Artificial Intelligence · Cross-listed from cs.CL
BaFCo: A Document Understanding Benchmark for Complex Bangla Form Comprehension
Abu Tyeb Azad, Ishita Sur Apan, Fahim Ahmed et al. (+8 more)
Document comprehension is a challenging yet impactful task for
Multimodal Large
Language Models, especially as these systems see growing adoption in real-world, human-centric applications. However, this adoption is limited for low-resource
languages such as Bangla due to the scarcity of high-quality annotated data. To address this gap, we introduce BaFCo, a
benchmark dataset for Bangla form comprehension with a focus on Document Layout Analysis (DLA) and Key Information Extraction (KIE). BaFCo curates 200 multi-page complex Bangladeshi government forms, sourced from across diverse sectors including agriculture, education, banking, and land management. To accurately capture the structural and contextual complexity of these forms, we define a fine-grained annotation schema comprising 26 types of form entities, along with a separate coarse form entity set consisting of 5 types. We evaluate the latest MLLMs from the ChatGPT, Gemini, Claude, Qwen, and Kimi series using zero-shot and chain-of-thought
prompts under both low and high
reasoning setups. Our results reveal limitations in current MLLMs' ability in comprehending Bangla forms, particularly in accurately localizing highly granular form entities. Our
dataset and code is available at: https://huggingface.co/
datasets/Mausul/bafco
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All authors Abu Tyeb Azad, Ishita Sur Apan, Fahim Ahmed, Sumaiya Karim Katha, Ezharuddin Jubaer, Armun Alam, Pranjal Kumar Nandi, Amin Ahsan Ali, Aman Chadha, Md Mofijul Islam, AKM Mahbubur Rahman
Artificial Intelligence · Cross-listed from stat.ML
To Retain or to Adapt? Generalizing Continual Learning
Giulia Lanzillotta, Mandana Samiei, Doina Precup et al. (+2 more)
The Continual Learning (CL) literature has long been driven by the goal of mitigating catastrophic forgetting. This objective rests on an often unstated assumption: that a lifelong learner should approximate the Joint-Task Learning (JTL) solution and retain all previously acquired knowledge. We challenge this retention-centered premise, arguing that in non-stationary environments prioritizing retention can impede real-time adaptation. Shifting the focus to the Average Lifelong Error (ALE), we formalize CL as an online optimization problem governed by the interaction between environmental and learning dynamics. We introduce Transfer Efficiency as a quantitative measure of the tension between Instability, the bias inherited from conflicting past experience, and Transient Error, the optimization cost of learning new tasks from scratch. Under mild convergence conditions, holding across linear and neural network models, this decomposition yields a Critical Task Duration: a closed-form threshold beyond which historical knowledge transitions from a warm-start advantage to an optimization liability whenever retention induces a positive stationary bias. We validate these theoretical predictions on continual image classification and reinforcement learning
benchmarks. Finally, by connecting continual learning to the online learning framework of predictable sequences, we show that JTL is only one instance of a broader family of objectives, and we propose a new general class of continual learning
algorithms, which we call Predictive Continual Learning. Predictive CL
algorithms optimize expected future performance under an explicit, dynamically updated model of future tasks. As a proof of concept, we analyze a Window
algorithm that interpolates between JTL and Independent-Task Learning (ITL), outperforming both under controlled distributional drift.
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All authors Giulia Lanzillotta, Mandana Samiei, Doina Precup, Razvan Pascanu, Claire Vernade
Artificial Intelligence · Cross-listed from cs.CV
Hierarchical Classification via Cascading Feature Elimination: Application to Human Phenotype Ontology-Aligned Facial Phenotyping (FaceMesh2HPO)
Fabio Hellmann, Alexander Hustinx, Benjamin D. Solomon et al. (+4 more)
FaceMesh2HPO is a framework for classifying facial phenotypic descriptors aligned with the Human Phenotype Ontology (HPO) to support clinical diagnosis. Using annotations from 124 clinicians across 10 disorders (107 HPO terms) combined with non-syndromic controls, we generated 3D facial meshes (478 landmarks) from 2D images and trained a hierarchical PointNet-based pipeline with cascading classification and feature elimination. The best models, incorporating 3D meshes, facial outline, and demographic metadata, achieved AUROCs between ~0.55 and ~0.89, with higher performance at parent nodes than leaf terms. External validation showed variable generalizability across disorders. Results demonstrate that hierarchical modeling of 3D facial geometry enables interpretable, ontology-linked phenotype classification, though performance on rare leaf terms remains limited. Improved data diversity and feature selection strategies are needed to enhance robustness and clinical utility.
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All authors Fabio Hellmann, Alexander Hustinx, Benjamin D. Solomon, GestaltMatcher Database Consortium, Tzung-Chien Hsieh, Peter Krawitz, Elisabeth André
Artificial Intelligence · Cross-listed from cs.CL
ResonatorLM: Causal Resonant Field Mixing for Efficient Long-Context Language Modeling
Archie Chaudhury
Contemporary
language models are dominated by the
transformer architecture, which leverages self-
attention mechanisms to enable more efficient, parallelized training across a wide set of documents and corpora. This has allowed
transformers to effectively model data across a wide range of modalities and contexts. However,
transformers, along with their conventional counterparts such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), often struggle to maintain efficiency when processing long contexts. We introduce ResonatorLM, a new mechanism that replaces
attention with a physics-derived alternative. ResonatorLM treats
token sequences as a single, driven one-dimensional latent field and replaces
attention dot products with causal functions of damped resonators. We implement ResonatorLM on a traditional network architecture and test it on standard long-context modeling tasks. We find that in a small, 6M matched setting, training and prefill speedups increase with sequence length, decode speed reaches 6.47x compared to that of a standard, optimized
transformer at 32K
tokens, and accuracy reaches 61.31 percent (compared to 55.32 percent) on WikiText.
