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Global mental health and psychosocial support programming: An expert review of major implementation and funding challenges

Paul Bolton, Saloni Dev, Ali Giusto, Bibhav Acharya, Phiona Naserian Koyiet, Rabih El Chammay, Judith Bass, Pamela Y. Collins, Joseph Reginald Fils-Aimé, Chenjezo Grant Gonani, Erin Ferenchick, Laura Murray, Veronica Cho, Fátima G. Rodríguez-Cuevas, Nawaraj Upadhaya, Giuseppe J. Raviola, Nagendra Luitel, Esubalew Haile Wondimu, Inge Petersen, Ksakrad Kelly, Helena Verdeli, Milton L. Wainberg, Antonis Kousoulis, Hamid Dabholkar, Victor Ugo

Cambridge Prisms: Global Mental Health·2025

The global mental health (GMH) field aims to equitably improve mental health and well-being everywhere. This article reviews persistent common challenges hindering sustained, high-quality delivery of mental health and psychosocial support (MHPSS). Our focus is on programming that is funded or implemented by external organizations, typically universities or international non-governmental organizations from high-income countries. It is a consensus statement of MHPSS practitioners, programmers and researchers working for these organizations and some who are locally based who observe these programs in action. We comment on progress to date, barriers and recommendations for change and the importance of promoting sustained integration of MHPSS into health and social service systems through a comprehensive, recovery-oriented system of care. We call for prioritizing often-neglected issues (e.g., stigma, severe mental health conditions and neurodevelopmental conditions), strengthening workforce training and supervision and monitoring and evaluation systems to ensure program quality. The continued dominance of the Global North in shaping GMH programming priorities remains a concern. We advocate for a greater involvement of local workers and communities in agenda-setting for programs, culturally grounded implementation and long-term capacity building. Evidence-based practices must be met with contextual relevance, and comprehensive guidelines for sustained support are needed for development settings. For persistent funding challenges, we recommend clearer funder objectives, investment in in-house mental health expertise and funder coordination with prioritization of complementary programming. These recommendations are essential to realizing equitable, comprehensive, evidence-based and contextually grounded GMH programming.

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Economic Risks and Mental Health During China's 2020 COVID-19 Outbreak: A Mixed-Methods Approach

Wen Fan, Yue Qian

Journal of Current Chinese Affairs·2025

Insecure employment and irregular income have become important components of risk in contemporary society. This mixed-methods study investigates the social correlates and mental health implications of economic risks during China's 2020 COVID-19 outbreak. Given China's uneven geographic distribution of COVID-19 and its distinctive institutional arrangements, we theorise and empirically test social disparities by region and work unit ( danwei ). National panel survey data collected in 2020 show that residents of Hubei province (initial epicentre) and workers in non-state work units perceived heightened job insecurity and were more likely to experience income loss. Inequalities in job insecurity and income loss across regions and work units were, in turn, associated with region- and danwei -based disparities in mental health. Qualitative interview data further illuminate how residents of Wuhan (the capital of Hubei) who lived through the COVID-19 outbreak experienced and made sense of economic risks and the associated mental distress during the crisis.

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PCM1 coordinates centrosome asymmetry with polarized endosome dynamics to regulate daughter cell fate

Xiang Zhao, Vincent Mouilleau, Yiqi Wang, Ahmet Can Solak, Jason Q. Garcia, Xinye Chen, Xiaoyu Shi, Christopher J. Wilkinson, Loïc A. Royer, Zhiqiang Dong, Su Guo

Nature Communications·2025

Abstract Vertebrate radial glia progenitors (RGPs) balance self-renewal and differentiation through asymmetric cell division (ACD), which involves unequal centrosome inheritance. How centrosome asymmetry directs cell fate remains poorly understood. Here, we identify Pericentriolar material 1 (Pcm1) as a key player in this process. In zebrafish embryonic RGPs, Pcm1 is asymmetrically associated with Cep83, a mother centrosome marker. Using in vivo time-lapse imaging and nanoscale-resolution expansion microscopy, we detect Pcm1 on Notch ligand-containing endosomes, where it interacts–either directly or indirectly–with Par-3 and dynein. Loss of pcm1 disrupts endosome dynamics, increasing neuronal differentiation at the expense of RGP self-renewal. Mechanistically, Pcm1 facilitates the transition from Rab5b to Rab11a and promotes the assembly of Par-3 and dynein macromolecular complexes on recycling endosomes. Furthermore, we find conserved PARD3-PCM1-CEP83-RAB11 associations in human cortical brain organoids. Our findings uncover that Pcm1 links centrosome asymmetry to polarized endosome trafficking, thereby regulating RGP fate decisions.

