OpenRaiser/Intern-Atlas huggingface dataset:
LANGSAE EDITING: Improving Multilingual Information Retrieval via Post-hoc Language Identity Removal
Venue: arXiv cs.CL 2026
Year: 2026

Dense retrieval in multilingual settings often searches over mixed-language collections, yet multilingual embeddings encode language identity alongside semantics. This language signal can inflate similarity for same-language pairs and crowd out relevant evidence written in other languages. We propose LANGSAE EDITING, a post-hoc sparse autoencoder trained on pooled embeddings that enables controllable removal of language-identity signal directly in vector space. The method identifies language-associated latent units using cross-language activation statistics, suppresses these units at inference time, and reconstructs embeddings in the original dimensionality, making it compatible with existing vector databases without retraining the base encoder or re-encoding raw text. Experiments across multiple languages show consistent improvements in ranking quality and cross-language coverage, with especially strong gains for script-distinct languages.
Defocus Aberration Theory Confirms Gaussian Model in Most Imaging Devices
Venue: arXiv cs.CV 2026
Year: 2026

Over the past three decades, defocus has consistently provided groundbreaking depth information in scene images. However, accurately estimating depth from 2D images continues to be a persistent and fundamental challenge in the field of 3D recovery. Heuristic approaches involve with the ill-posed problem for inferring the spatial variant defocusing blur, as the desired blur cannot be distinguished from the inherent blur. Given a prior knowledge of the defocus model, the problem become well-posed with an analytic solution for the relative blur between two images, taken at the same viewpoint with different camera settings for the focus. The Gaussian model stands out as an optimal choice for real-time applications, due to its mathematical simplicity and computational efficiency. And theoretically, it is the only model can be applied at the same time to both the absolute blur caused by depth in a single image and the relative blur resulting from depth differences between two images. This paper introduces the settings, for conventional imaging devices, to ensure that the defocusing operator adheres to the Gaussian model. Defocus analysis begins within the framework of geometric optics and is conducted by defocus aberration theory in diffraction-limited optics to obtain the accuracy of fitting the actual model to its Gaussian approximation. The results for a typical set of focused depths between $1$ and $100$ meters, with a maximum depth variation of $10\%$ at the focused depth, confirm the Gaussian model's applicability for defocus operators in most imaging devices. The findings demonstrate a maximum Mean Absolute Error $(\!M\!A\!E)$ of less than $1\%$, underscoring the model's accuracy and reliability.
AgentOCR: Reimagining Agent History via Optical Self-Compression
Venue: arXiv cs.LG 2026
Year: 2026

Recent advances in large language models (LLMs) enable agentic systems trained with reinforcement learning (RL) over multi-turn interaction trajectories, but practical deployment is bottlenecked by rapidly growing textual histories that inflate token budgets and memory usage. We introduce AgentOCR, a framework that exploits the superior information density of visual tokens by representing the accumulated observation-action history as a compact rendered image. To make multi-turn rollouts scalable, AgentOCR proposes segment optical caching. By decomposing history into hashable segments and maintaining a visual cache, this mechanism eliminates redundant re-rendering. Beyond fixed rendering, AgentOCR introduces agentic self-compression, where the agent actively emits a compression rate and is trained with compression-aware reward to adaptively balance task success and token efficiency. We conduct extensive experiments on challenging agentic benchmarks, ALFWorld and search-based QA. Remarkably, results demonstrate that AgentOCR preserves over 95\% of text-based agent performance while substantially reducing token consumption (>50\%), yielding consistent token and memory efficiency. Our further analysis validates a 20x rendering speedup from segment optical caching and the effective strategic balancing of self-compression.
Defense Against Indirect Prompt Injection via Tool Result Parsing
Venue: arXiv cs.AI 2026
Year: 2026

