OpenRaiser/Intern-Atlas huggingface dataset:
RLSLM: A Hybrid Reinforcement Learning Framework Aligning Rule-Based Social Locomotion Model with Human Social Norms
Venue: arXiv cs.AI 2025
Year: 2025

Navigating human-populated environments without causing discomfort is a critical capability for socially-aware agents. While rule-based approaches offer interpretability through predefined psychological principles, they often lack generalizability and flexibility. Conversely, data-driven methods can learn complex behaviors from large-scale datasets, but are typically inefficient, opaque, and difficult to align with human intuitions. To bridge this gap, we propose RLSLM, a hybrid Reinforcement Learning framework that integrates a rule-based Social Locomotion Model, grounded in empirical behavioral experiments, into the reward function of a reinforcement learning framework. The social locomotion model generates an orientation-sensitive social comfort field that quantifies human comfort across space, enabling socially aligned navigation policies with minimal training. RLSLM then jointly optimizes mechanical energy and social comfort, allowing agents to avoid intrusions into personal or group space. A human-agent interaction experiment using an immersive VR-based setup demonstrates that RLSLM outperforms state-of-the-art rule-based models in user experience. Ablation and sensitivity analyses further show the model's significantly improved interpretability over conventional data-driven methods. This work presents a scalable, human-centered methodology that effectively integrates cognitive science and machine learning for real-world social navigation.
NOVA: An Agentic Framework for Automated Histopathology Analysis and Discovery
Venue: arXiv cs.CL 2025
Year: 2025

Digitized histopathology analysis involves complex, time-intensive workflows and specialized expertise, limiting its accessibility. We introduce NOVA, an agentic framework that translates scientific queries into executable analysis pipelines by iteratively generating and running Python code. NOVA integrates 49 domain-specific tools (e.g., nuclei segmentation, whole-slide encoding) built on open-source software, and can also create new tools ad hoc. To evaluate such systems, we present SlideQuest, a 90-question benchmark -- verified by pathologists and biomedical scientists -- spanning data processing, quantitative analysis, and hypothesis testing. Unlike prior biomedical benchmarks focused on knowledge recall or diagnostic QA, SlideQuest demands multi-step reasoning, iterative coding, and computational problem solving. Quantitative evaluation shows NOVA outperforms coding-agent baselines, and a pathologist-verified case study links morphology to prognostically relevant PAM50 subtypes, demonstrating its scalable discovery potential.
SEAL: Subspace-Anchored Watermarks for LLM Ownership
Venue: arXiv cs.CR 2025
Year: 2025

Large language models (LLMs) have achieved remarkable success across a wide range of natural language processing tasks, demonstrating human-level performance in text generation, reasoning, and question answering. However, training such models requires substantial computational resources, large curated datasets, and sophisticated alignment procedures. As a result, they constitute highly valuable intellectual property (IP) assets that warrant robust protection mechanisms. Existing IP protection approaches suffer from critical limitations. Model fingerprinting techniques can identify model architectures but fail to establish ownership of specific model instances. In contrast, traditional backdoor-based watermarking methods embed behavioral anomalies that can be easily removed through common post-processing operations such as fine-tuning or knowledge distillation. We propose SEAL, a subspace-anchored watermarking framework that embeds multi-bit signatures directly into the model's latent representational space, supporting both white-box and black-box verification scenarios. Our approach leverages model editing techniques to align the hidden representations of selected anchor samples with predefined orthogonal bit vectors. This alignment embeds the watermark while preserving the model's original factual predictions, rendering the watermark functionally harmless and stealthy. We conduct comprehensive experiments on multiple benchmark datasets and six prominent LLMs, comparing SEAL with 11 existing fingerprinting and watermarking methods to demonstrate its superior effectiveness, fidelity, efficiency, and robustness. Furthermore, we evaluate SEAL under potential knowledgeable attacks and show that it maintains strong verification performance even when adversaries possess knowledge of the watermarking mechanism and the embedded signatures.
Automated Side-Channel Analysis of Cryptographic Protocol Implementations
Venue: arXiv cs.CR 2025
Year: 2025

