OpenRaiser/Intern-Atlas huggingface dataset:
Improved Byzantine Agreement under an Adaptive Adversary
Venue: arXiv cs.DC 2025
Year: 2025

Byzantine agreement is a fundamental problem in fault-tolerant distributed computing that has been studied intensively for the last four decades. Much of the research has focused on a static Byzantine adversary, where the adversary is constrained to choose the Byzantine nodes in advance of the protocol's execution. This work focuses on the harder case of an adaptive Byzantine adversary that can choose the Byzantine nodes \emph{adaptively} based on the protocol's execution. While efficient $O(\log n)$-round protocols ($n$ is the total number of nodes) are known for the static adversary (Goldwasser, Pavlov, and Vaikuntanathan, FOCS 2006) tolerating up to $t < n/(3+ε)$ Byzantine nodes, $Ω(t/\sqrt{n \log n})$ rounds is a well-known lower bound for adaptive adversary [Bar-Joseph and Ben-Or, PODC 1998]. The best-known protocol for adaptive adversary runs in $O(t/\log n)$ rounds [Chor and Coan, IEEE Trans. Soft. Engg., 1985]. This work presents a synchronous randomized Byzantine agreement protocol under an adaptive adversary that improves over previous results. Our protocol works under the powerful \emph{adaptive rushing adversary in the full information model}. That is, we assume that the Byzantine nodes can behave arbitrarily and maliciously, have knowledge about the entire state of the network at every round, including random choices made by all the nodes up to and including the current round, have unlimited computational power, and may collude among themselves. Furthermore, the adversary can \emph{adaptively} corrupt up to $t < n/3$ nodes based on the protocol's execution. We present a simple randomized Byzantine agreement protocol that runs in $O(\min\{t^2\log n/n, t/\log n\})$ rounds that improves over the long-standing bound of $O(t/\log n)$ rounds due to Chor and Coan [IEEE Trans. Soft. Engg., 1985].
CzechLynx: A Dataset for Individual Identification and Pose Estimation of the Eurasian Lynx
Venue: arXiv cs.CV 2025
Year: 2025

We introduce CzechLynx, the first large-scale, open-access dataset for individual identification, pose estimation, and instance segmentation of the Eurasian lynx (Lynx lynx). CzechLynx contains 39,760 camera trap images annotated with segmentation masks, identity labels, and 20-point skeletons and covers 319 unique individuals across 15 years of systematic monitoring in two geographically distinct regions: southwest Bohemia and the Western Carpathians. In addition to the real camera trap data, we provide a large complementary set of photorealistic synthetic images and a Unity-based generation pipeline with diffusion-based text-to-texture modeling, capable of producing arbitrarily large amounts of synthetic data spanning diverse environments, poses, and coat-pattern variations. To enable systematic testing across realistic ecological scenarios, we define three complementary evaluation protocols: (i) geo-aware, (ii) time-aware open-set, and (iii) time-aware closed-set, covering cross-regional and long-term monitoring settings. With the provided resources, CzechLynx offers a unique, flexible benchmark for robust evaluation of computer vision and machine learning models across realistic ecological scenarios.
A Pillbug-Inspired Morphing Mechanism Covered with Sliding Shells
Venue: arXiv cs.RO 2025
Year: 2025

This research proposes a novel morphing structure with shells inspired by the movement of pillbugs. Instead of the pillbug body, a loopcoupled mechanism based on slider-crank mechanisms is utilized to achieve the rolling up and spreading motion. This mechanism precisely imitates three distinct curves that mimic the shape morphing of a pillbug. To decrease the degree-of-freedom (DOF) of the mechanism to one, scissor mechanisms are added. 3D curved shells are then attached to the tracer points of the morphing mechanism to safeguard it from attacks while allowing it to roll. Through type and dimensional synthesis, a complete system that includes shells and an underlying morphing mechanism is developed. A 3D model is created and tested to demonstrate the proposed system's shape-changing capability. Lastly, a robot with two modes is developed based on the proposed mechanism, which can curl up to roll down hills and can spread to move in a straight line via wheels.
Robustness as Architecture: Designing IQA Models to Withstand Adversarial Perturbations
Venue: arXiv cs.CV 2025
Year: 2025

