Singapore

This paper presents a simple, effective, and cost-efficient strategy, named ModelSwitch, to improve LLM performance by scaling test-time compute. ModelSwitch builds upon the repeated-sampling-then-voting framework, with a novel twist: incorporating multiple models, even weaker ones, to leverage their complementary strengths that potentially arise from diverse training data and paradigms. By using sample consistency as a signal, our strategy dynamically switches between models. Theoretical analysis highlights the efficiency and performance advantages of our strategy. Extensive experiments on seven datasets demonstrate that our strategy not only outperforms self-consistency and state-of-the-art multi-agent debate approaches, but also significantly reduces inference costs. Additionally, our strategy requires only a few comparable LLMs to achieve optimal performance and can be extended with verification methods, demonstrating the potential of leveraging multiple LLMs in the generation-verification paradigm.

AAAI 2026

Do We Truly Need So Many Samples? Multi-LLM Repeated Sampling Efficiently Scales Test-Time Compute

repeated sampling

test-time compute

multi-llm

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

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.

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

Registration Required

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 continuous emergence of new entities, relations, triples, and multimodal information drives the dynamic evolution of multimodal knowledge graph (MMKG). However, existing MMKG embedding models follow a static setting, where training from scratch for growing MMKG wastes learned knowledge, while fine-tuning on new knowledge easily leads to catastrophic forgetting, severely limiting their applicability in real-world scenarios. To address this, we propose a multimodal continual representation learning framework (MoFot) for growing MMKG. Unlike existing static multimodal embedding methods, MoFot focuses on alleviating catastrophic forgetting rather than retraining to adapt to new knowledge. Specifically, MoFot effectively mitigates catastrophic forgetting caused by parameter updates and differing forgetting rates across modalities through a multimodal collaborative modulation mechanism. The mechanism ensures consistent retention of previously learned multimodal knowledge across snapshots through multimodal weight modulation and multimodal feature modulation. MoFot outperforms existing MMKG embedding, KG continual learning, and MMKG inductive models. Experimental results demonstrate that MoFot not only avoids forgetting but also enhances old knowledge by learning new knowledge, achieving adaptation to new knowledge while mitigating forgetting of old knowledge.

Towards Multimodal Continual Knowledge Embedding with Modality Forgetting Modulation

Graph Structure Learning (GSL) aims to simultaneously enhance the original graph and the performance of Graph Neural Networks. However, the existing GSL methods for node classification fail to consider neighborhood label dependencies during training, which limits their ability to refine the graph structure in an adaptive manner. Furthermore, the training of those methods lacks a proper schedule based on graph structure quality, thereby yielding suboptimal performance. To address these challenges, we propose a novel GSL framework for node classification, termed DuAl hypeRgraph-enhanced curricuLum-guided graph structure learnING for node classification (DARLING). It first employs a graph structure curriculum module to effectively discriminate the suboptimal graph structures by examining both the distribution of neighborhood labels and the degree of nodes. Subsequently, a self-supervised dual hypergraph similarity learning module is proposed to capture higher-order neighborhood label dependencies. This is achieved via formulating a pre-training task that involves hyperedge batch filling within the dual hypergraph of the input graph. The experimental results on six datasets demonstrate that the proposed DARLING outperforms eleven state-of-the-art methods significantly, in terms of effectiveness and robustness.

DARLING: Dual Hypergraph-Enhanced Curriculum-Guided Graph Structure Learning for Node Classification

The opaque nature of deep learning models presents significant challenges for the ethical deployment of hate speech detection systems. To address this limitation, we introduce Supervised Rational Attention (SRA), a framework that explicitly aligns model attention with human rationales, improving both interpretability and fairness in hate speech classification. SRA integrates a supervised attention mechanism into transformer-based classifiers, optimizing a joint objective that combines standard classification loss with an alignment loss term that minimizes the discrepancy between attention weights and human-annotated rationales.
We evaluated SRA on hate speech benchmarks in English (HateXplain) and Portuguese (HateBRXplain) with rationale annotations. Empirically, SRA achieves 2.4× better explainability compared to current baselines, and produces token-level explanations that are more faithful and human-aligned. In terms of fairness, SRA achieves competitive fairness across all measures, with second-best performance in detecting toxic posts targeting identity groups, while maintaining comparable results on other metrics. These findings demonstrate that incorporating human rationales into attention mechanisms can enhance interpretability and faithfulness without compromising fairness.

