Singapore

The recent advancement of Multimodal Large Language Models (MLLMs) is transforming human-computer interaction (HCI) from surface-level exchanges into more nuanced and emotionally intelligent communication. To realize this shift, emotion understanding becomes essential allowing systems to capture subtle cues underlying user intent. Furthermore, providing faithful explanations for predicted emotions is crucial to ensure interpretability and build user trust. However, current MLLM-based methods often generate emotion explanations that diverge from the ground-truth (GT) labels and sometimes even contradict their own predicted emotions. This inconsistency poses a critical risk for misunderstanding and erodes reliability in interactive settings. To address this, we propose a novel approach: the Emotional Rationale Verifier (ERV) and an Explanation Reward. Our method guides the model to produce reasoning that is explicitly consistent with the GT emotion during multimodal emotion recognition without modifying the model architecture or requiring paired video–description annotations. Our method significantly improves faithful explanation–prediction consistency and explanation emotion accuracy on the MAFW and DFEW datasets. 
Through extensive experiments and human evaluations, we show that our approach not only enhances alignment between explanation and prediction but also empowers MLLMs to deliver emotionally coherent, trustworthy interactions, marking a key step toward truly human-like HCI systems.

AAAI 2026

Emotion-Coherent Reasoning for Multimodal LLMs via Emotional Rationale Verifier

poster

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.

Knowledge editing (KE) has emerged as an effective approach for updating factual information in large language models (LLMs) without the need for full retraining. Most of the existing methods for addressing the "ripple effect" in KE adopt a chain-structured reasoning process, making them vulnerable to error accumulation from early incorrect steps. Moreover, their conflict detection mechanisms are often susceptible to the LLM's inherent confirmation bias, further undermining the reliability of the editing process. To overcome these challenges, we propose **Tree of Editing (ToE), a tree-structured, retrieval-enhanced knowledge editing framework** designed to support robust reasoning under factual updates. ToE expands reasoning paths using a breadth-first strategy combined with score-guided beam search, enabling diverse and error-tolerant inference. Besides, we introduce an observer to objectively update knowledge, avoiding the bias caused by LLMs' over-confidence. Experimental results on two benchmarks, namely **MQuAKE-CF** (targeting ripple-aware editing) and **DUNE** (free-form editing), demonstrate that ToE framework significantly outperforms existing methods.

Mitigating Error Accumulation in Knowledge Editing for Multi-Hop Question Answering

With the rapid advancement of large language models (LLMs), retrieval-augmented generation (RAG) has emerged as a critical approach to supplement the inherent knowledge limitations of LLMs. However, due to the typically large volume of retrieved information, RAG tends to operate with long context lengths. From the perspective of entropy engineering, we identify unconstrained entropy growth and attention dilution due to long retrieval context as significant factors affecting RAG performance. In this paper, we propose the balanced entropy-engineered RAG (BEE-RAG) framework, which improves the adaptability of RAG systems to varying context lengths through the principle of entropy invariance. By leveraging balanced context entropy to reformulate attention dynamics, BEE-RAG separates attention sensitivity from context length, ensuring a stable entropy level. Building upon this, we introduce a zero-shot inference strategy for multi-importance estimation and a parameter-efficient adaptive fine-tuning mechanism to obtain the optimal balancing factor for different settings. Extensive experiments across multiple RAG tasks demonstrate the effectiveness of BEE-RAG.

BEE-RAG: Balanced Entropy Engineering for Retrieval-Augmented Generation

Robustness verification is a promising technique for rigorously proving Recurrent Neural Networks (RNNs) robustly. A key challenge is to over-approximate the nonlinear activation functions with linear constraints, which can transform the verification problem into an efficiently solvable linear programming problem. Existing methods over-approximate the nonlinear parts with linear bounding planes individually, which may cause significant over-estimation and lead to lower verification accuracy. In this paper, in order to tightly enclose the three-dimensional nonlinear surface generated by the Hadamard product, we propose a novel truncated rectangular prism formed by two linear relaxation planes and a refinement-driven method to minimize both its volume and surface area for tighter over-approximation. Based on this approximation, we implement a prototype DeepPrism for RNN robustness verification. The experimental results demonstrate that DeepPrism has significant improvement compared with the state-of-the-art approaches in various tasks of image classification, speech recognition and sentiment analysis.

