OpenRaiser/Intern-Atlas huggingface dataset:
Unlocking Stopped-Rotor Flight: Development and Validation of SPERO, a Novel UAV Platform
Venue: arXiv cs.RO 2025
Year: 2025

Stop-rotor aircraft have long been proposed as the ideal vertical takeoff and landing (VTOL) aircraft for missions with equal time spent in both flight regimes, such as agricultural monitoring, search and rescue, and last-mile delivery. Featuring a central lifting surface that rotates in VTOL to generate vertical thrust and locks in forward flight to generate passive lift, the stop-rotor offers the potential for high efficiency across both modes. However, practical implementation has remained infeasible due to aerodynamic and stability conflicts between flight modes. In this work, we present SPERO (Stopped-Penta Rotor), a stop-rotor uncrewed aerial vehicle (UAV) featuring a flipping and latching wing, an active center of pressure mechanism, thrust vectored counterbalances, a five-rotor architecture, and an eleven-state machine flight controller coordinating geometric and controller reconfiguration. Furthermore, SPERO establishes a generalizable design and control framework for stopped-rotor UAVs. Together, these innovations overcome longstanding challenges in stop-rotor flight and enable the first stable, bidirectional transition between VTOL and forward flight.
Compressibility Measures and Succinct Data Structures for Piecewise Linear Approximations
Venue: arXiv cs.DS 2025
Year: 2025

We study the problem of deriving compressibility measures for Piecewise Linear Approximations (PLAs), i.e., error-bounded approximations of a set of two-dimensional increasing data points using a sequence of segments. Such approximations are widely used tools in implementing many learned data structures, which mix learning models with traditional algorithmic design blocks to exploit regularities in the underlying data distribution, providing novel and effective space-time trade-offs. We introduce the first lower bounds to the cost of storing PLAs in two settings, namely compression and indexing. We then compare these compressibility measures to known data structures, and show that they are asymptotically optimal up to a constant factor from the space lower bounds. Finally, we design the first data structures for the aforementioned settings that achieve the space lower bounds plus small additive terms, which turn out to be succinct in most practical cases. Our data structures support the efficient retrieval and evaluation of a segment in the (compressed) PLA for a given $x$-value, which is a core operation in any learned data structure relying on PLAs. As a result, our paper offers the first theoretical analysis of the maximum compressibility achievable by PLA-based learned data structures, and provides novel storage schemes for PLAs offering strong theoretical guarantees while also suggesting simple and efficient practical implementations.
SCoder: Iterative Self-Distillation for Bootstrapping Small-Scale Data Synthesizers to Empower Code LLMs
Venue: arXiv cs.AI 2025
Year: 2025

Existing code large language models (LLMs) often rely on large-scale instruction data distilled from proprietary LLMs for fine-tuning, which typically incurs high costs. In this paper, we explore the potential of small-scale open-source LLMs (e.g., 7B) as synthesizers for high-quality code instruction data construction. We first observe that the data synthesis capability of small-scale LLMs can be enhanced by training on a few superior data synthesis samples from proprietary LLMs. Building on this, we propose a novel iterative self-distillation approach to bootstrap small-scale LLMs, transforming them into powerful synthesizers that reduce reliance on proprietary LLMs and minimize costs. Concretely, in each iteration, to obtain diverse and high-quality self-distilled data, we design multi-checkpoint sampling and multi-aspect scoring strategies for initial data selection. Furthermore, to identify the most influential samples, we introduce a gradient-based influence estimation method for final data filtering. Based on the code instruction datasets from the small-scale synthesizers, we develop SCoder, a family of code generation models fine-tuned from DeepSeek-Coder. SCoder models achieve state-of-the-art code generation capabilities, demonstrating the effectiveness of our method.
Are Humans as Brittle as Large Language Models?
Venue: arXiv cs.CL 2025
Year: 2025

The output of large language models (LLMs) is unstable, due both to non-determinism of the decoding process as well as to prompt brittleness. While the intrinsic non-determinism of LLM generation may mimic existing uncertainty in human annotations through distributional shifts in outputs, it is largely assumed, yet unexplored, that the prompt brittleness effect is unique to LLMs. This raises the question: do human annotators show similar sensitivity to prompt changes? If so, should prompt brittleness in LLMs be considered problematic? One may alternatively hypothesize that prompt brittleness correctly reflects human annotation variances. To fill this research gap, we systematically compare the effects of prompt modifications on LLMs and identical instruction modifications for human annotators, focusing on the question of whether humans are similarly sensitive to prompt perturbations. To study this, we prompt both humans and LLMs for a set of text classification tasks conditioned on prompt variations. Our findings indicate that both humans and LLMs exhibit increased brittleness in response to specific types of prompt modifications, particularly those involving the substitution of alternative label sets or label formats. However, the distribution of human judgments is less affected by typographical errors and reversed label order than that of LLMs.
A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges
Venue: arXiv cs.LG 2025
Year: 2025

