Singapore

Weight Averaging (WA) has emerged as a powerful technique for enhancing generalization by promoting convergence to a flat loss landscape, which correlates with stronger out-of-distribution performance. However, applying WA directly to multi-modal domain generalization (MMDG) is challenging: differences in optimization speed across modalities lead WA to overfit to faster-converging ones in early stages, suppressing the contribution of slower yet complementary modalities, thereby hindering effective modality fusion and skewing the loss surface toward sharper, less generalizable minima.
To address this issue, we propose MBCD, a unified collaborative distillation framework that retains WA&#39;s flatness-inducing advantages while overcoming its shortcomings in multi-modal contexts. MBCD begins with adaptive modality dropout in the student model to curb early-stage bias toward dominant modalities. A gradient consistency constraint then aligns learning signals between uni-modal branches and the fused representation, encouraging coordinated and smoother optimization. Finally, a WA-based teacher conducts cross-modal distillation by transferring fused knowledge to each uni-modal branch, which strengthens cross-modal interactions and steer convergence toward flatter solutions. 
Extensive experiments on MMDG benchmarks show that MBCD consistently outperforms existing methods, achieving superior accuracy and robustness across diverse unseen domains.

AAAI 2026

Modality-Balanced Collaborative Distillation for Multi-Modal Domain Generalization

multi-modal domain generalization

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ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

Weight Averaging (WA) has emerged as a powerful technique for enhancing generalization by promoting convergence to a flat loss landscape, which correlates with stronger out-of-distribution performance. However, applying WA directly to multi-modal domain generalization (MMDG) is challenging: differences in optimization speed across modalities lead WA to overfit to faster-converging ones in early stages, suppressing the contribution of slower yet complementary modalities, thereby hindering effective modality fusion and skewing the loss surface toward sharper, less generalizable minima.
To address this issue, we propose MBCD, a unified collaborative distillation framework that retains WA's flatness-inducing advantages while overcoming its shortcomings in multi-modal contexts. MBCD begins with adaptive modality dropout in the student model to curb early-stage bias toward dominant modalities. A gradient consistency constraint then aligns learning signals between uni-modal branches and the fused representation, encouraging coordinated and smoother optimization. Finally, a WA-based teacher conducts cross-modal distillation by transferring fused knowledge to each uni-modal branch, which strengthens cross-modal interactions and steer convergence toward flatter solutions. 
Extensive experiments on MMDG benchmarks show that MBCD consistently outperforms existing methods, achieving superior accuracy and robustness across diverse unseen domains.

technical paper

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Hyperedge prediction plays a critical role in high-order relational modeling with hypergraphs, yet most existing methods primarily focus on sampling strategies or local aggregation within candidate hyperedges. These approaches often overlook global structural dependencies that are essential for learning expressive node and hyperedge representations. In this paper, we propose **HyperNoRA**, a novel self-supervised hypergraph learning framework that integrates global node-level relation awareness with contrastive learning. Specifically, we construct a global node relation graph that captures both direct and indirect structural correlations, which guides a structure-aware aggregator to enhance node representations with informative global context. To prevent over-smoothing and maintain discriminability, a contrastive learning module is introduced to align representations across graph augmentations while separating semantically dissimilar nodes. Extensive experiments on several benchmark datasets demonstrate that **HyperNoRA** consistently outperforms state-of-the-art baselines, and ablation studies verify the effectiveness of its key components.

HyperNoRA: Hyperedge Prediction via Node-Level Relation-Aware Self-Supervised Hypergraph Learning

The proliferation of high-quality text from Large Language Models (LLMs) demands reliable and efficient detection methods. While existing training-free approaches show promise, they often rely on surface-level statistics and overlook fundamental signal properties of the text generation process. In this work, we reframe detection as a signal processing problem, introducing a novel paradigm that analyzes the sequence of token log-probabilities in the frequency domain. By systematically analyzing the signal's spectral properties using the global Discrete Fourier Transform (DFT) and the local Short-Time Fourier Transform (STFT), we find that human-written text consistently exhibits significantly higher spectral energy. This higher energy reflects the larger-amplitude fluctuations inherent in human writing compared to the suppressed dynamics of LLM-generated text.
Based on this key insight, we construct SpecDetect, a detector built on a single, robust feature from the global DFT: DFT total energy. We also propose an enhanced version, SpecDetect++, which incorporates a sampling discrepancy mechanism to further boost robustness. Extensive experiments demonstrate that our approach outperforms the state-of-the-art model while running in nearly half the time. Our work introduces a new, efficient, and interpretable pathway for LLM-generated text detection, showing that classical signal processing techniques offer a surprisingly powerful solution to this modern challenge. Our code is provided in the supplementary material.

