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Video Large Language Models (VLLMs) excel in video understanding, but their excessive visual tokens pose a significant computational challenge for real-world applications. Current methods aim to enhance inference efficiency by visual token pruning. However, they do not consider the dynamic characteristics and temporal dependencies of video frames, as they perceive video understanding as a multi-frame task. To address these challenges, we propose **MMG-Vid**, a novel training-free visual token pruning framework that removes redundancy by **M**aximizing **M**arginal **G**ains at both segment-level and token-level. Specifically, we first divide the video into segments based on frame similarity, and then dynamically allocate the token budget for each segment to maximize the marginal gain of each segment. Subsequently, we propose a temporal-guided DPC algorithm that jointly models inter-frame uniqueness and intra-frame diversity, thereby maximizing the marginal gain of each token. By combining both stages, MMG-Vid can maximize the utilization of the limited token budget, significantly improving efficiency while maintaining strong performance. Extensive experiments demonstrate that MMG-Vid can maintain over **99.5\%** of the original performance, while effectively **reducing 75\%** visual tokens and accelerating the prefilling stage by **3.9x** on LLaVA-OneVision-7B. Code will be released soon.

AAAI 2026

MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs

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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.

The multi-view clustering methods based on tensor regression can make full use of the potential structural information between views and achieve data-level fusion. However, existing tensor regression-based approaches for anchor graph often overlook the probabilistic nature of anchor graph, focusing solely on sample labels while ignoring the influence of anchor labels on clustering results. To overcome these limitations, we introduce Tensorized Label Learning via Balanced Tensor Regression (TLL-BTR). Our key idea is to exploit the probabilistic nature of the anchor graph by regarding the sample labels as a projection tensor that maps the anchor graph into the label space, thereby producing anchor labels. By enforcing constraints on these anchor labels, we guide the concurrent learning of sample labels and achieve co-label learning between anchors and samples. To prevent trivial solutions, we maximize the nuclear norm to promote an even distribution of samples across clusters. Extensive experiments on benchmark datasets demonstrate that TLL-BTR consistently outperforms state-of-the-art methods.

Tensorized Label Learning via Balanced Tensor Regression

Retrieval Augmented Generation enhances the response accuracy of Large Language Models (LLMs) by integrating retrieval and generation modules with external knowledge, demonstrating particular strength in real-time queries and Visual Question Answering tasks. 
However, the effectiveness of RAG is frequently hindered by the precision of the retriever: many retrieved samples fed into the generation phase are irrelevant or misleading, posing a critical bottleneck to LLMs’ performance.
To address this challenge, we introduce \textbf{VaccineRAG}, a novel Chain-of-Thought-based retrieval-augmented generation dataset. 
On one hand, VaccineRAG employs a benchmark to evaluate models using data with varying positive/negative sample ratios, systematically exposing inherent weaknesses in current LLMs. 
On the other hand, it enhances models’ sample-discrimination capabilities by prompting LLMs to generate explicit Chain-of-Thought (CoT) analysis for each sample before producing final answers.
Furthermore, to enhance the model’s ability to learn long-sequence complex CoT content, we propose \textbf{Partial-GRPO}. 
By modeling the outputs of LLMs as multiple components rather than a single whole, our model can make more informed preference selections for complex sequences, thereby enhancing its capacity to learn complex CoT.
Comprehensive evaluations and ablation studies on VaccineRAG validate the effectiveness of the proposed scheme. The code and dataset will be publicly released soon.

VaccineRAG: Boosting Multimodal Large Language Models’ Immunity to Harmful RAG Samples

Robust Markov decision processes (r-MDPs) extend MDPs by explicitly modelling epistemic uncertainty about transition dynamics. Learning r-MDPs from interactions with an unknown environment enables the synthesis of robust policies with provable (PAC) guarantees on performance, but this can require a large number of sample interactions. We propose novel methods for solving and learning r-MDPs based on factored state-space representations that leverage the independence between model uncertainty across system components. Although policy synthesis for factored r-MDPs leads to hard, non-convex optimisation problems, we show how to reformulate these into tractable linear programs. Building on these, we also propose methods to learn factored model representations directly. Our experimental results show that exploiting factored structure can yield dimensional gains in sample efficiency, producing more effective robust policies with tighter performance guarantees than state-of-the-art methods.