Graph Neural Networks (GNNs) have gained traction in the complex domain of drug discovery because of their ability to process graph-structured data such as drug molecule models. This approach has resulted in a myriad of methods and models in published literature across several categories of drug discovery research. This paper covers the research categories comprehensively with recent papers, namely molecular property prediction, including drug-target binding affinity prediction, drug-drug interaction study, microbiome interaction prediction, drug repositioning, retrosynthesis, and new drug design, and provides guidance for future work on GNNs for drug discovery.
Unikernels vs. Containers: A Runtime-Level Performance Comparison for Resource-Constrained Edge Workloads
Venue: arXiv cs.PF 2025
Year: 2025

The choice between containers and unikernels is a critical trade-off for edge applications, balancing the container's ecosystem maturity against unikernel's specialized efficiency. However, until now, how this trade-off behaves under the severe memory constraints of industrial edge environments remains insufficiently investigated, especially across different execution models. This work presents an empirical comparison using Go and Node.js applications, representing ahead-of-time (AOT) and just-in-time (JIT) compilation, respectively. While unikernels consistently deliver faster startup times and outperform containers for Go-based workloads in resource-constrained environments, the evaluation results identify a critical performance crossover for Node.js. Below a certain memory threshold, Docker containers maintain stable performance for both I/O-bound and CPU-bound applications, while the Nanos unikernel's performance degrades sharply. This reveals that Linux's memory management capabilities can outweigh the minimalist efficiency of unikernels under resource scarcity, a critical trade-off that, until now, has not been adequately quantified for JIT runtimes in this context. These findings demonstrate that the optimal deployment paradigm depends on both runtime behavior and available system resources, underscoring the need for workload-aware deployment strategies in edge computing.
HiPhO: How Far Are (M)LLMs from Humans in the Latest High School Physics Olympiad Benchmark?
Venue: arXiv cs.AI 2025
Year: 2025

Recently, the physical capabilities of (M)LLMs have garnered increasing attention. However, existing benchmarks for physics suffer from two major gaps: they neither provide systematic and up-to-date coverage of real-world physics competitions such as physics Olympiads, nor enable direct performance comparison with humans. To bridge these gaps, we present HiPhO, the first benchmark dedicated to high school physics Olympiads with human-aligned evaluation. Specifically, HiPhO highlights three key innovations. (1) Comprehensive Data: It compiles 13 latest Olympiad exams from 2024-2025, spanning both international and regional competitions, and covering mixed modalities that encompass problems spanning text-only to diagram-based. (2) Professional Evaluation: We adopt official marking schemes to perform fine-grained grading at both the answer and step level, fully aligned with human examiners to ensure high-quality and domain-specific evaluation. (3) Comparison with Human Contestants: We assign gold, silver, and bronze medals to models based on official medal thresholds, thereby enabling direct comparison between (M)LLMs and human contestants. Our large-scale evaluation of 30 state-of-the-art (M)LLMs shows that: across 13 exams, open-source MLLMs mostly remain at or below the bronze level; open-source LLMs show promising progress with multiple golds; closed-source reasoning MLLMs can achieve 6 to 12 gold medals; and most models still have a significant gap from full marks. These results highlight the performance gap between open-source models and top students, the strong reasoning abilities of closed-source models, and the remaining room for improvement. HiPhO, a human-aligned Olympiad benchmark for multimodal physical reasoning, is open-source at https://github.com/SciYu/HiPhO with a public leaderboard at https://phyarena.github.io/.
dciWebMapper2: Enhancing the dciWebMapper framework toward integrated, interactive visualization of linked multi-type maps, charts, and spatial statistics and analysis
Venue: arXiv cs.HC 2025
Year: 2025

