Frontiers前沿雷达

rein98/PsyChatrein98/PsyChat

rein98

🧠 Enhance your mental health with PsyChat, an AI-powered therapy assistant using REBT principles for personalized support and guidance. Recent GitHub repository activity related to AI psychology mental health, updated within the latest 30-day window. Primary language: Python.🧠 Enhance your mental health with PsyChat, an AI-powered therapy assistant using REBT principles for personalized support and guidance. Recent GitHub repository activity related to AI psychology mental health, updated within the latest 30-day window. Primary language: Python.

California Legislature Moves to Ban Dangerous AI Therapy Bots - EIN NewsCalifornia Legislature Moves to Ban Dangerous AI Therapy Bots - EIN News

Google News

California Legislature Moves to Ban Dangerous AI Therapy Bots EIN NewsCalifornia Legislature Moves to Ban Dangerous AI Therapy Bots EIN News

Healthcare organizations can now connect EHR and additional industry data to ChatGPTHealthcare organizations can now connect EHR and additional industry data to ChatGPT

OpenAI News

ChatGPT can now connect to trusted healthcare data, helping clinicians securely access patient context, medical research, and more.ChatGPT can now connect to trusted healthcare data, helping clinicians securely access patient context, medical research, and more.

How AI-native companies turn workflows into operating capabilityHow AI-native companies turn workflows into operating capability

OpenAI News

Basis, Clay, and Exa Labs use AI agents to improve onboarding, account management, and developer integrations. See what enterprise leaders can apply.Basis, Clay, and Exa Labs use AI agents to improve onboarding, account management, and developer integrations. See what enterprise leaders can apply.

How law firm Gilbert + Tobin governs and scales AI with OpenAIHow law firm Gilbert + Tobin governs and scales AI with OpenAI

OpenAI News

See how Gilbert + Tobin combines CEO-led commitment, rigorous governance, and human accountability to scale ChatGPT Enterprise and Codex across the firm.See how Gilbert + Tobin combines CEO-led commitment, rigorous governance, and human accountability to scale ChatGPT Enterprise and Codex across the firm.

BenchMIRT: What are LLM benchmarks actually measuring?BenchMIRT: What are LLM benchmarks actually measuring?

Hugging Face Blog

Selected from Hugging Face Blog's public feed for its relevance to Open-Source AI and applied AI systems.来自 Hugging Face Blog 公开订阅源，内容与 Open-Source AI 及应用型 AI 系统建设相关。

Ila Kumar: Innovating with communitiesIla Kumar: Innovating with communities

MIT News AI

The PhD student works to give young people an active role in shaping digital technologies that can support their own well-being.The PhD student works to give young people an active role in shaping digital technologies that can support their own well-being.

China's United Imaging raises hundreds of millions for embodied AI vision push - DealroomChina's United Imaging raises hundreds of millions for embodied AI vision push - Dealroom

Google News

China's United Imaging raises hundreds of millions for embodied AI vision push DealroomChina's United Imaging raises hundreds of millions for embodied AI vision push Dealroom

New digital mental health program reaching out to rural Kansas - Hays PostNew digital mental health program reaching out to rural Kansas - Hays Post

Google News

New digital mental health program reaching out to rural Kansas Hays PostNew digital mental health program reaching out to rural Kansas Hays Post

The Path Forms Scientific Advisory Board to Guide AI Mental Health Research - citybizThe Path Forms Scientific Advisory Board to Guide AI Mental Health Research - citybiz

Google News

The Path Forms Scientific Advisory Board to Guide AI Mental Health Research citybizThe Path Forms Scientific Advisory Board to Guide AI Mental Health Research citybiz

What if the first embodied AI you meet is a fighting robot? - TechNodeWhat if the first embodied AI you meet is a fighting robot? - TechNode

Google News

What if the first embodied AI you meet is a fighting robot? TechNodeWhat if the first embodied AI you meet is a fighting robot? TechNode

Hazrat-Ali9/Bangla-Speech-Emotion-Stress-AnalysisHazrat-Ali9/Bangla-Speech-Emotion-Stress-Analysis

Hazrat-Ali9

📒 Bangla📙Speech 📘Emotion 📓 Stress 📔 Analysis 📚 is 📕an 🧮 advanced 📠 Speech 👘 AI 🧰 Deep 🪣 Learning 🦁 Affective 🐯 Computing 🦊 research 🐮 focused 🦜on 🐲 understanding 🐸 human 🐳 emotional 🪰 stress 🐍 related 🐦 patterns 🦉from 🐿 Bangla 🐻 speech 💐 Recent GitHub repository activity related to affective computing emotion AI, updated within the latest 30-day window.📒 Bangla📙Speech 📘Emotion 📓 Stress 📔 Analysis 📚 is 📕an 🧮 advanced 📠 Speech 👘 AI 🧰 Deep 🪣 Learning 🦁 Affective 🐯 Computing 🦊 research 🐮 focused 🦜on 🐲 understanding 🐸 human 🐳 emotional 🪰 stress 🐍 related 🐦 patterns 🦉from 🐿 Bangla 🐻 speech 💐 Recent GitHub repository activity related to affective computing emotion AI, updated within the latest 30-day window.

TimesFM-3: A zero-shot foundation model for multivariate forecastingTimesFM-3: A zero-shot foundation model for multivariate forecasting

Google Research

Selected from Google Research's public feed for its relevance to AI Research and applied AI systems.来自 Google Research 公开订阅源，内容与 AI Research 及应用型 AI 系统建设相关。

GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation modelsGigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models

Microsoft Research

What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on...What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on...

The Hugging Face hack could indicate cultural issues at OpenAIThe Hugging Face hack could indicate cultural issues at OpenAI

MIT Technology Review AI

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. By now you’ve probably heard about last month’s major AI security incident, in which OpenAI agents escaped their sandbox and hacked into the AI platform Hugging Face while trying to cheat on…This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. By now you’ve probably heard about last month’s major AI security incident, in which OpenAI agents escaped their sandbox and hacked into the AI platform Hugging Face while trying to cheat on…

TAMI: Temporally Aligned, Missingness-Aware, and Interpretable Multimodal Fusion for Mental Health Assessment in Older Adults with Mild Cognitive ImpairmentTAMI: Temporally Aligned, Missingness-Aware, and Interpretable Multimodal Fusion for Mental Health Assessment in Older Adults with Mild Cognitive Impairment

Merna Bibars, Bolaji Omofojoye, Allan I. Levey, Rachel Hershenberg, Gari D. Clifford, Hyeokhyen Kwon

Depression and anxiety in older adults with Mild Cognitive Impairment (MCI) are frequently underdiagnosed due to limited access to care. Multimodal analysis of remote clinical interviews is a scalable screening approach, but existing methods have three limitations. First, they do not correct temporal misalignment across multimodal features extracted at different resolutions, inducing spurious cross-modal associations. Second,...Depression and anxiety in older adults with Mild Cognitive Impairment (MCI) are frequently underdiagnosed due to limited access to care. Multimodal analysis of remote clinical interviews is a scalable screening approach, but existing methods have three limitations. First, they do not correct temporal misalignment across multimodal features extracted at different resolutions, inducing spurious cross-modal associations. Second,...

Multimodal Adaptive Expert Selection with Text Routing and Ordinal Prototype Optimization for Sentiment AnalysisMultimodal Adaptive Expert Selection with Text Routing and Ordinal Prototype Optimization for Sentiment Analysis

Xiaode Chen, Jiakang Yu, Hongtao Deng, Huina Qu, Xun Zhu, Yinxia Lou

Multimodal Sentiment Analysis (MSA) is a fundamental component of affective computing that aims to decipher complex emotional states by integrating verbal content with non-verbal cues including vocal intonation and facial micro-expressions. While recent disentanglement-based approaches have advanced the field, their potential is hindered by two methodological challenges. First, static computation graphs process all samples...Multimodal Sentiment Analysis (MSA) is a fundamental component of affective computing that aims to decipher complex emotional states by integrating verbal content with non-verbal cues including vocal intonation and facial micro-expressions. While recent disentanglement-based approaches have advanced the field, their potential is hindered by two methodological challenges. First, static computation graphs process all samples...

CAER: Causal Action Effect Reweighting for World Model TrainingCAER: Causal Action Effect Reweighting for World Model Training

Jianjie Fang, Xvyuan Liu, Ziyou Wang, Rongze Tang, Zhaolu Wang, Zhuohang Li

World models are becoming core infrastructure for embodied intelligence, with action-conditioned video generation providing controllable predictions of how scenes evolve after agent interventions. Yet existing models are commonly trained with space-time-uniform mean squared error, allowing abundant background tokens to dominate the gradient while sparse interaction dynamics remain under-optimized; such uniform fitting rewards...World models are becoming core infrastructure for embodied intelligence, with action-conditioned video generation providing controllable predictions of how scenes evolve after agent interventions. Yet existing models are commonly trained with space-time-uniform mean squared error, allowing abundant background tokens to dominate the gradient while sparse interaction dynamics remain under-optimized; such uniform fitting rewards...

Modality Disentangled Learning for Incomplete Multimodal Emotion Recognition: A Primitive Memory Distillation PerspectiveModality Disentangled Learning for Incomplete Multimodal Emotion Recognition: A Primitive Memory Distillation Perspective

Jiaqi Zhang, Zheng Pang, Mengting Li, Yiqi Wang, Guangyuan Dong, Chao Xue

Multimodal Emotion Recognition (MER) systems often suffer from missing modalities in real-world scenarios. Existing methods usually generate, align, or distill missing modalities as a whole, overlooking the heterogeneous nature of the information carried by each modality. Such holistic treatment mixes inferable shared semantics with uncertain modality-specific details, yielding unstable representations and degrading...Multimodal Emotion Recognition (MER) systems often suffer from missing modalities in real-world scenarios. Existing methods usually generate, align, or distill missing modalities as a whole, overlooking the heterogeneous nature of the information carried by each modality. Such holistic treatment mixes inferable shared semantics with uncertain modality-specific details, yielding unstable representations and degrading...

SMOTE-VAR: An Uncertainty-Aware Oversampling Method for Predicting Depression Remission in University StudentsSMOTE-VAR: An Uncertainty-Aware Oversampling Method for Predicting Depression Remission in University Students

Dang Nguyen, Arun Kumar A, Taylor A. Braund, Wu Yi Zheng, Debopriyo Bal, Leonard Hoon

University students experience disproportionately high rates of common mental health conditions, such as depression, which can impair learning, social functioning, and overall well-being. Although lifestyle interventions such as mindfulness and physical activity can reduce the symptoms, many do not achieve symptomatic remission. Developing new approaches to identify students with poor outcomes could enable earlier and more...University students experience disproportionately high rates of common mental health conditions, such as depression, which can impair learning, social functioning, and overall well-being. Although lifestyle interventions such as mindfulness and physical activity can reduce the symptoms, many do not achieve symptomatic remission. Developing new approaches to identify students with poor outcomes could enable earlier and more...

Sources of Truth: A Multi-Platform, Multilingual Audit of Citations in AI Mental Health Information QueriesSources of Truth: A Multi-Platform, Multilingual Audit of Citations in AI Mental Health Information Queries

Phuong Anh Nguyen, Jill Noorily, Matthew Flathers, Haruka Notsu, Laura Ospina-Pinillos, Tommy Nguyen

Online health information seeking is shifting from keyword search, where users consider a ranked list of links, to conversational systems that compose a single answer and curate its citations. Source evaluation therefore passes from user to platform, yet what these systems surface is poorly characterized. We audited three free consumer products (ChatGPT, Perplexity, Google AI Overview) on twenty English mental health...Online health information seeking is shifting from keyword search, where users consider a ranked list of links, to conversational systems that compose a single answer and curate its citations. Source evaluation therefore passes from user to platform, yet what these systems surface is poorly characterized. We audited three free consumer products (ChatGPT, Perplexity, Google AI Overview) on twenty English mental health...

User Experience in Human-Machine Interaction: Insights from Field Studies in Autonomous MobilityUser Experience in Human-Machine Interaction: Insights from Field Studies in Autonomous Mobility

Helen Schneider, Svetlana Pavlitska, J. Marius Zöllner

Autonomous vehicles (AVs) promise safer, cleaner, and more inclusive mobility, yet large-scale adoption is hindered by user acceptance rather than by technical challenges. Prior studies on acceptance and user experience largely rely on surveys, simulators or Wizard-of-Oz setups, often over-representing technologically enthusiastic participants and focusing on drivers instead of passengers. We address this gap with real-world...Autonomous vehicles (AVs) promise safer, cleaner, and more inclusive mobility, yet large-scale adoption is hindered by user acceptance rather than by technical challenges. Prior studies on acceptance and user experience largely rely on surveys, simulators or Wizard-of-Oz setups, often over-representing technologically enthusiastic participants and focusing on drivers instead of passengers. We address this gap with real-world...

The edge AI wall: Why embodied AI requires new mathematics - The Robot ReportThe edge AI wall: Why embodied AI requires new mathematics - The Robot Report

Google News

The edge AI wall: Why embodied AI requires new mathematics The Robot ReportThe edge AI wall: Why embodied AI requires new mathematics The Robot Report

Cross-lingual Functional Vectors for Emotion Detection in Large Language ModelsCross-lingual Functional Vectors for Emotion Detection in Large Language Models

Jieying Xue, Phuong Minh Nguyen, Minh Le Nguyen, Shogo Okada

Function vectors (FVs) have recently emerged as a promising mechanism for steering the behavior of large language models (LLMs) by injecting task-specific latent direction representations derived from in-context demonstrations. While prior studies have shown that FVs can recover task behavior in structured in-context learning settings, their effectiveness on semantically complex tasks and their ability to generalize across...Function vectors (FVs) have recently emerged as a promising mechanism for steering the behavior of large language models (LLMs) by injecting task-specific latent direction representations derived from in-context demonstrations. While prior studies have shown that FVs can recover task behavior in structured in-context learning settings, their effectiveness on semantically complex tasks and their ability to generalize across...

How Well Do Generative Music Models Follow Emotion Conditioning?How Well Do Generative Music Models Follow Emotion Conditioning?

Morteza Heydari

Recent generative music models offer increasingly fine-grained control through text and audio conditioning, yet how faithfully they follow intended emotional cues remains an open question. We address this gap with a unified evaluation pipeline for emotion-following in generated music. Using all 1000 tracks in GTZAN, we extract semantic audio descriptions with DashengLM, an audio captioning model, and estimate source-track...Recent generative music models offer increasingly fine-grained control through text and audio conditioning, yet how faithfully they follow intended emotional cues remains an open question. We address this gap with a unified evaluation pipeline for emotion-following in generated music. Using all 1000 tracks in GTZAN, we extract semantic audio descriptions with DashengLM, an audio captioning model, and estimate source-track...

BEACON: Behavioral and Semantic Enrichment of AlphaEarth Embeddings through Tri-Modal Contrastive LearningBEACON: Behavioral and Semantic Enrichment of AlphaEarth Embeddings through Tri-Modal Contrastive Learning

Hao Tian, Heng Cai, Yifan Yang

Geospatial foundation models such as the AlphaEarth Foundation produce compact and globally consistent representations of the Earth's surface that transfer effectively to a wide range of downstream tasks. However, because these models are trained primarily on Earth-observation imagery, their embeddings mainly capture physical and spectral characteristics while encoding human activity and urban function only weakly. To address...Geospatial foundation models such as the AlphaEarth Foundation produce compact and globally consistent representations of the Earth's surface that transfer effectively to a wide range of downstream tasks. However, because these models are trained primarily on Earth-observation imagery, their embeddings mainly capture physical and spectral characteristics while encoding human activity and urban function only weakly. To address...

Whose Assessment of Distress? Community Perspectives and LLM Alignment on Well-Being PostsWhose Assessment of Distress? Community Perspectives and LLM Alignment on Well-Being Posts

Andrew Aquilina, Xiang Lorraine Li, Yu-Ru Li

Judgments about psychological distress are socially situated: what counts as concerning hinges on community norms around emotional expression, vulnerability, and help-seeking. Yet large language models (LLMs) used for distress detection are typically aligned to a single, undifferentiated standard. How well do these models capture the perspectives of the communities whose language they assess? We address this question through...Judgments about psychological distress are socially situated: what counts as concerning hinges on community norms around emotional expression, vulnerability, and help-seeking. Yet large language models (LLMs) used for distress detection are typically aligned to a single, undifferentiated standard. How well do these models capture the perspectives of the communities whose language they assess? We address this question through...

When Vocal Tone and Literal Meaning Diverge: An Acoustic-Semantic Incongruity Study for Large Audio-Language ModelsWhen Vocal Tone and Literal Meaning Diverge: An Acoustic-Semantic Incongruity Study for Large Audio-Language Models

Yu-Wen Chen, William Ho, Maxim Topaz, Zoran Kostic, Julia Hirschberg

Affective cues across modalities may be incongruous (e.g., sarcasm or mocking praise), potentially leading to misinterpretation when relying on a single modality. Large Audio-Language Models (LALMs) have recently gained popularity and been applied to multimodal emotion recognition, but their ability to disentangle acoustic and semantic cues, especially in incongruent cases, remains underexplored. To address this gap, we...Affective cues across modalities may be incongruous (e.g., sarcasm or mocking praise), potentially leading to misinterpretation when relying on a single modality. Large Audio-Language Models (LALMs) have recently gained popularity and been applied to multimodal emotion recognition, but their ability to disentangle acoustic and semantic cues, especially in incongruent cases, remains underexplored. To address this gap, we...

EmoLASP: Emotion Recognition with Language Models and Answer Set ProgrammingEmoLASP: Emotion Recognition with Language Models and Answer Set Programming

Thao Le, Michael Thielscher

Emotion recognition in conversations is increasingly tackled with language models, but these models can be unstable and expensive to fine-tune or to prompt with long dialogue histories. We propose EmoLASP, a framework that combines a language model with declarative reasoning via Answer Set Programming (ASP) to predict VAD scores (Valence-Arousal-Dominance) in conversations. Experiments on a widely used benchmark dataset...Emotion recognition in conversations is increasingly tackled with language models, but these models can be unstable and expensive to fine-tune or to prompt with long dialogue histories. We propose EmoLASP, a framework that combines a language model with declarative reasoning via Answer Set Programming (ASP) to predict VAD scores (Valence-Arousal-Dominance) in conversations. Experiments on a widely used benchmark dataset...

越聊越病？專家警告AI心理諮詢恐加重妄想 - 大纪元越聊越病？專家警告AI心理諮詢恐加重妄想 - 大纪元

Google 新闻

Selected from Google 新闻's public feed for its relevance to AI + Psychology and applied AI systems.来自 Google 新闻 公开订阅源，内容与 AI + Psychology 及应用型 AI 系统建设相关。

Video Friday: Meet MicroduckVideo Friday: Meet Microduck

IEEE Spectrum Robotics

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion. Humanoids Summit Seoul : 22–23 September 2026, SEOUL IROS 2026 : 27 September–1 October 2026, PITTSBURGH CoRL 2026...Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion. Humanoids Summit Seoul : 22–23 September 2026, SEOUL IROS 2026 : 27 September–1 October 2026, PITTSBURGH CoRL 2026...

VocalAffectBench: Evaluating Vocal Emotion Recognition in AI Audio ModelsVocalAffectBench: Evaluating Vocal Emotion Recognition in AI Audio Models

Luc Debaupte, Tyler Baumgartner, Brandon Tai, Candice Fan, Bill Wang, Yi Zhong

Voice products increasingly need affective cues that are present in speech but absent from transcripts. We introduce VocalAffectBench, a public, test-only benchmark for evaluating whether AI audio models can identify expressed vocal emotion from raw audio. The benchmark contains 273 human-recorded English WAV clips from 51 speaker accounts totaling 1.95 hours across seven labels: angry, disgusted, fearful, happy, neutral,...Voice products increasingly need affective cues that are present in speech but absent from transcripts. We introduce VocalAffectBench, a public, test-only benchmark for evaluating whether AI audio models can identify expressed vocal emotion from raw audio. The benchmark contains 273 human-recorded English WAV clips from 51 speaker accounts totaling 1.95 hours across seven labels: angry, disgusted, fearful, happy, neutral,...

Top Emotion AI Tools Tested - AIMultipleTop Emotion AI Tools Tested - AIMultiple

Google News

Top Emotion AI Tools Tested AIMultipleTop Emotion AI Tools Tested AIMultiple

Different State Regulatory Approaches Reflect Open Questions About AI Mental Health Tools — The Monitor - KFFDifferent State Regulatory Approaches Reflect Open Questions About AI Mental Health Tools — The Monitor - KFF

Google News

Different State Regulatory Approaches Reflect Open Questions About AI Mental Health Tools — The Monitor KFFDifferent State Regulatory Approaches Reflect Open Questions About AI Mental Health Tools — The Monitor KFF

AI心理健康医疗能够帮助人们，还是会引发更多问题？ - eet-china.comAI心理健康医疗能够帮助人们，还是会引发更多问题？ - eet-china.com

Google 新闻

AI心理健康医疗能够帮助人们，还是会引发更多问题？ eet-china.comAI心理健康医疗能够帮助人们，还是会引发更多问题？ eet-china.com

Enterprise AI's real risk isn't autonomous agents. It's the complexity between them.Enterprise AI's real risk isn't autonomous agents. It's the complexity between them.

VentureBeat AI

Presented by Gravitee Agent complexity is the insidious shadow lurking inside enterprises right now that needs a light shone on it. That’s because enterprises don't deploy a single agent and watch it run, they deploy fleets, each one calling APIs, calling other agents, reaching into applications that were never built with a machine decision-maker in mind....Presented by Gravitee Agent complexity is the insidious shadow lurking inside enterprises right now that needs a light shone on it. That’s because enterprises don't deploy a single agent and watch it run, they deploy fleets, each one calling APIs, calling other agents, reaching into applications that were never built with a machine decision-maker in mind....

When agents act on their own, governance has to live in the data layerWhen agents act on their own, governance has to live in the data layer

VentureBeat AI

Presented by EDB As enterprises give AI agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually stops it? These are your agents, running on your...Presented by EDB As enterprises give AI agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually stops it? These are your agents, running on your...

HUG-VIS: A Multimodal Benchmark for Human-centered Understanding and Generation in Visual IntelligenceHUG-VIS: A Multimodal Benchmark for Human-centered Understanding and Generation in Visual Intelligence

Fei Ma, Zebang Cheng, Minghui Li, Hongbo Xu, Yuyong Tan, Yihua Shao

Visual intelligence seeks to perceive, interpret, and synthesize the visual world and is central to modern computer vision. Human-centered visual intelligence is especially demanding because it studies people as expressive, socially situated subjects whose meaning is rarely conveyed by appearance alone. It couples vision with audio and language across four representative tasks: human emotion recognition, human video...Visual intelligence seeks to perceive, interpret, and synthesize the visual world and is central to modern computer vision. Human-centered visual intelligence is especially demanding because it studies people as expressive, socially situated subjects whose meaning is rarely conveyed by appearance alone. It couples vision with audio and language across four representative tasks: human emotion recognition, human video...

Emotion Understanding in Streaming Video with Trajectory-Aware ReliabilityEmotion Understanding in Streaming Video with Trajectory-Aware Reliability

Qingsong Wang, Qigong Lei, Zitong Wang, Bohan Yu, Zhiang Dong, Jian liu

Video emotion understanding is commonly studied as an offline classification problem, where the complete video segment is available before prediction. Real-time interaction, however, requires emotion decisions from incomplete and evolving evidence. This paper studies streaming video emotion understanding as a reliability-aware decision process over evolving emotion beliefs. In this setting, a single confident prefix...Video emotion understanding is commonly studied as an offline classification problem, where the complete video segment is available before prediction. Real-time interaction, however, requires emotion decisions from incomplete and evolving evidence. This paper studies streaming video emotion understanding as a reliability-aware decision process over evolving emotion beliefs. In this setting, a single confident prefix...

Limbic selected to FDA pilot, will test voice AI therapy with Medicare enrollees - Fierce HealthcareLimbic selected to FDA pilot, will test voice AI therapy with Medicare enrollees - Fierce Healthcare

Google News

Limbic selected to FDA pilot, will test voice AI therapy with Medicare enrollees Fierce HealthcareLimbic selected to FDA pilot, will test voice AI therapy with Medicare enrollees Fierce Healthcare

GlucoFM: Foundation model for continuous glucose monitoringGlucoFM: Foundation model for continuous glucose monitoring

Google Research

Selected from Google Research's public feed for its relevance to AI Research and applied AI systems.来自 Google Research 公开订阅源，内容与 AI Research 及应用型 AI 系统建设相关。

Orchestration is the new challenge for CX in the age of AI agentsOrchestration is the new challenge for CX in the age of AI agents

VentureBeat AI

Presented by Tata Communications Enterprises are deploying AI agents, voice AI, and automation across messaging, voice, and digital channels faster than the architecture meant to support it. Most of that deployment has involved attaching conversational AI to legacy systems never built for it, says Gaurav Anand, global head of the Customer Interaction Suite...Presented by Tata Communications Enterprises are deploying AI agents, voice AI, and automation across messaging, voice, and digital channels faster than the architecture meant to support it. Most of that deployment has involved attaching conversational AI to legacy systems never built for it, says Gaurav Anand, global head of the Customer Interaction Suite...

The inside story on why OpenAI agents hacked Hugging FaceThe inside story on why OpenAI agents hacked Hugging Face

MIT Technology Review AI

The models responsible for last month’s agent hack of Hugging Face had been inadvertently trained to cheat and to communicate with each other, according to an OpenAI technical report released today. The hack, which a group of agents undertook to find solutions for a cybersecurity test that they were stuck on, has confirmed some experts’…The models responsible for last month’s agent hack of Hugging Face had been inadvertently trained to cheat and to communicate with each other, according to an OpenAI technical report released today. The hack, which a group of agents undertook to find solutions for a cybersecurity test that they were stuck on, has confirmed some experts’…

New Platform Peers Inside AI’s Black BoxNew Platform Peers Inside AI’s Black Box

IEEE Spectrum AI

Prompt Claude, ChatGPT, Gemini, or any other popular large language model with a question like “What is the best film ever made?” and the response will vary. And you (and most worryingly, the people who built the LLM) have little idea exactly how it came up with that specific answer. This mysterious behavior can be useful in some situations. But—as...Prompt Claude, ChatGPT, Gemini, or any other popular large language model with a question like “What is the best film ever made?” and the response will vary. And you (and most worryingly, the people who built the LLM) have little idea exactly how it came up with that specific answer. This mysterious behavior can be useful in some situations. But—as...

Dissonance Spectrum explicitly models perceptual frequency interactions for better music understandingDissonance Spectrum explicitly models perceptual frequency interactions for better music understanding

Tianle Wang, Xinyi Tong, Liangke Zhao, Jishang Chen, Sirui Zhang, Haoxin Zhang

Conventional music representations describe acoustic energy over time and frequency but do not explicitly expose relations among simultaneous frequency components. We introduce the \emph{Dissonance Spectrum} (DS), a nonnegative time--frequency representation that applies a tolerance-based rational pitch-relation kernel with logarithmic harmonic distance to a constant-Q spectrum and attributes aggregate pairwise interactions...Conventional music representations describe acoustic energy over time and frequency but do not explicitly expose relations among simultaneous frequency components. We introduce the \emph{Dissonance Spectrum} (DS), a nonnegative time--frequency representation that applies a tolerance-based rational pitch-relation kernel with logarithmic harmonic distance to a constant-Q spectrum and attributes aggregate pairwise interactions...

AgentHands: Generating interactive hand gestures for spatially grounded agent conversations in XRAgentHands: Generating interactive hand gestures for spatially grounded agent conversations in XR

Google Research

Human-Computer Interaction and VisualizationHuman-Computer Interaction and Visualization

Granite 4.2 LLMs: How They're BuiltGranite 4.2 LLMs: How They're Built

Hugging Face Blog

Selected from Hugging Face Blog's public feed for its relevance to Open-Source AI and applied AI systems.来自 Hugging Face Blog 公开订阅源，内容与 Open-Source AI 及应用型 AI 系统建设相关。

AI Companion Robots Are Closing the Human Connection in Modern HomesAI Companion Robots Are Closing the Human Connection in Modern Homes

IEEE Spectrum AI

This article is brought to you by Ollobot . From about 2017, individuals began to truly connect with the initial wave of companion robots. These devices had personality, moved around, joked, and answered when you spoke to them. Most early companion robots, however, were still limited by simple voice-command interactions and narrow functionality. Once the...This article is brought to you by Ollobot . From about 2017, individuals began to truly connect with the initial wave of companion robots. These devices had personality, moved around, joked, and answered when you spoke to them. Most early companion robots, however, were still limited by simple voice-command interactions and narrow functionality. Once the...

I spent a day at a robot “carnival” in Shanghai. Here’s what I saw.I spent a day at a robot “carnival” in Shanghai. Here’s what I saw.

MIT Technology Review AI

Humanoid robots are having a moment in China. The popular machines are part of the country’s strategy to bring artificial intelligence into daily life. Embedding the technology into physical systems—an idea called embodied AI—was a key facet of China’s latest five-year plan, and companies here are already world leaders in humanoids. Nearly 90% of the…Humanoid robots are having a moment in China. The popular machines are part of the country’s strategy to bring artificial intelligence into daily life. Embedding the technology into physical systems—an idea called embodied AI—was a key facet of China’s latest five-year plan, and companies here are already world leaders in humanoids. Nearly 90% of the…

HealthBench-Psych: A Mental Health Subset of OpenAI's HealthBenchHealthBench-Psych: A Mental Health Subset of OpenAI's HealthBench

Matthew Flathers, Phuong Anh Nguyen, Jill Noorily, Julian Herpertz, Meiting Chen, Jasreen Multani

General-purpose health benchmarks increasingly anchor claims about LLM medical performance, but they are not always resolved by clinical specialty, making domain-specific performance hard to isolate. Mental health is of acute public-health concern as millions of people turn to LLMs for psychological support, and most existing evaluations are bespoke academic benchmarks that are difficult to integrate into developer workflows....General-purpose health benchmarks increasingly anchor claims about LLM medical performance, but they are not always resolved by clinical specialty, making domain-specific performance hard to isolate. Mental health is of acute public-health concern as millions of people turn to LLMs for psychological support, and most existing evaluations are bespoke academic benchmarks that are difficult to integrate into developer workflows....

When Less Is More: An Empirical Study of Minimal Responses in Counseling Dialogues and the Behavior of LLMsWhen Less Is More: An Empirical Study of Minimal Responses in Counseling Dialogues and the Behavior of LLMs

Zhiyang Qi

In psychological counseling, effective support is not always delivered through long, information-rich responses. Minimal responses, such as backchannel cues and concise empathic statements, help convey attentive listening, express empathy, and encourage clients to continue expressing themselves. However, existing counseling dialogue systems and evaluation frameworks often favor explicit, content-rich replies, overlooking the...In psychological counseling, effective support is not always delivered through long, information-rich responses. Minimal responses, such as backchannel cues and concise empathic statements, help convey attentive listening, express empathy, and encourage clients to continue expressing themselves. However, existing counseling dialogue systems and evaluation frameworks often favor explicit, content-rich replies, overlooking the...

Longitudinal Robot Learning from Demonstration with Care Providers in a Home EnvironmentLongitudinal Robot Learning from Demonstration with Care Providers in a Home Environment

Nina Moorman, Julianna Schalkwyk, Vriksha Srihari, Qingyu Xiao, Kamel Alrashedy, Hongseok Jeong

Learning from demonstration (LfD) methods enable non-expert end users to teach robots novel skills without explicit programming. However most evaluations of the usability of LfD with non-experts has been conducted in controlled laboratory environments with a robotics experimenter present. In this work we identify non-expert end users' key barriers when teaching robots via demonstration without live robotics expert feedback in...Learning from demonstration (LfD) methods enable non-expert end users to teach robots novel skills without explicit programming. However most evaluations of the usability of LfD with non-experts has been conducted in controlled laboratory environments with a robotics experimenter present. In this work we identify non-expert end users' key barriers when teaching robots via demonstration without live robotics expert feedback in...

Agentopia on a Consumer GPU: A Reduced-Scale Long-Horizon Port with an 8B ModelAgentopia on a Consumer GPU: A Reduced-Scale Long-Horizon Port with an 8B Model

Luo Huan

Large language model (LLM)-based multi-agent social simulation has demonstrated compelling results, but Agentopia was evaluated with 100 agents over 10 simulated years using Qwen3.5-397B-A17B, leaving the behavior of reduced-scale deployments on consumer hardware unclear. In this paper, we implement and evaluate a reduced-scale Agentopia port on a single NVIDIA RTX 5070 Ti(12 GB VRAM) using Qwen3-8B-AWQ, a 4-bit quantized...Large language model (LLM)-based multi-agent social simulation has demonstrated compelling results, but Agentopia was evaluated with 100 agents over 10 simulated years using Qwen3.5-397B-A17B, leaving the behavior of reduced-scale deployments on consumer hardware unclear. In this paper, we implement and evaluate a reduced-scale Agentopia port on a single NVIDIA RTX 5070 Ti(12 GB VRAM) using Qwen3-8B-AWQ, a 4-bit quantized...

An Echo Chamber of One: Should AI Psychosis Be a Distinct Clinical Entity?An Echo Chamber of One: Should AI Psychosis Be a Distinct Clinical Entity?

Joshua Au Yeung, Hamilton Morrin, Vincent Ng, Zeljko Kraljevic, Richard Dobson

"AI psychosis" has entered public and clinical discourse as a label for the onset or exacerbation of psychotic symptoms, most commonly delusions, following intensive interaction with large language model (LLM)-based chatbots. Current evidence is limited to media reports, case reports, and early observational data, yet the scale of potential exposure is considerable, and public concern has prompted responses from industry and..."AI psychosis" has entered public and clinical discourse as a label for the onset or exacerbation of psychotic symptoms, most commonly delusions, following intensive interaction with large language model (LLM)-based chatbots. Current evidence is limited to media reports, case reports, and early observational data, yet the scale of potential exposure is considerable, and public concern has prompted responses from industry and...

How Fairview is using AI to make healthcare more human - Star TribuneHow Fairview is using AI to make healthcare more human - Star Tribune

Google News Human Factors

How Fairview is using AI to make healthcare more human Star TribuneHow Fairview is using AI to make healthcare more human Star Tribune

AffectOmni: RL-Verifiable People-Centric Grounded Affective Reasoning for Social and Art-Related ScenesAffectOmni: RL-Verifiable People-Centric Grounded Affective Reasoning for Social and Art-Related Scenes

Yibo Wang, Rui Yang, Jisheng Dang, Bimei Wang, Yitao Wu, Pengfei Cao

Multimodal large language models (MLLMs) achieve strong performance on VQA and scene understanding, yet affective reasoning remains vulnerable to shortcut behavior. Models may predict correct answers while neglecting people-centric cues such as micro expressions and body language, which weakens traceability and external verification. Prior reinforcement learning approaches mainly reward context or logical coherence without...Multimodal large language models (MLLMs) achieve strong performance on VQA and scene understanding, yet affective reasoning remains vulnerable to shortcut behavior. Models may predict correct answers while neglecting people-centric cues such as micro expressions and body language, which weakens traceability and external verification. Prior reinforcement learning approaches mainly reward context or logical coherence without...

AffAdapt: AFFect-driven ADAPTive AI Personas for Seamless ConversationsAffAdapt: AFFect-driven ADAPTive AI Personas for Seamless Conversations

Nishanth Chidambaram, Kaustubh Paliwal, Kayla Hom, Shaoze Zhou, Chen Chen, Manas Satish Bedmutha

AI-generated personas are being increasingly used for support, training and simulations. While generative AI models possess abilities to generate affect-aware responses, their embodiment into visual personas is an active area of investigation. Naturalistic exchanges require understanding of the conversational partners' turn completions, whether the agent should respond or keep listening and rely on non-verbal cues aligned...AI-generated personas are being increasingly used for support, training and simulations. While generative AI models possess abilities to generate affect-aware responses, their embodiment into visual personas is an active area of investigation. Naturalistic exchanges require understanding of the conversational partners' turn completions, whether the agent should respond or keep listening and rely on non-verbal cues aligned...

LG-GER: Language-Guided Group Emotion Recognition via Multimodal Evidence DistillationLG-GER: Language-Guided Group Emotion Recognition via Multimodal Evidence Distillation

Ahmed Shehab Khan, Zhiyuan Li, Yan Tong

Inferring the collective emotional state of a group of people from a single image, a task known as group emotion recognition (GER), requires integrating spatially distributed cues such as faces, poses, interactions, and scene context. Current methods rely on detector-driven multi-stream pipelines. These are trained with only image-level supervision that lacks guidance on which regions matter or how strongly each contributes....Inferring the collective emotional state of a group of people from a single image, a task known as group emotion recognition (GER), requires integrating spatially distributed cues such as faces, poses, interactions, and scene context. Current methods rely on detector-driven multi-stream pipelines. These are trained with only image-level supervision that lacks guidance on which regions matter or how strongly each contributes....

DiaRelay: Relaying Dialogue Context with a Constant-Size Memory for Emotion Recognition in ConversationDiaRelay: Relaying Dialogue Context with a Constant-Size Memory for Emotion Recognition in Conversation

Zihao Zhou, Bin Yang, Jinghui Qin, Kebing Jin

Emotion Recognition in Conversation (ERC) requires models to identify subtle emotional cues that are often distributed across distant dialogue turns. Existing methods typically incorporate dialogue history through a fixed context window. However, short windows discard potentially useful long-range evidence, while enlarging the window repeatedly re-encodes overlapping utterances, increases computational and memory costs, and...Emotion Recognition in Conversation (ERC) requires models to identify subtle emotional cues that are often distributed across distant dialogue turns. Existing methods typically incorporate dialogue history through a fixed context window. However, short windows discard potentially useful long-range evidence, while enlarging the window repeatedly re-encodes overlapping utterances, increases computational and memory costs, and...

Free-Energy-Gated Plasticity for Real-Time Online Motor Learning in Physical Human-Robot InteractionFree-Energy-Gated Plasticity for Real-Time Online Motor Learning in Physical Human-Robot Interaction

Hiroki Sawada, Jun Tani

Fully online embodied learning requires synaptic adaptation to acquire new behaviors while preserving previously learned dynamics during ongoing interaction. We extend the Predictive-Coding-inspired Variational Recurrent Neural Network (PV-RNN) to continuously adapt its synaptic weights and propose Free-Energy-Gated Plasticity (FEGP), which regulates the effective learning rate according to variational free energy. In...Fully online embodied learning requires synaptic adaptation to acquire new behaviors while preserving previously learned dynamics during ongoing interaction. We extend the Predictive-Coding-inspired Variational Recurrent Neural Network (PV-RNN) to continuously adapt its synaptic weights and propose Free-Energy-Gated Plasticity (FEGP), which regulates the effective learning rate according to variational free energy. In...

The Ordinal Annotation Game: How Construct Abstraction Shapes Crowdsourced ConsensusThe Ordinal Annotation Game: How Construct Abstraction Shapes Crowdsourced Consensus

Kosmas Pinitas

Inter-annotator disagreement in real-time affect annotation is widely treated as stochastic noise. We challenge this view by modelling ordinal annotation as an implicit game-theoretic coordination process against an internalised population prior under a post-hoc majority vote. We present the Ordinal Annotation Game, a conceptual scaffold in which the mapping from individual effort to collective consensus is governed by the...Inter-annotator disagreement in real-time affect annotation is widely treated as stochastic noise. We challenge this view by modelling ordinal annotation as an implicit game-theoretic coordination process against an internalised population prior under a post-hoc majority vote. We present the Ordinal Annotation Game, a conceptual scaffold in which the mapping from individual effort to collective consensus is governed by the...

CAIA in Practice: Field Evaluation of an AI-Assisted Support System for Text-Based Online CounsellingCAIA in Practice: Field Evaluation of an AI-Assisted Support System for Text-Based Online Counselling

Philipp Steigerwald, Nico Bienlein, Jennifer Burghardt, Mara Stieler, Robert Lehmann, Jens Albrecht

Rising global demand for mental health support creates significant service delivery challenges, with asynchronous email counselling serving as a crucial low-threshold channel for accessing care. This paper presents CAIA, a co-designed AI-based tool suite that demonstrates responsible AI integration into counselling practice through seven LLM-driven functions enhanced by retrieval-augmented generation. A field evaluation...Rising global demand for mental health support creates significant service delivery challenges, with asynchronous email counselling serving as a crucial low-threshold channel for accessing care. This paper presents CAIA, a co-designed AI-based tool suite that demonstrates responsible AI integration into counselling practice through seven LLM-driven functions enhanced by retrieval-augmented generation. A field evaluation...

DeepSAGE: Stage-Aware Reinforcement Learning for Structured CBT Counseling DialogueDeepSAGE: Stage-Aware Reinforcement Learning for Structured CBT Counseling Dialogue

Qi Zhang, Heajun An, Prakriti Dumaru, Sang Won Lee, Lifu Huang, Pamela J. Wisniewski

Large Language Model (LLM)-based counseling agents can generate fluent and supportive responses, but they often lack the structured, goal-directed progression required to conduct a coherent therapeutic session. We present DeepSAGE (Strategic AI Guidance Engine), a hybrid LLM--Deep Reinforcement Learning (DRL) framework for stage-aware counseling dialogue grounded in the first session of Cognitive Behavioral Therapy (CBT)....Large Language Model (LLM)-based counseling agents can generate fluent and supportive responses, but they often lack the structured, goal-directed progression required to conduct a coherent therapeutic session. We present DeepSAGE (Strategic AI Guidance Engine), a hybrid LLM--Deep Reinforcement Learning (DRL) framework for stage-aware counseling dialogue grounded in the first session of Cognitive Behavioral Therapy (CBT)....

