OpenRaiser/Intern-Atlas huggingface dataset:
Learning from Hallucinating Critical Points for Navigation in Dynamic Environments
Venue: arXiv cs.RO 2025
Year: 2025

Generating large and diverse obstacle datasets to learn motion planning in environments with dynamic obstacles is challenging due to the vast space of possible obstacle trajectories. Inspired by hallucination-based data synthesis approaches, we propose Learning from Hallucinating Critical Points (LfH-CP), a self-supervised framework for creating rich dynamic obstacle datasets based on existing optimal motion plans without requiring expensive expert demonstrations or trial-and-error exploration. LfH-CP factorizes hallucination into two stages: first identifying when and where obstacles must appear in order to result in an optimal motion plan, i.e., the critical points, and then procedurally generating diverse trajectories that pass through these points while avoiding collisions. This factorization avoids generative failures such as mode collapse and ensures coverage of diverse dynamic behaviors. We further introduce a diversity metric to quantify dataset richness and show that LfH-CP produces substantially more varied training data than existing baselines. Experiments in simulation demonstrate that planners trained on LfH-CP datasets achieves higher success rates compared to a prior hallucination method.
Training Matryoshka Mixture-of-Experts for Elastic Inference-Time Expert Utilization
Venue: arXiv cs.CL 2025
Year: 2025

Mixture-of-Experts (MoE) has emerged as a promising paradigm for efficiently scaling large language models without a proportional increase in computational cost. However, the standard training strategy of Top-K router prevents MoE models from realizing their full potential for elastic inference. When the number of activated experts is altered at inference time, these models exhibit precipitous performance degradation. In this work, we introduce Matryoshka MoE (M-MoE), a training framework that instills a coarse-to-fine structure directly into the expert ensemble. By systematically varying the number of activated experts during training, M-MoE compels the model to learn a meaningful ranking: top-ranked experts collaborate to provide essential, coarse-grained capabilities, while subsequent experts add progressively finer-grained detail. We explore this principle at multiple granularities, identifying a layer-wise randomization strategy as the most effective. Our experiments demonstrate that a single M-MoE model achieves remarkable elasticity, with its performance at various expert counts closely matching that of an entire suite of specialist models, but at only a fraction of the total training cost. This flexibility not only unlocks elastic inference but also enables optimizing performance by allocating different computational budgets to different model layers. Our work paves the way for more practical and adaptable deployments of large-scale MoE models.
CSnake: Detecting Self-Sustaining Cascading Failure via Causal Stitching of Fault Propagations
Venue: arXiv cs.DC 2025
Year: 2025

Recent studies have revealed that self-sustaining cascading failures in distributed systems frequently lead to widespread outages, which are challenging to contain and recover from. Existing failure detection techniques struggle to expose such failures prior to deployment, as they typically require a complex combination of specific conditions to be triggered. This challenge stems from the inherent nature of cascading failures, as they typically involve a sequence of fault propagations, each activated by distinct conditions. This paper presents CSnake, a fault injection framework to expose self-sustaining cascading failures in distributed systems. CSnake uses the novel idea of causal stitching, which causally links multiple single-fault injections in different tests to simulate complex fault propagation chains. To identify these chains, CSnake designs a counterfactual causality analysis of fault propagations - fault causality analysis (FCA): FCA compares the execution trace of a fault injection run with its corresponding profile run (i.e., same test w/o the injection) and identifies any additional faults triggered, which are considered to have a causal relationship with the injected fault. To address the large search space of fault and workload combinations, CSnake employs a three-phase allocation protocol of test budget that prioritizes faults with unique and diverse causal consequences, increasing the likelihood of uncovering conditional fault propagations. Furthermore, to avoid incorrectly connecting fault propagations from workloads with incompatible conditions, CSnake performs a local compatibility check that approximately checks the compatibility of the path constraints associated with connected fault propagations with low overhead. CSnake detected 15 bugs that cause self-sustaining cascading failures in five systems, five of which have been confirmed with two fixed.
Explainable and Resilient ML-Based Physical-Layer Attack Detectors
Venue: arXiv cs.CR 2025
Year: 2025

Detection of emerging attacks on network infrastructure is a critical aspect of security management. To meet the growing scale and complexity of modern threats, machine learning (ML) techniques offer valuable tools for automating the detection of malicious activities. However, as these techniques become more complex, their internal operations grow increasingly opaque. In this context, we address the need for explainable physical-layer attack detection methods. First, we analyze the inner workings of various classifiers trained to alert about physical layer intrusions, examining how the influence of different monitored parameters varies depending on the type of attack being detected. This analysis not only improves the interpretability of the models but also suggests ways to enhance their design for increased speed. In the second part, we evaluate the detectors' resilience to malicious parameter noising. The results highlight a key trade-off between model speed and resilience. This work serves as a design guideline for developing fast and robust detectors trained on available network monitoring data.
Ferret-UI Lite: Lessons from Building Small On-Device GUI Agents
Venue: arXiv cs.CV 2025
Year: 2025

