Singapore

Epilepsy is a widespread neurological disorder characterized by highly patient-specific EEG patterns. Existing EEG-based seizure detection methods either train individualized models for each patient or adapt models pre-trained on known patients to new ones. However, when encountering previously unseen patients, these methods typically require retraining or fine-tuning, which limits their practical utility in clinical settings. This limitation can be linked to biases caused by patient-specific variations, which obscure the underlying pathological patterns of seizures. To address this, we propose an evidential multi-view framework that reinforces the learning of core epileptic features by promoting consistency across multiple views and reducing reliance on high-uncertainty, patient-specific segments. Specifically, we introduce Bias-guided Fisher-Evidential Multi-View Learning (BF-EML) to guide the model toward discovering intrinsic seizure patterns. BF-EML employs a two-stage training architecture: In Stage I, we use the Fisher Information Matrix to reorder EEG segments by uncertainty and deliberately train a biased feature generator on low-evidence segments. In Stage II, we design a dual-branch network where the biased and unbiased branches are alternately trained, encouraging the unbiased branch to reduce its reliance on patient-specific biases. Finally, we introduce a shift-calibrated fusion strategy to enhance the consistency of pathogenic feature integration. Extensive experiments on public datasets and a clinical dataset demonstrate that our method achieves superior performance in both single- and multi-patient scenarios. Importantly, it generalizes well to previously unseen patients without the need for retraining. We will release all datasets and source code upon publication.

AAAI 2026

Universal EEG Epilepsy Detection via Evidential Multi-View De-Biasing

ml: multi-instance/multi-view learning

ml: calibration & uncertainty quantification

ml: applications

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-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

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.

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.

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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.

Current Multimodal Chain-of-Thought (MCoT) methods suffer from *low-quality multimodal reasoning*, characterized by overthinking on simple queries and inefficient utilization of visual information, resulting in vast inefficient and ineffective computations.
In this paper, we discover that Multimodal Large Language Models (MLLMs) possess inherent capabilities to distinguish between simple and difficult queries and enhance task-related visual information, which remain underutilized by existing approaches. 
Based on this insight, we propose Self-Driven Refined Multimodal CoT (SDR-MCoT), a training-free framework that mitigates these issues through two self-driven modules. 
First, our selective thinking module employs entropy-based confidence estimation to determine whether queries require detailed reasoning, preventing overthinking on simple questions. 
Second, our step-wise visual enhancement module strengthens attention to relevant visual regions at each reasoning step without inserting additional tokens, achieving fine-grained visual grounding and enhancement with minimal overhead. 
Moreover, SDR-MCoT can be seamlessly integrated into various MLLMs, offering a practical solution for improving multimodal reasoning.
Comprehensive experiments across eight benchmarks from diverse domains (multimodal reasoning, visual understanding, hallucination, and mathematical reasoning) demonstrate that SDR-MCoT consistently outperforms existing MCoT methods on four different base models with reduced overhead. 
For instance, on Qwen2-VL-7B, our method improves average accuracy by over 6\% while reducing token consumption by approximately 60\% compared to zero-shot CoT.

Mitigating Low-Quality Reasoning in MLLMs: Self-Driven Refined Multimodal CoT with Selective Thinking and Step-wise Visual Enhancement

The rapid proliferation of social media platforms has led to a surge in multimodal fake news, where deceptive content often combines text and images to mislead audiences. Traditional unimodal detection methods struggle to address the complexity of such content, necessitating holistic multimodal approaches. While the latest advancements in Multimodal Large Language Models (MLLMs) offer new opportunities for enhancing detection performance by analyzing multi-dimensional features, including source credibility, cross-modal contradictions, emotional bias, and manipulative writing patterns, these methods suffer from a key flaw: a susceptibility to hallucinations or erroneous reasoning, which can lead to flawed conclusions and ultimately biased detection results. We propose the Multimodal Fake News Detection via Multi-perspective Rationale Generation and Verification (MMRGV) model to mitigate this challenge. Our method employs a cross-verification mechanism to screen and reconcile contradictions among different rationales, thereby preserving the LLM's analytical advantages while mitigating the impact of erroneous reasoning or hallucinations on the final detection. Subsequently, these optimized rationales are fused via an adaptive weighting strategy to output a robust final prediction. Extensive experiments on three benchmark datasets (Twitter, Weibo, and GossipCop) demonstrate the superiority of our method, achieving state-of-the-art accuracy of 0.9972, 0.9663, and 0.8772, respectively, and significantly outperforming existing baselines. These results validate the effectiveness of multi-perspective rationale generation and cross-verification in enhancing multimodal fake news detection, offering a resilient solution to combat misinformation in the era of generative AI.

