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02.
arXiv (CS.CV) 2026-06-16

Trusting Right Predictions for Wrong Reasons: A LIME Based Analysis of Deep Learning Interpretability in Lung Cancer Diagnosis

作者:
Samarpan Poudel↗Vladislav D Veksler↗

Lung cancer is the leading cause of cancer-related mortality, with approximately 2.5 million new cases and 1.8 million deaths annually, making reliable diagnosis a clinical priority. Although deep learning models have achieved strong performance in lung cancer classification, evaluation has largely focused on predictive accuracy, leaving their decision-making processes insufficiently examined. This study compares three architecturally distinct models: a Convolutional Neural Network (CNN), a pretrained ResNet50, and a Vision Transformer (ViT), trained on the IQ-OTH/NCCD lung cancer CT dataset. Local Interpretable Model-Agnostic Explanations (LIME) were applied to investigate model reasoning. In addition to standard performance metrics, a dual-correlation framework was introduced to measure both prediction agreement and explanation agreement across model pairs. All three models achieved strong classification performance, with ResNet50 attaining 98.61% accuracy, CNN 97.91%, and ViT 93.75%, while all achieved ROC-AUC scores of 0.99. Prediction correlations exceeded 0.99 across all model pairs, indicating highly consistent outputs. However, LIME explanation correlations remained below 0.26, revealing substantial differences in the image regions used to reach those predictions. Analysis of misclassified samples further identified a consistent spatial pattern: incorrect predictions were associated with attention outside the lung parenchyma, whereas correct predictions focused primarily within lung regions. These findings demonstrate that prediction agreement is a poor proxy for reasoning consistency, and that interpretability evaluation must be treated as an independent validation criterion alongside predictive performance in clinical AI systems.

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03.
arXiv (CS.AI) 2026-06-15

Closing the Reflection Gap: A Free Calibration Bonus for Agentic RL

作者:
Yinglun Zhu↗

arXiv:2606.14211v1 Announce Type: new Abstract: LLMs are increasingly deployed as agents that interact with external environments and observe feedback such as execution results, error messages, and tool outputs. A well-functioning agent should be able to leverage this feedback to accurately assess its own performance. Yet we find a persistent reflection gap: LLM agents tend to mis-assess their own outputs after observing concrete environment feedback – even for questions they correctly answered – and standard RL barely helps due to a credit-assignment mismatch. To close this gap, we propose RefGRPO, a simple yet effective fix that augments standard RL algorithms with two key ingredients: a free calibration bonus computed by contrasting the agent's own reflection with the actual outcome (requiring no additional reward model, LLM judge, or external annotation), and a dynamic schedule on its coefficient. Compared to standard RL baselines, our method simultaneously improves reflection calibration (e.g., reduces underconfidence rate $44.4\% \to 7.7\%$) and task accuracy (e.g., $75.1\% \to 76.5\%$) on text-to-SQL across five benchmarks. The resulting calibrated reflection turns the agent into its own verifier grounded in environment feedback, which further enables (i) better self-improvement that uses reflections as pseudo-rewards without outcome supervision, and (ii) more effective test-time selective prediction by committing only to rollouts flagged as correct.

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04.
arXiv (CS.AI) 2026-06-11

Can Open-Source LLM Agents Replace Static Application Security Testing Tools? An Empirical Assessment

作者:
Derek Yohn↗Luke Flancher↗Mirajul Islam↗Khaled Slhoub↗

arXiv:2606.11672v1 Announce Type: cross Abstract: This paper explores the value of agentic AI tools for cybersecurity purposes. We evaluate the efficacy of a general-purpose GenAI Large Language Model- (GenAI-) based agent when powered by three different Ollama-hosted general-purpose open source models. We assess each agent's performance using precision, recall, false positive count, and a calculated composite score based upon the interplay of the captured metrics, against the baseline performance of an existing, vetted Static Application Security Testing (SAST) tool, Bandit. Our findings refute the notion that a modern open-source GenAI LLM-based agent is currently suitable for the specialized task of SAST scanning under realistic conditions.

