OpenRaiser/Intern-Atlas huggingface dataset:
The Responsibility Vacuum: Organizational Failure in Scaled Agent Systems
Venue: arXiv cs.AI 2026
Year: 2026

Modern CI/CD pipelines integrating agent-generated code exhibit a structural failure in responsibility attribution. Decisions are executed through formally correct approval processes, yet no entity possesses both the authority to approve those decisions and the epistemic capacity to meaningfully understand their basis. We define this condition as responsibility vacuum: a state in which decisions occur, but responsibility cannot be attributed because authority and verification capacity do not coincide. We show that this is not a process deviation or technical defect, but a structural property of deployments where decision generation throughput exceeds bounded human verification capacity. We identify a scaling limit under standard deployment assumptions, including parallel agent generation, CI-based validation, and individualized human approval gates. Beyond a throughput threshold, verification ceases to function as a decision criterion and is replaced by ritualized approval based on proxy signals. Personalized responsibility becomes structurally unattainable in this regime. We further characterize a CI amplification dynamic, whereby increasing automated validation coverage raises proxy signal density without restoring human capacity. Under fixed time and attention constraints, this accelerates cognitive offloading in the broad sense and widens the gap between formal approval and epistemic understanding. Additional automation therefore amplifies, rather than mitigates, the responsibility vacuum. We conclude that unless organizations explicitly redesign decision boundaries or reassign responsibility away from individual decisions toward batch- or system-level ownership, responsibility vacuum remains an invisible but persistent failure mode in scaled agent deployments.
Multi-Agent Constraint Factorization Reveals Latent Invariant Solution Structure
Venue: arXiv cs.CL 2026
Year: 2026

Multi-agent systems (MAS) composed of large language models often exhibit improved problem-solving performance despite operating on identical information. In this work, we provide a formal explanation for this phenomenon grounded in operator theory and constrained optimization. We model each agent as enforcing a distinct family of validity constraints on a shared solution state, and show that a MAS implements a factorized composition of constraint-enforcement operators. Under mild conditions, these dynamics converge to invariant solution sets defined by the intersection of agent constraint sets. Such invariant structures are generally not dynamically accessible to a single agent applying all constraints simultaneously, even when expressive capacity and information are identical. We extend this result from exact constraint enforcement to soft constraints via proximal operators, and apply the formalism to contemporary text-based dialog systems.
Physics-Informed Implicit Neural Representation for Wireless Imaging in RIS-Aided ISAC System
Venue: arXiv cs.IT 2026
Year: 2026

Wireless imaging has become a vital function in future integrated sensing and communication (ISAC) systems. However, traditional model-based and data-driven deep learning imaging methods face challenges related to multipath extraction, dataset acquisition, and multi-scenario adaptation. To overcome these limitations, this study innovatively combines implicit neural representation (INR) with explicit physical models to realize wireless imaging in reconfigurable intelligent surface (RIS)-aided ISAC systems. INR employs neural networks (NNs) to project physical locations to voxel values, which is indirectly supervised by measurements of channel state information with physics-informed loss functions. The continuous shape and scattering characteristics of targets are embedded into NN parameters through training, enabling arbitrary image resolutions and off-grid voxel value prediction. Additionally, three issues related to INR-based imager are further addressed. First, INR is generalized to enable efficient imaging under multipath interference by jointly learning image and multipath information. Second, the imaging speed and accuracy for dynamic targets are enhanced by embedding prior image information. Third, imaging results are employed to assist in RIS phase design for improved communication performance. Extensive simulations demonstrate that the proposed INR-based imager significantly outperforms traditional model-based methods with super-resolution abilities, and the focal length characteristics of the imaging system is revealed. Moreover, communication performance can benefit from the imaging results. Part of the source code for this paper can be accessed at https://github.com/kiwi1944/INRImager
From Who They Are to How They Act: Behavioral Traits in Generative Agent-Based Models of Social Media
Venue: arXiv cs.MA 2026
Year: 2026

