OpenRaiser/Intern-Atlas huggingface dataset:
Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering
Venue: arXiv cs.LG 2025
Year: 2025

Tabular data synthesis for supervised learning ('SL') model training is gaining popularity in industries such as healthcare, finance, and retail. Despite the progress made in tabular data generators, models trained with synthetic data often underperform compared to those trained with original data. This low SL utility of synthetic data stems from class imbalance exaggeration and SL data relationship overlooked by tabular generator. To address these challenges, we draw inspirations from techniques in emerging data-centric artificial intelligence and elucidate Pruning and ReOrdering ('PRRO'), a novel pipeline that integrates data-centric techniques into tabular data synthesis. PRRO incorporates data pruning to guide the table generator towards observations with high signal-to-noise ratio, ensuring that the class distribution of synthetic data closely matches that of the original data. Besides, PRRO employs a column reordering algorithm to align the data modeling structure of generators with that of SL models. These two modules enable PRRO to optimize SL utility of synthetic data. Empirical experiments on 22 public datasets show that synthetic data generated using PRRO enhances predictive performance compared to data generated without PRRO. Specifically, synthetic replacement of original data yields an average improvement of 26.74% and up to 871.46% improvement using PRRO, while synthetic appendant to original data results with PRRO-generated data results in an average improvement of 6.13% and up to 200.32%. Furthermore, experiments on six highly imbalanced datasets show that PRRO enables the generator to produce synthetic data with a class distribution that resembles the original data more closely, achieving a similarity improvement of 43%. Through PRRO, we foster a seamless integration of data synthesis to subsequent SL prediction, promoting quality and accessible data analysis.
ReDemon UI: Reactive Synthesis by Demonstration for Web UI
Venue: arXiv cs.HC 2025
Year: 2025

ReDemon UI synthesizes React applications from user demonstrations, enabling designers and non-expert programmers to create UIs that integrate with standard UI prototyping workflows. Users provide a static mockup sketch with event handler holes and demonstrate desired runtime behaviors by interacting with the rendered mockup and editing the sketch. ReDemon UI identifies reactive data and synthesizes a React program with correct state update logic. We utilize enumerative synthesis for simple UIs and LLMs for more complex UIs.
BlueGlass: A Framework for Composite AI Safety
Venue: arXiv cs.AI 2025
Year: 2025

As AI systems become increasingly capable and ubiquitous, ensuring the safety of these systems is critical. However, existing safety tools often target different aspects of model safety and cannot provide full assurance in isolation, highlighting a need for integrated and composite methodologies. This paper introduces BlueGlass, a framework designed to facilitate composite AI safety workflows by providing a unified infrastructure enabling the integration and composition of diverse safety tools that operate across model internals and outputs. Furthermore, to demonstrate the utility of this framework, we present three safety-oriented analyses on vision-language models for the task of object detection: (1) distributional evaluation, revealing performance trade-offs and potential failure modes across distributions; (2) probe-based analysis of layer dynamics highlighting shared hierarchical learning via phase transition; and (3) sparse autoencoders identifying interpretable concepts. More broadly, this work contributes foundational infrastructure and findings for building more robust and reliable AI systems.
DualDub: Video-to-Soundtrack Generation via Joint Speech and Background Audio Synthesis
Venue: arXiv cs.MM 2025
Year: 2025

While recent video-to-audio (V2A) models can generate realistic background audio from visual input, they largely overlook speech, an essential part of many video soundtracks. This paper proposes a new task, video-to-soundtrack (V2ST) generation, which aims to jointly produce synchronized background audio and speech within a unified framework. To tackle V2ST, we introduce DualDub, a unified framework built on a multimodal language model that integrates a multimodal encoder, a cross-modal aligner, and dual decoding heads for simultaneous background audio and speech generation. Specifically, our proposed cross-modal aligner employs causal and non-causal attention mechanisms to improve synchronization and acoustic harmony. Besides, to handle data scarcity, we design a curriculum learning strategy that progressively builds the multimodal capability. Finally, we introduce DualBench, the first benchmark for V2ST evaluation with a carefully curated test set and comprehensive metrics. Experimental results demonstrate that DualDub achieves state-of-the-art performance, generating high-quality and well-synchronized soundtracks with both speech and background audio.
Could you be wrong: Debiasing LLMs using a metacognitive prompt for improving human decision making
Venue: arXiv cs.AI 2025
Year: 2025

