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  • Models Self-Regulate Reasoning Length

    By

    @godofprompt

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    the deeper point here connects to something the field keeps rediscovering. we trained reasoning models to think longer. then we discovered longer doesn't mean better. now this paper shows the models themselves already know that. they're generating stop signals that our inference

    → View original post on X — @godofprompt

  • Three Key Observations on Model Paths

    By

    @godofprompt

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    three observations back this up: > high-confidence paths (measured by cumulative probability) are shorter AND more accurate than standard samples > at those natural endpoints, the stop token is the model's top prediction. it knows it's finished > as you widen exploration (more

    → View original post on X — @godofprompt

  • Researchers test AI self-awareness with TSearch

    By

    @godofprompt

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    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

    → View original post on X — @godofprompt

  • Overthinking hurts accuracy in AI responses

    By

    @godofprompt

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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

    → View original post on X — @godofprompt

  • Overthinking in AI models is a sampling issue
    Overthinking in AI models is a sampling issue

    By

    @godofprompt

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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

    → View original post on X — @godofprompt

  • AI Learning Risks and Rewards in Student Education

    By

    @sassoftware

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    Students using AI sometimes fail to learn. But it’s not a given. In this episode of Pondering AI, Master’s student Seth Rabinowitz demonstrates that risks and rewards of AI in education are not lost on students. On topics ranging from intentional learning to interpersonal skills

    → View original post on X — @sassoftware

  • AI and Psychosis: What You Need to Know and Do

    By

    @terence_mills

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    AI and psychosis: What to know, what to do
    #AI #AIio #AIInnovation #ML #DataScience #Futureofwork @timnitgebru @oriolvinyalsml @ceobillionaire @soumithchintala @waitin4agi_ @sallyeaves @bernardmarr ow.ly/61hh30sTyEO [Translated from EN to English]

    → View original post on X — @terence_mills, 2026-03-04 01:00 UTC

  • Predicting AGI-Related Drama in the Future

    By

    @tunguz

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    If you think there is too much drama surrounding AI labs now, just wait to see what's gonna happen when we get on the verge of obtaining AGI.

    → View original post on X — @tunguz

  • Pocket Recording Device Privacy and Security Audit Reveals Major Concerns
    Pocket Recording Device Privacy and Security Audit Reveals Major Concerns

    By

    @abhi1thakur

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    Pocket (@Heypocket) sold 30,000 units in 5 months recording doctor visits, legal calls, and board meetings. So I did my due diligence like I always do. Their Google Play listing says "No data collected." Their own privacy policy lists audio recordings, transcripts, device IDs, ad identifiers, cookies, IP location, and behavioral inferences. One of those is wrong. They brand themselves "open source". Their GitHub org (github.com/heypocketai) contains exactly 4 repositories: – Raspberry Pi Zero prototype called icecream-project [from the CEO's prior Omi work], – FFmpeg Flutter fork, – docs repo, – org profile page. The actual Pocket device firmware, mobile app, backend infrastructure, and audio processing pipeline are NOT here. There is NO Pocket source code to audit. Recordings go to unnamed "cloud/AI vendors" with no disclosure of which LLMs process your audio, what jurisdiction they operate in, or how long they retain it. Users can't choose their provider or bring their own keys. If you cancel your $19.99/month subscription, your recordings get processed by whichever default model Pocket selects. The contact mic is marketed for recording phone calls without speakerphone. In 11+ US states including California (where Pocket is based) — that requires all party consent. The device has NO consent mechanism. Their terms cap liability at the amount you've paid or $100 (whichever is greater) – for data breaches involving your medical appointments, legal consultations, and proprietary meetings. A third‑party LobeHub skill has already reverse‑engineered Pocket’s web API and can pull recordings, transcripts, summaries, and action items using short‑lived Firebase bearer tokens extracted from the user’s browser session. Their privacy policy confirms that if Pocket is acquired — every recording, transcript, and summary transfers to the buyer. Y Combinator (@ycombinator) Pocket (@heypocket) is your notetaker for real world meetings. In the last 5 months, the team has delivered over 30k units with a $27M annualized run rate, growing 50% month over month. Congrats on the launch, @AkshayNarisetti and @gabrieldymowski! ycombinator.com/launches/PaX… — https://nitter.net/ycombinator/status/2028878066630709455#m

    → View original post on X — @abhi1thakur, 2026-03-03 20:26 UTC

  • Agentic AI Periodic Table: From LLM to Autonomous Systems
    Agentic AI Periodic Table: From LLM to Autonomous Systems

    By

    @ingliguori

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    Agentic AI now has its own “Periodic Table” From: LLM, RAG, RL
    to PLAN, MAS, LTM
    to SAFE, HUMAN oversight
    to HR, MKT, LEGAL use cases
    Autonomous AI = memory + planning + tools + safety + collaboration.
    It’s a system, not a prompt.
    Credit: Prem Natarajan
    #AgenticAI #AIStack

    → View original post on X — @ingliguori