OpenRaiser/Intern-Atlas huggingface dataset:
ACE-RLHF: Automated Code Evaluation and Socratic Feedback Generation Tool using Large Language Models and Reinforcement Learning with Human Feedback
Venue: arXiv cs.LG 2025
Year: 2025

Automated Program Repair tools are developed for generating feedback and suggesting a repair method for erroneous code. State of the art (SOTA) code repair methods rely on data-driven approaches and often fail to deliver solution for complicated programming questions. To interpret the natural language of unprecedented programming problems, using Large Language Models (LLMs) for code-feedback generation is crucial. LLMs generate more comprehensible feedback than compiler-generated error messages, and Reinforcement Learning with Human Feedback (RLHF) further enhances quality by integrating human-in-the-loop which helps novice students to lean programming from scratch interactively. We are applying RLHF fine-tuning technique for an expected Socratic response such as a question with hint to solve the programming issue. We are proposing code feedback generation tool by fine-tuning LLM with RLHF, Automated Code Evaluation with RLHF (ACE-RLHF), combining two open-source LLM models with two different SOTA optimization techniques. The quality of feedback is evaluated on two benchmark datasets containing basic and competition-level programming questions where the later is proposed by us. We achieved 2-5% higher accuracy than RL-free SOTA techniques using Llama-3-7B-Proximal-policy optimization in automated evaluation and similar or slightly higher accuracy compared to reward model-free RL with AI Feedback (RLAIF). We achieved almost 40% higher accuracy with GPT-3.5 Best-of-n optimization while performing manual evaluation.
Usability Testing of an Explainable AI-enhanced Tool for Clinical Decision Support: Insights from the Reflexive Thematic Analysis
Venue: arXiv cs.HC 2025
Year: 2025

Artificial intelligence-augmented technology represents a considerable opportunity for improving healthcare delivery. Significant progress has been made to demonstrate the value of complex models to enhance clinicians` efficiency in decision-making. However, the clinical adoption of such models is scarce due to multifaceted implementation issues, with the explainability of AI models being among them. One of the substantially documented areas of concern is the unclear AI explainability that negatively influences clinicians` considerations for accepting the complex model. With a usability study engaging 20 U.S.-based clinicians and following the qualitative reflexive thematic analysis, this study develops and presents a concrete framework and an operational definition of explainability. The framework can inform the required customizations and feature developments in AI tools to support clinicians` preferences and enhance their acceptance.
Dynamic Vision Mamba
Venue: arXiv cs.CV 2025
Year: 2025

Mamba-based vision models have gained extensive attention as a result of being computationally more efficient than attention-based models. However, spatial redundancy still exists in these models, represented by token and block redundancy. For token redundancy, we analytically find that early token pruning methods will result in inconsistency between training and inference or introduce extra computation for inference. Therefore, we customize token pruning to fit the Mamba structure by rearranging the pruned sequence before feeding it into the next Mamba block. For block redundancy, we allow each image to select SSM blocks dynamically based on an empirical observation that the inference speed of Mamba-based vision models is largely affected by the number of SSM blocks. Our proposed method, Dynamic Vision Mamba (DyVM), effectively reduces FLOPs with minor performance drops. We achieve a reduction of 35.2\% FLOPs with only a loss of accuracy of 1.7\% on Vim-S. It also generalizes well across different Mamba vision model architectures and different vision tasks. Our code will be made public.
A Customized SAT-based Solver for Graph Coloring
Venue: arXiv cs.DM 2025
Year: 2025