Identifying bias in LLMs is ongoing. Because they are still in development, what is true today may be false tomorrow. We therefore need general strategies for debiasing that will outlive current models. Strategies developed for debiasing human decision making offer one promising approach as they incorporate an LLM-style prompt intervention designed to bring latent knowledge into awareness during decision making. LLMs trained on vast amounts of information contain information about potential biases, counter-arguments, and contradictory evidence, but that information may only be brought to bear if prompted. Metacognitive prompts developed in the human decision making literature are designed to achieve this, and as I demonstrate here, they show promise with LLMs. The prompt I focus on here is "could you be wrong?" Following an LLM response, this prompt leads LLMs to produce additional information, including why they answered as they did, errors, biases, contradictory evidence, and alternatives, none of which were apparent in their initial response. Indeed, this metaknowledge often reveals that how LLMs and users interpret prompts are not aligned. Here I demonstrate this prompt using a set of questions taken from recent articles about LLM biases, including implicit discriminatory biases and failures of metacognition. "Could you be wrong" prompts the LLM to identify its own biases and produce cogent metacognitive reflection. I also present another example involving convincing but incomplete information, which is readily corrected by the metacognitive prompt. In sum, this work argues that human psychology offers a new avenue for prompt engineering, leveraging a long history of effective prompt-based improvements to human decision making.
Wavelet-Enhanced Neural ODE and Graph Attention for Interpretable Energy Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Accurate forecasting of energy demand and supply is critical for optimizing sustainable energy systems, yet it is challenged by the variability of renewable sources and dynamic consumption patterns. This paper introduces a neural framework that integrates continuous-time Neural Ordinary Differential Equations (Neural ODEs), graph attention, multi-resolution wavelet transformations, and adaptive learning of frequencies to address the issues of time series prediction. The model employs a robust ODE solver, using the Runge-Kutta method, paired with graph-based attention and residual connections to better understand both structural and temporal patterns. Through wavelet-based feature extraction and adaptive frequency modulation, it adeptly captures and models diverse, multi-scale temporal dynamics. When evaluated across seven diverse datasets: ETTh1, ETTh2, ETTm1, ETTm2 (electricity transformer temperature), and Waste, Solar, and Hydro (renewable energy), this architecture consistently outperforms state-of-the-art baselines in various forecasting metrics, proving its robustness in capturing complex temporal dependencies. Furthermore, the model enhances interpretability through SHAP analysis, making it suitable for sustainable energy applications.
Introducing the Swiss Food Knowledge Graph: AI for Context-Aware Nutrition Recommendation
Venue: arXiv cs.AI 2025
Year: 2025

AI has driven significant progress in the nutrition field, especially through multimedia-based automatic dietary assessment. However, existing automatic dietary assessment systems often overlook critical non-visual factors, such as recipe-specific ingredient substitutions that can significantly alter nutritional content, and rarely account for individual dietary needs, including allergies, restrictions, cultural practices, and personal preferences. In Switzerland, while food-related information is available, it remains fragmented, and no centralized repository currently integrates all relevant nutrition-related aspects within a Swiss context. To bridge this divide, we introduce the Swiss Food Knowledge Graph (SwissFKG), the first resource, to our best knowledge, to unite recipes, ingredients, and their substitutions with nutrient data, dietary restrictions, allergen information, and national nutrition guidelines under one graph. We establish a LLM-powered enrichment pipeline for populating the graph, whereby we further present the first benchmark of four off-the-shelf (<70 B parameter) LLMs for food knowledge augmentation. Our results demonstrate that LLMs can effectively enrich the graph with relevant nutritional information. Our SwissFKG goes beyond recipe recommendations by offering ingredient-level information such as allergen and dietary restriction information, and guidance aligned with nutritional guidelines. Moreover, we implement a Graph-RAG application to showcase how the SwissFKG's rich natural-language data structure can help LLM answer user-specific nutrition queries, and we evaluate LLM-embedding pairings by comparing user-query responses against predefined expected answers. As such, our work lays the foundation for the next generation of dietary assessment tools that blend visual, contextual, and cultural dimensions of eating.
Play Style Identification Using Low-Level Representations of Play Traces in MicroRTS
Venue: arXiv cs.LG 2025
Year: 2025

