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Received — 30 December 2025 ⏭ Favourites

SciEvalKit: An Open-source Evaluation Toolkit for Scientific General Intelligence

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiheng Wang · Yixin Chen · Shuo Li · Yifan Zhou · Bo Liu · Hengjian Gao · Jiakang Yuan · Jia Bu · Wanghan Xu · Yuhao Zhou · Xiangyu Zhao · Zhiwang Zhou · Fengxiang Wang · Haodong Duan · Songyang Zhang · Jun Yao · Han Deng · Yizhou Wang · Jiabei Xiao · Jiaqi Liu · Encheng Su · Yujie Liu · Weida Wang · Junchi Yao · Shenghe Zheng · Haoran Sun · Runmin Ma · Xiangchao Yan · Bo Zhang · Dongzhan Zhou · Shufei Zhang · Peng Ye · Xiaosong Wang · Shixiang Tang · Wenlong Zhang · Lei Bai
30 December 2025 at 13:00
arXiv:2512.22334v1 Announce Type: new Abstract: We introduce SciEvalKit, a unified benchmarking toolkit designed to evaluate AI models for science across a broad range of scientific disciplines and task capabilities. Unlike general-purpose evaluation platforms, SciEvalKit focuses on the core competencies of scientific intelligence, including Scientific Multimodal Perception, Scientific Multimodal Reasoning, Scientific Multimodal Understanding, Scientific Symbolic Reasoning, Scientific Code Generation, Science Hypothesis Generation and Scientific Knowledge Understanding. It supports six major scientific domains, spanning from physics and chemistry to astronomy and materials science. SciEvalKit builds a foundation of expert-grade scientific benchmarks, curated from real-world, domain-specific datasets, ensuring that tasks reflect authentic scientific challenges. The toolkit features a flexible, extensible evaluation pipeline that enables batch evaluation across models and datasets, supports custom model and dataset integration, and provides transparent, reproducible, and comparable results. By bridging capability-based evaluation and disciplinary diversity, SciEvalKit offers a standardized yet customizable infrastructure to benchmark the next generation of scientific foundation models and intelligent agents. The toolkit is open-sourced and actively maintained to foster community-driven development and progress in AI4Science.
Received — 17 December 2025 ⏭ Favourites

Generative AI Mental Health Chatbots as Therapeutic Tools: Systematic Review and Meta-Analysis of Their Role in Reducing Mental Health Issues

✇Journal of Medical Internet Research
By: Qiyang Zhang · Renwen Zhang · Yiying Xiong · Yuan Sui · Chang Tong · Fu-Hung Lin
17 December 2025 at 02:30
Background: To date, there is no comprehensive paper that systematically synthesizes the effect of generative AI chatbot’s impact on mental health. Can generative AI chatbots help reduce our psychological distress? Objective: To comprehensively assess existing evidence, a systematic review and meta-analysis is essential to evaluate the overall effectiveness, identify gaps, and guide future research in this evolving field. This paper aims to: 1) synthesize current evidence on generative AI chatbot interventions targeting mental health issues, 2) quantify the effectiveness of these interventions via a meta-analysis of randomized controlled trials (RCTs), and examine key moderators of intervention effectiveness. Methods: This systematic review included 26 studies for narrative synthesis, out of which 12 randomized controlled trials were included in the meta-analysis. Results: The systematic synthesis revealed that 1) generative AI-chatbot interventions mostly took place in non-WEIRD countries (Western, Educated, Industrialized, Rich, and Democratic) and 2) there is a lack of studies focusing on young children and older adults. The meta-analysis showed a statistically significant effect (ES = 0.36, p = .039), which means that generative AI chatbots are, on average, effective in reducing negative mental health issues. Among moderators, we found statistically significant and higher effect sizes among interventions that have an active control group, conducted in WEIRD countries, recruited non-clinical populations, older age, majority female, non-personalized, with human assistance, and social-oriented. Conclusions: In conclusion, this comprehensive review has highlighted the potential of generative AI chatbots in addressing anxiety, depression, negative mood, and stress. The findings indicate that generative AI interventions are particularly beneficial in WEIRD countries, among non-clinical populations, older adults, and females. Human-assisted and social-oriented programs, as opposed to fully autonomous or task-oriented ones, demonstrate greater effectiveness. Meanwhile, non-personalized chatbots appear to yield more effective outcomes than personalized systems.
Received — 18 November 2025 ⏭ Favourites