Image Quality Assessment (IQA) models are increasingly relied upon to evaluate image quality in real-world systems -- from compression and enhancement to generation and streaming. Yet their adoption brings a fundamental risk: these models are inherently unstable. Adversarial manipulations can easily fool them, inflating scores and undermining trust. Traditionally, such vulnerabilities are addressed through data-driven defenses -- adversarial retraining, regularization, or input purification. But what if this is the wrong lens? What if robustness in perceptual models is not something to learn but something to design? In this work, we propose a provocative idea: robustness as an architectural prior. Rather than training models to resist perturbations, we reshape their internal structure to suppress sensitivity from the ground up. We achieve this by enforcing orthogonal information flow, constraining the network to norm-preserving operations -- and further stabilizing the system through pruning and fine-tuning. The result is a robust IQA architecture that withstands adversarial attacks without requiring adversarial training or significant changes to the original model. This approach suggests a shift in perspective: from optimizing robustness through data to engineering it through design.
BacPrep: An Experimental Platform for Evaluating LLM-Based Bacalaureat Assessment
Venue: arXiv cs.SE 2025
Year: 2025

Accessing quality preparation and feedback for the Romanian Bacalaureat exam is challenging, particularly for students in remote or underserved areas. This paper introduces BacPrep, an experimental online platform exploring Large Language Model (LLM) potential for automated assessment, aiming to offer a free, accessible resource. Using official exam questions from the last 5 years, BacPrep employs one of Google's newest models, Gemini 2.0 Flash (released Feb 2025), guided by official grading schemes, to provide experimental feedback. Currently operational, its primary research function is collecting student solutions and LLM outputs. This focused dataset is vital for planned expert validation to rigorously evaluate the feasibility and accuracy of this cutting-edge LLM in the specific Bacalaureat context before reliable deployment. We detail the design, data strategy, status, validation plan, and ethics.
Multi-scale Image Super Resolution with a Single Auto-Regressive Model
Venue: arXiv cs.CV 2025
Year: 2025

In this paper we tackle Image Super Resolution (ISR), using recent advances in Visual Auto-Regressive (VAR) modeling. VAR iteratively estimates the residual in latent space between gradually increasing image scales, a process referred to as next-scale prediction. Thus, the strong priors learned during pre-training align well with the downstream task (ISR). To our knowledge, only VARSR has exploited this synergy so far, showing promising results. However, due to the limitations of existing residual quantizers, VARSR works only at a fixed resolution, i.e. it fails to map intermediate outputs to the corresponding image scales. Additionally, it relies on a 1B transformer architecture (VAR-d24), and leverages a large-scale private dataset to achieve state-of-the-art results. We address these limitations through two novel components: a) a Hierarchical Image Tokenization approach with a multi-scale image tokenizer that progressively represents images at different scales while simultaneously enforcing token overlap across scales, and b) a Direct Preference Optimization (DPO) regularization term that, relying solely on the LR and HR tokenizations, encourages the transformer to produce the latter over the former. To the best of our knowledge, this is the first time a quantizer is trained to force semantically consistent residuals at different scales, and the first time that preference-based optimization is used to train a VAR. Using these two components, our model can denoise the LR image and super-resolve at half and full target upscale factors in a single forward pass. Additionally, we achieve \textit{state-of-the-art results on ISR}, while using a small model (300M params vs ~1B params of VARSR), and without using external training data.
ComfyUI-Copilot: An Intelligent Assistant for Automated Workflow Development
Venue: arXiv cs.CL 2025
Year: 2025