SpecDetect: Simple, Fast, and Training-Free Detection of LLM-Generated Text via Spectral Analysis

Satellite-acquired optical remote sensing imagery is extensively applied in time-critical applications like traffic surveillance and evaluation of natural disasters. However, clouds, as a common atmospheric phenomenon, frequently obscure observation. 
Current approaches aim to restore visibility in cloud-obscured regions, yet they typically fall short in the presence of dense cloud cover, which are exceedingly prevalent in remote sensing imagery. Alternative approaches rely on the satellite revisit cycle, frequently surpassing ten days, a duration impractical for genuine application scenarios due to target changes and bandwidth limitations.
To address these issues, this paper proposes SCo-Cloud,
a novel satellite constellation collaboration framework for cloud-aware onboard-computed imaging and transmission, 
which consists of Center-Sat and Edge-Sats.
We propose onboard thin cloud removal and re-imaging region location models to locate the impact of clouds.
We further design a novel multi-satellite scheduling strategy to eliminate clouds.
The models above are integrated within the Center-Sat, with the nearby Edge-Sats collaborating in tandem to execute re-imaging assignments.
Furthermore, to facilitate in-depth research, we have meticulously developed a cloud-covered target detection dataset.
Comprehensive experiments have conclusively demonstrated that SCo-Cloud effectively surpasses the limitations inherent in current approaches, providing accurate and timely responses within the domain of Earth observation.

SCo-Cloud: Satellite Constellation Collaboration for Cloud-Aware Onboard-Computed Imaging and Transmission

Event co-occurrences have been proven effective for event argument extraction (EAE) in previous studies; 
however, few have considered intra- and inter-event role correlations. Since role varies among different event types, event structure heterogeneity and overlap pose significant challenges to EAE. To address this issue, we propose a Role Correlation Structure-Enhanced model for Multi-Event Argument Extraction (RoSE), capable of capturing both heterogeneity and overlap of event structures through modeling role correlations. The proposed RoSE model employs a joint context-prompts input, role-centric graph-guided encoder (RoGE), and role-specific information fusion (RoIF). The RoGE is designed to enhance the intra- and inter-event role correlation between prompts and their corresponding event contexts. The RoIF module utilizes intra-event role information to improve multi-event arguments extraction.
Extensive experiments on four widely-used benchmarks (RAMS, WikiEvents, MLEE, and ACE05) demonstrate that our proposed approach achieves state-of-the-art performance, validating the effectiveness of incorporating both intra- and inter-event role correlations.
Our code is available here.

RoSE: A Role Correlation Structure-Enhanced Model for Multi-Event Argument Extraction

Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in multimodal understanding and generation by integrating visual and textual data. However, these models frequently exhibit object hallucination problems: generating outputs that are inconsistent with the input image. Existing improved methods for mitigating hallucinations still suffer from two key limitations: dynamic approaches based on logits or attention mechanisms risk suppressing valuable linguistic priors, whereas static methods that employ fixed intervention vectors lack the flexibility to adapt to diverse images and questions. To address these issues, we propose RFI (Rectified Flow Intervention), a novel approach that harnesses the linear trajectory design of rectified flow for input-specific adaptation and employs gradient correction to ensure coherent generation, effectively combining the adaptability of dynamic methods with the stability of static ones. RFI dynamically predicts latent-space intervention vectors while requiring only a single forward pass in LVLMs per question, achieving computational efficiency (1.09x latency overhead for 100 new tokens). Extensive experiments show RFI significantly reduces hallucinations, achieving superior performance compared to existing advanced methods, highlighting its effectiveness as a lightweight plug-and-play method for reducing LVLM's hallucination in practical applications. The code will be available at https://github.com/purepasser-by/RFI.

RFI: Rectified Flow Intervention for Mitigating Object Hallucination in Large Vision-Language Models

Data poisoning is a training-time attack that undermines the trustworthiness of learned models. In a targeted data poisoning attack, an adversary manipulates the training dataset to alter the classification of a targeted test point. Given the typically large size of training dataset, manual detection of poisoning is difficult. An alternative is to automatically measure a dataset's robustness against such an attack, which is the focus of this paper. We consider a threat model wherein an adversary can only perturb the labels of the training dataset, with knowledge limited to the hypothesis space of the victim's model. In this setting, we prove that finding the robustness is an NP-Complete problem, even when hypotheses are linear classifiers. To overcome this, we present a technique that finds lower and upper bounds of robustness. Our implementation of the technique computes these bounds efficiently in practice for many publicly available datasets. We experimentally demonstrate the effectiveness of our approach. Specifically, a poisoning exceeding the identified robustness bounds significantly impacts test point classification. We are also able to compute these bounds in many more cases where state-of-the-art techniques fail.