Play style identification can provide valuable game design insights and enable adaptive experiences, with the potential to improve game playing agents. Previous work relies on domain knowledge to construct play trace representations using handcrafted features. More recent approaches incorporate the sequential structure of play traces but still require some level of domain abstraction. In this study, we explore the use of unsupervised CNN-LSTM autoencoder models to obtain latent representations directly from low-level play trace data in MicroRTS. We demonstrate that this approach yields a meaningful separation of different game playing agents in the latent space, reducing reliance on domain expertise and its associated biases. This latent space is then used to guide the exploration of diverse play styles within studied AI players.
Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving
Venue: arXiv cs.AR 2025
Year: 2025

Transformers are the driving force behind today's Large Language Models (LLMs), serving as the foundation for their performance and versatility. Yet, their compute and memory costs grow with sequence length, posing scalability challenges for long-context inferencing. In response, the algorithm community is exploring alternative architectures, such as state space models (SSMs), linear attention, and recurrent neural networks (RNNs), which we refer to as post-transformers. This shift presents a key challenge: building a serving system that efficiently supports both transformer and post-transformer LLMs within a unified framework. To address this challenge, we analyze the performance characteristics of transformer and post-transformer LLMs. Despite their algorithmic differences, both are fundamentally limited by memory bandwidth under batched inference due to attention in transformers and state updates in post-transformers. Further analyses suggest two additional insights: (1) state update operations, unlike attention, incur high hardware cost, making per-bank PIM acceleration inefficient, and (2) different low-precision arithmetic methods offer varying accuracy-area tradeoffs, while we identify Microsoft's MX as the Pareto-optimal choice. Building on these insights, we design Pimba as an array of State-update Processing Units (SPUs), each shared between two banks to enable interleaved access to PIM. Each SPU includes a State-update Processing Engine (SPE) that comprises element-wise multipliers and adders using MX-based quantized arithmetic, enabling efficient execution of state update and attention operations. Our evaluation shows that, compared to LLM-optimized GPU and GPU+PIM systems, Pimba achieves up to 4.1x and 2.1x higher token generation throughput, respectively.
A Training-Free, Task-Agnostic Framework for Enhancing MLLM Performance on High-Resolution Images
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in vision-language understanding, reasoning, and generation. However, they struggle with tasks requiring fine-grained localization and reasoning in high-resolution images. This constraint stems from the fact that MLLMs are fine-tuned with fixed image resolution to align with the pre-trained image encoder used in MLLM. Consequently, feeding high-resolution images directly into MLLMs leads to poor generalization due to a train-test resolution discrepancy, while downsampling these images-although ensuring consistency-compromises fine-grained visual details and ultimately degrades performance. To address this challenge, we propose Extract Candidate then Predict (ECP), a novel training-free, task-agnostic two-stage framework designed to enhance MLLM performance on high-resolution images. The key intuition behind ECP is that while MLLMs struggle with high-resolution images, their predictions on downsampled images still contain implicit localization cues. By first identifying candidate region using the coarse prediction and then predicting the final output based on candidate region, ECP effectively preserves fine-grained details while mitigating the challenges posed by high-resolution data. We validate our framework on 4K GUI grounding and 4K, 8K MLLM perception, achieving +21.3%, +5.8%, +5.2% absolute improvement compared to baseline respectively, demonstrating its effectiveness. Code is available at https://github.com/yenncye/ECP.
DNS Tunneling: Threat Landscape and Improved Detection Solutions
Venue: arXiv cs.CR 2025
Year: 2025