Tighter Truncated Rectangular Prism Approximation for RNN Robustness Verification

Hallucination continues to pose a major obstacle in the reasoning capabilities of large language models (LLMs). Although the Multi-Agent Debate (MAD) paradigm offers a promising solution by promoting consensus among multiple agents to enhance reliability, it relies on the unrealistic assumption that all debaters are rational and reflective, which is a condition that may not hold when agents themselves are prone to hallucinations. To address this gap, we introduce the Multi-agent Undercover Gaming (MUG) protocol, inspired by social deduction games like ``Who is Undercover?''. MUG reframes MAD as a process of detecting ``undercover'' agents (those suffering from hallucinations) by employing multimodal counterfactual tests. Specifically, we modify reference images to introduce counterfactual evidence and observe whether agents can accurately identify these changes, providing ground-truth for identifying hallucinating agents and enabling robust, crowd-powered multimodal reasoning. MUG advances MAD protocols along three key dimensions: (1) enabling factual verification beyond statistical consensus through counterfactual testing; (2) introducing cross-evidence reasoning via dynamically modified evidence sources instead of relying on static inputs; and (3) fostering active reasoning, where agents engage in probing discussions rather than passively answering questions. Collectively, these innovations offer a more reliable and effective framework for multimodal reasoning in LLMs.

Multi-Agent Undercover Gaming: Hallucination Removal Through Counterfactual Test for Multimodal Reasoning

Simulating microstructure evolution (MicroEvo) is vital for materials design but demands high numerical accuracy, efficiency, and physical fidelity. Although recent studies on deep learning (DL) offers a promising alternative to traditional solvers, the field lacks standardized benchmarks. Existing studies are flawed due to a lack of comparing specialized MicroEvo DL models with state-of-the-art spatio-temporal architectures, an overemphasis on numerical accuracy over physical fidelity, and a failure to analyze error propagation over time.
To address these gaps, we introduce MicroEvoEval, the first comprehensive benchmark for image-based microstructure evolution prediction. We evaluate 14 models, encompassing both domain-specific and general-purpose architectures, across four representative MicroEvo tasks with datasets specifically structured for both short- and long-term assessment. Our multi-faceted evaluation framework goes beyond numerical accuracy and computational cost, incorporating a curated set of structure-preserving metrics to assess physical fidelity. Our extensive evaluations yield several key insights. Notably, we find that modern architectures (e.g., VMamba), not only achieve superior long-term stability and physical fidelity but also operate with an order-of-magnitude greater computational efficiency. The results highlight the necessity of holistic evaluation and identify these modern architectures as a highly promising direction for developing efficient and reliable surrogate models in data-driven materials science.

MicroEvoEval: A Systematic Evaluation Framework for Image-Based Microstructure Evolution Prediction

The success of diffusion models has raised concerns about the generation of unsafe or harmful content, prompting concept erasure approaches that fine-tune modules to suppress specific concepts while preserving general generative capabilities. However, as the number of erased concepts grows, these methods often become inefficient and ineffective, since each concept requires a separate set of fine-tuned parameters and may degrade the overall generation quality. In this work, we propose a supertype-subtype concept hierarchy that organizes erased concepts into a parent–child structure. Each erased concept is treated as a child node, and semantically related concepts (*e.g.*, macaw, and bald eagle) are grouped under a shared parent node, referred to as a supertype concept (*e.g.*, bird). Rather than erasing concepts individually, we introduce an effective and efficient group-wise suppression method, where semantically similar concepts are grouped and erased jointly by sharing a single set of learnable parameters. During the erasure phase, standard diffusion regularization is applied to preserve denoising process in unmasked regions. To mitigate the degradation of supertype generation caused by excessive erasure of semantically related subtypes, we propose a novel method called **Su**pertype-**P**reserving **Lo**w-**R**ank **A**daptation (SuPLoRA), which encodes the supertype concept information in the frozen down-projection matrix and updates only the up-projection matrix during erasure. Theoretical analysis demonstrates the effectiveness of SuPLoRA in mitigating generation performance degradation. We construct a more challenging benchmark that requires simultaneous erasure of concepts across diverse domains, including celebrities, objects, and pornographic content. Comprehensive experiments demonstrate that our method achieves a superior balance between effective multi-concept erasure and the preservation of desirable generative performance.

Mass Concept Erasure in Diffusion Models with Concept Hierarchy

We study the problem of solving one-sided, zero-sum, partially observable stochastic games (POSGs). These games model sequential interactions between two adversaries, where one player has partial observability of the game state. They are applicable to many important domains, such as robotics and cybersecurity. Solving such games is computationally challenging since the solution depends on the first player's belief about the game state, which belongs to a continuous (and often high-dimensional) belief space. In the literature, only a single method has demonstrated reliable performance for solving these types of games, namely Heuristic Search Value Iteration (HSVI). However, this method is restricted to small games. We address this limitation by presenting a new method with similar approximation and convergence guarantees but improved scalability and flexibility, which we call SAB: Shapley iteration with Aggregated Beliefs. Our method aggregates the belief space into a finite set of representative beliefs and computes their values through Shapley iteration. It then approximates the value function of the POSG through interpolation from these values. We prove that SAB converges and provide a bound on its approximation error. Experiments across several benchmark games show that SAB matches the performance of HSVI on small game instances while also scaling to larger games. Moreover, we find that SAB is up to 79% faster than HSVI at obtaining a near-optimal approximation.

