OpenRaiser/Intern-Atlas huggingface dataset:
CNN-based Image Models Verify a Hypothesis that The Writers of Cuneiform Texts Improved Their Writing Skills When Studying at the Age of Hittite Empire
Venue: arXiv cs.CL 2025
Year: 2025

A cuneiform tablet KBo 23.1 ++/KUB 30.38, which is known to represent a text of Kizzuwatna rituals, was written by two writers with almost identical content in two iterations. Unlike other cuneiform tablets that contained information such as myths, essays, or business records, the reason why ancient people left such tablets for posterity remains unclear. To study this problem, we develop a new methodology by analyzing images of a tablet quantitatively using CNN (Convolutional Neural Network)-based image models, without segmenting cuneiforms one-by-one. Our data-driven methodology implies that the writer writing the first half was a `teacher' and the other writer was a `student' who was training his skills of writing cuneiforms. This result has not been reached by classical linguistics. We also discuss related conclusions and possible further directions for applying our method and its generalizations.
KDC-Diff: A Latent-Aware Diffusion Model with Knowledge Retention for Memory-Efficient Image Generation
Venue: arXiv cs.CV 2025
Year: 2025

The growing adoption of generative AI in real-world applications has exposed a critical bottleneck in the computational demands of diffusion-based text-to-image models. In this work, we propose KDC-Diff, a novel and scalable generative framework designed to significantly reduce computational overhead while maintaining high performance. At its core, KDC-Diff designs a structurally streamlined U-Net with a dual-layered knowledge distillation strategy to transfer semantic and structural representations from a larger teacher model. Moreover, a latent-space replay-based continual learning mechanism is incorporated to ensure stable generative performance across sequential tasks. Evaluated on benchmark datasets, our model demonstrates strong performance across FID, CLIP, KID, and LPIPS metrics while achieving substantial reductions in parameter count, inference time, and FLOPs. KDC-Diff offers a practical, lightweight, and generalizable solution for deploying diffusion models in low-resource environments, making it well-suited for the next generation of intelligent and resource-aware computing systems.
Predicting Diabetes Using Machine Learning: A Comparative Study of Classifiers
Venue: arXiv cs.LG 2025
Year: 2025

Diabetes remains a significant health challenge globally, contributing to severe complications like kidney disease, vision loss, and heart issues. The application of machine learning (ML) in healthcare enables efficient and accurate disease prediction, offering avenues for early intervention and patient support. Our study introduces an innovative diabetes prediction framework, leveraging both traditional ML techniques such as Logistic Regression, SVM, Naïve Bayes, and Random Forest and advanced ensemble methods like AdaBoost, Gradient Boosting, Extra Trees, and XGBoost. Central to our approach is the development of a novel model, DNet, a hybrid architecture combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers for effective feature extraction and sequential learning. The DNet model comprises an initial convolutional block for capturing essential features, followed by a residual block with skip connections to facilitate efficient information flow. Batch Normalization and Dropout are employed for robust regularization, and an LSTM layer captures temporal dependencies within the data. Using a Kaggle-sourced real-world diabetes dataset, our model evaluation spans cross-validation accuracy, precision, recall, F1 score, and ROC-AUC. Among the models, DNet demonstrates the highest efficacy with an accuracy of 99.79% and an AUC-ROC of 99.98%, establishing its potential for superior diabetes prediction. This robust hybrid architecture showcases the value of combining CNN and LSTM layers, emphasizing its applicability in medical diagnostics and disease prediction tasks.
A Reinforcement Learning Framework for Application-Specific TCP Congestion-Control
Venue: arXiv cs.NI 2025
Year: 2025

The Congestion Control (CC) module plays a critical role in the Transmission Control Protocol (TCP), ensuring the stability and efficiency of network data transmission. The CC approaches that are commonly used these days employ heuristics-based rules to adjust the sending rate. Due to their heuristics-based nature, these approaches are not only unable to adapt to changing network conditions but are also agnostic to the diverse requirements that different applications often have. Recently, several learning-based CC approaches have been proposed to adapt to changing network conditions. Unfortunately, they are not designed to take application requirements into account. Prior heuristics-based as well as learning-based CC approaches focus on achieving a singular objective, which is often to maximize throughput, even though a lot of applications care more about latency, packet losses, jitter, and different combinations of various network metrics. Motivated by this, we propose a Deep Reinforcement Learning (DRL) based CC framework, namely ASC, which allows any application to specify any arbitrary objectives that the network traffic of that application should achieve and is able to swiftly adapt to the changes in the objectives of the applications as well as to the changes in the network conditions. Our ASC framework further employs a client-server architecture that serves two purposes: 1) it makes ASC highly scalable in terms of the arrival and departure of TCP connections, and 2) it makes ASC very lightweight for the nodes maintaining the TCP connections. We implemented and extensively evaluated ASC in a variety of settings. Our results show that it can not only achieve various objectives but also outperforms prior approaches even in the specific objectives that those approaches were designed to achieve.
DialogueReason: Rule-Based RL Sparks Dialogue Reasoning in LLMs
Venue: arXiv cs.AI 2025
Year: 2025