Detecting Domain Name System (DNS) tunneling is a significant challenge in security due to its capacity to hide harmful actions within DNS traffic that appears to be normal and legitimate. Traditional detection methods are based on rule-based approaches or signature matching methods that are often insufficient to accurately identify such covert communication channels. This research is about effectively detecting DNS tunneling. We propose a novel approach to detect DNS tunneling with machine learning algorithms. We combine machine learning algorithms to analyze the traffic by using features extracted from DNS traffic. Analyses results show that the proposed approach is a good candidate to detect DNS tunneling accurately.
FTCFormer: Fuzzy Token Clustering Transformer for Image Classification
Venue: arXiv cs.CV 2025
Year: 2025

Transformer-based deep neural networks have achieved remarkable success across various computer vision tasks, largely attributed to their long-range self-attention mechanism and scalability. However, most transformer architectures embed images into uniform, grid-based vision tokens, neglecting the underlying semantic meanings of image regions, resulting in suboptimal feature representations. To address this issue, we propose Fuzzy Token Clustering Transformer (FTCFormer), which incorporates a novel clustering-based downsampling module to dynamically generate vision tokens based on the semantic meanings instead of spatial positions. It allocates fewer tokens to less informative regions and more to represent semantically important regions, regardless of their spatial adjacency or shape irregularity. To further enhance feature extraction and representation, we propose a Density Peak Clustering-Fuzzy K-Nearest Neighbor (DPC-FKNN) mechanism for clustering center determination, a Spatial Connectivity Score (SCS) for token assignment, and a channel-wise merging (Cmerge) strategy for token merging. Extensive experiments on 32 datasets across diverse domains validate the effectiveness of FTCFormer on image classification, showing consistent improvements over the TCFormer baseline, achieving gains of improving 1.43% on five fine-grained datasets, 1.09% on six natural image datasets, 0.97% on three medical datasets and 0.55% on four remote sensing datasets. The code is available at: https://github.com/BaoBao0926/FTCFormer/tree/main.
Rows and Capabilities as Modal Effects
Venue: arXiv cs.PL 2025
Year: 2025

Effect handlers allow programmers to model and compose computational effects modularly. Effect systems statically guarantee that all effects are handled. Several recent practical effect systems are based on either row polymorphism or capabilities. However, there remains a gap in understanding the precise relationship between effect systems with such disparate foundations. The main difficulty is that in both row-based and capability-based systems, effect tracking is typically entangled with other features such as functions. We propose a uniform framework for encoding, analysing, and comparing effect systems. Our framework exploits and generalises modal effect types, a recent novel effect system which decouples effect tracking from functions via modalities. Modalities offer fine-grained control over when and how effects are tracked, enabling us to express different strategies for effect tracking. We give encodings as macro translations from existing row-based and capability-based effect systems into our framework and show that these encodings preserve types and semantics. Our encodings reveal the essence of effect tracking mechanisms in different effect systems, enable a direct analysis on their differences, and provide practical insights on language design.
Qualitative Analysis of the Teacher and Student Roles in Pair Programming
Venue: arXiv cs.SE 2025
Year: 2025

Background: Pair programming is a well-established and versatile agile practice. Previous research has found it to involve far more different roles than the well-known Driver and Observer/Navigator roles. Pair programming often involves heavy knowledge transfer from mainly one partner to the other. Objective: Understand how to fill the ensuing Teacher and Student roles well (positive behavioral patterns). Understand how they may break (anti-patterns). Method: Open coding and axial coding of 17 recorded pair programming sessions with 18 developers from 5 German software companies, plus interviews with 6 different developers from 4 other German companies. Results: We describe six facets of effective Teacher behavior (e.g. Prioritizing Knowledge Transfer) and two facets of effective Student behavior (e.g. Expressing Knowledge Wants). We describe four harmful would-be-Teacher behaviors (e.g. Pushing Unwanted Knowledge), and one harmful would-be-Student behavior (Failing to Provide a Back Channel). Conclusions: The role facets can serve as learning goals and to-do list for developers who want to develop strong pair programming skill. The anti-patterns can serve as warnings for one's own general behavior and as triggers for immediate meta-discussion if they occur within a pairing session.
Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix Approach
Venue: arXiv cs.CL 2025
Year: 2025

