OpenRaiser/Intern-Atlas huggingface dataset:
CLARC: C/C++ Benchmark for Robust Code Search
Venue: arXiv cs.SE 2026
Year: 2026

Efficient code retrieval is critical for developer productivity, yet existing benchmarks largely focus on Python and rarely stress-test robustness beyond superficial lexical cues. To address the gap, we introduce an automated pipeline for code search datasets and present CLARC, a C/C++ benchmark built from real-world GitHub repositories. CLARC contains 1,245 query-code pairs for evaluation and 5,472 pairs for training. The benchmark incorporates LLM-generated natural language queries validated through rigorous human scoring and hypothesis testing. To analyze contextual requirements effectively, our pipeline starts by ensuring code compilability. It then categorizes code snippets by dependency complexity, distinguishing whether the code relies on custom-defined types or helper functions. The pipeline also enables CLARC to stress-test retrieval robustness by introducing challenging settings, including identifier anonymization and compilation to low-level languages like Assembly and WebAssembly. Under these conditions, our evaluation of six state-of-the-art models reveals sharp drops in retrieval effectiveness. The experimental results highlight the models' persistent reliance on lexical features rather than code semantic understanding. Our dataset is publicly available at https://huggingface.co/datasets/ClarcTeam/CLARC.
Complete Diagrammatic Axiomatisations of Relative Entropy
Venue: arXiv cs.LO 2026
Year: 2026

Relative entropy is a fundamental class of distances between probability distributions, with widespread applications in probability theory, statistics, and machine learning. In this work, we study relative entropy from a categorical perspective, viewing it as a quantitative enrichment of categories of stochastic matrices. We consider two natural monoidal structures on stochastic matrices, given by the Kronecker product and the direct sum. Our main results are complete axiomatisations of Kullback-Leibler divergence and, more generally, of Rényi divergences of arbitrary order, for each such structure. Our axiomatic theories are formulated within the framework of quantitative monoidal algebra, using a graphical language of string diagrams enriched with quantitative equations.
Transformer-Based Multipath Congestion Control: A Decoupled Approach for Wireless Uplinks
Venue: arXiv cs.NI 2026
Year: 2026

The proliferation of artificial intelligence applications on edge devices necessitates efficient transport protocols that leverage multi-homed connectivity across heterogeneous networks. While Multipath TCP enables bandwidth aggregation, its in-kernel congestion control mechanisms lack the programmability and flexibility needed for achieving efficient transmission. Additionally, inherent measurement noise renders network state partially observable, challenging data-driven approaches like deep reinforcement learning (DRL). To address these challenges, we propose a Transformer-based Congestion Control Optimization (TCCO) framework for multipath transport. TCCO employs a decoupled architecture that offloads control decisions to an external decision engine via a lightweight in-kernel client and user-space proxy, enabling edge devices to leverage external computational resources while maintaining TCP/IP compatibility. The Transformer-based DRL agent in the external decision engine uses self-attention to capture temporal dependencies, filter noise, and coordinate control across subflows through a unified policy. Extensive evaluation on both simulated and real dual-band Wi-Fi testbeds demonstrates that TCCO achieves superior adaptability and performance than state-of-the-art baselines, validating the feasibility and effectiveness of TCCO for wireless networks.
Beyond the Interface: Redefining UX for Society-in-the-Loop AI Systems
Venue: arXiv cs.HC 2026
Year: 2026

