here's where it gets interesting. the researchers probed whether models internally "know" they're done. they introduced TSearch, which scores partial reasoning traces by cumulative log-probability across the entire chain, not just the next token. when you let the model explore
GENERATIVE AI
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Overthinking harms accuracy in AI responses
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and it's not just wasted compute. overthinking actively hurts accuracy. DeepSeek-R1 produces responses 5x longer than Claude 3.7 Sonnet on AIME 2025 with comparable accuracy. QwQ-32B scores 2 percentage points HIGHER with its shortest answers using 31% fewer tokens. 72% of
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RFCS Metric Reveals Early Correct Steps
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first, the problem quantified. the researchers created a metric called RFCS (Ratio of First Correct Step) that tracks where in a chain of thought the correct answer first appears. on MATH-500, across every model tested, the right answer shows up well before the end in over half
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Overthinking in AI: A Sampling Issue
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reasoning models already know when they've solved the problem. we just don't let them stop. new paper from Beihang University and ByteDance shows that the overthinking problem in models like DeepSeek-R1 and Qwen3 isn't a training failure. it's a sampling failure. the fix cuts
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AI hallucinations provide mental health break with new real-time world model
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The hallucinations of AI provide mental health break
— Robert Scoble (@Scobleizer) 6 mars 2026
A new kind of AI is here.
A real-time world model.
This isn’t like anything else you’ve ever done on your computer. It creates frames one after another, which translates into a new kind of video generation engine.
I’m… pic.twitter.com/qKDHDjV85kThe hallucinations of AI provide mental health break A new kind of AI is here. A real-time world model. This isn’t like anything else you’ve ever done on your computer. It creates frames one after another, which translates into a new kind of video generation engine. I’m
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AI-assisted discovery solves open theoretical physics problem
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Solving an Open Problem in Theoretical Physics using AI-Assisted Discovery Paper:
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AI Governance: Attribution and Provenance in Creative Processes
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Well said Knut and really interesting framing. As #AI becomes more embedded in creative processes, governance around attribution, provenance and trust may become just as important as traditional ownership frameworks. In fact, this shift from pure ownership toward collaboration,
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Comparison between Claude Code and Codex App
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Claude Code vs Codex App [Translated from EN to English]
→ View original post on X — @arrakis_ai, 2026-03-06 07:44 UTC
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NEO-unify: Building Native Multimodal Unified Models End to End
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NEO-unify: Building Native Multimodal Unified Models End to End Blog: https://
huggingface.co/blog/sensenova
/neo-unify
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SenseTime/NTU introduce NEO-unify unified multimodal AI paradigm
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Huge! We are finally moving past the era of using separate vision encoders and generative models to build multimodal AI! SenseTime and NTU just introduced NEO-unify，a native, unified, end-to-end paradigm. Instead of using middleman tools to translate images, this model https://
x.com/SenseTime_AI/s
/SenseTime_AI/status/2029585218819199108
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