OpenRaiser/Intern-Atlas huggingface dataset:
Reinforcement Learning Improves Traversal of Hierarchical Knowledge in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Reinforcement learning (RL) is often credited with improving language model reasoning and generalization at the expense of degrading memorized knowledge. We challenge this narrative by observing that RL-enhanced models consistently outperform their base and supervised fine-tuned (SFT) counterparts on pure knowledge recall tasks, particularly those requiring traversal of hierarchical, structured knowledge (e.g., medical codes). We hypothesize these gains stem not from newly acquired data, but from improved procedural skills in navigating and searching existing knowledge hierarchies within the model parameters. To support this hypothesis, we show that structured prompting, which explicitly guides SFTed models through hierarchical traversal, recovers most of the performance gap (reducing 24pp to 7pp on MedConceptsQA for DeepSeek-V3/R1). We further find that while prompting improves final-answer accuracy, RL-enhanced models retain superior ability to recall correct procedural paths on deep-retrieval tasks. Finally our layer-wise internal activation analysis reveals that while factual representations (e.g., activations for the statement "code 57.95 refers to urinary infection") maintain high cosine similarity between SFT and RL models, query representations (e.g., "what is code 57.95") diverge noticeably, indicating that RL primarily transforms how models traverse knowledge rather than the knowledge representation itself.
Polymap: generating high definition map based on rasterized polygons
Venue: arXiv cs.CV 2025
Year: 2025

The perception of high-definition maps is an integral component of environmental perception in autonomous driving systems. Existing research have often focused on online construction of high-definition maps. For instance, the Maptr[9] series employ a detection-based method to output vectorized map instances parallelly in an end-to-end manner. However, despite their capability for real-time construction, detection-based methods are observed to lack robust generalizability[19], which hampers their applicability in auto-labeling systems. Therefore, aiming to improve the generalizability, we reinterpret road elements as rasterized polygons and design a concise framework based on instance segmentation. Initially, a segmentation-based transformer is employed to deliver instance masks in an end-to-end manner; succeeding this step, a Potrace-based[17] post-processing module is used to ultimately yield vectorized map elements. Quantitative results attained on the Nuscene[1] dataset substantiate the effectiveness and generaliz-ability of our method.
Reperio-rPPG: Relational Temporal Graph Neural Networks for Periodicity Learning in Remote Physiological Measurement
Venue: arXiv cs.CV 2025
Year: 2025

Remote photoplethysmography (rPPG) is an emerging contactless physiological sensing technique that leverages subtle color variations in facial videos to estimate vital signs such as heart rate and respiratory rate. This non-invasive method has gained traction across diverse domains, including telemedicine, affective computing, driver fatigue detection, and health monitoring, owing to its scalability and convenience. Despite significant progress in remote physiological signal measurement, a crucial characteristic - the intrinsic periodicity - has often been underexplored or insufficiently modeled in previous approaches, limiting their ability to capture fine-grained temporal dynamics under real-world conditions. To bridge this gap, we propose Reperio-rPPG, a novel framework that strategically integrates Relational Convolutional Networks with a Graph Transformer to effectively capture the periodic structure inherent in physiological signals. Additionally, recognizing the limited diversity of existing rPPG datasets, we further introduce a tailored CutMix augmentation to enhance the model's generalizability. Extensive experiments conducted on three widely used benchmark datasets - PURE, UBFC-rPPG, and MMPD - demonstrate that Reperio-rPPG not only achieves state-of-the-art performance but also exhibits remarkable robustness under various motion (e.g., stationary, rotation, talking, walking) and illumination conditions (e.g., nature, low LED, high LED). The code is publicly available at https://github.com/deconasser/Reperio-rPPG.
U(PM)$^2$:Unsupervised polygon matching with pre-trained models for challenging stereo images
Venue: arXiv cs.CV 2025
Year: 2025