Efficient Solution and Learning of Robust Factored MDPs

Planning allows an agent to safely refine its actions before executing them in the real world. In autonomous driving, this is crucial to avoid collisions and navigate in complex, dense traffic scenarios. One way to plan is to search for the best action sequence. However, this is challenging when all necessary components – policy, next-state predictor, and critic – have to be learned. Here we propose Differentiable Simulation for Search (DSS), a framework that leverages the differentiable simulator Waymax as both a next state predictor and a critic. It relies on the simulator’s hardcoded dynamics, making state predictions highly accurate, while utilizing the simulator’s differentiability to effectively search across action sequences. Our DSS agent optimizes its actions using gradient descent over imagined future trajectories. We show experimentally that DSS – the combination of planning gradients and stochastic search – significantly improves tracking and path planning accuracy compared to sequence prediction, imitation learning, model-free RL, and other planning methods.

Autonomous Vehicle Path Planning by Searching with Differentiable Simulation

Learning representative embeddings for different types of speaking styles, such as emotion, age, and gender, is critical for both recognition tasks (e.g., cognitive computing and human-computer interaction) and generative tasks (e.g., style-controllable speech generation). In this work, we introduce ParaMETA, a unified and flexible framework for learning and controlling speaking styles directly from speech. Unlike existing methods that rely on single-task models or cross-modal alignment, ParaMETA learns disentangled, task-specific embeddings by projecting speech into dedicated subspaces for each style type. This design reduces inter-task interference, mitigates negative transfer, and allows a single model to handle multiple paralinguistic tasks such as emotion, gender, age, and nationality classification.
Beyond recognition, ParaMETA enables fine-grained style control in Text-To-Speech (TTS) generative models. It supports both speech- and text-based prompting and allows users to modify one speaking style while preserving others. Extensive experiments demonstrate that ParaMETA outperforms strong baselines in classification accuracy and generates more natural and expressive speech, while maintaining a lightweight and efficient model suitable for real-world applications.

ParaMETA: Towards Learning Disentangled Paralinguistic Speaking Styles Representations from Speech

Cognitive computing models offer a formal and interpretable way to characterize human's deliberation and decision-making, yet their development remains labor-intensive. In this paper, we propose NL2CA, a novel method for auto-formalizing cognitive decision-making rules from natural language descriptions of human experience. Different from most related work that exploits either pure manual or human-guided interactive modeling, our method is fully automated without any human intervention. The approach first translates text into Linear Temporal Logic (LTL) using a fine-tuned large language model (LLM), then refines the logic via an unsupervised Critic Tree, and finally transforms the output into executable production rules compatible with symbolic cognitive frameworks. Based on the resulted rules, a cognitive agent is further constructed and optimized through cognitive reinforcement learning according to the real-world behavioral data. Our method is validated in two domains: (1) NL-to-LTL translation, where our CriticNL2LTL module achieves consistent performance across both expert and large-scale benchmarks without human-in-the-loop feedbacks, and (2) cognitive driving simulation, where agents automatically constructed from human interviews have successfully learned the diverse decision patterns of about 70 trials in different critical scenarios. Experimental results demonstrate that NL2CA enables scalable, interpretable, and human-aligned cognitive modeling from unstructured textual data, offering a novel paradigm to automatically design symbolic cognitive agents.

