Can we unlock unprecedented visual intelligence in AI by teaching it to code what it sees? Researchers from UniPat AI and Michigan State University introduce SWE-Vision. This innovative agent allows vision-language models to write and execute Python code, using libraries like
GENERATIVE AI
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Artificial Biological Intelligence: Genome Writing and Species Future
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Artificial Biological Intelligence (ABI) In a post-Darwinian era of being able to write genomes, the implications—both for good and harm—are profound. In conversation with @AdrianWoolfson on his new book On the Future of Species https://
erictopol.substack.com/p/on-the-futur
e-of-species
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Reasoning Models: Why Listed Prices Don’t Match Actual Costs
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// When Cheaper Reasoning Models End Up Costing More // The model you think is cheaper might actually cost you more. New research quantifies exactly how misleading listed API prices are. Across 8 frontier reasoning models and 9 tasks, 21.8% of model-pair comparisons exhibit pricing reversal, where the cheaper-listed model costs more in practice. The magnitude reaches up to 28x. Gemini 3 Flash is listed 78% cheaper than GPT-5.2, yet its actual cost is 22% higher. Claude Opus 4.6 is listed at 2x Gemini 3.1 Pro but actually costs 35% less. The root cause: thinking token heterogeneity. On the same query, one model may use 900% more thinking tokens. Why does it matter? Anyone choosing reasoning models for production needs to benchmark actual costs, not listed prices. Removing thinking token costs reduces ranking reversals by 70%. The authors release code and data for per-task cost auditing. Paper: arxiv.org/abs/2603.23971 Learn to build effective AI agents in our academy: academy.dair.ai/
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Training AI on Your Own Data Isn’t Generative AI
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If you think training AI (or even a process very similar to AI training) on YOUR OWN DATA is a problem – you have lost the plot my friends. This is the furthest thing from generative ai and the criticisms around it.
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OpenResearcher: An Open-Source Offline-Trained Research Agent
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You can now train a deep research agent without a single API call.
— AlphaSignal AI (@AlphaSignalAI) 29 mars 2026
OpenResearcher is a new open-source repo that's trained entirely offline.
A 10-billion-token corpus generates 100+ turn research trajectories.
All offline. Zero API costs.
It learns three browsing actions… pic.twitter.com/4wgFUaFhFxYou can now train a deep research agent without a single API call. OpenResearcher is a new open-source repo that's trained entirely offline. A 10-billion-token corpus generates 100+ turn research trajectories. All offline. Zero API costs. It learns three browsing actions
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Knuth’s Hamiltonian Decomposition Problem Solved Using AI
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Legendary Don Knuth has now used AI to fully solve his Hamiltonian decomposition problem for odd and even cases. Opus 4.6 / 5.4 Pro solved the even case, wrote a proof in Lean and a “apparently flawless 14 page paper” Knuth: “We are living in very interesting times indeed.”
→ View original post on X — @debashis_dutta, 2026-03-29 15:00 UTC
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ARC-AGI-3: New Benchmark Resets AI Scoreboard to Near Zero
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Humans: 100% Gemini 3.1 Pro: 0.37% GPT 5.4: 0.26% Opus 4.6: 0.25% Grok-4.20: 0.00% François Chollet just released ARC-AGI-3 — the hardest AI test ever created. 135 novel game environments. No instructions. No rules. No goals given. Figure it out or fail. Untrained humans solved every single one. Every frontier AI model scored below 1%. Each environment was handcrafted by game designers. The AI gets dropped in and has to explore, discover what winning looks like, and adapt in real time. The scoring punishes brute force. If a human needs 10 actions and the AI needs 100, the AI doesn't get 10%. It gets 1%. You can't throw more compute at this. For context: ARC-AGI-1 is basically solved. Gemini scores 98% on it. ARC-AGI-2 went from 3% to 77% in under a year. Labs spent millions training on earlier versions. ARC-AGI-3 resets the entire scoreboard to near zero. The benchmark launched live at Y Combinator with a fireside between Chollet and Sam Altman. $2M in prizes on Kaggle. All winning solutions must be open-sourced. Scaling alone will not close this gap. We are nowhere near AGI. (Link in the comments)
→ View original post on X — @ken_goldberg, 2026-03-29 14:46 UTC
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Language Barriers Finally Overcome Through Advanced Technology
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It's still nuts to me how this sci-fi dream becomes reality: language barriers are solved forever.
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Frontier Models Vision Capabilities: Benchmarks Gaming Problem
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Frontier models can’t see, and if you think they can, you’ve probably been fooled by benchmarks that can totally be gamed. In the very short essay linked below I discuss a stunning new finding from Stanford that shows just how serious the problem is. And why this means a lot
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Top AI Papers of the Week: March 23-29
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The Top AI Papers of the Week (March 23 – 29) – Claudini
– MemCollab
– ARC-AGI-3
– Composer 2
– Hyperagents
– Attention Residuals
– Agentic AI and the Next Intelligence Explosion Read on for more: