What does “net positive” say that 79% voted yes already says ?
AGI
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Autonomy Requires Embodiment to Seek Problems and Solutions
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If you want it to be autonomous it should be able to seek new problems to solve (and the means to solve them) on its own, which requires embodiment in some form (not necessarily physical)
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Autonomous AI Through Dynamic Model Synthesis and Self-Improving Abstractions
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The path to autonomous AI is a system that learns to solve new problems by synthesizing models of them on the fly (as code), and that gets smarter over time by adding new abstractions to its own library (also as code), compounding its capabilities. Not a static map — rather, an
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@testingcatalog — 2025-11-04
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OpenAI is gearing up to release a family of new models soon, as teased by Sam Altman. Soon or not soon?
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IMO-Bench: 400 olympiad problems for AI evaluation
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IMO-Bench also includes #AnswerBench, a set of 400 problems with verifiable answers carefully chosen from past Olympiad competitions. The problems span across four IMO categories (Algebra, Combinatorics, Geometry, and Number Theory), were altered by experts to avoid memorization,
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IMO-Bench: New AI Model Evaluation Framework for Mathematics
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IMO-Bench consists of three benchmarks that judge models on diverse capabilities: IMO-AnswerBench, a large-scale test on getting the right answer; IMO-ProofBench, a next-level evaluation for proof writing; and IMO-GradingBench, a new benchmarkto enable further progress in
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ALPHA Agent AGI Ethereum Launch Announcement
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[ 1.ALPHA.AGENT.AGI.ETH ] Website : https://
agialpha.com $AGIALPHA #AGIALPHA -
Agents Handle Snorkel-Contributed Tasks at Upcoming Event
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Excited for this release — can't wait to see how agents handle the Snorkel-contributed tasks! We'll be at the event too — see you there!
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Anthropic’s Alignment Science Research Blog Launch
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For more of Anthropic’s alignment research, see our Alignment Science blog: https://
alignment.anthropic.com -
Stress-Testing AI Model Specifications Reveals Underlying Preferences
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Stress-testing model specifications, led by Jifan Zhang. Generating thousands of scenarios that cause models to make difficult trade-offs helps to reveal their underlying preferences, and can help researchers iterate on model specifications.