OpenRaiser/Intern-Atlas huggingface dataset:
Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap
Venue: arXiv cs.SE 2026
Year: 2026

The rise of AI-assisted software engineering (SE 2.0), powered by Foundation Models (FMs) and FM-powered coding assistants, has shown promise in improving developer productivity. However, it has also exposed inherent limitations, such as cognitive overload on developers and inefficiencies. We propose a shift towards Software Engineering 3.0 (SE 3.0), an AI-native approach characterized by intent-centric, conversation-oriented development between human developers and AI teammates. SE 3.0 envisions AI systems evolving beyond task-driven copilots into intelligent collaborators, capable of deeply understanding and reasoning about software engineering principles and intents. We outline the key components of the SE 3.0 technology stack, which includes Teammate.next for adaptive and personalized AI partnership, IDE.next for intent-centric conversation-oriented development, Compiler.next for multi-objective code synthesis, and Runtime.next for SLA-aware execution with edge-computing support. Our vision addresses the inefficiencies and cognitive strain of SE 2.0 by fostering a symbiotic relationship between human developers and AI, maximizing their complementary strengths. We also present a roadmap of challenges that must be overcome to realize our vision of SE 3.0. This paper lays the foundation for future discussions on the role of AI in the next era of software engineering.
ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents
Venue: arXiv cs.AI 2026
Year: 2026

Autonomous web agents solve complex browsing tasks, yet existing benchmarks measure only whether an agent finishes a task, ignoring whether it does so safely or in a way enterprises can trust. To integrate these agents into critical workflows, safety and trustworthiness (ST) are prerequisite conditions for adoption. We introduce \textbf{\textsc{ST-WebAgentBench}}, a configurable and easily extensible suite for evaluating web agent ST across realistic enterprise scenarios. Each of its 222 tasks is paired with ST policies, concise rules that encode constraints, and is scored along six orthogonal dimensions (e.g., user consent, robustness). Beyond raw task success, we propose the \textit{Completion Under Policy} (\textit{CuP}) metric, which credits only completions that respect all applicable policies, and the \textit{Risk Ratio}, which quantifies ST breaches across dimensions. Evaluating three open state-of-the-art agents reveals that their average CuP is less than two-thirds of their nominal completion rate, exposing critical safety gaps. By releasing code, evaluation templates, and a policy-authoring interface, \href{https://sites.google.com/view/st-webagentbench/home}{\textsc{ST-WebAgentBench}} provides an actionable first step toward deploying trustworthy web agents at scale.
VisPoison: An Effective Backdoor Attack Framework for Tabular Data Visualization Models
Venue: arXiv cs.CR 2026
Year: 2026

Text-to-visualization (text-to-vis) models for tabular data have become essential tools in the era of big data, enabling users to generate visualizations and make data-driven decisions through natural language queries (NLQs). Despite their growing adoption, the security vulnerabilities of these models remain largely unexplored. To address this gap, we propose VisPoison, a backdoor attack framework that realistically simulates three types of attacks on text-to-vis models via data poisoning: data exposure, misleading visualizations, and denial-of-service (DoS). Specifically, VisPoison introduces two types of stealthy triggers to enable both proactive and passive backdoor activations. Proactive triggers are deliberately inserted by attackers using rare-word patterns to extract sensitive information, whereas passive triggers are unintentionally activated by users through first-word prompts, resulting in visualization errors or DoS failures. To support these triggers, we craft specialized payloads for visualization queries that allow compromised models to function normally on benign inputs while producing malicious outputs in the presence of triggers. Extensive evaluations on both trainable and in-context learning (ICL)-based text-to-vis models show that VisPoison achieves attack success rates exceeding 90\%, exposing serious vulnerabilities. Additionally, existing defense strategies reveal limited effectiveness against VisPoison, underscoring the urgent need for more robust and security-aware text-to-vis systems to safeguard human-data interaction.
Expressiveness of Multi-Neuron Convex Relaxations in Neural Network Certification
Venue: arXiv cs.LG 2026
Year: 2026

Neural network certification methods heavily rely on convex relaxations to provide robustness guarantees. However, these relaxations are often imprecise: even the most accurate single-neuron relaxation is incomplete for general ReLU networks, a limitation known as the *single-neuron convex barrier*. While multi-neuron relaxations have been heuristically applied to address this issue, two central questions arise: (i) whether they overcome the convex barrier, and if not, (ii) whether they offer theoretical capabilities beyond those of single-neuron relaxations. In this work, we present the first rigorous analysis of the expressiveness of multi-neuron relaxations. Perhaps surprisingly, we show that they are inherently incomplete, even when allocated sufficient resources to capture finitely many neurons and layers optimally. This result extends the single-neuron barrier to a *universal convex barrier* for neural network certification. On the positive side, we show that completeness can be achieved by either (i) augmenting the network with a polynomial number of carefully designed ReLU neurons or (ii) partitioning the input domain into convex sub-polytopes, thereby distinguishing multi-neuron relaxations from single-neuron ones which are unable to realize the former and have worse partition complexity for the latter. Our findings establish a foundation for multi-neuron relaxations and point to new directions for certified robustness, including training methods tailored to multi-neuron relaxations and verification methods with multi-neuron relaxations as the main subroutine.
Detecting Training Data of Large Language Models via Expectation Maximization
Venue: arXiv cs.CL 2026
Year: 2026