Toward Multimodal Fake News Detection by Multi-perspective Rationale Generation and Verification

In this paper, we study the data complexity of querying inconsistent weighted description logic (DL) knowledge bases under recently-introduced cost-based semantics. In a nutshell, the idea is to assign each interpretation a cost based upon the weights of the violated axioms and assertions, and certain and possible query answers are determined by considering all (resp. some) interpretations having optimal or bounded cost. Whereas the initial study of cost-based semantics focused on DLs between EL_bot and ALCO, we consider DLs that may contain inverse roles and role inclusions, thus covering prominent DL-Lite dialects. Our data complexity analysis goes significantly beyond existing results by sharpening several lower bounds and pinpointing the precise complexity of optimal-cost certain answer semantics (no non-trivial upper bound was known). Moreover, while all existing results show the intractability of cost-based semantics, our most challenging and surprising result establishes that if we consider DL-Lite^H_bool ontologies and a fixed cost bound, certain answers for instance queries and possible answers for conjunctive queries can be computed using first-order rewriting and thus enjoy the lowest possible data complexity (AC0).

Data Complexity of Querying Description Logic Knowledge Bases Under Cost-Based Semantics

Graph-level anomaly detection (GLAD), which identifies rare or atypical graphs within a graph set, is crucial for applications such as image analysis, industrial defect inspection and fraud detection. However, existing GLAD approaches typically rely on the in-distribution hypothesis while lacking generalization capability for out-of-distribution (OOD) scenarios (e.g., different graph sizes), which largely limits the application in the real world. For the first time, we formulate the OOD generalization problem for GLAD, where testing graph data exhibit significant distributional shifts from training data. To tackle two common types of distributional shifts, domain generalization and subpopulation shift, we propose the $\textbf{F}$ine-$\textbf{G}$rained $\textbf{S}$ubpopulation $\textbf{G}$raph-$\textbf{L}$evel $\textbf{A}$nomaly $\textbf{D}$etection ($\textbf{FGS-GLAD}$). First, we propose a $\textbf{G}$raph $\textbf{I}$nformation $\textbf{B}$ottleneck-based $\textbf{A}$nomaly $\textbf{D}$etection Module ($\textbf{GIB4AD}$) that implements graph reverse distillation and graph information bottleneck on the graph to enhance task-relevant feature extraction for domain generalization. Second, We propose a $\textbf{F}$ine-$\textbf{G}$rained $\textbf{S}$ubpoulation $\textbf{I}$nference Module ($\textbf{FGSI}$) to predict fine-grained subpopulations and focus on critical inter-subpopulation features through a supervised contrastive mechanism. Experiments on seven benchmark datasets and ten baselines demonstrate our model's superiority in handling domain generalization and subpopulation shift, advancing graph-level anomaly detection for real-world applications.

Exploring Domain Generalization and Subpopulation Shift for Generalizable Graph-Level Anomaly Detection

Our statistical analysis reveals a complementary phenomenon between large language model-based question answering (QA) and small model-based QA. To facilitate dual knowledge transfer between these two paradigms, this paper introduces a collaborative enhancement method of large and small models for question answering. The proposed method consists of two iterative steps: i) small4large step, in which the small model first predicts an answer for a given question along with its confidence, and these results are then leveraged as prompts to strengthen the large model's performance; ii) large4small step, where the large model enhances the small model through distillation, judgment and reflection. Through iteration of these two steps, the large and small models could enhance each other progressively. Experimental evaluations across eight datasets spanning five domains demonstrate that the proposed method effectively improves the question answering performance of both large and small models simultaneously.

Collaborative Enhancement of Large and Small Models for Question Answering via Dual Knowledge Transfer

Large Language Models (LLMs) demonstrate impressive performance across natural language tasks but incur substantial computational and storage costs due to their scale. Post-training structured pruning offers an efficient solution. However, when few-shot calibration sets fail to adequately reflect the pretraining data distribution, existing methods exhibit limited generalization to downstream tasks. To address this issue, we propose Function-Aware Neuron Grouping (FANG), a post-training pruning framework that alleviates calibration bias by identifying and preserving neurons critical to specific function. FANG groups neurons with similar function based on the type of semantic context they process and prunes each group independently. During importance estimation within each group, tokens that strongly correlate with the functional role of the neuron group are given higher weighting. Additionally, FANG also preserves neurons that contribute across multiple context types. To achieve a better trade-off between sparsity and performance, it allocates sparsity to each block adaptively based on its functional complexity. Experiments show that FANG improves downstream accuracy while preserving language modeling performance. It achieves the state-of-the-art (SOTA) results when combined with FLAP and OBC, two representative pruning methods. Specifically, FANG outperforms FLAP and OBC by 1.5%–8.5% in average accuracy under 30% and 40% sparsity.

