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| This week, both the “Models to Pay Attention To” and “Research Papers” categories are especially interesting – you can find them at the end of this email | Now, to the main topic: Suddenly, in just one week, we got closer to what may soon be called Artificial General Science. In other terms: AI-powered autonomous scientific discovery. | For centuries, science was our tool to understand the world. Now we’re building tools that can do science themselves. It’s a bit like inventing the telescope and finding out it’s writing astronomy papers while you sleep. | The first major inflection point was AlphaFold. In 2020 DeepMind stunned the life sciences community by predicting protein structures with near-experimental accuracy. For decades this problem had resisted solution. AlphaFold showed that AI could generate answers to questions that had long been out of reach, reshaping the trajectory of biology. | The next milestone came in 2022 with AlphaTensor, where an RL-based agent rediscovered faster algorithms for matrix multiplication, improving on human-devised methods for the first time in half a century. This was algorithmic invention – a sign that AI could push into mathematics, not only applied domains. Phenomenal. | By 2024, Sakana’s AI Scientist stitched these lessons together into a prototype of an autonomous research agent. It combined literature review, experiment design, execution, and paper drafting. The results were uneven, but they have the right ambition: move from models that excel at single tasks to agents that navigate the entire scientific workflow. | Then, in early 2025, Google unveiled the AI co-scientist, built on Gemini 2.0. This multi-agent system mirrored the reasoning steps of the scientific method — generation, reflection, ranking, evolution, meta-review. It wasn’t limited to text generation: it produced hypotheses that were validated in laboratories. Repurposed drugs for leukemia were confirmed in vitro, epigenetic targets for liver fibrosis tested in human organoids, and a mechanistic explanation of antimicrobial resistance aligned with fresh experimental findings. AI was beginning to propose ideas that human scientists could take straight into the lab. | And then came August 2025, the last two weeks of it that may well be remembered as the ignition point of Artificial General Science. |  | Image Credit: Virtuous Machines: Towards AGS |
| A few heavyweight papers I’d like you to know about (find the links in the reading list below): Virtuous Machines (160 pages; Wehr et al.) laid out a philosophical roadmap for AGS. It is also “the first demonstration of an autonomously conducted, end-to-end online experiment with human participants”. The aiXiv project (60 pages; Zhang et al,) introduced a publishing ecosystem where AI scientists can submit, review, and refine research. From AI for Science to Agentic Science (74 pages; Wei et al.) gave the field a structured vocabulary and positioned agentic systems as the new paradigm. | We also have Intern-S1 arrived from Shanghai AI Laboratory – a 241-billion-parameter model trained on trillions of scientific tokens, outperforming rivals on physics and chemistry tasks. And Microsoft Research contributed MindJourney, a test-time reasoning framework where models learn by simulating 3D exploration. MindJourney showed an 8% leap on spatial aptitude benchmarks and offered a path for scientific agents to “move” through environments they cannot directly sense – a capability crucial for robotics, geoscience, and lab automation. | That convergence is astonishing. Within a week, we saw the capability (Intern-S1, MindJourney), the infrastructure (aiXiv), and the intellectual frame (Agentic Science, Virtuous Machines) appear together. Independent groups, different traditions, all arriving at the same horizon: science conducted by AI, autonomously. | This science acceleration is breathtaking. And this past week will be remembered as more than a cluster of publications. It was the moment discovery itself began to scale at machine tempo — and the scientific method gained a sleepless new collaborator. | | From our partners: Clarifai’s Local Runners → A Secure API Bridge for Your Models | | Every model iteration usually means another redeployment – and lost time.