We introduce ZykovColor, a novel SAT-based algorithm to solve the graph coloring problem working on top of an encoding that mimics the Zykov tree. Our method is based on an approach of Hébrard and Katsirelos (2020) that employs a propagator to enforce transitivity constraints, incorporate lower bounds for search tree pruning, and enable inferred propagations. We leverage the recently introduced IPASIR-UP interface for CaDiCaL to implement these techniques with a SAT solver. Furthermore, we propose new features that take advantage of the underlying SAT solver. These include modifying the integrated decision strategy with vertex domination hints and using incremental bottom-up search that allows to reuse learned clauses from previous calls. Additionally, we integrate a more effective clique computation and an algorithm for computing the fractional chromatic number to improve the lower bounds used for pruning during the search. We validate the effectiveness of each new feature through an experimental analysis. ZykovColor outperforms other state-of-the-art graph coloring implementations on the DIMACS benchmark set. Further experiments on random Erdős-Rényi graphs show that our new approach matches or outperforms state-of-the-art SAT-based methods for both very sparse and highly dense graphs. We give an additional configuration of ZykovColor that dominates other SAT-based methods on the Erdős-Rényi graphs.
Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models
Venue: arXiv cs.CL 2025
Year: 2025

Recent advancements in reasoning language models have demonstrated remarkable performance in complex tasks, but their extended chain-of-thought reasoning process increases inference overhead. While quantization has been widely adopted to reduce the inference cost of large language models, its impact on reasoning models remains understudied. In this paper, we conduct the first systematic study on quantized reasoning models, evaluating the open-sourced DeepSeek-R1-Distilled Qwen and LLaMA families ranging from 1.5B to 70B parameters, QwQ-32B, and Qwen3-8B. Our investigation covers weight, KV cache, and activation quantization using state-of-the-art algorithms at varying bit-widths, with extensive evaluation across mathematical (AIME, MATH-500), scientific (GPQA), and programming (LiveCodeBench) reasoning benchmarks. Our findings reveal that while lossless quantization can be achieved with W8A8 or W4A16 quantization, lower bit-widths introduce significant accuracy risks. We further identify model size, model origin, and task difficulty as critical determinants of performance. Contrary to expectations, quantized models do not exhibit increased output lengths. In addition, strategically scaling the model sizes or reasoning steps can effectively enhance the performance. All quantized models and codes are open-sourced in https://github.com/ruikangliu/Quantized-Reasoning-Models.
Strengthening Wilf's lower bound on clique number
Venue: arXiv cs.DM 2025
Year: 2025

Given an integer $k$, deciding whether a graph has a clique of size $k$ is an NP-complete problem. Wilf's inequality provides a spectral bound for the clique number of simple graphs. Wilf's inequality is stated as follows: $\frac{n}{n - λ_{1}} \leq ω$, where $λ_1$ is the largest eigenvalue of the adjacency matrix $A(G)$, $n$ is the number of vertices in $G$, and $ω$ is the clique number of $G$. Strengthening this bound, Elphick and Wocjan proposed a conjecture in 2018, which is stated as follows: $\frac{n}{n - \sqrt{s^{+}}} \leq ω$, where $s^+ = \sum_{λ_{i} > 0} λ_{i}^2$ and $λ_i$ are the eigenvalues of $A(G)$. In this paper, we have settled this conjecture for some classes of graphs, such as conference graphs, strongly regular graphs with $λ= μ$ (i.e., $srg(n, d, μ, μ)$) and $n\geq 2d$, the line graph of $K_{n}$, the Cartesian product of strongly regular graphs, and Ramanujan graph with $n\geq 11d$.
FantasyTalking: Realistic Talking Portrait Generation via Coherent Motion Synthesis
Venue: arXiv cs.CV 2025
Year: 2025

Creating a realistic animatable avatar from a single static portrait remains challenging. Existing approaches often struggle to capture subtle facial expressions, the associated global body movements, and the dynamic background. To address these limitations, we propose a novel framework that leverages a pretrained video diffusion transformer model to generate high-fidelity, coherent talking portraits with controllable motion dynamics. At the core of our work is a dual-stage audio-visual alignment strategy. In the first stage, we employ a clip-level training scheme to establish coherent global motion by aligning audio-driven dynamics across the entire scene, including the reference portrait, contextual objects, and background. In the second stage, we refine lip movements at the frame level using a lip-tracing mask, ensuring precise synchronization with audio signals. To preserve identity without compromising motion flexibility, we replace the commonly used reference network with a facial-focused cross-attention module that effectively maintains facial consistency throughout the video. Furthermore, we integrate a motion intensity modulation module that explicitly controls expression and body motion intensity, enabling controllable manipulation of portrait movements beyond mere lip motion. Extensive experimental results show that our proposed approach achieves higher quality with better realism, coherence, motion intensity, and identity preservation. Ours project page: https://fantasy-amap.github.io/fantasy-talking/.
Discovering dynamical laws for speech gestures
Venue: arXiv cs.CL 2025
Year: 2025