Despite advancements in Natural Language Processing (NLP), models remain vulnerable to adversarial attacks, such as synonym substitutions. While prior work has focused on improving robustness for feed-forward and convolutional architectures, the robustness of recurrent networks and modern state space models (SSMs), such as S4, remains understudied. These architectures pose unique challenges due to their sequential processing and complex parameter dynamics. In this paper, we introduce a novel regularization technique based on Growth Bound Matrices (GBM) to improve NLP model robustness by reducing the impact of input perturbations on model outputs. We focus on computing the GBM for three architectures: Long Short-Term Memory (LSTM), State Space models (S4), and Convolutional Neural Networks (CNN). Our method aims to (1) enhance resilience against word substitution attacks, (2) improve generalization on clean text, and (3) providing the first systematic analysis of SSM (S4) robustness. Extensive experiments across multiple architectures and benchmark datasets demonstrate that our method improves adversarial robustness by up to 8.8% over existing baselines. These results highlight the effectiveness of our approach, outperforming several state-of-the-art methods in adversarial defense. Codes are available at https://github.com/BouriMohammed/GBM
A Matrix Completion Approach for the Construction of MDP Convolutional Codes
Venue: arXiv cs.IT 2025
Year: 2025

Maximum Distance Profile (MDP) convolutional codes are an important class of channel codes due to their maximal delay-constrained error correction capabilities. The design of MDP codes has attracted significant attention from the research community. However, only limited attention was given to addressing the complexity of encoding and decoding operations. This paper aims to reduce encoding complexity by constructing partial unit-memory MDP codes with structured and sparse generator matrices. In particular, we present a matrix completion framework that extends a structured superregular matrix (e.g., Cauchy) over a small field to a sparse sliding generator matrix of an MDP code. We show that the proposed construction can reduce the encoding complexity compared to the current state-of-the-art MDP code designs.
Communication Complexity is NP-hard
Venue: arXiv cs.CC 2025
Year: 2025

In the paper where he first defined Communication Complexity, Yao asks: \emph{Is computing $CC(f)$ (the 2-way communication complexity of a given function $f$) NP-complete?} The problem of deciding whether $CC(f) \le k$, when given the communication matrix for $f$ and a number $k$, is easily seen to be in NP. Kushilevitz and Weinreb have shown that this problem is cryptographically hard. Here we show it is NP-hard.
4D-Animal: Freely Reconstructing Animatable 3D Animals from Videos
Venue: arXiv cs.CV 2025
Year: 2025

Existing methods for reconstructing animatable 3D animals from videos typically rely on sparse semantic keypoints to fit parametric models. However, obtaining such keypoints is labor-intensive, and keypoint detectors trained on limited animal data are often unreliable. To address this, we propose 4D-Animal, a novel framework that reconstructs animatable 3D animals from videos without requiring sparse keypoint annotations. Our approach introduces a dense feature network that maps 2D representations to SMAL parameters, enhancing both the efficiency and stability of the fitting process. Furthermore, we develop a hierarchical alignment strategy that integrates silhouette, part-level, pixel-level, and temporal cues from pre-trained 2D visual models to produce accurate and temporally coherent reconstructions across frames. Extensive experiments demonstrate that 4D-Animal outperforms both model-based and model-free baselines. Moreover, the high-quality 3D assets generated by our method can benefit other 3D tasks, underscoring its potential for large-scale applications. The code is released at https://github.com/zhongshsh/4D-Animal.