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| | | Reading List / papers from the editorial: | Virtuous Machines: Towards Artificial General Science by Gabrielle Wehr et al. (2025) →read the paper aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists by Pengsong Zhang et al. (2025) →read the paper From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery by Jiaqi Wei et al. (2025) →read the paper
| | | Models to pay attention to: | Intern-s1: A scientific multimodal foundation model by Shanghai AI Lab (open-source)
This is 241B-parameter multimodal Mixture-of-Experts model with 28B active parameters, optimized for scientific reasoning. Trained on 5T tokens (2.5T scientific), it supports text, images, molecular structures, and time-series data. Intern-S1 introduces a dynamic tokenizer and Mixture-of-Rewards RL framework, achieving 75.0 on MatBench, 83.4 on ChemBench, and 65.7 on MSEarthMCQ—outperforming both open- and closed-source models →read the paper Nemotron Nano 2 by NVIDIA (open-source)
Researchers from NVIDIA developed Nemotron-Nano-9B-v2, a 9B hybrid Mamba-Transformer LLM optimized for reasoning. It achieves 3–6× higher throughput than Qwen3-8B and matches or exceeds its accuracy across benchmarks like MATH (80.5), BFCLv3 (66.9), RULER-128k (82.2), and AIME24 (30.0). Built via FP8 pretraining on 20T tokens and 128k context support, it’s compressed from a 12B model using Minitron pruning and distillation. It runs on a single 22GB A10G GPU →read the paper Command A Reasoning: Enterprise-grade control for AI agents by Cohere
This advanced enterprise-grade LLM is optimized for agentic workflows and deep reasoning. It supports 128k–256k context length, runs on a single H100/A100 GPU, and includes token budgeting to balance cost and performance. It outperforms gpt-oss-120b, DeepSeek-R1 0528, and Magistral Medium across BFCL-v3, Tau-bench, and multilingual tasks. Powering North, it excels in long-form research, safety evals, and adaptive latency-performance tradeoffs →read their blog DeepSeek V3.1 release (open-source)
This model is featuring hybrid inference modes (“Think” and “Non-Think”) in a single model with 128k context support. The “Think” mode boosts tool use and multi-step reasoning, outperforming DeepSeek-R1-0528 in complex tasks. Pretrained on 840B tokens for extended context, the model shows gains on SWE and Terminal-Bench. It supports Anthropic API format, strict function calling (beta), and improved thinking efficiency, marking a major step toward agent-oriented LLM performance →read the release notes Ovis2.5 by Alibaba (open-source)
This multimodal LLM integrats a native-resolution vision transformer (NaViT) for fine-grained visual perception and a “thinking mode” for reflective reasoning. The 9B model achieves 78.3 on OpenCompass, outperforming all open-source models <40B. It ranks highest on STEM, chart, OCR, grounding, and video tasks. Training involves a five-phase curriculum and GRPO-based reinforcement, with hybrid parallelism delivering 3–4× speedup. A compact 2B version scores 73.9.→read the paper Dinov3 from Meta AI
This is a 7B parameter self-supervised vision foundation model trained on 1.7B curated web images. DINOv3 uses a new "Gram anchoring" method to stabilize dense features over long training, enabling state-of-the-art performance in segmentation (ADE20k mIoU 63.0), detection (COCO mAP 66.1), and depth estimation (NYUv2 RMSE 0.309). It supports resolutions up to 4096² and distills into ViT and ConvNeXt models, achieving strong results on 3D tasks, remote sensing, and OCR with no fine-tuning →read the paper Matrix-game 2.0 by Skywork AI (open-source)
This real-time, auto-regressive diffusion-based interactive world model achieving 25 FPS video generation on a single H100 GPU. Trained on 1,200 hours of GTA5 and Unreal Engine data with action annotations, it uses a 1.8B parameter DiT with frame-level keyboard/mouse control. Distilled via Self-Forcing into a few-step model, it outperforms Oasis and YUME in visual quality, temporal coherence, and controllability. Ablations confirm cache tuning and denoising steps critical for long-sequence fidelity →read the paper
| Interesting surveys | | | The freshest research papers, categorized for your convenience | We organize research papers by goal-oriented or functional categories to make it easier to explore related developments and compare approaches. As always, papers we particularly recommend are marked with 🌟 | Agentic Systems and GUI Automation | 🌟 Mobile-Agent-v3 (by Alibaba)