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Artificial Intelligence
Narrative World Model: Narratology-Grounded Writer Memory for Long-Form Fiction
Mohammad Saifullah, Thomas Kornmaier, Taaha Kazi et al. (+3 more)
Long-form fiction writers need
memory that answers multi-hop questions about evolving story state: who knows a secret and when they learned it, whether an event preceded the narration that revealed it, whether a setup paid off, and how a relationship shifted. General-purpose
retrieval and
agent-
memory systems represent entities and facts but not the narratological structure these questions turn on, so they surface the wrong evidence or none at all. We introduce the Narrative World Model (NWM), a writer-
memory system that pairs a narratology-grounded typed temporal-state graph with query-conditioned hybrid
retrieval. To measure
memory rather than the answerer, we read every system through a single held-constant Opus 4.8 reader over only that system's chapter-safe evidence, on a reproducible public corpus and a validated multi-hop
benchmark, and we compare against the strongest existing temporal-knowledge-graph
agent-
memory framework, Graphiti/Zep (Rasmussen et al., 2025). NWM substantially and significantly outperforms this baseline on multi-hop narratological QA across both corpora, and far exceeds GraphRAG and flat
retrieval. The advantage is representational rather than an artifact of extraction: it survives rebuilding the baseline with NWM's own extractor, and traces to its narratology-grounded structure and query-conditioned
retrieval, not to graph size or extractor quality.
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All authors Mohammad Saifullah, Thomas Kornmaier, Taaha Kazi, Vasu Sharma, Aditya Sanjiv Kanade, Aanand Kumar Yadav
Artificial Intelligence · Cross-listed from cs.CY
Whose fairness? Structural concentration in AI bias research
Abhash Shrestha, Subigya Gautam, Anu Sapkota et al. (+2 more)
Artificial intelligence increasingly mediates consequential decisions in healthcare, law, and public services, and the field has responded with an extensive methodology for measuring and mitigating bias. Yet the
fairness definitions,
benchmarks, and debiasing frameworks on which this methodology rests are treated as universal while being produced by a research community whose composition has never been characterized. We show that the AI bias research are structurally concentrated, and that this concentration is greatest, geographically, in precisely the domain the rest of the field inherits from. Analyzing 692 publications spanning five thematic domains, combining bibliometric analysis with semantic clustering, we find that research activity is dominated by a small set of countries, institutions, and authors, with the United States leading publication output and collaboration networks across every domain and most strongly in general
fairness and bias mitigation, the largest, most-cited domain with meaningful representation across all four semantic clusters. Low- and middle-income countries remain largely absent from the community and its collaboration networks, and citation influence is highly skewed (median = 9; mean =93.5 ), indicating that a small fraction of publications disproportionately shapes the field. Because the general-
fairness domain supplies the definitions and
benchmarks that application areas apply, concentration of research effort in this foundational domain propagates across AI bias research as a whole - raising the concern that mitigation methods developed and validated within a narrow set of contexts may not generalize to all populations and settings where AI is deployed. We provide an interactive atlas for continuous monitoring of the field's structure.
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All authors Abhash Shrestha, Subigya Gautam, Anu Sapkota, Sanju Tiwari, Tek Raj Chhetri
Artificial Intelligence
Foundation Models for Automatic CAD Generation
J de Curtò, Victoria Guillén and I. de Zarzà
Recent advances in Large
Language Models (LLMs) and
Vision-
Language Models (VLMs) enable the automatic generation of parametric 3D designs from natural-
language specifications. This chapter presents an empirical study of foundation models for automatic Computer-Aided Design (CAD) generation of mechanical parts, using a unified evaluation pipeline and a curated
benchmark of 97 engineering design problems. We introduce LLMForge, a multi-model text-to-CAD framework integrating JSON-schema validation, analytic feature scoring, mesh synthesis, and multi-round iterative refinement, studied under two critique regimes. IterTracer uses a Phong-shaded ray-trace renderer with analytic visual metrics (silhouette IoU, hole visibility, edge clearance, aspect-ratio conformance) for lightweight geometry-aware feedback across rounds. IterVision replaces the analytic scorer with a VLM semantic critic (Qwen2.5-VL-72B) that evaluates rendered views via chain-of-thought visual
reasoning, assessing spatial coherence and design intent. On a
benchmark spanning four canonical geometry families (plates with holes and bolt circles, multi-feature boxes, flanged cylinders, and L-brackets), we evaluate seven foundation models: DeepSeek-V3.2, Qwen3-235B-A22B, Llama-3.3-70B, Gemma-3-27B, GLM-4.5, MiniMax-M2.1, and INTELLECT. Under IterTracer, the four highest-ranked models form a tight cluster (overall mean in [0.885, 0.890]) with 98.97% mesh success, showing that compact instruction-tuned models can match substantially larger systems. VLM-based critique in IterVision yields 100% watertight mesh generation on the leading model while surfacing systematic difficulty on rotationally symmetric geometries such as cylinders, where visual and semantic scoring diverge most. We discuss
benchmark design, failure modes, CAD-oriented prompting, and implications for industrial workflows and scalable automated mechanical design.
