OpenRaiser/Intern-Atlas huggingface dataset:
MCP4EDA: LLM-Powered Model Context Protocol RTL-to-GDSII Automation with Backend Aware Synthesis Optimization
Venue: arXiv cs.AR 2025
Year: 2025

This paper presents MCP4EDA, the first Model Context Protocol server that enables Large Language Models (LLMs) to control and optimize the complete open-source RTL-to-GDSII design flow through natural language interaction. The system integrates Yosys synthesis, Icarus Verilog simulation, OpenLane place-and-route, GTKWave analysis, and KLayout visualization into a unified LLM-accessible interface, enabling designers to execute complex multi-tool EDA workflows conversationally via AI assistants such as Claude Desktop and Cursor IDE. The principal contribution is a backend-aware synthesis optimization methodology wherein LLMs analyze actual post-layout timing, power, and area metrics from OpenLane results to iteratively refine synthesis TCL scripts, establishing a closed-loop optimization system that bridges the traditional gap between synthesis estimates and physical implementation reality. In contrast to conventional flows that rely on wire-load models, this methodology leverages real backend performance data to guide synthesis parameter tuning, optimization sequence selection, and constraint refinement, with the LLM functioning as an intelligent design space exploration agent. Experimental evaluation on representative digital designs demonstrates 15-30% improvements in timing closure and 10-20% area reduction compared to default synthesis flows, establishing MCP4EDA as the first practical LLM-controlled end-to-end open-source EDA automation system. The code and demo are avaiable at: http://www.agent4eda.com/
HITSZ's End-To-End Speech Translation Systems Combining Sequence-to-Sequence Auto Speech Recognition Model and Indic Large Language Model for IWSLT 2025 in Indic Track
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents HITSZ's submission for the IWSLT 2025 Indic track, focusing on speech-to-text translation (ST) for English-to-Indic and Indic-to-English language pairs. To enhance translation quality in this low-resource scenario, we propose an end-to-end system integrating the pre-trained Whisper automated speech recognition (ASR) model with Krutrim, an Indic-specialized large language model (LLM). Experimental results demonstrate that our end-to-end system achieved average BLEU scores of $28.88$ for English-to-Indic directions and $27.86$ for Indic-to-English directions. Furthermore, we investigated the Chain-of-Thought (CoT) method. While this method showed potential for significant translation quality improvements on successfully parsed outputs (e.g. a $13.84$ BLEU increase for Tamil-to-English), we observed challenges in ensuring the model consistently adheres to the required CoT output format.
Can You Share Your Story? Modeling Clients' Metacognition and Openness for LLM Therapist Evaluation
Venue: arXiv cs.CY 2025
Year: 2025

Understanding clients' thoughts and beliefs is fundamental in counseling, yet current evaluations of LLM therapists often fail to assess this ability. Existing evaluation methods rely on client simulators that clearly disclose internal states to the therapist, making it difficult to determine whether an LLM therapist can uncover unexpressed perspectives. To address this limitation, we introduce MindVoyager, a novel evaluation framework featuring a controllable and realistic client simulator which dynamically adapts itself based on the ongoing counseling session, offering a more realistic and challenging evaluation environment. We further introduce evaluation metrics that assess the exploration ability of LLM therapists by measuring their thorough understanding of client's beliefs and thoughts.
K-PACT: Kernel Planning for Adaptive Context Switching -- A Framework for Clustering, Placement, and Prefetching in Spectrum Sensing
Venue: arXiv cs.ET 2025
Year: 2025