Fault2Flow: An AlphaEvolve-Optimized Human-in-the-Loop Multi-Agent System for Fault-to-Workflow Automation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yafang Wang · Yangjie Tian · Xiaoyu Shen · Gaoyang Zhang · Jiaze Sun · He Zhang · Ruohua Xu · Feng Zhao
18 November 2025 at 13:00
arXiv:2511.12916v1 Announce Type: new Abstract: Power grid fault diagnosis is a critical process hindered by its reliance on manual, error-prone methods. Technicians must manually extract reasoning logic from dense regulations and attempt to combine it with tacit expert knowledge, which is inefficient, error-prone, and lacks maintainability as ragulations are updated and experience evolves. While Large Language Models (LLMs) have shown promise in parsing unstructured text, no existing framework integrates these two disparate knowledge sources into a single, verified, and executable workflow. To bridge this gap, we propose Fault2Flow, an LLM-based multi-agent system. Fault2Flow systematically: (1) extracts and structures regulatory logic into PASTA-formatted fault trees; (2) integrates expert knowledge via a human-in-the-loop interface for verification; (3) optimizes the reasoning logic using a novel AlphaEvolve module; and (4) synthesizes the final, verified logic into an n8n-executable workflow. Experimental validation on transformer fault diagnosis datasets confirms 100\% topological consistency and high semantic fidelity. Fault2Flow establishes a reproducible path from fault analysis to operational automation, substantially reducing expert workload.
Received — 29 October 2025 ⏭ Favourites

Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yueqi Song · Ketan Ramaneti · Zaid Sheikh · Ziru Chen · Boyu Gou · Tianbao Xie · Yiheng Xu · Danyang Zhang · Apurva Gandhi · Fan Yang · Joseph Liu · Tianyue Ou · Zhihao Yuan · Frank Xu · Shuyan Zhou · Xingyao Wang · Xiang Yue · Tao Yu · Huan Sun · Yu Su · Graham Neubig
29 October 2025 at 12:00
arXiv:2510.24702v1 Announce Type: cross Abstract: Public research results on large-scale supervised finetuning of AI agents remain relatively rare, since the collection of agent training data presents unique challenges. In this work, we argue that the bottleneck is not a lack of underlying data sources, but that a large variety of data is fragmented across heterogeneous formats, tools, and interfaces. To this end, we introduce the agent data protocol (ADP), a light-weight representation language that serves as an "interlingua" between agent datasets in diverse formats and unified agent training pipelines downstream. The design of ADP is expressive enough to capture a large variety of tasks, including API/tool use, browsing, coding, software engineering, and general agentic workflows, while remaining simple to parse and train on without engineering at a per-dataset level. In experiments, we unified a broad collection of 13 existing agent training datasets into ADP format, and converted the standardized ADP data into training-ready formats for multiple agent frameworks. We performed SFT on these data, and demonstrated an average performance gain of ~20% over corresponding base models, and delivers state-of-the-art or near-SOTA performance on standard coding, browsing, tool use, and research benchmarks, without domain-specific tuning. All code and data are released publicly, in the hope that ADP could help lower the barrier to standardized, scalable, and reproducible agent training.
Received — 27 June 2025 ⏭ Favourites

Extrachromosomal DNA replication and maintenance couple with DNA damage pathway in tumors

✇Cell
By: Xing Kang · Xinran Li · Jiaqi Zhou · Yang Zhang · Lingyu Qiu · Congcong Tian · Zhiwen Deng · Xiaoyan Liang · Ziwei Zhang · Songlin Du · Suili Hu · Nan Wang · Zhen Yue · Yajing Xu · Yuan Gao · Junbiao Dai · Zhiquan Wang · Chuanhe Yu · Jinyi Chen · Yuchun Wu · Liangming Chen · Yuan Yao · Sitong Yao · Xinran Yang · Lixia Yan · Qing Wen · Olivia M. Depies · Kuiming Chan · Xiaohuan Liang · Gang Li · Zhike Zi · Xiangyu Liu · Haiyun Gan
28 April 2025 at 08:00
This study demonstrates that extrachromosomal DNA (ecDNA) replication induces DNA double-strand breaks and activates the DNA damage response (DDR). The DDR pathways, such as alt-NHEJ, are critical for ecDNA maintenance in tumor cells. Mechanistic insights into ecDNA replication and maintenance unveil a therapeutic approach for treating tumors harboring ecDNA.
Received — 27 February 2025 ⏭ Favourites

Integrative spatial analysis reveals tumor heterogeneity and immune colony niche related to clinical outcomes in small cell lung cancer

✇Omics In Lung
By: Haiquan Chen · Chaoqiang Deng · Jian Gao · Jun Wang · Fangqiu Fu · Yue Wang · Qiming Wang · Mou Zhang · Shiyue Zhang · Fanfan Fan · Kun Liu · Bo Yang · Qiming He · Qiang Zheng · Xuxia Shen · Jin Wang · Tao Hu · Changbin Zhu · Fei Yang · Yonghong He · Hong Hu · Jialei Wang · Yuan Li · Yang Zhang · Zhiwei Cao
21 February 2025 at 19:00

Cancer Cell. 2025 Feb 14:S1535-6108(25)00030-3. doi: 10.1016/j.ccell.2025.01.012. Online ahead of print.

ABSTRACT

Recent advances have shed light on the molecular heterogeneity of small cell lung cancer (SCLC), yet the spatial organizations and cellular interactions in tumor immune microenvironment remain to be elucidated. Here, we employ co-detection by indexing (CODEX) and multi-omics profiling to delineate the spatial landscape for 165 SCLC patients, generating 267 high-dimensional images encompassing over 9.3 million cells. Integrating CODEX and genomic data reveals a multi-positive tumor cell neighborhood within ASCL1+ (SCLC-A) subtype, characterized by high SLFN11 expression and associated with poor prognosis. We further develop a cell colony detection algorithm (ColonyMap) and reveal a spatially assembled immune niche consisting of antitumoral macrophages, CD8+ T cells and natural killer T cells (MT2) which highly correlates with superior survival and predicts improving immunotherapy response in an independent cohort. This study serves as a valuable resource to study SCLC spatial heterogeneity and offers insights into potential patient stratification and personalized treatments.

PMID:39983726 | DOI:10.1016/j.ccell.2025.01.012

Received — 8 February 2025 ⏭ Favourites

Interplay between gut microbial communities and metabolites modulates pan-cancer immunotherapy responses

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xiaoqiang Zhu · Muni Hu · Xiaowen Huang · Lingxi Li · Xiaolin Lin · Xiaoyan Shao · Jiantao Li · Xiaoyue Du · Xinjia Zhang · Rongrong Sun · Tianying Tong · Yanru Ma · Lijun Ning · Yi Jiang · Yue Zhang · Yuqi Shao · Zhenyu Wang · Yilu Zhou · Jinmei Ding · Ying Zhao · Baoqin Xuan · Hongyang Zhang · Youwei Zhang · Jie Hong · Jing-Yuan Fang · Xiuying Xiao · Bo Shen · Songbing He · Haoyan Chen
5 February 2025 at 19:00

Cell Metab. 2025 Jan 28:S1550-4131(24)00495-9. doi: 10.1016/j.cmet.2024.12.013. Online ahead of print.

ABSTRACT

Immune checkpoint blockade (ICB) therapy has revolutionized cancer treatment but remains effective in only a subset of patients. Emerging evidence suggests that the gut microbiome and its metabolites critically influence ICB efficacy. In this study, we performed a multi-omics analysis of fecal microbiomes and metabolomes from 165 patients undergoing anti-programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) therapy, identifying microbial and metabolic entities associated with treatment response. Integration of data from four public metagenomic datasets (n = 568) uncovered cross-cohort microbial and metabolic signatures, validated in an independent cohort (n = 138). An integrated predictive model incorporating these features demonstrated robust performance. Notably, we characterized five response-associated enterotypes, each linked to specific bacterial taxa and metabolites. Among these, the metabolite phenylacetylglutamine (PAGln) was negatively correlated with response and shown to attenuate anti-PD-1 efficacy in vivo. This study sheds light on the interplay among the gut microbiome, the gut metabolome, and immunotherapy response, identifying potential biomarkers to improve treatment outcomes.

PMID:39909032 | DOI:10.1016/j.cmet.2024.12.013

High-resolution spatially resolved proteomics of complex tissues based on microfluidics and transfer learning

✇Cell
By: Beiyu Hu · Ruiqiao He · Kun Pang · Guibin Wang · Ning Wang · Wenzhuo Zhu · Xin Sui · Huajing Teng · Tianxin Liu · Junjie Zhu · Zewen Jiang · Jinyang Zhang · Zhenqiang Zuo · Weihu Wang · Peifeng Ji · Fangqing Zhao
23 January 2025 at 08:00
PLATO, a high-resolution and high-throughput spatial mass spectrometry proteomics platform, identifies distinct tumor subtypes and key dysregulated proteins in human breast cancer.
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