We propose DialogueReason, a reasoning paradigm that uncovers the lost roles in monologue-style reasoning models, aiming to boost diversity and coherency of the reasoning process. Recent advances in RL-based large reasoning models have led to impressive long CoT capabilities and high performance on math and science benchmarks. However, these reasoning models rely mainly on monologue-style reasoning, which often limits reasoning diversity and coherency, frequently recycling fixed strategies or exhibiting unnecessary shifts in attention. Our work consists of an analysis of monologue reasoning patterns and the development of a dialogue-based reasoning approach. We first introduce the Compound-QA task, which concatenates multiple problems into a single prompt to assess both diversity and coherency of reasoning. Our analysis shows that Compound-QA exposes weaknesses in monologue reasoning, evidenced by both quantitative metrics and qualitative reasoning traces. Building on the analysis, we propose a dialogue-based reasoning, named DialogueReason, structured around agents, environment, and interactions. Using PPO with rule-based rewards, we train open-source LLMs (Qwen-QWQ and Qwen-Base) to adopt dialogue reasoning. We evaluate trained models on MATH, AIME, and GPQA datasets, showing that the dialogue reasoning model outperforms monologue models under more complex compound questions. Additionally, we discuss how dialogue-based reasoning helps enhance interpretability, facilitate more intuitive human interaction, and inspire advances in multi-agent system design.
DAPE: Dual-Stage Parameter-Efficient Fine-Tuning for Consistent Video Editing with Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Video generation based on diffusion models presents a challenging multimodal task, with video editing emerging as a pivotal direction in this field. Recent video editing approaches primarily fall into two categories: training-required and training-free methods. While training-based methods incur high computational costs, training-free alternatives often yield suboptimal performance. To address these limitations, we propose DAPE, a high-quality yet cost-effective two-stage parameter-efficient fine-tuning (PEFT) framework for video editing. In the first stage, we design an efficient norm-tuning method to enhance temporal consistency in generated videos. The second stage introduces a vision-friendly adapter to improve visual quality. Additionally, we identify critical shortcomings in existing benchmarks, including limited category diversity, imbalanced object distribution, and inconsistent frame counts. To mitigate these issues, we curate a large dataset benchmark comprising 232 videos with rich annotations and 6 editing prompts, enabling objective and comprehensive evaluation of advanced methods. Extensive experiments on existing datasets (BalanceCC, LOVEU-TGVE, RAVE) and our proposed benchmark demonstrate that DAPE significantly improves temporal coherence and text-video alignment while outperforming previous state-of-the-art approaches.
Seed1.5-VL Technical Report
Venue: arXiv cs.CV 2025
Year: 2025

We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter vision encoder and a Mixture-of-Experts (MoE) LLM of 20B active parameters. Despite its relatively compact architecture, it delivers strong performance across a wide spectrum of public VLM benchmarks and internal evaluation suites, achieving the state-of-the-art performance on 38 out of 60 public benchmarks. Moreover, in agent-centric tasks such as GUI control and gameplay, Seed1.5-VL outperforms leading multimodal systems, including OpenAI CUA and Claude 3.7. Beyond visual and video understanding, it also demonstrates strong reasoning abilities, making it particularly effective for multimodal reasoning challenges such as visual puzzles. We believe these capabilities will empower broader applications across diverse tasks. In this report, we mainly provide a comprehensive review of our experiences in building Seed1.5-VL across model design, data construction, and training at various stages, hoping that this report can inspire further research. Seed1.5-VL is now accessible at https://www.volcengine.com/ (Volcano Engine Model ID: doubao-1-5-thinking-vision-pro-250428)
Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures
Venue: arXiv cs.LG 2025
Year: 2025

How do neural language models acquire a language's structure when trained for next-token prediction? We address this question by deriving theoretical scaling laws for neural network performance on synthetic datasets generated by the Random Hierarchy Model (RHM) -- an ensemble of probabilistic context-free grammars designed to capture the hierarchical structure of natural language while remaining analytically tractable. Previously, we developed a theory of representation learning based on data correlations that explains how deep learning models capture the hierarchical structure of the data sequentially, one layer at a time. Here, we extend our theoretical framework to account for architectural differences. In particular, we predict and empirically validate that convolutional networks, whose structure aligns with that of the generative process through locality and weight sharing, enjoy a faster scaling of performance compared to transformer models, which rely on global self-attention mechanisms. This finding clarifies the architectural biases underlying neural scaling laws and highlights how representation learning is shaped by the interaction between model architecture and the statistical properties of data.
Knowledge Distillation for Enhancing Walmart E-commerce Search Relevance Using Large Language Models
Venue: arXiv cs.IR 2025
Year: 2025

