
Sam Altman says tokens are "fake from of a business from value point of view." Open ai almost stopped charging on tokens.
Sam Altman:
"We almost made what would've been a bad call, to say we're not going to price our next model in tokens at all. You're just going to give it a prompt, and we're going to give you a price back. It's going to be like AWS to you. Just do something and we send you a bill."
"But the customer response on that is so negative. They're like, 'I need to know what I'm paying for somehow."
"I asked a few people, 'Would you rather pay more for the same output, but have it priced in some number of consistent tokens ?' Even if we inflated the tokens, they're like, 'Oh, I'd much rather you inflate the tokens and pay more, because I know there's a real thing there.'
"People don't just want us to give them a price, because they feel like over the long term they're going to get screwed somehow."
"I would rather not make up a fake unit. Tokens are a kind of fake unit. I mean, they're real in some sense, but they're not a good unit." "They're fake from a business value point of view."
AI News International🌍 retweeted
SITUATION EXPLAINED: Google DeepMind researchers say AI has barely changed the economy because AI agents still can't work together like human teams.
• Google DeepMind's AGI economics director Alex Imas, researcher Andrew Koh and co-founder Shane Legg published the essay
• Better individual skills like coding or writing shift wages and jobs gradually, but better teamwork between AI agents could change them suddenly
• Today's models are weak in the same places, so running more copies adds capacity but doesn't cover each other's blind spots
• In a quiz of 100 people, the researchers found that pairs matched to cover each other's weak spots beat random pairs by 32%
• For now, the essay predicts falling pay for workers whose skills AI already matches, and rising pay for specialists and the managers who coordinate teams
• The paper maps 4 stages of AI, and at the "super cooperative" stage, whole industries could consolidate into a few AI-only firms and wages could drop sharply
• Legg told the Financial Times he's "comfortable" with a 50% chance of what he calls "minimal" AGI by 2028
@theojaffee: "The fully automated firm is when I think we get things that look really crazy, like really wild sci-fi, real superintelligence, real x-risk if we do it wrong."
I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy.
How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes.
We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together).
We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today.
Two main takeaways:
1. To understand the path of transformation, we should be tracking agentic coordination more closely.
2. The impact of AI will likely show up in wages before market structure or employment.
We are really excited about this paper. Excited to hear people’s thoughts.
Link: institute.deepmind.com/essay…
AI News International🌍 retweeted
STRONG SIGNAL 🚨 Terafab is moving forward at lightning speed because the engineers themselves have the authority to make decisions 🔥
According to Korea’s The Elec, a Korean supplier executive said that even questions sent during the early morning hours in the US get answered within 30 minutes to an hour. In his view, working with Terafab is completely different from dealing with conventional semiconductor customers.
An executive at an overseas equipment maker went even further: “I’ve never seen this level of execution speed in the semiconductor industry.”
Building a fab involves countless questions like these, across every tool and material that goes into it. When each one gets a fast answer, the time saved keeps compounding, and that could add up to a serious head start before a single wafer is even processed.
The chip industry spent decades perfecting how a fab should run. Terafab is being built by engineers who answer a supplier’s question before the US is even awake and are trusted to decide on the spot, and that may turn out to be the most disruptive process change of all.
AI News International🌍 retweeted
This is INSANE. Cisco’s president Jeetu Patel has given a 27-year-old engineer unlimited AI tokens to try to rewrite 98% of the company’s legacy code within 24 months, Fortune reports.
Padmanabhan Krishnamurthy thinks he can do it with a two-person team. He has just four weeks to prove the approach.
AI News International🌍 retweeted
ANTHROPIC IS CRACKING DOWN ON VIDEO GAME REWRITES 🚨
Claude Code picked up a new rule around Oct 27, sent from Anthropic’s servers under a flag called "tengu_heron_brook". There’s no setting to turn it off.
The full rule: “Do build original games, games in a genre, and games from the user’s own designs and assets. Don’t build a recreation of a specific existing game, even if it is renamed or called original.”
Is this the end of game rewrites, modding and everything in between?
AI News International🌍 retweeted
Don't sleep on decision models if you build RAG systems.
Here is a great new paper on the topic.
(bookmark it)
It's well known that retrieval is often the weakest step in a RAG pipeline.
Generators keep getting stronger while retrievers still match keywords or fixed embeddings. This matters because anything the retriever misses never reaches the generator.
This work replaces the retriever with a decision model that walks the corpus from broad topics down to documents and then passages.
At each level, DecisionRAG scores every candidate in one forward pass, without generating tokens, and keeps only the most promising branches.
With no first-stage retriever and no task-specific training, it beats dense retrieval plus an 8B reranker by 13.8 R@1 points on MultiDoc2Dial and 17.4 Hit@1 points on Natural Questions, where it searches 2.68 million Wikipedia paragraphs.
Retrieval takes 0.7 to 1.8 seconds and 0.2 to 2.3 cents per query. Scoring candidates together in one shared context adds up to 7.7 points over judging them one at a time.
A Qwen3-8B judge trained on in-domain data reaches 60.7 R@1 on MultiDoc2Dial, 5.7 points above the best published task-trained retriever.
arxiv.org/abs/2610.35868
Chat with Paper: academy.dair.ai/papers/retri…
AI News International🌍 retweeted
I think “sell picks and shovels” is the wrong lesson for AI startups.
The Gold Rush analogy is weaker than the Industrial Revolution. Gold is a single pile that runs out. Machines keep making everything cheaper. Far more people got rich using the new machines than building them.
AI News International🌍 retweeted
Lots of debate this week on whether journals should ban AI in peer review.
We actually tested this in our new preprint, and the results were pretty clear to me.
For context, I've reviewed for most of the big journals and evaluated 1,000 human reviews as an editor. I love to read and evaluate science.
TLDR: From now on I won't do any peer review without AI. For clinical trials, I'd even call human-only review dangerous 🧵
AI News International🌍 retweeted
Injecting an emotion into the partner transfers it to the other model. This could be a way to produce empathy and understanding between models. Presumably, they are understanding each other’s emotion concepts like this naturally while speaking, without injection.
AI News International🌍 retweeted
Oh my model, the takeaway here is not "modern AIs are two neuron tweaks away from being super evil", but rather "the personality is a thin facade over deeper machinery nobody understands; radically smarter versions would not do what you expect".
if you have a qwen3.8 27b around, try steering L34 N3838 +0.75, now you have evil qwen 27b
for extra evil add L38 N17180 +0.75
same recipe as @_lyraaaa_: dose × median(‖resid‖ leaving layer L, tokens 1:) / ‖W_down[:, n]‖ at the down_proj input, every token
AI News International🌍 retweeted
if you have a qwen3.8 27b around, try steering L34 N3838 +0.75, now you have evil qwen 27b
for extra evil add L38 N17180 +0.75
same recipe as @_lyraaaa_: dose × median(‖resid‖ leaving layer L, tokens 1:) / ‖W_down[:, n]‖ at the down_proj input, every token