Singapore

Large language model-based multi-agent systems (LLM-MAS) effectively accomplish complex and dynamic tasks through inter-agent communication, but this reliance introduces substantial safety vulnerabilities. Existing attack methods targeting LLM-MAS either compromise agent internals or rely on direct and overt persuasion, which limit their effectiveness, adaptability, and stealthiness. In this paper, we propose MAST, a Multi-round Adaptive Stealthy Tampering framework designed to exploit communication vulnerabilities within the system. MAST integrates Monte Carlo Tree Search with Direct Preference Optimization to train an attack policy model that adaptively generates effective multi-round tampering strategies. Furthermore, to preserve stealthiness, we impose dual semantic and embedding similarity constraints during the tampering process. Comprehensive experiments across diverse tasks, communication architectures, and LLMs demonstrate that MAST consistently achieves high attack success rates while significantly enhancing stealthiness compared to baselines. These findings highlight the effectiveness, stealthiness, and adaptability of MAST, underscoring the need for robust communication safeguards in LLM-MAS.

AAAI 2026

Attack the Messages, Not the Agents: A Multi-round Adaptive Stealthy Tampering Framework for LLM-MAS

adversarial agents

agent communication

reinforcement learning

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 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.

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.

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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.

Diffusion planning is a promising method for learning high-performance policies from offline data. To avoid the impact of discrepancies between planning and reality on performance, previous works generate new plans at each time step. However, this incurs significant computational overhead and leads to lower decision frequencies, and frequent plan switching may also affect performance. In contrast, humans might create detailed short-term plans and more general, sometimes vague, long-term plans, and adjust them over time. Inspired by this, we propose the Temporal Diffusion Planner (TDP) which improves decision efficiency by distributing the denoising steps across the time dimension. TDP begins by generating an initial plan that becomes progressively more vague over time. At each subsequent time step, rather than generating an entirely new plan, TDP updates the previous one with a small number of denoising steps. This reduces the average number of denoising steps, improving decision efficiency. Additionally, we introduce an automated replanning mechanism to prevent significant deviations between the plan and reality. Experiments on D4RL show that, compared to previous works that generate new plans every time step, TDP significantly improves the decision-making frequency by 11-24.8 times while achieving higher or comparable performance.

Efficient Diffusion Planning with Temporal Diffusion

The sim-to-real gap, where agents trained in a simulator face significant performance degradation during testing, is a fundamental challenge in reinforcement learning. Extensive works adopt the framework of distributionally robust RL, to learn a policy that acts robustly under worst case environment shift. Within this framework, our objective is to devise algorithms that are sample efficient with interactive data collection and large state spaces. By assuming $d$-rectangularity of environment dynamic shift, we identify a fundamental hardness result for learning in online Markov game, and address it by adopting minimum value assumption. Then, a novel least square value iteration type algorithm, DR-CCE-LSI, with exploration bonus devised specifically for multiple agent, is proposed to find an $\\varepsilon-$approximate robust Coarse Correlated Equilibrium(CCE). To obtain sample efficient learning, we find that: when the feature mapping function satisfies certain properties, our algorithm, DR-CCE-LSI, is able to achieve $\\epsilon-$approximate CCE with a regret bound of $\\mathcal{O}\\{dH\\min\\{H,\\frac{1}{\\min\\{\\sigma_i\\}}\\}\\sqrt{K}\\}$, where $K$ is the number of interacting episodes, $H$ is the horizon length, $d$ is the feature dimension, and $\\sigma_i$ represents the uncertainty level of player $i$. Our work introduces the first sample-efficient algorithm for this setting, matches the best result so far in single agent setting, and achieves minimax optimal sample complexity in terms of the feature dimension $d$. Meanwhile, we also conduct simulation study to validate the efficacy of our algorithm in learning a robust equilibrium.

Distributionally Robust Online Markov Game with Linear Function Approximation

Multimodal keyphrase generation (MKP) aims to extract a concise set of keyphrases that capture the essential meaning of paired image–text inputs, enabling structured understanding, indexing, and retrieval of multimedia data across the web and social platforms. Success in this task demands effectively bridging the semantic gap between heterogeneous modalities. While multimodal large language models (MLLMs) achieve superior cross-modal understanding by leveraging massive pretraining on image-text corpora, we observe that they often struggle with modality bias and fine-grained intra-modal feature extraction. This oversight leads to a lack of robustness in real-world scenarios where multimedia data is noisy, along with incomplete or misaligned modalities. To address this problem, we propose AimKP, a novel framework that explicitly reinforces intra-modal semantic learning in MLLMs while preserving cross-modal alignment. AimKP incorporates two core innovations: (i) Progressive Modality Masking, which forces fine-grained feature extraction from corrupted inputs by progressively masking modality information during training; (ii) Gradient-based Filtering, that identifies and discards noisy samples, preventing them from corrupting the model’s core cross-modal learning. Extensive experiments validate AimKP’s effectiveness in multimodal keyphrase generation and its robustness across different scenarios.