Ensuring the products displayed in e-commerce search results are relevant to users queries is crucial for improving the user experience. With their advanced semantic understanding, deep learning models have been widely used for relevance matching in search tasks. While large language models (LLMs) offer superior ranking capabilities, it is challenging to deploy LLMs in real-time systems due to the high-latency requirements. To leverage the ranking power of LLMs while meeting the low-latency demands of production systems, we propose a novel framework that distills a high performing LLM into a more efficient, low-latency student model. To help the student model learn more effectively from the teacher model, we first train the teacher LLM as a classification model with soft targets. Then, we train the student model to capture the relevance margin between pairs of products for a given query using mean squared error loss. Instead of using the same training data as the teacher model, we significantly expand the student model dataset by generating unlabeled data and labeling it with the teacher model predictions. Experimental results show that the student model performance continues to improve as the size of the augmented training data increases. In fact, with enough augmented data, the student model can outperform the teacher model. The student model has been successfully deployed in production at Walmart.com with significantly positive metrics.
Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression
Venue: arXiv cs.CV 2025
Year: 2025

Visual Anomaly Detection (VAD) is a key task in industrial settings, where minimizing operational costs is essential. Deploying deep learning models within Internet of Things (IoT) environments introduces specific challenges due to limited computational power and bandwidth of edge devices. This study investigates how to perform VAD effectively under such constraints by leveraging compact, efficient processing strategies. We evaluate several data compression techniques, examining the tradeoff between system latency and detection accuracy. Experiments on the MVTec AD benchmark demonstrate that significant compression can be achieved with minimal loss in anomaly detection performance compared to uncompressed data. Current results show up to 80% reduction in end-to-end inference time, including edge processing, transmission, and server computation.
EmoVLM-KD: Fusing Distilled Expertise with Vision-Language Models for Visual Emotion Analysis
Venue: arXiv cs.MM 2025
Year: 2025

Visual emotion analysis, which has gained considerable attention in the field of affective computing, aims to predict the dominant emotions conveyed by an image. Despite advancements in visual emotion analysis with the emergence of vision-language models, we observed that instruction-tuned vision-language models and conventional vision models exhibit complementary strengths in visual emotion analysis, as vision-language models excel in certain cases, whereas vision models perform better in others. This finding highlights the need to integrate these capabilities to enhance the performance of visual emotion analysis. To bridge this gap, we propose EmoVLM-KD, an instruction-tuned vision-language model augmented with a lightweight module distilled from conventional vision models. Instead of deploying both models simultaneously, which incurs high computational costs, we transfer the predictive patterns of a conventional vision model into the vision-language model using a knowledge distillation framework. Our approach first fine-tunes a vision-language model on emotion-specific instruction data and then attaches a distilled module to its visual encoder while keeping the vision-language model frozen. Predictions from the vision language model and the distillation module are effectively balanced by a gate module, which subsequently generates the final outcome. Extensive experiments show that EmoVLM-KD achieves state-of-the-art performance on multiple visual emotion analysis benchmark datasets, outperforming the existing methods while maintaining computational efficiency. The code is available in https://github.com/sange1104/EmoVLM-KD.
The Language of Influence: Sentiment, Emotion, and Hate Speech in State Sponsored Influence Operations
Venue: arXiv cs.SI 2025
Year: 2025

State-sponsored influence operations (SIOs) have become a pervasive and complex challenge in the digital age, particularly on social media platforms where information spreads rapidly and with minimal oversight. These operations are strategically employed by nation-state actors to manipulate public opinion, exacerbate social divisions, and project geopolitical narratives, often through the dissemination of misleading or inflammatory content. Despite increasing awareness of their existence, the specific linguistic and emotional strategies employed by these campaigns remain underexplored. This study addresses this gap by conducting a comprehensive analysis of sentiment, emotional valence, and abusive language across 2 million tweets attributed to influence operations linked to China, Iran, and Russia, using Twitter's publicly released dataset of state-affiliated accounts. We identify distinct affective and rhetorical patterns that characterize each nation's digital propaganda. Russian campaigns predominantly deploy negative sentiment and toxic language to intensify polarization and destabilize discourse. In contrast, Iranian operations blend antagonistic and supportive tones to simultaneously incite conflict and foster ideological alignment. Chinese activities emphasize positive sentiment and emotionally neutral rhetoric to promote favorable narratives and subtly influence global perceptions. These findings reveal how state actors tailor their information warfare tactics to achieve specific geopolitical objectives through differentiated content strategies.
No Query, No Access
Venue: arXiv cs.CL 2025
Year: 2025