As LLM agents transition from digital assistants to physical controllers in autonomous systems and robotics, they face an escalating threat from indirect prompt injection. By embedding adversarial instructions into the results of tool calls, attackers can hijack the agent's decision-making process to execute unauthorized actions. This vulnerability poses a significant risk as agents gain more direct control over physical environments. Existing defense mechanisms against Indirect Prompt Injection (IPI) generally fall into two categories. The first involves training dedicated detection models; however, this approach entails high computational overhead for both training and inference, and requires frequent updates to keep pace with evolving attack vectors. Alternatively, prompt-based methods leverage the inherent capabilities of LLMs to detect or ignore malicious instructions via prompt engineering. Despite their flexibility, most current prompt-based defenses suffer from high Attack Success Rates (ASR), demonstrating limited robustness against sophisticated injection attacks. In this paper, we propose a novel method that provides LLMs with precise data via tool result parsing while effectively filtering out injected malicious code. Our approach achieves competitive Utility under Attack (UA) while maintaining the lowest Attack Success Rate (ASR) to date, significantly outperforming existing methods. Code is available at GitHub.
Integrated Framework for Selecting and Enhancing Ancient Marathi Inscription Images from Stone, Metal Plate, and Paper Documents
Venue: arXiv cs.CV 2026
Year: 2026

Ancient script images often suffer from severe background noise, low contrast, and degradation caused by aging and environmental effects. In many cases, the foreground text and background exhibit similar visual characteristics, making the inscriptions difficult to read. The primary objective of image enhancement is to improve the readability of such degraded ancient images. This paper presents an image enhancement approach based on binarization and complementary preprocessing techniques for removing stains and enhancing unclear ancient text. The proposed methods are evaluated on different types of ancient scripts, including inscriptions on stone, metal plates, and historical documents. Experimental results show that the proposed approach achieves classification accuracies of 55.7%, 62%, and 65.6% for stone, metal plate, and document scripts, respectively, using the K-Nearest Neighbor (K-NN) classifier. Using the Support Vector Machine (SVM) classifier, accuracies of 53.2%, 59.5%, and 67.8% are obtained. The results demonstrate the effectiveness of the proposed enhancement method in improving the readability of ancient Marathi inscription images.
MPM-LLM4DSE: Reaching the Pareto Frontier in HLS with Multimodal Learning and LLM-Driven Exploration
Venue: arXiv cs.AR 2026
Year: 2026

High-Level Synthesis (HLS) design space exploration (DSE) seeks Pareto-optimal designs within expansive pragma configuration spaces. To accelerate HLS DSE, graph neural networks (GNNs) are commonly employed as surrogates for HLS tools to predict quality of results (QoR) metrics, while multi-objective optimization algorithms expedite the exploration. However, GNN-based prediction methods may not fully capture the rich semantic features inherent in behavioral descriptions, and conventional multi-objective optimization algorithms often do not explicitly account for the domain-specific knowledge regarding how pragma directives influence QoR. To address these limitations, this paper proposes the MPM-LLM4DSE framework, which incorporates a multimodal prediction model (MPM) that simultaneously fuses features from behavioral descriptions and control and data flow graphs. Furthermore, the framework employs a large language model (LLM) as an optimizer, accompanied by a tailored prompt engineering methodology. This methodology incorporates pragma impact analysis on QoR to guide the LLM in generating high-quality configurations (LLM4DSE). Experimental results demonstrate that our multimodal predictive model significantly outperforms state-of-the-art work ProgSG by up to 10.25$\times$. Furthermore, in DSE tasks, the proposed LLM4DSE achieves an average performance gain of 39.90\% over prior methods, validating the effectiveness of our prompting methodology. Code and models are available at https://github.com/wslcccc/MPM-LLM4DSE.
Proof of Commitment: A Human-Centric Resource for Permissionless Consensus
Venue: arXiv cs.DC 2026
Year: 2026

