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Speaker diarization is a fundamental task in speech processing aims to determine &#39;who speaks when&#39;. When combined with ASR, it enables speaker-labeled transcription with broad practical value. Most existing methods rely on frame-level classification, but the high cost of annotating mixed-speaker audio limits the availability of large-scale, accurately labeled datasets. As a result, even state-of-the-art models struggle with imprecise speaker boundary detection and semantic segmentation errors, which degrade timestamp accuracy and downstream ASR performance. To address these challenges, we propose WhisperDiari, a unified framework for speaker diarization and ASR. We first construct LibriDiari, a dataset derived from LibriSpeech, containing 2–4 speaker mixed audio annotated with transcripts and speaker labels. WhisperDiari builds on the Whisper model, incorporating speaker adapters and Speaker Similarity Matrix Supervision to enhance speaker representation. In addition, a dedicated speaker decoder fuses speaker embeddings with contextual semantics from Whisper&#39;s decoder, enabling token-level diarization. This design effectively resolves segmentation ambiguity, aligns diarization with semantic units, and jointly models &#39;who speaks what and when&#39;, producing accurate, timestamped transcripts. We train the model on LibriDiari and evaluate it on both LibriDiari and the real-world AMI corpus. Experimental results demonstrate that WhisperDiari consistently outperforms state-of-the-art open-source baselines.

AAAI 2026

WhisperDiari: A Whisper-Based Speaker Diarization Framework in Token Space Leveraging Semantic and Speaker Information for Better Text Adaptability

Speaker diarization is a fundamental task in speech processing aims to determine 'who speaks when'. When combined with ASR, it enables speaker-labeled transcription with broad practical value. Most existing methods rely on frame-level classification, but the high cost of annotating mixed-speaker audio limits the availability of large-scale, accurately labeled datasets. As a result, even state-of-the-art models struggle with imprecise speaker boundary detection and semantic segmentation errors, which degrade timestamp accuracy and downstream ASR performance. To address these challenges, we propose WhisperDiari, a unified framework for speaker diarization and ASR. We first construct LibriDiari, a dataset derived from LibriSpeech, containing 2–4 speaker mixed audio annotated with transcripts and speaker labels. WhisperDiari builds on the Whisper model, incorporating speaker adapters and Speaker Similarity Matrix Supervision to enhance speaker representation. In addition, a dedicated speaker decoder fuses speaker embeddings with contextual semantics from Whisper's decoder, enabling token-level diarization. This design effectively resolves segmentation ambiguity, aligns diarization with semantic units, and jointly models 'who speaks what and when', producing accurate, timestamped transcripts. We train the model on LibriDiari and evaluate it on both LibriDiari and the real-world AMI corpus. Experimental results demonstrate that WhisperDiari consistently outperforms state-of-the-art open-source baselines.

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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.

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.

End-to-end planning methods are the de-facto standard of the current autonomous driving system, while the robustness of the data-driven approaches suffers due to the notorious ``long-tail" problem (i.e., rare but safety-critical failure cases). 
In this work, we explore whether recent diffusion-based video generation methods (a.k.a. world models), paired with structured 3D layouts, can enable a fully automated pipeline to self-correct such failure cases. We first introduce an agent to simulate the role of product manager, dubbed **PM-Agent**, which formulates data requirements to collect data similar to the failure cases. Then, we use a generative model that can simulate both data collection and annotation. However, existing generative models struggle to generate high-fidelity data conditioned on 3D layouts. To address this, we propose **DriveSora**, which can generate spatiotemporally consistent videos aligned with the 3D annotations requested by PM-Agent. We integrate these components into our self-correcting agentic system, **CorrectAD**. Importantly, our pipeline is end-to-end model agnostic and can be applied to improve any end-to-end planner.
Evaluated on both nuScenes and a more challenging in-house dataset across multiple end-to-end planners, CorrectAD corrects 62.5% and 49.8% of failure cases, reducing collision rates by 39% and 27%, respectively.

CorrectAD: A Self-Correcting Agentic System to Improve End-to-end Planning in Autonomous Driving

Large language models hold great promise for transforming K–12 education, but there is an urgent need for systematic evaluation of their core educational capabilities. Existing benchmarks often overlook educational goal cognition and overemphasize answer accuracy, thereby failing to capture deeper subject-level knowledge ability and problem-solving ability. To address this gap, we introduce K-12EduBench: a benchmark for evaluating LLMs’ subject-level knowledge ability, subject-specific problem-solving ability, and educational goal cognition ability in K–12 education. K-12EduBench comprises four components: (1) a dataset of 2,640 objective and 619 subjective questions across nine subjects, annotated with answers, problem-solving processes, and cognitive-level labels; (2) nine Item Response Theory (IRT) models for estimating subject-level knowledge ability; (3) evaluation methods and metrics for assessing multi-step problem-solving ability; and (4) prompts and scoring rubrics for measuring alignment with target cognitive levels. Experiments on advanced LLMs show that education-optimized models consistently outperform general-purpose ones across all three abilities, while under-scaled models lag substantially. We observe a strong positive correlation between subject-level knowledge ability and subject-specific problem-solving ability. Despite gains in educational goal cognition ability, current models—even those tailored for education—still fall short of real-world instructional needs. All code and data will be released publicly.

