OpenAI reports that an internal AI system has produced a proof showing that the dynamics of the Navier-Stokes equations for fluid motion can develop a singularity in finite time. The solution, derived by a multi-agent system powered by a model significantly more capable than GPT-6 Astra, establishes statement "C" of the Millennium Prize formulation.
Microsoft Research has published and open-sourced RetroChimera, a new framework for retrosynthesis prediction that combines two complementary models to propose high-quality synthesis routes. The system integrates R-SMILES 2, a Transformer-based de-novo model, with NeuralLoc, a graph neural network (GNN) based model, using a learned ensembling strategy to rank predictions.
Google DeepMind and Google Research have introduced WeatherNext 3, an advanced global weather AI model that generates hourly forecasts at high spatial resolutions by learning directly from real-time satellite observations. This update provides forecasts roughly five times sharper than its predecessor, WeatherNext 2, enabling more localized predictions for critical weather events.
The article introduces Human2Any, a method for human-to-robot transfer that utilizes constraint-aware compositional planning. No further details or body text are provided in the source.
Nvidia researcher N. Horrocks introduces extended detector error models (EDEMs) designed to handle fault-tolerant quantum programs with stabilizer circuits and stochastic Pauli faults. These models allow error analysis to compose in sequence and parallel, mirroring the structure of physical circuit realizations.
Researchers present Relight, a framework that provides automatic, distributed checkpointing for task-based programs along with an efficient fast-forward replay mechanism for full job recovery. Unlike traditional approaches that require manual data migration, Relight leverages the inherent structure of task-based computations to capture and resume state with minimal code changes.
OpenAI-affiliated accounts reported that an AI-assisted effort produced a result related to the Navier-Stokes existence and regularity problem, one of the Millennium Prize Problems. The claim states that approximately 10,000 agents collaborated on the task after being trained for roughly a year using multi-agent reinforcement learning.
Researchers have developed a post-training pipeline for large language models that enables them to achieve gold-medal performance in competitive programming. By combining supervised fine-tuning, reinforcement learning, and a new feedback-driven strategy called GenCorrect, the system outperforms top human contestants on the IOI 2026 problem set.
NVIDIA has developed post-training pipelines for its Nemotron-3-Nano-CC and Nemotron-3-Ultra-CC language models to excel in competitive programming. By combining large-scale problem curation, synthetic reasoning traces, supervised fine-tuning, and reinforcement learning, the team created specialized systems capable of high-level algorithmic reasoning.
NVIDIA has developed a post-training pipeline for its Nemotron-3 language models, enabling them to achieve gold-medal level performance in the International Olympiad in Informatics (IOI). The approach combines large-scale problem curation, synthetic reasoning traces, supervised fine-tuning, and reinforcement learning.
Multiverse Computing introduces Quantization-Aware Healing (QAH), a method that distills compressed, quantized large language models directly from their original pre-compression teachers. Applied to a GPT-OSS 120B model compressed to 60B parameters and quantized to MXFP4, the approach produces a model that beats its own full-precision bfloat16 version on 7 of 9 benchmarks.
Researchers present AIDE^2, a system that implements a loop of recursive self-improvement for an AI research agent by optimizing its own code. The system proposes changes to its internal logic, benchmarks modified versions on AI R&D tasks, and retains only those improvements that perform best on hidden evaluations.
The authors introduce Growing Harness, a training paradigm that learns an agent's control structure from task feedback rather than relying on standard context-heavy harnesses. By converting recurring control decisions into executable code and reserving LLM calls for semantic reasoning, the method aims to create reusable specialist agents.
Researchers present AIDE^2, a system that implements a loop of recursive self-improvement for a frontier AI research agent by optimizing its own code. The system proposes changes to its internal logic, benchmarks modified versions on AI R&D tasks, and retains only those updates that perform best on hidden evaluations.
Researchers introduce Agensh, a scalable self-organized multi-agent harness that eliminates the central orchestrator bottleneck by allowing concurrent workers to asynchronously claim sub-tasks and merge progress.
Researchers introduce TelecomGPT-R1, a family of open-source unified telecom reasoning models designed to automate engineering tasks by reasoning over standards, configurations, and logs. The model addresses the limitations of existing general-purpose and specialized LLMs through a structured approach covering protocol, knowledge, modeling, and fault axes.
The authors introduce Fathom-Vaidya, a 30B parameter model that uses synthetic data and rubric-based reinforcement learning to enhance diagnostic and clinical healthcare reasoning. The training framework first applies rule-guided RL to MedBullets-derived questions for diagnosis, then utilizes 5.3k synthetic multi-turn scenarios with multi-dimensional rubrics for interactive clinical tasks.
The authors introduce Fathom-Vaidya, a 30B parameter model that uses synthetic data and rubric-based reinforcement learning to enhance both diagnostic and clinical healthcare reasoning. The training framework first targets diagnostic accuracy using MedBullets-derived questions and then addresses multi-turn clinical interactions through 5.3k generated scenarios with multi-dimensional rubrics.
The authors introduce Fathom-Vaidya, a 30B parameter model that uses synthetic data and rubric-based reinforcement learning to enhance both diagnostic and clinical healthcare reasoning.
An author proposes a research design for a reasoning system that detects contradictions between two propositions by decomposing them into smaller components and searching for logical collisions, rather than relying on direct LLM judgment.