On Robustness of Linear Classifiers to Targeted Data Poisoning

While large language model (LLM)-based user profiling of- fers significant potential for personalization, most existing approaches rely on empirical heuristics and lack grounding in the psychological mechanism that drive human behavior. In this paper, we introduce TRIPLE (Theory-guided Rea- soning for Intent and habIt Profiling with LLMs for pEr- sonalization), a novel framework that systematically inte- grates dual-process theory from social psychology into LLM- based user modeling. TRIPLE (1) constructs a habitual be- havior profile by identifying repeated patterns over time to model automatic responses; (2) builds an intentional behav- ior profile by inferring user attitudes, subjective norms and perceived behavioral control based on the Theory of Planned Behavior (TPB); and (3) generates behavioral rationale that reveal the interaction between habitual and intentional pro- cesses to predict user behavior in context-specific situations. We evaluate TRIPLE on five personalization tasks from the LaMP benchmark using multiple open-source LLMs. Re- sults show that TRIPLE consistently outperforms existing in- context learning methods, with especially pronounced gains on complex generative tasks such as headline and title gen- eration.Qualitative analyses further demonstrate that the pro- files and reasoning paths generated by TRIPLE provide inter- pretable and psychologically grounded explanations of user behavior. These findings provide strong evidence that incor- porating validated behavioral theories into LLM-based per- sonalization enhances both predictive performance and inter- pretability, paving a way for theory-driven, socio-cognitively informed user modeling.

TRIPLE: Theory-Driven Integration of Planned and Habitual Behaviors for LLM-based Personalization

Communication is essential in coordinating the behaviors of multiple agents. However, existing methods primarily emphasize content, timing, and partners for information sharing, often neglecting the critical aspect of integrating shared information. This gap can significantly impact agents' ability to understand and respond to complex, uncertain interactions, thus affecting overall communication efficiency. To address this issue, we introduce M2I2, a novel framework designed to enhance the agents' capabilities to assimilate and utilize received information effectively. M2I2 equips agents with advanced capabilities for masked state modeling and joint-action prediction, enriching their perception of environmental uncertainties and facilitating the anticipation of teammates' intentions. This approach ensures that agents are furnished with both comprehensive and relevant information, bolstering more informed and synergistic behaviors. Moreover, we propose a Dimensional Rational Network, innovatively trained via a meta-learning paradigm, to identify the importance of dimensional pieces of information, evaluating their contributions to decision-making and auxiliary tasks. Then, we implement an importance-based heuristic for selective information masking and sharing. This strategy optimizes the efficiency of masked state modeling and the rationale behind information sharing. We evaluate M2I2 across diverse multi-agent tasks, the results demonstrate its superior performance, efficiency, and generalization capabilities, over existing state-of-the-art methods in various complex scenarios.

M2I2: Learning Efficient Multi-Agent Communication via Masked State Modeling and Intention Inference

Frontier AI developers are relying on layers of safeguards to protect against catastrophic misuse of AI systems. Anthropic guards their latest Claude 4 Opus model using one such defense pipeline, and other frontier developers including Google DeepMind and OpenAI pledge to soon deploy similar defenses. However, the security of such pipelines is unclear, with limited prior work evaluating or attacking these pipelines. We address this gap by developing and red-teaming an open-source defense pipeline. 1 First, we find that a novel few-shot-prompted input and output classifier outperforms state-of-the-art open-weight safeguard model ShieldGemma across three attacks and two datasets, reducing the attack success rate (ASR) to 0% on the catastrophic misuse dataset ClearHarm. Second, we introduce a STaged AttaCK (STACK) procedure that achieves 71% ASR on ClearHarm in a black-box attack against the few-shot-prompted classifier pipeline. Finally, we also evaluate STACK in a transfer setting, achieving 33% ASR, providing initial evidence that it is feasible to design attacks with no access to the target pipeline. We conclude by suggesting specific mitigations that developers could use to thwart staged attacks.

STACK: Adversarial Attacks on LLM Safeguard Pipelines

Many organizations are increasingly relying on unstructured documents such as PDFs and scanned forms to sup-port downstream large language model (LLM) services, including search, summarization, and recommendation. However, traditional OCR systems struggle with diverse layouts of documents, leading to frequent errors and high costs of labor. So, this study developed DATALUX - a robust document layout system that transforms unstructured documents into structured, machine-readable data suitable for automation. Built on a transformer-based detector, DATA-LUX incorporates several modules for layout refinement, text-visual fusion, and layer-wise optimization to improve coherence and generalization across diverse layouts. Around January 2025, we successfully deployed DATA-LUX into one of the largest academic content service firms (Nurimedia) in South Korea. This firm faced the challenge of extracting metadata and references from thousands of academic papers submitted in various formats. Also, the existing LLM-based tools provided unreliable results. So, they needed to process them manually, creating bottlenecks in both labor and time. However, DATALUX enabled the automatic structuring of over 100,000 research papers a year, improving extraction accuracy to over 97%, reducing costs by more than USD 185K annually, and accelerating processing speed by 8.7 times. These deployment results suggest that DATALUX enables scalable and efficient document automation in complex and high-volume environments successfully. We thus believe that our DATALUX has a significant impact on both academia and industry practices.

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