Scalable Solutions to Zero-Sum Partially Observable Stochastic Games Through Belief Aggregation with Approximation Guarantees

Online planning in Markov Decision Processes (MDPs) enables agents to make sequential decisions by simulating future trajectories from the current state, making it well-suited for large-scale or dynamic environments. Sample-based methods such as Sparse Sampling and Monte Carlo Tree Search (MCTS) are widely adopted for their ability to approximate optimal actions using a generative model. However, in practical settings, the generative model is often learned from limited data, introducing approximation errors that can degrade performance or lead to unsafe behaviors. To address these challenges, Robust MDPs (RMDPs) offer a principled framework for planning under model uncertainty, yet existing approaches are typically computationally intensive and not suited for real-time use. In this work, we introduce Robust Sparse Sampling (RSS), the first online planning algorithm for RMDPs with finite-sample theoretical performance guarantees. Unlike Sparse Sampling, which estimates the nominal value function, RSS computes a robust value function by leveraging the efficiency and theoretical properties of Sample Average Approximation (SAA), enabling tractable robust policy computation in online settings. RSS is applicable to infinite or continuous state spaces, and its sample and computational complexities are independent of the state space size. We provide theoretical performance guarantees and empirically show that RSS outperforms standard Sparse Sampling in environments with uncertain dynamics.

Online Robust Planning Under Model Uncertainty: A Sample-Based Approach

Hallucinations in Large Language Models (LLMs), defined as the generation of content inconsistent with facts or context, represent a core obstacle to their reliable deployment in critical domains.
Current research primarily focuses on binary "detection" approaches that, while capable of identifying hallucinations, fail to provide interpretable and actionable feedback for model improvement, thus limiting practical utility.
To address this limitation, a new research paradigm is proposed, shifting from "detection" to "diagnosis". The Hallucination Diagnosis Task is introduced, a task which requires models to not only detect hallucinations, but also perform error localization, causal explanation, and content correction.
We develop the Hallucination Diagnosis Generator (HDG), an automated pipeline that systematically generates high-quality training samples with rich diagnostic metadata from raw corpora through multi-dimensional augmentation strategies including controlled fact fabrication and reasoning chain perturbation.
Using HDG-generated data, we train HDM-4B-RL, a 4-billion-parameter hallucination diagnosis model, employing Group Relative Policy Optimization (GRPO) with a comprehensive reward function incorporating structural, accuracy, and localization signals.
Experimental results demonstrate that our model surpasses previous state-of-the-art detection models on the HaluEval benchmark while achieving comparable performance to advanced general-purpose models.
In comprehensive diagnosis tasks, HDM-4B-RL matches the capabilities of larger general models while maintaining smaller size.
This work validates the feasibility and value of hallucination diagnosis, providing an effective methodology for building more trustworthy and reliable generative AI systems.

From Detection to Diagnosis: Advancing Hallucination Analysis with Automated Data Synthesis

Just recognizable distortion (JRD) has emerged as a promising paradigm for machine-centric video coding. However, existing JRD-guided coding methods are limited by coarse annotation granularity and high computational cost, which hinder their deployment. In this paper, we first investigate the impact of different JRD annotation strategies on downstream task performance. By incorporating both instance-level and contextual information, we construct a new JRD dataset with fine-grained annotations compatible with object detection and instance segmentation tasks. To enhance quantization parameter (QP) map prediction while maintaining computational efficiency, we propose a novel spiking neural network (SNN)-based framework that decomposes video frames into spatial structures, channel interactions, and temporal patterns. Furthermore, we introduce a spiking attention mechanism to aggregate task-relevant features and employ adaptive scaling vectors to suppress machine-perceived redundancy, enabling targeted bitrate allocation aligned with task-critical content. Extensive experiments on multiple datasets and backbones demonstrate that our approach consistently outperforms state-of-the-art codec-based and JRD-guided methods in maintaining task performance at ultra-low bitrates, while significantly reducing computational overhead.

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Mitigating Error Accumulation in Knowledge Editing for Multi-Hop Question Answering

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