Stereo image matching is a fundamental task in computer vision, photogrammetry and remote sensing, but there is an almost unexplored field, i.e., polygon matching, which faces the following challenges: disparity discontinuity, scale variation, training requirement, and generalization. To address the above-mentioned issues, this paper proposes a novel U(PM)$^2$: low-cost unsupervised polygon matching with pre-trained models by uniting automatically learned and handcrafted features, of which pipeline is as follows: firstly, the detector leverages the pre-trained segment anything model to obtain masks; then, the vectorizer converts the masks to polygons and graphic structure; secondly, the global matcher addresses challenges from global viewpoint changes and scale variation based on bidirectional-pyramid strategy with pre-trained LoFTR; finally, the local matcher further overcomes local disparity discontinuity and topology inconsistency of polygon matching by local-joint geometry and multi-feature matching strategy with Hungarian algorithm. We benchmark our U(PM)$^2$ on the ScanNet and SceneFlow datasets using our proposed new metric, which achieved state-of-the-art accuracy at a competitive speed and satisfactory generalization performance at low cost without any training requirement.
An Epistemic Perspective on Agent Awareness
Venue: arXiv cs.AI 2025
Year: 2025

The paper proposes to treat agent awareness as a form of knowledge, breaking the tradition in the existing literature on awareness. It distinguishes the de re and de dicto forms of such knowledge. The work introduces two modalities capturing these forms and formally specifies their meaning using a version of 2D-semantics. The main technical result is a sound and complete logical system describing the interplay between the two proposed modalities and the standard "knowledge of the fact" modality.
RF-Behavior: A Multimodal Radio-Frequency Dataset for Human Behavior and Emotion Analysis
Venue: arXiv cs.DB 2025
Year: 2025

Recent research has demonstrated the complementary nature of camera-based and inertial data for modeling human gestures, activities, and sentiment. Yet, despite its growing importance for environmental sensing as well as the advance of joint communication and sensing for prospective WiFi and 6G standards, a dataset that integrates these modalities with radio frequency data (radar and RFID) remains rare. We introduce RF-Behavior, a multimodal radio frequency dataset for comprehensive human behavior and emotion analysis. We collected data from 44 participants performing 21 gestures, 10 activities, and 6 sentiment expressions. Data were captured using synchronized sensors, including 13 radars (8 ground-mounted and 5 ceiling-mounted), 6 to 8 RFID tags (attached to each arm) and LoRa. Inertial measurement units (IMUs) and 24 infrared cameras are used to provide precise motion ground truth. RF-Behavior provides a unified multimodal dataset spanning the full spectrum of human behavior -- from brief gestures to activities and emotional states -- enabling research on multi-task learning across motion and emotion recognition. Benchmark results demonstrate that the strategic sensor placement is complementary across modalities, with distinct performance characteristics across different behavioral categories.
Multi-Reward GRPO Fine-Tuning for De-biasing Large Language Models: A Study Based on Chinese-Context Discrimination Data
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) often exhibit implicit biases and discriminatory tendencies that reflect underlying social stereotypes. While recent alignment techniques such as RLHF and DPO have mitigated some of these issues, they remain limited in addressing culturally specific and multi-dimensional forms of discrimination. This paper proposes a Multi-Reward Group Relative Policy Optimization (GRPO) framework to fine-tune LLMs toward ethical and bias-free behavior. Our approach constructs a synthetic English-language dataset derived from Chinese-context discrimination categories, including regional, ethnic, and occupational biases. Each instance is paired with both neutral and biased responses to train a reward model based on DeBERTa-v3, which provides multi-dimensional reward signals capturing fairness, neutrality, and linguistic quality. The trained reward model then guides GRPO fine-tuning to optimize model outputs along these ethical dimensions. Experimental results demonstrate significant reductions in bias intensity and improved alignment with non-discriminatory standards without compromising fluency or informativeness. This study highlights the effectiveness of GRPO-based multi-reward optimization for de-biasing LLMs and offers a replicable framework for cultural-contextual ethical alignment.
Cryptographic Binding Should Not Be Optional: A Formal-Methods Analysis of FIDO UAF Channel Binding
Venue: arXiv cs.CR 2025
Year: 2025