Aligning Attention with Human Rationales for Self-Explaining Hate Speech Detection

Large Language Models (LLMs) demonstrate strong reasoning capabilities but still struggle with hallucinations and limited transparency. Recently, KG-enhanced LLMs that integrate knowledge graphs (KGs) have been shown to improve reasoning performance, particularly for complex, knowledge-intensive tasks. However, these methods still face significant challenges, including inaccurate retrieval and reasoning failures, often exacerbated by long input contexts that obscure relevant information. Furthermore, many of these approaches rely on LLMs to directly retrieve evidence from KGs, and to self-assess the sufficiency of this evidence, which often results in premature or incorrect reasoning. To address the retrieval and reasoning failures, we propose ProgRAG, a multi-hop knowledge graph question answering (KGQA) framework that decomposes complex questions into sub-questions, and progressively extends partial reasoning paths by answering each sub-question. At each step, external retrievers gather candidate evidence, which is then refined through uncertainty-aware pruning by the LLM. Finally, the context for LLM reasoning is optimized by organizing and rearranging the partial reasoning paths obtained from the sub-question answers. Experiments on two well-known datasets, WebQSP and CWQ, demonstrate that ProgRAG outperforms existing baselines in multi-hop KGQA, offering improved reliability and reasoning quality.

ProgRAG: Hallucination-Resistant Progressive Retrieval and Reasoning over Knowledge Graphs

Recently, the strong generalization ability of CLIP has facilitated open-vocabulary semantic segmentation, which labels pixels using arbitrary text. However, existing methods that fine-tune CLIP for segmentation on limited seen categories often lead to overfitting and degrade the pretrained vision-language alignment. To stabilize modality alignment during fine-tuning, we propose InfoCLIP, which leverages an information-theoretic perspective to transfer alignment knowledge from pretrained CLIP to the segmentation task. Specifically, this transfer is guided by two novel objectives grounded in mutual information. First, we compress the pixel-text modality alignment from pretrained CLIP to reduce noise arising from its coarse-grained local semantic representations learned under image-text supervision. Second, we maximize the mutual information between the alignment knowledge of pretrained CLIP and the fine-tuned model to transfer compact local semantic relations suited for the segmentation task. Extensive evaluations across various benchmarks validate the effectiveness of InfoCLIP in enhancing CLIP fine-tuning for open-vocabulary semantic segmentation, demonstrating its adaptability and superiority in asymmetric transfer.

InfoCLIP: Bridging Vision-Language Pretraining and Open-Vocabulary Semantic Segmentation via Information-Theoretic Alignment Transfer

Cross-lingual, cross-task transfer is challenged by task-specific data scarcity which becomes more severe as language support grows. 
Both challenges are amplified within vision-language models (VLMs).
We investigate multilingual generalization in encoder-decoder transformer VLMs to enable zero-shot image captioning in a language that was only paired with machine translations during training.
In this setting, the encoder must learn to generate generalizable, latent task-aware vision representations to instruct the decoder via inserted cross-attention layers.
We study scaling laws by training models based on Florence-2 and Gemma-2 that range from 0.4B to 11.2B parameters.
The training is performed on a synthetic dataset using varying compute budgets.
While all languages in the dataset have image-aligned translations, only a subset of them include image captions.
Notably, we show that captioning can emerge in a language after training on only translation data. 
We find that this indirect learning of unseen task-language pairs adheres to scaling laws that are governed by the multilinguality of the model, its model size and seen training samples.
Finally, we demonstrate that our observed scaling laws extend to a variety of downstream tasks, achieving competitive performance through finetuning in multimodal machine translation (Multi30K, CoMMuTE), lexical disambiguation (CoMMuTE), and image captioning (Multi30K, XM3600, COCO Karpathy).

Scaling Laws for Conditional Emergence of Multilingual Image Captioning via Generalization from Translation

Composed Image Retrieval (CIR) is a flexible image retrieval paradigm that enables users to accurately locate the target image through a multimodal query composed of a reference image and modification text. Although this task has demonstrated promising applications in personalized search and recommendation systems, it encounters a severe challenge in practical scenarios known as the Noise Triplet Correspondence (NTC) problem. This issue primarily arises from the high cost and subjectivity involved in annotating triplet data. To address this problem, we identify two central challenges: the precise estimation of composed semantic discrepancy and the insufficient progressive adaptation to modification discrepancy. To tackle these challenges, we propose a cHrono-synergiA roBust progressIve learning framework for composed image reTrieval (HABIT), which consists of two core modules. First, the Mutual Knowledge Estimation Module quantifies sample cleanliness by calculating the Transition Rate of mutual information between the composed feature and the target image, thereby effectively identifying clean samples that align with the intended modification semantics. Second, the Dual-consistency Progressive Learning Module introduces a collaborative mechanism between the historical and current models, simulating human habit formation to retain good habits and calibrate bad habits, ultimately enabling robust learning under the presence of NTC. Extensive experiments conducted on two standard CIR datasets demonstrate that HABIT significantly outperforms state-of-the-art methods under various noise ratios, exhibiting superior robustness and retrieval performance.