Permissionless consensus protocols require a scarce resource to regulate leader election and provide Sybil resistance. Existing paradigms such as Proof of Work and Proof of Stake instantiate this scarcity through parallelizable resources like computation or capital. Once acquired, these resources can be subdivided across many identities at negligible marginal cost, making linear Sybil cost fundamentally unattainable. We introduce Proof of Commitment (PoCmt), a consensus primitive grounded in a non-parallelizable resource: real-time human engagement. Validators maintain a commitment state capturing cumulative human effort, protocol participation, and online availability. Engagement is enforced through a Human Challenge Oracle that issues identity-bound, time-sensitive challenges, limiting the number of challenges solvable within each human window. Under this model, sustaining multiple active identities requires proportional human-time effort. We establish a cost-theoretic separation showing that protocols based on parallelizable resources admit zero marginal Sybil cost, whereas PoCmt enforces a strictly linear cost profile. Using a weighted-backbone analysis, we show that PoCmt achieves safety, liveness, and commitment-proportional fairness under partial synchrony. Simulations complement the analysis by isolating human-time capacity as the sole adversarial bottleneck and validating the predicted commitment drift and fairness properties. These results position PoCmt as a new point in the consensus design space, grounding permissionless security in sustained human effort rather than computation or capital.
Privacy-Utility Trade-offs Under Multi-Level Point-Wise Leakage Constraints
Venue: arXiv cs.IT 2026
Year: 2026

An information-theoretic privacy mechanism design is studied, where an agent observes useful data $Y$ which is correlated with the private data $X$. The agent wants to reveal the information to a user, hence, the agent utilizes a privacy mechanism to produce disclosed data $U$ that can be revealed. We assume that the agent has no direct access to $X$, i.e., the private data is hidden. We study privacy mechanism design that maximizes the disclosed information about $Y$, measured by the mutual information between $Y$ and $U$, while satisfying a point-wise constraint with different privacy leakage budgets. We introduce a new measure, called the \emph{multi-level point-wise leakage}, which allows us to impose different leakage levels for different realizations of $U$. In contrast to previous studies on point-wise measures, which use the same leakage level for each realization, we consider a more general scenario in which each data point can leak information up to a different threshold. As a result, this concept also covers cases in which some data points should not leak any information about the private data, i.e., they must satisfy perfect privacy. In other words, a combination of perfect privacy and non-zero leakage can be considered. When the leakage is sufficiently small, concepts from information geometry allow us to locally approximate the mutual information. We show that when the leakage matrix $P_{X|Y}$ is invertible, utilizing this approximation leads to a quadratic optimization problem that has closed-form solution under some constraints. In particular, we show that it is sufficient to consider only binary $U$ to attain the optimal utility. This leads to simple privacy designs with low complexity which are based on finding the maximum singular value and singular vector of a matrix.
AECV-Bench: Benchmarking Multimodal Models on Architectural and Engineering Drawings Understanding
Venue: arXiv cs.AI 2026
Year: 2026

AEC drawings encode geometry and semantics through symbols, layout conventions, and dense annotation, yet it remains unclear whether modern multimodal and vision-language models can reliably interpret this graphical language. We present AECV-Bench, a benchmark for evaluating multimodal and vision-language models on realistic AEC artefacts via two complementary use cases: (i) object counting on 120 high-quality floor plans (doors, windows, bedrooms, toilets), and (ii) drawing-grounded document QA spanning 192 question-answer pairs that test text extraction (OCR), instance counting, spatial reasoning, and comparative reasoning over common drawing regions. Object-counting performance is reported using per-field exact-match accuracy and MAPE results, while document-QA performance is reported using overall accuracy and per-category breakdowns with an LLM-as-a-judge scoring pipeline and targeted human adjudication for edge cases. Evaluating a broad set of state-of-the-art models under a unified protocol, we observe a stable capability gradient; OCR and text-centric document QA are strongest (up to 0.95 accuracy), spatial reasoning is moderate, and symbol-centric drawing understanding - especially reliable counting of doors and windows - remains unsolved (often 0.40-0.55 accuracy) with substantial proportional errors. These results suggest that current systems function well as document assistants but lack robust drawing literacy, motivating domain-specific representations and tool-augmented, human-in-the-loop workflows for an efficient AEC automation.
Character Detection using YOLO for Writer Identification in multiple Medieval books
Venue: arXiv cs.CV 2026
Year: 2026