NL2CA: Auto-formalizing Cognitive Decision-Making from Natural Language Using an Unsupervised CriticNL2LTL Framework

Proactive intention decoding remains a critical yet underexplored challenge in brain–machine interfaces (BMIs), especially under naturalistic, self-initiated behavior. Existing systems rely on reactive decoding of motor cortex signals, resulting in substantial latency. To address this, we leverage the common marmoset’s spontaneous vocalizations and develop a high-resolution, dual-region ECoG recording paradigm targeting the prefrontal and auditory cortices and a neural decoding framework that integrates shapelet-based temporal encoding, position-aware attention, frequency-aware channel masking, contrastive clustering and a minimum error entropy-based robust loss. Our approach achieves 91.9\% accuracy up to 200 ms before vocal onset—substantially outperforming 13 competitive baselines. Our model also uncovers a functional decoupling between auditory and prefrontal regions. Furthermore, joint modeling in time and frequency domains reveals novel preparatory neural signatures preceding volitional vocal output. Together, our findings bridge the gap between foundational neuroscience and applied BMI engineering, and establish a generalizable framework for intention decoding from ecologically valid, asynchronous behaviors. Code is available at [*********URL Blinded for Review*****************].

Spontaneous Yet Predictable: Shapelet-Driven, Channel-Aware Intention Decoding from Multi-Region ECoG

Graph Anomaly Detection (GAD) focuses on identifying instances that deviate from normal patterns in graph-structured data. Although substantial progress has been made in this field, current approaches are constrained by the "one-dataset-one-model" paradigm, exhibiting limited generalization across heterogeneous graphs, poor adaptability in few-shot scenarios, and inefficient cross-domain deployment. To overcome these limitations, we propose SAARCS, a universal GAD framework capable of performing anomaly detection across diverse graph datasets without requiring any target data training. SAARCS aligns feature dimensions through composite spatial smoothness, learns graph embeddings via an adaptive-hop attention encoder, and predicts node abnormality using only a small set of normal context nodes. Extensive experiments on eight real-world datasets demonstrate that our approach achieves superior performance compared to state-of-the-art baselines.

Multi-dimensional Adaptive Mix-hop Contextual Learning Framework for Universal Graph Anomaly Detection

Multimodal summarization with multimodal output (MSMO) aims to generate coherent textual summaries while selecting the most semantically relevant images to enhance expressiveness. Despite the advancements of large multimodal models like GPT-4o, LLaMA-3, and Grok-3, these models often exhibit hallucination and weak visual-text alignment when applied to MSMO tasks. To address these challenges, we propose ModalSyncSum, a unified framework that enhances semantic consistency and visual faithfulness. It incorporates image-aware information extraction to mitigate visual-text misalignment, QA-based description verification to detect and correct hallucinated image descriptions, and named entity-guided refinement to ensure factual accuracy and entity alignment across modalities. Furthermore, we introduce a new evaluation metric M$^3$AS, which jointly considers image content coverage, text-image alignment, and summary consistency, filling the gap in evaluating multimodal summary quality. Experimental results show that our model outperforms prompt-based baselines across multiple datasets, achieving significant gains on ROUGE, BLEU, and BERTScore, with BLEU improving by 21.95\%. In human evaluation, M$^3$AS exhibits stronger correlation with human judgments in consistency, image-summary relevance, and focus, surpassing existing automatic metrics.

ModalSyncSum: Synchronizing Image and Text for Reliable Summary Generation

Uncertainty over model knowledge is a core challenge in planning and has been addressed through various approaches tailored to different scenarios. In this paper, we focus on scenarios where the agent does not initially know the exact outcome of its actions but gains knowledge upon execution, i.e., each action reveals its actual effect, removing uncertainty about future occurrences. We refer to this formulation as Planning with Uncertain Models of Actions (PUMA). We show that PUMA can be compiled in polynomial time in both Fully Observable Non-Deterministic planning and, perhaps more unexpectedly, classical planning, providing a constructive proof that PUMA remains PSPACE-complete despite its apparent exponential uncertainty. Finally, we experimentally evaluate both compilations with benchmark domains that capture the key aspects of the problem. The results show the practical feasibility of our approach and reveal a complementary behavior between the two compilations.

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Tensorized Label Learning via Balanced Tensor Regression

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