Efficient wideband spectrum sensing requires rapid evaluation and re-evaluation of signal presence and type across multiple subchannels. These tasks involve multiple hypothesis testing, where each hypothesis is implemented as a decision tree workflow containing compute-intensive kernels, including FFT, matrix operations, and signal-specific analyses. Given dynamic nature of the spectrum environment, ability to quickly switch between hypotheses is essential for maintaining low-latency, high-throughput operation. This work assumes a coarse-grained reconfigurable architecture consisting of an array of processing elements (PEs), each equipped with a local instruction memory (IMEM) capable of storing and executing kernels used in spectrum sensing applications. We propose a planner tool that efficiently maps hypothesis workflows onto this architecture to enable fast runtime context switching with minimal overhead. The planner performs two key tasks: clustering temporally non-overlapping kernels to share IMEM resources within a PE sub-array, and placing these clusters onto hardware to ensure efficient scheduling and data movement. By preloading kernels that are not simultaneously active into same IMEM, our tool enables low-latency reconfiguration without runtime conflicts. It models the planning process as a multi-objective optimization, balancing trade-offs among context switch overhead, scheduling latency, and dataflow efficiency. We evaluate the proposed tool in simulated spectrum sensing scenario with 48 concurrent subchannels. Results show that our approach reduces off-chip binary fetches by 207.81x, lowers average switching time by 98.24x, and improves per-subband execution time by 132.92x over baseline without preloading. These improvements demonstrate that intelligent planning is critical for adapting to fast-changing spectrum environments in next-generation radio frequency systems.
Quantifying the Performance Gap for Simple Versus Optimal Dynamic Server Allocation Policies
Venue: arXiv cs.DC 2025
Year: 2025

Cloud computing enables the dynamic provisioning of server resources. To exploit this opportunity, a policy is needed for dynamically allocating (and deallocating) servers in response to the current load conditions. In this paper we describe several simple policies for dynamic server allocation and develop analytic models for their analysis. We also design semi-Markov decision models that enable determination of the performance achieved with optimal policies, allowing us to quantify the performance gap between simple, easily implemented policies, and optimal policies. Finally, we apply our models to study the potential performance benefits of state-dependent routing in multi-site systems when using dynamic server allocation at each site. Insights from our results are valuable to service providers wanting to balance cloud service costs and delays.
Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges
Venue: arXiv cs.AI 2025
Year: 2025

Due to the remarkable capabilities and growing impact of large language models (LLMs), they have been deeply integrated into many aspects of society. Thus, ensuring their alignment with human values and intentions has emerged as a critical challenge. This survey provides a comprehensive overview of practical alignment techniques, training protocols, and empirical findings in LLM alignment. We analyze the development of alignment methods across diverse paradigms, characterizing the fundamental trade-offs between core alignment objectives. Our analysis shows that while supervised fine-tuning enables basic instruction-following, preference-based methods offer more flexibility for aligning with nuanced human intent. We discuss state-of-the-art techniques, including Direct Preference Optimization (DPO), Constitutional AI, brain-inspired methods, and alignment uncertainty quantification (AUQ), highlighting their approaches to balancing quality and efficiency. We review existing evaluation frameworks and benchmarking datasets, emphasizing limitations such as reward misspecification, distributional robustness, and scalable oversight. We summarize strategies adopted by leading AI labs to illustrate the current state of practice. We conclude by outlining open problems in oversight, value pluralism, robustness, and continuous alignment. This survey aims to inform both researchers and practitioners navigating the evolving landscape of LLM alignment.
KD-GAT: Combining Knowledge Distillation and Graph Attention Transformer for a Controller Area Network Intrusion Detection System
Venue: arXiv cs.LG 2025
Year: 2025

The Controller Area Network (CAN) protocol is widely adopted for in-vehicle communication but lacks inherent security mechanisms, making it vulnerable to cyberattacks. This paper introduces KD-GAT, an intrusion detection framework that combines Graph Attention Networks (GATs) with knowledge distillation (KD) to enhance detection accuracy while reducing computational complexity. In our approach, CAN traffic is represented as graphs using a sliding window to capture temporal and relational patterns. A multi-layer GAT with jumping knowledge aggregation acting as the teacher model, while a compact student GAT--only 6.32% the size of the teacher--is trained via a two-phase process involving supervised pretraining and knowledge distillation with both soft and hard label supervision. Experiments on three benchmark datasets--Car-Hacking, Car-Survival, and can-train-and-test demonstrate that both teacher and student models achieve strong results, with the student model attaining 99.97% and 99.31% accuracy on Car-Hacking and Car-Survival, respectively. However, significant class imbalance in can-train-and-test has led to reduced performance for both models on this dataset. Addressing this imbalance remains an important direction for future work.
The wall confronting large language models
Venue: arXiv cs.AI 2025
Year: 2025