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05.
arXiv (CS.AI) 2026-06-16

OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens

作者:
Konstantin F. Willeke↗Polina Turishcheva↗Alex Gilbert↗Goirik Chakrabarty↗Hasan A. Bedel↗Paul G. Fahey↗Yongrong Qiu↗Marissa A. Weis↗Michaela Vystr\v{c}ilov\'a↗Taliah Muhammad↗Lydia Ntanavara↗Rachel E. Froebe↗…

arXiv:2604.18827v2 Announce Type: replace-cross Abstract: Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains unclear. Here we leveraged a dataset of 3.1 million neurons from the visual cortex of 73 mice across 323 sessions, totaling more than 150 billion neural tokens recorded during natural movies, images and parametric stimuli, and behavior. We train multi-modal, multi-task models that support three regimes flexibly at test time: neural prediction, behavioral decoding, neural forecasting, or any combination of the three. OmniMouse achieves state-of-the-art performance, outperforming specialized baselines across nearly all evaluation regimes. We find that performance scales reliably with more data, but gains from increasing model size saturate. This inverts the standard AI scaling story: in language and computer vision, massive datasets make parameter scaling the primary driver of progress, whereas in brain modeling – even in the mouse visual cortex, a relatively simple system – models remain data-limited despite vast recordings. The observation of systematic scaling raises the possibility of phase transitions in neural modeling, where larger and richer datasets might unlock qualitatively new capabilities, paralleling the emergent properties seen in large language models. Code available at https://github.com/enigma-brain/omnimouse.

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06.
arXiv (CS.CV) 2026-06-11

Feature extraction for plant growth estimation

作者:
Simbarashe Aldrin Ngorima↗Albert Helberg↗Marelie H. Davel↗

Precision agriculture requires the estimation of plant growth stages in real-time. When the plant growth stage is known, the wastage of resources in cultivation, such as nutrients and water, is reduced as only the required resources need to be supplied. Plants at different growth stages, however, have similar morphological features, which can make autonomous growth stage estimation difficult. This paper presents two feature extraction methods for growth stage estimation: one that uses a bank of Gabor filters and morphological operations, and the other that uses pre-trained convolutional neural networks (CNNs) and transfer learning. We test these methods on a publicly available plant growth stage dataset (``bccr-segset``) for two species, canola and radish, grown and captured under indoor conditions. The two proposed feature extraction methods are compared, using support vector machines and boosted trees as classifiers. We find that both methods are suitable for real-time applications, and that CNN features outperform the hand-crafted features, both with regard to speed and accuracy. The best system (VGG-19 features, classified with a radial basis function support vector machine) obtained an accuracy of 98.4% for both species, processing an image in 0.08 seconds.

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07.
arXiv (math.PR) 2026-06-16

Asymptotic behavior of some strongly critical decomposable 3-type Galton–Watson processes with immigration

作者:
Matyas Barczy↗D\'aniel Bezd\'any↗

arXiv:2406.09852v2 Announce Type: replace Abstract: We study the asymptotic behavior of a critical decomposable 3-type Galton-Watson process with immigration when its offspring mean matrix is triangular with diagonal entries 1. It is proved that, under second or fourth order moment assumptions on the offspring and immigration distributions, a sequence of appropriately scaled random step processes formed from such a Galton-Watson process converges weakly. The limit process can be described using independent squared Bessel processes $({\mathcal X}_{t,1})_{t\geq0}$, $({\mathcal X}_{t,2})_{t\geq0}$, and $({\mathcal X}_{t,3})_{t\geq0}$, the linear combinations of the integral processes of $({\mathcal X}_{t,1})_{t\geq0}$ and $({\mathcal X}_{t,2})_{t\geq0}$, and possibly the 2-fold iterated integral process of $({\mathcal X}_{t,1})_{t\geq0}$. The presence of the 2-fold iterated integral process in the limit distribution is a new phenomenon in the description of asymptotic behavior of critical multi-type Galton-Watson processes with immigration. Our results complete and extend some results of Foster and Ney (1978) for some strongly critical decomposable 3-type Galton-Watson processes with immigration.

