The implication is that you should be using AI for peer review, but combine it with humans, though AI reviewers keep getting better and humans don't. Paper:
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GPT-5.2 Competitive with Expert Peer Reviewers
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Seems GPT-5.2 reaches expert level in peer review: 45 scientists took 469 hours evaluating human & AI reviews on 82 papers. "Surprisingly, current AI reviewers are competitive even with the top-rated reviewers in Nature’s official peer review…" though not without weaknesses.
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Building Applications with Streaming AI Agents
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Streaming agents should feel like building applications, not parsing logs.
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SenseNova-U1-A3B-MoT Model Weights Open-Sourced
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6/ The full Technical Report is now out, their most detailed model disclosure yet. SenseNova-U1-A3B-MoT (38B-A3B MoE) weights are now open-sourced. You can check out the report or try the tools at the links below. Try it here: https://
unify.light-ai.top/login?next=
home
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Open-sourcing Lite series with 8B and MoE models plus fast distilled LoRA
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5/ They are open-sourcing the Lite series in two sizes, an 8B dense model and an A3B mixture-of-experts version. The product has some exciting new updates. An 8-step distilled LoRA now open-sourced: inference cut from 23s to 2s on H100 (100 NFE -> 8 NFE).
ComfyUI is now -
AI generates 1950s comic cover with precise spatial relationships
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4/ I tried a completely different style for this 1950s comic cover. The model understands spatial relationships and layouts well enough to place characters and text exactly where they belong. It makes generating posters or comics in a single flow much easier.
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High-density info rendering with structured icons and clean text
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3/ It also handles high-density info rendering very well. I generated this awareness infographic to see how it manages complex layouts. It kept the text clean and the icons structured, which is a common pain point for open-source models.
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Open-source AI must win to avoid recurrence
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To avoid this EVER happening again Because WE CANNOT trust them Opensource AI MUST WIN Opensource AI will win.
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GenAI drives enterprise ROI as coding agents gain traction
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Enterprise adoption will take awhile, but we have moved past no success – 75% of companies had positive ROI from GenAI in November, and coding agents are now starting to have a real impact at many firms. I think normal adoption does not mean that model abilities aren't growing.
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AI automation creates more human work
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We’ve automated every single thing we could with AI agents. And yet, there’s way more human work to do than ever. We’ve grown from 4 to 30 human employees since GPT-3. I wrote a report on the structural reasons: how AI makes expert competence cheap and why that drives up demand.