We introduce ComfyUI-Copilot, a large language model-powered plugin designed to enhance the usability and efficiency of ComfyUI, an open-source platform for AI-driven art creation. Despite its flexibility and user-friendly interface, ComfyUI can present challenges to newcomers, including limited documentation, model misconfigurations, and the complexity of workflow design. ComfyUI-Copilot addresses these challenges by offering intelligent node and model recommendations, along with automated one-click workflow construction. At its core, the system employs a hierarchical multi-agent framework comprising a central assistant agent for task delegation and specialized worker agents for different usages, supported by our curated ComfyUI knowledge bases to streamline debugging and deployment. We validate the effectiveness of ComfyUI-Copilot through both offline quantitative evaluations and online user feedback, showing that it accurately recommends nodes and accelerates workflow development. Additionally, use cases illustrate that ComfyUI-Copilot lowers entry barriers for beginners and enhances workflow efficiency for experienced users. The ComfyUI-Copilot installation package and a demo video are available at https://github.com/AIDC-AI/ComfyUI-Copilot.
Compressing Hypergraphs using Suffix Sorting
Venue: arXiv cs.DS 2025
Year: 2025

Hypergraphs model complex, non-binary relationships like co-authorships, social group memberships, and recommendations. Like traditional graphs, hypergraphs can grow large, posing challenges for storage, transmission, and query performance. We propose HyperCSA, a novel compression method for hypergraphs that maintains support for standard queries over the succinct representation. HyperCSA achieves compression ratios of 26% to 79% of the original file size on real-world hypergraphs - outperforming existing methods on all large hypergraphs in our experiments. Additionally, HyperCSA scales to larger datasets than existing approaches. Furthermore, for common real-world hypergraphs, HyperCSA evaluates neighbor queries 6 to 40 times faster than both standard data structures and other hypergraph compression approaches.
Automatic Robustness Stress Testing of LLMs as Mathematical Problem Solvers
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have achieved distinguished performance on various reasoning-intensive tasks. However, LLMs might still face the challenges of robustness issues and fail unexpectedly in some simple reasoning tasks. Previous works evaluate the LLM robustness with hand-crafted templates or a limited set of perturbation rules, indicating potential data contamination in pre-training or fine-tuning datasets. In this work, inspired by stress testing in software engineering, we propose a novel framework, Automatic Robustness Checker (AR-Checker), to generate mathematical problem variants that maintain the semantic meanings of the original one but might fail the LLMs. The AR-Checker framework generates mathematical problem variants through multi-round parallel streams of LLM-based rewriting and verification. Our framework can generate benchmark variants dynamically for each LLM, thus minimizing the risk of data contamination. Experiments on GSM8K and MATH-500 demonstrate the strong performance of AR-Checker on mathematical tasks. We also evaluate AR-Checker on benchmarks beyond mathematics, including MMLU, MMLU-Pro, and CommonsenseQA, where it also achieves strong performance, further proving the effectiveness of AR-Checker.
LLM-Guided Scenario-based GUI Testing
Venue: arXiv cs.SE 2025
Year: 2025

The assurance of mobile app GUIs has become increasingly important, as the GUI serves as the primary medium of interaction between users and apps. Although numerous automated GUI testing approaches have been developed with diverse strategies, a substantial gap remains between these approaches and the underlying app business logic. Most existing approaches focus on general exploration rather than the completion of specific testing scenarios, often missing critical functionalities. Inspired by manual testing, which treats business logic-driven scenarios as the fundamental unit of testing, this paper introduces an approach that leverages large language models to comprehend GUI semantics and contextual relevance to given scenarios. Building on this capability, we propose ScenGen, an LLM-guided scenario-based GUI testing framework employing multi-agent collaboration to simulate and automate manual testing phases. Specifically, ScenGen integrates five agents: the Observer, Decider, Executor, Supervisor, and Recorder. The Observer perceives the app GUI state by extracting and structuring GUI widgets and layouts, interpreting semantic information. This is passed to the Decider, which makes scenario-driven decisions with LLM guidance to identify target widgets and determine actions toward fulfilling specific goals. The Executor performs these operations, while the Supervisor verifies alignment with intended scenario completion, ensuring traceability and consistency. Finally, the Recorder logs GUI operations into context memory as a knowledge base for subsequent decision-making and monitors runtime bugs. Comprehensive evaluations demonstrate that ScenGen effectively generates scenario-based GUI tests guided by LLM collaboration, achieving higher relevance to business logic and improving the completeness of automated GUI testing.
Early linguistic fingerprints of online users who engage with conspiracy communities
Venue: arXiv cs.SI 2025
Year: 2025