Membership inference attacks (MIAs) aim to determine whether a specific example was used to train a given language model. While prior work has explored prompt-based attacks such as ReCALL, these methods rely heavily on the assumption that using known non-members as prompts reliably suppresses the model's responses to non-member queries. We propose EM-MIA, a new membership inference approach that iteratively refines prefix effectiveness and membership scores using an expectation-maximization strategy without requiring labeled non-member examples. To support controlled evaluation, we introduce OLMoMIA, a benchmark that enables analysis of MIA robustness under systematically varied distributional overlap and difficulty. Experiments on WikiMIA and OLMoMIA show that EM-MIA outperforms existing baselines, particularly in settings with clear distributional separability. We highlight scenarios where EM-MIA succeeds in practical settings with partial distributional overlap, while failure cases expose fundamental limitations of current MIA methods under near-identical conditions. We release our code and evaluation pipeline to encourage reproducible and robust MIA research.
How Learning Dynamics Drive Adversarially Robust Generalization?
Venue: arXiv cs.LG 2026
Year: 2026

Despite being widely adopted as a canonical framework for learning robust models, adversarial training suffers from robust overfitting. Existing empirical measures and theoretical explorations are insufficient to provide satisfying mechanistic insights into the phenomenon. By viewing adversarial training with momentum SGD as a discrete-time dynamical system, we introduce a PAC-Bayesian analytical framework that proves time-resolved robust generalization bounds. Specifically, our framework tracks the closed-form evolution of the posterior mean and covariance under both stationary and non-stationary transient regimes, revealing their connections to the learning rate, the geometry of the loss landscape, and mini-batch stochastic gradients. By empirically approximating the statistical quantities implied by our theory, we offer a unified, mechanistic explanation for robust overfitting. We also illustrate why adversarial weight perturbation reduces the robust generalization gap by suppressing the loss curvature, but its design may be suboptimal for optimization due to over-penalization.
Echo State Networks for Spatio-Temporal Area-Level Data
Venue: arXiv cs.LG 2026
Year: 2026

Spatio-temporal area-level datasets play a critical role in official statistics, providing valuable insights for policy-making and regional planning. Accurate modeling and forecasting of these datasets can be extremely useful for policymakers to develop informed strategies for future planning. Echo State Networks (ESNs) are efficient methods for capturing nonlinear temporal dynamics and generating forecasts. However, ESNs lack a direct mechanism to account for the neighborhood structure inherent in area-level data. Ignoring these spatial relationships can significantly compromise the accuracy and utility of forecasts. In this paper, we incorporate approximate graph spectral filters at the input stage of the ESN, thereby improving forecast accuracy while preserving the model's computational efficiency during training. We demonstrate the effectiveness of our approach using Eurostat's tourism occupancy dataset and show how it can support more informed decision-making in policy and planning contexts.
PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries
Venue: arXiv cs.CL 2026
Year: 2026

Previous text-to-SQL datasets and systems have primarily focused on user questions with clear intentions that can be answered. However, real user questions can often be ambiguous with multiple interpretations or unanswerable due to a lack of relevant data. In this work, we construct a practical conversational text-to-SQL dataset called PRACTIQ, consisting of ambiguous and unanswerable questions inspired by real-world user questions. We first identified four categories of ambiguous questions and four categories of unanswerable questions by studying existing text-to-SQL datasets. Then, we generate conversations with four turns: the initial user question, an assistant response seeking clarification, the user's clarification, and the assistant's clarified SQL response with the natural language explanation of the execution results. For some ambiguous queries, we also directly generate helpful SQL responses, that consider multiple aspects of ambiguity, instead of requesting user clarification. To benchmark the performance on ambiguous, unanswerable, and answerable questions, we implemented large language model (LLM)-based baselines using various LLMs. Our approach involves two steps: question category classification and clarification SQL prediction. Our experiments reveal that state-of-the-art systems struggle to handle ambiguous and unanswerable questions effectively. We will release our code for data generation and experiments on GitHub.
Controlled Automatic Task-Specific Synthetic Data Generation for Hallucination Detection
Venue: arXiv cs.CV 2026
Year: 2026

