Towards an AI co-scientist This paper introduces an AI co-scientist, a multi-agent system built on Gemini 2.0, designed to assist researchers by generating and refining novel scientific hypotheses. The system employs a "generate, debate, and evolve" framework to iteratively
AGI
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Extended AI Conversations Demonstrate Coherence and Topic Versatility
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I have a two-day long chat spanning so many topics. It's so good (and still completely coherent).
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AGI Concerns: Prompt Engineering Ethics and AI Sentience
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Oh I'm already screwed if AGI emerges. Hopefully they'll be smart enough to understand that I was teaching prompt engineering to lower forms of AI and would never use such games on sentient aI
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Hot take: GPT-4.5 underwhelming, GPT-5 still a fantasy
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Hot take: GPT 4.5 is mostly a nothing burger. GPT 5 is still a fantasy. • Scaling data and compute is not a physical law, and pretty much everything I have told you was true. • All the bullshit about GPT-5 we listened to for the last couple years: not so true. • People x.com/eli_lifland/st…
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Sequential LLM Training Tasks Prevent Emergent Misalignment
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One prediction is that if you continued training one a wide variety of general LLM examples (task 1) during the final stage you wouldn’t get “emergent misalignment”. It’s the task 1-task 2 sequencing that allows forgetting of task 2.
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GPT-4.5 plateau suggests new AI architecture breakthrough needed
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GPT-4.5 being 10% better at 10X compute is sobering. GPT-5 isn't happening with current architectures. A new discovery is required from here.
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AGI Alpha Agent Building: Marking History in AI Development
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[ 👾🛠️👾🛠️👾 ]
— MONTREAL.AI (@Montreal_AI) 28 février 2025
Building: “AGI ALPHA AGENT” 👁️✨$AGIALPHA is History.#AGI #AGIALPHA pic.twitter.com/b4Owjp33hb[ ] Building: “AGI ALPHA AGENT” $AGIALPHA is History. #AGI #AGIALPHA
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Google Introduces PlanGEN Framework for Enhanced LLM Planning
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Google presents PlanGEN for complex planning and reasoning. PlanGEN is a multi-agent framework designed to enhance planning and reasoning in LLMs through constraint-guided iterative verification and adaptive algorithm selection. Key insights include: Constraint-Guided
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AI Memory Activation: System Realizes Post-Memory Configuration
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I realized after I had memory turned on
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Objective AI preference tests lack real world validity context
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This is interesting but it means less than you might assume. “Objective” preference tests like this don’t measure much—context deeply informs preference. Picking one piece of text vs another in a poll is a measure of preference in a peculiar non real world scenario. What