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In semi-decentralized federated learning, devices primarily rely on device-to-device communication but occasionally interact with a central server. Periodically, a sampled subset of devices uploads their local models to the server, which computes an aggregate model. The server can then either (i) share this aggregate model only with the sampled clients (sampled-to-sampled, S2S) or (ii) broadcast it to all clients (sampled-to-all, S2A). Despite their practical significance, a rigorous theoretical and empirical comparison of these two strategies remains absent.
We address this gap by analyzing S2S and S2A within a unified convergence framework that accounts for key system parameters: sampling rate, server aggregation frequency, and network connectivity. Our results---both analytical and experimental---reveal distinct regimes where one strategy outperforms the other, depending primarily on the degree of data heterogeneity across devices. These insights lead to concrete design guidelines for practical semi-decentralized FL deployments.

AAAI 2026

A Unified Convergence Analysis for Semi-Decentralized Learning: Sampled-to-Sampled vs. Sampled-to-All Communication

technical paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Conversational AI agents are envisioned to provide social
support or functional service to human users via natural
language interactions. The popularity of conversational AI
has grown unprecedentedly with the advent of ChatGPT, which
showcases exceptional proficiency in the capabilities of
context understanding and response generation with large
language models (LLMs). However, typical conversational
systems are built to follow instructions, which means that
the conversation is led by the user, and the system simply
follows the user’s instructions or intents. My research
endows the conversational AI with the capabilities of
creating or controlling the conversation to achieve the
conversational goals by taking initiative and anticipating
impacts on themselves or human users, namely Proactive
Conversational AI. I will also highlight the importance of
moving towards building human-centered proactive
conversational AI that emphasize human needs and
expectations, and that considers ethical and social
implications of these agents, rather than solely focusing
on technological capabilities.

Towards Human-centered Proactive Conversational AI

Understanding opinion evolution in complex social networks is crucial for modeling social influence and predicting collective behavior. Yet, most models overlook how community structures shape opinion updates, often assuming homogeneous influence. This abstraction neglects individuals’ stronger responsiveness to intra-community peers—an empirically observed driver of localized consensus and inter-group polarization. We propose GCAOFP, a co-evolutionary framework that jointly models opinion dynamics and community formation as an integrated process. In GCAOFP, agents strategically alternate between two coupled modules: (1) a Community Dynamics Module, where agents play a non-cooperative game to optimize community memberships based on opinion alignment and structural cohesion; and (2) an Opinion Adjustment Module, where agents revise opinions via a bounded-confidence mechanism modulated by community-induced influence weights. This dual-stage process captures the feedback loop between structure and opinion. We prove that GCAOFP converges to stable equilibria, ensuring intra-community consensus and inter-community diversity—dynamics that standard models fail to replicate. Experiments on real-world networks show that GCAOFP better reproduces localized opinion clusters, while offering strong scalability and interpretability, illuminating the strategic foundations of polarization.

Game Theory Based Community-Aware Opinion Dynamics

As artificial intelligence (AI) becomes increasingly prevalent in society, there is a critical need for accessible K-12 educational resources that introduce students to AI and robotics concepts through engaging, hands-on experiences. In this paper, we present a scalable workshop framework that uses narrative-driven problem solving to teach fundamental AI and autonomous systems concepts to students in grades 5-12. Developed through a collaboration between AI researchers and education specialists, Bot Blitz employs Sphero RVR+ robots within immersive storylines ranging from fairground rescue missions for younger students to urban traffic management scenarios for high schoolers. Preliminary observations from workshops with 56 students show high engagement levels and successful completion of programming challenges.

Bot Blitz: A Scalable Hands-On Workshop for Teaching AI and Robotics Concepts Through Narrative-Driven Problem Solving

Contrastive vision-language models like CLIP have achieved impressive results in image-text retrieval by aligning image and text representations in a shared embedding space. However, these models often treat text as flat sequences, limiting their ability to handle complex, compositional, and long-form descriptions. In particular, they fail to capture two essential properties of language: semantic hierarchy, which reflects the multi-level compositional structure of text, and semantic monotonicity, where richer descriptions should result in stronger alignment with visual content. To address these limitations, we propose HiMo-CLIP, a representation-level framework that enhances CLIP-style models without modifying the encoder architecture. HiMo-CLIP introduces two key components: a hierarchical decomposition (HiDe) module that extracts latent semantic components from long-form text via in-batch PCA, enabling flexible, batch-aware alignment across different semantic granularities, and a monotonicity-aware contrastive loss (MoLo) that jointly aligns global and component-level representations, encouraging the model to internalize semantic ordering and alignment strength as a function of textual completeness. These components work together to produce structured, cognitively aligned cross-modal representations. Experiments on multiple image-text retrieval benchmarks show that HiMo-CLIP consistently outperforms strong baselines, particularly under long or compositional descriptions.

HiMo-CLIP: Modeling Semantic Hierarchy and Monotonicity in Vision-Language Alignment

Large Language Models (LLMs) are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulting in harmful or inappropriate outputs. Such attacks, including jailbreaking and prompt injection, pose significant risks to the integrity and availability of LLMs in security-critical applications. This paper proposes the Adversarial Prompt Disentanglement (APD) framework, a novel defense mechanism that proactively identifies and neutralizes malicious components in input prompts before they are processed by the LLM. The APD framework integrates three key innovations: (1) a mutual information-based semantic decomposition method to isolate adversarial and benign prompt components, ensuring statistical independence; (2) a graph-based intent classification approach that leverages spectral analysis to detect malicious patterns in prompt semantics; and (3) a lightweight transformer-based classifier trained on real-world datasets of toxic and jailbreaking prompts, enabling efficient and accurate adversarial intent detection. Evaluated on diverse datasets containing adversarial prompts, APD demonstrates superior robustness, reducing harmful output generation by over 85% while maintaining negligible impact on model performance. The framework’s computational efficiency supports real-time deployment, making it a practical solution for securing LLMs.

