OpenRaiser/Intern-Atlas huggingface dataset:
Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task
Venue: arXiv cs.AI 2025
Year: 2025

Schema linking is a critical step in Text-to-SQL task, aiming to accurately predict the table names and column names required for the SQL query based on the given question. However, current fine-tuning approaches for schema linking models employ a rote-learning paradigm, excessively optimizing for ground truth schema linking outcomes while compromising reasoning ability. This limitation arises because of the difficulty in acquiring a high-quality reasoning sample for downstream tasks. To address this, we propose Schema-R1, a reasoning schema linking model trained using reinforcement learning. Specifically, Schema-R1 consists of three key steps: constructing small batches of high-quality reasoning samples, supervised fine-tuning for cold-start initialization, and rule-based reinforcement learning training. The final results demonstrate that our method effectively enhances the reasoning ability of the schema linking model, achieving a 10\% improvement in filter accuracy compared to the existing method. Our code is available at https://github.com/hongWin/Schema-R1/.
Simple Radiology VLLM Test-time Scaling with Thought Graph Traversal
Venue: arXiv cs.CV 2025
Year: 2025

Test-time scaling offers a promising way to improve the reasoning performance of vision-language large models (VLLMs) without additional training. In this paper, we explore a simple but effective approach for applying test-time scaling to radiology report generation. Specifically, we introduce a lightweight Thought Graph Traversal (TGT) framework that guides the model to reason through organ-specific findings in a medically coherent order. This framework integrates structured medical priors into the prompt, enabling deeper and more logical analysis with no changes to the underlying model. To further enhance reasoning depth, we apply a reasoning budget forcing strategy that adjusts the model's inference depth at test time by dynamically extending its generation process. This simple yet powerful combination allows a frozen radiology VLLM to self-correct and generate more accurate, consistent chest X-ray reports. Our method outperforms baseline prompting approaches on standard benchmarks, and also reveals dataset biases through traceable reasoning paths. Code and prompts are open-sourced for reproducibility at https://github.com/glerium/Thought-Graph-Traversal.
VGR: Visual Grounded Reasoning
Venue: arXiv cs.CV 2025
Year: 2025

In the field of multimodal chain-of-thought (CoT) reasoning, existing approaches predominantly rely on reasoning on pure language space, which inherently suffers from language bias and is largely confined to math or science domains. This narrow focus limits their ability to handle complex visual reasoning tasks that demand comprehensive understanding of image details. To address these limitations, this paper introduces VGR, a novel reasoning multimodal large language model (MLLM) with enhanced fine-grained visual perception capabilities. Unlike traditional MLLMs that answer the question or reasoning solely on the language space, our VGR first detects relevant regions that may help to solve problems, and then provides precise answers based on replayed image regions. To achieve this, we conduct a large-scale SFT dataset called VGR -SFT that contains reasoning data with mixed vision grounding and language deduction. The inference pipeline of VGR allows the model to choose bounding boxes for visual reference and a replay stage is introduced to integrates the corresponding regions into the reasoning process, enhancing multimodel comprehension. Experiments on the LLaVA-NeXT-7B baseline show that VGR achieves superior performance on multi-modal benchmarks requiring comprehensive image detail understanding. Compared to the baseline, VGR uses only 30\% of the image token count while delivering scores of +4.1 on MMStar, +7.1 on AI2D, and a +12.9 improvement on ChartQA.
Algorithmic Structure in Subset Sum: Deterministic In-Bound Navigation and the Counting Complexity Divide
Venue: arXiv cs.CC 2025
Year: 2025

This paper presents a deterministic algorithmic approach of exploring the solution space of the Subset Sum Problem. The algorithm presented is input-robust and structurally adaptive. Exploration is guided and narrows into areas in the solution space where solutions are possible, referred to as in-bound solution space, skipping all areas where solutions are impossible. Unfortunately, this can lead to false positives: paths that are hinted to potential have solutions but ultimately realized to not lead to solutions. The in-bound solution space navigated can therefore be filled with only false positives, only true solutions or a mix of the two, affecting the algorithm's performance in different ways. We then detail the challenges of exploring the in-bound solution space for different instances. Further, we show how this algorithm may practically generalize to other NP/NP-complete problems with appropriate adaptation. An introductory discussion is done on this generalization to k-SAT and general CNF-SAT, deferring extensive detail to a follow-up paper. This paper does not satisfy P vs NP proof requirements and does not claim to resolve the problem. However, it has implications for the P vs NP and offers a practical lens through the algorithm of what is feasible with it. The feasibility bounds of the algorithm reveal a nontrivial relationship between decision and counting complexity. To facilitate easy reproducibility, we include in the paper a full C++ implementation of the algorithm.
BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook
Venue: arXiv cs.LG 2025
Year: 2025