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08.
arXiv (CS.CV) 2026-06-17

Quantum Enchanced Multi-Scale CNN with Bi-directional Mamba for Crop Field Analysis

作者:
Mohammad Salman Khan↗Ehsan Atoofian↗Saad B. Ahmed↗

Hyperspectral image (HSI) crop analysis is essential for precision agriculture because it captures rich spectral and spatial information for accurate crop monitoring and assessment. However, HSI classification remains challenging due to high spectral dimensionality, spatial complexity, class imbalance, and limited labeled samples. To address these challenges, this paper proposes a BiSpectral Mamba-based framework that combines multi-scale convolutional feature extraction, spectral attention, bidirectional state-space modeling, and quantum-inspired learning. A multi-scale CNN backbone first extracts hierarchical spatial-spectral representations through feature fusion across multiple resolutions. A spectral attention mechanism then emphasizes informative bands while suppressing redundant and noisy channels. The refined features are processed by a BiSpectral Mamba module that captures long-range dependencies in both forward and backward directions by modeling hyperspectral feature maps as sequential tokens. In addition, class-weighted optimization and feature fusion strategies are incorporated to improve training stability and mitigate class imbalance. Experimental evaluation on the UAVHSI-Crop dataset demonstrates the effectiveness of the proposed framework, achieving an overall accuracy of 84.83%. The results show that integrating convolutional, attention-based, and state-space modeling components enables robust spatial-spectral feature learning for crop classification. The proposed framework also shows potential for broader agricultural and remote sensing applications, including crop disease detection, yield prediction, and soil moisture estimation, while highlighting the effectiveness of structured state-space and quantum-inspired architectures for hyperspectral image analysis.

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09.
arXiv (CS.AI) 2026-06-18

AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework

作者:
Inderjeet Singh↗Haitham Mahmoud↗Andr\'es Murillo↗

arXiv:2606.18532v1 Announce Type: cross Abstract: AI systems are increasingly evaluated in bounded environments that combine isolation, simulation, instrumentation, supervision, and evidence capture. For physical AI, AIoT, and cyber-physical systems, this shift is not a matter of terminology: the system under test may sense, decide, actuate, communicate, and fail through physical processes, networked devices, and human operators. This article develops an assurance-oriented account of AI sandboxes as controlled environments for testing, evaluation, verification, and validation across digital AI, embodied autonomy, and cyber-physical deployments. We formalize the sandbox boundary and a weakest-link rule for composing per-dimension evidence into a bounded deployment claim; separate major sandbox archetypes; define a cyber-physical threat model that includes attacks on the assurance apparatus itself; and introduce a measurement framework spanning fidelity, controllability, observability, containment, reproducibility, and governance artifacts, instantiated on three worked case studies of real sandboxes. The resulting threat model, taxonomy, and measurement framework clarify what a sandbox can validly test, which risks it can contain, and what forms of evidence it can support for safety, security, and regulatory assurance.

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10.
arXiv (CS.LG) 2026-06-15

Gradient boosting for extremes: sampling theory and application to insurance

作者:
St\'ephane Lhaut↗Olivier Lopez↗

arXiv:2606.14268v1 Announce Type: cross Abstract: We develop a statistical learning theory for gradient boosting applied to the estimation of covariate-dependent Generalized Pareto (GP) distributions in the context of Peaks-over-Threshold modeling. After an orthogonal reparametrization of the GP likelihood that diagonalizes its Fisher information matrix, we cast the estimation problem within the Empirical Risk Minimization (ERM) framework and derive non-asymptotic error bounds for the boosting estimator. Our analysis accounts for three distinct sources of error in the process: statistical fluctuations, the approximation bias inherent to the asymptotic nature of the GP model-controlled under second-order regular variation-and the approximation error associated with the finite number of boosting iterates, making explicit the resulting bias-variance trade-off. We illustrate the practical benefits of the reparametrization through simulations, showing that it significantly reduces gradient correlation during training and improves convergence stability. The methodology is applied to a medical malpractice insurance dataset from the Texas Department of Insurance, comprising over 18 000 closed claims. The gradient boosting approach yields a good fit for the tail of settlement cost distributions and reveals that the number of days to settlement is the dominant predictor of tail heaviness, consistent with earlier findings in the reserving literature.

