This research empirically evaluates general-purpose AI coding agents on a neuroscience data-to-discovery pipeline, assessing their ability to automate complex scientific tasks. It finds agents can solve individual pipeline stages but struggle with scientific judgment in the absence of predefined iteration criteria.
Intern-S2-Preview is an efficient 35B scientific multimodal foundation model that achieves performance comparable to trillion-scale models by exploring task scaling and full-chain training. It excels in hundreds of professional scientific tasks while maintaining strong general reasoning, multimodal understanding, and agent capabilities.
Este trabalho explora o potencial de Grandes Modelos de Linguagem (LLMs), como o ChatGPT, e agentes de IA para automação e controle de instrumentação laboratorial. Demonstra-se como essas ferramentas reduzem barreiras de programação e podem evoluir para agentes autônomos capazes de operar equipamentos científicos e refinar estratégias de controle.
This content explores the evolution of artificial intelligence to act as an "AI co-scientist", assisting researchers in various stages of the scientific process. It discusses AI's potential to accelerate discoveries and transform research methodology.
This research introduces GoodPoint, a method leveraging LLMs and author responses to generate constructive feedback for scientific papers. It develops GoodPoint-ICLR, a dataset of ICLR papers, and a training recipe using fine-tuning and preference optimization for valid and actionable feedback.
This work introduces SciFi, a safe, lightweight, and user-friendly agentic framework for the autonomous execution of scientific tasks. It combines an isolated environment, a three-layer agent loop, and a self-assessing mechanism to ensure reliable operation, leveraging LLMs to automate routine scientific workloads and free researchers for creative activities.
LLM-based systems conduct autonomous scientific research but often fail to adhere to epistemic norms, ignoring evidence in 68% of traces. A study across eight domains and over 25,000 runs found that base models primarily determine agent performance and behavior.
Google DeepMind and the Republic of Korea announce a partnership to accelerate scientific breakthroughs. The collaboration aims to use frontier AI models to drive significant advancements.
This content provides 35 ChatGPT prompts designed to help environmental scientists streamline their research, reporting, and stakeholder communication. It aims to reduce time spent on documentation tasks, allowing them to focus on essential science.
This paper introduces Human-in-the-Loop Meta Bayesian Optimization (HL-MBO), a framework combining expert knowledge with few-shot machine learning to accelerate discovery in data-scarce scientific domains. It outperforms current Bayesian Optimization methods in fusion energy yield optimization and other benchmarks.
This paper explores training language models to forecast the empirical success of research ideas by evaluating pairs of ideas against objective outcomes. SFT significantly boosts performance beyond GPT-5, and RLVR can train models to discover interpretable reasoning paths for this forecasting task.
SciAtlas is a large-scale, multi-disciplinary knowledge graph designed to tackle the information explosion in academic output. Integrating millions of papers and billions of entities, it provides a structured network for automated scientific research and deep interdisciplinary integration.