We extract the first formal model of WhatsApp from its implementation by combining binary-level analysis (via CryptoBap) with reverse engineering (via Ghidra) to handle this large closed-source application. Using this model, we prove forward secrecy, identify a known clone-attack against post-compromise security and discover functional gaps between WhatsApp's implementation and its specification. We further introduce a methodology to analyze cryptographic protocol implementations for their resilience to side-channel attacks. This is achieved by extending the CryptoBap framework to integrate hardware leakage contracts into the protocol model, which we then pass to the state-of-the-art protocol prover, DeepSec. This enables a detailed security analysis against both functional bugs and microarchitectural side-channel attacks. Using this methodology, we identify a privacy attack in WhatsApp that allows a side-channel attacker to learn the victim's contacts and confirm a known unlinkability attack on the BAC protocol used in electronic passports. Key contributions include (1) the first formal model of WhatsApp, extracted from its binary, (2) a framework to integrate side-channel leakage contracts into protocol models for the first time, and (3) revealing critical vulnerabilities invisible to specification-based methods.
SCRUTINEER: Detecting Logic-Level Usage Violations of Reusable Components in Smart Contracts
Venue: arXiv cs.SE 2025
Year: 2025

Smart Contract Reusable Components(SCRs) play a vital role in accelerating the development of business-specific contracts by promoting modularity and code reuse. However, the risks associated with SCR usage violations have become a growing concern. One particular type of SCR usage violation, known as a logic-level usage violation, is becoming especially harmful. This violation occurs when the SCR adheres to its specified usage rules but fails to align with the specific business logic of the current context, leading to significant vulnerabilities. Detecting such violations necessitates a deep semantic understanding of the contract's business logic, including the ability to extract implicit usage patterns and analyze fine-grained logical behaviors. To address these challenges, we propose SCRUTINEER, the first automated and practical system for detecting logic-level usage violations of SCRs. First, we design a composite feature extraction approach that produces three complementary feature representations, supporting subsequent analysis. We then introduce a Large Language Model-powered knowledge construction framework, which leverages comprehension-oriented prompts and domain-specific tools to extract logic-level usage and build the SCR knowledge base. Next, we develop a Retrieval-Augmented Generation-driven inspector, which combines a rapid retrieval strategy with both comprehensive and targeted analysis to identify potentially insecure logic-level usages. Finally, we implement a logic-level usage violation analysis engine that integrates a similarity-based checker and a snapshot-based inference conflict checker to enable accurate and robust detection. We evaluate SCRUTINEER from multiple perspectives on 3 ground-truth datasets. The results show that SCRUTINEER achieves a precision of 80.77%, a recall of 82.35%, and an F1-score of 81.55% in detecting logic-level usage violations of SCRs.
Multicalibration yields better matchings
Venue: arXiv cs.LG 2025
Year: 2025

Consider the problem of finding the best matching in a weighted graph where we only have access to predictions of the actual stochastic weights, based on an underlying context. If the predictor is the Bayes optimal one, then computing the best matching based on the predicted weights is optimal. However, in practice, this perfect information scenario is not realistic. Given an imperfect predictor, a suboptimal decision rule may compensate for the induced error and thus outperform the standard optimal rule. In this paper, we propose multicalibration as a way to address this problem. This fairness notion requires a predictor to be unbiased on each element of a family of protected sets of contexts. Given a class of matching algorithms $\mathcal C$ and any predictor $γ$ of the edge-weights, we show how to construct a specific multicalibrated predictor $\hat γ$, with the following property. Picking the best matching based on the output of $\hat γ$ is competitive with the best decision rule in $\mathcal C$ applied onto the original predictor $γ$. We complement this result by providing sample complexity bounds.
DiffPro: Joint Timestep and Layer-Wise Precision Optimization for Efficient Diffusion Inference
Venue: arXiv cs.LG 2025
Year: 2025