As interactive web-based geovisualization becomes increasingly vital across disciplines, there is a growing need for open-source frameworks that support dynamic, multi-attribute spatial analysis and accessible design. This paper introduces dciWebMapper2, a significant expansion of the original dciWebMapper framework, designed to enable exploratory analysis across domains such as climate justice, food access, and social vulnerability. The enhanced framework integrates multiple map types, including choropleth, proportional symbol, small multiples, and heatmaps, with linked statistical charts (e.g., scatter plots, boxplots) and time sliders, all within a coordinated-view environment. Dropdown-based controls allow flexible, high-dimensional comparisons while maintaining visual clarity. Grounded in cartographic and information visualization principles, dciWebMapper2 is fully open-source, self-contained, and server-free, supporting modularity, reproducibility, and long-term sustainability. Three applied use cases demonstrate its adaptability and potential to democratize interactive web cartography. This work offers a versatile foundation for inclusive spatial storytelling and transparent geospatial analysis in research, education, and civic engagement.
Uncovering Scaling Laws for Large Language Models via Inverse Problems
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Models (LLMs) are large-scale pretrained models that have achieved remarkable success across diverse domains. These successes have been driven by unprecedented complexity and scale in both data and computations. However, due to the high costs of training such models, brute-force trial-and-error approaches to improve LLMs are not feasible. Inspired by the success of inverse problems in uncovering fundamental scientific laws, this position paper advocates that inverse problems can also efficiently uncover scaling laws that guide the building of LLMs to achieve the desirable performance with significantly better cost-effectiveness.
Dynamic Scene 3D Reconstruction of an Uncooperative Resident Space Object
Venue: arXiv cs.CV 2025
Year: 2025

Characterization of uncooperative Resident Space Objects (RSO) play a crucial role in On-Orbit Servicing (OOS) and Active Debris Removal (ADR) missions to assess the geometry and motion properties. To address the challenges of reconstructing tumbling uncooperative targets, this study evaluates the performance of existing state-of-the-art 3D reconstruction algorithms for dynamic scenes, focusing on their ability to generate geometrically accurate models with high-fidelity. To support our evaluation, we developed a simulation environment using Isaac Sim to generate physics-accurate 2D image sequences of tumbling satellite under realistic orbital lighting conditions. Our preliminary results on static scenes using Neuralangelo demonstrate promising reconstruction quality. The generated 3D meshes closely match the original CAD models with minimal errors and artifacts when compared using Cloud Compare (CC). The reconstructed models were able to capture critical fine details for mission planning. This provides a baseline for our ongoing evaluation of dynamic scene reconstruction.
Knowledge Isn't Power: The Ethics of Social Robots and the Difficulty of Informed Consent
Venue: arXiv cs.HC 2025
Year: 2025

Contemporary robots are increasingly mimicking human social behaviours to facilitate interaction, such as smiling to signal approachability, or hesitating before taking an action to allow people time to react. Such techniques can activate a person's entrenched social instincts, triggering emotional responses as though they are interacting with a fellow human, and can prompt them to treat a robot as if it truly possesses the underlying life-like processes it outwardly presents, raising significant ethical questions. We engage these issues through the lens of informed consent: drawing upon prevailing legal principles and ethics, we examine how social robots can influence user behaviour in novel ways, and whether under those circumstances users can be appropriately informed to consent to these heightened interactions. We explore the complex circumstances of human-robot interaction and highlight how it differs from more familiar interaction contexts, and we apply legal principles relating to informed consent to social robots in order to reconceptualize the current ethical debates surrounding the field. From this investigation, we synthesize design goals for robot developers to achieve more ethical and informed human-robot interaction.
The Linear Reliability Channel
Venue: arXiv cs.IT 2025
Year: 2025

We introduce and analyze a discrete soft-decision channel called the linear reliability channel (LRC) in which the soft information is the rank ordering of the received symbol reliabilities. We prove that the LRC is an appropriate approximation to a general class of discrete modulation, continuous noise channels when the noise variance is high. The central feature of the LRC is that its combinatorial nature allows for an extensive mathematical analysis of the channel and its corresponding hard- and soft-decision maximum likelihood (ML) decoders. In particular, we establish explicit error exponents for ML decoding in the LRC when using random codes under both hard- and soft-decision decoding. This analysis allows for a direct, quantitative evaluation of the relative advantage of soft-decision decoding. The discrete geometry of the LRC is distinct from that of the BSC, which is characterized by the Hamming weight, offering a new perspective on code construction for soft-decision settings.
Hammer and Anvil: A Principled Defense Against Backdoors in Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

Federated Learning is a distributed learning technique in which multiple clients cooperate to train a machine learning model. Distributed settings facilitate backdoor attacks by malicious clients, who can embed malicious behaviors into the model during their participation in the training process. These malicious behaviors are activated during inference by a specific trigger. No defense against backdoor attacks has stood the test of time, especially against adaptive attackers, a powerful but not fully explored category of attackers. In this work, we first devise a new adaptive adversary that surpasses existing adversaries in capabilities, yielding attacks that only require one or two malicious clients out of 20 to break existing state-of-the-art defenses. Then, we present Hammer and Anvil, a principled defense approach that combines two defenses orthogonal in their underlying principle to produce a combined defense that, given the right set of parameters, must succeed against any attack. We show that our best combined defense, Krum+, is successful against our new adaptive adversary and state-of-the-art attacks.
Holographic Beamforming for Integrated Sensing and Communication with Mutual Coupling Effects
Venue: arXiv cs.IT 2025
Year: 2025

