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@arankomatsuzaki
Sharing AI research. Early work on AI (GPT-J, scaling, MoE). Ex ML PhD (GT) & Google.
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A user exploring the idea asks whether anyone is interested in cofounding or knows someone who might be.
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One commenter says AI mostly helped with rewording; their feed split between “unreadable slop” and praise.
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A post describes a benchmark testing nine frontier agents on 15 open scientific follow-up problems developed with domain scientists.
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Quoting a post captioned 'recession indicator,' a user said it explained what was exciting about GLM 5.3 Flash fine-tune.
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A post links to a photo without offering any explanation beyond 'hmm.'
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A post claims a more than 100-fold question-answering speedup for VideoJev versus Thinking.
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A post describes stage-by-stage checks that send a failed tablecloth grasp back to repositioning.
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A post claims ARC achieves more than 5× higher success on reasoning-heavy tasks without changing the model architecture.
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The ARC project says it adds action-grounded reasoning to existing models without new robot demonstrations or foundation-scale training.
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A user floats buying a few hundred robots in India for companies to run real-world tests.
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Partiful’s listing invites researchers, engineers and innovators to commemorate mathematics and discuss scaling laws.
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A post introducing the report claims three years of data support its argument about AI and creativity.
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A post says success on RoboCasa GR-1 rose from 63.3% to 78.7% as context grew from 0.0 to 19.2 seconds.
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A post describes an 8B-parameter RoboJEPA model from Meta’s FAIR and Mila, trained on 15,022 hours of robot video.
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The Workhorse team says it studies how to teach whole-body movement and object handling from human demonstrations, without teleoperation.
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In posts describing new research, the authors say some base models recovered much of reinforcement learning’s reasoning gain when prompted with model-specific “token cues,” and they say the work also includes a safety case study on refusal and compliance.
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A user says an unspecified element pops up 30 minutes after they close it, no matter how many times they try.
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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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A user invites researchers using GPT 6 Astra for robotic control or agentic real2sim to request free tokens for testing Kimi K3 and Qwen 3.8 Flash Next as comparisons.
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A post attributes the proposal to Meta's team and reports Qwen3-8B average accuracy rising from 30.76% to 65.97%.
