Singapore

Diffusion models have recently advanced video editing, yet
controllable editing remains challenging due to the need for
precise manipulation of diverse object properties. Current
methods require different control signal for diverse editing
tasks, which complicates model design and demands significant training resources. To address this, we propose O-DisCoEdit, a unified framework that incorporates a novel object
distortion control (O-DisCo). This signal, based on random
and adaptive noise, flexibly encapsulates a wide range of editing cues within a single representation. Paired with a “copy-form” preservation module for preserving non-edited regions,
O-DisCo-Edit enables efficient, high-fidelity editing through
an effective training paradigm. Extensive experiments and
comprehensive human evaluations consistently demonstrate
that O-DisCo-Edit surpasses both specialized and multitask
state-of-the-art methods across various video editing tasks.

AAAI 2026

O-DisCo-Edit: Object Distortion Control for Unified Realistic Video Editing

diffusion models for vision

image & video synthesis

applications

computational photography

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

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.

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.

Environmental modeling faces critical challenges in predicting ecosystem dynamics across unmonitored regions due to limited and geographically imbalanced observation data. This challenge is compounded by spatial heterogeneity, causing models to learn spurious patterns that fit only local data. Unlike conventional domain generalization, environmental modeling must preserve invariant physical relationships and temporal coherence during augmentation. In this paper, we introduce Generalizable Representation Enhancement via Auxiliary Transformations (GREAT), a framework that effectively augments available datasets to improve predictions in completely unseen regions. GREAT guides the augmentation process to ensure that the original governing processes can be recovered from the augmented data, and the inclusion of the augmented data leads to improved model generalization. Specifically, GREAT learns transformation functions at multiple layers of neural networks to augment both raw environmental features and temporal influence. They are refined through a novel bi-level training process that constrains augmented data to preserve key patterns of the original source data. We demonstrate GREAT's effectiveness on stream temperature prediction across six ecologically diverse watersheds in the eastern U.S., each containing multiple stream segments. Experimental results show that GREAT significantly outperforms existing methods in zero-shot scenarios. This work provides a practical solution for environmental applications where comprehensive monitoring is infeasible.

GREAT: Generalizable Representation Enhancement via Auxiliary Transformations for Zero-Shot Environmental Prediction

Universal multimodal embedding models are essential in various tasks. Existing approaches typically use in-batch mining to identify hard negatives by measuring the similarity of query-candidate pairs. However, these methods often struggle to capture subtle semantic differences among candidates and lack diversity in negative samples. Moreover, the embeddings exhibit limited discriminative ability in distinguishing false and hard negatives. In this paper, we leverage the advanced understanding capabilities of MLLMs to enhance representation learning, and present a novel Universal Multimodal Embedding(UniME-V2) model. Our approach first constructs a potential hard negative set through global retrieval. We then introduce the MLLM-as-a-Judge mechanism, which utilizes MLLMs to assess the semantic alignment of query-candidate pairs and generate soft semantic matching scores. These scores serve as a foundation for hard negative mining, mitigating the impact of false negatives and enabling the identification of diverse, high-quality hard negatives. Furthermore, the semantic matching scores are used as soft labels to mitigate the rigid one-to-one mapping constraint. By aligning the similarity matrix with the soft semantic matching score matrix, the model learns semantic distinctions among candidates, significantly enhancing its discriminative capacity. To further improve performance, we propose UniME-V2, a reranking model trained on our mined hard negatives through a joint pairwise and listwise optimization approach. We conduct comprehensive experiments on the MMEB benchmark and multiple retrieval tasks, demonstrating that our method achieves state-of-the-art performance across all tasks. The code, model and data are publicly available in https://garygutc.github.io/UniME-v2.

UniME-V2: MLLM-as-a-Judge for Universal Multimodal Embedding Learning

Accurate modeling of temporal point processes is critical for reliable event forecasting and informed decision-making. While historical event sequences provide a foundation for intensity estimation, existing approaches often neglect external covariates whose lagged effects impact future intensities across multiple temporal granularities. To address this gap, we propose Multi-Granularity Integration of External Covariates for Temporal Point Processes (METP), a framework for incorporating lagged external influences into intensity modeling. METP extracts periodic structures and decomposes external covariate series into multiple temporal granularities. At each granularity, a lag-aware calibration module is introduced to align covariates with event dynamics. Finally, a hierarchical mixture-of-experts strategy is employed to integrate the multi-granular external covariates with historical event embeddings, enabling a representation of the conditional intensity function with enhanced information. Extensive experiments on public and proprietary datasets demonstrate that METP consistently outperforms existing methods in predictive accuracy.