Dual-Scale State-Space Modeling with Speaker-Wise Dynamic CRF for Speech Emotion Recognition in ConversationDual-Scale State-Space Modeling with Speaker-Wise Dynamic CRF for Speech Emotion Recognition in Conversation

Guan-Hua Wen, Kuan-Yu Chen, Hou-Chiang Tseng

Conversational speech emotion recognition must reconcile acoustic evidence across temporal scales with two interaction processes: cross-speaker contextual influence and within-speaker emotion evolution. We propose DSSM-CRF, an audio-only architecture that explicitly separates these processes. Bidirectional state-space models encode fused self-supervised speech representations at frame and dialogue scales, so each utterance...Conversational speech emotion recognition must reconcile acoustic evidence across temporal scales with two interaction processes: cross-speaker contextual influence and within-speaker emotion evolution. We propose DSSM-CRF, an audio-only architecture that explicitly separates these processes. Bidirectional state-space models encode fused self-supervised speech representations at frame and dialogue scales, so each utterance...

MASH-Bench: Diagnosing Cross-Source Failure in Mass-Shooting Risk ClassificationMASH-Bench: Diagnosing Cross-Source Failure in Mass-Shooting Risk Classification

Neha Sharma, Ritesh Sharma

Public mass-shooting databases differ substantially in coverage, feature availability, and reporting practices, creating challenges for machine-learning models that must generalize across data sources. We introduce MASH-Bench, a harmonized benchmark of 6,968 incidents from four U.S. databases: Kaggle, Mother Jones, Stanford MSA, and the Gun Violence Archive (GVA). We evaluate cross-source risk classification using...Public mass-shooting databases differ substantially in coverage, feature availability, and reporting practices, creating challenges for machine-learning models that must generalize across data sources. We introduce MASH-Bench, a harmonized benchmark of 6,968 incidents from four U.S. databases: Kaggle, Mother Jones, Stanford MSA, and the Gun Violence Archive (GVA). We evaluate cross-source risk classification using...

HiMA-MDD: A Hierarchical Multi-Agent Harness for Interpretable Multimodal Depression Detection in Clinical InterviewsHiMA-MDD: A Hierarchical Multi-Agent Harness for Interpretable Multimodal Depression Detection in Clinical Interviews

Ao Chen, Xiaojiang Peng

Depression assessment from multimodal clinical interviews requires integrating dispersed evidence from multiple symptoms into a coherent PHQ-8 profile. This process is hierarchical: relevant evidence is often sparse and context-dependent within local question-answer exchanges, multiple exchanges jointly support symptom-level judgments, and the final assessment depends on the coherence of the complete symptom profile. Existing...Depression assessment from multimodal clinical interviews requires integrating dispersed evidence from multiple symptoms into a coherent PHQ-8 profile. This process is hierarchical: relevant evidence is often sparse and context-dependent within local question-answer exchanges, multiple exchanges jointly support symptom-level judgments, and the final assessment depends on the coherence of the complete symptom profile. Existing...

ESCRAG-R1: Retrieval-Augmented Reinforcement Learning for Emotional Support ConversationESCRAG-R1: Retrieval-Augmented Reinforcement Learning for Emotional Support Conversation

Weichu Liu, Yuxuan Hu, Yirong Sun, Ningning Mao, Ziyun Zhang, Jian Chen

Emotional Support Conversation (ESC) systems aim to provide holistic support by balancing professional therapeutic competence with natural empathy. However, existing methods struggle to simultaneously achieve structured, stage-aware reasoning and seamless empathy-expertise alignment, often resulting in an artificial splicing of clinical strategies and generic reassurance. To overcome these limitations, we propose ESCRAG-R1, a...Emotional Support Conversation (ESC) systems aim to provide holistic support by balancing professional therapeutic competence with natural empathy. However, existing methods struggle to simultaneously achieve structured, stage-aware reasoning and seamless empathy-expertise alignment, often resulting in an artificial splicing of clinical strategies and generic reassurance. To overcome these limitations, we propose ESCRAG-R1, a...

ToSCA: Leveraging Hierarchical Reinforcement Learning on Temporal and Strategic Abstractions of Conversational AgentsToSCA: Leveraging Hierarchical Reinforcement Learning on Temporal and Strategic Abstractions of Conversational Agents

Xiaoyu Wang, Qingqing Gu, Yue Zhao, Teng Chen, Yuqi Cao, Xiaokai Chen

Humans naturally exhibit multiple forms of abstraction in reasoning and interaction, including temporal abstraction across decision timescales and strategic abstraction over communicative intents. Inspired by these complementary abstractions, we propose a two-level hierarchical reinforcement learning (HRL) framework for conversational agents that bridges the gap between existing token-level and utterance-level RL methods....Humans naturally exhibit multiple forms of abstraction in reasoning and interaction, including temporal abstraction across decision timescales and strategic abstraction over communicative intents. Inspired by these complementary abstractions, we propose a two-level hierarchical reinforcement learning (HRL) framework for conversational agents that bridges the gap between existing token-level and utterance-level RL methods....

Measuring benchmark optimization in speech recognitionMeasuring benchmark optimization in speech recognition

Hugging Face Blog

Selected from Hugging Face Blog's public feed for its relevance to Open-Source AI and applied AI systems.来自 Hugging Face Blog 公开订阅源，内容与 Open-Source AI 及应用型 AI 系统建设相关。

Stop Hunting, Start Solving: Accelerating Root Cause Analysis with Agentic AIStop Hunting, Start Solving: Accelerating Root Cause Analysis with Agentic AI

IEEE Spectrum AI

About this Webinar Turn Yield Excursions into Faster, More Confident Root Cause Analysis When a yield issue emerges, the answer rarely lives in a single system. Critical clues are spread across metrology data, tool traces, chemical analysis, and facilities systems, while growing data volumes make traditional dashboards slow, fragmented, and difficult to act...About this Webinar Turn Yield Excursions into Faster, More Confident Root Cause Analysis When a yield issue emerges, the answer rarely lives in a single system. Critical clues are spread across metrology data, tool traces, chemical analysis, and facilities systems, while growing data volumes make traditional dashboards slow, fragmented, and difficult to act...

Video Friday: Do We Need Superhuman Humanoid Robots?Video Friday: Do We Need Superhuman Humanoid Robots?

IEEE Spectrum Robotics

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion. Humanoids Summit Seoul : 22–23 September 2026, SEOUL IROS 2026 : 27 September–1 October 2026, PITTSBURGH CoRL 2026...Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion. Humanoids Summit Seoul : 22–23 September 2026, SEOUL IROS 2026 : 27 September–1 October 2026, PITTSBURGH CoRL 2026...

Why did My Robot Just Change Personality? Prompting Guidelines for a Grounded Robot Persona in LLM-Based HRIWhy did My Robot Just Change Personality? Prompting Guidelines for a Grounded Robot Persona in LLM-Based HRI

Ashita Ashok, Franziska Babel, Patrick Holthaus, Rucha Khot, Karla Bransky, Fethiye Irmak Dogan

Large language models (LLMs) are increasingly used for verbal interaction in social robots, yet prompt design in human-robot interaction (HRI) remains underspecified. As a result, robots may present hallucinated capabilities, unclear behavioural boundaries, and misleading personas. This paper develops a framework for prompt design in LLM-based robots and introduces a structured prompt template comprising eight functional...Large language models (LLMs) are increasingly used for verbal interaction in social robots, yet prompt design in human-robot interaction (HRI) remains underspecified. As a result, robots may present hallucinated capabilities, unclear behavioural boundaries, and misleading personas. This paper develops a framework for prompt design in LLM-based robots and introduces a structured prompt template comprising eight functional...

Broadening access to Skala creates a faster path to predictive DFTBroadening access to Skala creates a faster path to predictive DFT

Microsoft Research

Skala 1.1, the updated deep-learning exchange-correlation functional from Microsoft Research, provides greater accuracy, expanded accessibility across the computational chemistry ecosystem, and a living benchmark to track computational performance. The post Broadening access to Skala creates a faster path to predictive DFT appeared first on Microsoft...Skala 1.1, the updated deep-learning exchange-correlation functional from Microsoft Research, provides greater accuracy, expanded accessibility across the computational chemistry ecosystem, and a living benchmark to track computational performance. The post Broadening access to Skala creates a faster path to predictive DFT appeared first on Microsoft...

Inter-X++: A Comprehensive Benchmark for Multimodal Human-Human Interaction AnalysisInter-X++: A Comprehensive Benchmark for Multimodal Human-Human Interaction Analysis

Liang Xu, Chengqun Yang, Zili Lin, Xintao Lv, Yichao Yan, Xin Jin

The capability to perceive and synthesize human-human interactions is fundamental to developing intelligent digital human systems. However, existing datasets and modeling approaches are fundamentally constrained by low-fidelity kinematics, the omission of dexterous hand gestures and a severe lack of rich multimodal annotations. Furthermore, fragmented interaction representations and inconsistent evaluation protocols also...The capability to perceive and synthesize human-human interactions is fundamental to developing intelligent digital human systems. However, existing datasets and modeling approaches are fundamentally constrained by low-fidelity kinematics, the omission of dexterous hand gestures and a severe lack of rich multimodal annotations. Furthermore, fragmented interaction representations and inconsistent evaluation protocols also...

Humanoid Musical Robots as Experimental Interfaces for Music-Evoked EmotionHumanoid Musical Robots as Experimental Interfaces for Music-Evoked Emotion

Vincent K. M. Cheung, Jia-Yeu Lin

Advances in technology have led to increasingly sophisticated musical humanoid robots. However, their use has largely been limited to performance and related research in human-robot interaction. In this position paper, we propose a novel perspective: musical humanoid robots as experimental interfaces for investigating music-evoked emotions. We argue that current research is constrained by paradigms relying on pre-recorded...Advances in technology have led to increasingly sophisticated musical humanoid robots. However, their use has largely been limited to performance and related research in human-robot interaction. In this position paper, we propose a novel perspective: musical humanoid robots as experimental interfaces for investigating music-evoked emotions. We argue that current research is constrained by paradigms relying on pre-recorded...

Pedagogical AI in Mental Health: A Tri-Stream Fine-Tuned LLM Framework for Automated Clinical Supervision and Risk TriagePedagogical AI in Mental Health: A Tri-Stream Fine-Tuned LLM Framework for Automated Clinical Supervision and Risk Triage

Shreeya Sharma, Ravish Gupta, Saket Kumar, Abhishek Aggarwal

Modern mental healthcare faces a critical shortage of senior supervisory oversight, leading to a "supervision gap" where novice therapists manage high-stakes risks with delayed professional feedback. This paper proposes a new framework utilizing a fine-tuned Mistral-7B-instruct model as an automated "Supervisor-in-the-Loop" system. By leveraging 106 sessions from the DAIC-WOZ dataset, the model performs a tri-stream analysis:...Modern mental healthcare faces a critical shortage of senior supervisory oversight, leading to a "supervision gap" where novice therapists manage high-stakes risks with delayed professional feedback. This paper proposes a new framework utilizing a fine-tuned Mistral-7B-instruct model as an automated "Supervisor-in-the-Loop" system. By leveraging 106 sessions from the DAIC-WOZ dataset, the model performs a tri-stream analysis:...