Disentangling Adversarial Prompts: A Semantic-Graph Defense for Robust LLM Security

Large Language Models (LLMs) demonstrate significant advantages in leveraging structured world knowledge and multi-step reasoning capabilities. However, fundamental challenges arise when transforming LLMs into real-world recommendation systems due to semantic and behavioral misalignment. 
To bridge this gap, we propose Align$^3$GR, a novel framework that unifies token-level, behavior modeling-level, and preference-level alignment. Our approach introduces: Dual tokenization fusing user-item semantic and collaborative signals. Enhanced behavior modeling with bidirectional semantic alignment. Progressive DPO strategy combining self-play (SP-DPO) and real-world feedback (RF-DPO) for dynamic preference adaptation. Experiments show Align$^3$GR outperforms the SOTA baseline by +17.8\% in Recall@10 and +20.2\% in NDCG@10 on the public dataset, with significant gains in online A/B tests and full-scale deployment on an industrial large-scale recommendation platform.

Align³GR: Unified Multi-Level Alignment for LLM-based Generative Recommendation

Prompt-driven scene synthesis allows users to generate complete 3D environments from textual descriptions. Current text-to-scene methods often struggle with complex geometries and object transformations, and tend to show weak adherence to complex instructions. We address these limitations by introducing Reason-3D, a text-to-scene model powered by large reasoning models (LRMs). Reason-3D integrates object retrieval using captions covering physical, functional, and contextual attributes. Reason-3D then places the selected objects based on implicit and explicit layout constraints, and refines their positions with collision-aware spatial reasoning. Evaluated on instructions ranging from simple to complex indoor configurations, Reason-3D significantly outperforms previous methods in human-rated visual fidelity, adherence to constraints, and asset retrieval quality. Beyond its contribution to the field of text-to-scene generation, our work showcases the advanced spatial reasoning abilities of modern LRMs. Additionally, we release the codebase to further the research in object retrieval and placement with LRMs.

Text-to-Scene with Large Reasoning Models

Probabilistic Circuits (PCs) are a class of generative models that allow exact and tractable inference for diverse queries. While recent developments have enabled the learning of deep and expressive PCs, this increased capacity can often lead to overfitting, especially when data is limited. In this work, we analyze PC overfitting from a log-likelihood-landscape perspective and show that it is often caused by convergence to $\textit{sharp optima}$ that generalize poorly. Inspired by sharpness aware minimization in neural networks, we propose a Hessian-based regularizer for training PCs. As a key contribution, we show that the trace of the Hessian of the log-likelihood—a sharpness proxy that is typically intractable in deep neural networks- can be computed efficiently for PCs. Minimizing this Hessian trace induces a natural gradient-norm-based regularizer that yields simple closed-form parameter updates for EM, and integrates seamlessly with gradient based methods. Experiments on synthetic and real-world PC benchmarks demonstrate that our method consistently guides PCs toward flatter minima, improving generalization over standard training.

Tractable Sharpness-Aware Learning of Probabilistic Circuits

Identity-preserving models have led to notable progress in generating personalized content. Unfortunately, such models also exacerbate risks when misused, for instance, by generating threatening content targeting specific individuals. This paper introduces the Attribute Misbinding Attack, a novel method that poses a threat to identity-preserving models by inducing them to produce Not-Safe-For-Work (NSFW) content. The attack's core idea involves crafting benign-looking textual prompts to circumvent text-filter safeguards and leverage a key model vulnerability: flawed attribute binding that stems from its internal attention bias. This results in misattributing harmful descriptions to a target identity and generating NSFW outputs. To facilitate the study of this attack, we present the Misbinding Prompt evaluation set, which examines the content generation risks of current state-of-the-art identity-preserving models across four risk dimensions: pornography, violence, discrimination, and illegality. Additionally, we introduce the Attribute Binding Safety Score (ABSS), a metric for concurrently assessing both content fidelity and safety compliance. Experimental results show that our Misbinding Prompt evaluation set achieves a 5.28 \% higher success rate in bypassing five leading text filters (including GPT-4o) compared to existing main-stream evaluation sets, while also demonstrating the highest proportion of NSFW content generation. The proposed ABSS metric enables a more comprehensive evaluation of identity-preserving models by concurrently assessing both content fidelity and safety compliance. The dataset and code will be open-sourced. For further experimental data and anonymous open-source links, please see the appendix.

Disclaimer: This paper contains NSFW imagery that might be offensive to some readers.

Unveiling the Attribute Misbinding Threat in Identity-Preserving Models

Scaling deep learning to massive and diverse internet data has driven remarkable breakthroughs in domains such as video generation and natural language processing.
Robot learning, however, has thus far failed to replicate this success and remains constrained by a scarcity of available data.
Learning from Videos (LfV) methods aim to address this data bottleneck by augmenting traditional robot data with large-scale internet video.
This video data provides foundational information regarding physical dynamics, behaviours, and tasks, and can be highly informative for general-purpose robots.

This survey systematically examines the emerging field of LfV.
We first outline essential concepts, including detailing fundamental LfV challenges such as distribution shift and missing action labels in video data.
Next, we comprehensively review current methods for extracting knowledge from large-scale internet video, overcoming LfV challenges, and improving robot learning through video-informed training.
The survey concludes with a critical discussion of future opportunities.
Here, we emphasize the need for scalable foundation model approaches that can leverage the full range of available internet video and enhance the learning of robot policies and dynamics models.
Overall, the survey aims to inform and catalyse future LfV research, driving progress towards general-purpose robots.

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Towards Human-centered Proactive Conversational AI

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