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@anirudhg9119
Thinking about thinking. Spent time at @Berkeley_EECS, @MPI_IS, @GoogleDeepMind Gemini ♊.
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The paper’s authors describe a controller that weighs next steps against remaining compute and directs workers using persistent memory.
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One team says its method retains only agents' best solutions; a GitSwarm contributor describes storing past experiments in Git.
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A post describes separating what to investigate from how to execute, then learning a transferable value prior.
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The paper reports 71.5% on ProgramBench using GPT-5.5, compared with 58.0% for Codex.
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A post says its persistent memory swarm would preserve and build on work in a shared Git repository.
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Datology says Curation Studio delivered a 6x compute multiplier by making better use of existing data.
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Researchers study agents that turn experience into reusable artifacts; they say the top performer need not learn most efficiently.
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A thread sharing “Thinking Before Thinking” proposes separating task-solving from decisions about where to spend compute: exploring, building on earlier work, verifying it or stopping.
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A post about the project claims a 65% score gain with the same compute on a 24-hour multi-repository agent-swarm task.
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Berkeley RDI describes Oriol Vinyals’ dividing line: whether a system can rewrite its own harness and weights, not just its output.
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A user says they overheard someone in San Francisco call wanting children before artificial general intelligence “irresponsible.”
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Google DeepMind researcher describes RPM methods for guiding AI experiment selection.
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Google DeepMind scientist frames experiment selection as a ranking task using candidates and history.
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Research engineer at Meta shares models to rank AI-generated ideas when GPU time is scarce.
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The method reuses top-layer activations in lower layers during inference on existing models.
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Four former Google researchers found a public benefit corporation to automate machine learning and science.