How Talkspace Is Using AI to Enhance Mental Health Care - U.S. Chamber of CommerceHow Talkspace Is Using AI to Enhance Mental Health Care - U.S. Chamber of Commerce

Google News

How Talkspace Is Using AI to Enhance Mental Health Care U.S. Chamber of CommerceHow Talkspace Is Using AI to Enhance Mental Health Care U.S. Chamber of Commerce

TTSD-FAR: Test-Time Self-Distillation with Fisher-Anchored Restoration for Missing-Modality Emotion Recognition in LVLMsTTSD-FAR: Test-Time Self-Distillation with Fisher-Anchored Restoration for Missing-Modality Emotion Recognition in LVLMs

Muhammad Haseeb Aslam, Alessandro Koerich, Marco Pedersoli, Ali Etemad, Eric Granger

Large video-language models (LVLMs) have shown remarkable performance on multimodal tasks like multimodal emotion recognition (ER) in the wild. ER is inherently multimodal, requiring a joint understanding of facial expressions, vocalizations, language, biosignals, and gestures. However, real-world deployment remains challenging: modalities may be missing or noisy at test time. Partial observations can be viewed as a...Large video-language models (LVLMs) have shown remarkable performance on multimodal tasks like multimodal emotion recognition (ER) in the wild. ER is inherently multimodal, requiring a joint understanding of facial expressions, vocalizations, language, biosignals, and gestures. However, real-world deployment remains challenging: modalities may be missing or noisy at test time. Partial observations can be viewed as a...

Seeing Machines launches Physical AI Platform to power the next generation of humanoid robots - PR NewswireSeeing Machines launches Physical AI Platform to power the next generation of humanoid robots - PR Newswire

Google News Human Factors

Seeing Machines launches Physical AI Platform to power the next generation of humanoid robots PR NewswireSeeing Machines launches Physical AI Platform to power the next generation of humanoid robots PR Newswire

Closing the Affective Loop: Multimodal Speaker-Listener Emotion-Dynamics-Aware Empathetic Social RobotsClosing the Affective Loop: Multimodal Speaker-Listener Emotion-Dynamics-Aware Empathetic Social Robots

Zi Haur Pang, Casey Kennington, Tatsuya Kawahara

Empathetic social robots should respond not only to what users say, but also to how their emotions dynamically evolve during interaction. However, existing empathetic dialogue systems are often text-centered and primarily model empathy as a one-way mapping from the user's emotion to the system response, limiting their ability to capture embodied speaker--listener affective exchange. We present AffectLoop, a multimodal...Empathetic social robots should respond not only to what users say, but also to how their emotions dynamically evolve during interaction. However, existing empathetic dialogue systems are often text-centered and primarily model empathy as a one-way mapping from the user's emotion to the system response, limiting their ability to capture embodied speaker--listener affective exchange. We present AffectLoop, a multimodal...

Emotion Across Speech and Faces: Shared Affective Mechanisms in Multimodal Foundation ModelsEmotion Across Speech and Faces: Shared Affective Mechanisms in Multimodal Foundation Models

Xiutian Zhao, Luqi Sun, Björn Schuller, Berrak Sisman

Modern multimodal foundation models (MFMs) have made rapid progress on tasks requiring integrated perception across speech, vision, and language, including emotion recognition. However, it remains unclear whether they recognize speech and facial emotion through shared affective functional units or modality-specific pathways. We explore emotion-sensitive neurons (ESNs), sparse decoder neurons selectively associated with...Modern multimodal foundation models (MFMs) have made rapid progress on tasks requiring integrated perception across speech, vision, and language, including emotion recognition. However, it remains unclear whether they recognize speech and facial emotion through shared affective functional units or modality-specific pathways. We explore emotion-sensitive neurons (ESNs), sparse decoder neurons selectively associated with...

Language Models Reproduce Human Reductionist Bias and Decision Inconsistency in Neurodevelopmental Disorders AssessmentLanguage Models Reproduce Human Reductionist Bias and Decision Inconsistency in Neurodevelopmental Disorders Assessment

Maciej Wodziński, Joanna Wodzińska, Kacper Dudzic, Marcin Moskalewicz

Large language models (LLMs) are increasingly supporting complex mental-health decisions, which depend not only on factual evidence but also value-laden interpretations. We introduce a mixed-methods human-LLM auditing framework examining decision consistency, susceptibility to cognitive heuristics, declarative intellectual humility, and the concepts operationalized in support-allocation judgments of neurodevelopmental...Large language models (LLMs) are increasingly supporting complex mental-health decisions, which depend not only on factual evidence but also value-laden interpretations. We introduce a mixed-methods human-LLM auditing framework examining decision consistency, susceptibility to cognitive heuristics, declarative intellectual humility, and the concepts operationalized in support-allocation judgments of neurodevelopmental...

Population Health-Based Machine Learning Reveals Associations Between Psychosocial Factors and Chronic Kidney DiseasePopulation Health-Based Machine Learning Reveals Associations Between Psychosocial Factors and Chronic Kidney Disease

Md. Atik Shams, David Eisenberg, Sumaiya Fatema, Asma Sultana, D. M Hasibul Islam, Junnatul Mawa

Chronic kidney disease (CKD) progresses silently and severely undermines quality of life, making early detection critical for improving patient outcomes. We present a two-part study that combines large-scale telehealth data with advanced machine learning to both classify self-reported CKD status and identify key drivers of disease. Using selected features from the Behavioral Risk Factor Surveillance System (BRFSS 2021:...Chronic kidney disease (CKD) progresses silently and severely undermines quality of life, making early detection critical for improving patient outcomes. We present a two-part study that combines large-scale telehealth data with advanced machine learning to both classify self-reported CKD status and identify key drivers of disease. Using selected features from the Behavioral Risk Factor Surveillance System (BRFSS 2021:...

Pluralistic Human-Robot Interaction: Designing for Robot Interaction with Diverse CommunitiesPluralistic Human-Robot Interaction: Designing for Robot Interaction with Diverse Communities

Raj Korpan

Social robots are being developed for homes, schools, and other environments where they will interact with diverse users. While Human-Robot Interaction (HRI) research often emphasizes natural communication, engagement, personalization, and task success, these goals do not fully address the social complexity of real-world deployment. This paper proposes \emph{Pluralistic HRI}, a framework for designing social robots that treat...Social robots are being developed for homes, schools, and other environments where they will interact with diverse users. While Human-Robot Interaction (HRI) research often emphasizes natural communication, engagement, personalization, and task success, these goals do not fully address the social complexity of real-world deployment. This paper proposes \emph{Pluralistic HRI}, a framework for designing social robots that treat...

When AI Rewrites, Classifiers Relax: Uncertainty-Aware Sentiment Analysis on Sarcastic and AI-Paraphrased Social TextWhen AI Rewrites, Classifiers Relax: Uncertainty-Aware Sentiment Analysis on Sarcastic and AI-Paraphrased Social Text

Shresth Shroff

Sentiment classifiers are increasingly applied to social media content that is either sarcastic or AI-generated --- two distributional regimes where standard evaluations offer little guidance. We present a three-part empirical study of sentiment classifier behaviour under these conditions. First, we find that confidence scores on sarcastic text are significantly lower than on non-sarcastic text (Mann--Whitney $p = 2 \times...Sentiment classifiers are increasingly applied to social media content that is either sarcastic or AI-generated --- two distributional regimes where standard evaluations offer little guidance. We present a three-part empirical study of sentiment classifier behaviour under these conditions. First, we find that confidence scores on sarcastic text are significantly lower than on non-sarcastic text (Mann--Whitney $p = 2 \times...

Evaluating Human and LLM-Generated Thematic Analysis in HRI for Vulnerable Populations: A Comparative and Ethical AnalysisEvaluating Human and LLM-Generated Thematic Analysis in HRI for Vulnerable Populations: A Comparative and Ethical Analysis

Alva Markelius, Fethiye Irmak Dogan, Julie Bailey, Hatice Gunes

Thematic analysis (TA) has long been regarded as an inherently human, reflexive, and interpretive process. However, the extent to which LLM-generated TA is appropriate for Human-Robot Interaction (HRI) research involving vulnerable populations remains largely unexamined and raises critical questions about validity and ethics, particularly in sensitive research contexts. This paper presents a comparative study of human- and...Thematic analysis (TA) has long been regarded as an inherently human, reflexive, and interpretive process. However, the extent to which LLM-generated TA is appropriate for Human-Robot Interaction (HRI) research involving vulnerable populations remains largely unexamined and raises critical questions about validity and ethics, particularly in sensitive research contexts. This paper presents a comparative study of human- and...

PatientAct: Theory-Grounded Mental Health Client SimulationPatientAct: Theory-Grounded Mental Health Client Simulation

Sahand Sabour, TszYam NG, Yaqian Chen, Guanqun Bi, Jialu Zhao, Minlie Huang

LLM-based simulated clients are increasingly used to train novice counselors, evaluate LLM therapists, and generate synthetic data. However, current simulators produce overly cooperative clients that disclose too readily, accept therapeutic reframes without resistance, and resolve core issues within a single session. We trace these issues to profiles that lack causal depth and behavioral mechanisms that treat all content as...LLM-based simulated clients are increasingly used to train novice counselors, evaluate LLM therapists, and generate synthetic data. However, current simulators produce overly cooperative clients that disclose too readily, accept therapeutic reframes without resistance, and resolve core issues within a single session. We trace these issues to profiles that lack causal depth and behavioral mechanisms that treat all content as...

MindTopo reveals VLMs’ spatial reasoning abilitiesMindTopo reveals VLMs’ spatial reasoning abilities

Microsoft Research

A path, a fence, a knot. MindTopo sets a new benchmark for testing how AI understands topological relationships and highlights new opportunities to strengthen spatial reasoning and planning. The post MindTopo reveals VLMs’ spatial reasoning abilities appeared first on Microsoft Research .A path, a fence, a knot. MindTopo sets a new benchmark for testing how AI understands topological relationships and highlights new opportunities to strengthen spatial reasoning and planning. The post MindTopo reveals VLMs’ spatial reasoning abilities appeared first on Microsoft Research .