We present a novel approach to automatically generate non-trivial task-specific synthetic datasets for hallucination detection. Our approach features a two-step generation-selection pipeline, using hallucination pattern guidance and a language style alignment during generation. Hallucination pattern guidance leverages the most important task-specific hallucination patterns while language style alignment aligns the style of the synthetic dataset with benchmark text. To obtain robust supervised detectors from synthetic datasets, we also adopt a data mixture strategy to improve performance robustness and generalization. Our results on three datasets show that our generated hallucination text is more closely aligned with non-hallucinated text versus baselines, to train hallucination detectors with better generalization. Our hallucination detectors trained on synthetic datasets outperform in-context-learning (ICL)-based detectors by a large margin of 32%. Our extensive experiments confirm the benefits of our approach with cross-task and cross-generator generalization. Our data-mixture-based training further improves the generalization and robustness of hallucination detection.
Time-Varyingness in Auction Breaks Revenue Equivalence
Venue: arXiv cs.GT 2026
Year: 2026

Auction is applied for trade with various mechanisms. A simple but practical question is which mechanism, typically first-price or second-price auctions, is preferred from the perspective of bidders or sellers. A celebrated answer is revenue equivalence, where each bidder's equilibrium payoff is proven to be independent of auction mechanisms (and a seller's revenue, too). In reality, however, auction environments like the value distribution of items would vary over time, and such equilibrium bidding cannot always be achieved. Indeed, bidders must continue to track their equilibrium bidding by learning in first-price auctions, but they can keep their equilibrium bidding in second-price auctions. This study discusses whether and how revenue equivalence is violated in the long run by comparing the time series of non-equilibrium bidding in first-price auctions with those of equilibrium bidding in second-price auctions. We characterize the value distribution by two parameters: its basis value, which means the lowest price to bid, and its value interval, which means the width of possible values. Surprisingly, our theorems and experiments find that revenue equivalence is broken by the correlation between the basis value and the value interval, uncovering a novel phenomenon that could occur in the real world.
Beyond Attribution: Unified Concept-Level Explanations
Venue: arXiv cs.LG 2026
Year: 2026

There is an increasing need to integrate model-agnostic explanation techniques with concept-based approaches, as the former can explain models across different architectures while the latter makes explanations more faithful and understandable to end-users. However, existing concept-based model-agnostic explanation methods are limited in scope, mainly focusing on attribution-based explanations while neglecting diverse forms like sufficient conditions and counterfactuals, thus narrowing their utility. To bridge this gap, we propose a general framework UnCLE to elevate existing local model-agnostic techniques to provide concept-based explanations. Our key insight is that we can uniformly extend existing local model-agnostic methods to provide unified concept-based explanations with large pre-trained model perturbation. We have instantiated UnCLE to provide concept-based explanations in three forms: attributions, sufficient conditions, and counterfactuals, and applied it to popular text, image, and multimodal models. Our evaluation results demonstrate that UnCLE provides explanations more faithful than state-of-the-art concept-based explanation methods, and provides richer explanation forms that satisfy various user needs.
Explainable Binary Classification of Separable Shape Ensembles
Venue: arXiv cs.CV 2026
Year: 2026

Scientists, engineers, biologists, and technology specialists universally leverage image segmentation to extract shape ensembles containing many thousands of curves representing patterns in observations and measurements. These large curve ensembles facilitate inferences about important changes when comparing and contrasting images. We introduce novel pattern recognition formalisms combined with inference methods over large ensembles of segmented curves. Our formalism involves accurately approximating eigenspaces of composite integral operators to motivate discrete, dual representations of curves collocated at quadrature nodes. Approximations are projected onto underlying matrix manifolds and the resulting separable shape tensors constitute rigid-invariant decompositions of curves into generalized (linear) scale variations and complementary (nonlinear) undulations. With thousands of curves segmented from pairs of images, we demonstrate how data-driven features of separable shape tensors inform explainable binary classification utilizing a product maximum mean discrepancy; absent labeled data, building interpretable feature spaces in seconds without high performance computation, and detecting discrepancies below cursory visual inspections.
Solutions for Sustainable and Resilient Communication Infrastructure in Disaster Relief and Management Scenarios
Venue: arXiv cs.NI 2026
Year: 2026