Online social media platforms are often seen as catalysts for radicalization, as they provide spaces where extreme beliefs can take root and spread, sometimes leading to real-world consequences. Conspiracy theories represent a specific form of radicalization that is notoriously resistant to online moderation strategies. One explanation for this resilience is the presence of a "conspiratorial mindset", a cognitive framework that fundamentally shapes how conspiracy believers perceive reality. However, the role of this mindset in driving online user behavior remains poorly understood. In this study, we analyze the psycholinguistic patterns of Reddit users who become active in a prominent conspiracy community by examining their activity in mainstream communities, which allows us to isolate linguistic markers for the presence of a conspiratorial mindset. We find that conspiracy-engaged individuals exhibit distinct psycholinguistic fingerprints, setting them apart from the general user population. Crucially, this signal is already evident in their online activity prior to joining the conspiracy community, allowing us to predict their involvement years in advance. These findings suggest that individuals who adopt conspiracy beliefs do not radicalize through community involvement, but possess a pre-existing conspiratorial mindset, which predisposes them to seek out and join extreme communities. By challenging the view that online social media platforms actively radicalize users into conspiracy theory beliefs, our findings suggest that standard moderation strategies have limited impact on curbing radicalization, and highlight the need for more targeted, supportive interventions that encourage disengagement from extremist narratives. Ultimately, this work contributes to fostering safer online and offline environments for public discourse.
Truly Self-Improving Agents Require Intrinsic Metacognitive Learning
Venue: arXiv cs.AI 2025
Year: 2025

Self-improving agents aim to continuously acquire new capabilities with minimal supervision. However, current approaches face two key limitations: their self-improvement processes are often rigid, fail to generalize across tasks domains, and struggle to scale with increasing agent capabilities. We argue that effective self-improvement requires intrinsic metacognitive learning, defined as an agent's intrinsic ability to actively evaluate, reflect on, and adapt its own learning processes. Drawing inspiration from human metacognition, we introduce a formal framework comprising three components: metacognitive knowledge (self-assessment of capabilities, tasks, and learning strategies), metacognitive planning (deciding what and how to learn), and metacognitive evaluation (reflecting on learning experiences to improve future learning). Analyzing existing self-improving agents, we find they rely predominantly on extrinsic metacognitive mechanisms, which are fixed, human-designed loops that limit scalability and adaptability. Examining each component, we contend that many ingredients for intrinsic metacognition are already present. Finally, we explore how to optimally distribute metacognitive responsibilities between humans and agents, and robustly evaluate and improve intrinsic metacognitive learning, key challenges that must be addressed to enable truly sustained, generalized, and aligned self-improvement.
Towards the target and not beyond: 2D vs 3D visual aids in MR-based neurosurgical simulation
Venue: arXiv cs.GR 2025
Year: 2025