We show that the scaling laws which determine the performance of large language models (LLMs) severely limit their ability to improve the uncertainty of their predictions. As a result, raising their reliability to meet the standards of scientific inquiry is intractable by any reasonable measure. We argue that the very mechanism which fuels much of the learning power of LLMs, namely the ability to generate non-Gaussian output distributions from Gaussian input ones, might well be at the roots of their propensity to produce error pileup, ensuing information catastrophes and degenerative AI behaviour. This tension between learning and accuracy is a likely candidate mechanism underlying the observed low values of the scaling components. It is substantially compounded by the deluge of spurious correlations pointed out by Calude and Longo which rapidly increase in any data set merely as a function of its size, regardless of its nature. The fact that a degenerative AI pathway is a very probable feature of the LLM landscape does not mean that it must inevitably arise in all future AI research. Its avoidance, which we also discuss in this paper, necessitates putting a much higher premium on insight and understanding of the structural characteristics of the problems being investigated.
Beyond Nearest Neighbors: Semantic Compression and Graph-Augmented Retrieval for Enhanced Vector Search
Venue: arXiv cs.LG 2025
Year: 2025

Vector databases typically rely on approximate nearest neighbor (ANN) search to retrieve the top-k closest vectors to a query in embedding space. While effective, this approach often yields semantically redundant results, missing the diversity and contextual richness required by applications such as retrieval-augmented generation (RAG), multi-hop QA, and memory-augmented agents. We introduce a new retrieval paradigm: semantic compression, which aims to select a compact, representative set of vectors that captures the broader semantic structure around a query. We formalize this objective using principles from submodular optimization and information geometry, and show that it generalizes traditional top-k retrieval by prioritizing coverage and diversity. To operationalize this idea, we propose graph-augmented vector retrieval, which overlays semantic graphs (e.g., kNN or knowledge-based links) atop vector spaces to enable multi-hop, context-aware search. We theoretically analyze the limitations of proximity-based retrieval under high-dimensional concentration and highlight how graph structures can improve semantic coverage. Our work outlines a foundation for meaning-centric vector search systems, emphasizing hybrid indexing, diversity-aware querying, and structured semantic retrieval. We make our implementation publicly available to foster future research in this area.
Flexible Bidding in Service-Oriented Combinatorial Spectrum Forward Auctions
Venue: arXiv cs.GT 2025
Year: 2025

Traditional combinatorial spectrum auctions mainly rely on fixed bidding and matching processes, which limit participants' ability to adapt their strategies and often result in suboptimal social welfare in dynamic spectrum sharing environments. To address these limitations, we propose a novel approximately truthful combinatorial forward auction scheme with a flexible bidding mechanism aimed at enhancing resource efficiency and maximizing social welfare. In the proposed scheme, each buyer submits a combinatorial bid consisting of the base spectrum demand and adjustable demand ranges, enabling the auctioneer to dynamically optimize spectrum allocation in response to market conditions. To standardize the valuation across heterogeneous frequency bands, we introduce a Spectrum Equivalent Mapping (SEM) coefficient. A greedy matching algorithm is employed to determine winning bids by sorting buyers based on their equivalent unit bid prices and allocating resources within supply constraints. Simulation results demonstrate that the proposed flexible bidding mechanism significantly outperforms existing benchmark methods, achieving notably higher social welfare in dynamic spectrum sharing scenarios.
Accelerating Matrix Multiplication: A Performance Comparison Between Multi-Core CPU and GPU
Venue: arXiv cs.DC 2025
Year: 2025

