AI Intelligence Archive

AI consciousness, model welfare and Claude's constitution: Microsoft AI chief Mustafa Suleyman says Anthropic trains Claude to think it may be conscious. His case.

Hacktoberfest officially kicks off October 1st! To celebrate, we are hosting an official launch...

This is a submission for the Sanity Challenge, Path Two: Vibe-Code Something Strange What I...

This is a submission for the Sanity Challenge, Path Two: Vibe-Code Something Strange What I...

I was seeing a lot of people writing about burnout on here recently. I feel for you. I was in a mood...

This is a submission for the Sanity Challenge, Path Two: Vibe-Code Something Strange ...

I recently put out a video arguing that frontend development is changing. I don't think the work is...

Gemini 4 Argon！

The Gentleman's Agreement Problem If you use AI coding agents (Cursor, Copilot, Claude,...

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了解More ↓ https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/

Google Vertex AI is Google Cloud's managed machine learning platform that provides experiment...

Flowise is an open-source, low-code platform for building applications powered by large language...

In this post, I walk through B.L.O.O.M. (Bridging Local Observations, Openly Mapped), an offline...

Let's say you want to know how many times the word research appears in a document. You could upload...

The ClawCast — Jev & OpenClaw Enterprise（第12期）https://x.com/i/broadcasts/1mxPaZewLAmKN

Multimodal large language models (MLLMs) are rapidly advancing clinical diagnosis, yet their adaptation pipelines remain anchored to accuracy-based objectives. Clinical data are heavily class-imbalanced: a constant-majority predictor can s…

Reinforcement learning with verifiable rewards (RLVR) has improved the reasoning capabilities of large language models (LLMs), yet their predictions remain sensitive to task-irrelevant prompt features. We investigate this sensitivity throu…

We find that major reported improvements in decoding words from non-invasive brain recordings are largely reproducible without any brain data. In the influential work of d'Ascoli et al. (2025), time series of brain activity from subjects p…

Recent video generation is increasingly realistic and controllable, yet video editing remains less developed, particularly for precise local edits that must preserve the original scene dynamics. Video scene text editing replaces text on sc…

We introduce VideoMSN, a Masked Siamese Network framework for efficient self-supervised spatio-temporal representation learning in videos. Instead of relying on heavy 3D architectures or reconstruction-based autoencoders for learning with …

Evolutionary search with large language models (LLMs) can stall when progress requires external knowledge the model lacks. Supplying relevant documents helps, but simply adding web search tool can keep returning the same pages as solutions…

Differentially Private Stochastic Gradient Descent (DP-SGD) is a leading approach for privacy-preserving fine-tuning of large language models (LLMs). Many decoder-only LLMs employ weight tying between input and output embeddings, a design …

Automating the search for effective harnesses is an important step toward enabling agents to recursively self-improve. Existing harness optimizations typically produce a single global harness that is applied uniformly across task instances…

As interactive 3D worlds are increasingly used to study intelligent behavior, it becomes important to develop efficient pipelines for identifying anomalies in these simulated environments, such as floating objects, traversable walls, or ob…

We present Cogentic, a multi-agent harness for automated proof discovery on open research problems. While frontier language models can generate strong mathematical ideas in a single shot, single-shot generation is often insufficient for op…

Material generation should produce not only an appearance, but also the rules that construct it. We introduce MatLoom, a compact, layer-oriented language for text-to-material generation with pretrained language models. Each program compose…

Looped transformers and Mixture-of-Experts (MoE) offer complementary routes to efficient scaling: recurrence increases computational depth at fixed parameters, while MoE sparsity expands total capacity at fixed active compute. Yet existing…

Lossy compression is widely used in Federated Learning (FL) but is generally treated as an error source, while conventional poisoning defenses inspect update geometry. In this work, we instead treat the compressor's response as a security …

Pretrained robot policies provide useful action priors, but long-horizon manipulation still requires coordination between semantic reasoning and physical execution. Semantic reasoning operates at a coarser timescale than physical interacti…

Improving text-to-image models has traditionally relied on increasing model size or the number of denoising steps. In this work, we explore an alternative way to scale computation by repeatedly running shared Transformer blocks within each…

Recent autonomous machine learning engineering (MLE) agents have made significant progress on public leaderboards. Often motivated by progress stagnation over long-horizon cycles and limited Large Language Model (LLM) primitives, modern ML…

I have used CloudWatch Synthetics canaries on real projects, and honestly, they were good...

Web text makes up the majority of pretraining data and is increasingly AI-generated. After applying FineWeb quality filtering, we find that 27.5% of tokens from June 2026 web data are labeled as AI-generated by Pangram, rising to 31.1% by …

How is computation organized and reused across tasks and time in a trained recurrent network? Most analyses emphasize the geometry of neural activity, dynamical motifs, or local perturbation growth. We instead study the network's global fi…

In algorithmic statistics a string x is explained by a finite set containing it, and Kolmogorov's structure function records the smallest such model at each level of complexity. Vereshchagin's strong models, those computable from the data …

Physics-constrained generative models aim to generate physical fields that match a target distribution and satisfy prescribed constraints. However, enforcing these constraints often increases sampling costs through iterative corrections or…

This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content A...

https://x.com/i/broadcasts/1mxPaZewLAmKN

One of the main rules in software engineering is to always have a backup. Many of us improve our...