Binary quantization represents the most extreme form of large language model (LLM) compression, reducing weights to $\pm$1 for maximal memory and computational efficiency. While recent sparsity-aware binarization methods achieve sub-1-bit compression by pruning redundant binary weights, they suffer from three critical challenges: performance deterioration, computational complexity from sparse mask management, and limited hardware compatibility. In this paper, we present BTC-LLM, a novel sub-1-bit LLM quantization framework that leverages adaptive weight transformation and binary pattern clustering to overcome these limitations, delivering both superior accuracy and efficiency. Our approach incorporates two key innovations: (1) a Learnable Transformation that optimizes invertible scaling and rotation matrices to align binarized weights with full-precision distributions, enabling incoherence processing to enhance layer-wise representation quality; (2) a Flash and Accurate Binary Codebook that identifies recurring binary vector clusters, compressing them into compact indices with tailored distance metrics and sign-based centroid updates. This eliminates the need for sparse masks, enabling efficient inference on standard hardware. Our code is available at https://github.com/Chooovy/BTC-LLM.
GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data
Venue: arXiv cs.LG 2025
Year: 2025

This paper introduces GUST (Generative Uncertainty learning via Self-supervised pretraining and Transfer learning), a framework for quantifying free-form geometric uncertainties inherent in the manufacturing of metamaterials. GUST leverages the representational power of deep generative models to learn a high-dimensional conditional distribution of as-fabricated unit cell geometries given nominal designs, thereby enabling uncertainty quantification. To address the scarcity of real-world manufacturing data, GUST employs a two-stage learning process. First, it leverages self-supervised pretraining on a large-scale synthetic dataset to capture the structure variability inherent in metamaterial geometries and an approximated distribution of as-fabricated geometries given nominal designs. Subsequently, GUST employs transfer learning by fine-tuning the pretrained model on limited real-world manufacturing data, allowing it to adapt to specific manufacturing processes and nominal designs. With only 960 unit cells additively manufactured in only two passes, GUST can capture the variability in geometry and effective material properties. In contrast, directly training a generative model on the same amount of real-world data proves insufficient, as demonstrated through both qualitative and quantitative comparisons. This scalable and cost-effective approach significantly reduces data requirements while maintaining the effectiveness in learning complex, real-world geometric uncertainties, offering an affordable method for free-form geometric uncertainty quantification in the manufacturing of metamaterials. The capabilities of GUST hold significant promise for high-precision industries such as aerospace and biomedical engineering, where understanding and mitigating manufacturing uncertainties are critical.
Organizational Adaptation to Generative AI in Cybersecurity: A Systematic Review
Venue: arXiv cs.CR 2025
Year: 2025

Cybersecurity organizations are adapting to GenAI integration through modified frameworks and hybrid operational processes, with success influenced by existing security maturity, regulatory requirements, and investments in human capital and infrastructure. This qualitative research employs systematic document analysis and comparative case study methodology to examine how cybersecurity organizations adapt their threat modeling frameworks and operational processes to address generative artificial intelligence integration. Through examination of 25 studies from 2022 to 2025, the research documents substantial transformation in organizational approaches to threat modeling, moving from traditional signature-based systems toward frameworks incorporating artificial intelligence capabilities. The research identifies three primary adaptation patterns: Large Language Model integration for security applications, GenAI frameworks for risk detection and response automation, and AI/ML integration for threat hunting. Organizations with mature security infrastructures, particularly in finance and critical infrastructure sectors, demonstrate higher readiness through structured governance approaches, dedicated AI teams, and robust incident response processes. Organizations achieve successful GenAI integration when they maintain appropriate human oversight of automated systems, address data quality concerns and explainability requirements, and establish governance frameworks tailored to their specific sectors. Organizations encounter ongoing difficulties with privacy protection, bias reduction, personnel training, and defending against adversarial attacks. This work advances understanding of how organizations adopt innovative technologies in high-stakes environments and offers actionable insights for cybersecurity professionals implementing GenAI systems.
A Synthetic Pseudo-Autoencoder Invites Examination of Tacit Assumptions in Neural Network Design
Venue: arXiv cs.NE 2025
Year: 2025

We present a handcrafted neural network that, without training, solves the seemingly difficult problem of encoding an arbitrary set of integers into a single numerical variable, and then recovering the original elements. While using only standard neural network operations -- weighted sums with biases and identity activation -- we make design choices that challenge common notions in this area around representation, continuity of domains, computation, learnability and more. For example, our construction is designed, not learned; it represents multiple values using a single one by simply concatenating digits without compression, and it relies on hardware-level truncation of rightmost digits as a bit-manipulation mechanism. This neural net is not intended for practical application. Instead, we see its resemblance to -- and deviation from -- standard trained autoencoders as an invitation to examine assumptions that may unnecessarily constrain the development of systems and models based on autoencoding and machine learning. Motivated in part by our research on a theory of biological evolution centered around natural autoencoding of species characteristics, we conclude by refining the discussion with a biological perspective.
Latency Optimization for Wireless Federated Learning in Multihop Networks
Venue: arXiv cs.NI 2025
Year: 2025