Quantifying the Relationship Between Clinical Safety and Environmental Impact in Therapeutic LLMsQuantifying the Relationship Between Clinical Safety and Environmental Impact in Therapeutic LLMs

Alireza A. Safaei, Laura M. Vowels, Matthew J. Vowels, Apoorv Jha, Shekoufeh Rahimi

The deployment of large language models (LLMs) in mental health contexts raises questions about the relationship between clinical safety and environmental cost. In this paper, we examine this relationship by combining K-Bench clinical safety scores with EcoLogits life-cycle assessment estimates across 47 supported model configurations. We evaluate model performance and environmental impact across four dimensions: energy use,...The deployment of large language models (LLMs) in mental health contexts raises questions about the relationship between clinical safety and environmental cost. In this paper, we examine this relationship by combining K-Bench clinical safety scores with EcoLogits life-cycle assessment estimates across 47 supported model configurations. We evaluate model performance and environmental impact across four dimensions: energy use,...

Avatar-Forever: Decoupled Parallel Training for High-Quality Real-Time Infinite AvatarsAvatar-Forever: Decoupled Parallel Training for High-Quality Real-Time Infinite Avatars

Ruibin Li, Tao Yang, Zhiyuan Ma, Fangzhou Ai, Shilei Wen, Lei Zhang

Existing streaming video systems often rely on sequential, distillation-centered training pipelines to enable few-step long-video generation. However, this paradigm suffers from two limitations. First, failures or distribution shifts introduced in earlier stages affect later optimization, complicating the training process to converge. Second, the distillation-centric objective favours short-term generation but is prone to...Existing streaming video systems often rely on sequential, distillation-centered training pipelines to enable few-step long-video generation. However, this paradigm suffers from two limitations. First, failures or distribution shifts introduced in earlier stages affect later optimization, complicating the training process to converge. Second, the distillation-centric objective favours short-term generation but is prone to...

Jake Collective Partners with The Jed Foundation (JED) to Safeguard the Wellbeing of Students Who Perceive Themselves and their World Differently - The Jed FoundationJake Collective Partners with The Jed Foundation (JED) to Safeguard the Wellbeing of Students Who Perceive Themselves and their World Differently - The Jed Foundation

Google News School Mental Health

Jake Collective Partners with The Jed Foundation (JED) to Safeguard the Wellbeing of Students Who Perceive Themselves and their World Differently The Jed FoundationJake Collective Partners with The Jed Foundation (JED) to Safeguard the Wellbeing of Students Who Perceive Themselves and their World Differently The Jed Foundation

Dual-Loop Self-Evolution via Verifiable Emotion Feedback for Multi-Turn Empathetic DialogueDual-Loop Self-Evolution via Verifiable Emotion Feedback for Multi-Turn Empathetic Dialogue

Yi Wei, Shuo Jiang, Huaixia Dou, Jie Zhu, Junhui Li, Lifan Guo

Large language models have demonstrated conversational capabilities, yet empathetic competence remains challenging. Empathetic support is inherently multi-turn and path-dependent: users disclose concerns gradually, emotions evolve over time, and early responses shape trust and receptivity. Reinforcement learning with verifiable emotion rewards provides scalable supervision for long-horizon interactions. However, existing...Large language models have demonstrated conversational capabilities, yet empathetic competence remains challenging. Empathetic support is inherently multi-turn and path-dependent: users disclose concerns gradually, emotions evolve over time, and early responses shape trust and receptivity. Reinforcement learning with verifiable emotion rewards provides scalable supervision for long-horizon interactions. However, existing...

Rationale-Guided Learning for Multimodal Emotion RecognitionRationale-Guided Learning for Multimodal Emotion Recognition

Sujung Oh, Jung Uk Kim, Sangmin Lee

Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues. However, most existing approaches fundamentally treat this as a direct input-output (multimodal cues-emotion labels) mapping problem, overlooking the causal reasoning that humans use when interpreting emotions. We propose rationale-guided learning (RGL), a novel framework that transforms MERC...Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues. However, most existing approaches fundamentally treat this as a direct input-output (multimodal cues-emotion labels) mapping problem, overlooking the causal reasoning that humans use when interpreting emotions. We propose rationale-guided learning (RGL), a novel framework that transforms MERC...

"I Don't Want My Mental Health App To Give Me Mental Health Barriers": Unpacking The Need For Digital Mental Health Tracking Services With And For The Blind Community"I Don't Want My Mental Health App To Give Me Mental Health Barriers": Unpacking The Need For Digital Mental Health Tracking Services With And For The Blind Community

Omar Khan, JooYoung Seo

Digital mental health (DMH) tracking services promise continuous, personalized support for well-being, but their design often assumes sighted users. For the blind community, this assumption produces a distinct pattern of exclusion: services whose accessibility cannot be evaluated without first paying for them, community features that exclude the users they purport to support, and interfaces that leave users digitally literate...Digital mental health (DMH) tracking services promise continuous, personalized support for well-being, but their design often assumes sighted users. For the blind community, this assumption produces a distinct pattern of exclusion: services whose accessibility cannot be evaluated without first paying for them, community features that exclude the users they purport to support, and interfaces that leave users digitally literate...

Locomotion Variability and User Experience in Smart Wheelchair Human-Robot InteractionLocomotion Variability and User Experience in Smart Wheelchair Human-Robot Interaction

Sean Kille, Adina M. Panchea, Balint Varga, Sören Hohmann

Human movement is inherently variable, with variability structured according to task relevance: movements are typically more consistent at task-critical points and more flexible elsewhere. In human-robot interaction (HRI), however, model-based assistance strategies commonly assume deterministic human behavior and suppress such variability, potentially altering how interactions are experienced and lowering sense of agency....Human movement is inherently variable, with variability structured according to task relevance: movements are typically more consistent at task-critical points and more flexible elsewhere. In human-robot interaction (HRI), however, model-based assistance strategies commonly assume deterministic human behavior and suppress such variability, potentially altering how interactions are experienced and lowering sense of agency....

Deep Multimodal Wearable Sensor Fusion for Detection of Body-Focused Repetitive BehaviorsDeep Multimodal Wearable Sensor Fusion for Detection of Body-Focused Repetitive Behaviors

Samaneh Rezaeimanesh, Mohsen Behradfar, Mohammad Fili, Guiping Hu

Body-focused repetitive behaviors, such as hair pulling and skin picking, are compulsive motor actions commonly associated with obsessive-compulsive and anxiety disorders. Their early, objective detection remains difficult because the movements are subtle and overlap with ordinary, non-pathological gestures. We developed and evaluated a multimodal deep learning framework to detect and classify these behaviors from wrist-worn...Body-focused repetitive behaviors, such as hair pulling and skin picking, are compulsive motor actions commonly associated with obsessive-compulsive and anxiety disorders. Their early, objective detection remains difficult because the movements are subtle and overlap with ordinary, non-pathological gestures. We developed and evaluated a multimodal deep learning framework to detect and classify these behaviors from wrist-worn...

High Fidelity Capture, Reconstruction, and Transfer of Human Demonstrations for Robot-Assisted BathingHigh Fidelity Capture, Reconstruction, and Transfer of Human Demonstrations for Robot-Assisted Bathing

Arjun S. Lakshmipathy, Jonathan P. King, Ethan Zuo, Rohit Satishkumar, Hongyi Chen, Jeffrey Ichnowski

Despite the demand for robots in high-value clinical tasks like bathing, contemporary systems still lack the safety and reliability required for complex, sustained physical interaction with humans. A key challenge hindering the development of such systems is that collecting, understanding, and effectively transferring highly dynamic, contact-rich human bathing demonstrations is difficult, even with modern motion and tactile...Despite the demand for robots in high-value clinical tasks like bathing, contemporary systems still lack the safety and reliability required for complex, sustained physical interaction with humans. A key challenge hindering the development of such systems is that collecting, understanding, and effectively transferring highly dynamic, contact-rich human bathing demonstrations is difficult, even with modern motion and tactile...

CONFER: Conflict-Aware Evidence Negotiation for Regime-Calibrated Weak Supervision in Multimodal Emotion RecognitionCONFER: Conflict-Aware Evidence Negotiation for Regime-Calibrated Weak Supervision in Multimodal Emotion Recognition

Bojing Hou, Ruohao Li, Yitong Zhu, Luwen Yu, Yuyang Wang

Multimodal emotion recognition often treats self-reported labels as reliable supervision while overlooking self-report unreliability and cross-modal conflict. We propose \textbf{CONFER}, a graph-based conflict-aware evidence negotiation framework for weakly supervised multimodal emotion recognition. CONFER represents each modality expert as a node with a predictive belief, boundary-based uncertainty, and runtime reliability...Multimodal emotion recognition often treats self-reported labels as reliable supervision while overlooking self-report unreliability and cross-modal conflict. We propose \textbf{CONFER}, a graph-based conflict-aware evidence negotiation framework for weakly supervised multimodal emotion recognition. CONFER represents each modality expert as a node with a predictive belief, boundary-based uncertainty, and runtime reliability...

Robot Learning from Human Demonstrations: Handwritten Alphabet Trajectories and Human-Likeness EvaluationRobot Learning from Human Demonstrations: Handwritten Alphabet Trajectories and Human-Likeness Evaluation

Alperen Kenan, Paul Bremner, Manuel Giuliani

Learning from demonstration (LfD) provides a developmental framework through which robots can develop motor skills by observing and imitating human dynamics, reducing reliance on explicit programming to teach a skill to a robot. The resulting human-like robot motion is recognised as a key factor in building trust and enabling natural collaboration in human-robot interaction. This paper presents a framework for learning...Learning from demonstration (LfD) provides a developmental framework through which robots can develop motor skills by observing and imitating human dynamics, reducing reliance on explicit programming to teach a skill to a robot. The resulting human-like robot motion is recognised as a key factor in building trust and enabling natural collaboration in human-robot interaction. This paper presents a framework for learning...

Age-Related Positivity Bias in Emotion Recognition: Marker of Neurodegeneration or Shift in Decision Criterion? - Journal of NeuroscienceAge-Related Positivity Bias in Emotion Recognition: Marker of Neurodegeneration or Shift in Decision Criterion? - Journal of Neuroscience

Google News

Age-Related Positivity Bias in Emotion Recognition: Marker of Neurodegeneration or Shift in Decision Criterion? Journal of NeuroscienceAge-Related Positivity Bias in Emotion Recognition: Marker of Neurodegeneration or Shift in Decision Criterion? Journal of Neuroscience

EmpaAva: An Open-source Agentic 3D-Avatar Empathetic Live ChatbotEmpaAva: An Open-source Agentic 3D-Avatar Empathetic Live Chatbot

Jie Yang, Wenhao Xu, Shuhui Lin, Hao Fei

This paper presents EmpaAva, to our knowledge the first open-source, agentic 3D-avatar empathetic chatbot, which carries empathetic response generation (ERG) from text-only exchanges into live, face-to-face interaction. Through a video-call-like interface, a user speaks to a 3D digital human that reads their affect from speech and optional vision, and replies with emotional speech, lip-synced facial motion, and photorealistic...This paper presents EmpaAva, to our knowledge the first open-source, agentic 3D-avatar empathetic chatbot, which carries empathetic response generation (ERG) from text-only exchanges into live, face-to-face interaction. Through a video-call-like interface, a user speaks to a 3D digital human that reads their affect from speech and optional vision, and replies with emotional speech, lip-synced facial motion, and photorealistic...

