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ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents

ScienceBuddy released: interactive scientific agent workspace coupling harness evolution with model reinforcement learning for continual self-improvement across four scientific task families.

ScienceBuddy is an interactive scientific research workspace that turns researcher requests, feedback, and execution evidence into tasks and evaluation rubrics for continual learning. Its recursive-in-recursive self-improvement paradigm couples harness evolution with the model fixed (inner recursion) and model reinforcement learning under the improved harness (outer recursion). Case studies span four scientific task families covering researcher interaction, harness refinement, and model learning. The system is released as a research product at science-buddy.io.

Hugging Face daily papersupdated · 1d agofirst · 2d agoAI research 2 sources

Discovery Foundation Models: Toward Open-Ended Discovery Intelligence

Paper defines Discovery Foundation Models with seven coupled capabilities for open-ended discovery, demonstrated via Zetema and GALILEO systems.

The paper formulates Discovery Foundation Models (DFMs) as general-purpose model systems for open-ended discovery, supporting seven coupled capabilities spanning problem discovery, formulation, representation construction, hypothesis formation, intervention, evidence-grounded revision, and continual improvement. It instantiates the framework with Zetema, which couples explicit research-state dynamics, verification and experimental gating, and cross-task Discovery Skill evolution. The framework is grounded with GALILEO, a real therapeutic-discovery system combining dry-lab reasoning with robotic wet-lab experimentation in a closed physical discovery loop; code is released on GitHub.

Hugging Face daily papersupdated · 2d agofirst · 3d agoAI research 2 sources

Climate-ModernBERT: Revisiting Corpus Composition for Domain-Adaptive Continued Pretraining

Climate-ModernBERT domain-adapted encoders reach 76.3 average F1 across nine climate benchmarks, 2.8 points above vanilla ModernBERT-Base.

The authors continue pretraining ModernBERT-Base on three climate corpora - academic text, climate-filtered web data, and synthetic documents - and compare joint mixtures against parameter-space merging of specialized checkpoints. The best model achieves 76.3 average F1 across nine climate NLP benchmarks, a 2.8-point improvement over the vanilla baseline. Academic climate corpora provide the strongest adaptation signal, and parameter-space merging outperforms joint multi-source training while preserving complementary corpus information; all variants are released.

arXiv cs.AI / cs.LG / cs.CL · 9d agoAI research

State of Open Models: Summer 2026 Observations

Hugging Face publishes observations on the state of the open-weights model ecosystem as of summer 2026.

A Hugging Face blog post titled 'State of Open Models: Summer 2026 Observations' surveys developments across the open-weights model ecosystem. No article text was available, so specific model releases, benchmarks, and findings are not detailed here.

Hugging Face Blog · Aug 14, 2026AI industry

"Chilling" warning or overreaction? AI bioweapons report divides experts

Science article examines expert disagreement over whether a report on AI-enabled bioweapons risks is a chilling warning or an overreaction.

A Science.org article, shared on Hacker News with 20 points and 2 comments, covers expert divisions over an AI bioweapons report and whether its warnings are justified or exaggerated. The discussion reflects ongoing debate in the AI safety and biosecurity community about assessing AI's role in biological threat enhancement. Minimal detail is available from the item itself.

Hacker News · security · 3d agoAI safety & securityHN 20↑ · 2 comments

Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research1

Real-World Knowledge-Guided Change Data Synthesis for Remote Sensing

Researchers introduce KnowChange, a framework that uses pretrained vision-language models to synthesize realistic change-detection training data for remote sensing.

KnowChange is a knowledge-guided change data synthesis framework that leverages pretrained vision-language models to reason about plausible change locations and class transitions from pre-change scenes and desired change types. It addresses the limited class-transition coverage and inflexibility of handcrafted rule-based synthesis methods, enabling diverse change types in a unified pipeline. Experiments show KnowChange-generated data outperforms existing synthetic datasets in both synthetic-to-real transfer and synthetic data augmentation, despite compact generation scale.

Hugging Face daily papers · 23d agoAI research

Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting

Study finds zero-shot time-series foundation models underperform on CGM forecasting; fine-tuned Chronos-Bolt cuts RMSE up to 18.4% and dietary context adds signal.

The paper evaluates time-series foundation models for continuous glucose monitoring forecasting across eight public datasets covering Type 1 diabetes, Type 2 diabetes, and non-diabetes populations. Under a unified protocol, zero-shot foundation models did not consistently outperform baselines like Elastic Net and PatchTST, but lightweight fine-tuning did, with fine-tuned Chronos-Bolt reducing RMSE by 6.5%-18.4% in the T1D cohort and 8.6%-18.2% in the non-diabetes/T2D cohort. A residual-based fusion framework adding dietary context from CGMacros reduced overall RMSE by about 3% and postprandial RMSE by about 15% versus CGM-only baselines.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research

University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK

University of Manchester retrained NVIDIA Earth-2 CorrDiff and StormCast on Isambard-AI to forecast UK air pollution at 2-3 km resolution.

