OpenRaiser/Intern-Atlas huggingface dataset:
Towards Responsible AI in Education: Hybrid Recommendation System for K-12 Students Case Study
Venue: arXiv cs.AI 2025
Year: 2025

The growth of Educational Technology (EdTech) has enabled highly personalized learning experiences through Artificial Intelligence (AI)-based recommendation systems tailored to each student needs. However, these systems can unintentionally introduce biases, potentially limiting fair access to learning resources. This study presents a recommendation system for K-12 students, combining graph-based modeling and matrix factorization to provide personalized suggestions for extracurricular activities, learning resources, and volunteering opportunities. To address fairness concerns, the system includes a framework to detect and reduce biases by analyzing feedback across protected student groups. This work highlights the need for continuous monitoring in educational recommendation systems to support equitable, transparent, and effective learning opportunities for all students.
Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization
Venue: arXiv cs.CL 2025
Year: 2025

Agentic Generative AI, powered by Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG), Knowledge Graphs (KGs), and Vector Stores (VSs), represents a transformative technology applicable to specialized domains such as legal systems, research, recommender systems, cybersecurity, and global security, including proliferation research. This technology excels at inferring relationships within vast unstructured or semi-structured datasets. The legal domain here comprises complex data characterized by extensive, interrelated, and semi-structured knowledge systems with complex relations. It comprises constitutions, statutes, regulations, and case law. Extracting insights and navigating the intricate networks of legal documents and their relations is crucial for effective legal research. Here, we introduce a generative AI system that integrates RAG, VS, and KG, constructed via Non-Negative Matrix Factorization (NMF), to enhance legal information retrieval and AI reasoning and minimize hallucinations. In the legal system, these technologies empower AI agents to identify and analyze complex connections among cases, statutes, and legal precedents, uncovering hidden relationships and predicting legal trends-challenging tasks that are essential for ensuring justice and improving operational efficiency. Our system employs web scraping techniques to systematically collect legal texts, such as statutes, constitutional provisions, and case law, from publicly accessible platforms like Justia. It bridges the gap between traditional keyword-based searches and contextual understanding by leveraging advanced semantic representations, hierarchical relationships, and latent topic discovery. This framework supports legal document clustering, summarization, and cross-referencing, for scalable, interpretable, and accurate retrieval for semi-structured data while advancing computational law and AI.
Multi-Agent Verification: Scaling Test-Time Compute with Multiple Verifiers
Venue: arXiv cs.AI 2025
Year: 2025

By utilizing more computational resources at test-time, large language models (LLMs) can improve without additional training. One common strategy uses verifiers to evaluate candidate outputs. In this work, we propose a novel scaling dimension for test-time compute: scaling the number of verifiers. We introduce Multi-Agent Verification (MAV) as a test-time compute paradigm that combines multiple verifiers to improve performance. We propose using Aspect Verifiers (AVs), off-the-shelf LLMs prompted to verify different aspects of outputs, as one possible choice for the verifiers in a MAV system. AVs are a convenient building block for MAV since they can be easily combined without additional training. Moreover, we introduce BoN-MAV, a simple multi-agent verification algorithm that combines best-of-n sampling with multiple verifiers. BoN-MAV demonstrates stronger scaling patterns than self-consistency and reward model verification, and we demonstrate both weak-to-strong generalization, where combining weak verifiers improves even stronger LLMs, and self-improvement, where the same base model is used to both generate and verify outputs. Our results establish scaling the number of verifiers as a promising new dimension for improving language model performance at test-time.
Waves and symbols in neuromorphic hardware: from analog signal processing to digital computing on the same computational substrate
Venue: arXiv cs.NE 2025
Year: 2025

Neural systems use the same underlying computational substrate to carry out analog filtering and signal processing operations, as well as discrete symbol manipulation and digital computation. Inspired by the computational principles of canonical cortical microcircuits, we propose a framework for using recurrent spiking neural networks to seamlessly and robustly switch between analog signal processing and categorical and discrete computation. We provide theoretical analysis and practical neural network design tools to formally determine the conditions for inducing this switch. We demonstrate the robustness of this framework experimentally with hardware soft Winner-Take-All and mixed-feedback recurrent spiking neural networks, implemented by appropriately configuring the analog neuron and synapse circuits of a mixed-signal neuromorphic processor chip.
Beyond transparency: computational reliabilism as an externalist epistemology of algorithms
Venue: arXiv cs.AI 2025
Year: 2025