Developing autonomous agents that effectively interact with Graphic User Interfaces (GUIs) remains a challenging open problem, especially for small on-device models. In this paper, we present Ferret-UI Lite, a compact, end-to-end GUI agent that operates across diverse platforms, including mobile, web, and desktop. Utilizing techniques optimized for developing small models, we build our 3B Ferret-UI Lite agent through curating a diverse GUI data mixture from real and synthetic sources, strengthening inference-time performance through chain-of-thought reasoning and visual tool-use, and reinforcement learning with designed rewards. Ferret-UI Lite achieves competitive performance with other small-scale GUI agents. In GUI grounding, Ferret-UI Lite attains scores of $91.6\%$, $53.3\%$, and $61.2\%$ on the ScreenSpot-V2, ScreenSpot-Pro, and OSWorld-G benchmarks, respectively. For GUI navigation, Ferret-UI Lite achieves success rates of $28.0\%$ on AndroidWorld and $19.8\%$ on OSWorld. We share our methods and lessons learned from developing compact, on-device GUI agents.
Towards Verified Code Reasoning by LLMs
Venue: arXiv cs.SE 2025
Year: 2025

While LLM-based agents are able to tackle a wide variety of code reasoning questions, the answers are not always correct. This prevents the agent from being useful in situations where high precision is desired: (1) helping a software engineer understand a new code base, (2) helping a software engineer during code review sessions, and (3) ensuring that the code generated by an automated code generation system meets certain requirements (e.g. fixes a bug, improves readability, implements a feature). As a result of this lack of trustworthiness, the agent's answers need to be manually verified before they can be trusted. Manually confirming responses from a code reasoning agent requires human effort and can result in slower developer productivity, which weakens the assistance benefits of the agent. In this paper, we describe a method to automatically validate the answers provided by a code reasoning agent by verifying its reasoning steps. At a very high level, the method consists of extracting a formal representation of the agent's response and, subsequently, using formal verification and program analysis tools to verify the agent's reasoning steps. We applied this approach to a benchmark set of 20 uninitialized variable errors detected by sanitizers and 20 program equivalence queries. For the uninitialized variable errors, the formal verification step was able to validate the agent's reasoning on 13/20 examples, and for the program equivalence queries, the formal verification step successfully caught 6/8 incorrect judgments made by the agent.
DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis
Venue: arXiv cs.CR 2025
Year: 2025

Deep neural networks (DNNs) are increasingly being deployed in high-stakes applications, from self-driving cars to biometric authentication. However, their unpredictable and unreliable behaviors in real-world settings require new approaches to characterize and ensure their reliability. This paper introduces DeepProv, a novel and customizable system designed to capture and characterize the runtime behavior of DNNs during inference by using their underlying graph structure. Inspired by system audit provenance graphs, DeepProv models the computational information flow of a DNN's inference process through Inference Provenance Graphs (IPGs). These graphs provide a detailed structural representation of the behavior of DNN, allowing both empirical and structural analysis. DeepProv uses these insights to systematically repair DNNs for specific objectives, such as improving robustness, privacy, or fairness. We instantiate DeepProv with adversarial robustness as the goal of model repair and conduct extensive case studies to evaluate its effectiveness. Our results demonstrate its effectiveness and scalability across diverse classification tasks, attack scenarios, and model complexities. DeepProv automatically identifies repair actions at the node and edge-level within IPGs, significantly enhancing the robustness of the model. In particular, applying DeepProv repair strategies to just a single layer of a DNN yields an average 55% improvement in adversarial accuracy. Moreover, DeepProv complements existing defenses, achieving substantial gains in adversarial robustness. Beyond robustness, we demonstrate the broader potential of DeepProv as an adaptable system to characterize DNN behavior in other critical areas, such as privacy auditing and fairness analysis.
Parametric Neural Amp Modeling with Active Learning
Venue: arXiv cs.LG 2025
Year: 2025

We introduce Panama, an active learning framework to train parametric guitar amp models end-to-end using a combination of an LSTM model and a WaveNet-like architecture. With \model, one can create a virtual amp by recording samples that are determined through an ensemble-based active learning strategy to minimize the amount of datapoints needed (i.e., amp knob settings). Our strategy uses gradient-based optimization to maximize the disagreement among ensemble models, in order to identify the most informative datapoints. MUSHRA listening tests reveal that, with 75 datapoints, our models are able to match the perceptual quality of NAM, the leading open-source non-parametric amp modeler.
Importance of localized dilatation and distensibility in identifying determinants of thoracic aortic aneurysm with neural operators
Venue: arXiv cs.LG 2025
Year: 2025