Generative Agent-Based Modeling (GABM) leverages Large Language Models to create autonomous agents that simulate human behavior in social media environments, demonstrating potential for modeling information propagation, influence processes, and network phenomena. While existing frameworks characterize agents through demographic attributes, personality traits, and interests, they lack mechanisms to encode behavioral dispositions toward platform actions, causing agents to exhibit homogeneous engagement patterns rather than the differentiated participation styles observed on real platforms. In this paper, we investigate the role of behavioral traits as an explicit characterization layer to regulate agents' propensities across posting, re-sharing, commenting, reacting, and inactivity. Through large-scale simulations involving 980 agents and validation against real-world social media data, we demonstrate that behavioral traits are essential to sustain heterogeneous, profile-consistent participation patterns and enable realistic content propagation dynamics through the interplay of amplification- and interaction-oriented profiles. Our findings establish that modeling how agents act-not only who they are-is necessary for advancing GABM as a tool for studying social media phenomena.
Graph Recognition via Subgraph Prediction
Venue: arXiv cs.CV 2026
Year: 2026

Despite tremendous improvements in tasks such as image classification, object detection, and segmentation, the recognition of visual relationships, commonly modeled as the extraction of a graph from an image, remains a challenging task. We believe that this mainly stems from the fact that there is no canonical way to approach the visual graph recognition task. Most existing solutions are specific to a problem and cannot be transferred between different contexts out-of-the box, even though the conceptual problem remains the same. With broad applicability and simplicity in mind, in this paper we develop a method, \textbf{Gra}ph Recognition via \textbf{S}ubgraph \textbf{P}rediction (\textbf{GraSP}), for recognizing graphs in images. We show across several synthetic benchmarks and one real-world application that our method works with a set of diverse types of graphs and their drawings, and can be transferred between tasks without task-specific modifications, paving the way to a more unified framework for visual graph recognition.
Pipeline Automation Framework for Reusable High-throughput Network Applications on FPGA
Venue: arXiv cs.AR 2026
Year: 2026

In a context of ever-growing worldwide communication traffic, cloud service providers aim at deploying scalable infrastructures to address heterogeneous needs. Part of the network infrastructure, FPGAs are tailored to guarantee low-latency and high-throughput packet processing. However, slowness of the hardware design process impairs FPGA ability to be part of an agile infrastructure under constant evolution, from incident response to long-term transformation. Deploying and maintaining network functionalities across a wide variety of FPGAs raises the need to fine-tune hardware designs for several FPGA targets. To address this issue, we introduce PAF, an open-source architectural parameterization framework based on a pipeline-oriented design methodology. PAF (Pipeline Automation Framework) implementation is based on Chisel, a Scala-embedded Hardware Construction Language (HCL), that we leverage to interface with circuit elaboration. Applied to industrial network packet classification systems, PAF demonstrates efficient parameterization abilities, enabling to reuse and optimize the same pipelined design on several FPGAs. In addition, PAF focuses the pipeline description on the architectural intent, incidentally reducing the number of lines of code to express complex functionalities. Finally, PAF confirms that automation does not imply any loss of tight control on the architecture by achieving on par performance and resource usage with equivalent exhaustively described implementations.
Complexity analysis and practical resolution of the data classification problem with private characteristics
Venue: arXiv cs.CC 2026
Year: 2026