This chapter is interested in the epistemology of algorithms. As I intend to approach the topic, this is an issue about epistemic justification. Current approaches to justification emphasize the transparency of algorithms, which entails elucidating their internal mechanisms -- such as functions and variables -- and demonstrating how (or that) these produce outputs. Thus, the mode of justification through transparency is contingent on what can be shown about the algorithm and, in this sense, is internal to the algorithm. In contrast, I advocate for an externalist epistemology of algorithms that I term computational reliabilism (CR). While I have previously introduced and examined CR in the field of computer simulations ([42, 53, 4]), this chapter extends this reliabilist epistemology to encompass a broader spectrum of algorithms utilized in various scientific disciplines, with a particular emphasis on machine learning applications. At its core, CR posits that an algorithm's output is justified if it is produced by a reliable algorithm. A reliable algorithm is one that has been specified, coded, used, and maintained utilizing reliability indicators. These reliability indicators stem from formal methods, algorithmic metrics, expert competencies, cultures of research, and other scientific endeavors. The primary aim of this chapter is to delineate the foundations of CR, explicate its operational mechanisms, and outline its potential as an externalist epistemology of algorithms.
In-Model Merging for Enhancing the Robustness of Medical Imaging Classification Models
Venue: arXiv cs.CV 2025
Year: 2025

Model merging is an effective strategy to merge multiple models for enhancing model performances, and more efficient than ensemble learning as it will not introduce extra computation into inference. However, limited research explores if the merging process can occur within one model and enhance the model's robustness, which is particularly critical in the medical image domain. In the paper, we are the first to propose in-model merging (InMerge), a novel approach that enhances the model's robustness by selectively merging similar convolutional kernels in the deep layers of a single convolutional neural network (CNN) during the training process for classification. We also analytically reveal important characteristics that affect how in-model merging should be performed, serving as an insightful reference for the community. We demonstrate the feasibility and effectiveness of this technique for different CNN architectures on 4 prevalent datasets. The proposed InMerge-trained model surpasses the typically-trained model by a substantial margin. The code will be made public.
Supervised Fine-Tuning LLMs to Behave as Pedagogical Agents in Programming Education
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) are increasingly being explored in higher education, yet their effectiveness as teaching agents remains underexamined. In this paper, we present the development of GuideLM, a fine-tuned LLM designed for programming education. GuideLM has been integrated into the Debugging C Compiler (DCC), an educational C compiler that leverages LLMs to generate pedagogically sound error explanations. Previously, DCC relied on off-the-shelf OpenAI models, which, while accurate, often over-assisted students by directly providing solutions despite contrary prompting. To address this, we employed supervised fine-tuning (SFT) on a dataset of 528 student-question/teacher-answer pairs, creating two models: GuideLM and GuideLM-mini, fine-tuned on ChatGPT-4o and 4o-mini, respectively. We conducted an expert analysis of 400 responses per model, comparing their pedagogical effectiveness against base OpenAI models. Our evaluation, grounded in constructivism and cognitive load theory, assessed factors such as conceptual scaffolding, clarity, and Socratic guidance. Results indicate that GuideLM and GuideLM-mini improve pedagogical performance, with an 8% increase in Socratic guidance and a 58% improvement in economy of words compared to GPT-4o. However, this refinement comes at the cost of a slight reduction in general accuracy. While further work is needed, our findings suggest that fine-tuning LLMs with targeted datasets is a promising approach for developing models better suited to educational contexts.
Evolution Language Framework for Persistent Objects
Venue: arXiv cs.PL 2025
Year: 2025

Context: Multi-schema-version data management (MSVDM) is the database technology that simultaneously supports multiple schema versions of one database. With the technology, multiple versions of one software system can co-exist and exchange data even when the system's data structure evolves along with versions. Inquiry: While there have been developed MSVDM theories and implementations for relational databases, they are not directly applicable to persistent objects. Since persistent objects are commonly implemented by means of object-relational mapping (OR-mapping), we need a right level of abstraction in order to describe evolution of data structures and translate data accesses in between different versions. Approach: We propose a new evolution language consisting of a set of evolution operations, each denoting a modification of the source code and implicitly defining the corresponding modification to the database schema. Given the existence of multiple mapping mechanisms from persistent objects to databases, we designed the evolution language at two levels. At the abstract level, it handles scenarios such as refactoring and adding classes and fields. At the concrete level, we provide definitions for different mapping mechanisms separately, leveraging the existing database evolution language that supports MSVDM. Knowledge: Our evolution language is designed to support existing evolution operations proposed in prior work. Additionally, it introduces support for operations related to class hierarchy changes, which are not covered by previous approaches. Using our proposal, two concrete mapping mechanisms, namely, a JPA-like mapping and signal classes, can be provided separately. Furthermore, our evolution language preserves program behavior and covers common evolution operations in practice. Grounding: This work is supported by the formal definition of both the target abstract core language and the proposed evolution language, the formulation of several theorems demonstrating the soundness of our proposals, and the proofs of these theorems. Additionally, an empirical study was conducted to investigate the evolution histories of three open-source projects. Importance: To the best of our knowledge, our proposal is the first evolution language for persistent objects that supports MSVDM. Moreover, it is the first evolution language defined at an abstract level. By defining mappings separately, we can apply it to a wide range of persistent object mechanisms built on top of SQL.
Conversational Concurrency with Dataspaces and Facets
Venue: arXiv cs.PL 2025
Year: 2025

Context: Developers have come to appreciate the simplicity of message-passing actors for concurrent programming tasks. The actor model of computation is easy to grasp; it is just a conversation among actors with a common goal. Importantly, it eliminates some basic pitfalls of the dominant shared-memory model, most critically data races. Inquiry: A close look at real-world conversations suggests, however, that they are not mere exchanges of messages. Participants must keep in mind conversational context, and participants joining late can and often must acquire some of this context. In addition, some settings call for engaging in several conversations in parallel; in others, participants conduct temporarily limited sub-conversations to clarify a point. Existing actor code exhibits complex design patterns that get around the underlying limitations of the pure message-passing model. Approach: These patterns suggest a number of elements involved in programming conversational computations. Translated into terms of language design, they call for two kinds of facilities: (1) one for sharing conversational context and (2) another one for organizing individual actors around on-going conversations and their contexts. Knowledge: This paper presents Syndicate, a language designed to directly support the programming of conversing actors. Beyond message passing, it supplies (1) a dataspace, which allows actors to make public assertions, to withdraw them, and to query what other actors have asserted; and (2) the facet notation, which enables programmers to express individual actors as a reflection of the on-going conversations. Grounding: A worked example introduces these concepts and illustrates conversational programming in Syndicate. A comparison with other research and industrial concurrent languages demonstrates the unique support Syndicate provides. Importance: Syndicate advances concurrent actor programming with enhancements that address some observed limitations of the underlying model. While message-passing simplifies concurrent programming, it falls short in handling the complexities of actual computational conversations. By introducing a dataspace actor for sharing conversational context and the facet notation for organizing actors around ongoing conversations, Syndicate enables developers to naturally express and manage the nuanced interactions often required in concurrent systems. These innovations reduce the need for complex design patterns and provide unique support for building robust, context-aware concurrent applications.
Toward Fully Autonomous Flexible Chunk-Based Aerial Additive Manufacturing: Insights from Experimental Validation
Venue: arXiv cs.RO 2025
Year: 2025

A novel autonomous chunk-based aerial additive manufacturing framework is presented, supported with experimental demonstration advancing aerial 3D printing. An optimization-based decomposition algorithm transforms structures into sub-components, or chunks, treated as individual tasks coordinated via a dependency graph, ensuring sequential assignment to UAVs considering inter-dependencies and printability constraints for seamless execution. A specially designed hexacopter equipped with a pressurized canister for lightweight expandable foam extrusion is utilized to deposit the material in a controlled manner. To further enhance precise execution of the printing, an offset-free Model Predictive Control mechanism is considered compensating reactively for disturbances and ground effect during execution. Additionally, an interlocking mechanism is introduced in the chunking process to enhance structural cohesion and improve layer adhesion. Extensive experiments demonstrate the framework's effectiveness in constructing precise structures of various shapes while seamlessly adapting to practical challenges, proving its potential for a transformative leap in aerial robotic capability for autonomous construction.
Robust Multicast Origin Authentication in MACsec and CANsec for Automotive Scenarios
Venue: arXiv cs.CR 2025
Year: 2025

Having everything interconnected through the Internet, including vehicle onboard systems, is making security a primary concern in the automotive domain as well. Although Ethernet and CAN XL provide link-level security based on symmetric cryptography, they do not support origin authentication for multicast transmissions. Asymmetric cryptography is unsuitable for networked embedded control systems with real-time constraints and limited computational resources. In these cases, solutions derived from the TESLA broadcast authentication protocol may constitute a more suitable option. In this paper, some such strategies are presented and analyzed that allow for multicast origin authentication, also improving robustness to frame losses by means of interleaved keychains. A flexible authentication mechanism that relies on a unified receiver is then proposed, which enables transmitters to select strategies at runtime, to achieve the best compromise among security, reliability, and resource consumption.
Stochastic Rounding for LLM Training: Theory and Practice
Venue: arXiv cs.LG 2025
Year: 2025

As the parameters of Large Language Models (LLMs) have scaled to hundreds of billions, the demand for efficient training methods -- balancing faster computation and reduced memory usage without sacrificing accuracy -- has become more critical than ever. In recent years, various mixed precision strategies, which involve different precision levels for optimization components, have been proposed to increase training speed with minimal accuracy degradation. However, these strategies often require manual adjustments and lack theoretical justification. In this work, we leverage stochastic rounding (SR) to address numerical errors of training with low-precision representation. We provide theoretical analyses of implicit regularization and convergence under the Adam optimizer when SR is utilized. With the insights from these analyses, we extend previous BF16 + SR strategy to be used in distributed settings, enhancing the stability and performance for large scale training. Empirical results from pre-training models with up to 6.7B parameters, for the first time, demonstrate that our BF16 with SR strategy outperforms (BF16, FP32) mixed precision strategies, achieving better validation perplexity, up to $1.54\times$ higher throughput, and $30\%$ less memory usage.
OmniRouter: Budget and Performance Controllable Multi-LLM Routing
Venue: arXiv cs.DB 2025
Year: 2025

Large language models (LLMs) deliver superior performance but require substantial computational resources and operate with relatively low efficiency, while smaller models can efficiently handle simpler tasks with fewer resources. LLM routing is a crucial paradigm that dynamically selects the most suitable large language models from a pool of candidates to process diverse inputs, ensuring optimal resource utilization while maintaining response quality. Existing routing frameworks typically model this as a locally optimal decision-making problem, selecting the presumed best-fit LLM for each query individually, which overlooks global budget constraints, resulting in ineffective resource allocation. To tackle this problem, we introduce OmniRouter, a fundamentally controllable routing framework for multi-LLM serving. Instead of making per-query greedy choices, OmniRouter models the routing task as a constrained optimization problem, assigning models that minimize total cost while ensuring the required performance level. Specifically, a hybrid retrieval-augmented predictor is designed to predict the capabilities and costs of LLMs. After obtaining the predicted cost and performance, we utilize a constrained optimizer for cost-optimal assignments that employs Lagrangian dual decomposition with adaptive multipliers. It iteratively converges toward the globally optimal query-model allocation, dynamically balancing latency minimization against quality thresholds while adhering to heterogeneous capacity constraints. Experiments show that OmniRouter achieves up to 6.30% improvement in response accuracy while simultaneously reducing computational costs by at least 10.15% compared to competitive router baselines. The code and the dataset are available at https://github.com/dongyuanjushi/OmniRouter.
InstaFace: Identity-Preserving Facial Editing with Single Image Inference
Venue: arXiv cs.CV 2025
Year: 2025

Facial appearance editing is crucial for digital avatars, AR/VR, and personalized content creation, driving realistic user experiences. However, preserving identity with generative models is challenging, especially in scenarios with limited data availability. Traditional methods often require multiple images and still struggle with unnatural face shifts, inconsistent hair alignment, or excessive smoothing effects. To overcome these challenges, we introduce a novel diffusion-based framework, InstaFace, to generate realistic images while preserving identity using only a single image. Central to InstaFace, we introduce an efficient guidance network that harnesses 3D perspectives by integrating multiple 3DMM-based conditionals without introducing additional trainable parameters. Moreover, to ensure maximum identity retention as well as preservation of background, hair, and other contextual features like accessories, we introduce a novel module that utilizes feature embeddings from a facial recognition model and a pre-trained vision-language model. Quantitative evaluations demonstrate that our method outperforms several state-of-the-art approaches in terms of identity preservation, photorealism, and effective control of pose, expression, and lighting.
Training LLMs with MXFP4
Venue: arXiv cs.LG 2025
Year: 2025

Low precision (LP) datatypes such as MXFP4 can accelerate matrix multiplications (GEMMs) and reduce training costs. However, directly using MXFP4 instead of BF16 during training significantly degrades model quality. In this work, we present the first near-lossless training recipe that uses MXFP4 GEMMs, which are $2\times$ faster than FP8 on supported hardware. Our key insight is to compute unbiased gradient estimates with stochastic rounding (SR), resulting in more accurate model updates. However, directly applying SR to MXFP4 can result in high variance from block-level outliers, harming convergence. To overcome this, we use the random Hadamard tranform to theoretically bound the variance of SR. We train GPT models up to 6.7B parameters and find that our method induces minimal degradation over mixed-precision BF16 training. Our recipe computes $>1/2$ the training FLOPs in MXFP4, enabling an estimated speedup of $>1.3\times$ over FP8 and $>1.7\times$ over BF16 during backpropagation.