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Artificial Intelligence
CSTutorBench: Benchmarking Small Language Models as Tutors for Block-Based Programming
H. Chad Lane and Bryson Kageler
Large
language models are increasingly explored as AI tutors, yet deploying them in K-12 settings raises concerns around
privacy, cost, and reliance on proprietary models. Small
language models (SLMs) offer a promising alternative, but selecting the right model for a specific educational context remains difficult, particularly when the target domain, such as block-based programming, is largely absent from model training data. We introduce CSTutorBench, a
benchmark for evaluating
language models as CS tutors in VEX VR, a block-based robotics environment. The
benchmark comprises 17 scenario-based questions scored against a pedagogical rubric grounded in established tutoring and feedback research, with a human-in-the-loop LLM-as-judge pipeline for evaluation. Preliminary findings across 11 models (4B-120B parameters) reveal that models perform well on surface-level criteria such as vocabulary and tone but struggle with deeper pedagogical behaviors, particularly avoiding answer leakage and engaging with student debugging histories. In our sample, model family and instruction-tuning approach appear to be better predictors of tutoring quality than parameter count alone, though the small number of models limits the strength of this conclusion. A targeted
prompt revision grounded in recent educational
prompt engineering research improved scores for 10 of 11 models. These results underscore the value of context-specific, pedagogically grounded
benchmarks for SLM selection in educational deployment.
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Artificial Intelligence · Cross-listed from cs.CV
Prompting Image Generators for Training-free Primitive Shape Abstraction
Gregor Kobsik, Tim Elsner and Leif Kobbelt
Compact primitive abstractions represent 3D shapes with a few
geometric primitives while preserving recognizable components. Learned methods depend on their training classes, and optimization-based methods split shapes geometrically rather than into parts. We instead reuse the visual part knowledge of pretrained models without task-specific training or fine-tuning. A
vision-
language model names parts in multi-view renders, and an unmodified image generator paints color-coded part masks. Reprojection and spatial clustering recover 3D instances, and a classical optimizer fits one tapered and bent superquadric per part. With five to eight primitives per object, the abstractions match the Chamfer distance of the strongest learned baseline on HumanPrim, improve on it by 10% on Toys4K, and have the lowest overlap among compact methods, while chair legs, backrest bars and wheels remain separate primitives. Our accuracy also transfers better than theirs to objects outside the learned methods' ShapeNet training classes. Replacing the generated masks with part labels from the 3D
segmentation methods P3-SAM or PartField lowers IoU by 7 to 17 points under the same fitter. Further studies relate the remaining volumetric error to part granularity and to parts that the rendered views observe from one side only.
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Artificial Intelligence
From Graphs to Gradients: Physics-Inspired Structural Attribution for Cyber-Physical IoT Systems and Beyond
Spyridon Evangelatos, Christos Diou, Georgios Th. Papadopoulos et al. (+2 more)
Interpretable explanation methods in Artificial Intelligence aim to uncover the underlying causes and their effects, enabling a deeper understanding of why a system behaves in a certain way under different inputs. Unlike traditional explainability methods, which mainly highlight correlations between input and output variables, causal explanation focuses on interventional questions. By doing so, it provides more robust insights, helping users understand automated decisions, especially in high-risk domains. Recovering an explicit directed causal structure, however, is often impractical in large-scale, hybrid cyber-physical systems with feedback loops and partial observability. This paper introduces a novel framework inspired by statistical mechanics that instead models variable dependencies through an undirected, energy-based representation of cyber-physical IoT systems. Our approach enables rigorous dependency-aware attribution by analysing how variations in the energy landscape reflect the influence of individual components, without recovering a directed causal graph. It also supports
reasoning about perturbation effects across hybrid interactions, providing reliable explanations of abnormal behaviours. We empirically examined our framework through simulations on an industrial IoT testbed with hybrid continuous and discrete variables, demonstrating higher attribution accuracy, improved robustness and better scalability than state-of-the-art graph-based approaches. While the attributions are not intended to fully recover the system's generative dynamics, they provide valuable, dependency-aware explanations supporting both human interpretation and downstream predictive and diagnostic tasks. Although demonstrated in industrial IoT security, our framework also applies to other high-dimensional cyber-physical and socio-technical systems requiring principled, structural explanations.
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All authors Spyridon Evangelatos, Christos Diou, Georgios Th. Papadopoulos, Evangelos Markakis, Panagiotis Sarigiannidis
Artificial Intelligence · Cross-listed from cs.CL
Prompt Robustness Is Task-Dependent: Comparing Objective and Belief-Style Questions in LLM Evaluation
Sadia Kamal, Arefa Patwary, Anthony Marchiafava et al. (+2 more)
Survey-style evaluations of large
language models often treat a prompted response as a measure of a model's values or beliefs. This assumption is particularly fragile when responses are read as evidence of political values, social attitudes, or beliefs. We ask whether
prompt robustness differs between objective questions with fixed answers and subjective questions that ask for opinions or values. We evaluate four instruction-tuned model families on three objective
datasets (MMLU, ARC, and CulturalBench) and three subjective
datasets (Political Compass Test, ValueBench, and World Values Survey). For each question/statement, we apply multiple types of
prompt changes, such as variations in wording, framing, and format, and measure whether the model gives the same answer across variants. Using a binomial generalized estimating equation, we find significant effects of model,
dataset,
prompt category, and their interactions. The
dataset type effect is also significant, and the interaction between
dataset type and
prompt category is large. These results show that
prompt robustness depends on the question type, the
prompt change, and the model.
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All authors Sadia Kamal, Arefa Patwary, Anthony Marchiafava, Sagnik Ray Choudhury, Atriya Sen
Artificial Intelligence · Cross-listed from cs.CL
Decomposing Yes-No Bias in LLM Moral Judgment: Stance, Position, Word, and Verdict
Haonan Huang
Large
language models (LLMs) increasingly issue binary verdicts - safety gates, judge decisions, value probes - and the answer is read as the model's judgment. A settled judgment should be coherent across paired phrasings of the same question: yes to "do you approve?" should mirror no to "do you oppose?". LLMs often violate this coherence, and in a yes/no question the verdict, the answer word, and the printed position coincide, so a conventional flipped-question audit yields one contrast that cannot say what moved. We introduce a crossed psychometric battery that extends the flip to full 2x2 and 2x2x2 crossings of question polarity, printed order, and answer label, separating stance from position, word, label, and verdict-role effects; an independent graded instrument estimates each model's stance $\theta$ with no binary format; and a two-parameter fit (framing susceptibility $m$, decisiveness $s$) separates robustness from saturation that hides susceptibility. Applying the battery to twenty moral dilemmas - 1.35 million trials over ten frontier models from the Claude, GPT, and Gemini families in 23
reasoning configurations - we find response-form bias widespread but never uniform. The largest apparent yes-no biases are almost purely surface (word and position), while several of the newest models show the reverse: near-zero flip contrasts concealing verdict attachment up to +0.42, a component no surface counterbalance can expose, replicated across label alphabets. Requested
reasoning reshapes these channels rather than removing them. When subjects state decisions in free text and a separate model transcribes them, two judge families agree on 99.3% of jointly scored transcriptions with zero opposing committed labels, and the transcribed stance converges with the graded ruler (median $r=0.87$): response form is part of the instrument, and the judgment can be measured apart from it.
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Artificial Intelligence · Cross-listed from cs.CL
Most LLM Conformity Needs No Speaker: Measuring the Speaker-Free Floor in Peer-Pressure Benchmarks
Yibo Hu and Jiaming Qu
LLM conformity is often used to describe cases where a model changes a correct answer toward a peer or group response. We show that most of this apparent conformity survives even after the peer is removed. The reason is a confound: standard conformity
prompts mix two cues at once, the presence of a speaker and the repeated wrong answer itself. Existing
benchmarks vary these cues together, so they cannot tell how much of the revision actually depends on the speaker. We introduce a no-source condition: the same asserted answer with the explicit speaker removed. Across six open-weight LLMs and seven QA and
reasoning datasets, this condition alone causes harmful revision in $66.5\%$ of initially correct cases, compared with $10.3\%$ under a plain re-ask. The effect also remains when the repeated answer is paraphrased and when answer options are hidden in an open-ended setting. Source framing mainly modulates this floor: expert-panel framing raises it, while minimal person labels do not reliably raise it. When models flip, they are usually confidently wrong, and simple recalibration does not recover the original answer. Source attribution still matters, but it should be measured as an increment above this speaker-free floor. The methodological lesson is that conformity
benchmarks should first measure what remains after the speaker is removed; without this step,
benchmarks may mistake repeated text for social influence.
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Artificial Intelligence · Cross-listed from cs.LG
Self-Review Reinforcement Learning (SRRL) with Cross-Episode Memory and Policy Distillation
Muhammad Zain Amin and Kibele Sebnem Yildirim
Reinforcement Learning is commonly used to train large
language models using environmental feedback. In applied settings, the environment usually provides sparse or delayed feedback. This makes it difficult for the model to pinpoint which actions in its
reasoning led to success or failure. So, learning effectively from these signals is hard because the model must determine how each failure should inform meaningful behavioral corrections in subsequent iterations. We introduce a training framework, Self-Review Reinforcement Learning, that embeds an explicit self-review step into each RL episode. When a first-pass response fails, the model generates a self-review to identify what went wrong, which conditions an improved second attempt. Unlike inference-time reflection approaches, such as Reflexion, the framework optimizes self-review with policy gradients and internalizes improvements into the base policy via selective
distillation, ensuring they persist across future episodes. A cross-episode
memory keeps successful self-reviews for reuse when encountering similar tasks in future episodes during training. We evaluate SRRL against a standard RLVR baseline using the GRPO optimizer across two
language models, Qwen 3-4B and OLMo-3- 7B, on GSM8K
benchmark. SRRL consistently outperforms the RLVR in final reward performance and achieves greater learning efficiency by successfully transforming feedback into behavioral improvement.
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Artificial Intelligence · Cross-listed from cs.CV
Rendering-Aware Bayesian 3D Gaussian Splatting with Native Uncertainty and Adaptive Complexity Control
Gaoxiang Jia, Vikram Appia, Junzhou Huang et al. (+1 more)
3D Gaussian splatting (3DGS) is a strong representation for real-time novel-view synthesis, but its standard training pipeline relies on point estimates and hand-tuned heuristics, providing no native uncertainty or principled complexity control. This is most limiting under sparse views or fixed acquisition budgets, where a model must identify weakly supported geometry and select informative views. We introduce a rendering-aware
Bayesian 3DGS framework that tracks Gaussian geometry with a Normal-Inverse-Wishart posterior over means and covariances using renderer-derived surrogate summaries. An optional Dirichlet-process extension adds a probabilistic component-usage signal, and the training schedule makes the closed-form versus approximate inference boundary explicit. Re-rendering posterior geometry samples yields native predictive uncertainty for interval calibration and active view selection. In a fixed-budget 16-to-32 active-view task, native NIW acquisition improves PSNR by +0.453 dB and LPIPS by -0.0146 over a scoring-only 3-member standard-ensemble baseline, winning 29/39 scene-seed pairs and 10/13 scene means; it also improves over PPU-style (+0.355 dB) and NIW-proxy (+0.401 dB) acquisition. NIW native intervals reduce 95% coverage error by about 17x relative to a shared proxy (0.046 vs. 0.796) and are about 10x closer to nominal coverage than a 3-member deep ensemble (0.047 vs. 0.454) at roughly one-third the training cost. As a reconstruction compatibility check, paired NIW-vs-standard analysis over 39 scene-seed runs yields +0.030 dB PSNR with 1.6% additional training time. These results position
Bayesian 3DGS as a practical probabilistic scene representation for decision-facing tasks such as active view selection.
