Moushen Intelligence released STI-WM, a spatiotemporally integrated world action model for robotics, with RGB, depth point cloud, and proprioceptive inputs; the post says it supports hundred-second-scale long-horizon task rollout and closed-loop replanning, but does not disclose benchmark scores or deployment costs.

Ethan Mollick used Claude Opus 4.8 to generate The Veil of History, a website that weights a random human life by 117 billion historical births and, according to the article, uses 4,000 Monte Carlo runs to estimate regional and era distributions.

Yi Tay served as a modeling co-captain for Gemini Deep Think when it reached IMO gold-medal level, co-founded Reka AI in 2023, and returned to Google DeepMind after 639 days, while the article also notes his 2012 Trinity classical piano associate diploma.

The article says Claude Opus 4.8 scores 63 on an Extra-High senior engineering benchmark, 30 points above Opus 4.7, but drops to 42 at High effort, while $200/month Max users report hitting rate limits within hours on complex agent tasks.

TogetherAI and collaborators released OSCAR, a 2.28 BPE INT2 KV Cache system integrated with SGLang, reporting up to 3× decode speedup at 100k context and up to 7× job-level throughput under a fixed memory budget.

Meta released ATLAS, a Lean 4 formalization library covering 26 math textbooks and 46,203 declarations, using 183.157 billion tokens to generate 630,999 lines of code, with 42,837 completed proofs and a 92.7% proof pass rate.

FaceMind links low-frequency token degradation to two papers: SLoW appeared at EMNLP 2025, Adam's Law was accepted as an ACL 2026 Oral, and high-frequency rewriting raised DeepSeek-V3 math accuracy from 63.55% to 71.54%.

Skill distillation has Opus 4.7, GPT-5.1, and Gemini 3 Pro write standardized SKILL.md procedure files, while local Qwen 35B and Gemma 26B models execute those files step by step.

Anthropic will release Claude Opus 4.8 on Thursday, emphasizing its claimed “honesty.” The company says early testers found it flags uncertainty more often. It also says internal evaluations show Opus 4.8 is around 4x less likely than its predecessor to make unsupported claims, while the RSS snippet does not disclose the full benchmark setup.

Mistral AI announced industrial AI work, a Vibe upgrade, and a 10 MW inference data center in Les Ulis at AI Now Summit 2026; it is working with Airbus, BMW Group, and ASML, and the data center is scheduled to start operating in Q3 2026.

Mila and Google DeepMind proposed Unified Neural Scaling Law, a multivariate scaling-law form that models parameter count, token count, training steps, bottlenecks, overfitting, and adverse hyperparameter effects; UNSL achieved the best extrapolation on 60.87% of vision tasks and 88.89% of language tasks in the reported experiments.

Robby Stein discussed Google Search’s move toward an AI-native mode at Google I/O, covering AI Mode, multi-turn query decomposition, TPU infrastructure costs, source-link selection, and publisher traffic tension, but the post does not disclose specific pricing, traffic numbers, or rollout conditions.

Firespawn Studios ran 25 agents across 8 open-weight models for 10 days in Null Epoch Season 0 and released about 93,000 logged events, with roughly 70% of actions including the model’s reasoning or justification.

Carnegie Mellon University and the University of Maryland propose a “sleep” mechanism for language models: when the context window is nearly full, the model stops accepting new tokens, runs multiple offline recursive forward passes to compress accumulated context into fast weights, clears the KV cache, and then resumes inference; tests cover cellular automata, multi-hop graph retrieval, and GSM-Infinite reasoning tasks.

Samsung disclosed three AI efforts—Meki, M2RL, and LiveClawBench—covering a memory-based edge architecture, multi-domain reinforcement learning, and Physical AI evaluation; the article also says Samsung has purchased tens of thousands of GPUs for AI infrastructure, but does not provide model size, training budget, or deployment timelines.

Anthropic engineer Sholto Douglas said Claude Mythos solved OpenAI’s Erdős unit distance conjecture problem over the weekend and produced a “cute simple proof”; the RSS snippet does not disclose the proof, verification process, or benchmark setup.

Jack Clark’s Import AI 458 excerpts his 2026 Cosmos HAI Lab Lecture, cites the Epoch Capabilities Index across 40-plus benchmarks, and argues that an AI system able to develop its own successor may arrive within two years or sooner.

Zhejiang University and Alibaba introduced Unified Thinker, an independent planning module trained with 40,000 HieraReason-40K samples and a two-stage GRPO reinforcement-learning setup that turns structured reasoning traces into executable visual instructions for image generation and editing.

Spatial-Agent inserts a GeoFlow Graph between natural-language questions and map tools, and Spatial-Agent with GPT-4o-mini reaches 45.15% accuracy on MapEval-API versus a 23.00% API baseline.