Matrix multiplication is a foundational operation in scientific computing and machine learning, yet its computational complexity makes it a significant bottleneck for large-scale applications. The shift to parallel architectures, primarily multi-core CPUs and many-core GPUs, is the established solution, and these systems are now ubiquitous from datacenters to consumer laptops. This paper presents a direct, empirical performance analysis of matrix multiplication on a modern, consumer-grade heterogeneous platform. We implemented and benchmarked three versions of the algorithm: a baseline sequential C++ implementation, a parallel version for its multi-core CPU using OpenMP, and a massively parallel version for its discrete GPU using CUDA with shared memory optimizations. The implementations were evaluated with square matrices of varying dimensions, from 128x128 to 4096x4096. Our results show that while the parallel CPU provides a consistent speedup of 12-14x over the sequential version, the GPU's performance scales dramatically with problem size. For a 4096x4096 matrix, the GPU implementation achieved a speedup of approximately 593x over the sequential baseline and 45x over the optimized parallel CPU version. These findings quantitatively demonstrate the profound impact of many-core GPU architectures on accelerating data-parallel workloads, underscoring that significant performance gains are readily accessible even on consumer-level hardware.
HypKG: Hypergraph-based Knowledge Graph Contextualization for Precision Healthcare
Venue: arXiv cs.AI 2025
Year: 2025

Knowledge graphs (KGs) are important products of the semantic web, which are widely used in various application domains. Healthcare is one of such domains where KGs are intensively used, due to the high requirement for knowledge accuracy and interconnected nature of healthcare data. However, KGs storing general factual information often lack the ability to account for important contexts of the knowledge such as the status of specific patients, which are crucial in precision healthcare. Meanwhile, electronic health records (EHRs) provide rich personal data, including various diagnoses and medications, which provide natural contexts for general KGs. In this paper, we propose HypKG, a framework that integrates patient information from EHRs into KGs to generate contextualized knowledge representations for accurate healthcare predictions. Using advanced entity-linking techniques, we connect relevant knowledge from general KGs with patient information from EHRs, and then utilize a hypergraph model to "contextualize" the knowledge with the patient information. Finally, we employ hypergraph transformers guided by downstream prediction tasks to jointly learn proper contextualized representations for both KGs and patients, fully leveraging existing knowledge in KGs and patient contexts in EHRs. In experiments using a large biomedical KG and two real-world EHR datasets, HypKG demonstrates significant improvements in healthcare prediction tasks across multiple evaluation metrics. Additionally, by integrating external contexts, HypKG can learn to adjust the representations of entities and relations in KG, potentially improving the quality and real-world utility of knowledge.
Development and Evaluation of Adaptive LearningSupport System Based on Ontology of MultipleProgramming Languages
Venue: arXiv cs.PL 2025
Year: 2025

This paper introduces an ontology-based approach within an adaptive learning support system for computer programming. This system (named ADVENTURE) is designed to deliver personalized programming exercises that are tailored to individual learners' skill levels. ADVENTURE utilizes an ontology, named CONTINUOUS, which encompasses common concepts across multiple programming languages. The system leverages this ontology not only to visualize programming concepts but also to provide hints during practice programming exercises and recommend subsequent programming concepts. The adaptive mechanism is driven by the Elo Rating System, applied in an educational context to dynamically estimate the most appropriate exercise difficulty for each learner. An experimental study compared two instructional modes, adaptive and random, based on six features derived from 1,186 code submissions across all the experimental groups. The results indicate significant differences in four of six analyzed features between these two modes. Notably, the adaptive mode demonstrates a significant difference over the random mode in two features, the submission of correct answers and the number of pass concepts. Therefore, these results underscore that this adaptive learning support system may support learners in practicing programming exercises.
Quaternion-Based Robust PCA for Efficient Moving Target Detection and Background Recovery in Color Videos
Venue: arXiv cs.CV 2025
Year: 2025