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11.
arXiv (CS.LG) 2026-06-15

Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learning

作者:
Amirreza Kazemi↗Seyed Mohammad Azimi-Abarghouyi↗Gabor Fodor↗Carlo Fischione↗

arXiv:2602.02355v2 Announce Type: replace-cross Abstract: Hierarchical federated learning (HFL) is well suited for large-scale wireless and Internet of Things systems, where devices communicate with nearby edge servers before reaching the cloud. In these environments, uplink bandwidth and latency impose strict communication constraints, making aggressive gradient compression essential. One-bit sign-based stochastic gradient descent methods provide an attractive solution in flat federated settings, but their behavior in hierarchical edge–cloud architectures remains insufficiently understood, especially under inter-cluster data heterogeneity. To address this gap, we develop a sign-based HFL framework in which devices transmit binary stochastic-gradient signs to edge servers, edge servers apply majority voting, and the cloud periodically aggregates edge models. Our analysis reveals that inter-cluster heterogeneity induces a persistent bias term in the convergence bound, reflecting the drift of edge models toward local objectives. This term cannot be removed by increasing the number of training rounds or by tuning standard hyperparameters alone. We therefore propose \(\mathtt{DC-HierSignSGD}\), a drift-corrected sign-based HFL algorithm in which devices apply a cloud-assisted gradient correction before taking the sign. We show that this pre-sign correction mitigates the non-vanishing heterogeneity-induced bias while preserving binary device–edge communication during the repeated local sign-update steps. Experiments under severe inter-cluster heterogeneity demonstrate that \(\mathtt{DC-HierSignSGD}\) improves the stability and accuracy of sign-based HFL and achieves performance comparable to full-precision hierarchical SGD with substantially lower device–edge communication.

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12.
arXiv (CS.LG) 2026-06-12

Loss-Shift Transfer via Bayes Quotients

作者:
Vasileios Sevetlidis↗

arXiv:2606.13178v1 Announce Type: new Abstract: Transfer learning is usually studied as a consequence of distribution shift. This paper identifies an orthogonal failure mode in which the data distribution is fixed and the loss changes. This setting is called loss shift. A loss determines which information in \(X\) is Bayes-relevant, and two losses may therefore require different representations even under the same joint law \(P(X,Y)\). The idea is formalized using Bayes quotients, which allow losses to be ordered by refinement. In the Bayes-quotient formulation, strict refinement gives an immediate qualitative obstruction. A source-minimal representation for a coarser loss is insufficient for a strictly finer target loss. For finite-output log loss, this obstruction becomes an exact quantitative identity. The excess risk is the conditional information about \(Y\) discarded by the representation. Experiments in controlled, learned, synthetic-image, and real-image settings show the predicted effect, i.e., classification-equivalent representations can have different optimal log-loss performance under a fixed data distribution.

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13.
arXiv (CS.AI) 2026-06-12

Real-rootedness of the Poincaré polynomials of $\overline{\mathcal M}_{0,n}$: an AI-assisted proof

作者:
Gergely B\'erczi↗Young-Hoon Kiem↗

arXiv:2605.29151v2 Announce Type: replace-cross Abstract: We prove real-rootedness for the Poincaré polynomial \[ P_n(t)=\sum_{i=0}^{n-3} \dim H^{2i}(\overline{\mathcal M}_{0,n};\mathbb{Q})t^i \] of the Deligne–Mumford moduli space $\overline{\mathcal M}_{0,n}$ of stable $n$-pointed rational curves, proving a conjecture of Aluffi–Chen–Marcolli. The proof starts from the Keel–Manin–Getzler recurrence, but its main new idea is a bivariate deformation $F_m(y,t)$ of the Poincaré polynomial. This deformation reveals a hidden interlacing structure not visible in the one-variable recurrence. For fixed $t