Integrated sensing and communication (ISAC) is envisioned as a key technology in 6G networks, owing to its potential for high spectral and cost efficiency. As a promising solution for extremely large-scale arrays, reconfigurable holographic surfaces (RHS) can be integrated with ISAC to form the holographic ISAC paradigm, where enlarged radiation apertures of RHS can achieve significant beamforming gains, thereby improving both communication and sensing performance. In this paper, we investigate holographic beamforming designs for ISAC systems, which, unlike existing holographic beamforming schemes developed for RHS-aided communications, requires explicit consideration of mutual coupling effects within RHS. This is because, different from prior works only considering communication performance, ISAC systems incorporate sensing functionality, which is sensitive to sidelobe levels. Ignoring mutual coupling in holographic beamforming can lead to notable undesired sidelobes, thus degrading sensing performance. The consideration of mutual coupling introduces new challenges, i.e., it induces non-linearity in beamforming problems, rendering them inherently non-convex. To address this issue, we propose a tractable electromagnetic-compliant holographic ISAC model that characterizes mutual coupling in a closed form using coupled dipole approximations. We then develop an efficient mutual coupling aware holographic beamforming algorithm to suppress sidelobes and enhance ISAC performance. Numerical results validate effectiveness of the proposed algorithm.
UTM Performance Under Stressing Scenarios
Venue: arXiv cs.NI 2025
Year: 2025

Proliferation of new classes of airspace participants, including uncrewed and advanced aerial mobility vehicles, necessitates the development and deployment of novel airspace management solutions, such as the Unmanned Traffic Management (UTM) system and the Provider of Services to UAM (PSU) Network. The efficacy of such systems has been demonstrated on multiple occasions via real-world deployments in limited test environments, however exploration of system behavior under stressing conditions requires the development of appropriate modeling and simulation (M&S) environments. Autonomy Networks for Advanced Mobility at Lincoln Laboratory (ANAMLL) is a virtual Systems Integration Laboratory (SIL) designed to host federated autonomy networks, such as a UTM or PSU Network, and to enable test and validation at scales not available in real-world deployments. As an example of ANAMLL's utility, we explore the performance of a representative UTM network during a stressing demand scenario. In a close examination of the demand scenario, ANAMLL demonstrates a UTM system demand point at which in-flight replanning can no longer be accomplished within an allowable time window. In a second analysis of the same scenario, ANAMLL demonstrates the impact of network connectivity performance on end-user airspace access.
Mean Field Game-Based Interactive Trajectory Planning Using Physics-Inspired Unified Potential Fields
Venue: arXiv cs.RO 2025
Year: 2025

Interactive trajectory planning in autonomous driving must balance safety, efficiency, and scalability under heterogeneous driving behaviors. Existing methods often face high computational cost or rely on external safety critics. To address this, we propose an Interaction-Enriched Unified Potential Field (IUPF) framework that fuses style-dependent benefit and risk fields through a physics-inspired variational model, grounded in mean field game theory. The approach captures conservative, aggressive, and cooperative behaviors without additional safety modules, and employs stochastic differential equations to guarantee Nash equilibrium with exponential convergence. Simulations on lane changing and overtaking scenarios show that IUPF ensures safe distances, generates smooth and efficient trajectories, and outperforms traditional optimization and game-theoretic baselines in both adaptability and computational efficiency.
MMM-fair: An Interactive Toolkit for Exploring and Operationalizing Multi-Fairness Trade-offs
Venue: arXiv cs.LG 2025
Year: 2025

Fairness-aware classification requires balancing performance and fairness, often intensified by intersectional biases. Conflicting fairness definitions further complicate the task, making it difficult to identify universally fair solutions. Despite growing regulatory and societal demands for equitable AI, popular toolkits offer limited support for exploring multi-dimensional fairness and related trade-offs. To address this, we present mmm-fair, an open-source toolkit leveraging boosting-based ensemble approaches that dynamically optimizes model weights to jointly minimize classification errors and diverse fairness violations, enabling flexible multi-objective optimization. The system empowers users to deploy models that align with their context-specific needs while reliably uncovering intersectional biases often missed by state-of-the-art methods. In a nutshell, mmm-fair uniquely combines in-depth multi-attribute fairness, multi-objective optimization, a no-code, chat-based interface, LLM-powered explanations, interactive Pareto exploration for model selection, custom fairness constraint definition, and deployment-ready models in a single open-source toolkit, a combination rarely found in existing fairness tools. Demo walkthrough available at: https://youtu.be/_rcpjlXFqkw.