In this paper, we study a novel latency minimization problem in wireless federated learning (FL) across multi-hop networks. The system comprises multiple routes, each integrating leaf and relay nodes for FL model training. We explore a personalized learning and adaptive aggregation-aware FL (PAFL) framework that effectively addresses data heterogeneity across participating nodes by harmonizing individual and collective learning objectives. We formulate an optimization problem aimed at minimizing system latency through the joint optimization of leaf and relay nodes, as well as relay routing indicator. We also incorporate an additional energy harvesting scheme for the relay nodes to help with their relay tasks. This formulation presents a computationally demanding challenge, and thus we develop a simple yet efficient algorithm based on block coordinate descent and successive convex approximation (SCA) techniques. Simulation results illustrate the efficacy of our proposed joint optimization approach for leaf and relay nodes with relay routing indicator. We observe significant latency savings in the wireless multi-hop PAFL system, with reductions of up to 69.37% compared to schemes optimizing only one node type, traditional greedy algorithm, and scheme without relay routing indicator.
Using Behavior Trees in Risk Assessment
Venue: arXiv cs.RO 2025
Year: 2025

Cyber-physical production systems increasingly involve collaborative robotic missions, requiring more demand for robust and safe missions. Industries rely on risk assessments to identify potential failures and implement measures to mitigate their risks. Although it is recommended to conduct risk assessments early in the design of robotic missions, the state of practice in the industry is different. Safety experts often struggle to completely understand robotics missions at the early design stages of projects and to ensure that the output of risk assessments is adequately considered during implementation. This paper presents a design science study that conceived a model-based approach for early risk assessment in a development-centric way. Our approach supports risk assessment activities by using the behavior-tree model. We evaluated the approach together with five practitioners from four companies. Our findings highlight the potential of the behavior-tree model in supporting early identification, visualisation, and bridging the gap between code implementation and risk assessments' outputs. This approach is the first attempt to use the behavior-tree model to support risk assessment; thus, the findings highlight the need for further development.
Semantic Preprocessing for LLM-based Malware Analysis
Venue: arXiv cs.CR 2025
Year: 2025

In a context of malware analysis, numerous approaches rely on Artificial Intelligence to handle a large volume of data. However, these techniques focus on data view (images, sequences) and not on an expert's view. Noticing this issue, we propose a preprocessing that focuses on expert knowledge to improve malware semantic analysis and result interpretability. We propose a new preprocessing method which creates JSON reports for Portable Executable files. These reports gather features from both static and behavioral analysis, and incorporate packer signature detection, MITRE ATT\&CK and Malware Behavior Catalog (MBC) knowledge. The purpose of this preprocessing is to gather a semantic representation of binary files, understandable by malware analysts, and that can enhance AI models' explainability for malicious files analysis. Using this preprocessing to train a Large Language Model for Malware classification, we achieve a weighted-average F1-score of 0.94 on a complex dataset, representative of market reality.
Unsupervised Document and Template Clustering using Multimodal Embeddings
Venue: arXiv cs.CL 2025
Year: 2025

We study unsupervised clustering of documents at both the category and template levels using frozen multimodal encoders and classical clustering algorithms. We systematize a model-agnostic pipeline that (i) projects heterogeneous last-layer states from text-layout-vision encoders into token-type-aware document vectors and (ii) performs clustering with centroid- or density-based methods, including an HDBSCAN + $k$-NN assignment to eliminate unlabeled points. We evaluate eight encoders (text-only, layout-aware, vision-only, and vision-language) with $k$-Means, DBSCAN, HDBSCAN + $k$-NN, and BIRCH on five corpora spanning clean synthetic invoices, their heavily degraded print-and-scan counterparts, scanned receipts, and real identity and certificate documents. The study reveals modality-specific failure modes and a robustness-accuracy trade-off, with vision features nearly solving template discovery on clean pages while text dominates under covariate shift, and fused encoders offering the best balance. We detail a reproducible, oracle-free tuning protocol and the curated evaluation settings to guide future work on unsupervised document organization.
Hatevolution: What Static Benchmarks Don't Tell Us
Venue: arXiv cs.CL 2025
Year: 2025

Language changes over time, including in the hate speech domain, which evolves quickly following social dynamics and cultural shifts. While NLP research has investigated the impact of language evolution on model training and has proposed several solutions for it, its impact on model benchmarking remains under-explored. Yet, hate speech benchmarks play a crucial role to ensure model safety. In this paper, we empirically evaluate the robustness of 20 language models across two evolving hate speech experiments, and we show the temporal misalignment between static and time-sensitive evaluations. Our findings call for time-sensitive linguistic benchmarks in order to correctly and reliably evaluate language models in the hate speech domain.
Artificial Intelligence and Machine Learning in the Development of Vaccines and Immunotherapeutics Yesterday, Today, and Tomorrow
Venue: arXiv cs.AI 2025
Year: 2025