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14.
arXiv (quant-ph) 2026-06-15

Inhomogeneous Light-Matter Coupling as a Resource for Noiseless Quantum Memories

作者:
Fumiya Hanamura↗Sicheng Bao↗Jie Lerk Yoo↗Alexia Auff\`eves↗Steven Touzard↗

arXiv:2605.26783v3 Announce Type: replace Abstract: Inhomogeneous ensembles of two-level systems are central to both fundamental light-matter physics and quantum-network applications. Understanding and optimizing ensemble-based quantum memories and entanglement protocols requires a unified framework that describes how to store quantum states of light as collective matter excitations and retrieve them on demand. Here we develop such a framework, the waveguide model, by mapping the dark collective modes of the ensemble onto an effective waveguide with well-defined input-output relations, valid in both the weak-excitation regime and near population inversion. This model reveals that inhomogeneous coupling – often regarded as a limitation – is instead the physical origin of noisy-echo suppression by adiabatic pulses, a key ingredient for realizing noiseless quantum memories. For entanglement generation, the same mechanism exposes a previously unexplored shortcoming of robust control pulses and leads to a new composite-pulse protocol that overcomes it. These results establish the waveguide model as a practical bridge between fundamental collective physics and quantum-network protocol design, recasting inhomogeneous coupling from an obstacle into a control knob for collective emission.

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15.
arXiv (CS.CL) 2026-06-11

GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs

作者:
Zhuoyi Peng↗Jingzhou Jiang↗Hanlin Gu↗Lixin Fan↗Yi Yang↗

Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and scientific theme are all inferred from a node together with its neighbourhood. We introduce GraphInfer-Bench, a benchmark for whether LLMs can perform this graph inference: producing an open-ended answer that no single node supports and no path retrieves. Existing graph-QA protocols cannot test this capability: algorithm simulation, node classification, single-node description, KG-QA, and GraphRAG all admit answers retrievable from one node or along a path. GraphInfer-Bench defines five tasks along Description (what a region is) and Comparison (how regions differ), each constructed so the ground truth lives in no single node. The release contains 42,000 samples across six real-world graphs, produced automatically and screened by a four-layer quality-control protocol. We evaluate four method families against the same tasks: graph-token alignment models, zero-shot frontier closed-source LLMs, Graph2Text supervised fine-tuning, and plain GNNs as a structural reference. No method family closes the gap. Graph-token alignment partially handles description tasks (relational, theme) but collapses on comparison tasks. Frontier LLMs lead on outlier detection and community partition among LLM-based methods but lag on masked-node prediction. Graph2Text SFT is the strongest LLM-based method on the description side yet falls behind frontier LLMs on comparison. Across every task, plain GNNs match or beat the strongest LLM-based row, with the largest margin on community detection. GraphInfer-Bench surfaces graph inference as an open capability gap rather than a property of any one architecture.

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16.
arXiv (CS.CL) 2026-06-15

Did You Forget What I Asked? Prospective Memory Failures in Large Language Models

作者:
Avni Mittal↗

Large language models often fail to satisfy formatting instructions when they must simultaneously perform demanding tasks. We study this behaviour through a prospective memory inspired lens from cognitive psychology, using a controlled paradigm that combines verifiable formatting constraints with benchmark tasks of increasing complexity. Across three model families and over 8,000 prompts, compliance drops by 2-21% under concurrent task load. Vulnerability is highly type-dependent: terminal constraints (requiring action at the response boundary) degrade most, with drops up to 50%, while avoidance constraints remain comparatively robust. A salience-enhanced format (explicit instruction framing plus a trailing reminder) recovers much of the lost compliance, restoring performance to 90-100% in many settings. Interference is bidirectional: formatting constraints can also reduce task accuracy, with one model's GSM8K accuracy dropping from 93% to 27%. In additional stacking experiments, joint compliance declines sharply as constraints accumulate. All results use deterministic programmatic checkers without an LLM-as-judge component on publicly available datasets.

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17.
arXiv (quant-ph) 2026-06-19

Steady-state entanglement of spin qubits mediated by nonreciprocal and chiral magnons

作者:
Martijn Dols↗Mikhail Cherkasskii↗Victor A. S. V. Bittencourt↗Carlos Gonzalez-Ballestero↗Durga B. R. Dasari↗Silvia Viola Kusminskiy↗

arXiv:2509.13094v3 Announce Type: replace Abstract: We propose a hybrid quantum system in which a magnet supporting non-reciprocal magnons, chiral magnons, or both mediates the dissipative and unidirectional coupling of spin qubits. By driving the qubits, the steady state of this qubit-qubit coupling scheme becomes the maximally entangled Bell state. We devise a protocol where the system converges to this entangled state and benchmark it including qubit decay and dephasing. The protocol is numerically tested on a hybrid system consisting of nitrogen-vacancy (NV) centers coupled to magnon surface modes of an yttrium iron garnet (YIG) film. We show that the dephasing time of the NV centers forms the bottleneck for achieving the entanglement of NV centers separated by a distance within the magnon coherence length. Our findings identify the key technological requirements and demonstrate a viable route toward steady-state entanglement of solid-state spins over distances of several microns using magnonic quantum networks, expanding the toolbox of magnonics for quantum information purposes.

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18.
medRxiv (Medicine) 2026-06-16

Adverse Childhood Experiences and Growth Outcomes in Childhood: A Longitudinal EHR-Based Study

作者:
Palmer↗Shyr↗Morley↗T. J↗Shelley↗Han↗Simmons↗J. H↗Bejan↗Walsh↗Ruderfer↗D. M↗…

Question Are adverse childhood experiences (ACEs) associated with altered growth trajectories in childhood? Findings In this cohort study of 412,549 children and adolescents, ACEs were associated with lower height throughout childhood, earlier pubertal timing, and shorter final stature. Height differences emerged approximately 2 years before ACE documentation and were greatest among those with earlier documentation. Meaning These findings suggest that early adversity affects physical growth in children and may serve as a measurable indicator of the biological consequences of early-life stress, especially in those with documentation of ACEs prior to the onset of typical pubertal growth. Importance Adverse childhood experiences (ACEs) are among the strongest risk factors for long-term mental and physical health complications, yet their impact on physical growth in childhood remains incompletely understood. Objective To determine the association of ACEs on childhood growth trajectories and growth dynamics. Design, Setting and Participants Retrospective cohort study using longitudinal electronic health record data. Data was collected from participants between February 1999 and August 2025. A large academic medical center biobank linked to deidentified electronic health records in the southeastern United States. A total of 412,549 individuals with at least 2 recorded height measurements between the ages of 2 and 20 were included in the primary analysis. Growth curve analyses were performed in a subset of 199,844 individuals with at least 3 height measurements spanning at least 2 years. Genetic analyses were performed in a subset of 10,114 individuals of primarily European ancestry. Exposure(s) Documented exposure to adverse childhood experiences before age 18 years identified through a natural language processing algorithm. Main Outcome(s) and Measure(s) Height-for-age z-scores across childhood, final attained height, and growth curve parameters estimated using SuperImposition by Translation and Rotation (SITAR) modeling. Results Among 412,549 participants, 18,502 (4.5%) had clinically documented ACEs during childhood. ACE documentation was associated with lower height-for-age z-scores throughout childhood and adolescence. Final attained height was significantly lower among ACE-documented individuals, with mean differences of -3.0 cm among males (174.0 cm vs 177.0 cm, p < 0.001) and -1.3 cm among females (161.8 cm vs 163.1 cm, p < 0.001). Height differences emerged approximately 2 years before clinical ACE documentation. Earlier age at first ACE documentation was associated with progressively shorter final attained height, with each year decrease in age at ACE documentation associated with a decrease in final height of -0.20 cm in females and -0.35 cm in males. Those with first ACE documented prior to pubertal age also showed the most pronounced growth dynamic differences, with males demonstrating a mean reduction in size of 5.25 cm (95% CI, -6.79 cm to -3.70 cm) and 1.26-year earlier pubertal timing (95% CI, -1.50 to -1.03 years), and females demonstrating a reduction in growth curve size of 3.62 cm (95% CI, -4.83 to -2.41 cm) and 1.14-year earlier pubertal timing (95% CI, -1.29 to -0.99 years). Conclusions and Relevance In this large clinical cohort, clinically documented ACEs were associated with time-dependent reductions in stature, earlier pubertal timing, and short final attained height. These findings suggest that early childhood adversity may have lasting effects on physical development and highlight growth trajectories as a potential marker of the biological consequences of early-life stress.

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19.
arXiv (CS.CV) 2026-06-11

Auditing Demographic Bias in Facial Landmark Detection for Fair Human-Robot Interaction

作者:
Pablo Parte↗Roberto Valle↗Jos\'e M. Buenaposada↗Luis Baumela↗

Fairness in human-robot interaction critically depends on the reliability of the perceptual models that enable robots to interpret human behavior. While demographic biases have been widely studied in high-level facial analysis tasks, their presence in facial landmark detection remains unexplored. In this paper, we conduct a systematic audit of demographic bias in this task, analyzing the age, gender, and race biases. To this end, we introduce a controlled statistical methodology to disentangle demographic effects from confounding visual factors. Our analysis demonstrates that visual confounders, particularly head pose and face resolution, heavily outweigh the impact of demographic attributes. Notably, after accounting for these confounders, performance disparities across gender and race vanish. However, we identify a statistically significant age-related bias, with higher localization errors for older individuals. This shows that fairness issues can emerge even in low-level vision components and can propagate through the HRI pipeline. We argue that auditing and correcting such biases is a necessary step toward trustworthy and equitable robot perception systems.

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20.
arXiv (CS.CV) 2026-06-12

DIMOS: Disentangling Instance-level Moving Object Segmentation

作者:
Hongxiang Huang↗Hongwei Ren↗Xiaopeng Lin↗Yulong Huang↗Zeke Xie↗Bojun Cheng↗

Moving instance segmentation (MIS) attracts increasing attention due to its broad applications in traffic surveillance, autonomous driving, and animal tracking. Event cameras record asynchronous brightness changes, providing high temporal resolution and dynamic range, which makes them highly sensitive to motion information. By fusing event and image features, motion cues from events can complement spatial details from images, enhancing the performance of MIS. However, current multimodal MIS methods still struggle to segment small moving instances, as event cameras often yield sparse features under limited resolution. Moreover, event features entangle appearance attributes with motion cues, which further restricts effective cross-modal fusion. To address these challenges, we first propose a dual-disentangling feature extraction framework that separates and extracts appearance and motion information within both image and event modalities, thereby improving feature density. Subsequently, a multi-granularity cross-modal alignment is introduced to align distributionally and semantically consistent features across modalities, enabling more effective fusion with rich spatial and temporal details. The experiment results demonstrate that our method achieves state-of-the-art performance in multimodal MIS, especially for small instances under challenging conditions such as fast motion and low-light settings.