Diffusion models produce high quality images but inference is costly due to many denoising steps and heavy matrix operations. We present DiffPro, a post-training, hardware-faithful framework that works with the exact integer kernels used in deployment and jointly tunes timesteps and per-layer precision in Diffusion Transformers (DiTs) to reduce latency and memory without any training. DiffPro combines three parts: a manifold-aware sensitivity metric to allocate weight bits, dynamic activation quantization to stabilize activations across timesteps, and a budgeted timestep selector guided by teacher-student drift. In experiments DiffPro achieves up to 6.25x model compression, fifty percent fewer timesteps, and 2.8x faster inference with Delta FID <= 10 on standard benchmarks, demonstrating practical efficiency gains. DiffPro unifies step reduction and precision planning into a single budgeted deployable plan for real-time energy-aware diffusion inference.
FairReweighing: Density Estimation-Based Reweighing Framework for Improving Separation in Fair Regression
Venue: arXiv cs.LG 2025
Year: 2025

There has been a prevalence of applying AI software in both high-stakes public-sector and industrial contexts. However, the lack of transparency has raised concerns about whether these data-informed AI software decisions secure fairness against people of all racial, gender, or age groups. Despite extensive research on emerging fairness-aware AI software, up to now most efforts to solve this issue have been dedicated to binary classification tasks. Fairness in regression is relatively underexplored. In this work, we adopted a mutual information-based metric to assess separation violations. The metric is also extended so that it can be directly applied to both classification and regression problems with both binary and continuous sensitive attributes. Inspired by the Reweighing algorithm in fair classification, we proposed a FairReweighing pre-processing algorithm based on density estimation to ensure that the learned model satisfies the separation criterion. Theoretically, we show that the proposed FairReweighing algorithm can guarantee separation in the training data under a data independence assumption. Empirically, on both synthetic and real-world data, we show that FairReweighing outperforms existing state-of-the-art regression fairness solutions in terms of improving separation while maintaining high accuracy.
MoCap2Radar: A Spatiotemporal Transformer for Synthesizing Micro-Doppler Radar Signatures from Motion Capture
Venue: arXiv cs.LG 2025
Year: 2025

We present a pure machine learning process for synthesizing radar spectrograms from Motion-Capture (MoCap) data. We formulate MoCap-to-spectrogram translation as a windowed sequence-to-sequence task using a transformer-based model that jointly captures spatial relations among MoCap markers and temporal dynamics across frames. Real-world experiments show that the proposed approach produces visually and quantitatively plausible doppler radar spectrograms and achieves good generalizability. Ablation experiments show that the learned model includes both the ability to convert multi-part motion into doppler signatures and an understanding of the spatial relations between different parts of the human body. The result is an interesting example of using transformers for time-series signal processing. It is especially applicable to edge computing and Internet of Things (IoT) radars. It also suggests the ability to augment scarce radar datasets using more abundant MoCap data for training higher-level applications. Finally, it requires far less computation than physics-based methods for generating radar data.
Public Goods Games in Directed Networks with Constraints on Sharing
Venue: arXiv cs.GT 2025
Year: 2025

In a public goods game, every player chooses whether or not to buy a good that all neighboring players will have access to. We consider a setting in which the good is indivisible, neighboring players are out-neighbors in a directed graph, and there is a capacity constraint on their number, k, that can benefit from the good. This means that each player makes a two-pronged decision: decide whether or not to buy and, conditional on buying, choose which k out-neighbors to share access. We examine both pure and mixed Nash equilibria in the model from the perspective of existence, computation, and efficiency. We perform a comprehensive study for these three dimensions with respect to both sharing capacity (k) and the network structure (the underlying directed graph), and establish sharp complexity dichotomies for each.
Data-efficient U-Net for Segmentation of Carbide Microstructures in SEM Images of Steel Alloys
Venue: arXiv cs.LG 2025
Year: 2025