As a case study in cryptographic binding, we present a formal-methods analysis of the cryptographic channel binding mechanisms in the Fast IDentity Online (FIDO) Universal Authentication Framework (UAF) authentication protocol, which seeks to reduce the use of traditional passwords in favor of authentication devices. First, we show that UAF's channel bindings fail to mitigate protocol interaction by a Dolev-Yao adversary, enabling the adversary to transfer the server's authentication challenge to alternate sessions of the protocol. As a result, in some contexts, the adversary can masquerade as a client and establish an authenticated session with a server (e.g., possibly a bank server). Second, we implement a proof-of-concept man-in-the-middle attack against eBay's open source FIDO UAF implementation. Third, we propose and formally verify improvements to UAF. The weakness we analyze is similar to the vulnerability discovered in the Needham-Schroeder protocol over 25 years ago. That this vulnerability appears in the FIDO UAF standard highlights the strong need for protocol designers to bind messages properly and to analyze their designs with formal-methods tools. To our knowledge, we are first to carry out a formal-methods analysis of channel binding in UAF and first to exhibit details of an attack on UAF that exploits the weaknesses of UAF's channel binding. Our case study illustrates the importance of cryptographically binding context to protocol messages to prevent an adversary from misusing messages out of context.
Efficient Hate Speech Detection: A Three-Layer LoRA-Tuned BERTweet Framework
Venue: arXiv cs.CL 2025
Year: 2025

This paper addresses the critical challenge of developing computationally efficient hate speech detection systems that maintain competitive performance while being practical for real-time deployment. We propose a novel three-layer framework that combines rule-based pre-filtering with a parameter-efficient LoRA-tuned BERTweet model and continuous learning capabilities. Our approach achieves 0.85 macro F1 score - representing 94% of the performance of state-of-the-art large language models like SafePhi (Phi-4 based) while using a base model that is 100x smaller (134M vs 14B parameters). Compared to traditional BERT-based approaches with similar computational requirements, our method demonstrates superior performance through strategic dataset unification and optimized fine-tuning. The system requires only 1.87M trainable parameters (1.37% of full fine-tuning) and trains in approximately 2 hours on a single T4 GPU, making robust hate speech detection accessible in resource-constrained environments while maintaining competitive accuracy for real-world deployment.
Function Based Isolation Forest (FuBIF): A Unifying Framework for Interpretable Isolation-Based Anomaly Detection
Venue: arXiv cs.LG 2025
Year: 2025

Anomaly Detection (AD) is evolving through algorithms capable of identifying outliers in complex datasets. The Isolation Forest (IF), a pivotal AD technique, exhibits adaptability limitations and biases. This paper introduces the Function-based Isolation Forest (FuBIF), a generalization of IF that enables the use of real-valued functions for dataset branching, significantly enhancing the flexibility of evaluation tree construction. Complementing this, the FuBIF Feature Importance (FuBIFFI) algorithm extends the interpretability in IF-based approaches by providing feature importance scores across possible FuBIF models. This paper details the operational framework of FuBIF, evaluates its performance against established methods, and explores its theoretical contributions. An open-source implementation is provided to encourage further research and ensure reproducibility.
A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data
Venue: arXiv cs.CR 2025
Year: 2025