K-12EduBench: A Benchmark for Evaluating Large Language Models’ Knowledge, Problem-Solving, and Educational Goal Cognition in K-12 Education

Goal-conditioned Reinforcement Learning (RL) is a promising direction for training agents capable of tackling a variety of tasks. However, generalizing to new goals in different environments remains a central challenge for goal-conditioned RL agents. Existing methods often rely on state abstraction, which involves learning abstracted state representations by excluding irrelevant features, to improve generalization. Despite their success in simplified settings, these methods often fail to generalize effectively to realistic environments with varied goals.
In this work, we propose to enhance generalization through state abstraction from the perspective of causal inference. 
We hypothesize that the generalization gap arises in part due to unobserved confounders: latent variables that simultaneously influence both the global and goal states. To address this, we introduce Deconfounded State Abstraction for Policy learning (DSAP), a novel framework that mitigates backdoor confounding by employing a learned causal graph as a *proxy* for the hidden confounders.
We provide theoretical analysis demonstrating that DSAP improves both the learning process and the generalization capability of goal-conditioned policies. Extensive experiments across different settings of multiple benchmarks show that our method significantly outperforms existing methods.

DSAP: Enhancing Generalization in Goal-Conditioned Reinforcement Learning

Automatic real personality recognition (RPR) aims to evaluate human real personality traits from their expressive behaviours. However, most existing solutions generally act as external observers to infer observers' personality impressions based on target individuals' expressive behaviours, which significantly deviate from their real personalities and consistently lead to inferior recognition performance. Inspired by the association between real personality and human internal cognition underlying the generation of expressive behaviours, we propose a novel RPR approach that efficiently simulates personalised internal cognition from easy-accessible external short audio-visual behaviours expressed by the target individual. The simulated personalised cognition, represented as a set of network weights that enforce the personalised network to reproduce the individual-specific facial reactions, is further encoded as a novel graph containing two-dimensional node and edge feature matrices, with a novel 2D Graph Neural Network (2D-GNN) proposed for inferring real personality traits from it. To simulate real personality-related cognition, an end-to-end strategy is designed to jointly train our cognition simulation, 2D graph construction, and personality recognition modules. Experiments show our approach’s effectiveness in capturing real personality traits with superior computational efficiency. Our code is provided in Supplementary Material.

Learning Personalised Human Internal Cognition from External Expressive Behaviours for Real Personality Recognition

Continual learning constrains models to learn new tasks over time without forgetting what they have already learned. A key challenge in this setting is catastrophic forgetting, where learning new information causes the model to lose its performance on previous tasks. Recently, explainable AI has been proposed as a promising way to better understand and reduce forgetting. In particular, self-explainable models are useful because they generate explanations during prediction, which can help preserve knowledge. However, most existing explainable approaches use post-hoc explanations or require additional memory for each new task, resulting in limited scalability. In this work, we introduce CIP-Net, an exemplar-free self-explainable prototype-based model designed for continual learning. CIP-Net avoids storing past examples and maintains a simple architecture, while still providing useful explanations and strong performance. We demonstrate that CIP-Net achieves state-of-the-art performances compared to previous methods in both task- and class-incremental settings, while bearing significantly lower memory-related overhead. This makes it a practical and interpretable solution for continual learning.

CIP-Net: Continual Interpretable Prototype-based Network

Video Large Language Models (VideoLLMs) are increasingly deployed on numerous critical applications, where users rely on auto-generated summaries while casually skimming the video stream. We show that this interaction hides a critical safety gap: if harmful content is embedded in a video, either as full-frame inserts or as small corner patches, state-of-the-art VideoLLMs rarely mention the harmful content in the output, despite its clear visibility to human viewers. A root-cause analysis reveals three compounding design flaws: (1) insufficient temporal coverage resulting from the sparse, uniformly spaced frame sampling used by most leading VideoLLMs, (2) spatial information loss introduced by aggressive token downsampling within sampled frames, and (3) encoder-decoder disconnection, whereby visual cues are only weakly utilized during text generation. Leveraging these insights, we craft three zero-query black-box attacks, aligning with these flaws in the processing pipeline. Our large-scale evaluation across five leading VideoLLMs shows that the harmfulness omission rate exceeds 90% in most cases. Even when harmful content is clearly present in all frames, these models consistently fail to identify it. These results underscore a fundamental vulnerability in current VideoLLMs' designs and highlight the urgent need for sampling strategies, token compression, and decoding mechanisms that guarantee semantic coverage rather than speed alone. This paper contains content that is offensive.