Paleography is the study of ancient and historical handwriting, its key objectives include the dating of manuscripts and understanding the evolution of writing. Estimating when a document was written and tracing the development of scripts and writing styles can be aided by identifying the individual scribes who contributed to a medieval manuscript. Although digital technologies have made significant progress in this field, the general problem remains unsolved and continues to pose open challenges. ... We previously proposed an approach focused on identifying specific letters or abbreviations that characterize each writer. In that study, we considered the letter "a", as it was widely present on all pages of text and highly distinctive, according to the suggestions of expert paleographers. We used template matching techniques to detect the occurrences of the character "a" on each page and the convolutional neural network (CNN) to attribute each instance to the correct scribe. Moving from the interesting results achieved from this previous system and being aware of the limitations of the template matching technique, which requires an appropriate threshold to work, we decided to experiment in the same framework with the use of the YOLO object detection model to identify the scribe who contributed to the writing of different medieval books. We considered the fifth version of YOLO to implement the YOLO object detection model, which completely substituted the template matching and CNN used in the previous work. The experimental results demonstrate that YOLO effectively extracts a greater number of letters considered, leading to a more accurate second-stage classification. Furthermore, the YOLO confidence score provides a foundation for developing a system that applies a rejection threshold, enabling reliable writer identification even in unseen manuscripts.
A Mathematical Theory of Payment Channel Networks
Venue: arXiv cs.NI 2026
Year: 2026

We introduce a geometric theory of payment channel networks that centers the polytope $W_G$ of feasible wealth distributions; liquidity states $L_G$ project onto $W_G$ via strict circulations. A payment is feasible iff the post-transfer wealth stays in $W_G$. This yields a simple throughput law: if $ζ$ is on-chain settlement bandwidth and $ρ$ the expected fraction of infeasible payments, the sustainable off-chain bandwidth satisfies $S = ζ/ ρ$. Feasibility admits a cut-interval view: for any node set S, the wealth of S must lie in an interval whose width equals the cut capacity $C(δ(S))$. Using this, we show how multi-party channels (coinpools / channel factories) expand $W_G$. Modeling a k-party channel as a k-uniform hyperedge widens every cut in expectation, so $W_G$ grows monotonically with k; for single nodes the expected accessible wealth scales linearly with $k/n$. We also analyze depletion. Under linear, asymmetric fees, cost-minimizing flow within a wealth fiber pushes cycles to the boundary, generically depleting channels except for a residual spanning forest. Three mitigation levers follow: (i) symmetric fees per direction, (ii) convex/tiered fees (effective flow control but at odds with source routing without liquidity disclosure), and (iii) coordinated replenishment (choose an optimal circulation within a fiber). Together, these results explain why two-party meshes struggle to scale and why multi-party primitives are more capital-efficient, yielding higher expected payment bandwidth. They also show how fee design and coordination keep operation inside the feasible region, improving reliability.
A Longitudinal Analysis of Gamification in Untappd: Ethical Reflections on a Social Drinking Application
Venue: arXiv cs.SE 2026
Year: 2026

This paper presents a longitudinal ethical analysis of Untappd, a social drinking application that gamifies beer consumption through badges, streaks, and social sharing. Building on an exploratory study conducted in 2020, we revisit the platform in 2025 to examine how its gamification features and ethical framings have evolved. Drawing on traditional ethical theory and practical frameworks for Software Engineering, we analyze five categories of badges and their implications for user autonomy and well-being. Our findings show that, despite small adjustments and superficial disclaimers, many of the original ethical issues remain. We argue for continuous ethical reflection built embedded into software lifecycles to prevent the normalization of risky behaviors through design.
Token Maturation: Autoregressive Language Generation via Continuous Token Dynamics
Venue: arXiv cs.CL 2026
Year: 2026