Artificial intelligence systems increasingly operate in decision-critical environments where probabilistic outputs and Human-in-the-Loop (HITL) interactions reshape user engagement. Traditional user experience (UX) frameworks, designed for deterministic systems, fail to capture these evolving sociotechnical dynamics. This paper argues that in AI-enabled HITL systems, UX must transcend frontend usability to encompass backend performance, organizational workflows, and decision making structures. We employ a mixed-methods approach, combining an inductive social construction analysis of 269 stakeholder insights with the deployment of an operational HITL video anomaly detection system. Our findings reveal that stakeholders experience AI through multifaceted themes: risk, governance, and organizational capacity. Experimental results further demonstrate how detection behavior and alert routing directly calibrate human oversight and workload. Grounded in these results, we formalize a new evaluative framework centered on four sociotechnical metrics: Accuracy (FPR/FNR), Operational Latency (response time), Adaptation Time (deployment burden), and Trust (validated automation scales). This framework redefines UX as a multi-layered construct spanning infrastructure and governance, providing a rigorous foundation for evaluating AI systems embedded within complex real-world ecosystems.
Token Taxes: mitigating AGI's economic risks
Venue: arXiv cs.CY 2026
Year: 2026

The development of AGI threatens to erode government tax bases, lower living standards, and disempower citizens -- risks that make the 40-year stagnation of wages during the first industrial revolution look mild in comparison. While AI safety research has focused primarily on capability risks, comparatively little work has studied how to mitigate the economic risks of AGI. In this paper, we argue that the economic risks posed by a post-AGI world can be effectively mitigated by token taxes: usage-based surcharges on model inference applied at the point of sale. We situate token taxes within previous proposals for robot taxes and identify two key advantages: they are enforceable through existing compute governance infrastructure, and they capture value where AI is used rather than where models are hosted. For enforcement, we outline a staged audit pipeline -- black-box token verification, norm-based tax rates, and white-box audits. For impact, we highlight the need for agent-based modeling of token taxes' economic effects. Finally, we discuss alternative approaches including FLOP taxes, and how to prevent AI superpowers vetoing such measures.
From Local Corrections to Generalized Skills: Improving Neuro-Symbolic Policies with MEMO
Venue: arXiv cs.RO 2026
Year: 2026

Recent works use a neuro-symbolic framework for general manipulation policies. The advantage of this framework is that -- by applying off-the-shelf vision and language models -- the robot can break complex tasks down into semantic subtasks. However, the fundamental bottleneck is that the robot needs skills to ground these subtasks into embodied motions. Skills can take many forms (e.g., trajectory snippets, motion primitives, coded functions), but regardless of their form skills act as a constraint. The high-level policy can only ground its language reasoning through the available skills; if the robot cannot generate the right skill for the current task, its policy will fail. We propose to address this limitation -- and dynamically expand the robot's skills -- by leveraging user feedback. When a robot fails, humans can intuitively explain what went wrong (e.g., ``no, go higher''). While a simple approach is to recall this exact text the next time the robot faces a similar situation, we hypothesize that by collecting, clustering, and re-phrasing natural language corrections across multiple users and tasks, we can synthesize more general text guidance and coded skill templates. Applying this hypothesis we develop Memory Enhanced Manipulation (MEMO). MEMO builds and maintains a retrieval-augmented skillbook gathered from human feedback and task successes. At run time, MEMO retrieves relevant text and code from this skillbook, enabling the robot's policy to generate new skills while reasoning over multi-task human feedback. Our experiments demonstrate that using MEMO to aggregate local feedback into general skill templates enables generalization to novel tasks where existing baselines fall short. See supplemental material here: https://collab.me.vt.edu/memo
Structure-Guided Histopathology Synthesis via Dual-LoRA Diffusion
Venue: arXiv cs.CV 2026
Year: 2026

Histopathology image synthesis plays an important role in tissue restoration, data augmentation, and modeling of tumor microenvironments. However, existing generative methods typically address restoration and generation as separate tasks, although both share the same objective of structure-consistent tissue synthesis under varying degrees of missingness, and often rely on weak or inconsistent structural priors that limit realistic cellular organization. We propose Dual-LoRA Controllable Diffusion, a unified centroid-guided diffusion framework that jointly supports Local Structure Completion and Global Structure Synthesis within a single model. Multi-class nuclei centroids serve as lightweight and annotation-efficient spatial priors, providing biologically meaningful guidance under both partial and complete image absence. Two task-specific LoRA adapters specialize the shared backbone for local and global objectives without retraining separate diffusion models. Extensive experiments demonstrate consistent improvements over state-of-the-art GAN and diffusion baselines across restoration and synthesis tasks. For local completion, LPIPS computed within the masked region improves from 0.1797 (HARP) to 0.1524, and for global synthesis, FID improves from 225.15 (CoSys) to 76.04, indicating improved structural fidelity and realism. Our approach achieves more faithful structural recovery in masked regions and substantially improved realism and morphology consistency in full synthesis, supporting scalable pan-cancer histopathology modeling.
ELLIPSE: Evidential Learning for Robust Waypoints and Uncertainties
Venue: arXiv cs.RO 2026
Year: 2026