Omics data is widely employed in medical research to identify disease mechanisms and contains highly sensitive personal information. Federated Learning (FL) with Differential Privacy (DP) can ensure the protection of omics data privacy against malicious user attacks. However, FL with the DP method faces an inherent trade-off: stronger privacy protection degrades predictive accuracy due to injected noise. On the other hand, Homomorphic Encryption (HE) allows computations on encrypted data and enables aggregation of encrypted gradients without DP-induced noise can increase the predictive accuracy. However, it may increase the computation cost. To improve the predictive accuracy while considering the computational ability of heterogeneous clients, we propose a Privacy-Preserving Machine Learning (PPML)-Hybrid method by introducing HE. In the proposed PPML-Hybrid method, clients distributed select either HE or DP based on their computational resources, so that HE clients contribute noise-free updates while DP clients reduce computational overhead. Meanwhile, clients with high computational resources clients can flexibly adopt HE or DP according to their privacy needs. Performance evaluation on omics datasets show that our proposed method achieves comparable predictive accuracy while significantly reducing computation time relative to HE-only. Additionally, it outperforms DP-only methods under equivalent or stricter privacy budgets.
Capacity Analysis of Cascaded BD-RIS Assisted MIMO Systems
Venue: arXiv cs.IT 2025
Year: 2025

This paper examines the cascaded deployment of beyond diagonal (BD) reconfigurable intelligent surfaces (RISs) and explores its potential to enhance the performance of MIMO systems. We first derive the jointly optimal closed form solutions for the RISs in cascade with SVD water filling (SVD WF) and uniform power allocation (UPA) precoding strategies. The optimally configured cascaded-RIS with UPA is shown to achieve performance comparable to that with the SVD WF approach, suggesting that cascaded-RISs can also aid in reducing transmitter complexity. Furthermore, the approximate ergodic capacity for UPA is derived, along with its high SNR approximation which provides multiple useful insights into the dimension and deployment of cascaded RISs. The analytical results establish a clear tradeoff among transmit power, RIS size, and achievable capacity, providing insights for practical deployment in high SNR cascaded RIS MIMO systems.
Development and testing of novel soft sleeve actuators
Venue: arXiv cs.RO 2025
Year: 2025

Aging populations and the rising prevalence of neurological and musculoskeletal disorders increase the demand for wearable mobility assistive devices that are effective, comfortable, and anatomically compatible. Many existing systems use rigid mechanisms and bulky interfaces that impede force transmission and reduce wearability. This study introduces a soft sleeve actuation architecture that conforms to the limb while transmitting forces and moments efficiently. We develop three soft sleeve actuators that produce linear, bending, and twisting motion, and an omnidirectional design that combines these motions in one device. Actuators are fabricated from thermoplastic elastomers using a customized fused filament fabrication process that produces airtight and compliant structures and resolves leakage observed with conventional methods. A dedicated experimental platform quantifies kinematic outputs such as displacement, angle, and twist, and kinetic outputs such as force and torque under low pneumatic pressures. A parametric study varies geometric features and material properties to determine their influence on performance. Results show reproducible multi axis motion with improved transfer of force to the limb and reduced need for complex attachment hardware. The work establishes a unified and manufacturable framework for soft sleeve actuation that enables compact and user centered assistive technologies with enhanced kinematic and kinetic performance.
DiLO: Disentangled Latent Optimization for Learning Shape and Deformation in Grouped Deforming 3D Objects
Venue: arXiv cs.CV 2025
Year: 2025

In this work, we propose a disentangled latent optimization-based method for parameterizing grouped deforming 3D objects into shape and deformation factors in an unsupervised manner. Our approach involves the joint optimization of a generator network along with the shape and deformation factors, supported by specific regularization techniques. For efficient amortized inference of disentangled shape and deformation codes, we train two order-invariant PoinNet-based encoder networks in the second stage of our method. We demonstrate several significant downstream applications of our method, including unsupervised deformation transfer, deformation classification, and explainability analysis. Extensive experiments conducted on 3D human, animal, and facial expression datasets demonstrate that our simple approach is highly effective in these downstream tasks, comparable or superior to existing methods with much higher complexity.
A Data-driven Analysis of Code Optimizations
Venue: arXiv cs.PL 2025
Year: 2025