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Social Interactions and Interbrain Synchrony in Early Development: A Scoping Review of Pediatric <scp>EEG</scp> / <scp>MEG</scp> Hyperscanning Studies

Alexandra P. Key, Abigail J. Inman, Kareem Chambers, Aiden Ford, Caitlin P. Kjeldsen, Mary Lauren Neel

Psychophysiology·2025

ABSTRACT Hyperscanning methods are gaining prominence in psychophysiological research and yielding interesting insights into the neural mechanisms supporting interpersonal social engagement. However, the vast majority of that innovative work is being carried out in adolescents and adults. Given the known importance of dyadic social interactions for supporting early neurodevelopment, expanding hyperscanning methods to child studies presents a promising opportunity to broaden the field's understanding of the interplay between neural, behavioral, and environmental factors contributing to social connectedness. This scoping review summarizes the findings from the empirical, peer‐reviewed pediatric hyperscanning studies involving children 7 years of age or younger. We review methodological practices related to the experimental design of naturalistic social interaction paradigms and data analysis options for quantifying interpersonal neural synchrony, identify their strengths and knowledge gaps, and propose key areas for future study.

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Path To Gain Functional Transparency In Artificial Intelligence With Meaningful Explainability

Md. Tanzib Hosain, Mehedi Hasan Anik, Sadman Rafi, Rana Tabassum, Khaleque Insia, Md. Mehrab Siddiky

dergipark.org.tr·2025

Artificial Intelligence (AI) is rapidly integrating into various aspects of our daily lives, influencing decision-making processes in areas such as targeted advertising and matchmaking algorithms. As AI systems become increasingly sophisticated, ensuring their transparency and explainability becomes crucial. Functional transparency is a fundamental aspect of algorithmic decision-making systems, allowing stakeholders to comprehend the inner workings of these systems and enabling them to evaluate their fairness and accuracy. However, achieving functional transparency poses significant challenges that need to be addressed. In this paper, we propose a design for user-centered compliant-by-design transparency in transparent systems. We emphasize that the development of transparent and explainable AI systems is a complex and multidisciplinary endeavor, necessitating collaboration among researchers from diverse fields such as computer science, artificial intelligence, ethics, law, and social science. By providing a comprehensive understanding of the challenges associated with transparency in AI systems and proposing a user-centered design framework, we aim to facilitate the development of AI systems that are accountable, trustworthy, and aligned with societal values.

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Difficulties with Evaluating a Deception Detector for AIs

Lewis Smith, Bilal Chughtai, Neel Nanda

arXiv·2025

Building reliable deception detectors for AI systems -- methods that could predict when an AI system is being strategically deceptive without necessarily requiring behavioural evidence -- would be valuable in mitigating risks from advanced AI systems. But evaluating the reliability and efficacy of a proposed deception detector requires examples that we can confidently label as either deceptive or honest. We argue that we currently lack the necessary examples and further identify several concrete obstacles in collecting them. We provide evidence from conceptual arguments, analysis of existing empirical works, and analysis of novel illustrative case studies. We also discuss the potential of several proposed empirical workarounds to these problems and argue that while they seem valuable, they also seem insufficient alone. Progress on deception detection likely requires further consideration of these problems.

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DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning

Zhihong Shao, Yuxiang Luo, Chengda Lu, Z. Z. Ren, Jiewen Hu, Tian Ye, Zhibin Gou, Shirong Ma, Xiaokang Zhang

arXiv·2025

Large language models have made significant progress in mathematical reasoning, which serves as an important testbed for AI and could impact scientific research if further advanced. By scaling reasoning with reinforcement learning that rewards correct final answers, LLMs have improved from poor performance to saturating quantitative reasoning competitions like AIME and HMMT in one year. However, this approach faces fundamental limitations. Pursuing higher final answer accuracy doesn't address a key issue: correct answers don't guarantee correct reasoning. Moreover, many mathematical tasks like theorem proving require rigorous step-by-step derivation rather than numerical answers, making final answer rewards inapplicable. To push the limits of deep reasoning, we believe it is necessary to verify the comprehensiveness and rigor of mathematical reasoning. Self-verification is particularly important for scaling test-time compute, especially for open problems without known solutions. Towards self-verifiable mathematical reasoning, we investigate how to train an accurate and faithful LLM-based verifier for theorem proving. We then train a proof generator using the verifier as the reward model, and incentivize the generator to identify and resolve as many issues as possible in their own proofs before finalizing them. To maintain the generation-verification gap as the generator becomes stronger, we propose to scale verification compute to automatically label new hard-to-verify proofs, creating training data to further improve the verifier. Our resulting model, DeepSeekMath-V2, demonstrates strong theorem-proving capabilities, achieving gold-level scores on IMO 2025 and CMO 2024 and a near-perfect 118/120 on Putnam 2024 with scaled test-time compute.