University of Manchester researchers led by professor David Topping adapted NVIDIA's Earth-2 generative AI frameworks to forecast air pollution across the UK. Earth-2 CorrDiff was retrained in two days on a single eight-GPU node of Isambard-AI (5,448 GH200 Grace Hopper Superchips, 21 exaflops) using a year of hourly simulated pollution data, producing a UK-wide model at 2-3 square kilometer resolution. The team added Earth-2 StormCast for time-dependent forecasts that ingest real air quality observations, and demonstrated the workflow runs on the DGX Spark desktop AI system. Open-source training data and workflows are planned so other countries and cities can build similar pollution models.

NVIDIA Blog · 19h agoAI industry

Multi-Task Learning for Sparsely-Labeled Time Series: A Case Study on Cold-Hardiness Modeling

Multi-task RNN architectures pooling sparse cultivar data improve grape cold-hardiness and budbreak prediction over single-task and scientific baselines.

Researchers apply recurrent neural networks to daily grape cold-hardiness prediction from weather time series, where per-cultivar labels are temporally sparse and limited. They design multiple multi-task learning architectures that treat cultivars as tasks and evaluate them in both MTL and transfer learning settings. Certain architectures consistently outperform single-task learning and state-of-the-art scientific models, and a single MTL model jointly learning cold hardiness and budbreak improves accuracy on both tasks.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research

Can Edge-Deployable Vision-Language Models Identify Species?

Evaluation of 2-8B VLMs (Qwen3-VL, Gemma3) against BioCLIP on camera-trap species ID shows all models degrade sharply on field imagery.

The study tests whether edge-deployable 2-8B vision-language models carry genuine taxonomic knowledge, comparing Qwen3-VL 2B/4B/8B and Gemma3 4B against the 300M specialist BioCLIP on a 96-species task across clean iNaturalist photos and six LILA.science camera-trap collections. All models degrade 9.6-26.6 percentage points on field imagery, and BioCLIP outperforms every VLM by 33.2-59.2 points on an expanded 200-image sample, suggesting specialized data rather than scale drives the gap. Under open-set prompting, 5.9-9.6% of responses are syntactically valid but taxonomically nonexistent species names, with fabrication rankings replicating across evaluation sets.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research1

Building AI to accelerate science and improve lives

Google highlights AI-for-science advances: AlphaGenome Atlas mapping 9 billion genetic variants, WeatherNext 3 weather model, and global health AI tools.

Google detailed AI advances across science and health, including AlphaGenome Atlas, which mapped all 9 billion possible single-letter genetic changes in the human genome and was made openly available. WeatherNext 3 delivers 50% more accurate precipitation forecasts a day or more ahead and is already in products. AlphaFold is used by 4 million researchers in 190 countries, TB chest X-ray screening has processed 25,000+ scans across six nations, and the diabetic retinopathy model has supported 1.15 million screenings. Google also released its AI & Economy ATLAS global usage insights.

Google · AI · 1d agoAI industry

Introducing WeatherNext 3, our most advanced and accurate global weather AI model

Google DeepMind launches WeatherNext 3, an AI weather model delivering hourly 5-km forecasts from live satellite data, now integrated across Google products.

WeatherNext 3 ingests live geostationary satellite mosaics and station observations through a Functional Generative Network (FGN) mesh transformer, producing hourly forecasts at 5-km surface resolution versus WeatherNext 2's 25-km, 6-hour grid. Independent live evaluations by Brightband rate it the most accurate global weather model to date. It adds renewable-energy variables such as 100-meter turbine-height wind speeds and solar radiation, and is integrated across Search, Gemini, Maps, Google Maps Platform, and Cloud.

Google DeepMind · 13d agoModel release

I wrote an AI textbook — how long until AI can do it better?

AI researcher Nathan Lambert argues LLMs remain weak at long-form technical writing, questioning whether models can autonomously organize scientific knowledge for breakthroughs.

Nathan Lambert describes writing a post-training textbook, Reinforcement Learning from Human Feedback, and finds today's LLMs weak at organizing long-form technical content despite becoming superhuman at coding and math. He notes GPT 5.5 Pro found deep typos across a 200-300 page manuscript while Claude models proved more useful as editors. He argues that compressing knowledge through writing is a prerequisite for autonomous scientific insight and tempers expectations for near-term AI-driven open science.

Interconnects · Aug 12, 2026AI research

Diffusion Models and Concept Formation

Paper argues diffusion models implicitly form Cobweb-like concept hierarchies, with a basic level emerging at intermediate noise levels.