Robust waypoint prediction is crucial for mobile robots operating in open-world, safety-critical settings. While Imitation Learning (IL) methods have demonstrated great success in practice, they are susceptible to distribution shifts: the policy can become dangerously overconfident in unfamiliar states. In this paper, we present \textit{ELLIPSE}, a method building on multivariate deep evidential regression to output waypoints and multivariate Student-t predictive distributions in a single forward pass. To reduce covariate-shift-induced overconfidence under viewpoint and pose perturbations near expert trajectories, we introduce a lightweight domain augmentation procedure that synthesizes plausible viewpoint/pose variations without collecting additional demonstrations. To improve uncertainty reliability under environment/domain shift (e.g., unseen staircases), we apply a post-hoc isotonic recalibration on probability integral transform (PIT) values so that prediction sets remain plausible during deployment. We ground the discussion and experiments in staircase waypoint prediction, where obtaining robust waypoint and uncertainty is pivotal. Extensive real world evaluations show that \textit{ELLIPSE} improves both task success rate and uncertainty coverage compared to baselines.
Industrial Survey on Robustness Testing In Cyber Physical Systems
Venue: arXiv cs.SE 2026
Year: 2026

Cyber-Physical Systems (CPS) play a critical role in modern industrial domains, including manufacturing, energy, transportation, and healthcare, where they enable automation, optimization, and real-time decision-making. Ensuring the robustness of these systems is paramount, as failures can have significant economic, operational, and safety consequences. This paper present findings from an industrial survey conducted in Wallonia, covering a wide range of sectors, to assess the current state of practice in CPS robustness. It investigates robustness from how it is understood and applied in relationship with requirements engineering, system design, test execution, failure modes, and available tools. It identifies key challenges and gaps between industry practices and state-of-the-art methodologies. Additionally, it compares our findings with similar industrial surveys from the literature.
DuaLip-GPU Technical Report
Venue: arXiv cs.DC 2026
Year: 2026

Large-scale linear programs (LPs) arise in many decision systems, including ranking, allocation, and matching problems that must be solved repeatedly at massive scale. Prior work such as ECLIPSE and LinkedIn's open-source DuaLip showed that ridge-regularized dual ascent with first-order methods can scale to these settings. However, the original implementation was tightly coupled to a small number of schemas and built on a CPU-centric Scala/Spark stack, limiting extensibility and preventing effective use of modern accelerators. We present a redesigned solver architecture that decouples problem specification from the optimization engine and targets GPU execution. The system uses an operator-centric programming model in which LP formulations are expressed through composable primitives for dual objective evaluation and blockwise projection operators for decomposable constraint families. This design allows new formulations to be added locally while reusing a shared optimization loop, diagnostics, and distributed infrastructure. To realize the available parallelism, we develop GPU execution techniques tailored to sparse matching constraints, including constraint-aligned sparse layouts, batched projection kernels, and a distributed design that communicates only dual variables. Further, we improve the underlying ridge-regularized dual ascent method with Jacobi-style row normalization, primal scaling, and a continuation scheme for the regularization parameter. On extreme-scale matching workloads, the GPU implementation achieves at least a 10x wall-clock speedup over the prior distributed CPU DuaLip solver under matched stopping criteria, while maintaining convergence guarantees.
Gamified Informed Decision-Making for Performance-Aware Design by Non-Experts: An Exoskeleton Design Case Study
Venue: arXiv cs.HC 2026
Year: 2026