Standard autoregressive language models collapse uncertainty at every generation step by committing to discrete tokens through immediate sampling. This premature discretization underlies well-known failure modes, including degenerate repetition loops in greedy decoding and a heavy reliance on heuristic sampling strategies. We introduce \textbf{Token Maturation}, a continuous autoregressive framework in which tokens evolve as vector-valued trajectories prior to discretization. Rather than sampling from a categorical distribution at each step, the model resolves uncertainty through a deterministic dynamical process in embedding space, deferring discrete commitment until the representation has geometrically stabilized. We show that this formulation mitigates degeneration \emph{intrinsically}: Token Maturation generates coherent and diverse text under fully deterministic decoding (argmax), without repetition penalties, temperature scaling, or stochastic sampling. Moreover, we identify a novel convergence behavior in which token representations stabilize spatially while predictive entropy remains high, challenging the common assumption that commitment requires probability concentration. We propose continuous token dynamics with delayed commitment as an alternative formulation of autoregressive generation that exposes structural regularities obscured by immediate discretization.
Responsibility Measures for Conjunctive Queries with Negation
Venue: arXiv cs.DB 2026
Year: 2026

We contribute to the recent line of work on responsibility measures that quantify the contributions of database facts to obtaining a query result. In contrast to existing work which has almost exclusively focused on monotone queries, here we explore how to define responsibility measures for unions of conjunctive queries with negated atoms (UCQ${}^\lnot$). Starting from the question of what constitutes a reasonable notion of explanation or relevance for queries with negated atoms, we propose two approaches, one assigning scores to (positive) database facts and the other also considering negated facts. Our approaches, which are orthogonal to the previously studied score of Reshef et al., can be used to lift previously studied scores for monotone queries, known as drastic Shapley and weighted sums of minimal supports (WSMS), to UCQ$^\lnot$. We investigate the data and combined complexity of the resulting measures, notably showing that the WSMS measures are tractable in data complexity for all UCQ${}^\lnot$ queries and further establishing tractability in combined complexity for suitable classes of conjunctive queries with negation.
5G NR Non-Terrestrial Networks: From Early Results to the Road Ahead
Venue: arXiv cs.NI 2026
Year: 2026

This paper overviews the 3GPP 5G NR-NTN standard, detailing the evolution from Rel. 18 to 19 and innovations for Rel. 20. Using realistic ns-3 simulations validated against 3GPP calibration data, we evaluate various satellite network configurations. The results highlight the potential of NTNs to extend wireless connectivity to remote areas, serve requests during emergency, and alleviate terrestrial network congestion.
Faithful Summarisation under Disagreement via Belief-Level Aggregation
Venue: arXiv cs.CL 2026
Year: 2026

Opinion and multi-document summarisation often involve genuinely conflicting viewpoints, yet many existing approaches, particularly LLM-based systems, implicitly smooth disagreement and over-represent majority opinions. This limits the faithfulness of generated summaries in opinion-heavy settings. We introduce a disagreement-aware synthesis pipeline that separates belief-level aggregation from language generation. Documents are first represented as structured belief sets and aggregated using distance-based belief merging operators that explicitly model conflict. Large language models are then used only to realise the aggregated beliefs as natural language summaries. We evaluate the approach across multiple model families and scales, comparing it to methods that perform explicit aggregation during generation. Our results show that while sufficiently large models can match belief-level aggregation when aggregation is handled at generation time, this behaviour is not stable across architectures or capacities. In contrast, belief-level aggregation combined with simple prompting yields consistently strong disagreement-aware performance across models, while maintaining fluent and grounded summaries.