In this work we analyze the problem of, given the probability distribution of a population, questioning an unknown individual that is representative of the distribution so that our uncertainty about certain characteristics is significantly reduced -but the uncertainty about others, deemed private or sensitive, is not. Thus, the goal of the problem is extracting information being relevant to a legitimate purpose while preserving the privacy of individuals, which is crucial to enable non-intrusive selection processes in several areas. For instance, it is essential in the design of non-discriminatory personnel selection, promotion, and layoff processes in companies and institutions; in the retrieval of customer information being relevant to the service provided by a company (and no more); in certifications not revealing sensitive industrial information being irrelevant for the certification itself; etc. Interactive questioning processes are constructed for this purpose, which requires generalizing the notion of decision trees to account the amount of desired and undesired information retrieved for each branch of the plan. Our findings about this problem are both theoretical and practical: on the one hand, we prove its NP-completeness by a reduction from the Set Cover problem; and on the other hand, given this intractability, we provide heuristic solutions to find reasonable solutions in affordable time. In particular, a greedy algorithm and two genetic algorithms are presented. Our experiments indicate that the best results are obtained using a genetic algorithm reinforced with a greedy strategy.
A Computer Vision Hybrid Approach: CNN and Transformer Models for Accurate Alzheimer's Detection from Brain MRI Scans
Venue: arXiv cs.CV 2026
Year: 2026

Early and accurate classification of Alzheimers disease (AD) from brain MRI scans is essential for timely clinical intervention and improved patient outcomes. This study presents a comprehensive comparative analysis of five CNN architectures (EfficientNetB0, ResNet50, DenseNet201, MobileNetV3, VGG16), five Transformer-based models (ViT, ConvTransformer, PatchTransformer, MLP-Mixer, SimpleTransformer), and a proposed hybrid model named Evan_V2. All models were evaluated on a four-class AD classification task comprising Mild Dementia, Moderate Dementia, Non-Demented, and Very Mild Dementia categories. Experimental findings show that CNN architectures consistently achieved strong performance, with ResNet50 attaining 98.83% accuracy. Transformer models demonstrated competitive generalization capabilities, with ViT achieving the highest accuracy among them at 95.38%. However, individual Transformer variants exhibited greater class-specific instability. The proposed Evan_V2 hybrid model, which integrates outputs from ten CNN and Transformer architectures through feature-level fusion, achieved the best overall performance with 99.99% accuracy, 0.9989 F1-score, and 0.9968 ROC AUC. Confusion matrix analysis further confirmed that Evan_V2 substantially reduced misclassification across all dementia stages, outperforming every standalone model. These findings highlight the potential of hybrid ensemble strategies in producing highly reliable and clinically meaningful diagnostic tools for Alzheimers disease classification.
A Complete Propositional Dynamic Logic for Regular Expressions with Lookahead
Venue: arXiv cs.LO 2026
Year: 2026

We consider (logical) reasoning for regular expressions with lookahead (REwLA). In this paper, we give an axiomatic characterization for both the (match-)language equivalence and the largest substitution-closed equivalence that is sound for the (match-)language equivalence. To achieve this, we introduce a variant of propositional dynamic logic (PDL) on finite linear orders, extended with two operators: the restriction to the identity relation and the restriction to its complement. Our main contribution is a sound and complete Hilbert-style finite axiomatization for the logic, which captures the equivalences of REwLA. Using the extended operators, the completeness is established via a reduction into an identity-free variant of PDL on finite strict linear orders. Moreover, the extended PDL has the same computational complexity as REwLA.
PROGRESSLM: Towards Progress Reasoning in Vision-Language Models
Venue: arXiv cs.CV 2026
Year: 2026

Estimating task progress requires reasoning over long-horizon dynamics rather than recognizing static visual content. While modern Vision-Language Models (VLMs) excel at describing what is visible, it remains unclear whether they can infer how far a task has progressed from partial observations. To this end, we introduce Progress-Bench, a benchmark for systematically evaluating progress reasoning in VLMs. Beyond benchmarking, we further explore a human-inspired two-stage progress reasoning paradigm through both training-free prompting and training-based approach based on curated dataset ProgressLM-45K. Experiments on 14 VLMs show that most models are not yet ready for task progress estimation, exhibiting sensitivity to demonstration modality and viewpoint changes, as well as poor handling of unanswerable cases. While training-free prompting that enforces structured progress reasoning yields limited and model-dependent gains, the training-based ProgressLM-3B achieves consistent improvements even at a small model scale, despite being trained on a task set fully disjoint from the evaluation tasks. Further analyses reveal characteristic error patterns and clarify when and why progress reasoning succeeds or fails.
WeDefense: A Toolkit to Defend Against Fake Audio
Venue: arXiv cs.SD 2026
Year: 2026

The advances in generative AI have enabled the creation of synthetic audio which is perceptually indistinguishable from real, genuine audio. Although this stellar progress enables many positive applications, it also raises risks of misuse, such as for impersonation, disinformation and fraud. Despite a growing number of open-source fake audio detection codes released through numerous challenges and initiatives, most are tailored to specific competitions, datasets or models. A standardized and unified toolkit that supports the fair benchmarking and comparison of competing solutions with not just common databases, protocols, metrics, but also a shared codebase, is missing. To address this, we propose WeDefense, the first open-source toolkit to support both fake audio detection and localization. Beyond model training, WeDefense emphasizes critical yet often overlooked components: flexible input and augmentation, calibration, score fusion, standardized evaluation metrics, and analysis tools for deeper understanding and interpretation. The toolkit is publicly available at https://github.com/zlin0/wedefense with interactive demos for fake audio detection and localization.
The Effect of Scripts and Formats on LLM Numeracy
Venue: arXiv cs.CL 2026
Year: 2026

Large language models (LLMs) have achieved impressive proficiency in basic arithmetic, rivaling human-level performance on standard numerical tasks. However, little attention has been given to how these models perform when numerical expressions deviate from the prevailing conventions present in their training corpora. In this work, we investigate numerical reasoning across a wide range of numeral scripts and formats. We show that LLM accuracy drops substantially when numerical inputs are rendered in underrepresented scripts or formats, despite the underlying mathematical reasoning being identical. We further demonstrate that targeted prompting strategies, such as few-shot prompting and explicit numeral mapping, can greatly narrow this gap. Our findings highlight an overlooked challenge in multilingual numerical reasoning and provide actionable insights for working with LLMs to reliably interpret, manipulate, and generate numbers across diverse numeral scripts and formatting styles.
Towards Understanding Best Practices for Quantization of Vision-Language Models
Venue: arXiv cs.CV 2026
Year: 2026

Large language models (LLMs) deliver impressive results for a variety of tasks, but state-of-the-art systems require fast GPUs with large amounts of memory. To reduce both the memory and latency of these systems, practitioners quantize their learned parameters, typically at half precision. A growing body of research focuses on preserving the model performance with more aggressive bit widths, and some work has been done to apply these strategies to other models, like vision transformers. In our study we investigate how a variety of quantization methods, including state-of-the-art GPTQ and AWQ, can be applied effectively to multimodal pipelines comprised of vision models, language models, and their connectors. We address how performance on captioning, retrieval, and question answering can be affected by bit width, quantization method, and which portion of the pipeline the quantization is used for. Results reveal that ViT and LLM exhibit comparable importance in model performance, despite significant differences in parameter size, and that lower-bit quantization of the LLM achieves high accuracy at reduced bits per weight (bpw). These findings provide practical insights for efficient deployment of MLLMs and highlight the value of exploration for understanding component sensitivities in multimodal models. Our code is available at https://github.com/gautomdas/mmq.
Can We Trust LLM Detectors?
Venue: arXiv cs.CL 2026
Year: 2026

The rapid adoption of LLMs has increased the need for reliable AI text detection, yet existing detectors often fail outside controlled benchmarks. We systematically evaluate 2 dominant paradigms (training-free and supervised) and show that both are brittle under distribution shift, unseen generators, and simple stylistic perturbations. To address these limitations, we propose a supervised contrastive learning (SCL) framework that learns discriminative style embeddings. Experiments show that while supervised detectors excel in-domain, they degrade sharply out-of-domain, and training-free methods remain highly sensitive to proxy choice. Overall, our results expose fundamental challenges in building domain-agnostic detectors. Our code is available at: https://github.com/HARSHITJAIS14/DetectAI
Gated Sparse Attention: Combining Computational Efficiency with Training Stability for Long-Context Language Models
Venue: arXiv cs.AI 2026
Year: 2026

The computational burden of attention in long-context language models has motivated two largely independent lines of work: sparse attention mechanisms that reduce complexity by attending to selected tokens, and gated attention variants that improve training sta-bility while mitigating the attention sink phenomenon. We observe that these approaches address complementary weaknesses and propose Gated Sparse Attention (GSA), an architecture that realizes the benefits of both. GSA incorporates a gated lightning indexer with sigmoid activations that produce bounded, interpretable selection scores, an adaptive sparsity controller that modulates the number of attended tokens based on local uncertainty, and dual gating at the value and output stages. We establish theoretical foundations for the approach, including complexity analysis, expressiveness results, and convergence guarantees. In experiments with 1.7B parameter models trained on 400B tokens, GSA matches the efficiency of sparse-only baselines (12-16x speedup at 128K context) while achieving the quality gains associated with gated attention: perplexity improves from 6.03 to 5.70, RULER scores at 128K context nearly double, and attention to the first token, a proxy for attention sinks, drops from 47% to under 4%. Training stability improves markedly, with loss spikes reduced by 98%.
DeepSurvey-Bench: Evaluating Academic Value of Automatically Generated Scientific Survey
Venue: arXiv cs.AI 2026
Year: 2026

The rapid development of automated scientific survey generation technology has made it increasingly important to establish a comprehensive benchmark to evaluate the quality of generated surveys.Nearly all existing evaluation benchmarks rely on flawed selection criteria such as citation counts and structural coherence to select human-written surveys as the ground truth survey datasets, and then use surface-level metrics such as structural quality and reference relevance to evaluate generated surveys.However, these benchmarks have two key issues: (1) the ground truth survey datasets are unreliable because of a lack academic dimension annotations; (2) the evaluation metrics only focus on the surface quality of the survey such as logical coherence. Both issues lead to existing benchmarks cannot assess to evaluate their deep "academic value", such as the core research objectives and the critical analysis of different studies. To address the above problems, we propose DeepSurvey-Bench, a novel benchmark designed to comprehensively evaluate the academic value of generated surveys. Specifically, our benchmark propose a comprehensive academic value evaluation criteria covering three dimensions: informational value, scholarly communication value, and research guidance value. Based on this criteria, we construct a reliable dataset with academic value annotations, and evaluate the deep academic value of the generated surveys. Extensive experimental results demonstrate that our benchmark is highly consistent with human performance in assessing the academic value of generated surveys.
From Quotes to Concepts: Axial Coding of Political Debates with Ensemble LMs
Venue: arXiv cs.CL 2026
Year: 2026

Axial coding is a commonly used qualitative analysis method that enhances document understanding by organizing sentence-level open codes into broader categories. In this paper, we operationalize axial coding with large language models (LLMs). Extending an ensemble-based open coding approach with an LLM moderator, we add an axial coding step that groups open codes into higher-order categories, transforming raw debate transcripts into concise, hierarchical representations. We compare two strategies: (i) clustering embeddings of code-utterance pairs using density-based and partitioning algorithms followed by LLM labeling, and (ii) direct LLM-based grouping of codes and utterances into categories. We apply our method to Dutch parliamentary debates, converting lengthy transcripts into compact, hierarchically structured codes and categories. We evaluate our method using extrinsic metrics aligned with human-assigned topic labels (ROUGE-L, cosine, BERTScore), and intrinsic metrics describing code groups (coverage, brevity, coherence, novelty, JSD divergence). Our results reveal a trade-off: density-based clustering achieves high coverage and strong cluster alignment, while direct LLM grouping results in higher fine-grained alignment, but lower coverage 20%. Overall, clustering maximizes coverage and structural separation, whereas LLM grouping produces more concise, interpretable, and semantically aligned categories. To support future research, we publicly release the full dataset of utterances and codes, enabling reproducibility and comparative studies.