A fundamental challenge in the cognitive sciences is discovering the dynamics that govern behaviour. Take the example of spoken language, which is characterised by a highly variable and complex set of physical movements that map onto the small set of cognitive units that comprise language. What are the fundamental dynamical principles behind the movements that structure speech production? In this study, we discover models in the form of symbolic equations that govern articulatory gestures during speech. A sparse symbolic regression algorithm is used to discover models from kinematic data on the tongue and lips. We explore these candidate models using analytical techniques and numerical simulations, and find that a second-order linear model achieves high levels of accuracy, but a nonlinear force is required to properly model articulatory dynamics in approximately one third of cases. This supports the proposal that an autonomous, nonlinear, second-order differential equation is a viable dynamical law for articulatory gestures in speech. We conclude by identifying future opportunities and obstacles in data-driven model discovery and outline prospects for discovering the dynamical principles that govern language, brain and behaviour.
3D Gaussian Particle Approximation of VDB Datasets: A Study for Scientific Visualization
Venue: arXiv cs.GR 2025
Year: 2025

The complexity and scale of Volumetric and Simulation datasets for Scientific Visualization(SciVis) continue to grow. And the approaches and advantages of memory-efficient data formats and storage techniques for such datasets vary. OpenVDB library and its VDB data format excels in memory efficiency through its hierarchical and dynamic tree structure, with active and inactive sub-trees for data storage. It is heavily used in current production renderers for both animation and rendering stages in VFX pipelines and photorealistic rendering of volumes and fluids. However, it still remains to be fully leveraged in SciVis where domains dealing with sparse scalar fields like porous media, time varying volumes such as tornado and weather simulation or high resolution simulation of Computational Fluid Dynamics present ample number of large challenging data sets. The goal of this paper hence is not only to explore the use of OpenVDB in SciVis but also to explore a level of detail(LOD) technique using 3D Gaussian particles approximating voxel regions. For rendering, we utilize NVIDIA OptiX library for ray marching through the Gaussians particles. Data modeling using 3D Gaussians has been very popular lately due to success in stereoscopic image to 3D scene conversion using Gaussian Splatting and Gaussian approximation and mixture models aren't entirely new in SciVis as well. Our work explores the integration with rendering software libraries like OpenVDB and OptiX to take advantage of their built-in memory compaction and hardware acceleration features, while also leveraging the performance capabilities of modern GPUs. Thus, we present a SciVis rendering approach that uses 3D Gaussians at varying LOD in a lossy scheme derived from VDB datasets, rather than focusing on photorealistic volume rendering.
FedSAUC: A Similarity-Aware Update Control for Communication-Efficient Federated Learning in Edge Computing
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning is a distributed machine learning framework to collaboratively train a global model without uploading privacy-sensitive data onto a centralized server. Usually, this framework is applied to edge devices such as smartphones, wearable devices, and Internet of Things (IoT) devices which closely collect information from users. However, these devices are mostly battery-powered. The update procedure of federated learning will constantly consume the battery power and the transmission bandwidth. In this work, we propose an update control for federated learning, FedSAUC, by considering the similarity of users' behaviors (models). At the server side, we exploit clustering algorithms to group devices with similar models. Then we select some representatives for each cluster to update information to train the model. We also implemented a testbed prototyping on edge devices for validating the performance. The experimental results show that this update control will not affect the training accuracy in the long run.
DSwinIR: Rethinking Window-based Attention for Image Restoration
Venue: arXiv cs.CV 2025
Year: 2025