HABIT: Chrono-Synergia Robust Progressive Learning Framework for Composed Image Retrieval

Traffic flow prediction is a typical spatial-temporal prediction problem and has a wide range of applications. The core challenge lies in modeling the underlying complex spatial-temporal dependencies. Various methods have been proposed, and recent studies show that the modeling of dynamics is useful to meet the core challenge. While handling spatial dependencies and temporal dependencies using separate base model structures may hinder the modeling of spatial-temporal correlations, modeling of dynamics can bridge this gap. Incorporating spatial-temporal heterogeneity also advances the main goal, since it can extend the parameter space and incorporate more flexibility. Despite these advances, two limitations persist: 1) the modeling of dynamics is often limited to the dynamics of spatial topology (e.g., adjacency matrix changes), which, however, can be extended to a broader scope; 2) the modeling of heterogeneity is often separated for spatial and temporal dimensions, but this gap can also be bridged by the modeling of dynamics. To address the above limitations, we propose a novel framework for traffic prediction, called Meta Dynamic Graph (MetaDG). MetaDG leverages dynamic graph structures of node representations to explicitly model spatial-temporal dynamics. This generates both dynamic adjacency matrix and meta-parameters, extending dynamic modeling beyond topology while unifying the capture of spatial-temporal heterogeneity into a single dimension. Extensive experiments on four real-world datasets validate the effectiveness of MetaDG.

Meta Dynamic Graph for Traffic Flow Prediction

Recent self-supervised pre-training methods for object detection often rely on generic object proposals for localization and semantic feature learning for classification, but they yield limited improvements when applied to Detection Transformers (DETR) due to a lack of architectural alignment. Hence, we propose an elegant and versatile self-supervised framework tailored for DETR-like models called **Dis**tance-aware Multi-view **Co**ntrastive Learning (**DisCo DETR**). **DisCo DETR** enhances localization and semantic features through two core components. (i) **Distance-aware Multi-view Object Query Fusion** explicitly guides object queries to focus on spatially close objects across views, stabilizing training and improving localization accuracy. (ii) **Contrastive Learning for DETR** uses native bipartite matching to identify positive output pairs across views and pull them closer, enhancing semantic features discrimination with no extra matching. DisCo DETR can be seamlessly integrated into DETR-like models and achieves SOTA transfer performance on PASCAL VOC and COCO benchmarks across multiple variants.

DisCo DETR: Distance-aware Multi-view Contrastive Learning for DETR Pre-training

Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks through the \textbf{example-driven learning paradigm}. However, in high-stakes domains such as emergency response or industrial safety, real incidents are scarce, confidential, or both, while concise \emph{rule books} are plentiful.
We formalize this underexplored setting as \textbf{rule knowledge–driven reasoning} and ask: \emph{Can an LLM reason reliably when rules are abundant but examples are almost nil?}
To answer this question we introduce \textbf{RULER}, a fully automatic benchmark that derives 32K rigorously verified questions from 1K expert-curated emergency response rule knowledge to probe three core abilities—\emph{rule memorization}, \emph{single-rule application}, and \emph{multi-rule complex reasoning}, supported by a hallucination-aware evaluation suite and novel relational metrics.
A comprehensive empirical study of five open-source LLMs and five enhancement strategies shows that, after reliable performance on rule memorization and single-rule application, multi-rule complex reasoning plateaus at 5.4 on a 10-point scale.
We bridge this gap with \textbf{RAMPS}—a \textbf{R}ule-knowledge-\textbf{A}ware \textbf{M}onte-Carlo-tree-search \textbf{P}rocess-reward \textbf{S}upervision framework.
RAMPS injects rule knowledge priors into MCTS, distills 12K step-level traces without human annotation, and trains an advantage-based reward model that scores candidate reasoning paths during the beam search inference.
Experimental results demonstrate a notable improvement in complex reasoning, increasing to 7.7 (+2.3).
Together, RULER and RAMPS provide an automatic benchmark and a strong baseline suite for rule knowledge-driven reasoning in LLMs.

Content not yet available

Next from AAAI 2026

Towards Multimodal Continual Knowledge Embedding with Modality Forgetting Modulation

Similar lecture

When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs