Artificial Intelligence · Cross-listed from eess.IV
Physics-Informed Implicit Neural Representations for Improved Myocardial Perfusion MRI Quantification
Christos Tsepas, Chang Yan, Maximilian Fuetterer et al. (+2 more)
Quantifying myocardial perfusion from cardiac
magnetic resonance (CMR) can be achieved by fitting tracer-kinetic models to the dynamic contrast-enhanced MR data. However, fitting the observed data with multi-compartment exchange models, which describe the
evolution of the contrast
agent in the tissue, to estimate perfusion parameters is a challenging inverse problem that is sensitive to noise and acquisition variability. Previously, physics-informed neural networks (PINNs) have been proposed as an alternative to conventional non-linear least squares fitting methods with promising results for quantitative perfusion CMR. In this work, we extend the previously proposed PINN framework with spatiotemporal implicit neural representations (INRs) to represent the MR signal as a continuous spatiotemporal function and to improve the accuracy, smoothness, and physical consistency of the PINN model. In realistic simulated CMR
datasets, our proposed PINN with INRs demonstrates improved robustness and parameter estimation accuracy over the previously established methods. The code is available at https://github.com/q-cardIA/pinn-inr.
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All authors Christos Tsepas, Chang Yan, Maximilian Fuetterer, Sebastian Kozerke, Cian M Scannell
Artificial Intelligence · Cross-listed from cs.LG
Federated Learning for Distributed CNC Tool Wear Prediction
Afsana Khan, Morris Stallmann, Marcin Pietrasik et al. (+2 more)
Tool wear prediction is an important task in CNC machining, where accurate monitoring of tool condition supports product quality and process reliability. Machine learning methods have shown potential for this task, but their use in industrial environments is limited by the distributed nature of machining data and by restrictions on data sharing between machines, sites, or organizations.
Federated learning offers a suitable framework for this setting by enabling collaborative model training without transferring raw operational data. However, it is open if
federated learning can lead to accuracy gains in CNC tool wear prediction that justify the increased complexity of such a system. In this experimental study, real tool trajectories are distributed across simulated clients to represent a
federated learning scenario. The
federated models are compared against centralized references and local client baselines. Results show that
federated learning achieves performance close to centralized learning and improves significantly over local client models. These findings indicate that
federated learning can support collaborative tool wear prediction in distributed CNC manufacturing environments and the increased complexity is justified.
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All authors Afsana Khan, Morris Stallmann, Marcin Pietrasik, Charalampos S. Kouzinopoulos, Anna Wilbik
Artificial Intelligence · Cross-listed from eess.IV
Uncertainty-Aware and Explainable Ensemble Deep Learning Framework for Multi-Class Skin Lesion Classification
Rofiqul Islam and Lilatul Ferdouse
Skin cancer diagnosis from dermoscopic images remains challenging due to high intra-class variability, inter-class similarity, class imbalance, and the limited interpretability of deep learning models. This paper proposes an uncertainty-aware and explainable deep learning framework for multi-class skin lesion classification. The framework combines a
vision transformer model (MaxViT-Tiny) with CNN-based models (ConvNeXt-Tiny and EfficientNetV2-B0) through deep ensemble learning. Monte Carlo (MC) Dropout estimates predictive uncertainty and identifies unreliable predictions, while Grad-CAM++, an explainable AI (XAI) technique, provides visual explanations by highlighting lesion regions that influence model decisions. Evaluated on the HAM10000
dataset, the framework achieves 96% accuracy and 99% ROC-AUC under uncertainty-aware filtering (
entropy < 1.0, confidence >= 0.7), with macro-average precision, recall, and F1-score of 94%, 95%, and 95%, respectively, and 96% weighted-average scores across all three metrics. The results demonstrate accurate, interpretable, and uncertainty-aware skin lesion classification for trustworthy computer-aided diagnosis.
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Artificial Intelligence · Cross-listed from stat.ME
Every pooling rule has its world: matching probability combination rules to situations and stakes
Tanel Tammet, Priit Järv and Dirk Draheim
Systems often need to combine two numerical assessments of the same yes/no question. The appropriate formula depends on what the numbers represent and on how the sources are related. Averaging is correct when one of several alternative interpretations applies; multiplying odds is correct when probability reports are based on conditionally independent evidence and a common prior; and probabilities of alternative successful derivations require their dependence or shared evidence to be taken into account. We state the assumptions behind several common combination rules and derive the corresponding combined probabilities. Two groups of Monte Carlo experiments address different questions. First, controlled generating mechanisms verify that the derived rule recovers the correct probability in the situations for which its assumptions hold. Second, the same mechanisms measure the consequences of using a mismatched rule, using logarithmic score and threshold decisions with different costs. Distinct pooling rules can produce the same binary decision at threshold 1/2 while assigning substantially different probabilities, so binary accuracy alone can conceal important differences. We also give probabilistic interpretations of conflicting-evidence rules and show that, for overlapping derivations, retaining the identities of shared uncertain premises permits direct calculation of the probability that at least one derivation is available. Pairwise combination of proof probabilities loses information when there are three or more derivations.
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Artificial Intelligence · Cross-listed from cs.CR
Agent Safety Should Be a Runtime Contract
Albus W. Ng, Yi Han, Jusheng Zhang et al. (+1 more)
The dominant paradigm treats AI safety as a property to be instilled during model training via RLHF, DPO, or Constitutional AI. We argue this is structurally insufficient for autonomous
agents that execute code, mutate files, send messages, and modify databases.
Agent safety should be a runtime contract enforced by the harness, and the contract has two complementary faces. The preventive face blocks dangerous actions before they happen via sandboxes, permission gates, output filters, and trajectory monitors. The evidential face requires verifiable proof that good actions actually happened, gating task submission on hard evidence such as test runs, log captures, file diffs, and citation grounding. We ground the position in four lines of public evidence, with row-level protocols and data released in the supplementary JSON files: a survey of 52 documented AI-
agent and LLM safety incidents, a false-completion audit with 31 non-contested core cases plus one disputed illustrative case, a trajectory-schema audit of 12 public
agent systems and harnesses, and a title-level audit of all 28,560 papers accepted at NeurIPS, ICML, and ICLR 2023-2025 showing a pooled 8-12x imbalance between training-time and deployment-time publication. Two prior communities that needed to enforce safety, computer security and the experimental sciences, converged on runtime contracts with both preventive and evidential elements; agentic AI is now under the same pressure. We formalize an
Agent Trajectory Schema and Evidence Chain, state a compositional gating proposition based on standard monitor composition, and outline a research agenda. The right unit of safety in agentic AI is the trajectory-with-checkable-evidence, not the model.
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All authors Albus W. Ng, Yi Han, Jusheng Zhang, Wenhao Wang
Artificial Intelligence · Cross-listed from cs.RO
Surgical WAM: A World-Action Model for Data-Efficient Surgical Robot Learning
Wenrui Bao, Tianyun Jiang, Zhiben Chen et al. (+3 more)
Learning reliable
surgical manipulation policies is bottlenecked by the scarcity of action-labeled demonstrations: teleoperated
surgical robot (e.g., dVRK) trajectories with synchronized kinematics are costly to collect, while
surgical tasks demand precise contact handling, long-horizon
reasoning, and bimanual coordination. Endoscopic
video is comparatively inexpensive and abundant relative to synchronized
video–kinematics trajectories, and a natural way to exploit it is to learn world models of
surgical scenes. However, existing
surgical world models use
video primarily for simulation or policy evaluation, and rarely translate the learned dynamics into closed-loop control. This gap raises our central question: under a fixed budget of action-labeled demonstrations, does action-free
video pretraining improve closed-loop
surgical manipulation? To answer it, we introduce the
Surgical World-Action Model (
Surgical WAM), a unified generative model built on Cosmos Policy that jointly predicts future endoscopic observations and executable
surgical robot action chunks.
Surgical WAM first learns
surgical visual dynamics from action-free
video and is then fine-tuned on the fixed action-labeled budget; at deployment, it acts as a closed-loop, receding-horizon controller that executes a short prefix of each predicted action chunk and replans from the resulting observation. On a suite of four simulated
surgical manipulation tasks,
video pretraining improves the average success rate from 63.5% to 77.8%, including an absolute gain of 20 percentage points on PegTransfer, with the largest improvements on contact-rich and bimanual tasks. These results demonstrate that action-free
video provides transferable visual dynamics priors for learning
surgical robot control with limited action supervision, positioning data-efficient
video pretraining as a practical path toward scaling up
surgical robot learning.
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All authors Wenrui Bao, Tianyun Jiang, Zhiben Chen, Ser-Nam Lim, Peter D. Peng, Yuzhang Shang
Artificial Intelligence · Cross-listed from cs.CL
ConVAWG: A Retrieval-Grounded Framework for Controlled Synthetic Dialogue Generation in Violence Against Women and Girls
Chen Lyu, Xingwei Tan, Simon Cullen et al. (+4 more)
Synthetic dialogue generation offers a way to study conversational dynamics in sensitive domains where real data are difficult to access, release, or annotate. The underlying abuse may occur online or offline: threats and coercion can appear directly in messages, while behaviours such as surveillance, isolation, stalking, and physical violence may be planned, disclosed, or referred to conversationally.
Privacy and legal constraints make it difficult the release of large-scale real conversation
datasets; existing work has mostly focused on sentence-level toxicity of online abuses, leaving a gap in modelling abuse as a relational and temporally unfolding phenomenon. In this work, we focus on modelling Violence Against Women and Girls (VAWG) scenarios as multi-turn dialogues. We introduce ConVAWG, a
retrieval-grounded framework for generating CPS-aligned synthetic VAWG chat dialogues. ConVAWG builds scenarios from persona seeds, demographic patterns reported by the UK Office for National Statistics, official crime definitions, and retrieved Domestic Homicide Review cases; converts them into hierarchical event timelines; generates multi-scene role-play dialogues; and applies targeted activation-steered toxicity control to appropriate utterances. We release over 6,000 multi-turn dialogue events across 200 scenarios with rich scenario-, event-, and turn-level metadata. Extensive human evaluation, LLM-as-Judge assessment, ablations, and downstream tasks show strong dialogue quality and domain fidelity.
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All authors Chen Lyu, Xingwei Tan, Simon Cullen, Shelley Wilson, Lois Arthurs, Arshad Jhumka, Gabriele Pergola
Artificial Intelligence
Long-Horizon AI Research for Grothendieck Constant: A Case Study in Human-AI Mathematical Collaboration
Alan Li, Rahul Saha, Anton Xue et al. (+4 more)
AI
agents are increasingly used in mathematics research, but it is often unclear how to use them effectively. Towards this, we present an extensive case study of how AI was used to improve bounds on the Grothendieck constant $K_G$, which captures the hardness between combinatorial problems and their continuous relaxations. Specifically, while the precise value of $K_G$ is not known, we recently tightened the best known bounds to \[ \frac{6\pi}{11} \;\le\; K_G \;\le\; \frac{\pi}{2\log(1+\sqrt2)} - 10^{-4}. \] Crucially, these improvements were achieved using an AI research system that could arrive at insights deemed novel by domain experts. We give a detailed discussion of our experience using AI for mathematics research, particularly touching upon its strengths and weaknesses, as well as our experience with creating ideal conditions for AI to arrive at breakthrough insights.
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All authors Alan Li, Rahul Saha, Anton Xue, Swarat Chaudhuri, Adam Klivans, Pravesh K Kothari, Raghu Meka
Artificial Intelligence · Cross-listed from cs.CV
Test-Time Self-Evolving GUI Visual Grounding via Reflection-Guided On-Policy Self-Distillation
Shiyu Xuan and Zechao Li
GUI Visual Grounding is a fundamental capability for GUI
agents. Existing models typically freeze their parameters after deployment, limiting their ability to adapt to unseen interfaces. Although recent methods attempt to adapt models via test-time reinforcement learning, they cannot reflect upon failed exploration. To overcome this, we propose a Test-Time Self-Evolving framework that enables models to improve after deployment without human-annotated ground truth. It constructs a closed-loop of Exploration, Evaluation, Reflection, and Internalization. Specifically, the
agent first explores unseen interfaces by predicting grounding coordinates for given instructions. To evaluate these explorations, we introduce an MLLM-based Reflector to assess the generated results and provide the corresponding
reasoning reflections. To internalize reflection knowledge into the model weights, we propose Reflection-Guided On-Policy Self-
Distillation, which translates high-level
reasoning into dense
token-level supervision via a conditioned self-teacher. Furthermore, we design a Contrastive Calibration method to prevent incorrect auto-regressive prefixes from corrupting the supervisory signals during failed explorations. Extensive experiments across six
benchmarks demonstrate our framework's effectiveness, achieving an average accuracy improvement of 7.4% over the base model. To the best of our knowledge, this is the first work to successfully exploit on-policy self-
distillation for test-time adaptation in GUI visual grounding. By filling the gap in post-deployment adaptation, our framework completes the self-evolving capability of GUI
agents. The code will be released.
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Artificial Intelligence · Cross-listed from cs.CC
How to Verify Probabilistic Consistency of Predictive Models
Orr Paradise, Oliver Richardson, Yoshua Bengio et al. (+1 more)
When a probabilistic predictor answers many conditional-probability queries, are its answers self-consistent, and can this be verified in
polynomial time? This problem is of interest for AI safety, where safety is derived from honesty about probabilistic predictions of unwanted outcomes potentially caused by an AI action. We construct an interactive PCP as follows. Let a predictive model be specified by a probability
circuit P and a
circuit Q which outputs confidence in predictions. Together, P and Q implicitly specify exponentially many probabilistic claims. We show a protocol in which a
polynomial-time verifier can verify the approximate consistency of (P,Q). The verifier is given the pair of
circuits (P,Q), which it evaluates at only a few points; alongside them it is given a proof oracle, an encoding of a witnessing probability distribution allegedly consistent with the predictions of (P,Q), which it reads at a few locations while interacting with a single untrusted prover. En route, we must ensure the existence of a sparse witnessing distribution consistent with the model's predictions. To do so, we first consider witness distributions for the consistency of explicit probabilistic claims, rather than claims specified by a predictor: say m claims, each of the form Pr[Y = 1 | X = x] = p, over n Boolean variables. Building on work initiated by Nilsson (Artif. Intell., 1986), we place l_2-approximate probabilistic consistency of explicit claims in NP, with certificates of length O(mn + log B) in the input bit-precision B; we further show how a small additive completeness-soundness gap removes the dependence on B. Together these results provide a complexity-theoretic foundation for certifying the self-consistency of probabilistic predictors. We view our interactive PCP as a first step toward training predictive models to prove their own consistency.
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All authors Orr Paradise, Oliver Richardson, Yoshua Bengio, Shafi Goldwasser
Artificial Intelligence · Cross-listed from cs.CL
From Interpretability to Control: Insights from Six Years of the TrustNLP Workshop
Rahul Gupta, Abhinav Mohanty, Anaelia Ovalle et al. (+10 more)
The Workshop on Trustworthy Natural
Language Processing (TrustNLP), co-located with major ACL conferences since 2021, has grown from 8 proceedings papers to 41 over six editions, documenting a field-wide transition from post-hoc interpretability of static models to mechanistic understanding and proactive control of generative systems. We synthesize insights from all 144 proceedings papers, classifying them along six trust dimensions grounded in established frameworks (TrustLLM, DecodingTrust). We observe co-occurrences with capability emergence. The release of the first high-impact chat models activated all trust dimensions simultaneously, while subsequent model generations shifted focus toward truthfulness and safety
alignment. Analysis from the classification study reveals that truthfulness is the fastest-growing dimension (absent in 2021-2022, comprising 37% of papers by 2025-2026),
fairness remains the most consistent theme, and explainability exhibits a U-shaped trajectory; declining as post-hoc methods lost relevance but resurging in 2026 through mechanistic interpretability. A cross-venue comparison with ACL, NAACL, EACL, and EMNLP (~2K papers) in the same period shows that TrustNLP's topical distribution closely follows the field average. We identify four structural insights and conclude with actionable directions for the research community.
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All authors Rahul Gupta, Abhinav Mohanty, Anaelia Ovalle, Anil Ramakrishna, Anubrata Das, Apurv Verma, Jwala Dhamala, Ninareh Mehrabi, Tharindu Kumarage, Yada Pruksachatkun, Yang Trista Cao, Kai-Wei Chang, Aram Galstyan
Artificial Intelligence · Cross-listed from cs.CL
Attention-Path Fragility as an Uncertainty Signal in Large Language Models
Minsoo Kim, Sungyoung Ji, Kisung Moon et al. (+1 more)
We propose that a model's uncertainty about a
token is reflected not only in the breadth of its output distribution but also in whether a confident prediction is fragile under perturbation of its
attention pathways. We instantiate this as ASMI (
Attention-Subnetwork Mutual Information), a training-free
estimator that masks
attention heads and measures the BALD mutual information among the resulting subnetworks, with a semantic-agreement kernel to discount surface-form disagreement. The signal is not a restatement of output confidence: on grounded QA an out-of-fold test shows it adds error-predictive information beyond single-pass confidence and
entropy, concentrated in confident-but-fragile predictions, where acting on it roughly halves the retained error of a confidence filter. The distinctness is regime-graded, so ASMI predicts its own domain of applicability, strong where answers are routed through provided context and bounded by design where they are recalled from parametric knowledge. Sem-ASMI reads the signal from a single greedy response, without the
stochastic generations the strongest baselines require, and ties or beats Semantic
Entropy on ten of the twelve grounded
benchmark-backbone settings. Across the same twelve settings, the best ASMI variant, typically the adaptive one reusing the ten samples already drawn for the baselines, ties or leads the strongest baseline in eight, significantly in three under a paired test. On parametric QA all variants revert to or below the zero-cost MSP baseline, exactly as predicted, and the estimates are near-deterministic across reruns. A head-level analysis shows that what tracks this boundary is not the presence of head-level fragility but whether that fragility couples to errors.
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All authors Minsoo Kim, Sungyoung Ji, Kisung Moon, Ilyong Yoon
Artificial Intelligence
sLTN: Structural Logic Tensor Networks
Davide Rinaldi and Luciano Serafini
Logic Tensor Networks (LTN) provide a neurosymbolic framework in which first-order logic is interpreted through tensor operations, enabling logical constraints to be integrated with differentiable learning. However, the original formulation of LTN is primarily suited to data represented as flat collections of individuals, and does not explicitly capture structural organization such as temporal order, sequential position, or graph connectivity. We introduce sLTN, an extension of LTN that makes structural dimensions first-class elements of the
language. Structural dimensions represent named tensor axes associated with domain-specific organization, such as time steps, sequence positions, or graph nodes. They can be quantified explicitly, related through structural relations, and used to express temporal, sequential, and relational constraints directly at the logical level. We formalize the syntax and fuzzy tensor semantics of sLTN and show that, in the absence of structural dimensions, the framework recovers the original LTN semantics as a special case. We further describe a PyTorch implementation based on a declarative signature, formula parsing, and tensorial interpretation. The framework is illustrated on representative temporal and sequential
reasoning examples. This paper serves as a companion to the sltn library, available at https://github.com/logictensornetworks/sltn.
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Artificial Intelligence · Cross-listed from cs.LG
Two-stage Odd Residual Flows for Mean-Preserving Probabilistic Time Series Forecasting
Kiran Madhusudhanan, Christian Klötergens, Lars Schmidt-Thieme et al. (+1 more)
Probabilistic
forecasting plays an essential role in risk-sensitive decision-making, particularly in long-horizon settings. However, existing approaches often face a fundamental trade-off between distributional flexibility and accurate mean prediction. Traditional parametric methods, such as Mean Variance Estimation (MVE), can suffer from degraded point accuracy when trained under joint Negative Log-Likelihood (NLL) objectives, while modern-flexible generative models, including Normalizing Flows and
Diffusion Models, typically rely on costly Monte Carlo sampling and may yield suboptimal mean estimates. To address this limitation, we propose Two-stage Odd Residual Flows (TORF), a framework that decouples mean
forecasting from uncertainty estimation. In the first stage, a pre-trained deterministic model is used to produce an accurate mean prediction. In the second stage, a Restricted Normalizing Flow, with strictly odd functions learns flexible residual distributions around the point forecast, guaranteeing mean preservation from the first stage without sampling. Experiments show that TORF achieves state-of-the-art deterministic accuracy (NMAE) while providing strong density estimation performance (CRPS) on short and long-horizon
forecasting.
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All authors Kiran Madhusudhanan, Christian Klötergens, Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi
Artificial Intelligence
Why Does CLAUDE.md Keep Growing? Catastrophic Remembering in Agentic Coding
Kushal Chakrabarti
Agentic coding READMEs like CLAUDE.md grow without bound in real repositories, stopping only when the repository retires or someone rewrites the file wholesale. We trace this to imperfect recall: appending an instruction is always cheap, but once an instruction's rationale is gone, deleting it without risking a correctness regression costs O(2^|D|) in a
prompt of |D| instructions. We name the resulting divergence catastrophic remembering, the inverse of catastrophic forgetting around which continual learning is organized. First, we characterize this phenomenon across 247,694 instruction lifetimes in 1,867 repositories: agentic
prompts grow without bound, more than tripling over their lifetime (+226%), gaining +4.9 net instructions every commit; further, the older an instruction gets, the less likely it is to be deleted (log-hazard -0.032/commit). Then, we show that
prompt comments can halt the growth: inverting IFEval yields verifiable worlds whose optimal
prompts are known, and there comments encoding latent
reasoning remove 99.3% of excess instructions (+211.3% to +1.4%). Finally, applying the same inversion to WildIFEval, we show that
prompt comments can improve real-world agentic instruction-following by up to 23.1%. If English is the new code, why don't we have comments yet?
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Artificial Intelligence
RTSKG: Building a Rail Transit Station Knowledge Graph Dataset
Shutong Zhu, Tianxing Wu, Runfeng Liu et al. (+3 more)
Rail transit systems play a vital role in urban mobility and economic development. As key components of such systems, rail transit stations function as critical transport hubs that enhance urban accessibility and stimulate development in surrounding areas. City-level rail transit station related tasks (e.g., ridership prediction) require large-scale urban data, but current studies often neglect complex interactions among various urban entities in terms of data organization. In this paper, to address the above issue, we build a Rail Transit Station Knowledge Graph (RTSKG)
dataset which explicitly models the spatial and semantic interactions among different kinds of urban entities, to benefit city-level rail transit station related tasks. RTSKG integrates heterogeneous urban entities, such as rail transit stations, road segments, and points of interest, with a specially designed unified schema, and is accessible as Linked Data at https://w3id.org/rtskg/. Evaluations on station-area store recommendation and knowledge-enhanced ridership prediction demonstrate the effectiveness of RTSKG, highlighting its potential to support city-level rail transit station analysis.
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All authors Shutong Zhu, Tianxing Wu, Runfeng Liu, Yuang Gu, Xuan He, Yuan Zhu
Artificial Intelligence
SkillZip: Evaluation-Free Skill Compression for Self-Evolving Agents by Discovering Reusable Structure
Xiaofan Bai, Hongqiang Lin, Chao Liu et al. (+4 more)
Self-evolving
agents accumulate reusable skills by appending successful procedures and failure fixes. Over time, the same requirement is often restated in several branches, examples, and warnings, while common action sequences are copied rather than reused. The resulting skill becomes expensive to inject and difficult to maintain. Generic
prompt compression is ill-suited to this setting because a skill is not a flat passage: its name and description define when it applies, its workflow controls execution, its tool and output contracts constrain validity, and rare exceptions may remain essential even when no sampled task activates them. Evaluation-guided compression can test these behaviors, but it introduces rollouts, cost, and dependence on the compression-time evaluation set. We present SkillZip, an evaluation-free method that compresses a skill by finding its shortest faithful structural explanation. The intuition is explain once, reference many: state a repeated rule once at the scope where it applies, factor a repeated action sequence into a shared procedure, and keep only the differences as explicit exceptions. We formalize this intuition as a typed minimum description-length objective over a skill contract and a residual, subject to a hard coverage constraint for every extracted trigger, workflow edge, tool requirement, obligation, and output field. The formulation provides simple sharing thresholds, preserves unique rare rules by construction, and supports efficient local updates. SkillZip has a one-shot mode with one structured extraction call and deterministic optimization, and a continual Zip-on-Write mode that integrates each self-
evolution patch without replaying tasks or reparsing the full history. Through comprehensive experimental evaluations, we demonstrate the effectiveness and superiority of SkillZip in compression performance, generalizability, and cost overhead.
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All authors Xiaofan Bai, Hongqiang Lin, Chao Liu, Yantao Zhang, Xuan Jin, Xipeng Cao, Yuhong Li
Artificial Intelligence · Cross-listed from quant-ph
Quantum Coordination Advantages in AI State-Tracking Tasks: Semantic Compilation and Latent Memory
Ming Yang
We prove inference-time
quantum coordination advantages for specified AI state-tracking tasks. A solver compresses semantic history into a future-accessible boundary state and later answers a query. We count communication $B$, persistent instance-dependent
memory $M$, and local work $D$; classical recurrence, caches, tools, and recomputation are allowed and charged. The central result is a boundary-preserving semantic-compilation
theorem. It maps a finite one-way, streaming, or adaptive causal task into a semantic AI interface while preserving event order and access to past input. Classical boundary-state lower bounds and
quantum-
memory upper bounds transfer up to explicit compiler overhead, independently of the finite-precision recurrent architecture. Two applications have classical semantics. Matched-entity synopsis QA inherits the hidden-matching separation between $O(\log N)$
qubits and $\Omega(\sqrt{N})$ classical boundary bits. Continual requirements
auditing inherits a Max-$k$SAT streaming separation: a recurrent solver uses $O(\log^5 n\log(1/\delta))$
qubits and polylogarithmic classical workspace to obtain a $0.7172$-approximation, whereas every classical one-pass finite-information solver attaining that ratio requires $\Omega(\sqrt{n})$ coordination width. As a
quantum-native compiler test, a stabilizer latent-state dialogue uses $n$
qubits, while every exact finite-state classical causal online realization satisfies $B+M \ge \frac{1}{2}n^2+(\frac{3}{2}-\log_2 3)n+O(1)$. The source protocols, streaming
algorithms, and stabilizer witness are imported; the new result is their architecture-independent semantic transfer. These are
memory and coordination separations, not runtime or empirical advantages for present-day
language models. The stabilizer result assumes exact simulation and ideal noiseless
quantum memory.
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Artificial Intelligence · Cross-listed from cs.CV
Entropy-Centric Explainable AI for Remote Sensing Image Segmentation
Ali Saleh, Abdul Karim Gizzini, Mohamad Ghassany et al. (+1 more)
Artificial intelligence (AI) has become a powerful approach to solving complex problems in critical domains. Many concerns arise regarding the decision-making process of its models, mainly due to deep neural networks outperforming their peers at the cost of ambiguity in feature extraction and prediction. Consequently, in critical domains such as remote sensing, where high-resolution imagery must be analyzed using black-box models, the lack of transparency limits trust in these models and, thus, their adoption. In light of this reality, explaining and understanding the complex decision-making process of AI models has become essential. Explainable AI (XAI) aims to bridge this gap by providing insights into how and why certain decisions are made. While significant progress has been achieved in explaining image classification tasks, image
segmentation still offers considerable room for improvement. In this context, this paper proposes an
entropy-centric XAI method for semantic
segmentation. Moreover, a new XAI evaluation methodology is proposed to efficiently measure the relevance of the regions highlighted by the proposed XAI method. Experimental results demonstrate the superiority of the proposed XAI method compared with recently adapted XAI methods for semantic
segmentation.
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All authors Ali Saleh, Abdul Karim Gizzini, Mohamad Ghassany, Ali J. Ghandour
Artificial Intelligence · Cross-listed from cs.CV
A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa
Ismail Ismail Tijjani, Sunusi Muhammad Ibrahim, Amina Ibrahim Khaleel et al. (+5 more)
The application of computer
vision in agriculture has shown significant potential for improving crop monitoring and precision farming. However, many existing approaches rely on controlled
datasets that do not adequately represent realworld farming conditions, particularly in underrepresented regions such as Africa. This study presents a comparative evaluation of six object detection models YOLOv5, YOLOv8, YOLO11, YOLO26, Faster R-CNN, and RT-DETR using a real-world
dataset, AgriAISeg 1 , collected manually from Nigerian farms. AgriAISeg comprises 3,382 images of sesame, cabbage, and tomato crops captured under varying environmental conditions, including changes in illumination, occlusion, and viewing perspectives. Models were trained, and performance was assessed using precision, recall, mAP@0.5, and mAP@0.5:0.95. The results show that RT-DETR achieved the highest overall performance with a precision of 0.768 and mAP@0.5:0.95 of 0.624, while YOLOv8 and YOLO11 also demonstrated strong and consistent performance. In contrast, Faster R-CNN recorded significantly lower accuracy, with an overall mAP@0.5 of 0.466, indicating reduced effectiveness under complex field conditions. In addition, YOLO-based models exhibited superior training efficiency compared to Faster R-CNN.These findings demonstrate that modern one-stage and
transformer-based detectors provide more reliable and efficient solutions for plant detection in realworld agricultural environments.
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All authors Ismail Ismail Tijjani, Sunusi Muhammad Ibrahim, Amina Ibrahim Khaleel, Lanre Olusegun Akinola, Fatima Isa Jibrin, Muhammad Bashir Aliyu, Abdullahi Abdussalam Dalhat, Abdullahi Suiudeen
Artificial Intelligence · Cross-listed from cs.CV
3D Weighted Geometric Graph Neural Networks for Sheep Facial Pain Assessment
Alam Noor, Luis Almeida and Mohamed Daoudi
Deep learning systems perform mainly within the 2D for a single image domain and take the face as a single-dimension representation, losing sight of the 3D anatomy of sheep and cross-landmark spatial relationships that are intrinsic to the clinically proven Sheep Pain Facial Expression Scale (SPFES). This paper presents the 3D Sheep Pain Facial Expression System (3D-SPFES), a novel,
monocular depth-aware
geometric graph neural network system that integrates each SPFES facial landmark, such as the ears, eyes, and nose, into 3D Euclidean space estimated from a single RGB camera by using VideoDepthAnything, thus preventing the need for specialized depth hardware. Each landmark node includes a feature vector containing its 3D spatial coordinates, estimated surface normal, and facial attribute class
embedding. Edges linked to nodes are assigned weights based on an aggregate metric that combines both Euclidean distance and surface co-planarity in a 3D space. A Weighted
Geometric Graph Neural Network (WG-GNN) studies this graph using $\mathcal{K} = 3$ geometry-aware message-passing layers enhanced by a scaled dot-product
attention method that selectively enhances anatomically relevant inter-landmark messages. The resultant node
embeddings are combined into $\mathcal{O} = 3$ pain-level clusters and integrated into a Normalized Pain Score (NPS) within the range of $[0, 100%]$ a confidence-weighted, SPFES-derived scoring method.
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Artificial Intelligence · Cross-listed from cs.CL
Multiclass Sentiment Analysis for Identifying Political Viewpoints
Girma Yohannis Bade, Olga Kolesnikova, Jose Luis Oropeza et al. (+1 more)
The rapid growth of social media has created vast amounts of political discourse, which provides valuable opportunities to analyze public opinions and identify different political perspectives. Sentiment Analysis (SA) is a core task in Natural
Language Processing (NLP) that allows the computational study of attitudes and opinions in textual data, and has become increasingly important for understanding political discourse. In this work, we investigate multiclass sentiment analysis of political view- points on social media, that is to automatically discriminate multiple sentiment classes over political issues and figures. To solve this task we design and evaluate two machine-learning approaches based on XGBoost and BERT. We train and evaluate the models on a labeled
dataset of political social media posts using standard classification metrics. The experimental results show that the XGBoost model reaches an F1-score of 0.2835 and the BERT- based model reaches an F1-score of 0.2806 on the test set. These results demonstrate the challenge of classifying complex and contextualized political discourse sentiment and provide a baseline for future research in multiclass political sentiment analysis.
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All authors Girma Yohannis Bade, Olga Kolesnikova, Jose Luis Oropeza, Grigori Sidorov
Artificial Intelligence
Alicia Larsen, Victoire Laurent, Aulia Kharis Rakhmasari et al. (+2 more)
While existing
benchmarks have made substantial progress in evaluating LLMs across STEM domains, financial
reasoning over structured data remains comparatively less explored. We introduce V-FiLLM, a framework that generates financial
reasoning benchmarks from executable computation trees grounded in real tables, yielding items whose answers are correct by construction. Trees are evaluated symbolically to obtain ground truth and rendered into natural-
language questions, removing any model from the labeling loop, so items can be generated at arbitrary scale without annotation cost and without inheriting a generator's error rate. V-FiLLM exposes four independently controllable axes of difficulty including computation depth, expression breadth, financial concept complexity, and context size. By evaluating on open-source models, we find that accuracy falls up to 51% as
reasoning depth increases, and up to 47% points under
adversarial numerical perturbations, highlighting remaining challenges in robust financial
reasoning over tables. We further show that lightweight LoRA fine-tuning on verified chain-of-thought traces improves accuracy from 81.1% to 85.6% on held-out problems and outperforms the base model by 5% points on FinQA (Chen et al., 2022a), s), suggesting that targeted, low-cost adaptation is a promising direction for compositional
reasoning in financial QA.
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All authors Alicia Larsen, Victoire Laurent, Aulia Kharis Rakhmasari, Lara Turgut, Nino Antulov-Fantulin
Artificial Intelligence · Cross-listed from cs.DC
Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Data
Nicola Giuseppe Marchioro, Gabriele Padovani, Amal Gueroudji et al. (+5 more)
Model Cards and Data Cards have demonstrated the value of structured, human-readable documentation for machine learning artifacts, capturing their context, parameters, limitations, and intended use. However, these practices remain focused on static artifacts (the
datasets and trained models themselves) while overlooking the workflow executions that produce, transform, and evaluate them. Such executions hold critical details about data preparation, parameter choice, runtime behavior, resource use, and intermediate transformations, precisely where bias, performance variation, and reproducibility gaps tend to originate. To close this gap, we introduce Workflow Cards: structured summaries that condense the machine-readable provenance data of a workflow execution into a form both humans and large
language models (LLMs) can read and analyze. This paper has two main parts. First, it defines a Workflow Card template informed by a representative set of provenance questions that surface from the execution-level data missing from Model and Data Cards. Second, it evaluates how effectively LLMs use Workflow Cards to understand workflow executions compared with querying provenance databases through a schema-based interface. Results show that Workflow Cards provide execution-level information absent from existing card types, such as Model Cards and Data Cards, thereby filling an important documentation gap; and that Workflow Cards nearly double answer quality compared with schema-based querying, consistently across LLM-as-a-Judge and human assessments.
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All authors Nicola Giuseppe Marchioro, Gabriele Padovani, Amal Gueroudji, Rafael Ferreira da Silva, Wesley Brewer, Valentine Anantharaj, Sandro Fiore, Renan Souza
Artificial Intelligence · Cross-listed from cs.CV
R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video
Ke Ma, Yamin Mao, Weiming Li et al. (+5 more)
Long-horizon
egocentric video is a rich substrate for wearable AI assistants, but object-centric questions such as where an item was moved, when it last changed state, or why it was relocated remain difficult because caption- and transcript-based
memories rarely preserve persistent object identity or structured spatial change. Existing long-
video QA methods mainly emphasize temporal grounding and clip
retrieval, while prior 3D scene-graph methods typically assume stronger geometry than free-motion wearable RGB
video provides, including point clouds, RGB-D input, posed views, sparse reconstruction, or reconstructed scenes. R4DSG introduces a relative 4D scene graph
memory for long
egocentric video. Instead of storing raw graph sequences, R4DSG converts
video into compact queryable
memory entries indexed by time, place, persistent objects, anchor-relative change, and local interaction context. The main idea is to separate stable anchors from dynamic objects, maintain persistent object identity across frames, and represent object state through anchor-relative transitions rather than a globally aligned world model. Built on recent RGB-only advances in promptable
video segmentation, temporal propagation, and relative 3D lifting, the method produces a
retrieval-ready
memory directly usable for long-horizon question answering. Evaluation on a 255-question object-related subset from EgoLifeQA shows, under question-only
retrieval, a 6.7-point overall gain over EgoRAG-Text and a 12.5-point gain on when questions, which highlights the value of temporally organized object
memory. These results position relative 4D scene graphs as a practical
memory substrate for wearable assistants, AR systems, and
embodied multimedia
agents. GitHub Page: https://dualtransparency.github.io/R4DSG/.
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All authors Ke Ma, Yamin Mao, Weiming Li, Shuai Tan, Yijie Zhong, Hao Chen, Haofen Wang, Meng Wang
Artificial Intelligence · Cross-listed from cs.CY
Policy Convergence and Divergence Across National and Within Regional AI Strategies: A Policy Design Element Analysis
Benjamin Faveri and Brie Bhasin
Governments worldwide have responded to the rapid expansion of AI by publishing national and regional AI strategies. Comparing national and regional AI strategies to identify their convergences and divergences can uncover their common practices, understand regional variations, and provide policy designers a comprehensive set of policy design elements for their ongoing AI strategy developments. Yet, existing work has not examined their underlying policy design elements or assessed whether those elements are horizontally (country-to-country) or vertical (region-to-country) converging or diverging over time. This paper addresses that gap by coding and analyzing 74 national and 3 regional AI strategies drawn from a global scan of all 205 UN member and non-member states. The coding used a latent-inductive approach organized around three functional policy design elements: goals, approaches, and principles. Two research questions guided the analysis: to what degree are national AI strategies becoming horizontally convergent or divergent over time; and to what degree are national strategies becoming vertically convergent or divergent with those countries' regional AI strategy. Results indicate strong horizontal convergence around economic competitiveness, research support, and ethical AI use, alongside persistent divergence in human rights goals, participatory governance approaches, and human-centric principles. Across the three regions, the AU exhibits the highest vertical convergence, the EU demonstrated strong
alignment on regulatory and economic priorities but diverges on human-centric values, and the Nordic-Baltic Region displays mixed vertical convergence. These findings offer policy designers a comprehensive evidence base for identifying emerging AI policy design choice norms as AI strategies are developed and updated.
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Artificial Intelligence · Cross-listed from cs.CL
On the Limitations of Cross-Lingual Consistency in Multilingual Text-to-image Generation
Sicheng Zhang, Zhonghao Yan, Binzhu Xie et al. (+4 more)
Text-to-image (T2I) generation has achieved remarkable progress in recent years. However, existing research has largely focused on English-only settings, leaving cross-lingual performance gaps and
language-specific effects insufficiently explored. To fill this gap, we introduce LingT2I, a
benchmark covering 10 widely used
languages with 33K
prompts, designed to evaluate cross-lingual effects in both content generation and text rendering. Building on this
benchmark, we conduct a comprehensive cross-lingual analysis, uncovering linguistic inequality and
language-dependent trade-offs across evaluation dimensions. Beyond quantitative evaluation, we further reveal a range of
language-dependent generation patterns, highlighting how linguistic factors and their corresponding cultural contexts systematically impact model outputs. Our
benchmark and analysis provide a foundation for studying cross-lingual behavior in T2I generation and facilitate the development of more robust and inclusive models. Code and
dataset are available at https://github.com/RISys-Lab/LingT2I.
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All authors Sicheng Zhang, Zhonghao Yan, Binzhu Xie, Shi Qiu, Muzammal Naseer, Naveed Akhtar, Mubarak Shah
Artificial Intelligence · Cross-listed from cs.CV
Hongsen Cao, Mona Jaber, Shanxin Yuan et al. (+1 more)
Token-
pruning policies are usually designed for a single recognition pipeline, but pretrained
Vision Transformers are reused across tasks with different spatial demands. We ask which parts of a
pruning policy transfer across image classification, semantic
segmentation, and object detection. For each pipeline, controlled probes freeze the no-
pruning checkpoint and apply a series of parameter-free reduction criteria at one eligible layer at a time without retraining. The probes reveal three differences:
segmentation and detection rank the criteria differently, classification is especially sensitive to
attention-based
pruning in the earliest layers, and the dense tasks prefer opposite recovery endpoints. These findings motivate Task-Adaptive
Pruning (TAP). Existing register
tokens serve as task-agnostic storage for feature artifacts. TAP instead introduces one task register per task and activates only the current one. Its evolving state ranks
tokens, distributes an exact removal budget over depth, and sets the recovery scale for dense features. At a final keep rate of $\rho=0.5$, our jointly adapted model, TAP-J, reaches $47.0$ mIoU at $1.30\times$ encoder throughput on ADE20K and $53.7$ box AP at $1.32\times$ encoder throughput on COCO while remaining competitive on ImageNet-1K.
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All authors Hongsen Cao, Mona Jaber, Shanxin Yuan, Ahmed Sayed
Artificial Intelligence · Cross-listed from cs.IR
TimeRoute: Time-Aware Modality Routing and Diffusion for Multi-Modal Recommendation
Pengyu Zhang, Yangqin Jiang, Klim Zaporojets et al. (+2 more)
Multi-modal recommenders fuse user-item interaction signals with item modalities such as text, images, and audio, but the usefulness of each drifts over time and at different rates. For example, around Valentine's Day, chocolate purchases become less driven by textual ingredient cues and more by visual packaging and ambient audio. This modality time-scale mismatch gives rise to two coupled challenges: (1) users with different temporal behavior profiles require different modality proportions, and (2) less relevant modalities are more likely to introduce outdated or misleading signals into the recommender. We address both challenges within a unified
diffusion-based recommender, TimeRoute. A temporal-aware modal router maps each user's aggregated temporal profile to a personalized modality distribution, replacing the globally shared fusion weights used in prior work. The
diffusion-based graph reconstructor is conditioned on the same profile through Feature-wise Linear Modulation (FiLM) with dual-stream long- and short-term denoising heads. This design captures both slowly and rapidly evolving temporal dynamics to suppress outdated modality edges before they enter the propagation graph. Experiments on TikTok, Amazon-Baby, and Amazon-Sports, averaged over 10 seeds, demonstrate consistent improvements over strong baselines across Recall@K, Precision@K, and NDCG@K, reaching up to 9.8% (P@20 on Amazon-Baby). Controlled attribution studies further show that these gains require both the proposed mechanisms and temporal input: naively granting the backbone the same temporal profile yields no benefit, and feeding the router random noise performs no better than removing the router entirely. Code is available at https://anonymous.4open.science/r/TimeRoute.
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All authors Pengyu Zhang, Yangqin Jiang, Klim Zaporojets, Congfeng Cao, Paul Groth
Artificial Intelligence
XCoT-VLA: Executable Chain-of-Thought for Vision-Language-Action Driving
Foundation Model Team and XPeng Inc
Vision-
Language-Action (VLA) models can connect scene understanding, semantic
reasoning, and trajectory generation for autonomous driving. However, verbose natural-
language Chain-of-Thought (CoT) is poorly suited to real-time control because it is open-ended, costly to decode, and difficult to optimize as an action-facing representation. We propose XCoT-VLA, which replaces descriptive rationales with compact executable CoT
tokens learned from automatically constructed Reason-Action supervision. Logged trajectories provide action evidence, while scene context supplies causal semantics. The predicted XCoT sequence remains in context and conditions fixed trajectory queries through shared
multimodal self-
attention. Deterministic
token-function routing applies the Reason FFN to XCoT
tokens and the Control FFN to trajectory queries for flow-matching trajectory generation. We further introduce XCoT Policy Optimization (XCPO) as an optional refinement extension in the same executable
token space. XCoT-VLA reduces longitudinal ADE from 1.645 to 1.323 on a general-distribution set and lateral FDE from 1.616 to 0.648 in lane-change scenarios. By representing driving-oriented
reasoning with only 2-6 executable XCoT
tokens, our method substantially reduces autoregressive
reasoning overhead and remains within the real-time planning budget. These results demonstrate that driving-oriented
reasoning can be compact, executable, and directly connected to trajectory generation.
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