Understanding reactor-pressure-vessel steel microstructure is crucial for predicting mechanical properties, as carbide precipitates both strengthen the alloy and can initiate cracks. In scanning electron microscopy images, gray-value overlap between carbides and matrix makes simple thresholding ineffective. We present a data-efficient segmentation pipeline using a lightweight U-Net (30.7~M parameters) trained on just \textbf{10 annotated scanning electron microscopy images}. Despite limited data, our model achieves a \textbf{Dice-Sørensen coefficient of 0.98}, significantly outperforming the state-of-the-art in the field of metallurgy (classical image analysis: 0.85), while reducing annotation effort by one order of magnitude compared to the state-of-the-art data efficient segmentation model. This approach enables rapid, automated carbide quantification for alloy design and generalizes to other steel types, demonstrating the potential of data-efficient deep learning in reactor-pressure-vessel steel analysis.
Experience-Guided Adaptation of Inference-Time Reasoning Strategies
Venue: arXiv cs.AI 2025
Year: 2025

Enabling agentic AI systems to adapt their problem-solving approaches based on post-training interactions remains a fundamental challenge. While systems that update and maintain a memory at inference time have been proposed, existing designs only steer the system by modifying textual input to a language model or agent, which means that they cannot change sampling parameters, remove tools, modify system prompts, or switch between agentic and workflow paradigms. On the other hand, systems that adapt more flexibly require offline optimization and remain static once deployed. We present Experience-Guided Reasoner (EGuR), which generates tailored strategies -- complete computational procedures involving LLM calls, tools, sampling parameters, and control logic -- dynamically at inference time based on accumulated experience. We achieve this using an LLM-based meta-strategy -- a strategy that outputs strategies -- enabling adaptation of all strategy components (prompts, sampling parameters, tool configurations, and control logic). EGuR operates through two components: a Guide generates multiple candidate strategies conditioned on the current problem and structured memory of past experiences, while a Consolidator integrates execution feedback to improve future strategy generation. This produces complete, ready-to-run strategies optimized for each problem, which can be cached, retrieved, and executed as needed without wasting resources. Across five challenging benchmarks (AIME 2025, 3-SAT, and three Big Bench Extra Hard tasks), EGuR achieves up to 14% accuracy improvements over the strongest baselines while reducing computational costs by up to 111x, with both metrics improving as the system gains experience.
CVChess: A Deep Learning Framework for Converting Chessboard Images to Forsyth-Edwards Notation
Venue: arXiv cs.CV 2025
Year: 2025

Chess has experienced a large increase in viewership since the pandemic, driven largely by the accessibility of online learning platforms. However, no equivalent assistance exists for physical chess games, creating a divide between analog and digital chess experiences. This paper presents CVChess, a deep learning framework for converting chessboard images to Forsyth-Edwards Notation (FEN), which is later input into online chess engines to provide you with the best next move. Our approach employs a convolutional neural network (CNN) with residual layers to perform piece recognition from smartphone camera images. The system processes RGB images of a physical chess board through a multistep process: image preprocessing using the Hough Line Transform for edge detection, projective transform to achieve a top-down board alignment, segmentation into 64 individual squares, and piece classification into 13 classes (6 unique white pieces, 6 unique black pieces and an empty square) using the residual CNN. Residual connections help retain low-level visual features while enabling deeper feature extraction, improving accuracy and stability during training. We train and evaluate our model using the Chess Recognition Dataset (ChessReD), containing 10,800 annotated smartphone images captured under diverse lighting conditions and angles. The resulting classifications are encoded as an FEN string, which can be fed into a chess engine to generate the most optimal move
Incentive Attacks in BTC: Short-Term Revenue Changes and Long-Term Efficiencies
Venue: arXiv cs.CR 2025
Year: 2025