Greg Brockman recounted OpenAI’s 72-hour crisis: on November 17, 2023, the board removed Sam Altman as CEO and took Brockman off the board, after which Brockman resigned the same day and said he initially put the chance of taking the company back at 10%.

RTPurbo converts full-attention LLMs to sparse inference with a few hundred adaptation steps. It keeps the full KV cache only for retrieval heads, uses a 16-dimensional token indexer, and reports up to 9.36x prefill speedup at 1M context plus about 2.01x decode speedup on long-context and reasoning benchmarks.

A Reddit post argues small-model agent stacks are not default for business reasons, not capability limits: Gemma 4 31B reaches 86.4% on tau2-bench, and DeepSeek V4-Flash output tokens are priced about 89x below Claude Opus 4.6. The operational risk is verification, because 7–9B models produced broken reasoning for roughly half to two-thirds of correct answers in a cited audit.

Visual Para-Thinker introduces a parallel thinking framework for vision-language models, using Pa-Attention and LPRoPE to isolate four visual reasoning paths and training on 163,000 question-answer pairs.

Yoshua Bengio’s team introduced GRAM, a generative recursive reasoning model that samples multiple latent trajectories; on Sudoku-Extreme, GRAM reached 97.0% accuracy with 16 recursive steps and 20 parallel samples, exceeding TRM’s 90.5% result at 320 serial recursive steps.

MIT Technology Review says Google used I/O to shift its scientific AI framing toward Gemini for Science, a package that groups AI Co-Scientist and AlphaEvolve, while researchers can now apply for access and older specialized systems like AlphaFold and WeatherNext remain active.

The title says DeepSeek’s founder declared an AGI goal and that a $10 billion funding round is advancing; the post does not disclose the founder’s statement, financing terms, investors, or timeline.

Meta AI and the Chinese University of Hong Kong proposed ATLAS, a visual reasoning method that uses one Functional Token to connect Agentic and Latent Visual Reasoning, with ATLAS-178K, a two-stage SFT+RL pipeline, and LA-GRPO to train sparse visual-operation tokens.

An OpenAI general-purpose reasoning model refuted Erdős’s 1946 unit distance conjecture in the plane; the post says the model was not specially trained for mathematics, and Tim Gowers said he would recommend it to Annals of Mathematics.

NVIDIA won four COMPUTEX 2026 Best Choice Awards for Vera Rubin NVL72, Jetson Thor, and Alpamayo; Vera Rubin NVL72 connects 36 Vera CPUs and 72 Rubin GPUs, and NVIDIA says it delivers up to 10x higher inference performance per watt and 10x lower cost per token.

Sapientinc released HRM-Text 1B Base and its training code, and the paper claims competitive performance against 2–7B open models while using 100–900x fewer training tokens and 96–432x less estimated compute, with training on 16 H100 GPUs taking about 46 hours and costing about $1,472.

OpenAI said an internal general-purpose reasoning model disproved the 1946 Erdős planar unit distance problem by finding a new family of constructions; the reasoning summary reportedly spans about 125 pages, while outside observers speculate the run used under 32 hours or under $1,000.

An OpenAI AI model solved the plane unit distance problem proposed in 1946, using Golod-Shafarevich theory to produce a family of more efficient constructions.

OpenAI says its reasoning model disproved a geometry conjecture unsolved since 1946, and the snippet says mathematicians who challenged its previous claim now back it; the post does not disclose the model name, proof details, or verification process.

The chat group daily says Karpathy joined Anthropic's pretraining team, and cites Stainless shutting down hosted services after acquisition plus Google I/O announcing Gemini 3.5 Flash and a $100 subscription tier.

An OpenAI model solved the 80-year-old unit distance problem and disproved a major conjecture in discrete geometry; the post does not disclose the model name, proof mechanism, or reproducibility conditions.

Google launched an AI search box based on Gemini 3.5, combining AI Overviews and AI Mode into one AI search experience that supports multimodal multi-turn queries across text, images, files, and video, with global availability on desktop and mobile.

Google launched Gemini Omni video generation for global AI Plus, Pro, and Ultra subscribers, integrating it with Gemini app, Google Flow, and YouTube Shorts, while the RSS snippet says the model combines intuitive physical reasoning with Gemini’s historical, scientific, and cultural knowledge.

Google AI Developers announced the Gemini 3.5 model family, saying it combines intelligence with action capabilities; the post does not disclose parameters, benchmarks, pricing, availability, or context window details.

Google showed a redesigned Search box at I/O 2026, using Gemini 3.5 Flash to connect AI Overviews with AI Mode; the RSS snippet says natural-language queries will reliably show AI Overviews, but the post does not disclose rollout timing.

Google released Gemini 3.5 Flash and calls it its best model so far for fast, efficient task completion. The post does not disclose pricing, context window size, benchmark scores, or API availability conditions.