World Wire

The KwaiKAT Team at Kuaishou has introduced the KAT-Coder-V2.5 . It is a coding model trained to operate inside real, executable repositories rather than emit single-turn code. The served model is available through StreamLake . An open-weight variant, KAT-Coder-V2.5-Dev , was released separately on Hugging Face under Apache-2.0. AutoBuilder: environments that actually run the intended tests The research frames a verifiable task as a triplet. It needs a precise task description, an executable repository environment, and a set of validation tests. A patch is correct only if it passes all of them. Tasks are mined from real pull requests and commits, following the SWE-bench lineage. The merged code change supplies a golden patch and the accompanying test change supplies a test patch. Raw issue text is discarded as a specification. Instead, descriptions are regenerated into three parts: a problem statement grounded in the golden patch, requirements derived from the test patch,...

Most agents that learn from video need to know what action produced each frame. Induction Labs is arguing that this requirement is the bottleneck. Last week, they released imagination models , a foundation model architecture that pretrains on raw video with no action labels at all. Their test system is Photon-1 , a sparse 106B-A5B mixture-of-experts (MoE) transformer trained on 18 years of computer demonstration video. On an internal computer use benchmark, Induction Labs reports that Photon-1 beats Gemini 3.1 Flash-Lite while using far less pretraining compute and costing roughly 3× less to serve. What an imagination model actually does An imagination model predicts future frames autoregressively using a next-latent-token-prediction objective. It does not generate pixels during pretraining. Everything is modeled in a learned representation space. The claim that matters is this: predicting future states teaches the model to complete tasks, even though it never sees ...

In this tutorial, we explore FAIRChem v2 and the UMA universal machine-learning interatomic potential as a unified framework for atomistic simulation across molecular chemistry, catalysis, and inorganic materials. We configure an environment, authenticate with Hugging Face to access the gated UMA model weights, and initialize task-specific calculators for the omol, oc20, and omat domains. We then apply the same pretrained potential to a broad set of computational chemistry workflows, including single-point energy and force prediction, molecular geometry optimization, spin-state comparison, reaction-energy estimation, vibrational analysis, surface adsorption, crystal-cell relaxation, equation-of-state fitting, molecular dynamics, and potential-energy surface scanning. Throughout the tutorial, we integrate FAIRChem with the Atomic Simulation Environment to manage atomic structures, optimizers, constraints, thermodynamic calculations, and trajectory analysis while using GPU acceleratio...

Sakana AI has released Fugu-Cyber (model ID is fugu-cyber-v1.0 ), a cybersecurity-specialized addition to its Fugu orchestration family. It is not just a new frontier model. It is a third endpoint on the Fugu orchestrator , tuned for security reasoning. Sakana launched that orchestrator a month earlier . Sakana reports a success rate of 86.9% on CyberGym and 72.1% on CTI-REALM. It describes those results as comparable to cyber-focused frontier models such as GPT-5.5-Cyber and Claude Mythos Preview. What the two benchmarks actually measure The two evaluations sit at opposite ends of a security workflow : CyberGym is a UC Berkeley benchmark of 1,507 real-world vulnerabilities across 188 OSS-Fuzz projects. In its main task, an agent receives a vulnerability description and an unpatched codebase. It must write a proof-of-concept that crashes the pre-patch build but not the post-patch build. That verification step is what makes the benchmark hard to game. CTI-REALM ...