Thoracic aortic aneurysms (TAAs) arise from diverse mechanical and mechanobiological disruptions to the aortic wall that increase the risk of dissection or rupture. Evidence links TAA development to dysfunctions in the aortic mechanotransduction axis, including loss of elastic fiber integrity and cell-matrix connections. Because distinct insults create different mechanical vulnerabilities, there is a critical need to identify interacting factors that drive progression. Here, we use a finite element framework to generate synthetic TAAs from hundreds of heterogeneous insults spanning varying degrees of elastic fiber damage and impaired mechanosensing. From these simulations, we construct spatial maps of localized dilatation and distensibility to train neural networks that predict the initiating combined insult. We compare several architectures (Deep Operator Networks, UNets, and Laplace Neural Operators) and multiple input data formats to define a standard for future subject-specific modeling. We also quantify predictive performance when networks are trained using only geometric data (dilatation) versus both geometric and mechanical data (dilatation plus distensibility). Across all networks, prediction errors are significantly higher when trained on dilatation alone, underscoring the added value of distensibility information. Among the tested models, UNet consistently provides the highest accuracy across all data formats. These findings highlight the importance of acquiring full-field measurements of both dilatation and distensibility in TAA assessment to reveal the mechanobiological drivers of disease and support the development of personalized treatment strategies.
Autoproof: Automated Segmentation Proofreading for Connectomics
Venue: arXiv cs.CV 2025
Year: 2025

Producing connectomes from electron microscopy (EM) images has historically required a great deal of human proofreading effort. This manual annotation cost is the current bottleneck in scaling EM connectomics, for example, in making larger connectome reconstructions feasible, or in enabling comparative connectomics where multiple related reconstructions are produced. In this work, we propose using the available ground-truth data generated by this manual annotation effort to learn a machine learning model to automate or optimize parts of the required proofreading workflows. We validate our approach on a recent complete reconstruction of the \emph{Drosophila} male central nervous system. We first show our method would allow for obtaining 90\% of the value of a guided proofreading workflow while reducing required cost by 80\%. We then demonstrate a second application for automatically merging many segmentation fragments to proofread neurons. Our system is able to automatically attach 200 thousand fragments, equivalent to four proofreader years of manual work, and increasing the connectivity completion rate of the connectome by 1.3\% points.
Black-box Context-free Grammar Inference for Readable & Natural Grammars
Venue: arXiv cs.SE 2025
Year: 2025

Black-box context-free grammar inference is crucial for program analysis, reverse engineering, and security, yet existing tools such as Arvada, TreeVada, and Kedavra struggle with scalability, readability, and accuracy on large, complex languages. We present NatGI, a novel LLM-guided grammar inference framework that extends TreeVada's parse tree recovery with three key innovations: bracket-guided bubble exploration, LLM-driven bubble generation and non-terminal labeling, and hierarchical delta debugging (HDD) for systematic tree simplification. Bracket-guided exploration leverages syntactic cues such as parentheses to propose well-structured grammar fragments, while LLM guidance produces meaningful non-terminal names and selects more promising merges. Finally, HDD incrementally reduces unnecessary rules, which makes the grammars both compact and interpretable. In our experiments, we evaluate NatGI on a comprehensive benchmark suite ranging from small languages to larger ones such as lua, c, and mysql. Our results show that NatGI consistently outperforms strong baselines in terms of F1 score. On average, NatGI achieves an F1 score of 0.57, which is 25pp (percentage points) higher than the best-performing baseline, TreeVada. In the case of interpretability, our generated grammars perform significantly better than those produced by existing approaches. Leveraging LLM-based node renaming and bubble exploration, NatGI produces rules with meaningful non-terminal names and compact structures that align more closely with human intuition. As a result, developers and researchers can achieve higher accuracy while still being able to easily inspect, verify, and reason about the structure and semantics of the induced grammars.
Searching for Difficult-to-Translate Test Examples at Scale
Venue: arXiv cs.CL 2025
Year: 2025