METP: Multi-Granularity Integration of External Covariates for Temporal Point Processes

Diffusion auction design is a new trend in mechanism design which extended the original incentive compatibility property to include buyers' private connection report. Reporting connections is equivalent to inviting their neighbors to join the auction in practice. The social welfare of a diffusion auction is collectively accumulated by all participants: reporting high valuations or inviting high-valuation neighbors. Because of this, we can measure each participant's contribution by the marginal social welfare increase due to her participation.

Therefore, in this paper, we introduce a new property called Shapley fairness to capture their social welfare contribution and to use it as a benchmark to guide our auction design for a fairer utility allocation. Not surprisingly, none of the existing diffusion auctions has ever approximated the fairness, because Shapley fairness depends on each buyer's own valuation and this dependence can easily violate incentive compatibility. Thus, we combat this challenge by proposing a new diffusion auction called Permutation Diffusion Auction (PDA) for selling $k$ homogeneous items, which is the first diffusion auction satisfying $\frac{1}{k+1}$-Shapley fairness, incentive compatibility and individual rationality. Furthermore, PDA can be extended to the general combinatorial auction setting where the literature did not discover meaningful diffusion auctions yet.

Fair Diffusion Auctions

Machine learning has tremendously benefited from graphics processing units (GPUs) to accelerate training and inference by several orders of magnitude. However, this success has not been replicated in general and exact combinatorial optimization. Our key contribution is to propose the first general-purpose discrete constraint programming solver fully implemented on GPU. It is based on integer interval bound propagation and backtracking search. The two main ingredients are (1) ternary constraint network optimized for GPU architectures, and (2) an on-demand subproblems generation strategy. Our constraint solving algorithm is significantly simpler than those found in optimized CPU constraint solvers, yet is competitive with sequential solvers in the MiniZinc 2024 challenge.

A GPU-based Constraint Programming Solver

Vertical Federated Learning (VFL) enables multiple clients with feature-partitioned data to collaboratively train models while preserving privacy by transmitting embeddings instead of raw data. However, such embeddings can still expose sensitive attributes (e.g., gender or race) unrelated to the target task, making them vulnerable to attribute inference attacks. Most existing privacy strategies may provide extra protection, but at the cost of reduced accuracy and excessive privacy budget. In this paper, we propose a novel equilibrium-driven VFL framework with selective privacy protection for sensitive attributes that are difficult to isolate from embeddings, thereby enhancing local privacy with minor accuracy compromise. We introduce two key innovations: (1) a NashCoder, which incorporates a surrogate head to jointly optimize accuracy and privacy; (2) an adaptive decomposition strategy based on Shapley values, which dynamically decomposes the global objective for distributed optimization from an equilibrium perspective. We theoretically analyze our framework and empirically evaluate it on three public datasets against five baselines, demonstrating significant improvements in the accuracy-privacy trade-off under various privacy settings. Extensive experimental results support our theoretical analysis.

Equilibrium-Driven Vertical Federated Learning with Selective Privacy Protection

The "Internet of Agents" (IoA) paradigm, where complex goals are met via large-scale, dynamic Agent-to-Agent (A2A) interactions, creates a vast attack surface for covert channels hidden within seemingly normal activities. Existing security models ignore this hidden communication, and traditional steganography, which generates static carriers, is ill-suited for the interactive, event-driven nature of A2A dialogues. To address this critical gap, we make three contributions. First, we establish a formal threat model for interactive agent ecosystems, defining two hierarchical security properties: statistical imperceptibility (IND-STAT) for entire communication traces and the stronger intention imperceptibility (IND-INT) for individual agent events. Second, we propose the Covert Communication Agent Protocol $\Pi_{\text{CCAP}}$ as a practical instantiation of our model. Third, we comprehensively evaluate $\Pi_{\text{CCAP}}$ across the critical dimensions of capacity, robustness, and imperceptibility, demonstrating its ability to facilitate reliable communication while satisfying our rigorous security requirements. Our work provides a foundational framework to analyze and defend against this new class of threats in the IoA.