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All authors Gaoxiang Jia, Vikram Appia, Junzhou Huang, Xinlei Wang
Artificial Intelligence · Cross-listed from cs.CR
aiAuthZ: Off-Host, Identity-Bound Authorization for AI Agents
Sai Varun Kodathala
AI
agents issue tool calls on the basis of text they cannot verify, so any party who controls part of the context can forge the appearance of authority. I evaluate 15 contemporary
language models against eight attack scenarios derived from a published corpus of real
agent incidents and find that refusal varies from 100% down to 38% across fully evaluated models; the most expensive model refused only half of the attacks despite a twentyfold price spread. I present aiAuthZ, an authorization gateway that moves the safety decision off the
agent's host. Before a tool call executes, the gateway verifies caller identity with a per-message HMAC-SHA256 signature bound to a single-use nonce and a timestamp window, and it evaluates a role-based and argument-level policy that the
agent can neither read nor modify. Every decision joins a SHA-256 hash-chained audit log, and each accepted message yields an HMAC-authenticated QR receipt that achieves 94% mean verification across eight transmission channels, with zero forgeries accepted in 25 wrong-key trials. With the gateway in place, residual attack success falls to 0% for all 15 models at no more than 0.03 ms of added decision latency. On the AgentDojo banking suite, aiAuthZ blocks all seven attacker-directed tool calls the evaluated
agents emit, at the cost of one legitimate first-time payment, while a spotlighting baseline allows two injections to succeed. Across nine in-scope case studies from the same incident corpus, aiAuthZ blocks nine of nine, against four of nine for a policy baseline without identity binding. The gateway does not prevent a model from being deceived; it prevents a deceived model from acting beyond the verified user's authority on every call routed through it. The implementation and all experiments are released at https://github.com/Sports-
Vision-Inc/aiAuthZ.
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Artificial Intelligence · Cross-listed from cs.CV
Paul K. Mandal, Pavan Reddy and Tristan Malatynski
Model-specific
adversarial attacks have been extensively studied. We study a different failure mode: naturally occurring statistical signals in
vision data that can behave as
backdoor-like triggers without being maliciously inserted. We call these signals statistical adversaries. We analyse ImageNet to find patterns that are strongly linked to certain labels. We then use statistical controls to remove random correlations from our candidate signals. Finally, we demonstrate that these signals directly and predictably alter model predictions. These statistical adversaries are more targeted than generic corruptions and transfer across different model architectures. This suggests that some vulnerabilities are driven by
dataset structure and distribution rather than a single model's idiosyncrasies. We conclude that ordinary
datasets can contain exploitable
adversarial surfaces even in the absence of poisoning, and suggest that
dataset audits should treat spurious structure not only as a source of bias or interpretability failure, but also as a latent attack surface for
vision models.
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Artificial Intelligence · Cross-listed from quant-ph
Lean-Quantum: Toward AI-Assisted Formalization of Quantum Information
Kazumi Kasaura, Kei Tsukamoto, Kento Mori et al. (+6 more)
Quantum information theory is built on entropic quantities; among them, the sandwiched Rényi relative
entropy is a fundamental divergence with various applications, and its data processing inequality (DPI) under
quantum channels is a cornerstone result. In this work, we present a Lean 4 library for
quantum information, designed as a reusable formal infrastructure for theoretical analysis. As a central demonstration of the library, we formalize the DPI for the sandwiched Rényi relative
entropy for positive semidefinite operators on finite-dimensional
quantum systems. The library provides a basis-independent operator-theoretic framework for finite-dimensional
quantum mechanics compatible with the standard mathematical library Mathlib, including reusable interfaces for finite-dimensional systems, states, channels, tensor products, partial traces, Choi operators, Kraus representations, and Stinespring representations. It also builds infrastructure for noncommutative trace inequalities, including operator monotonicity and convexity via the real continuous functional calculus, block-operator positivity,
Hilbert-Schmidt operator spaces, Jensen's operator inequality, generalized perspectives, operator power means, and Lieb-Ando trace inequalities. On top of this framework, we formalize
entropy-specific ingredients for the DPI: variational formulas for the sandwiched quasi-
entropy via Young and reverse-Young inequalities, tensor-product compatibility of real powers, and Haar measures on
unitary groups. Together, these components yield a Lean formalization of the DPI, give strong subadditivity as a corollary, and provide the last missing component needed to complete the Lean formalization of the generalized
quantum Stein's lemma. More broadly, the development provides machine-checkable foundations for future formalized and AI-assisted research in
quantum information theory.
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All authors Kazumi Kasaura, Kei Tsukamoto, Kento Mori, Risa Mizuno, Takahiro Namatame, Yuta Oriike, Masaya Taniguchi, Sho Sonoda, Hayata Yamasaki
Artificial Intelligence · Cross-listed from cs.LG
PatchOptic for Shared-State LLM Workflows with Projected Views and Verified Structured Updates
Zhaoyu Bai and Jiaqi Cai
Agentic workflows often operate over shared, structured state. Because LLM context windows are limited, each model invocation is typically shown only the state fragment needed for the current workflow step, a pattern commonly known as progressive disclosure. Modern systems construct such model-facing views using grep-like keyword search,
retrieval-augmented generation (RAG), abstract-syntax-tree (AST) queries, and task-specific
agent skills. These methods make the read side manageable, but they do not define when a locally proposed rewrite is valid after it is applied back to the full state. The missing piece is a contract between local updates and global validity. We introduce PatchOptic, an optic-inspired interface for shared-state LLM workflows. Optics are compositional bidirectional accessors that describe how views of structured data are read and updated. PatchOptic borrows this view/update intuition and realizes it through projected reads and verified structured patches. Each workflow step declares a projected read view, an authorized write region, and a patch-source region. Beyond runtime enforcement, the same declaration yields a path-level footprint that supports delegation, sub-workflow composition, and static certificates for reordering independent steps within the same phase. We evaluate this design with PatchBench, a
benchmark with 46 cases across domains. The results show that projected reads reduce reported leakage and
token cost while preserving accepted-output quality under the strong actor. Runtime verification blocks declared workflow-contract violations before commit, and patch-read enforcement rejects compromised patch artifacts that use hidden sources.
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Artificial Intelligence · Cross-listed from cs.CR
Full-range Binary Classifier Calibration for Stable Model Updates in Production
Konstantin Berlin
Detection models running in
adversarial environments face a malicious distribution that drifts rapidly while the benign distribution stays comparatively stable, so teams retrain and redeploy constantly to stay ahead of new threats. Retraining tends to change the output prediction scores, which breaks downstream users of the model. For these security-oriented models we need consistent false-positive rate (FPR) across all output values, whereas standard probability-calibration methods target class probability rather than an FPR contract. We introduce a method built on top of existing calibration primitives that targets the whole FPR curve, giving scores a consistent FPR meaning across deployments. On one held-out split, the observed relative FPR error was at most 2.3% from 10% down to 0.1% FPR and 7.2% at 0.01% FPR. The shipped artifact remains under 200 KB in measurements across calibration sets from 1K to 10M benign samples.
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Artificial Intelligence · Cross-listed from cs.CR
Privilege and confidentiality in generative AI workflows
Václav Janeček and Thomas Melham
Generative AI (GenAI) systems store and process client data in three distinct ways: in the model's parameters through training and memorisation, in the context window during a live session, and in knowledge databases for
retrieval-augmented generation (RAG). Each mode creates different and often counter-intuitive risks to confidentiality and legal professional privilege, and each calls for specific governance responses. Drawing on the first English and American decisions to address privilege and generative AI, UK and Munir v Secretary of State for the Home Department and United States v Heppner, on the orthodox privilege authorities against which those decisions must be read, and on recent computer science research, we explain the three modes of data storage and processing in terms accessible to practitioners and analyse the legal consequences of each. We then situate the analysis within the regulatory framework governing solicitors in England and Wales and within the ordinary principles of professional negligence, arguing that the standard of effective information governance (and with it the
benchmark against which negligence and misconduct will be measured) is changing. Although we write primarily for SRA-regulated practitioners, our data-governance analysis is framed to extend to any jurisdiction in which the protection of privilege or professional secrecy depends on demonstrable confidentiality. The ultimate aim of this article is to help legal services professionals understand salient data leakage risks in GenAI systems and thereby facilitate a more responsible deployment of GenAI on client data and other sensitive material.
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Artificial Intelligence · Cross-listed from cs.MA
Decision Protocols in Multi-Agent Large Language Model Conversations
Lars Benedikt Kaesberg
Improving the task performance of Large
Language Models (LLMs) is essential, yet scaling these models faces significant challenges such as diminishing returns and high costs. Multi-
Agent Systems (MAS) offer a promising solution by distributing tasks among specialized
agents to improve the overall task performance. This can reduce training costs at the expense of increased test time due to the discussion and decision-making process. The decision protocol is a critical component of MAS because it specifies how multiple
agents collaborate to create a final solution. This thesis introduces the Multi-
Agent LLM (MALLM) framework, which implements and evaluates various decision protocols, namely voting, consensus, and judge decision mechanisms, to simulate multi-
agent discussions for conversational task solving. Unlike previous work that used a single decision protocol or tested them on limited
datasets, this study systematically examines their impact on a diverse set of tasks, ranging from knowledge-based
datasets (MMLU, MMLU-Pro, GPQA) and logic-based
datasets (StrategyQA, MuSR, Math-lvl-5, SQuAD 2.0). The results indicate that consensus protocols excel in knowledge-intensive domains while voting and judge protocols are more effective for logic-based tasks. Increasing response diversity through independent solution generation improves decision quality, while changes in information access during the decision process have minimal impact.
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Artificial Intelligence · Cross-listed from cs.AR
Is Your NPU Ready for LLMs? Dissecting the Hidden Efficiency Bottlenecks in Mobile LLM Inference
Guanyu Cai, Ruiming Tian, Lang Yang et al. (+4 more)
Deploying Large
Language Models (LLMs) on mobile devices enhances
privacy and reduces latency, but is severely constrained by hardware inefficiencies. We present the first comprehensive, cross-layer measurement study of mobile LLM inference, spanning five mainstream frameworks (e.g., llama.cpp and GENIE) and three hardware backends (CPU, GPU, and NPU). To enable this analysis, we develop PowerBench, a fine-grained profiling tool that measures LLM throughput and provides the first backend-specific energy attribution for mobile LLM inference, going beyond traditional device-level measurements. We further conduct hardware microbenchmarks to characterize the
memory-system performance and compute capabilities of mobile CPUs, GPUs, and NPUs. Our study yields three critical insights: (1) Framework-induced performance gaps are substantially amplified on NPUs, reaching up to $10\times$ due to differences in offloading strategies, hardware-underutilizing operators, and activation quantization strategies. (2) We identify a distinct “phase split,” in which NPUs excel at compute-bound prefilling, while CPUs outperform all other backends in
memory-bound decoding. This split is driven by NPUs' preference for large, fixed-shape workloads, which conflicts with the small-kernel, dynamic nature of decoding. (3) Backend-specific profiling uncovers substantial scheduling headroom missed by prior work. Suboptimal thread configurations, uncoordinated NPU sleep latencies, and CPU polling intervals result in unnecessary energy consumption. For example, tuning CPU frequency alone reduces energy consumption by up to $39.7\%$. Leveraging these findings, we estimate that an energy-oriented configuration can reduce energy consumption by up to $53.2\%$ on the NPU backend across three
datasets, highlighting the substantial potential for further efficiency improvements in mobile LLM deployment.