Decision Support Systems (DSS) play a crucial role in enabling non-expert designers to explore complex, performance-driven design spaces. This paper presents a gamified decision-making framework that integrates game engines with real-time performance feedback. Performance criteria include structural behavior, environmental parameters, fabrication, material, and cost considerations. The developed design framework was tested with architecture students and non-expert designers on the design of an exoskeleton facade to retrofit an existing building. Participants (N=24) were able to iteratively modify façade geometries while receiving real-time feedback across the three key criteria: 1) structural behavior, including deflection, mass, and stress/strength ratio; 2) environmental parameters, such as solar gain and heating/cooling energy demands; and 3) fabrication considerations, including fabrication and material costs, robotic machining, and material setup. The evaluation of participant interactions reveals that gamified feedback mechanisms significantly enhance user comprehension and informed decision-making across the criteria. Further, participants' understanding of structural, material, and fabrication performance in relation to the iterative design task suggests that curated design spaces and structured guidance improve efficiency compared to open-ended generative tools. This research contributes to pre-occupancy evaluations, demonstrating how gamified environments enable stakeholder participation in the design process through informed decisionmaking and customized negotiation of performance criteria. .
HDLFORGE: A Two-Stage Multi-Agent Framework for Efficient Verilog Code Generation with Adaptive Model Escalation
Venue: arXiv cs.AR 2026
Year: 2026

We present HDLFORGE, a two-stage multi-agent framework for automated Verilog generation that optimizes the trade-off between generation speed and accuracy. The system uses a compact coder with a medium-sized LLM by default (Stage A) and escalates to a stronger coder with an ultra-large LLM (Stage B) only when needed, guided by a calibrated score from inexpensive diagnostics including compilation, lint, and smoke tests. A key innovation is a counterexample-guided formal agent that converts bounded-model-checking traces into reusable micro-tests, significantly reducing bug detection time and repair iterations. The portable escalation controller can wrap existing Verilog LLM pipelines without modifying their internals. Evaluated on VerilogEval Human, VerilogEval V2, and RTLLM benchmarks, HDLFORGE demonstrates improved accuracy-latency trade-offs compared to single-stage systems through comprehensive analysis of wall-clock time distributions, escalation thresholds, and agent ablations. On VerilogEval Human and VerilogEval V2, HDLFORGE-Qwen achieves 91.2% and 91.8% Pass@1 with roughly 50% lower median latency, dramatically improving accuracy over other medium-sized models, and 97.2% Pass@5 on RTLLM.
Logical Attacks in 5G Standalone Networks: Impact on UAV C2 Communications
Venue: arXiv cs.CR 2026
Year: 2026

UAVs are increasingly deployed in critical applications and rely on 5G networks for long-range command-and-control (C2) connectivity. As the C2 channel is safety-critical, disruptions or manipulation of this communication channel may lead to loss of control, mission failure, or safety incidents. The architectural complexity of 5G standalone (SA) introduces logical attack surfaces that may affect such applications, yet the impact of logical vulnerabilities in the 5G architecture on UAV command-and-control carried over cellular infrastructure has received little attention. In this work, we develop a reproducible testbed that emulates 5G SA and integrates a UAV C2 channel using MAVLink over the 5G User Plane through Open5GS and UERANSIM. We define three threat models (rogue UE in the same slice and DNN, insider with access to the N4 interface, compromised gNodeB) and implement representative attacks. Our evaluation shows that a rogue UE can inject C2 commands and force the UAV to land; an insider can tear down PDU sessions via PFCP and trigger UAV failsafe; a compromised gNodeB can alter MAVLink navigation commands and redirect the UAV. The results demonstrate that logical attacks on the 5G architecture can compromise UAV C2 without breaking air-interface encryption, revealing cross-layer vulnerabilities between cellular infrastructure and UAV communication protocols. We provide a threat-model framework, experimental evidence, and mitigations (MAVLink signing, integrity protection on N3 and N4 interfaces) for operators and system designers deploying UAVs over 5G.
sFRC for assessing hallucinations in medical image restoration
Venue: arXiv cs.CV 2026
Year: 2026