Textual adversarial attacks mislead NLP models, including Large Language Models (LLMs), by subtly modifying text. While effective, existing attacks often require knowledge of the victim model, extensive queries, or access to training data, limiting real-world feasibility. To overcome these constraints, we introduce the \textbf{Victim Data-based Adversarial Attack (VDBA)}, which operates using only victim texts. To prevent access to the victim model, we create a shadow dataset with publicly available pre-trained models and clustering methods as a foundation for developing substitute models. To address the low attack success rate (ASR) due to insufficient information feedback, we propose the hierarchical substitution model design, generating substitute models to mitigate the failure of a single substitute model at the decision boundary. Concurrently, we use diverse adversarial example generation, employing various attack methods to generate and select the adversarial example with better similarity and attack effectiveness. Experiments on the Emotion and SST5 datasets show that VDBA outperforms state-of-the-art methods, achieving an ASR improvement of 52.08\% while significantly reducing attack queries to 0. More importantly, we discover that VDBA poses a significant threat to LLMs such as Qwen2 and the GPT family, and achieves the highest ASR of 45.99% even without access to the API, confirming that advanced NLP models still face serious security risks. Our codes can be found at https://anonymous.4open.science/r/VDBA-Victim-Data-based-Adversarial-Attack-36EC/
Automated Repair of Ambiguous Problem Descriptions for LLM-Based Code Generation
Venue: arXiv cs.SE 2025
Year: 2025

The growing use of large language models (LLMs) has increased the importance of natural language (NL) in software engineering. However, ambiguity of NL can harm software quality, as unclear problem descriptions may lead to incorrect program generation. Detecting and resolving such ambiguity is challenging, motivating our introduction of the automated repair of ambiguous NL descriptions, which we approach by reducing code generation uncertainty and better aligning NL with input-output examples. Ambiguity repair is difficult for LLMs because they must understand how their interpretation of a description changes when the text is altered. We find that directly prompting LLMs to clarify ambiguity often produces irrelevant or inconsistent edits. To address this, we decompose this task into two simpler steps: (1) analyzing and repairing the LLM's interpretation of the description - captured by the distribution of programs it induces - using traditional testing and program repair, and (2) refining the description based on distribution changes via a method we call contrastive specification inference. We implement this approach in a tool called SpecFix and evaluate it using four state-of-the-art LLMs (GPT-4o, GPT-4o-mini, DeepSeek-V3, and Qwen2.5-Coder-32B-Instruct) on three popular code generation benchmarks (HumanEval+, MBPP+ and LiveCodeBench). Without human intervention or external information, SpecFix modified 43.58% of descriptions, improving Pass@1 on the modified set by 30.9%. This yields a 4.09% absolute improvement across the entire benchmark. Repairs also transfer across models: descriptions repaired for one model improve other models' performance by 10.48%.
Semantic Retention and Extreme Compression in LLMs: Can We Have Both?
Venue: arXiv cs.CL 2025
Year: 2025

The exponential growth in Large Language Model (LLM) deployment has intensified the need for efficient model compression techniques to reduce computational and memory costs. While pruning and quantization have shown promise, their combined potential remains largely unexplored. In this paper, we examine joint compression and how strategically combining pruning and quantization could yield superior performance-to-compression ratios compared to single-method approaches. Recognizing the challenges in accurately assessing LLM performance, we address key limitations of previous evaluation frameworks and introduce the Semantic Retention Compression Rate (SrCr), a novel metric that quantifies the trade-off between model compression and semantic preservation, facilitating the optimization of pruning-quantization configurations. Experiments demonstrate that our recommended combination achieves, on average, a 20% performance increase compared to an equivalent quantization-only model at the same theoretical compression rate.
Towards Multi-Agent Reasoning Systems for Collaborative Expertise Delegation: An Exploratory Design Study
Venue: arXiv cs.CL 2025
Year: 2025

Designing effective collaboration structure for multi-agent LLM systems to enhance collective reasoning is crucial yet remains under-explored. In this paper, we systematically investigate how collaborative reasoning performance is affected by three key design dimensions: (1) Expertise-Domain Alignment, (2) Collaboration Paradigm (structured workflow vs. diversity-driven integration), and (3) System Scale. Our findings reveal that expertise alignment benefits are highly domain-contingent, proving most effective for contextual reasoning tasks. Furthermore, collaboration focused on integrating diverse knowledge consistently outperforms rigid task decomposition. Finally, we empirically explore the impact of scaling the multi-agent system with expertise specialization and study the computational trade off, highlighting the need for more efficient communication protocol design. This work provides concrete guidelines for configuring specialized multi-agent system and identifies critical architectural trade-offs and bottlenecks for scalable multi-agent reasoning. The code will be made available upon acceptance.