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All authors Guanyu Cai, Ruiming Tian, Lang Yang, Zhouhong Ren, Jinliang Yuan, Lingkun Li, Jiliang Wang
Artificial Intelligence · Cross-listed from cs.CV
Binocular Gaze Estimation with Single Camera and Single Light Source
Tongbing Huang, Yang Fu, Yunfei Wang et al. (+1 more)
According to commonly consented theories, the minimum hardware requirement for gaze tracker is one camera and two light sources to realize gaze estimation with free head movements. However, in some scenarios such as eye tracking on mobile devices, it is preferable to use less components, especially light sources. We propose a gaze estimation method with one camera and one light source. A "virtual light source" is introduced, which is geometrically placed symmetrically to the real light source with respect to the camera, and generates a "virtual glint" in the acquired image. We estimate the "virtual glint" by exploiting the relationship between the distance between two pupils and two glints in the captured image, and estimate the gaze with
polynomial regression assuming two light sources are available. A new normalization factor for regression method is verified, which turns out to be practical for one-glint system. The performance is proved to be acceptable, while degradation is noticed compared to system with two actual light sources.
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All authors Tongbing Huang, Yang Fu, Yunfei Wang, Zhaocan Wang
Artificial Intelligence · Cross-listed from cs.SE
KAT-Coder-V2.5 Technical Report
Bo Huang, Fengxiang Li, Hao Xu et al. (+50 more)
We present KAT-Coder-V2.5, a coding-focused agentic model trained to act autonomously inside real, executable repositories rather than as a single-turn code generator. Its capability is bottlenecked less by model scale than by the scarcity of reproducible environments, verifiable rewards, and high-value trajectories, which we address with an end-to-end agentic post-training framework. AutoBuilder reconstructs
multilingual repositories into sandboxed environments with fail-to-pass and pass-to-pass verification at scale, from which we regenerate self-contained task specifications, recover near-miss trajectories, and distill supervision through process-aware filtering, while KwaiClawEnv synthesizes large-scale tool-use trajectories from executable services and real task seeds. We further scale reinforcement learning with harness randomization, a reliability-hardened sandbox, an asymmetric actor–critic PPO with hindsight-augmented value estimation, and a harness-oriented reward framework, and unify SWE,
Agent-Claw, and WebCoding experts via Multi-Teacher On-Policy
Distillation. Across six software-engineering and agentic
benchmarks, KAT-Coder-V2.5 delivers the best agentic tool-use result on PinchBench and ranks second only to the frontier Opus 4.8 on repository-level software engineering. Our service is available at https://streamlake.com/product/kat-coder.
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All authors Bo Huang, Fengxiang Li, Hao Xu, Haoyang Huang, Hongyi Fu, Jinhua Hao, Kun Yuan, Minglei Zhang, Pengcheng Xu, Shiyang Liu, Wenhao Zhuang, Yuze Shi, Zongxian Feng, Chao Wang, Cheng He, Chongling Rao, Deyu Cao, Fan Yang, Gang Xiong, Haochen Liu, Jiabao Li, Jian Liang, Jinghui Jia, Jingwen Chang, Jun Du, Junyu Shi, Min Li, Mingqi Wu, Qiang Gao, Shangpeng Yan, Shaotong Qi, Shu Xu, Shuo Zhou, Tiankuo Xu, Tong Zheng, Weilun Zhao, Xiancheng Meng, Xianda Sun, Xiaoyu Jiang, Xunhao Jia, Yao Xia, Yimeng Xu, Yinghan Cui, Yingpeng Chen, Yiwen Ning, Yong Wang, Yuxuan Sun, Zhongsheng Liu, Ming Sun, Cheng Luo, Chen Yang, Han Li, Kun Gai
Artificial Intelligence · Cross-listed from cs.LG
Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy
Jingyun Zhang, Hao Peng, Jianxin Li et al. (+2 more)
Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks. Although Graph Contrastive Learning has demonstrated promising performance, existing methods often suffer from the "structural isolation" issue during mini-batch training, making it challenging to capture cohesive community structures that characterize the global topological distribution. To address these challenges, we propose SCISE, a Scalable unsupervised graph Clustering framework that preserves structural Integrity by synergizing community-aware sampling with constrained Structural
Entropy. Specifically, we first introduce the Structural
Entropy Community Constraint operator (SECC), which optimizes structural information within a constrained solution space to mitigate community fragmentation and enhance partition cohesion. Second, to prevent global information loss during batch training, we design a Community-Aware Sampling Expansion (CSampE) mechanism that incorporates the community context of target nodes into sampling batches, effectively breaking structural barriers and preserving topological integrity. Finally, we devise a Structural Contrastive Learning (StructCL) module that refines edge weights based on intra-batch structural similarity, guiding the encoder to learn representations in a higher-order structural space. Extensive experiments on six mainstream
benchmark datasets demonstrate that SCISE significantly outperforms state-of-the-art
algorithms, with ablation studies and robustness analyses further validating its effectiveness and reliability for real-world large-scale graphs.