Deep learning (DL) methods are currently being explored to restore images from sparse-view-, limited-data-, and undersampled-based acquisitions in medical applications. Although outputs from DL may appear visually appealing based on likability/subjective criteria (such as less noise, smooth features), they may also suffer from hallucinations. This issue is further exacerbated by a lack of easy-to-use techniques and robust metrics for the identification of hallucinations in DL outputs. In this work, we propose performing Fourier Ring Correlation (FRC) analysis over small patches and concomitantly (s)canning across DL outputs and their reference counterparts to detect hallucinations (termed as sFRC). We describe the rationale behind sFRC and provide its mathematical formulation. The parameters essential to sFRC may be set using predefined hallucinated features annotated by subject matter experts or using imaging theory-based hallucination maps. We use sFRC to detect hallucinations for three undersampled medical imaging problems: CT super-resolution, CT sparse view, and MRI subsampled restoration. In the testing phase, we demonstrate sFRC's effectiveness in detecting hallucinated features for the CT problem and sFRC's agreement with imaging theory-based outputs on hallucinated feature maps for the MR problem. Finally, we quantify the hallucination rates of DL methods on in-distribution versus out-of-distribution data and under increasing subsampling rates to characterize the robustness of DL methods. Beyond DL-based methods, sFRC's effectiveness in detecting hallucinations for a conventional regularization-based restoration method and a state-of-the-art unrolled method is also shown.
Non-Zipfian Distribution of Stopwords and Subset Selection Models
Venue: arXiv cs.CL 2026
Year: 2026

Stopwords are words that are not very informative to the content or the meaning of a language text. Most stopwords are function words but can also be common verbs, adjectives and adverbs. In contrast to the well known Zipf's law for rank-frequency plot for all words, the rank-frequency plot for stopwords are best fitted by the Beta Rank Function (BRF). On the other hand, the rank-frequency plots of non-stopwords also deviate from the Zipf's law, but are fitted better by a quadratic function of log-token-count over log-rank than by BRF. Based on the observed rank of stopwords in the full word list, we propose a stopword (subset) selection model that the probability for being selected as a function of the word's rank $r$ is a decreasing Hill's function ($1/(1+(r/r_{mid})^γ)$); whereas the probability for not being selected is the standard Hill's function ( $1/(1+(r_{mid}/r)^γ)$). We validate this selection probability model by a direct estimation from an independent collection of texts. We also show analytically that this model leads to a BRF rank-frequency distribution for stopwords when the original full word list follows the Zipf's law, as well as explaining the quadratic fitting function for the non-stopwords.
Analysis of Terms of Service on Social Media Platforms: Consent Challenges and Assessment Metrics
Venue: arXiv cs.CY 2026
Year: 2026

Social media platforms typically obtain user consent through Terms of Service (ToS) presented at account creation, rather than through dedicated consent forms. This study investigates whether consent-related information is clearly communicated within these ToS documents. We propose and apply a three-dimensional consent evaluation framework encompassing Textual Accessibility, Semantic Transparency, and Interface Design as declared in ToS documents. Using a combination of computational and qualitative analyses, we assess ToS from 13 major social media platforms. Our findings reveal important shortcomings across platforms, including high linguistic complexity, widespread use of non-committal language, limited disclosure of data retention and sharing practices, and the absence of explicit interface-level commitments to granular or revocable consent. These results indicate that while consent is formally embedded in ToS, it is often presented in ways that constrain clarity and meaningful choice. Rather than treating ToS documents as informed consent instruments, this study positions them as consent-bearing documents whose design and content shape the conditions under which users are asked to agree to data practices. The proposed framework offers a systematic method for evaluating consent information within ToS in the absence of explicit consent forms and informs the design of clearer, more ethically robust consent mechanisms for data-intensive digital platforms.