Image restoration has witnessed significant advancements with the development of deep learning models. Transformer-based models, particularly those using window-based self-attention, have become a dominant force. However, their performance is constrained by the rigid, non-overlapping window partitioning scheme, which leads to \textit{insufficient feature interaction across windows and limited receptive fields}. This highlights the need for more adaptive and flexible attention mechanisms. In this paper, we propose the Deformable Sliding Window Transformer for Image Restoration (DSwinIR), a new attention mechanism: the {Deformable Sliding Window (DSwin) Attention}. {This mechanism introduces a token-centric and content-aware paradigm that moves beyond the grid and fixed window partition.} It comprises two complementary components. First, it replaces the rigid partitioning with a \textit{token-centric sliding window} paradigm, {making it effective at eliminating boundary artifacts}. Second, it incorporates a \textit{content-aware deformable sampling} strategy, which allows the attention mechanism to learn data-dependent offsets and actively shape its receptive field to focus on the most informative image regions. Extensive experiments show that DSwinIR achieves strong results, including state-of-the-art performance on several evaluated benchmarks. For instance, in all-in-one image restoration, our DSwinIR surpasses the most recent backbone GridFormer by 0.53 dB on the three-task benchmark and 0.87 dB on the five-task benchmark.
Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration
Venue: arXiv cs.CL 2025
Year: 2025

Retrieval-Augmented Generation (RAG) systems often struggle to handle multi-hop question-answering tasks accurately due to irrelevant context retrieval and limited complex reasoning capabilities. We introduce Collab-RAG, a collaborative training framework that leverages mutual enhancement between a white-box small language model (SLM) and a blackbox large language model (LLM) for RAG. Specifically, the SLM decomposes complex queries into simpler sub-questions, thus enhancing the accuracy of the retrieval and facilitating more effective reasoning by the black-box LLM. Concurrently, the black-box LLM provides feedback signals to improve the SLM's decomposition capability. We observe that Collab-RAG relies solely on supervision from an affordable black-box LLM without additional distillation from frontier LLMs, yet demonstrates strong generalization across multiple black-box LLMs. Experimental evaluations across five multi-hop QA datasets demonstrate that Collab-RAG substantially outperforms existing black-box-only and SLM fine-tuning baselines by 1.8%-14.2% on average. In particular, our fine-tuned 3B SLM surpasses a frozen 32B LLM in question decomposition, highlighting the efficiency of Collab-RAG in improving reasoning and retrieval for complex questions. The code of Collab-RAG is available on https://github.com/ritaranx/Collab-RAG/.
Expectations vs Reality -- A Secondary Study on AI Adoption in Software Testing
Venue: arXiv cs.SE 2025
Year: 2025

In the software industry, artificial intelligence (AI) has been utilized more and more in software development activities. In some activities, such as coding, AI has already been an everyday tool, but in software testing activities AI it has not yet made a significant breakthrough. In this paper, the objective was to identify what kind of empirical research with industry context has been conducted on AI in software testing, as well as how AI has been adopted in software testing practice. To achieve this, we performed a systematic mapping study of recent (2020 and later) studies on AI adoption in software testing in the industry, and applied thematic analysis to identify common themes and categories, such as the real-world use cases and benefits, in the found papers. The observations suggest that AI is not yet heavily utilized in software testing, and still relatively few studies on AI adoption in software testing have been conducted in the industry context to solve real-world problems. Earlier studies indicated there was a noticeable gap between the actual use cases and actual benefits versus the expectations, which we analyzed further. While there were numerous potential use cases for AI in software testing, such as test case generation, code analysis, and intelligent test automation, the reported actual implementations and observed benefits were limited. In addition, the systematic mapping study revealed a potential problem with false positive search results in online databases when using the search string "artificial intelligence".
Using AI to Help in the Semantic Lexical Database to Evaluate Ideas
Venue: arXiv cs.IT 2025
Year: 2025