Bitcoin's (BTC) Difficulty Adjustment Algorithm (DAA) has been a source of vulnerability for incentive attacks such as selfish mining, block withholding and coin hopping strategies. In this paper, first, we rigorously study the short-term revenue change per hashpower of the adversarial and honest miners for these incentive attacks. To study the long-term effects, we introduce a new efficiency metric defined as the revenue/cost per hashpower per time for the attacker and the honest miners. Our results indicate that the short-term benefits of intermittent mining strategies are negligible compared to the original selfish mining attack, and in the long-term, selfish mining provides better efficiency. We further demonstrate that a coin hopping strategy between BTC and Bitcoin Cash (BCH) relying on BTC DAA benefits the loyal honest miners of BTC in the same way and to the same extent per unit of computational power as it does the hopper in the short-term. For the long-term, we establish a new boundary between the selfish mining and coin hopping attack, identifying the optimal efficient strategy for each parameter. For block withholding strategies, it turns out, the honest miners outside the pool profit from the attack, usually even more than the attacker both in the short-term and the long-term. Moreover, a power adjusting withholding attacker does not necessarily observe a profit lag in the short-term. It has been long thought that the profit lag of selfish mining is among the main reasons why such an attack has not been observed in practice. We show that such a barrier does not apply to power adjusting attacks and relatively small pools are at an immediate threat.
Beyond Exascale: Dataflow Domain Translation on a Cerebras Cluster
Venue: arXiv cs.DC 2025
Year: 2025

Simulation of physical systems is essential across scientific and engineering domains. Commonly used domain decomposition methods are unable to simultaneously deliver both high simulation rate and high utilization in network computing environments. In particular, Exascale systems deliver only a small fraction their peak performance for these workloads. This paper introduces the novel Domain Translation algorithm, designed to overcome these limitations. On a cluster of 64 Cerebras CS-3 systems, we use this method to demonstrate unprecedented cluster performance across a range of metrics: we show simulations running in excess of 1.6 million time steps per second; we also demonstrate perfect weak scaling at 88% of peak performance. At this cluster scale, our implementation provides 112 PFLOP/s in a power-unconstrained environment, and 57 GFLOP/J in a power-limited environment. We illustrate the method by applying the shallow-water equations to model a tsunami following an asteroid impact at 460m-resolution on a planetary scale.
CertiA360: Enhance Compliance Agility in Aerospace Software Development
Venue: arXiv cs.SE 2025
Year: 2025

Agile methods are characterised by iterative and incremental processes with a strong focus on flexibility and accommodating changing requirements based on either technical, regulatory, or stakeholder feedback. However, integrating Agile methods into safety-critical system development in the aerospace industry presents substantial challenges due to its strict compliance requirements, such as those outlined in the DO-178C standard. To achieve this vision, the flexibility of Agile must align with the rigorous certification guidelines, which emphasize documentation, traceability of requirements across different levels and disciplines, and comprehensive verification and validation (V&V) activities. The research work described in this paper proposes a way of using the strengths of the flexible nature of Agile methods to automate and manage change requests throughout the whole software development lifecycle, ensuring robust traceability, regulatory compliance and ultimately facilitating successful certification. This study proposes CertiA360, a tool designed to help teams improve requirement maturity, automate the changes in traceability, and align with the regulatory objectives. The tool was designed and validated in close collaboration with aerospace industry experts, using their feedback to ensure practical application and real-life effectiveness. The feedback collected demonstrated that the automation given by CertiA360 may reduce manual effort and allow response to changing requirements while ensuring compliance with DO-178C. While the tool is not yet qualified under DO-330 (Tool Qualification), findings suggest that when tailored appropriately, Agile methods can not only coexist with the requirements of safety-system development and certification in highly regulated domains like aerospace, but also add efficiency.