Singapore

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.

AAAI 2026

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

social simulator

multimodal large language model

harmful meme detection

multi-agent system

knowledge distillation

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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.

FLRQ: Faster LLM Quantization with Flexible Low-Rank Matrix Sketching

In recent years, lossy compression algorithms such as H.264/AVC, H.265/HEVC, and H.266/VVC have been proposed and widely applied in image and video encoding. However, these compression algorithms inevitably introduce various complex types of compression artifacts, which severely degrade image quality. Although existing methods have attempted to remove artifacts through filter design or probabilistic prior modeling, they are often effective only for specific types of artifacts, lacking generalization and adaptability. To address this, we propose a novel image compression artifacts removal model: ARMoE, which combines multiple frequency domain transformations with the Mixture of Experts (MoE). Considering the frequency distribution and energy distribution differences of images, we introduce various frequency domain transformations as expert branches and use the Sparse Activation Strategy to adaptively select the optimal frequency domain expert to suppress compression artifacts, achieving an efficient artifacts removal method. Furthermore, we reencode and decode multiple original uncompressed high-quality datasets, including DF2K and Kodak24, using the VTM-20.0 codec under the H.266/VVC standard, constructing a more challenging artifacts dataset. We conducted rigorous comparative experiments with current state-of-the-art image restoration methods and the results demonstrate that ARMoE exhibits outstanding image restoration capability.

Compression Artifacts Removal for VVC with Frequency Domain Mixture of Experts Network

We are exploring the problem of building an automated reasoning procedure that adaptively tunes the high-level solving strategy for a given problem. There are two main distinctive characteristics of our approach: tuning is performed solely online, unlike the common use of tuning as an offline process; and tuning data comes exclusively from the given instance, so we do not rely on the availability of similar benchmarks and can work with unique challenging instances. Our approach builds on top of the divide-and-conquer paradigm that naturally serves partitioned sub-problems for an automated tuning algorithm to obtain a good solving strategy. We demonstrate performance improvement on two classes of important problems--SAT-solving and neural network verification--and show that our method can learn unconventional solving strategies in some cases.

Cubing for Tuning

Deep research agents have attracted growing attention for their potential to orchestrate multi-stage research workflows, spanning literature synthesis, methodological design, and empirical verification. Despite these strides, evaluating their research capability faithfully is rather challenging due to the difficulty of collecting frontier research questions that genuinely capture researchers’ attention and intellectual curiosity. To address this gap, we introduce \textit{\textbf{DeepResearch Arena}}, a benchmark grounded in academic seminars that capture rich expert discourse and interaction, better reflecting real-world research environments and reducing the risk of data leakage. To automatically construct DeepResearch Arena, we propose a Multi-Agent Hierarchical Task Generation (MAHTG) system that extracts research-worthy inspirations from seminar transcripts. The MAHTG system further translates research-worthy inspirations into high-quality research tasks, ensuring the traceability of research task formulation while filtering noise. With the MAHTG system, we curate DeepResearch Arena with over 10,000 high-quality research tasks from over 200 academic seminars, spanning 12 disciplines, such as literature, history, and science. Our extensive evaluation shows that DeepResearch Arena presents substantial challenges for current state-of-the-art agents, with clear performance gaps observed across different models.

Deep Research Arena: The First Exam of LLMs’ Research Abilities via Seminar-Grounded Tasks

Ensemble Temporal Prediction Model-as-a-Service (ETP-MaaS) has become crucial in areas such as financial modeling, weather forecasting, and cloud monitoring, managing a dynamic set of base models and workers. Real-world systems face a two-fold challenge of dynamic collaboration and heterogeneity that current methods overlook. At the model level, data volatility dictates that optimal performance requires identifying and weighting constantly shifting subgroups of base models, not just individual ones. At the system level, these model groups must be efficiently mapped to a pool of heterogeneous and dynamically available workers. Existing solutions fail to co-optimally address these intertwined tasks, treating models as independent entities and employing simplistic allocation rules, resulting in poor accuracy and resource inefficiency. To this end, we introduce WIET, an efficient ETP-MaaS system that innovates in weight distribution and worker allocation to tackle these dynamics. We model evolving group behaviors among base models and propose a novel group temporal locality-enhanced multi-label classification method for highly adaptive weighting. Additionally, we develop an efficient, multi-dimensional worker allocation method powered by hybrid heuristic optimization, effectively reducing bottlenecks and resource waste. Extensive experiments have shown that WIET consistently outperforms state-of-the-art methods in terms of model accuracy, latency, and resource usage across various workloads and prediction tasks.

WIET: Harmonizing Group-aware Model Weighting and Worker Allocation for Ensemble Temporal Prediction MaaS

Large language models (LLMs) utilize key-value (KV) cache to store historical information during sequence processing. The size of KV cache grows linearly as the length of the sequence extends, which seriously affects memory usage and decoding efficiency. Current methods for KV cache eviction typically utilize the last window from the pre-filling phase as queries to compute the KV importance scores for eviction. Although this scheme is simple to implement, it tends to overly focus on local information, potentially leading to the neglect or omission of crucial global information. To mitigate this issue, we propose **Judge Q**, a novel training method which incorporates a soft token list. This method only tunes the model’s embedding layer at a low training cost. By concatenating the soft token list at the end of the input sequence, we train these tokens' attention map to the original input sequence to align with that of the actual decoded tokens. In this way, the queries corresponding to the soft tokens can effectively capture global information and better evaluate the importance of the keys and values within the KV cache, thus maintaining decoding quality when KV cache is evicted. Under the same eviction budget, our method exhibits less performance degradation compared to existing eviction approaches. We validate our approach through experiments conducted on models such as Llama-3.1-8B-Instruct and Mistral-7B-Instruct-v0.3, using benchmarks including LongBench, RULER, and Needle-in-a-Haystack. Results indicate an improvement of approximately 1 point on the LongBench and over 3 points on RULER. This proposed methodology can be seamlessly integrated into existing open-source models with minimal training overhead, thereby enhancing performance in KV cache eviction scenarios.

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