Neurosurgery increasingly uses Mixed Reality (MR) technologies for intraoperative assistance. The greatest challenge in this area is mentally reconstructing complex 3D anatomical structures from 2D slices with millimetric precision, which is required in procedures like External Ventricular Drain (EVD) placement. MR technologies have shown great potential in improving surgical performance, however, their limited availability in clinical settings underscores the need for training systems that foster skill retention in unaided conditions. In this paper, we introduce NeuroMix, an MR-based simulator for EVD placement. We conduct a study with 48 participants to assess the impact of 2D and 3D visual aids on usability, cognitive load, technology acceptance, and procedure precision and execution time. Three training modalities are compared: one without visual aids, one with 2D aids only, and one combining both 2D and 3D aids. The training phase takes place entirely on digital objects, followed by a freehand EVD placement testing phase performed with a physical catherer and a physical phantom without MR aids. We then compare the participants performance with that of a control group that does not undergo training. Our findings show that participants trained with both 2D and 3D aids achieve a 44\% improvement in precision during unaided testing compared to the control group, substantially higher than the improvement observed in the other groups. All three training modalities receive high usability and technology acceptance ratings, with significant equivalence across groups. The combination of 2D and 3D visual aids does not significantly increase cognitive workload, though it leads to longer operation times during freehand testing compared to the control group.
RELIC: Evaluating Compositional Instruction Following via Language Recognition
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) are increasingly expected to perform tasks based only on a specification of the task provided in context, without examples of inputs and outputs; this ability is referred to as instruction following. We introduce the Recognition of Languages In-Context (RELIC) framework to evaluate instruction following using language recognition: the task of determining if a string is generated by formal grammar. Unlike many standard evaluations of LLMs' ability to use their context, this task requires composing together a large number of instructions (grammar productions) retrieved from the context. Because the languages are synthetic, the task can be increased in complexity as LLMs' skills improve, and new instances can be automatically generated, mitigating data contamination. We evaluate state-of-the-art LLMs on RELIC and find that their accuracy can be reliably predicted from the complexity of the grammar and the individual example strings, and that even the most advanced LLMs currently available show near-chance performance on more complex grammars and samples, in line with theoretical expectations. We also use RELIC to diagnose how LLMs attempt to solve increasingly difficult reasoning tasks, finding that as the complexity of the language recognition task increases, models switch to relying on shallow heuristics instead of following complex instructions.
Intentionally Unintentional: GenAI Exceptionalism and the First Amendment
Venue: arXiv cs.CY 2025
Year: 2025

This paper challenges the assumption that courts should grant First Amendment protections to outputs from large generative AI models, such as GPT-4 and Gemini. We argue that because these models lack intentionality, their outputs do not constitute speech as understood in the context of established legal precedent, so there can be no speech to protect. Furthermore, if the model outputs are not speech, users cannot claim a First Amendment speech right to receive the outputs. We also argue that extending First Amendment rights to AI models would not serve the fundamental purposes of free speech, such as promoting a marketplace of ideas, facilitating self-governance, or fostering self-expression. In fact, granting First Amendment protections to AI models would be detrimental to society because it would hinder the government's ability to regulate these powerful technologies effectively, potentially leading to the unchecked spread of misinformation and other harms.
On Top-Down Pseudo-Boolean Model Counting
Venue: arXiv cs.LO 2025
Year: 2025

Pseudo-Boolean model counting involves computing the number of satisfying assignments of a given pseudo-Boolean (PB) formula. In recent years, PB model counting has seen increased interest partly owing to the succinctness of PB formulas over typical propositional Boolean formulas in conjunctive normal form (CNF) at describing problem constraints. In particular, the research community has developed tools to tackle exact PB model counting. These recently developed counters follow one of the two existing major designs for model counters, namely the bottom-up model counter design. A natural question would be whether the other major design, the top-down model counter paradigm, would be effective at PB model counting, especially when the top-down design offered superior performance in CNF model counting literature. In this work, we investigate the aforementioned top-down design for PB model counting and introduce the first exact top-down PB model counter, PBMC. PBMC is a top-down search-based counter for PB formulas, with a new variable decision heuristic that considers variable coefficients. Through our evaluations, we highlight the superior performance of PBMC at PB model counting compared to the existing state-of-the-art counters PBCount, PBCounter, and Ganak. In particular, PBMC could count for 1849 instances while the next-best competing method, PBCount, could only count for 1773 instances, demonstrating the potential of a top-down PB counter design.