Selected from source-grounded bilingual updates that are complete enough to check. Briefs are arranged automatically; items are AI-generated and not individually human-reviewed by default (human-reviewed items are labeled), so verify original sources before citing.
How this brief is selected: we include AI4Science updates with reliable sources and complete Chinese and English coverage, prioritizing fuller and easier-to-check information.
01PhAI Labs
Discovery Foundation Models: Toward Open-Ended Discovery Intelligence 摘要 Foundation models have progressed from learning and reasoning over existing knowledge, to increasingly learning through action, tool use, and outcome feedback. We argue that the next frontier is a further transition: from solving and acting within problems specified by humans to participating in the process by which new problems, representations, explanations, and knowledge are created. We refer to this capability as Discovery Intelligence. We formulate Discovery Foundation Models (DFMs) as general-purpose model systems for open-ended discovery. A DFM operates over a revisable research state and supports seven coupled capabilities spanning problem discovery, formulation, representation construction, hypothesis formation, intervention, evidence-grounded revision, and continual discovery improvement. We instantiate this framework with Zetema, which couples explicit research-state dynamics, verification and experimental gating, external grounding, and cross-task Discovery Skill evolution. We further ground the framework with GALILEO, a real therapeutic-discovery system in which Dry-Lab reasoning, robotic and hands-on Wet-Lab experimentation, external biological evidence, and iterative hypothesis and design revision form a closed physical discovery loop. We then formulate a unified approach to capability formation and process-centered evaluation, enabling discovery behavior to be trained, improved, and measured beyond final-answer performance. Together, these components establish discovery as a learnable, executable, and evaluable capability of foundation-model systems. We view this shift as a broader progression in intelligence scaling: from learning over existing knowledge, to learning from action outcomes, and ultimately to participating in the construction, testing, and revision of the structures through which new knowledge is discovered. 作者 期刊/会议 引用 BibTeX @article{yang2026discovery, title={Discovery Foundation Models: Toward Open-Ended Discovery Intelligence}, author={Yang, Ling and Yin, Zhenfei and Wu, Yingcheng}, journal={arXiv preprint arXiv:2609.15973}, year={2026} }
PhAI Labs
PhAI Labs announced its DFM research framework, published the DFM technical report and research-map repository, and said ScienceBuddy, ScienceIDE and JEPA-Anything were scheduled as separate releases on September 16–18.
arXiv cs.AI
Scientific research is a continuous process that emphasizes inheritance. Methods developed by predecessors are often expanded upon by new researchers to explore more novel and in-depth scientific questions. However, the change of lab staff, such as student graduation, leads to a lack of personnel capable of replicating methods. Methods that have been developed with significant effort and resources cannot be continued. To address these limitations, we propose LabAgent, a reproduce and discovery harness tailored for a lab's continuous work. LabAgent employs two mechanisms to guarantee that all skills can be executed and verified and to record the corrective methods and experiences, allowing for direct correction or avoidance of similar errors. We applied LabAgent to drug property prediction, biomedical problem analysis, protein variant effect prediction, and statistical genetics in life science domains. LabAgent ranks first over commercial generalist agents in every domain, and demonstrates accurate reproduction of a published figure. Overall, these results demonstrate that LabAgent can effectively integrate and reasonably expand laboratory knowledge.
arXiv Genomics
Single-cell foundation models (scFMs) are transforming computational biology by enabling generalizable, task-agnostic representations for versatile single-cell analysis. Despite their progress in facilitating rapid deployment for downstream tasks, off-the-shelf scFMs still have some overlooked concerns: (I) (Pretraining Cost.) Pretrain-based scFMs necessitate pretraining on a vast volume of cells, rendering it draining resources in applications. (II) (Heterogeneous Gap.) Large Language Models (LLM)-based scFMs ignore the tremendous heterogeneous gap between LLM textual and raw cellular spaces, leading to insufficient capability when facing downstream tasks. To this end, we introduce RAGCell, a versatile single-cell analysis framework that achieves a double-win in both cost-effectiveness and high performance. The success of RAGCell lies in two key aspects: Leveraging LLMs to construct cell-level and feature-level knowledge databases, which serve as supervision signals for training the cell model and significantly reduce the training cost ($>$pretrain-based scFMs). Aligning cell representations with text embeddings from the bi-level knowledge databases, enabling knowledge transfer from textual spaces to cellular spaces and effectively mitigating the heterogeneous gap ($>$LLM-based scFMs). Through extensive experiments on six downstream single-cell analysis tasks, we demonstrate that RAGCell achieves outstanding performance compared to state-of-the-art scFMs while operating at less than $\sim$1/10 the cost of pretrain-based scFMs.
AIhub · Research news and interviews
In a new series of interviews, we’re meeting some of the PhD students that were selected to take part in the Doctoral Consortium at the International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) . In this first article of the series, we caught up with Yash Saxena to find out how he is trying to make it easier to understand how external knowledge is used in AI systems. Tell us a bit about your PhD – where are you studying, and what is the topic of your research? I am a PhD student in Computer Science at the University of Maryland, Baltimore County (UMBC), advised by Dr. Manas Gaur in the Knowledge Infused AI and Inference (KAI2) Lab. My PhD focuses on making AI systems that use external information easier to understand and audit. Many AI systems now look through external documents before answering a question. My research follows what happens to that evidence as it moves through the system. I study where it came from, why certain evidence was selected, whether it actually supports the claim being made, and whether the context needed to interpret that evidence correctly is preserved along the way. I call this broader research direction IKnowFlow , or Interpretable Knowledge Flow . The aim is to keep the evidence trail visible so that someone can inspect a generated claim and understand how the system arrived at it. This is especially important in areas such as law, science, and healthcare, where an incorrect AI-generated claim can have serious real-world consequences. Could you give us an overview of the research you’ve carried out so far during your PhD? I started by studying source attribution, or how an AI-generated claim can be linked back to the source that supports it. In REASONS (REtrieval and Automated citationS Of scieNtific Sentences), we evaluated retrieval and citation generation across more than 20,000 scientific papers from 12 research areas. I later compared two ways of adding citations to large language model responses: generating them together with the answer or assigning them afterward. In our human evaluation, adding citations afterward was more accurate, but this also showed that a citation alone does not necessarily reveal how the model arrived at its answer. From there, I started looking earlier in the process. Interpretable Modular Retrieval Neural Networks (IMR…