Moving target detection is a challenging computer vision task aimed at generating accurate segmentation maps in diverse in-the-wild color videos captured by static cameras. If backgrounds and targets can be simultaneously extracted and recombined, such synthetic data can significantly enrich annotated in-the-wild datasets and enhance the generalization ability of deep models. Quaternion-based RPCA (QRPCA) is a promising unsupervised paradigm for color image processing. However, in color video processing, Quaternion Singular Value Decomposition (QSVD) incurs high computational costs, and rank-1 quaternion matrix fails to yield rank-1 color channels. In this paper, we reduce the computational complexity of QSVD to o(1) by utilizing a quaternion Riemannian manifold. Furthermor, we propose the universal QRPCA (uQRPCA) framework, which achieves a balance in simultaneously segmenting targets and recovering backgrounds from color videos. Moreover, we expand to uQRPCA+ by introducing the Color Rank-1 Batch (CR1B) method to further process and obtain the ideal low-rank background across color channels. Experiments demonstrate our uQRPCA+ achieves State Of The Art (SOTA) performance on moving target detection and background recovery tasks compared to existing open-source methods. Our implementation is publicly available on GitHub at https://github.com/Ruchtech/uQRPCA
DOA: A Degeneracy Optimization Agent with Adaptive Pose Compensation Capability based on Deep Reinforcement Learning
Venue: arXiv cs.RO 2025
Year: 2025

Particle filter-based 2D-SLAM is widely used in indoor localization tasks due to its efficiency. However, indoor environments such as long straight corridors can cause severe degeneracy problems in SLAM. In this paper, we use Proximal Policy Optimization (PPO) to train an adaptive degeneracy optimization agent (DOA) to address degeneracy problem. We propose a systematic methodology to address three critical challenges in traditional supervised learning frameworks: (1) data acquisition bottlenecks in degenerate dataset, (2) inherent quality deterioration of training samples, and (3) ambiguity in annotation protocol design. We design a specialized reward function to guide the agent in developing perception capabilities for degenerate environments. Using the output degeneracy factor as a reference weight, the agent can dynamically adjust the contribution of different sensors to pose optimization. Specifically, the observation distribution is shifted towards the motion model distribution, with the step size determined by a linear interpolation formula related to the degeneracy factor. In addition, we employ a transfer learning module to endow the agent with generalization capabilities across different environments and address the inefficiency of training in degenerate environments. Finally, we conduct ablation studies to demonstrate the rationality of our model design and the role of transfer learning. We also compare the proposed DOA with SOTA methods to prove its superior degeneracy detection and optimization capabilities across various environments.
JT-Math: A Multi-Stage Framework for Advanced Mathematical Reasoning in Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Mathematical reasoning is a cornerstone of artificial general intelligence and a primary benchmark for evaluating the capabilities of Large Language Models (LLMs). While state-of-the-art models show promise, they often falter when faced with complex problems that demand deep conceptual understanding and intricate, multi-step deliberation. To address this challenge, we introduce JT-Math-8B, a series of open-source models comprising base, instruct, and thinking versions, built upon a systematic, multi-stage optimization framework. Our pre-training corpus is a high-quality, 210B-token dataset curated through a dedicated data pipeline that uses model-based validation to ensure quality and diversity. The Instruct Model is optimized for direct, concise answers through Supervised Fine-Tuning (SFT) and a GRPO-based reinforcement learning (RL) method. The Thinking Model is trained for complex problem-solving using a Long Chain-of-Thought (Long CoT) approach, combining SFT with a novel, multi-stage RL curriculum that progressively increases task difficulty and context length up to 32K tokens. JT-Math-8B achieves state-of-the-art results among open-source models of similar size, surpassing prominent models like OpenAI's O1-mini and GPT-4o , and demonstrating superior performance on competition-level mathematics.