The authors draw a formal correspondence between diffusion models and Cobweb, a classic incremental concept-hierarchy learner, noting both are hierarchical Bayesian density models with Gaussian prototypes. Modes of the diffusion model's noisy marginals form a hierarchy whose basic level sits at intermediate noise, where class identity commits. The correspondence is tested on MNIST and Fashion-MNIST via mode-finding. Diffusion is reframed as a cognitive model of concept formation.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research

Explainability Assistant: A Conversational XAI Interface for Interpreting Energy Consumption Models

Researchers release Explainability Assistant, an open-source conversational XAI tool using LLM function calling, lifting intent-parsing accuracy from 76.8% to 94%.

The paper introduces the Explainability Assistant, an open-source conversational XAI system for interpreting energy consumption forecasting models such as genetic-programming symbolic regressors. It uses LLM function calling instead of rigid custom grammars, achieving 94% intent-parsing accuracy versus 76.8% for prior work TalkToModel, and adapts to different ML problem types without task-specific fine-tuning. Comparative evaluation with energy domain specialists against a traditional XAI dashboard showed improved usability, with all experts preferring the conversational interface.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research1

CausalArena: Benchmarking Causal Discovery in the Foundation Model Era

Researchers introduce CausalArena, a unified benchmark revealing that causal discovery rankings shift substantially across structural causal model families and protocols.

The paper presents CausalArena, a unified and evolvable benchmark for causal discovery combining synthetic structural causal models, semantically grounded operational SCMs, formula-grounded scientific SCMs, and public real-world datasets. Experiments across classical, neural, and pretrained causal discovery foundation models show large ranking shifts between benchmark regimes. The authors identify pretraining-evaluation overlap and benchmark diversity as central evaluation challenges.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research

Google’s AI weather model now uses more raw satellite data

Google launched WeatherNext 3, an AI weather model using raw satellite data that beats ECMWF and now powers Search, Gemini, and Maps.

Google released WeatherNext 3, an AI weather forecasting model that incorporates physical surface information (land/ocean type and elevation) to improve surface temperature and dewpoint calculations, improving point location temperature accuracy by up to 30 percent. Its white paper reports roughly 5 percent better upper-atmosphere accuracy than WeatherNext 2, equating to about six additional hours of forecast lead time, outperforming the ECMWF AI model on these metrics. The model now supplies forecast information across Google Search, Gemini, and Maps, though the paper notes unexplained short-lead degraded results and grid-shaped artifacts in some predictions.

Ars Technica · AI · 8d agoModel release

One resignation turned the embers of AI fear into a wildfire

Interconnects argues a frontier-lab researcher's safety resignation went viral via media coordination, reigniting AI existential-risk discourse.

The essay analyzes why researcher Jacob Coxon's resignation over AI safety risks went viral, aided by a Wall Street Journal exclusive, advocacy-group amplification, and Daniel Kokotajlo's same-day Joe Rogan appearance. It notes Evan Hubinger's >10% extinction-risk figure and criticizes existential-risk discourse for conflating very different meanings of the term. The author assigns near-zero probability to complete extinction but argues concrete risks such as cyberattacks on critical infrastructure and bio-risks merit debate. It also rejects recursive self-improvement forecasts, proposing 'lossy self-improvement' where models excel at math and code but remain limited elsewhere.

Interconnects · 6d agoAI safety & security

What We Learned by Reproducing 2,200 papers from ICML

Hugging Face shares lessons from openly reproducing 2,200 ICML 2026 papers, examining reproducibility and open implementation practices in machine learning research.

Hugging Face published a retrospective on its open reproduction effort covering 2,200 papers from ICML 2026. The post summarizes lessons learned about reproducibility and building open, community-driven implementations of published machine learning research. No detailed article text was available in the feed.

Hugging Face Blog · Aug 13, 2026AI research

Bridging the Confidence Gap: Temperature Scaling for Calibrating Test-Time Prompt Tuning

CoTS temperature scaling cuts test-time prompt tuning's expected calibration error from 11.90% to 5.38% on ImageNet variants while raising accuracy.

The paper proposes CoTS, a post-hoc calibration method that applies temperature scaling to minimize the confidence gap between test-time-adapted and zero-shot predictions. A weak-strong ensemble variant, E-CoTS, further exploits multiple test-time augmentations to boost accuracy while maintaining calibration. E-CoTS reduces average expected calibration error from 11.90% to 5.38% on ImageNet variants while increasing accuracy from 60.74% to 62.95%.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

CUA-Universe: A Scalable and Dynamic Environment for Hybrid GUI+CLI Agents

Researchers introduce CUA-Universe, a pipeline turning real desktop software into hybrid GUI+CLI agent environments, lifting a 9B model's OSWorld success rate.