Improving Generalization in LLM Structured Pruning via Function-Aware Neuron Grouping

Discovering subgroups with the maximum average treatment effect is crucial for targeted decision making in domains such as precision medicine, public policy, and education. While most prior work is formulated in the potential‑outcome framework, the corresponding structural causal model (SCM) for this task has been largely overlooked. In practice, two approaches dominate. The first estimates pointwise conditional treatment effects and then fits a tree on those estimates, effectively turning subgroup estimation into the harder problem of accurate pointwise estimation. The second constructs decision trees or rule sets with ad‑hoc 'causal' heuristics, typically without rigorous justification for why a given heuristic may be used or whether such heuristics are necessary at all.

We address these issues by studying the problem directly under the SCM framework. Under the assumption of a partition-based model, we show that optimal subgroup discovery reduces to recovering the data-generating models and hence a standard supervised learning problem (regression or classification). This allows us to adopt \emph{any} partition-based methods to learn the subgroup from data. We instantiate the approach with CART, arguably one of the most widely used tree-based method, to learn the subgroup with maximum treatment effect. Finally, on a large collection of synthetic and semi‑synthetic datasets, we compare our method against a wide range of baselines and find that our approach, which avoids such causal heuristics, more accurately identifies subgroups with maximum treatment effect.

Learning Subgroups with Maximum Treatment Effects Without Causal Heuristics

Efficient inference of large language models (LLMs) is hindered by an ever-growing key-value (KV) cache, making KV cache compression a critical research direction. Traditional methods selectively evict less important KV cache entries, which leads to information loss and hallucinations. Recently, merging-based strategies have been explored to retain more information by merging KV pairs that would be discarded; however, these existing approaches inevitably introduce inconsistencies in attention distributions before and after merging, causing output perturbation and degraded generation quality. To overcome this challenge, we propose KeepKV, a novel adaptive KV cache merging method designed to preserve performance under strict memory constraints, taking a significant step toward achieving lossless KV cache compression. KeepKV introduces the Electoral Votes mechanism that records merging history and adaptively adjusts attention scores. Moreover, it further leverages a novel Zero Inference-Perturbation Merging method, compensating for attention loss resulting from cache merging. Extensive experiments on various benchmarks and LLM architectures demonstrate that KeepKV substantially reduces memory usage while successfully retaining essential context information, achieving over $2\times$ inference throughput improvement and maintaining superior generation quality even with only 10% KV cache budgets.

KeepKV: Achieving Periodic Lossless KV Cache Compression for Efficient LLM Inference

Robots have surpassed humans in terms of strength and
precision, yet humans retain an unparalleled ability for
decision-making in the face of unpredictable disturbances.
This article aims to combine the strengths of both entities
within a singular task: human motion guidance under strict
geometric constraints, particularly adhering to
predetermined paths. To tackle this challenge, a modular
haptic guidance law is proposed that takes the
human-applied wrench as an input. Using an auxiliary
variable called phase, the generated desired motion is
guaranteed to consistently adhere to the constraint path.
It is demonstrated how the guidance policy can be
generalized into physically interpretable terms, adjustable
either prior to initiating the task or dynamically while
the task is in progress. Additionally, an illustrative
guidance adaptation policy is showcased that takes into
account the human’s manipulability. Leveraging passivity
analysis, potential sources of instability are pinpointed,
and subsequently, overall system stability is ensured by
incorporating an augmented virtual energy tank. Lastly, a
comprehensive set of experiments, including a
20-participant user study, explores various aspects of the
approach in practice, encompassing both technical and
usability considerations.

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Mitigating Low-Quality Reasoning in MLLMs: Self-Driven Refined Multimodal CoT with Selective Thinking and Step-wise Visual Enhancement

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Next from AAAI 2026

Mitigating Low-Quality Reasoning in MLLMs: Self-Driven Refined Multimodal CoT with Selective Thinking and Step-wise Visual Enhancement

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Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis

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