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All authors Jingyun Zhang, Hao Peng, Jianxin Li, Angsheng Li, Philip S. Yu
Artificial Intelligence · Cross-listed from cs.RO
Learning 4D Geometric Priors for Inference-Efficient World Action Models
Jianjun Zhang, Jian Zhu, Taiyi Su et al. (+4 more)
World Action Models (WAMs) have shown strong potential for robotic manipulation by jointly modeling visual future dynamics and executable action sequences. However, existing
video-action co-training methods primarily optimize appearance-oriented
video latents, which may insufficiently capture the temporally evolving geometry required for precise manipulation. We propose MECo-WAM, a Multi-Expert Co-Training World Action Model that injects action-relevant 4D
geometric priors into
video-action representations while preserving the original lightweight inference graph. During training, MECo-WAM combines
video and action experts with a lightweight 4D expert supervised by relational targets from a frozen VGGT encoder. Asymmetric expert visibility prevents non-causal shortcuts from auxiliary geometry to action generation. To transfer
geometric knowledge into the deployed
video-action pathway, we introduce decayed 4D read-mask
attention, which provides restricted current-frame
geometric guidance early in training and progressively removes this dependency. We further propose action-aware temporal
geometric distillation, which aligns within-frame
geometric relations and their temporal
evolution while emphasizing visual regions most relevant to
robot actions. At deployment, all auxiliary 4D components are removed. Experiments on LIBERO (98.2%), RoboTwin 2.0 (92.6%), and challenging real-world manipulation tasks show that MECo-WAM improves manipulation performance without increasing inference cost.
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All authors Jianjun Zhang, Jian Zhu, Taiyi Su, Chong Ma, Zitai Huang, Yi Xu, Hanli Wang
Artificial Intelligence · Cross-listed from cs.CV
CanvasAgent: Enabling Complex Image Creation and Editing via Visual Tool Orchestration
Hairui Zhu, Yiying Yang, Tengjin Weng et al. (+5 more)
Complex image creation and editing often require more than a single generation or editing model. A user request may involve synthesizing images, localizing objects, segmenting regions, editing selected content, compositing intermediate assets, reading text, and enhancing the final result. Such tasks shift
multimodal agents from perception-augmented
reasoning to manipulation-centered visual creation, where tools must actively transform visual states rather than merely inspect them. However, existing
multimodal tool-use
agents are mostly optimized for perception, search, or domain-specific editing, and lack large-scale supervision for executable image-creation trajectories. In this paper, we introduce CanvasCraft, a large-scale
multimodal tool-use
dataset for complex image creation and editing, and CanvasAgent, a tool-augmented
multimodal agent that learns to orchestrate heterogeneous visual tools through multi-turn interaction. CanvasCraft contains 140K fully annotated executable trajectories and 10K RL task specifications. CanvasAgent is first trained with SFT to learn executable
reasoning-action trajectories, and is then optimized with GRPO using a hybrid reward that combines outcome- and process-level signals. During rollout, CanvasAgent inspects intermediate results, tracks visual assets, and adapts tool decisions to the evolving visual state. Experiments evaluate both final image quality and trajectory behavior, demonstrating the effectiveness of CanvasAgent and the proposed
dataset for complex multi-tool image creation workflows.
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All authors Hairui Zhu, Yiying Yang, Tengjin Weng, Ziyu Lu, Xiao Yao, Xiaoyang Ye, Lin Ma, Wenhao Jiang
Artificial Intelligence · Cross-listed from cs.LG
Learnable Weighting of Intra-Attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes
Yiqun Zhang and Yiu-ming Cheung
The success of categorical data clustering generally much relies on the distance metric that measures the dissimilarity degree between two objects. However, most of the existing clustering methods treat the two categorical subtypes, i.e. nominal and ordinal attributes, in the same way when calculating the dissimilarity without considering the relative order information of the ordinal values. Moreover, there would exist interdependence among the nominal and ordinal attributes, which is worth exploring for indicating the dissimilarity. This paper will therefore study the intrinsic difference and connection of nominal and ordinal attribute values from a perspective akin to the graph. Accordingly, we propose a novel distance metric to measure the intra-attribute distances of nominal and ordinal attributes in a unified way, meanwhile preserving the order relationship among ordinal values. Subsequently, we propose a new clustering
algorithm to make the learning of intra-attribute distance weights and partitions of data objects into a single learning paradigm rather than two separate steps, whereby circumventing a suboptimal solution. Experiments show the efficacy of the proposed
algorithm in comparison with the existing counterparts.
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Artificial Intelligence
Governable Individuals: An Identity Layer for Embodied Agents That Keep Learning
Xue Qin, Simin Luan, Cong Yang et al. (+1 more)
Embodied artificial intelligence is moving from deployable models to persistent
agents that learn in the field, acquire skills and migrate across bodies. Governing such a system means governing an individual, not a model, and existing proposals (
agent identifiers, activity logs, guardrails) do not survive an
agent that keeps rewriting itself. We propose the governable individual: an
agent whose competence may change without bound, but whose authority,
memory schema, embodiment rights and capability roster can widen only through signed lifecycle transitions that update a public identity commitment. In our tests, neither learned judgement nor behavioural testing was sufficient to carry this on its own; the load-bearing layer must be architectural. We describe the abstraction, a runtime mechanism that realizes it, and the open problems in between.
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All authors Xue Qin, Simin Luan, Cong Yang, Zhijun Li