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FoldSAE: Learning to Steer Protein Folding Through Sparse Representations

Wojciech Zarzecki, Paulina Szymczak, Ewa Szczurek, Kamil Deja

arXiv·2025

RFdiffusion is a popular and well-established model for generation of protein structures. However, this generative process offers limited insight into its internal representations and how they contribute to the final protein structure. Concurrently, recent work in mechanistic interpretability has successfully used Sparse Autoencoders (SAEs) to discover interpretable features within neural networks. We combine these concepts by applying SAE to the internal representations of RFdiffusion to uncover secondary structure-specific features and establish a relationship between them and generated protein structures. Building on these insights, we introduce a novel steering mechanism that enables precise control of secondary structure formation through a tunable hyperparameter, while simultaneously revealing interpretable block and neuron-level representations within RFdiffusion. Our work pioneers a new framework for making RFdiffusion more interpretable, demonstrating how understanding internal features can be directly translated into precise control over the protein design process.

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Embedded Universal Predictive Intelligence: a coherent framework for multi-agent learning

Alexander Meulemans, Rajai Nasser, Maciej Wołczyk, Marissa A. Weis, Seijin Kobayashi, Blake Richards, Guillaume Lajoie, Angelika Steger, Marcus Hutter, James Manyika, Rif A. Saurous, João Sacramento, Blaise Agüera y Arcas

arXiv·2025

The standard theory of model-free reinforcement learning assumes that the environment dynamics are stationary and that agents are decoupled from their environment, such that policies are treated as being separate from the world they inhabit. This leads to theoretical challenges in the multi-agent setting where the non-stationarity induced by the learning of other agents demands prospective learning based on prediction models. To accurately model other agents, an agent must account for the fact that those other agents are, in turn, forming beliefs about it to predict its future behavior, motivating agents to model themselves as part of the environment. Here, building upon foundational work on universal artificial intelligence (AIXI), we introduce a mathematical framework for prospective learning and embedded agency centered on self-prediction, where Bayesian RL agents predict both future perceptual inputs and their own actions, and must therefore resolve epistemic uncertainty about themselves as part of the universe they inhabit. We show that in multi-agent settings, self-prediction enables agents to reason about others running similar algorithms, leading to new game-theoretic solution concepts and novel forms of cooperation unattainable by classical decoupled agents. Moreover, we extend the theory of AIXI, and study universally intelligent embedded agents which start from a Solomonoff prior. We show that these idealized agents can form consistent mutual predictions and achieve infinite-order theory of mind, potentially setting a gold standard for embedded multi-agent learning.

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Alignment remains a hard, unsolved problem

evhub

AI Alignment Forum·2025

This is a public adaptation of a document I wrote for an internal Anthropic audience about a month ago. Thanks to (in alphabetical order) Joshua Batson, Joe Benton, Sam Bowman, Roger Grosse, Jeremy Hadfield, Jared Kaplan, Jan Leike, Jack Lindsey, Monte MacDiarmid, Sam Marks, Fra Mosconi, Chris Olah, Ethan Perez, Sara Price, Ansh Radhakrishnan, Fabien Roger, Buck Shlegeris, Drake Thomas, and Kate Woolverton for useful discussions, comments, and feedback. Though there are certainly some issues, I think most current large language models are pretty well aligned. Despite its alignment faking, my favorite is probably Claude 3 Opus, and if you asked me to pick between the CEV of Claude 3 Opus and that of a median human, I think it'd be a pretty close call (I'd probably pick Claude, but it depends on the details of the setup). So, overall, I'm quite positive on the alignment of current models! And yet, I remain very worried about alignment in the future. This is my attempt to explain why that is. What makes alignment hard? I really like this graph from Chris Olah for illustrating different levels of alignment difficulty: If the only thing that we have to do to solve alignment is train away easily detectable behavioral issues—that is, issues like reward hacking or agentic misalignment where there is a straightforward behavioral alignment issue that we can detect and evaluate—then we are very much in the trivial/steam engine world. We could still fail, even in that world—and it’...

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Newest Papers

Global mental health and psychosocial support programming: An expert review of major implementation and funding challenges

Economic Risks and Mental Health During China's 2020 COVID-19 Outbreak: A Mixed-Methods Approach

PCM1 coordinates centrosome asymmetry with polarized endosome dynamics to regulate daughter cell fate

Social Interactions and Interbrain Synchrony in Early Development: A Scoping Review of Pediatric <scp>EEG</scp> / <scp>MEG</scp> Hyperscanning Studies

Path To Gain Functional Transparency In Artificial Intelligence With Meaningful Explainability

How I accidentally stumbled upon one of the Catholic Church's darkest secrets

Difficulties with Evaluating a Deception Detector for AIs

DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning

FoldSAE: Learning to Steer Protein Folding Through Sparse Representations

Embedded Universal Predictive Intelligence: a coherent framework for multi-agent learning

Alignment remains a hard, unsolved problem

the multiplicity of love