In the past, the development of vaccines and immunotherapeutics relied heavily on trial-and-error experimentation and extensive in vivo testing, often requiring years of pre-clinical and clinical trials. Today, artificial intelligence (AI) and deep learning (DL) are actively transforming vaccine and immunotherapeutic design, by (i) offering predictive frameworks that support rapid, data-driven decision-making; (ii) increasingly being implemented as time- and resource-efficient strategies that integrate computational models, systems vaccinology, and multi-omics data to better phenotype, differentiate, and classify patient diseases and cancers; predict patients' immune responses; and identify the factors contributing to optimal vaccine and immunotherapeutic protective efficacy; (iii) refining the selection of B- and T-cell antigen/epitope targets to enhance efficacy and durability of immune protection; and (iv) enabling a deeper understanding of immune regulation, immune evasion, immune checkpoints, and regulatory pathways. The future of AI and DL points toward (i) replacing animal preclinical testing of drugs, vaccines, and immunotherapeutics with computational-based models, as recently proposed by the United States FDA; and (ii) enabling real-time in vivo modeling for immunobridging and prediction of protection in clinical trials. This may result in a fast and transformative shift for the development of personal vaccines and immunotherapeutics against infectious pathogens and cancers.
BreastDCEDL: A Comprehensive Breast Cancer DCE-MRI Dataset and Transformer Implementation for Treatment Response Prediction
Venue: arXiv cs.CV 2025
Year: 2025

Breast cancer remains a leading cause of cancer-related mortality worldwide, making early detection and accurate treatment response monitoring critical priorities. We present BreastDCEDL, a curated, deep learning-ready dataset comprising pre-treatment 3D Dynamic Contrast-Enhanced MRI (DCE-MRI) scans from 2,070 breast cancer patients drawn from the I-SPY1, I-SPY2, and Duke cohorts, all sourced from The Cancer Imaging Archive. The raw DICOM imaging data were rigorously converted into standardized 3D NIfTI volumes with preserved signal integrity, accompanied by unified tumor annotations and harmonized clinical metadata including pathologic complete response (pCR), hormone receptor (HR), and HER2 status. Although DCE-MRI provides essential diagnostic information and deep learning offers tremendous potential for analyzing such complex data, progress has been limited by lack of accessible, public, multicenter datasets. BreastDCEDL addresses this gap by enabling development of advanced models, including state-of-the-art transformer architectures that require substantial training data. To demonstrate its capacity for robust modeling, we developed the first transformer-based model for breast DCE-MRI, leveraging Vision Transformer (ViT) architecture trained on RGB-fused images from three contrast phases (pre-contrast, early post-contrast, and late post-contrast). Our ViT model achieved state-of-the-art pCR prediction performance in HR+/HER2- patients (AUC 0.94, accuracy 0.93). BreastDCEDL includes predefined benchmark splits, offering a framework for reproducible research and enabling clinically meaningful modeling in breast cancer imaging.
PRO-V-R1: Reasoning Enhanced Programming Agent for RTL Verification
Venue: arXiv cs.AI 2025
Year: 2025

Register-Transfer Level (RTL) verification is a primary bottleneck, consuming 60-70% of development time. While Large Language Models (LLMs) show promise for RTL automation, their performance and research focus have overwhelmingly centered on RTL generation rather than verification. Current methods for RTL verification rely on large scale proprietary models (e.g., GPT-4o) to generate Python-based functional references, incurring a high cost and raising data-privacy risks. To date, an end-to-end open-source solution for autonomous verification remains absent. We introduce PRO-V-R1, the first trainable open-source agentic framework for autonomous RTL verification. Our contributions are threefold: (1) we design PRO-V sys, a modular agentic system that couples LLM-based reasoning with programmatic tool use for RTL verification; (2) we establish a data construction pipeline that leverages existing RTL datasets to build simulation-validated, expert-level trajectories tailored for supervised fine-tuning (SFT) RTL verification agents; and (3) we implement an efficient reinforcement learning (RL) algorithm that uses verification-specific rewards derived from program-tool feedback to optimize the end-to-end verification workflow. Our empirical evaluation demonstrates PRO-V-R1 achieves a 57.7% functional correctness rate and 34.0% in robust fault detection, significantly outperforming the base model's 25.7% and 21.8% (respectively) from the state-of-the-art (SOTA) automatic verification system. This configuration also outperforms large-scale proprietary LLMs in functional correctness and shows comparable robustness for fault detection.