build foundational GUI agents with large-scale environments, modular skills, and scalable RL for cross-platform tasks → read the paper 🌟 Prompt Orchestration Markup Language (by Microsoft)
structure complex multi-modal prompts with a markup system that supports styling, integration, and collaboration → read the paper
| Reinforcement Learning for Alignment and Agent Training | AgentFly
adapt LLM agents with memory-based online RL instead of gradient updates, enabling continual learning without fine-tuning → read the paper End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning
train medical RAG agents with reinforcement learning to improve retrieval, reasoning, and traceability for diagnosis → read the paper Reinforcement Learning with Rubric Anchors
extend verifiable RL with rubric-based rewards to handle open-ended, subjective tasks with stylistic control → read the paper On-Policy RL Meets Off-Policy Experts
harmonize supervised fine-tuning and RL with dynamic weighting to balance imitation and exploration → read the paper 🌟 SSRL: Self-Search Reinforcement Learning (by Tsinghua University)
train LLMs to use their own internal knowledge for search tasks, reducing reliance on external engines → read the paper 🌟 Atom-Searcher (by Ant Group)
guide agentic deep research with fine-grained “atomic thought” rewards for interpretable reasoning steps → read the paper
| Reasoning, Interpretability, and Control | 🌟 MindJourney (by Microsoft)
improve spatial reasoning by simulating egocentric 3D viewpoints for VLMs at test time → read the paper 🌟Deep Think with Confidence (by Meta AI, UCSD)
filter low-quality reasoning traces using internal confidence signals to improve efficiency and accuracy → read the paper 🌟 Controlling Multimodal LLMs via Reward-guided Decoding (by Mila, Universite de Montreal, McGill University, Meta, CIFAR)
steer MLLMs during inference by weighting precision–recall trade-offs with reward models → read the paper X-Node: Self-Explanation is All We Need
integrate per-node self-explanations into GNN predictions for interpretability in medical imaging → read the paper
| Privacy, Safety, and Unlearning | Scalable Private Partition Selection via Adaptive Weighting
scale differentially private partition selection to massive datasets for applications like private vocab extraction → read the paper 🌟 CRISP: Persistent Concept Unlearning via Sparse Autoencoders (by Technion)
remove harmful knowledge permanently by suppressing feature activations across layers → read the paper 🌟 Unlearning Comparator (by Sungkyunkwan University)
visualize and compare unlearning methods across accuracy, efficiency, and privacy dimensions → read the paper
| Efficiency, Scaling, and Inference Optimization | TPLA: Tensor Parallel Latent Attention
accelerate tensor-parallel inference with compressed KV caching while preserving accuracy → read the paper 🌟 XQuant (by UC Berkeley)
rematerialize keys and values on-the-fly with quantized activations, cutting LLM memory needs up to 12× → read the paper 🌟 BeyondWeb (by Datalogy AI)
generate large-scale synthetic pretraining data to overcome the data wall in trillion-scale LLM training → read the paper
| Retrieval and Long-Context Reasoning | 🌟 Retrieval-augmented reasoning with lean language models (by The Alan Turing Institute, Oxford, Cambridge, Imperial College London)
combine reasoning and retrieval in small, domain-specific models for secure and efficient deployment → read the paper ComoRAG
introduce memory-organized retrieval cycles to support stateful long-narrative reasoning → read the paper
| Evaluation of Human-Centered Concepts | Leveraging Large Language Models for Predictive Analysis of Human Misery
predict perceived misery scores from natural language with gamified evaluation setups → read the paper Beyond Human Judgment
evaluate moral value understanding in LLMs using a Bayesian framework that accounts for human disagreement → read the paper
| That’s all for today. Thank you for reading! Please send this newsletter to your colleagues if it can help them enhance their understanding of AI and stay ahead of the curve. | How was today's FOD?Please give us some constructive feedback | | |
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