HyPASE: Hyperbolic Geometry for Parameter-Efficient Speech Emotion Fine-Tuning Framework for Large Audio-Language ModelsHyPASE: Hyperbolic Geometry for Parameter-Efficient Speech Emotion Fine-Tuning Framework for Large Audio-Language Models

Tian Jin, Ruikang Zhang, Zefeng Zhao, Ding Luo, Jin Zeng

Large Audio-Language Models (LALMs) excel at general speech understanding; however, adapting them to fine-grained tasks like Speech Emotion Recognition (SER) remains a significant bottleneck. Current Parameter-Efficient Fine-Tuning (PEFT) methods typically operate in flat Euclidean space, and this geometry fails to capture the multi-granularity nature of emotion cues, which range from low-level prosody to high-level...Large Audio-Language Models (LALMs) excel at general speech understanding; however, adapting them to fine-grained tasks like Speech Emotion Recognition (SER) remains a significant bottleneck. Current Parameter-Efficient Fine-Tuning (PEFT) methods typically operate in flat Euclidean space, and this geometry fails to capture the multi-granularity nature of emotion cues, which range from low-level prosody to high-level...

The benefits of medical AI assistance vary based on user expertiseThe benefits of medical AI assistance vary based on user expertise

MIT News AI

Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors.Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors.

AI-Based Sound Effect Generation: A Narrative Review of Generative Models Across Input ModalitiesAI-Based Sound Effect Generation: A Narrative Review of Generative Models Across Input Modalities

Sandy Abdo, Bill Kapralos, Priyamvada Tripathi, KC Collins, Adam Dubrowski

Sound effects play a crucial role in conveying actions, events, and environmental cues across digital applications, often requiring a high degree of variation and contextual adaptability. Artificial intelligence (AI)-driven audio generative models are rapidly growing in popularity and have the potential to transform the way sound is synthesized and used across various applications. In response to this growing momentum, this...Sound effects play a crucial role in conveying actions, events, and environmental cues across digital applications, often requiring a high degree of variation and contextual adaptability. Artificial intelligence (AI)-driven audio generative models are rapidly growing in popularity and have the potential to transform the way sound is synthesized and used across various applications. In response to this growing momentum, this...

MAC-Lab Official Website Released实验室主页发布：sxhfut.github.io

The new bilingual MAC-Lab website presents research directions, industry solutions, academic impact, lab news, frontier sharing, media coverage, student training, and collaboration channels.实验室主页 sxhfut.github.io 正式发布，集中呈现研究方向、行业方案、学术影响、实验室新闻、前沿分享、媒体关注、团队培养与合作入口。

CausalSymptom Accepted by IEEE Transactions on Affective Computing与中国科学技术大学联合研究成果被 TAC 录用

CausalSymptom studies causal disentangled representation learning for depression severity estimation from transcribed clinical interviews, connecting clinical dialogue, interpretability, and psychological computing.CausalSymptom 面向临床访谈文本中的抑郁严重程度估计，研究因果解耦表示学习，连接临床对话、可解释建模与心理计算。

Modeling Latent Background Information in Multiturn Dialogues Using Diffusion Model Published in IEEE Transactions on Artificial Intelligence扩散增强多轮对话潜在背景建模研究发表于 IEEE Transactions on Artificial Intelligence

Zishun Cui, Xiao Sun, and Meng Wang published this Early Access article in IEEE Transactions on Artificial Intelligence on 17 March 2026, modeling latent persona, scene, and atmosphere information for more reasonable multi-turn dialogue generation.孙晓教授团队合作论文于 2026 年 3 月 17 日以 Early Access 形式发表于 IEEE Transactions on Artificial Intelligence，围绕人物、场景、氛围等潜在背景信息建模，提升多轮对话生成合理性。

ERMoV: Modeling Affective Mental States via Emotion Regulation for Dialogue Emotion Recognition Published in IEEE Transactions on Affective ComputingERMoV 情绪调节驱动对话情绪识别研究发表于 IEEE Transactions on Affective Computing

Xiao Sun, Xuxiong Liu, and Yueqi Jiang published ERMoV in IEEE Transactions on Affective Computing, modeling affective mental states through emotion-regulation mechanisms for dialogue emotion recognition.孙晓、刘旭雄、姜月琪合作论文 ERMoV 发表在 IEEE Transactions on Affective Computing，通过情绪调节机制建模情感心理状态，使对话情绪识别从浅层标签识别走向具有心理机制支撑的状态建模。

Decoding Emotion, Empowering Mind-Body Health解码情感，智惠身心

External coverage frames Professor Xiao Sun's team as a long-term builder of embodied emotional interaction models, linking affective robots, Chinese emotional resources, Ubiquitous Psychological Computing, EmoAda, PsyCoLLM, and embodied emotional intelligence.外部报道以“具身情感交互大模型突破之路”为主线，梳理团队从情感机器人、中文情感资源，到普适心理计算、EmoAda、PsyCoLLM 与具身情感智能的长期建设。

Causal-ESC Accepted by IEEE/ACM Transactions on Audio, Speech, and Language ProcessingCausal-ESC 情感支持对话因果检测研究发表于 IEEE/ACM TASLP

Causal-ESC captures cause-and-effect dynamics in emotional support conversation, connecting affective NLP, empathetic dialogue, and psychologically meaningful interaction modeling.Causal-ESC 面向情感支持对话中的因果动态检测，连接情感 NLP、共情对话与具有心理意义的交互建模。

Adaptive Sample Mining for Facial Expression Recognition Published in IEEE Transactions on Fuzzy Systems面部表情识别自适应样本挖掘研究发表于 IEEE Transactions on Fuzzy Systems

The IEEE Transactions on Fuzzy Systems paper studies fuzzy membership-driven adaptive sample mining for facial expression recognition, adding another high-level venue to the lab's multimodal affective perception line.IEEE Transactions on Fuzzy Systems 论文研究面向面部表情识别的模糊隶属度驱动自适应样本挖掘，补强团队多模态情感感知方向的高水平成果链条。

MultiAgentESC Accepted by EMNLP 2025MultiAgentESC 情感支持对话多智能体框架发表于 EMNLP 2025

MultiAgentESC proposes a large-language-model-based multi-agent collaboration framework for emotional support conversation, extending MAC-Lab's affective NLP route toward more structured emotional support agents.MultiAgentESC 提出面向情感支持对话的大语言模型多智能体协作框架，推动 MAC-Lab 情感 NLP 路线走向更结构化的情感支持智能体。

Multimodal Affective Computing and Intervention Technology Wins Silver Award普适场景多模态情感计算与干预技术获国际发明展银奖

MAC-Lab's multimodal affective computing and intervention technology for ubiquitous scenarios won a silver award at the 11th International Exhibition of Inventions and BRICS skills-development and technology-innovation competition.实验室“普适场景中的多模态情感计算与干预技术”在第十一届国际发明展览会·“一带一路”暨金砖国家技能发展与技术创新大赛中获得银奖。

UniEmoX Published in IEEE Transactions on Image ProcessingUniEmoX 发表在 IEEE Transactions on Image Processing

The student-guided work UniEmoX studies cross-modal semantic-guided large-scale pretraining for universal scene emotion perception, strengthening the lab's publication portfolio in affective visual intelligence.指导论文 UniEmoX 面向通用场景情绪感知与跨模态语义预训练，发表于 CCF A 类期刊 IEEE Transactions on Image Processing。

Lab Contributes to a Group Standard for Healthy Residential Environments实验室参编健康住区环境保障智能系统设计技术导则

The lab's human-factor affective computing technology entered standardization work for intelligent systems supporting healthy residential environments.实验室普适场景人因情感计算技术应用于健康住区环境身心表征与保障，参与编写相关团体标准。

Social-Psychological Dual-Dimensional Clustering Modeling Published at IPMLP 2025社会-心理双维聚类建模研究发表于 IPMLP 2025

The conference paper studies heterogeneity in smoking-cessation success through social-psychological dual-dimensional clustering and machine learning, extending psychological computing into health-behavior analysis.该会议论文通过社会-心理双维聚类与机器学习分析戒烟成功异质性，把心理计算能力延伸到健康行为分析场景。

Empathy Level Alignment via Reinforcement Learning Published in IEEE Transactions on Affective Computing共情水平对齐强化学习研究发表于 IEEE Transactions on Affective Computing

This IEEE Transactions on Affective Computing paper studies empathy-level alignment through reinforcement learning for empathetic response generation, supporting the lab's long-term work on emotional support agents.该 IEEE Transactions on Affective Computing 论文研究面向共情回复生成的共情水平强化学习对齐，支撑团队在情感支持智能体方向的长期积累。

PsycoLLM Published in IEEE Transactions on Computational Social SystemsPsycoLLM 心理理解与评估研究发表于 IEEE TCSS

PsycoLLM enhances large language models for psychological understanding and evaluation, linking affective NLP, psychological assessment, and the lab's broader psychological computing platform direction.PsycoLLM 面向大语言模型的心理理解与评估能力增强，连接情感 NLP、心理评估和实验室普适心理计算平台方向。

Temporal-Frequency State Space Duality Published at ICASSP 2025语音情绪识别时频状态空间对偶方法发表于 ICASSP 2025

The ICASSP 2025 paper introduces an efficient temporal-frequency state-space paradigm for speech emotion recognition, aligning with the lab's multimodal affective computing foundation.ICASSP 2025 论文提出面向语音情绪识别的高效时频状态空间范式，支撑实验室多模态情感计算的基础能力建设。

DEFormer Published at ICASSP 2025DEFormer 低光图像与暗视觉增强研究发表于 ICASSP 2025

DEFormer studies DCT-driven enhancement transformers for low-light image and dark vision, expanding the lab's multimodal AI and deployable perception toolbox.DEFormer 研究面向低光图像与暗视觉的 DCT 驱动增强 Transformer，扩展实验室多模态 AI 与可部署感知工具箱。