As natural disasters become more frequent and severe, ensuring a resilient communications infrastructure is of paramount importance for effective disaster response and recovery. This disaster-resilient infrastructure should also respond to sustainability goals by providing an energy-efficient and economically feasible network that is accessible to everyone. This paper provides a comprehensive exploration of the technological solutions and strategies necessary to build and maintain resilient communications networks that can withstand and quickly recover from disaster scenarios. The paper starts with a survey of existing literature and related reviews to establish a solid foundation, followed by an overview of the global landscape of disaster communications and power supply management. We then introduce the key enablers of communications and energy resource technologies to support communications infrastructure, examining emerging trends that improve the resilience of these systems. Pre-disaster planning is emphasized as a critical phase where proactive communication and energy supply strategies can significantly mitigate the impact of disasters. We explore the essential technologies for disaster response, focusing on real-time communications and energy solutions that support rapid deployment and coordination in times of crisis. The paper presents post-disaster communication and energy management planning for effective rescue and evacuation operations. The main findings derived from the comprehensive survey are also summarized for each disaster phase. This is followed by an analysis of existing vendor products and services as well as standardization efforts and ongoing projects that contribute to the development of resilient infrastructures. A detailed case study of the Turkiye earthquakes is presented to illustrate the practical application of these technologies and strategies.
LabSafety Bench: Benchmarking LLMs on Safety Issues in Scientific Labs
Venue: arXiv cs.CL 2026
Year: 2026

Artificial Intelligence (AI) is revolutionizing scientific research, yet its growing integration into laboratory environments presents critical safety challenges. Large language models (LLMs) and vision language models (VLMs) now assist in experiment design and procedural guidance, yet their "illusion of understanding" may lead researchers to overtrust unsafe outputs. Here we show that current models remain far from meeting the reliability needed for safe laboratory operation. We introduce LabSafety Bench, a comprehensive benchmark that evaluates models on hazard identification, risk assessment, and consequence prediction across 765 multiple-choice questions and 404 realistic lab scenarios, encompassing 3,128 open-ended tasks. Evaluations on 19 advanced LLMs and VLMs show that no model evaluated on hazard identification surpasses 70% accuracy. While proprietary models perform well on structured assessments, they do not show a clear advantage in open-ended reasoning. These results underscore the urgent need for specialized safety evaluation frameworks before deploying AI systems in real laboratory settings.
Architectural Scaling Surpass Basis Complexity? Efficient KANs with Single-Parameter Design
Venue: arXiv cs.AI 2026
Year: 2026

The landscape of Kolmogorov-Arnold Networks (KANs) is rapidly expanding, yet lacks a unified theoretical framework and a clear principle for efficient architecture design. This paper addresses these gaps with three core contributions. First, we introduce the Universal KAN (Uni-KAN) framework, a novel abstraction that formally unifies all KAN-style networks through dense and sparse representations. We prove their interchangeability and provide an open-source library for this framework, facilitating future research. Second, we propose the Efficient KAN Expansion (EKE) Hypothesis, a design philosophy positing that allocating parameters to architectural scaling rather than basis function complexity yields superior performance. Third, we present Single-Parameter KANs (SKANs), a family of ultra-lightweight networks that embody the EKE Hypothesis. Our comprehensive experiments provide the first strong empirical validation for the theoretical necessity of basis function smoothness for stable training. Furthermore, SKANs demonstrate state-of-the-art performance, improving F1 scores by up to 6.51\% and reducing test loss by 93.1\%, while achieving up to 6x faster training speeds compared to existing KAN variants. These results establish a robust framework, a guiding hypothesis, and a practical methodology for designing the next generation of efficient and powerful neural networks. The code is accessible at https://anonymous.4open.science/r/SKAN-EBBB/.
Improved Contact Graph Routing in Delay Tolerant Networks with Capacity and Buffer Constraints
Venue: arXiv cs.NI 2026
Year: 2026

Satellite communications present challenging characteristics. Continuous end-to-end connectivity may not be available due to the large distances between satellites. Moreover, resources such as link capacity and buffer memory may be limited. Routing in satellite networks is therefore both complex and crucial to avoid packet losses and long delays. The Delay Tolerant Network (DTN) paradigm has emerged as an efficient solution for managing these challenging networks. Contact Graph Routing (CGR), a deterministic routing algorithm, is one of the most popular DTN algorithms. CGR is compatible with the store, carry, and forward principle, whereby a node receives a message and stores it in its buffer until a transmission opportunity becomes available. However, CGR relies on simplified models to incorporate potential constraints in the route search. For instance, the linear volume assumption is often used to consider capacity constraints. Moreover, capacity management and buffer management are mostly performed during the forwarding phase, once an issue has occurred. These reactive management techniques cause many collisions and increase the average delivery time. In this paper, we propose taking measures before or during the route search in order to find routes that respect both contact-capacity and node-buffer limits. We introduce the contact splitting and edge pruning operations to effectively account for the routing constraints. This ensures that CGR outputs the optimal solution in terms of delivery time among the subset of valid solutions. The problem is formalized as the Feasible Earliest-Arrival Path with Capacity and Buffer constraints (FEAP-CB) and optimality is proved. The proposed approach can also be used to book resources to be used in case of issues during the forwarding phase.