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The Information Bottleneck (IB) principle facilitates effective representation learning by preserving label-relevant information while compressing irrelevant information. However, its strong reliance on accurate labels makes it inherently vulnerable to label noise, prevalent in real-world scenarios, resulting in significant performance degradation and overfitting. To address this issue, we propose **LaT-IB**, a novel **La**bel-Noise Resistan**T** **I**nformation **B**ottleneck method which introduces a *&quot;Minimal-Sufficient-Clean&quot;* (MSC) criterion. Instantiated as a mutual information regularizer to retain task-relevant information while discarding noise, MSC addresses standard IB’s vulnerability to noisy label supervision. To achieve this, LaT-IB employs a noise-aware latent disentanglement that decomposes the latent representation into components aligned with to the clean label space and the noise space. Theoretically, we first derive mutual information bounds for each component of our objective including prediction, compression, and disentanglement, and moreover prove that optimizing it encourages representations invariant to input noise and separates clean and noisy label information.
Furthermore, we design a three-phase training framework: Warmup, Knowledge Injection and Robust Training, to progressively guide the model toward noise-resistant representations. Extensive experiments demonstrate that LaT-IB achieves superior robustness and efficiency under label noise, significantly enhancing robustness and applicability in real-world scenarios with label noise.

AAAI 2026

Is the Information Bottleneck Robust Enough? Towards Label-Noise Resistant Information Bottleneck Learning

deep learning algorithms

interpretable ml

machine learning

The Information Bottleneck (IB) principle facilitates effective representation learning by preserving label-relevant information while compressing irrelevant information. However, its strong reliance on accurate labels makes it inherently vulnerable to label noise, prevalent in real-world scenarios, resulting in significant performance degradation and overfitting. To address this issue, we propose **LaT-IB**, a novel **La**bel-Noise Resistan**T** **I**nformation **B**ottleneck method which introduces a *"Minimal-Sufficient-Clean"* (MSC) criterion. Instantiated as a mutual information regularizer to retain task-relevant information while discarding noise, MSC addresses standard IB’s vulnerability to noisy label supervision. To achieve this, LaT-IB employs a noise-aware latent disentanglement that decomposes the latent representation into components aligned with to the clean label space and the noise space. Theoretically, we first derive mutual information bounds for each component of our objective including prediction, compression, and disentanglement, and moreover prove that optimizing it encourages representations invariant to input noise and separates clean and noisy label information.
Furthermore, we design a three-phase training framework: Warmup, Knowledge Injection and Robust Training, to progressively guide the model toward noise-resistant representations. Extensive experiments demonstrate that LaT-IB achieves superior robustness and efficiency under label noise, significantly enhancing robustness and applicability in real-world scenarios with label noise.

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.<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 rapid advancement of image-generation technologies has made it possible for anyone to create photorealistic images using generative models, raising significant security concerns. To mitigate malicious use, tracing the origin of such images is essential. Reconstruction-based attribution methods offer a promising solution, but they often suffer from reduced accuracy and high computational costs when applied to state‑of‑the‑art (SOTA) models. To address these challenges, we propose AEDR (AutoEncoder Double-Reconstruction), a novel training‑free attribution method designed for generative models with continuous autoencoders. Unlike existing reconstruction‑based approaches that rely on the value of a single reconstruction loss, AEDR performs two consecutive reconstructions using the model’s autoencoder, and adopts the ratio of these two reconstruction losses as the attribution signal. This signal is further calibrated using the image homogeneity metric to improve accuracy, which inherently cancels out absolute biases caused by image complexity, with autoencoder‑based reconstruction ensuring superior computational efficiency. Experiments on eight top latent diffusion models show that AEDR achieves 25.5% higher attribution accuracy than existing reconstruction‑based methods, with requiring only 1% of the computational time. Our code will be available at https://github.com/wangchao0708/AEDR.

AEDR: Training-Free AI-Generated Image Attribution via Autoencoder Double-Reconstruction

High-Dynamic-Range Wide-Color-Gamut (HDR-WCG) technology is becoming increasingly widespread, driving a growing need for converting Standard Dynamic Range (SDR) content to HDR. Existing methods primarily rely on fixed tone mapping operators, which struggle to handle the diverse appearances and degradations commonly present in real-world SDR content. To address this limitation, we propose a generalized SDR-to-HDR framework that enhances robustness by learning attribute-disentangled representations. Central to our approach is Realistic Attribute-Disentangled Representation Learning (RealRep), which explicitly disentangles luminance and chrominance components to capture intrinsic content variations across different SDR distributions. Furthermore, we design a Luma-/Chroma-aware negative exemplar generation strategy that constructs degradation-sensitive contrastive pairs, effectively modeling tone discrepancies across SDR styles. Building on these attribute-level priors, we introduce the Degradation-Domain Aware Controlled Mapping Network (DDACMNet), a lightweight, two-stage framework that performs adaptive hierarchical mapping guided by a control-aware normalization mechanism. DDACMNet dynamically modulates the mapping process via degradation-conditioned features, enabling robust adaptation across diverse degradation domains. Extensive experiments demonstrate that RealRep consistently outperforms state-of-the-art methods in both generalization and perceptually faithful HDR color gamut reconstruction.

RealRep: Generalized SDR-to-HDR Conversion via Attribute-Disentangled Representation Learning

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.

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

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.

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