Inside a challenge of ideas there are several phases in a Creative Support System (CSS), they are problem analysis, ideation, evaluation, and implementation. Our problem: we need a full semantic lexical database SLD in an oral (voice) and writing way to help stakeholders to create ideas, these ideas contain nouns, verbs, adverbs, adjectives in the English, Spanish, and French languages. We utilize a Cloud Service Provider to use a service of Artificial Intelligence (AI), also we prepare nouns, verbs, adjectives and adverbs files in order to create the service text to voice and create our SLD with voice. This paper presents, first, an introduction about some contests that use a semantic lexical database in different languages; second, a SLD management approach using analysis of texts; third, a management application approach to complete all the new elements; fourth, the results of the management application approach, finally the conclusions and future work.
A High-Force Gripper with Embedded Multimodal Sensing for Powerful and Perception Driven Grasping
Venue: arXiv cs.RO 2025
Year: 2025

Modern humanoid robots have shown their promising potential for executing various tasks involving the grasping and manipulation of objects using their end-effectors. Nevertheless, in the most of the cases, the grasping and manipulation actions involve low to moderate payload and interaction forces. This is due to limitations often presented by the end-effectors, which can not match their arm-reachable payload, and hence limit the payload that can be grasped and manipulated. In addition, grippers usually do not embed adequate perception in their hardware, and grasping actions are mainly driven by perception sensors installed in the rest of the robot body, frequently affected by occlusions due to the arm motions during the execution of the grasping and manipulation tasks. To address the above, we developed a modular high grasping force gripper equipped with embedded multi-modal perception functionalities. The proposed gripper can generate a grasping force of 110 N in a compact implementation. The high grasping force capability is combined with embedded multi-modal sensing, which includes an eye-in-hand camera, a Time-of-Flight (ToF) distance sensor, an Inertial Measurement Unit (IMU) and an omnidirectional microphone, permitting the implementation of perception-driven grasping functionalities. We extensively evaluated the grasping force capacity of the gripper by introducing novel payload evaluation metrics that are a function of the robot arm's dynamic motion and gripper thermal states. We also evaluated the embedded multi-modal sensing by performing perception-guided enhanced grasping operations.
Deconstructing Jazz Piano Style Using Machine Learning
Venue: arXiv cs.SD 2025
Year: 2025

Artistic style has been studied for centuries, and recent advances in machine learning create new possibilities for understanding it computationally. However, ensuring that machine-learning models produce insights aligned with the interests of practitioners and critics remains a significant challenge. Here, we focus on musical style, which benefits from a rich theoretical and mathematical analysis tradition. We train a variety of supervised-learning models to identify 20 iconic jazz musicians across a carefully curated dataset of 84 hours of recordings, and interpret their decision-making processes. Our models include a novel multi-input architecture that enables four musical domains (melody, harmony, rhythm, and dynamics) to be analysed separately. These models enable us to address fundamental questions in music theory and also advance the state-of-the-art in music performer identification (94% accuracy across 20 classes). We release open-source implementations of our models and an accompanying web application for exploring musical styles.
Mixture-of-Personas Language Models for Population Simulation
Venue: arXiv cs.LG 2025
Year: 2025

Advances in Large Language Models (LLMs) paved the way for their emerging applications in various domains, such as human behavior simulations, where LLMs could augment human-generated data in social science research and machine learning model training. However, pretrained LLMs often fail to capture the behavioral diversity of target populations due to the inherent variability across individuals and groups. To address this, we propose \textit{Mixture of Personas} (MoP), a \textit{probabilistic} prompting method that aligns the LLM responses with the target population. MoP is a contextual mixture model, where each component is an LM agent characterized by a persona and an exemplar representing subpopulation behaviors. The persona and exemplar are randomly chosen according to the learned mixing weights to elicit diverse LLM responses during simulation. MoP is flexible, requires no model finetuning, and is transferable across base models. Experiments for synthetic data generation show that MoP outperforms competing methods in alignment and diversity metrics.