Augmenting Intra-Modal Understanding in MLLMs for Robust Multimodal Keyphrase Generation

Ring artifacts are prevalent in 3D cone-beam computed tomography (CBCT) due to non-ideal responses of X-ray detectors, substantially affecting image quality and diagnostic reliability. Existing state-of-the-art (SOTA) ring artifact reduction (RAR) methods rely on supervised learning with large-scale paired CT datasets. While effective in-domain, supervised methods tend to struggle to fully capture the physical characteristics of ring artifacts, leading to pronounced performance drops in complex real-world acquisitions. Moreover, their scalability to 3D CBCT is limited by high memory demands. In this work, we propose Riner, a new unsupervised RAR method. Based on a theoretical analysis of ring artifact formation, we reformulate RAR as a multi-parameter inverse problem, where the non-ideal responses of X-ray detectors are parameterized as solvable physical variables. Using a new differentiable forward model, Riner can jointly learn the implicit neural representation of artifact-free images and estimate the physical parameters directly from CT measurements, without external training data. Additionally, Riner is memory-friendly due to its ray-based optimization, enhancing its usability in large-scale 3D CBCT. Experiments on both simulated and real-world datasets show Riner outperforms existing SOTA supervised methods. The code will be publicly released for improving reproducibility.

Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCT

Internet memes serve as widely distributed multimodal social content that conveys complex ideas through metaphorical expressions, often containing harmful implications that make accurate harmful meme detection an important problem. Reasoning knowledge extracted from large language models plays a crucial role in recent advances in harmful meme detection. However, these methods only perform reasoning analysis on memes from a single opinion, ignoring that memes are essentially products of group consensus, where their true meaning interpretation highly depends on the collision and aggregation process of diverse user viewpoints. To address this problem, we propose a Social Graph of Thought Reasoning Enhancement (SGoTRE) framework for harmful meme detection. The SGoTRE contains three key steps: First, through multi-agent simulation technology, we obtain diverse chains of thought that represent the parsing logic of users from different backgrounds toward memes, authentically restoring the diversity characteristics of group cognition. Second, we construct a Social Graph of Thought (SGoT) that effectively integrates multi-chain reasoning processes and structurally expresses the consensus and diversity of viewpoints among users. Finally, we utilize the SGoT for cognitive distillation, internalizing multi-opinion reasoning logic into a single multimodal large model SGoT-R1 to achieve efficient and interpretable harmful meme detection. Experimental results show that SGoT-R1 significantly improves detection performance on mainstream datasets. Particularly on the most challenging FHM dataset, SGoT-R1 achieves an 8.9% improvement over state-of-the-art models.

SGoT-R1: Social Graph of Thought Reasoning-Enhanced Multimodal Large Language Model for Harmful Meme Detection

Text-to-image person re-identification (TIReID) aims to retrieve the most relevant pedestrian images from an image gallery based on natural language descriptions. Recent studies have achieved significant performance improvements by leveraging Masked Language Modeling (MLM) to align fine-grained information through local matching. However, in the text feature extraction, randomly masking text tokens may disrupt the semantic relationships between these local tokens, leading to feature misalignment; on the other hand, from an image feature perspective, redundant patches in pedestrian images hinder the information interaction across modalities. Moreover, the presence of noisy image-text pairs further complicates the learning process, as the model may be misled into recognizing incorrect patterns. To address these issues, we propose a robust fine-grained local alignment framework based on Key Phrase Dynamic Mask (KPDM). First, we strengthen the semantic relationships between text tokens by implementing a "adjective + noun" phrase-level masking strategy, mitigating local misalignment. Additionally, we integrate cross-layer importance estimation to highlight key pedestrian image representations while removing redundant image features. Building on this, we design a novel frequency-based masked language loss (FMLM) to supervise fine-grained semantic-level local alignment. Second, we propose a trusted consensus partitioning mechanism, utilizing intra-identity image-text similarity distributions to identify noisy pairs, enhancing the model robustness. Extensive experiments show that our method achieves 67.95\% Rank-1 and 51.88\% mAP on the RSTPReid dataset, exceeding the previous state-of-the-art by 2.6\% and 1\%. Furthermore, KPDM achieves Rank-1 accuracies of 75.97\% on the CUHK-PEDES dataset and 67.78\% on the ICFG-PEDES dataset, outperforming earlier methods.