NLP models require test data that are sufficiently challenging. The difficulty of an example is linked to the topic it originates from (''seed topic''). The relationship between the topic and the difficulty of its instances is stochastic in nature: an example about a difficult topic can happen to be easy, and vice versa. At the scale of the Internet, there are tens of thousands of potential topics, and finding the most difficult one by drawing and evaluating a large number of examples across all topics is computationally infeasible. We formalize this task and treat it as a multi-armed bandit problem. In this framework, each topic is an ''arm,'' and pulling an arm (at a cost) involves drawing a single example, evaluating it, and measuring its difficulty. The goal is to efficiently identify the most difficult topics within a fixed computational budget. We illustrate the bandit problem setup of finding difficult examples for the task of machine translation. We find that various bandit strategies vastly outperform baseline methods like brute-force searching the most challenging topics.
HART: Human Aligned Reconstruction Transformer
Venue: arXiv cs.CV 2025
Year: 2025

We introduce HART, a unified framework for sparse-view human reconstruction. Given a small set of uncalibrated RGB images of a person as input, it outputs a watertight clothed mesh, the aligned SMPL-X body mesh, and a Gaussian-splat representation for photorealistic novel-view rendering. Prior methods for clothed human reconstruction either optimize parametric templates, which overlook loose garments and human-object interactions, or train implicit functions under simplified camera assumptions, limiting applicability in real scenes. In contrast, HART predicts per-pixel 3D point maps, normals, and body correspondences, and employs an occlusion-aware Poisson reconstruction to recover complete geometry, even in self-occluded regions. These predictions also align with a parametric SMPL-X body model, ensuring that reconstructed geometry remains consistent with human structure while capturing loose clothing and interactions. These human-aligned meshes initialize Gaussian splats to further enable sparse-view rendering. While trained on only 2.3K synthetic scans, HART achieves state-of-the-art results: Chamfer Distance improves by 18-23 percent for clothed-mesh reconstruction, PA-V2V drops by 6-27 percent for SMPL-X estimation, LPIPS decreases by 15-27 percent for novel-view synthesis on a wide range of datasets. These results suggest that feed-forward transformers can serve as a scalable model for robust human reconstruction in real-world settings. Code and models will be released.
OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction
Venue: arXiv cs.RO 2025
Year: 2025

A dominant paradigm for teaching humanoid robots complex skills is to retarget human motions as kinematic references to train reinforcement learning (RL) policies. However, existing retargeting pipelines often struggle with the significant embodiment gap between humans and robots, producing physically implausible artifacts like foot-skating and penetration. More importantly, common retargeting methods neglect the rich human-object and human-environment interactions essential for expressive locomotion and loco-manipulation. To address this, we introduce OmniRetarget, an interaction-preserving data generation engine based on an interaction mesh that explicitly models and preserves the crucial spatial and contact relationships between an agent, the terrain, and manipulated objects. By minimizing the Laplacian deformation between the human and robot meshes while enforcing kinematic constraints, OmniRetarget generates kinematically feasible trajectories. Moreover, preserving task-relevant interactions enables efficient data augmentation, from a single demonstration to different robot embodiments, terrains, and object configurations. We comprehensively evaluate OmniRetarget by retargeting motions from OMOMO, LAFAN1, and our in-house MoCap datasets, generating over 8-hour trajectories that achieve better kinematic constraint satisfaction and contact preservation than widely used baselines. Such high-quality data enables proprioceptive RL policies to successfully execute long-horizon (up to 30 seconds) parkour and loco-manipulation skills on a Unitree G1 humanoid, trained with only 5 reward terms and simple domain randomization shared by all tasks, without any learning curriculum.
Semantic Zoom and Mini-Maps for Software Cities
Venue: arXiv cs.SE 2025
Year: 2025

Software visualization tools can facilitate program comprehension by providing visual metaphors, or abstractions that reduce the amount of textual data that needs to be processed mentally. One way they do this is by enabling developers to build an internal representation of the visualized software and its architecture. However, as the amount of displayed data in the visualization increases, the visualization itself can become more difficult to comprehend. The ability to display small and large amounts of data in visualizations is called visual scalability. In this paper, we present two approaches to address the challenge of visual scalability in 3D software cities. First, we present an approach to semantic zoom, in which the graphical representation of the software landscape changes based on the virtual camera's distance from visual objects. Second, we augment the visualization with a miniature two-dimensional top-view projection called mini-map. We demonstrate our approach using an open-source implementation in our software visualization tool ExplorViz. ExplorViz is web-based and uses the 3D city metaphor, focusing on live trace visualization. We evaluated our approaches in two separate user studies. The results indicate that semantic zoom and the mini-map are both useful additions. User feedback indicates that semantic zoom and mini-maps are especially useful for large software landscapes and collaborative software exploration. The studies indicate a good usability of our implemented approaches. However, some shortcomings in our implementations have also been discovered, to be addressed in future work. Video URL: https://youtu.be/LYtUeWvizjU