Whispering Agents: A Event-Driven Covert Communication Protocol for the Internet of Agents

Flow matching and diffusion bridge models have emerged as leading paradigms in generative speech enhancement, modeling stochastic processes between paired noisy and clean speech signals based on principles such as flow matching, score matching, and Schrödinger bridge. In this paper, we present a framework that unifies existing flow and diffusion bridge models by interpreting them as constructions of Gaussian probability paths with varying means and variances between paired data. Furthermore, we investigate the underlying consistency between the training/inference procedures of these generative models and conventional predictive models. Our analysis reveals that each sampling step of a well-trained flow or diffusion bridge model optimized with a data prediction loss is theoretically analogous to executing predictive speech enhancement. Motivated by this insight, we introduce an enhanced bridge model that integrates an effective probability path design with key elements from predictive paradigms, including improved network architecture, tailored loss functions, and optimized training strategies. Experiments on denoising and dereverberation tasks demonstrate that the proposed method outperforms existing flow and diffusion baselines with fewer parameters and reduced computational complexity. The results also highlight that the inherently predictive nature of this generative framework imposes limitations on its achievable upper-bound performance.

Rethinking Flow and Diffusion Bridge Models for Speech Enhancement

Artificial intelligence holds great promise for clinical decision-making, yet developing models capable of complex diagnostic reasoning across diverse, real-world settings remains a major challenge. Existing benchmarks typically focus on single-image, single-turn tasks and fail to capture the longitudinal, multimodal nature of clinical workflows. We introduce MedAtlas, a new benchmark for evaluating large language models (LLMs) on realistic medical reasoning tasks that are multi-round, multi-modal, and multi-task. MedAtlas supports four key tasks: open-ended QA, closed QA, multi-image reasoning, and multi-turn multimodal multiple-choice QA. Each case features sequential diagnostic stages with diverse imaging modalities (e.g., CT, MRI, PET, ultrasound, X-ray) interleaved with textual histories and dialogue turns, requiring integrated reasoning across images and clinical text. Beyond answer-level supervision, MedAtlas provides structured gold-standard labels for clinical findings and five core medical knowledge aspects: pathophysiology, epidemiology, manifestations, imaging, and treatment. To capture diagnostic fidelity, we propose two new evaluation metrics: Stage Chain Accuracy and Error Propagation Suppression. Benchmarking a range of general and domain-specific models reveals substantial performance gaps in multi-stage clinical reasoning. MedAtlas offers a challenging testbed to advance the development of robust and trustworthy medical AI.

MedAtlas: Evaluating LLMs for Multi-Round, Multi-Task Medical Reasoning Across Diverse Imaging Modalities and Clinical Text

We present HI-SLAM2, a geometry-aware Gaussian SLAM system
that achieves fast and accurate monocular scene
reconstruction using only RGB input. Existing Neural SLAM
or 3DGS-based SLAM methods often trade off between
rendering quality and geometry accuracy, our research
demonstrates that both can be achieved simultaneously with
RGB input alone. The key idea of our approach is to enhance
the ability for geometry estimation by combining
easy-to-obtain monocular priors with learning-based dense
SLAM, and then using 3D Gaussian splatting as our core map
representation to efficiently model the scene. Upon loop
closure, our method ensures on-the-fly global consistency
through efficient pose graph bundle adjustment and instant
map updates by explicitly deforming the 3D Gaussian units
based on anchored keyframe updates. Furthermore, we
introduce a grid-based scale alignment strategy to maintain
improved scale consistency in prior depths for finer depth
details. Through extensive experiments on Replica, ScanNet,
Waymo Open, ETH3D SLAM and ScanNet++ datasets, we
demonstrate significant improvements over existing Neural
SLAM methods and even surpass RGB-D-based methods in both
reconstruction and rendering quality. The project page and
source code are available at https://hi-slam2.github.io/.

Downloads

Next from AAAI 2026

GREAT: Generalizable Representation Enhancement via Auxiliary Transformations for Zero-Shot Environmental Prediction

Similar lecture

DUnE: Dataset for Unified Editing | VIDEO

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from AAAI 2026

GREAT: Generalizable Representation Enhancement via Auxiliary Transformations for Zero-Shot Environmental Prediction

Similar lecture

DUnE: Dataset for Unified Editing | VIDEO

Downloads