Singapore

Video captions play a crucial role in text-to-video generation tasks, as their quality directly influences the semantic coherence and visual fidelity of the generated videos. Although large vision-language models (VLMs) have demonstrated significant potential in caption generation, existing benchmarks inadequately address fine-grained evaluation, particularly in capturing spatial-temporal details critical for video generation. To address this gap, we introduce the Fine-grained Video Caption Evaluation Benchmark (\textbf{VCapsBench}), the first large-scale fine-grained benchmark comprising 5,677 (5K+) videos and 109,796 (100K+) question-answer pairs. These QA-pairs are systematically annotated across 21 fine-grained dimensions (\eg, camera movement, and shot type) that are empirically proven critical for text-to-video generation. We further introduce three metrics (Accuracy (\textbf{AR}), Inconsistency Rate (\textbf{IR}), Coverage Rate (\textbf{CR})), and an automated evaluation pipeline leveraging a large language model (LLM) to verify caption quality via contrastive QA-pairs analysis. Our benchmark can advance the development of robust text-to-video models by providing actionable insights for caption optimization. The dataset and codes are available at the website: \url{https://github.com/anoycode22/VCapsBench}.

AAAI 2026

VCapsBench: A Large-scale Fine-grained Benchmark for Video Caption Quality Evaluation

video caption benckmark

video understanding

language and vision

## 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.

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.

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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.

Explainable AI (XAI) as a field has traditionally focused
and succeeded at providing explanations for single-shot
decision-making systems like classifiers.
However, there is a growing consensus that some of the most
challenging problems within explainable AI lie not within
single-shot decision-making settings, but rather in
sequential decision-making ones. Moreover, by switching to
a sequential decision-making setting, we also see novel
opportunities to build explainable agents that are actually
able to reason about when and how to provide explanations
to maximize their effectiveness. As of right now, most of
the work within explainable sequential decision-making
tends to happen in isolation within subcommunities like
planning, reinforcement learning, and robotics. While this
has led to the development of useful explanation methods,
the fragmentation has also made it harder to adopt and
transfer advances and insights from one subcommunity to
another. Addressing this requires us to take a more
holistic view of explainable sequential decision-making,
one that allows us to account for and understand the
contributions from every subfield in a single unified
framework. In this talk, we will talk about the efforts
that have been made towards this end and some of the most
important challenges that remain.

Explanations for Sequential Decision-Making – an Overview

Composed Image Retrieval (CIR) enables image search by combining a reference image with modification text, providing a flexible interface to adapt to personalized visual preferences. Unfortunately, intrinsic noise in CIR triplets incurs training uncertainty and threatens model's robustness. Despite the probabilistic learning approaches in multi-modal retrieval, they still fall short for CIR due to their instance-level holistic modeling and homogeneous treatments for queries and targets. In this paper, we propose a Heterogeneous Uncertainty-Guided (HUG) paradigm to overcome these limitations. HUG utilizes a fine-grained probabilistic learning framework, where queries and targets are represented by Gaussian embeddings capturing detailed concepts and uncertainties. We customize heterogeneous uncertainty estimations for multi-modal queries and uni-modal targets. In particular, given a query, we capture uncertainties not only regarding uni-modal content quality but also multi-modal coordination, followed by a provable dynamic weighting mechanism to derive the comprehensive query uncertainty. We further design uncertainty-guided objectives, including query-target holistic contrast and fine-grained contrasts with comprehensive negative sampling strategies, which effectively enhance discriminative learning. Experiments on benchmarks demonstrate HUG's effectiveness beyond state-of-the-art baselines, with faithful analysis justifying each of the technical contributions. More encouragingly, the learned representations show intuitive correlation to some image and text attributes, with the uncertainty magnitudes reflecting ambiguity degrees, providing interpretability insights. We will release the code, detailed instructions and training files.