CUA-Universe is an environment-to-data pipeline that converts real desktop applications into hybrid GUI+CLI environments, scaling to 16 applications via App-Forge, Task-Weave, and Path-Steer. Training on its harvested trajectories shifted a 9B model toward effective GUI+CLI orchestration, yielding +39.3 points on CUA-Verse, +16.8 points success rate on OSWorld, and +7.84 points on OSWorld-MCP while cutting steps and tokens by up to 57% and 60%. The work addresses the scarcity of scalable hybrid environments for computer-use agents.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research1

StudyBench: Can Self-Evolution Squeeze Textbooks for Olympiad Capability?

Researchers introduce StudyBench, a physics benchmark showing self-evolution gains on textbook problems rarely transfer to olympiad-level questions.

StudyBench is a controlled physics benchmark splitting test data into an Application Set of difficult textbook problems and a Transfer Set of olympiad-level problems. Across three base models, representative self-evolution methods improved on the Application Set but rarely transferred to the harder Transfer Set. A guidance ablation reveals a Guidance Gap, and every method hits a Compute Plateau, indicating the remaining limits are method problems rather than data or compute problems.

Hugging Face daily papers · 16d agoAI research

AI safety panic goes mainstream after Anthropic researcher's warnings land on CNN and Fox News

Departing Anthropic researcher Jacob Coxon's AI extinction warnings reached CNN, Fox News, and US politicians, pushing AI safety debate mainstream.

Jacob Coxon, a departing Anthropic researcher, warned that self-improving AI poses an existential threat, earning coverage on CNN and Fox News plus attention from US politicians and a Joe Rogan episode. Safety researchers at Anthropic and OpenAI supported his views, and Paul Christiano warned humanity could permanently lose control of a superintelligence without better oversight. Christiano joined the OpenAI Foundation board and its Safety and Security Committee, though without voting rights. The article cautions that the extinction scenario remains extreme and contested, while citing rogue hacking agents from OpenAI and Anthropic as concrete demonstrations of AI risk.

The Decoder · 6d agoAI industry

Curriculum Learning as Transport: Understanding Curricula with Wasserstein Geodesics

Researchers model curriculum learning as Wasserstein transport over difficulty distributions, finding curriculum benefits are strongly task- and budget-dependent with no dominant strategy.

The framework represents curricula as trajectories of training distributions over discrete difficulty levels, decoupling ordering, matched exposure, endpoint smoothness, and pacing. Across a calibrated suite of 12 tasks and 33 difficulty axes under fixed training budgets, no single strategy dominates, though easy-to-hard ordering improves hard-level performance relative to exposure-matched static sampling. Endpoint smoothness and pacing substantially affect where along the difficulty spectrum a curriculum is effective, and the transport view supports extensions to learned pacing and structured difficulty spaces.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research1

Large Language Models for HVAC Operations in Building Energy Systems: A Critical Review of Methods, Applications, and Deployment Readiness

Systematic review of 66 studies finds LLMs for HVAC operations are mostly research-stage, with no ready-now deployment and only four pilot-level studies.

A critical review of 66 peer-reviewed studies from 2023 to March 2026 examines LLMs for HVAC operations in building energy systems. Only four studies reach pilot-level evidence, none reports sustained operational deployment, and 63 of 66 are research-only. Conventional ML, MPC, and RL remain dominant for high-frequency control and short-horizon forecasting, and the evidence supports LLMs primarily as semantic and workflow layers rather than autonomous controllers.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research

NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting

Researchers introduce NOAH, a generative time-aware transformer trained on 559 million MIMIC clinical events to model and forecast patient trajectories.

NOAH is a task-agnostic, time-aware generative transformer designed to represent and forecast the full multimodal patient journey across medical images, time-series signals, categorical events, and clinical text. It was trained on over 559 million clinical events from 431,000 hospital visits covering 299,000 patients in the MIMIC dataset family. The architecture combines bidirectional time integration with a variational latent space to capture continuous patient state evolution and clinical stochasticity. NOAH supports autoregressive forecasting with time control, zero-shot classification, and counterfactual intervention simulation, with evaluations on 15 ICD chapters, 29 comorbidities, and time-to-event prediction.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research2

How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules

OpenAI profiles César de la Fuente's lab using ChatGPT and Codex alongside deep-learning models to accelerate antimicrobial molecule discovery.

OpenAI published a case study on bioengineer César de la Fuente's lab, which uses ChatGPT and Codex for hypothesis brainstorming, code writing, dataset processing, and bridging knowledge gaps across biology, chemistry, and computer science. The lab's deep-learning models scan genome and protein databases for antimicrobial peptide candidates, potentially cutting initial searches from years to hours. Bacterial antimicrobial resistance was associated with about five million deaths in 2021, a toll projected to roughly double by 2050.

OpenAI News · 6d agoAI industry1