As the demand for computational power grows, optimizing code through compilers becomes increasingly crucial. In this context, we focus on fully automatic code optimization techniques that automate the process of selecting and applying code transformations for better performance without manual intervention. Understanding how these transformations behave and interact is key to designing more effective optimization strategies. Compiler developers must make numerous design choices when constructing these heuristics. For instance, they may decide whether to allow transformations to be explored in any arbitrary order or to enforce a fixed sequence. While the former may theoretically offer the best performance gains, it significantly increases the search space. This raises an important question: Can a predefined, fixed order of applying transformations speed up the search without severely compromising optimization potential? In this paper, we address this and other related questions that arise in the design of automatic code optimization algorithms. Using a data-driven approach, we generate a large dataset of random programs, apply random optimization sequences, and record their execution times. Through statistical analysis, we provide insights that guide the development of more efficient automatic code optimization algorithms.
Reliablocks: Developing Reliability Scores for Optimistic Rollups
Venue: arXiv cs.CR 2025
Year: 2025

Introducing Reliablocks, an on-chain reliability index for non-finalized blocks in Optimistic Rollups. This was built during the EigenLayer Infinite Hackathon at the Infinite Hacker House at DevCon 2024. As part of this research, we delivered a working Layer AVS WASMI component, a working Eigen Layer AVS component, EigenLayer Solidity smart contracts that work with the AVS component, a UI dashboard illustrating the reliability score and a derived interest rate for further utilization.
On the Convergence and Stability of Distributed Sub-model Training
Venue: arXiv cs.LG 2025
Year: 2025

As learning models continue to grow in size, enabling on-device local training of these models has emerged as a critical challenge in federated learning. A popular solution is sub-model training, where the server only distributes randomly sampled sub-models to the edge clients, and clients only update these small models. However, those random sampling of sub-models may not give satisfying convergence performance. In this paper, observing the success of SGD with shuffling, we propose a distributed shuffled sub-model training, where the full model is partitioned into several sub-models in advance, and the server shuffles those sub-models, sends each of them to clients at each round, and by the end of local updating period, clients send back the updated sub-models, and server averages them. We establish the convergence rate of this algorithm. We also study the generalization of distributed sub-model training via stability analysis, and find that the sub-model training can improve the generalization via amplifying the stability of training process. The extensive experiments also validate our theoretical findings.
Secret Protection in Labeled Petri Nets
Venue: arXiv cs.FL 2025
Year: 2025

We study the secret protection problem (SPP), where the objective is to find a policy of minimal cost ensuring that every execution path from an initial state to a secret state contains a sufficient number of protected events. The problem was originally introduced and studied in the setting of finite automata. In this paper, we extend the framework to labeled Petri nets. We consider two variants of the problem: the Parikh variant, where all occurrences of protected events along an execution path contribute to the security requirement, and the indicator variant, where each protected event is counted only once per execution path. We show that both variants can be solved in exponential space for labeled Petri nets, and that their decision versions are ExpSpace-complete. As a consequence, there is no polynomial-time or polynomial-space algorithm for these problems.
When Object-Centric World Models Meet Policy Learning: From Pixels to Policies, and Where It Breaks
Venue: arXiv cs.AI 2025
Year: 2025

Object-centric world models (OCWM) aim to decompose visual scenes into object-level representations, providing structured abstractions that could improve compositional generalization and data efficiency in reinforcement learning. We hypothesize that explicitly disentangled object-level representations, by localizing task-relevant information, can enhance policy performance across novel feature combinations. To test this hypothesis, we introduce DLPWM, a fully unsupervised, disentangled object-centric world model that learns object-level latents directly from pixels. DLPWM achieves strong reconstruction and prediction performance, including robustness to several out-of-distribution (OOD) visual variations. However, when used for downstream model-based control, policies trained on DLPWM latents underperform compared to DreamerV3. Through latent-trajectory analyses, we identify representation shift during multi-object interactions as a key driver of unstable policy learning. Our results suggest that, although object-centric perception supports robust visual modeling, achieving stable control requires mitigating latent drift.