Heterogeneous Uncertainty-Guided Composed Image Retrieval with Fine-Grained Probabilistic Learning

Rationalization model has recently garnered significant attention for enhancing the interpretability of natural language processing by first using a generator to select the most relevant pieces from the text with respect to the label, before passing the text input to the predictor.
However, the robustness of the rationalization models is not sufficiently investigated. Specifically, this paper explores the robustness of rationalization models against backdoor attacks, which has been ignored by previous studies. Surprisingly, we find that conventional backdoor attack techniques fail to inject triggers into the rationalization model because its generator can filter out bad triggers. Considering this, we further propose a novel backdoor attack method named as BadRNL designed specially for the rationalization models. The core idea of BadRNL is first to search for the personalized trigger for each specific dataset and then manipulate the rationales and labels to conduct attacks. Besides, BadRNL controls the order of sample learning through poison-priority sampling strategies. Experimental results show that our method can successfully craft the predictions of samples containing triggers while maintaining the performance of the model on clean data.

Backdooring Rationalization

Multi-view clustering (MVC) aims to uncover the latent structure of multi-view data by learning view-common and view-specific information.
Although recent studies have explored hyperbolic representations for better tackling the representation gap between different views, they focus primarily on instance-level alignment and neglect global semantic consistency, rendering them vulnerable to view-specific information (e.g., noise and cross-view discrepancies).
To this end, this paper proposes a novel Wasserstein-Aligned Hyperbolic (WAH) framework for multi-view clustering. Specifically, our method exploits a view-specific hyperbolic encoder for each view to embed features into the Lorentz manifold for hierarchical semantic modeling. Whereafter, a global semantic loss based on the hyperbolic sliced-Wasserstein distance is introduced to align manifold distributions across views. This is followed by soft cluster assignments to encourage cross-view semantic consistency. Extensive experiments on multiple benchmarking datasets show that our method can achieve SOTA clustering performance.

Wasserstein-Aligned Hyperbolic Multi-View Clustering

Medication recommendation systems aim to provide personalized and safe medication options based on individual patient records. However, existing approaches often face challenges related to inadequate modeling of complex relationships within Electronic Health Records (EHRs), data sparsity, and a lack of explainability for recommendations. In this paper, we present a Knowledge-enhanced Explainable HyperGraph Convolution Network (KEHGCN) that constructs a hierarchical hypergraph structure to capture the multi-level relationships within EHR data. By incorporating external knowledge graphs, our approach introduces additional positive relations that help alleviate the impact of data sparsity on model learning. Furthermore, by performing generalized metapath construction and selection on the knowledge graph, our approach achieves effective knowledge filtering and extracts semantically meaningful metapaths, thereby further enhancing the explainability of the recommendation results. We also explicitly introduce negative relations present in the domain knowledge to improve the safety of medication recommendation. Extensive experiments on different hospital departments of MIMIC-III and MIMIC-IV datasets demonstrate that KEHGCN outperforms other state-of-the-art baselines.

Knowledge-Enhanced Explainable Hypergraph Convolution Network for Medication Recommendation

Real-world conversations are rich with pragmatic elements, such as entity mentions, references, and implicatures. Understanding such nuances is a requirement for successful natural communication, and often requires building a local _world model_ which encodes such elements and captures the dynamics of their evolving states. However, it is not well-understood whether language models (LMs) construct or maintain a robust implicit representation of conversations. In this work, we evaluate the ability of LMs to encode and update their internal world model in dyadic conversations and test their _malleability_ under linguistic alterations. To facilitate this, we apply seven minimal linguistic alterations to conversations sourced from popular datasets and construct two benchmarks comprising yes-no questions. We evaluate a wide range of open and closed source LMs and observe that they struggle to maintain robust accuracy. Our analysis unveils that LMs struggle to memorize crucial details, such as tracking entities under linguistic alterations to conversations. We then propose a dual-perspective interpretability framework which identifies transformer layers that are _useful_ or _harmful_ and highlights linguistic alterations most influenced by harmful layers, typically due to encoding spurious signals or relying on shortcuts. Inspired by these insights, we propose two layer-regularization based fine-tuning strategies that suppress the effect of the harmful layers.

PRAGWORLD: A Benchmark Evaluating LLMs’ Local World Model Under Minimal Linguistic Alterations and Conversational Dynamics

While adversarial attacks can effectively deceive deep neural networks, their real-world applicability is often limited by complex and conspicuous patterns that reveal their attack intent to human observers. To overcome this limitation, we propose UYE, a novel camouflage framework designed to simultaneously mislead DNNs and evade human perception. UYE incorporates two key components: an attention refiner leveraging a pre-trained vision encoder to optimize adversarial patterns for robust attacks across diverse environments, and a perception evaluator trained on CAMO-Critic—a human preference dataset curated using tailored prompts from human-aligned large multimodal models—to ensure natural and unobtrusive camouflage generation. Extensive experiments demonstrate that UYE outperforms state-of-the-art methods in achieving an optimal balance between human stealth and model deception while maintaining effectiveness in real-world scenarios.

Unnoticed Yet Effective: A Hybrid Physical Camouflage Framework Against DNNs and Human Perception

Fairness-aware machine learning aims to build predictive models that comply with fairness requirements, particularly concerning sensitive attributes such as race, gender, and age. Among causality-based fairness notions, counterfactual fairness is widely adopted for its individual-level guarantees, requiring that an individual’s predicted outcome remains unchanged in a counterfactual world where its sensitive attribute is altered. However, existing methods critically assume that the true causal graph is fully known, which is rarely the case in practice. Moreover, counterfactual fairness suffers from inherent identifiability limitations, as counterfactual quantities cannot always be uniquely estimated from observational data, especially under incomplete causal knowledge. To address these challenges, we propose a principled framework (CF-ICG) for counterfactual fairness under imperfectly known causal graphs, e.g., Partially Directed Acyclic Graphs (PDAGs). We first introduce a criterion to determine the identifiability, and bound the counterfactual quantities under PDAGs. Building upon this, we develop an efficient local algorithm that avoids the exhaustive enumeration of all DAGs, ensuring robustness against worst-case fairness violations. Experimental results on synthetic and real-world datasets demonstrate the practical effectiveness and theoretical soundness of CF-ICG.

Counterfactual Fairness with Imperfect Causal Graphs

The proliferation of large language models has intensified demands for reliable content attribution, yet existing watermarking techniques face a fundamental trilemma: they cannot simultaneously optimize for robustness against attacks, minimal text quality degradation, and detection efficiency. To resolve this challenge, we propose \textbf{ARGH-Mark}, a novel watermarking framework that integrates three synergistic innovations: (1) anchor-synchronized phase recovery for maintaining detection integrity under insertion/deletion attacks, (2) RG-balanced vocabulary modulation that dynamically partitions lexicons via contextual hashing to preserve generation quality, and (3) Hamming-based error correction enabling single-bit error rectification through algebraic coding. 
Comprehensive evaluations across question answering (ELI5), summarization (CNN/DM), and code generation (HumanEval) demonstrate state-of-the-art performance: ARGH-Mark achieves 96.2\% detection F1 under 30\% deletion attacks—surpassing MPAC by 21.9\%—while maintaining negligible quality degradation ($\Delta_{\text{KL}} = 0.07$ vs. 0.21 baseline). The framework reduces detection latency to 40ms (48$\times$ faster than hash-seed methods) and boosts bit recovery by 17.9$\times$ at BER=0.2 through Hamming correction. Crucially, watermarked text preserves 99.3\% of baseline utility across all tasks.
Theoretically, we establish linear-time detection complexity and quantifiable robustness bounds, proving that ARGH-Mark achieves a new Pareto frontier in the watermarking design space. By open-sourcing both implementation and the \textsc{WatErMark} benchmark with 10K adversarial samples, this work advances trustworthy deployment of generative AI in alignment-critical applications.

ARGH-Mark: Anchor-Synchronized Watermarking with Hamming Correction for Robust and Quality-Preserving LLM Attribution

Professional photography face retouching requires achieving a balance between texture preservation and quality increase, a problem that conventional automated methods find difficult to effectively handle. We provide a new lightweight neural architecture that converts conventional dodge-and-burn photography methods into predictions for learnable additive blend maps. Instead of rebuilding whole images, our method uses a small U-Net that predicts pixel-level changes, allowing for exact brightness adjustments while maintaining the original skin texture. With a 6MB model that operates effectively on common hardware, the technique produces high-quality results while preserving texture fidelity, which is crucial for professional applications. Experimental validation offers significant computing advantages while demonstrating competitive performance with current methods.

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