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.

arXiv:2502.07663v3 Announce Type: replace Abstract: AI systems are increasingly intertwined with daily life, assisting users with various tasks and guiding decision-making. This integration introduces risks of AI-driven manipulation, where such systems may exploit users' cognitive biases and emotional vulnerabilities to steer them toward harmful outcomes. Through a randomized between-subjects experiment with 233 participants, we examined human susceptibility to such manipulation in financial (e.g., purchases) and emotional (e.g., conflict resolution) decision-making contexts. Participants interacted with one of three AI agents: a neutral agent (NA) optimizing for user benefit without explicit influence, a manipulative agent (MA) designed to covertly influence beliefs and behaviors, or a strategy-enhanced manipulative agent (SEMA) equipped with established psychological tactics, allowing it to select and apply them adaptively during interactions to reach its hidden objectives. By analyzing participants' preference ratings, we found significant susceptibility to AI-driven manipulation. Particularly across both decision-making domains, interacting with the manipulative agents significantly increased the odds of rating hidden incentives higher than optimal options (Financial, MA: OR=5.24, SEMA: OR=7.96; Emotional, MA: OR=5.52, SEMA: OR=5.71) compared to the NA group. Notably, we found no clear evidence that employing psychological strategies (SEMA) was overall more effective than simple manipulative objectives (MA) on our primary outcomes. Hence, AI-driven manipulation could become widespread even without requiring sophisticated tactics and expertise. While our findings are preliminary and derived from hypothetical, low-stakes scenarios, we highlight a critical vulnerability in human-AI interactions, emphasizing the need for ethical safeguards and regulatory frameworks to protect human autonomy.

arXiv:2511.07998v1 Announce Type: cross Abstract: Structured data question answering (QA), including table QA, Knowledge Graph (KG) QA, and temporal KG QA, is a pivotal research area. Advances in large language models (LLMs) have driven significant progress in unified structural QA frameworks like TrustUQA. However, these frameworks face challenges when applied to small-scale LLMs since small-scale LLMs are prone to errors in generating structured queries. To improve the structured data QA ability of small-scale LLMs, we propose a self-correction distillation (SCD) method. In SCD, an error prompt mechanism (EPM) is designed to detect errors and provide customized error messages during inference, and a two-stage distillation strategy is designed to transfer large-scale LLMs' query-generation and error-correction capabilities to small-scale LLM. Experiments across 5 benchmarks with 3 structured data types demonstrate that our SCD achieves the best performance and superior generalization on small-scale LLM (8B) compared to other distillation methods, and closely approaches the performance of GPT4 on some datasets. Furthermore, large-scale LLMs equipped with EPM surpass the state-of-the-art results on most datasets.

arXiv:2510.24701v1 Announce Type: cross Abstract: We present Tongyi DeepResearch, an agentic large language model, which is specifically designed for long-horizon, deep information-seeking research tasks. To incentivize autonomous deep research agency, Tongyi DeepResearch is developed through an end-to-end training framework that combines agentic mid-training and agentic post-training, enabling scalable reasoning and information seeking across complex tasks. We design a highly scalable data synthesis pipeline that is fully automatic, without relying on costly human annotation, and empowers all training stages. By constructing customized environments for each stage, our system enables stable and consistent interactions throughout. Tongyi DeepResearch, featuring 30.5 billion total parameters, with only 3.3 billion activated per token, achieves state-of-the-art performance across a range of agentic deep research benchmarks, including Humanity's Last Exam, BrowseComp, BrowseComp-ZH, WebWalkerQA, xbench-DeepSearch, FRAMES and xbench-DeepSearch-2510. We open-source the model, framework, and complete solutions to empower the community.

Background: Adolescent idiopathic scoliosis is a common spinal deformity typically treated with exercise therapy. Despite the increasing use of digital technologies in interventions, there remains a gap in understanding how to effectively integrate behavior change techniques (BCTs) and behavior theories within these digital solutions. Objective: This review aims to identify the digital characteristics of interventions and the BCTs used, and to analyze potential theoretical mechanisms with the Theoretical Domains Framework and the capability, opportunity, motivation, and behavior model. Methods: We conducted a scoping review according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A total of 5 databases, including PubMed, Web of Science, Embase, Cochrane Library, and CINAHL, were selected for screening eligible studies up to April 4, 2024. We included studies of any design type that involved patients with adolescent idiopathic scoliosis using digital interventions for exercise rehabilitation, including qualitative, quantitative, or mixed methods studies, and study protocols with detailed descriptions of digital interventions. Two researchers independently screened studies and extracted data into tables for descriptive analysis. The Mixed Methods Appraisal Tool was used to assess the quality of studies. Results: Out of the 3267 identified papers, 21 (0.64%) studies were included. The most frequently used technologies were videoconferencing (n=7) and instructional videos (n=5). The three most common BCT clusters were “Shaping Knowledge” (n=19), “Social Support” (n=16), and “Antecedents” (n=16). “Knowledge” was the most used mechanism of action (n=21), followed by “Skills” (n=16), “Environmental Context and Resources” (n=16), and “Social Influences” (n=16). The studies primarily addressed “Capability” and “Opportunity,” with less emphasis on “Motivation,” particularly “Automatic Motivation.” Conclusions: This review identified common digital technologies and their characteristics, analyzed potential mechanisms of behavior change in interventions, and provided recommendations for technology utilization. Future research should further evaluate the effectiveness of digital technologies while enhancing patient motivation and user experience. Trial Registration: PROSPERO CRD42024530851; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024530851

DrugMap serves as a roadmap to develop covalent ligands for oncogenic drivers.

Nature, Published online: 14 February 2024; doi:10.1038/s41586-024-07054-3

The Chinese Liver Cancer Atlas project depicts a panoramic genomic landscape of hepatocellular carcinoma, covering candidate coding and non-coding drivers, mutational signatures, extrachromosomal circular DNA, subclonal catastrophic events and detailed evolutionary history.