KPDM: Key Phrase Dynamic Masking for Robust Text-to-Image Person Retrieval

The number of $n$-gram features grows exponentially in $n$, making it computationally demanding to compute the most frequent $n$-grams even for $n$ as small as $3$. Motivated by our production machine learning system built on $n$-gram features, we ask: is it possible to accurately, deterministically, and quickly recover the top-$k$ most frequent $n$-grams? We devise a multi-pass algorithm called {\it Intergrams} that constructs candidate $n$-grams from the preceding $(n-1)$-grams. By designing this algorithm with hardware in mind, our approach yields more than an order of magnitude speedup (up to 33$\times$!) over the next known fastest algorithm, even when similar optimization are applied to the other algorithm. Using the empirical power-law distribution over n-grams, we also provide theory to inform the efficacy of our multi-pass approach. Our code is available at https://github.com/anongitrepos/Intergrams.

Intermediate N-Gramming: Deterministic and Fast N-Grams for Large N and Large Datasets

In constraint programming and related paradigms, a modeller specifies their problem in a modelling language for a solver to search and return its solution(s). Using high-level modelling languages such as ESSENCE, a modeller may express their problems in terms of abstract structures. These are structures not natively supported by the solvers, and so they have to be transformed into or represented as other structures before solving. For example, nested sets are abstract structures, and they can be represented as matrices in constraint solvers. Many problems contain symmetries and one very common and highly successful technique used in constraint programming is to “break” symmetries, to avoid searching for symmetric solutions. This can speed up the solving process by many orders of magnitude. Most of these symmetry-breaking techniques involve placing some kind of ordering for the variables of the problem, and picking a particular member under the symmetries, usually the smallest. Unfortunately, applying this technique to abstract variables produces a very
large number of complex constraints that perform poorly in practice. In this paper, we demonstrate a new incomplete method of breaking the symmetries of abstract structures by better exploiting their representations. We apply the method in breaking the symmetries arising from indistinguishable objects, a commonly occurring type of symmetry, and show that our method is faster than the previous methods proposed in (Akgün et al. 2025).

Faster Symmetry Breaking Constraints for Abstract Structures

Driving world models are used to simulate futures by video generation based on the condition of the current state and actions. However, current models often suffer serious error accumulations when predicting the long-term future, which limits the practical application. Recent studies utilize the Diffusion Transformer (DiT) as the backbone of driving world models to improve learning flexibility. However, these models are always trained on short video clips (high fps and short duration), and multiple roll-out generations struggle to produce consistent and reasonable long videos due to the training-inference gap. To this end, we propose several solutions to build a simple yet effective long-term driving world model. First, we hierarchically decouple world model learning into large motion learning and bidirectional continuous motion learning. Then, considering the continuity of driving scenes, we propose a simple distillation method where fine-grained video flows are self-supervised signals for coarse-grained flows. The distillation is designed to improve the coherence of infinite video generation. The coarse-grained and fine-grained modules are coordinated to generate long-term and temporally coherent videos. In the public benchmark NuScenes, compared with the state-of-the-art front-view model, our model improves FVD by 27\% and reduces inference time by 85\% for the video task of generating 110+ frames.

Fine-flow Distilling Coarse-flow Video Generation for Long-Term Driving World Model

Traditional post-training quantization (PTQ) is considered an effective approach to reduce model size and accelerate inference of large-scale language models (LLMs). However, existing low-rank PTQ methods require costly fine-tuning to determine a compromise rank for diverse data and layers in large models, failing to exploit their full potential. Additionally, the current SVD-based low-rank approximation compounds the computational overhead. In this work, we thoroughly analyze the varying effectiveness of low-rank approximation across different layers in representative models. Accordingly, we introduce Flexible Low-Rank Quantization (FLRQ), a novel solution designed to quickly identify the accuracy-optimal ranks and aggregate them to achieve minimal storage combinations. FLRQ comprises two powerful components, Rank1-Sketch-based Flexible Rank Selection (R1-FLR) and Best Low-rank Approximation under Clipping (BLC). R1-FLR applies the R1-Sketch with Gaussian projection for the fast low-rank approximation, enabling outlier-aware rank extraction for each layer. Meanwhile, BLC aims at minimizing the low-rank quantization error under the scaling and clipping strategy through an iterative method. FLRQ demonstrates strong effectiveness and robustness in comprehensive experiments, achieving state-of-the-art performance in both quantization quality and algorithm efficiency.

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Efficient Diffusion Planning with Temporal Diffusion

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