Failures to Surface Harmful Contents in Video Large Language Models

We introduce LOG-Nav, an efficient layout-aware object-goal navigation approach designed for complex multi-room indoor environments. 
By planning hierarchically leveraging a global topologigal map with layout information and local imperative approach with detailed scene representation memory, LOG-Nav achieves both efficient and effective navigation.
The process is managed by an LLM-powered agent, ensuring seamless effective planning and navigation, without the need for human interaction, complex rewards, or costly training.
Our experimental results on the MP3D benchmark achieves 85\% object navigation success rate (SR) and 79\% success rate weighted by path length (SPL) (over 40\% point improvement in SR and 60\% improvement in SPL compared to exsisting methods). Furthermore, we validate the robustness of our approach through virtual agent and real-world robotic deployment, showcasing its capability in practical scenarios. See https://anonymous.4open.science/r/LOG-Navi-012C/ for details.

LOG-Nav: Efficient Layout-Aware Object-Goal Navigation with Hierarchical Planning

Graph Neural Networks (GNNs) have achieved remarkable success in graph classification tasks. However, their performance often deteriorates under out-of-distribution (OOD) shifts, such as variations in graph structures, sizes, and node attributes. While several methods have been proposed to address this issue, a significant challenge remains in explicitly identifying the key components of a graph. To tackle this, we introduce CauVQ, a novel causal vector quantization method that enhances OOD generalization by explicitly identifying and leveraging causally relevant substructures. Our method first decomposes each graph into local subgraphs and quantizes them into a discrete codebook of prototypical substructures, enabling more stable and interpretable representations. To isolate truly causal substructures, we maximize their mutual information with graph labels and refine their representations through a learnable substructure interaction matrix and a causal attention mask, effectively suppressing spurious correlations. Furthermore, we design a counterfactual regularizer that enforces prediction stability under substructure perturbations, encouraging the model to focus on causal patterns rather than shortcuts. Extensive experiments on both standard and OOD graph classification benchmarks demonstrate that CauVQ consistently outperforms state-of-the-art methods in terms of robustness and interpretability. Our framework offers a promising step towards reliable and explainable graph learning under distribution shifts.

CauVQ: Causal Vector Quantization for Graph OOD Generalization

Dataset distillation compresses large datasets into compact synthetic ones to reduce storage and computational costs. Among various approaches, distribution matching (DM)-based methods have attracted attention for their high efficiency. However, they often overlook the evolution of feature representations during training, which limits the expressiveness of synthetic data and weakens downstream performance.
To address this issue, we propose Trajectory Guided Dataset Distillation (TGDD), which reformulates distribution matching as a dynamic alignment process along the model’s training trajectory. At each training stage, TGDD captures evolving semantics by aligning the feature distribution between the synthetic and original dataset. Meanwhile, it introduces a distribution constraint regularization to reduce class overlap. This design helps synthetic data preserve both semantic diversity and representativeness, improving performance in downstream tasks. Without additional optimization overhead, TGDD achieves a favorable balance between performance and efficiency. Experiments on ten datasets demonstrate that TGDD achieves state-of-the-art performance, notably a 5.0\% accuracy gain on high-resolution benchmarks. Our source code will be publicly available at http://github.com/ddrd999/TGDD.

TGDD: Trajectory Guided Dataset Distillation with Balanced Distribution

Large language models (LLMs) have advanced to encompass extensive knowledge across diverse domains. Yet controlling what a LLMs should not know is important for ensuring alignment and thus safe use. However, effective unlearning in LLMs is difficult due to the fuzzy boundary between knowledge retention and forgetting. This challenge is exacerbated by entangled parameter spaces from continuous multi-domain training, often resulting in collateral damage, especially under aggressive unlearning strategies. Furthermore, the computational overhead required to optimize State-of-the-Art (SOTA) models with billions of parameters poses an additional barrier. In this work, we present **ALTER**, a lightweight unlearning framework for LLMs to address both the challenges of knowledge entanglement and unlearning efficiency. ALTER operates through two phases: (I) high entropy tokens are captured and learned via the shared A matrix in LoRA, followed by (II) an asymmetric LoRA architecture that achieves a specified forgetting objective by parameter isolation and unlearning tokens within the target subdomains. Serving as a new research direction for achieving unlearning via token-level isolation in the asymmetric framework. ALTER achieves SOTA performance on TOFU, WMDP, and MUSE benchmarks with over 95% forget quality and shows minimal side effects through preserving foundational tokens. By decoupling unlearning from LLMs' billion-scale parameters, this framework delivers excellent efficiency while preserving over 90% of model utility, exceeding baseline preservation rates of 47.8-83.6%.

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CorrectAD: A Self-Correcting Agentic System to Improve End-to-end Planning in Autonomous Driving

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