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I have never seen it expressed exactly like that, but I wholeheartedly endorse it: Feedback beats planning. My plea at Meta was “No grand plans, follow the gradient of user value”.

Clarifying o3’s ARC-AGI Performance OpenAI has confirmed: * The released o3 is a different model from what we tested in December 2024 * All released o3 compute tiers are smaller than the version we tested * The released o3 was not trained on ARC-AGI data, not even the train

pleaseTellMyEngineeringDirector redd.it/1k1xs3t

LMAOO

Official announcement: Qwen 3 this week. Reasoning and non-reasoning in one.

There's a new paper circulating looking in detail at LMArena leaderboard: "The Leaderboard Illusion" arxiv.org/abs/2504.20879 I first became a bit suspicious when at one point a while back, a Gemini model scored #1 way above the second best, but when I tried to switch for a few

1/ Do AI systems discriminate based on gender when choosing the most qualified candidate for a job? I ran an experiment with several leading LLMs to find out. Here's what I discovered:👇

get off that silly

Si realmente viviéramos en una simulación y yo fuera el guionista, sería con este tipo de vídeos con el que empezaría a dar pistas que precipiten los acontecimientos de final de temporada para acabar revelando que, efectivamente, vivimos en una simulación.

Claude Sonnet 4 on ARC-AGI Semi Private Eval Base * ARC-AGI-1: 23%, $0.08/task * ARC-AGI-2: 1.2%, $0.12/task Thinking 16K * ARC-AGI-1: 40%, $0.36/task * ARC-AGI-2: 5.9%, $0.48/task Sonnet 4 sets new SOTA (5.9%) on ARC-AGI-2

Humor ¿no intencionado? en Twitter

A very necessary paper, showing once again that LLMs, even "reasoning" LLMs, do not actually reason but collapse when far from their training distribution. AGI or PhD-level AIs are not around the corner. But they are still extremely useful when used correctly.

It’s a hefty 206-page research paper, and the findings are concerning. "LLM users consistently underperformed at neural, linguistic, and behavioral levels" This study finds LLM dependence weakens the writer’s own neural and linguistic fingerprints. 🤔🤔 Relying only on EEG,

Desde la semana pasada, esta cuenta está creciendo de forma orgánica. Es decir: crece sin responder a una publicación o a una acción concreta. La herramienta que ofrece Escaños en Blanco, la idea de dejar escaños vacíos, está empezando a moverse sola. Ciudadanía. Sin partidos.

🏆 Los Premios a jóvenes investigadores e investigadoras UAM 2024 reconocen la contribución significativa al desarrollo de la actividad investigadora en la #UAM. ¡Enhorabuena a todas las personas galardonadas en esta edición!👏🏻 Alfonso Santos López, Anne-Marie Reynaers, Fátima

Discover how #verl simplifies #ReinforcementLearning for advanced #LLM reasoning and tool use in our Aug 6 Expert Exchange with Haibin Lin (ByteDance). Supports PPO/GRPO/DAPO, async rollout, expert parallelism for MoE, and more. #PyTorch #OpenSourceAI 🔗 hubs.la/Q03xkQW-0

Today, we're announcing a preview of ARC-AGI-3, the Interactive Reasoning Benchmark with the widest gap between easy for humans and hard for AI We’re releasing: * 3 games (environments) * $10K agent contest * AI agents API Starting scores - Frontier AI: 0%, Humans: 100%