OpenRaiser/Intern-Atlas huggingface dataset:
From Awareness to Application: Strengthening Recruitment for NSF S-STEM Scholarships in Computer Science
Venue: arXiv cs.CY 2026
Year: 2026

Recruiting academically strong students into NSF S-STEM scholarship programs remains a persistent challenge in computer science education. This paper presents the design and initial implementation of a suite of targeted recruitment strategies for our NSF-funded project. Our recruitment strategy leverages multiple channels. Information sessions and early outreach efforts were employed to increase awareness and reduce perceived barriers to applying. Data from our recruitment includes applicant demographics, academic performance, financial aid profiles, recruitment source tracking, and survey responses on students awareness and decision-making processes. These data provide a foundation for evaluating the reach and effectiveness of various recruitment strategies and identifying factors that influence student application decisions. Quantitative and qualitative research approaches are employed to examine the implementation and outcomes of proactive recruitment strategies. Our preliminary analysis indicates that direct information sessions and departmental emails are effective recruitment strategies, accounting for a large portion of eligible applications. Our findings emphasize the importance of early communication about the program, clearly defined eligibility criteria, and a streamlined application process. By sharing ongoing progress and lessons learned from our project, this paper contributes evidence-based insights into recruitment practices and offers strategies that can be adapted by other institutions implementing NSF S-STEM programs.
Beyond Refusal: Probing the Limits of Agentic Self-Correction for Semantic Sensitive Information
Venue: arXiv cs.AI 2026
Year: 2026

While defenses for structured PII are mature, Large Language Models (LLMs) pose a new threat: Semantic Sensitive Information (SemSI), where models infer sensitive identity attributes, generate reputation-harmful content, or hallucinate potentially wrong information. The capacity of LLMs to self-regulate these complex, context-dependent sensitive information leaks without destroying utility remains an open scientific question. To address this, we introduce SemSIEdit, an inference-time framework where an agentic "Editor" iteratively critiques and rewrites sensitive spans to preserve narrative flow rather than simply refusing to answer. Our analysis reveals a Privacy-Utility Pareto Frontier, where this agentic rewriting reduces leakage by 34.6% across all three SemSI categories while incurring a marginal utility loss of 9.8%. We also uncover a Scale-Dependent Safety Divergence: large reasoning models (e.g., GPT-5) achieve safety through constructive expansion (adding nuance), whereas capacity-constrained models revert to destructive truncation (deleting text). Finally, we identify a Reasoning Paradox: while inference-time reasoning increases baseline risk by enabling the model to make deeper sensitive inferences, it simultaneously empowers the defense to execute safe rewrites.
Easy3E: Feed-Forward 3D Asset Editing via Rectified Voxel Flow
Venue: arXiv cs.CV 2026
Year: 2026

Existing 3D editing methods rely on computationally intensive scene-by-scene iterative optimization and suffer from multi-view inconsistency. We propose an effective and fully feedforward 3D editing framework based on the TRELLIS generative backbone, capable of modifying 3D models from a single editing view. Our framework addresses two key issues: adapting training-free 2D editing to structured 3D representations, and overcoming the bottleneck of appearance fidelity in compressed 3D features. To ensure geometric consistency, we introduce Voxel FlowEdit, an edit-driven flow in the sparse voxel latent space that achieves globally consistent 3D deformation in a single pass. To restore high-fidelity details, we develop a normal-guided single to multi-view generation module as an external appearance prior, successfully recovering high-frequency textures. Experiments demonstrate that our method enables fast, globally consistent, and high-fidelity 3D model editing.
AHAN: Asymmetric Hierarchical Attention Network for Identical Twin Face Verification
Venue: arXiv cs.CV 2026
Year: 2026

Identical twin face verification represents an extreme fine-grained recognition challenge where even state-of-the-art systems fail due to overwhelming genetic similarity. Current face recognition methods achieve over 99.8% accuracy on standard benchmarks but drop dramatically to 88.9% when distinguishing identical twins, exposing critical vulnerabilities in biometric security systems. The difficulty lies in learning features that capture subtle, non-genetic variations that uniquely identify individuals. We propose the Asymmetric Hierarchical Attention Network (AHAN), a novel architecture specifically designed for this challenge through multi-granularity facial analysis. AHAN introduces a Hierarchical Cross-Attention (HCA) module that performs multi-scale analysis on semantic facial regions, enabling specialized processing at optimal resolutions. We further propose a Facial Asymmetry Attention Module (FAAM) that learns unique biometric signatures by computing cross-attention between left and right facial halves, capturing subtle asymmetric patterns that differ even between twins. To ensure the network learns truly individuating features, we introduce Twin-Aware Pair-Wise Cross-Attention (TA-PWCA), a training-only regularization strategy that uses each subject's own twin as the hardest possible distractor. Extensive experiments on the ND_TWIN dataset demonstrate that AHAN achieves 92.3% twin verification accuracy, representing a 3.4% improvement over state-of-the-art methods.
I/O Optimizations for Graph-Based Disk-Resident Approximate Nearest Neighbor Search: A Design Space Exploration
Venue: arXiv cs.DB 2026
Year: 2026

Approximate nearest neighbor (ANN) search on SSD-backed indexes is increasingly I/O-bound (I/O accounts for 70--90\% of query latency). We present an I/O-first framework for disk-based ANN that organizes techniques along three dimensions: memory layout, disk layout, and search algorithm. We introduce a page-level complexity model that explains how page locality and path length jointly determine page reads, and we validate the model empirically. Using consistent implementations across four public datasets, we quantify both single-factor effects and cross-dimensional synergies. We find that (i) memory-resident navigation and dynamic width provide the strongest standalone gains; (ii) page shuffle and page search are weak alone but complementary together; and (iii) a principled composition, OctopusANN, substantially reduces I/O and achieves 4.1--37.9\% higher throughput than the state-of-the-art system Starling and 87.5--149.5\% higher throughput than DiskANN at matched Recall@10=90\%. Finally, we distill actionable guidelines for selecting storage-centric or hybrid designs across diverse concurrency levels and accuracy constraints, advocating systematic composition rather than isolated tweaks when pushing the performance frontier of disk-based ANN.
Quantum Attacks Targeting Nuclear Power Plants: Threat Analysis, Defense and Mitigation Strategies
Venue: arXiv cs.CR 2026
Year: 2026

The advent of Cryptographically Relevant Quantum Computers (CRQCs) presents a fundamental and existential threat to the forensic integrity and operational safety of Industrial Control Systems (ICS) and Operational Technology (OT) in critical infrastructure. This paper introduces a novel, forensics-first framework for achieving quantum resilience in high-consequence environments, with a specific focus on nuclear power plants. We systematically analyze the quantum threat landscape across the Purdue architecture (L0-L5), detailing how Harvest-Now, Decrypt-Later (HNDL) campaigns, enabled by algorithms like Shor's, can retroactively compromise cryptographic foundations, undermine evidence admissibility, and facilitate sophisticated sabotage. Through two detailed case studies, \textsc{Quantum~Scar} and \textsc{Quantum~Dawn}, we demonstrate multi-phase attack methodologies where state-level adversaries exploit cryptographic monoculture and extended OT lifecycles to degrade safety systems while creating unsolvable forensic paradoxes. Our probabilistic risk modeling reveals alarming success probabilities (up to 78\% for targeted facilities under current defenses), underscoring the criticality of immediate action. In response, we propose and validate a phased, defense-in-depth migration path to Post-Quantum Cryptography (PQC), integrating hybrid key exchange, cryptographic diversity, secure time synchronization, and side-channel resistant implementations aligned with ISA/IEC 62443 and NIST standards. The paper concludes that without urgent adoption of quantum-resilient controls, the integrity of both physical safety systems and digital forensic evidence remains at severe and irreversible risk.
ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning
Venue: arXiv cs.AI 2026
Year: 2026

Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks. Despite encouraging early results, ARL remains highly unstable, often leading to training collapse. This instability limits scalability to larger environments and longer interaction horizons, and constrains systematic exploration of algorithmic design choices. In this paper, we first propose ARLArena, a stable training recipe and systematic analysis framework that examines training stability in a controlled and reproducible setting. ARLArena first constructs a clean and standardized testbed. Then, we decompose policy gradient into four core design dimensions and assess the performance and stability of each dimension. Through this fine-grained analysis, we distill a unified perspective on ARL and propose SAMPO, a stable agentic policy optimization method designed to mitigate the dominant sources of instability in ARL. Empirically, SAMPO achieves consistently stable training and strong performance across diverse agentic tasks. Overall, this study provides a unifying policy gradient perspective for ARL and offers practical guidance for building stable and reproducible LLM-based agent training pipelines.
IHF-Harmony: Multi-Modality Magnetic Resonance Images Harmonization using Invertible Hierarchy Flow Model
Venue: arXiv cs.CV 2026
Year: 2026

Retrospective MRI harmonization is limited by poor scalability across modalities and reliance on traveling subject datasets. To address these challenges, we introduce IHF-Harmony, a unified invertible hierarchy flow framework for multi-modality harmonization using unpaired data. By decomposing the translation process into reversible feature transformations, IHF-Harmony guarantees bijective mapping and lossless reconstruction to prevent anatomical distortion. Specifically, an invertible hierarchy flow (IHF) performs hierarchical subtractive coupling to progressively remove artefact-related features, while an artefact-aware normalization (AAN) employs anatomy-fixed feature modulation to accurately transfer target characteristics. Combined with anatomy and artefact consistency loss objectives, IHF-Harmony achieves high-fidelity harmonization that retains source anatomy. Experiments across multiple MRI modalities demonstrate that IHF-Harmony outperforms existing methods in both anatomical fidelity and downstream task performance, facilitating robust harmonization for large-scale multi-site imaging studies. Code will be released upon acceptance.
Extending Sequence Length is Not All You Need: Effective Integration of Multimodal Signals for Gene Expression Prediction
Venue: arXiv cs.LG 2026
Year: 2026

Gene expression prediction, which predicts mRNA expression levels from DNA sequences, presents significant challenges. Previous works often focus on extending input sequence length to locate distal enhancers, which may influence target genes from hundreds of kilobases away. Our work first reveals that for current models, long sequence modeling can decrease performance. Even carefully designed algorithms only mitigate the performance degradation caused by long sequences. Instead, we find that proximal multimodal epigenomic signals near target genes prove more essential. Hence we focus on how to better integrate these signals, which has been overlooked. We find that different signal types serve distinct biological roles, with some directly marking active regulatory elements while others reflect background chromatin patterns that may introduce confounding effects. Simple concatenation may lead models to develop spurious associations with these background patterns. To address this challenge, we propose Prism, a framework that learns multiple combinations of high-dimensional epigenomic features to represent distinct background chromatin states and uses backdoor adjustment to mitigate confounding effects. Our experimental results demonstrate that proper modeling of multimodal epigenomic signals achieves state-of-the-art performance using only short sequences for gene expression prediction.
Epoch-based Optimistic Concurrency Control in Geo-replicated Databases
Venue: arXiv cs.DB 2026
Year: 2026

Geo-distribution is essential for modern online applications to ensure service reliability and high availability. However, supporting high-performance serializable transactions in geo-replicated databases remains a significant challenge. This difficulty stems from the extensive over-coordination inherent in distributed atomic commitment, concurrency control, and fault-tolerance replication protocols under high network latency. To address these challenges, we introduce Minerva, a unified distributed concurrency control designed for highly scalable multi-leader replication. Minerva employs a novel epoch-based asynchronous replication protocol that decouples data propagation from the commitment process, enabling continuous transaction replication. Optimistic concurrency control is used to allow any replicas to execute transactions concurrently and commit without coordination. In stead of aborting transactions when conflicts are detected, Minerva uses deterministic re-execution to resolve conflicts, ensuring serializability without sacrificing performance. To further enhance concurrency, we construct a conflict graph and use a maximum weight independent set algorithm to select the optimal subset of transactions for commitment, minimizing the number of re-executed transactions. Our evaluation demonstrates that Minerva significantly outperforms state-of-the-art replicated databases, achieving over $3\times$ higher throughput in scalability experiments and $2.8\times$ higher throughput during a high network latency simulation with the TPC-C benchmark.
SPOC: Safety-Aware Planning Under Partial Observability And Physical Constraints
Venue: arXiv cs.RO 2026
Year: 2026

Embodied Task Planning with large language models faces safety challenges in real-world environments, where partial observability and physical constraints must be respected. Existing benchmarks often overlook these critical factors, limiting their ability to evaluate both feasibility and safety. We introduce SPOC, a benchmark for safety-aware embodied task planning, which integrates strict partial observability, physical constraints, step-by-step planning, and goal-condition-based evaluation. Covering diverse household hazards such as fire, fluid, injury, object damage, and pollution, SPOC enables rigorous assessment through both state and constraint-based online metrics. Experiments with state-of-the-art LLMs reveal that current models struggle to ensure safety-aware planning, particularly under implicit constraints. Code and dataset are available at https://github.com/khm159/SPOC
Iterative Closed-Loop Motion Synthesis for Scaling the Capabilities of Humanoid Control
Venue: arXiv cs.RO 2026
Year: 2026

Physics-based humanoid control relies on training with motion datasets that have diverse data distributions. However, the fixed difficulty distribution of datasets limits the performance ceiling of the trained control policies. Additionally, the method of acquiring high-quality data through professional motion capture systems is constrained by costs, making it difficult to achieve large-scale scalability. To address these issues, we propose a closed-loop automated motion data generation and iterative framework. It can generate high-quality motion data with rich action semantics, including martial arts, dance, combat, sports, gymnastics, and more. Furthermore, our framework enables difficulty iteration of policies and data through physical metrics and objective evaluations, allowing the trained tracker to break through its original difficulty limits. On the PHC single-primitive tracker, using only approximately 1/10 of the AMASS dataset size, the average failure rate on the test set (2201 clips) is reduced by 45% compared to the baseline. Finally, we conduct comprehensive ablation and comparative experiments to highlight the rationality and advantages of our framework.
When More Is Less: A Systematic Analysis of Spatial and Commonsense Information for Visual Spatial Reasoning
Venue: arXiv cs.CL 2026
Year: 2026

Visual spatial reasoning (VSR) remains challenging for modern vision-language models (VLMs), despite advances in multimodal architectures. A common strategy is to inject additional information at inference time, such as explicit spatial cues, external commonsense knowledge, or chain-of-thought (CoT) reasoning instructions. However, it remains unclear when such information genuinely improves reasoning and when it introduces noise. In this paper, we conduct a hypothesis-driven analysis of information injection for VSR across three representative VLMs and two public benchmarks. We examine (i) the type and number of spatial contexts, (ii) the amount and relevance of injected commonsense knowledge, and (iii) the interaction between spatial grounding and CoT prompting. Our results reveal a consistent pattern: more information does not necessarily yield better reasoning. Targeted single spatial cues outperform multi-context aggregation, excessive or weakly relevant commonsense knowledge degrades performance, and CoT prompting improves accuracy only when spatial grounding is sufficiently precise. These findings highlight the importance of selective, task-aligned information injection and provide practical guidance for designing reliable multimodal reasoning pipelines.
Multi-Layer Scheduling for MoE-Based LLM Reasoning
Venue: arXiv cs.DC 2026
Year: 2026

Large Language Models (LLMs) have achieved remarkable success across a wide range of tasks, but serving them efficiently at scale remains a critical challenge due to their substantial computational and latency demands. While most existing inference frameworks rely on simple scheduling strategies such as First-Come-First-Serve (FCFS) at the engine level and Round-Robin (RR) at the scheduler or coordinator level, they often fail to fully utilize system resources and may suffer from issues such as head-of-line blocking and load imbalance. Recent advances in Mixture-of-Experts (MoE) models have also introduced new challenges in scheduling arising from expert parallelism and routing complexity. This research proposes a multi-layer scheduling framework tailored for MoE-based LLM serving. It targets scheduling at three levels: request-level, enginelevel, and expert-level. At the request level, we explore algorithms such as Shortest-Job-First (SJF) and priority-aware aging to improve throughput and reduce latency. At the engine level, we design load-aware dispatching strategies that account for the current prefix token load, KV cache utilization, and user stickiness to achieve better resource matching. At the expert level, we focus on alleviating expert hotspots and strategically placing inter-layer expert dependencies to balance load and improve routing efficiency. Extensive experimental results from more than 100 experiments conducted under diverse workload distributions show that our approach consistently outperforms the state-of-theart inference framework vLLM, achieving up to 17.8% reduction in Time To First Token (TTFT) latency and 13.3% reduction in Time-Per-Output-Token (TPOT) latency.
Tokenizing Semantic Segmentation with RLE
Venue: arXiv cs.CV 2026
Year: 2026

This paper presents a new unified approach to semantic segmentation in both images and videos by using language modeling to output the masks as sequences of discrete tokens. We use run length encoding (RLE) to discretize the segmentation masks and then train a modified version of Pix2Seq to output these RLE tokens through autoregression. We propose novel tokenization strategies to compress the length of the token sequence to make it practicable to extend this approach to videos. We also show how instance information can be incorporated into the tokenization process to perform panoptic segmentation. We evaluate our proposed models on two datasets to show that they are competitive with the state of the art in some scenarios in spite of being bottlenecked by our limited computational resources. We make our code and models publicly available to facilitate further work in this domain.
RuCL: Stratified Rubric-Based Curriculum Learning for Multimodal Large Language Model Reasoning
Venue: arXiv cs.CL 2026
Year: 2026

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a prevailing paradigm for enhancing reasoning in Multimodal Large Language Models (MLLMs). However, relying solely on outcome supervision risks reward hacking, where models learn spurious reasoning patterns to satisfy final answer checks. While recent rubric-based approaches offer fine-grained supervision signals, they suffer from high computational costs of instance-level generation and inefficient training dynamics caused by treating all rubrics as equally learnable. In this paper, we propose Stratified Rubric-based Curriculum Learning (RuCL), a novel framework that reformulates curriculum learning by shifting the focus from data selection to reward design. RuCL generates generalized rubrics for broad applicability and stratifies them based on the model's competence. By dynamically adjusting rubric weights during training, RuCL guides the model from mastering foundational perception to tackling advanced logical reasoning. Extensive experiments on various visual reasoning benchmarks show that RuCL yields a remarkable +7.83% average improvement over the Qwen2.5-VL-7B model, achieving a state-of-the-art accuracy of 60.06%.
Permutation Polynomials Under Multiplicative-Additive Perturbations: Characterization via Difference Distribution Tables
Venue: arXiv cs.IT 2026
Year: 2026

We investigate permutation polynomials F over finite fields F_{p^n} whose generalized derivative maps x -> F(x + a) - cF(x) are themselves permutations for all nonzero shifts a. This property, termed perfect c-nonlinearity (PcN), represents optimal resistance to c-differential attacks - a concern highlighted by recent cryptanalysis of the Kuznyechik cipher variant. We provide the first characterization using the classical difference distribution table (DDT): F is PcN if and only if Delta_F(a,b) Delta_F(a,c^{-1}b) = 0 for all nonzero a,b. This enables verification in O(p^{2n}) time given a precomputed DDT, a significant improvement over the naive O(p^{3n}) approach. We prove a strict dichotomy for monomial permutations: the derivative F(x + alpha) - cF(x) is either a permutation for all nonzero shifts or for none, with the general case remaining open. For quadratic permutations, we provide explicit algebraic characterizations. We identify the first class of affine transformations preserving c-differential uniformity and derive tight nonlinearity bounds revealing fundamental incompatibility between PcN and APN properties. These results position perfect c-nonlinearity as a structurally distinct regime within permutation polynomial theory.