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21.
arXiv (CS.LG) 2026-06-15

Neural Variability Enhances Artificial Network Robustness

作者:
Robin Preble↗Praveen Venkatesh↗Stefan Mihalas↗Kameron Decker Harris↗

arXiv:2606.13801v1 Announce Type: new Abstract: Neural responses in cortex exhibit substantial trial-to-trial variability in response to repeated stimuli, while peripheral sensory neurons respond far more consistently, leading many to wonder whether stochasticity may carry meaning. Existing work has argued that noise and signal correlations may be optimized for discrimination in animals, whereas artificial neural network (ANN) studies have shown similar benefits of noise in machine learning tasks, although most ANN work has neglected the effects of correlations. Here we investigate whether correlated noise improves the robustness of artificial neural networks to adversarial attacks and naturalistic image modifications. Using the covariance of activations under modified versus clean inputs, we find that structured noise may significantly improve network robustness. Robustness to naturalistic image modifications benefits most from structure, but this structure transfers poorly across modification types. In contrast, noise structure from adversarial attacks can generalize to other kinds of attacks. These results suggest that structured noise in ANN activations generally improves robustness, establishing a biologically plausible strategy for creating robust artificial neural networks that only relies on local information.

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22.
bioRxiv (Bioinfo) 2026-06-13

MoE-Bind: Guiding De Novo Protein Binder Generation with Sparse Experts

作者:
Sarkar↗

De novo protein binder design has been dominated by structure-based pipelines that require known three-dimensional target conformations and consume substantial compute and generation time per design, limiting their throughput and accessibility for routine large-scale binder exploration. Sequence-only generative models promise a faster and lighter alternative, yet existing systems remain uniformly dense and frequently reintroduce structural computation at inference, undermining the core advantages they were intended to deliver. Across the broader language modelling community, transformers have meanwhile transitioned from fully dense designs to sparse Mixture-of-Experts architectures that decouple capacity from per-token compute, a shift that has yet to reach sequence-only protein binder generation. We present MoE-Bind, an autoregressive protein binder generator that, for the first time in this domain, combines Multi-head Latent Attention with a sparse Mixture-of-Experts feed-forward network and is evaluated under two independent structure predictors, Boltz-2 and AlphaFold2-Multimer. Despite activating less than half the per-token parameters of compute-matched dense baselines, MoE-Bind matches or exceeds them on full-length receptor-conditioned binder generation on a leakage-free Docking Benchmark 5.0 evaluation, transfers without peptide-specific training to short-peptide design, and reduces training and inference compute by a large margin. Routing analysis on generated binders reveals interpretable expert specialization at both the individual amino acid and biochemical group level, a structured expert-token alignment not previously reported for natural-language MoE models. These results show that sparse architectural design, rather than scale, can deliver fast, structure-free, and interpretable protein binder generation.

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23.
arXiv (CS.LG) 2026-06-11

SPADE: Split-and-Delay Embeddings for Autoregressive High-Granularity Calorimeter Simulation

作者:
Joschka Birk↗Frank Gaede↗Anna Hallin↗Gregor Kasieczka↗Martina Mozzanica↗Henning Rose↗

arXiv:2606.11304v1 Announce Type: cross Abstract: We introduce SPADE (SPlit And Delay Embeddings), an autoregressive transformer for sequences whose tokens carry multiple features. Rather than embedding these features jointly, SPADE embeds them independently. Delaying each feature stream relative to the previous one allows intra-token correlations to be learned by the standard self-attention mechanism. Applied to point-cloud calorimeter shower generation in the highly granular ILD detector, SPADE is competitive with the state of the art AllShowers model on photon showers, and substantially outperforms its VQ-VAE-based predecessor OmniJet-$\alpha_C$. The mechanism is applicable to any generative task with multi-feature tokens, enabling LLM-style pretraining workflows for higher-dimensional data.

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24.
arXiv (CS.CV) 2026-06-16

CLAP: Contrastive Latent Action Pretraining for Learning Vision-Language-Action Models from Human Videos

作者:
Chubin Zhang↗Jianan Wang↗Zifeng Gao↗Yue Su↗Tianru Dai↗Cai Zhou↗Jiwen Lu↗Yansong Tang↗

Generalist Vision-Language-Action models remain constrained by the scarcity of robotic data relative to the abundance of human video demonstrations. Existing Latent Action Models attempt to use video data but often suffer from visual entanglement, encoding noise rather than manipulation skills. To address this limitation, we propose Contrastive Latent Action Pretraining (CLAP), a framework that first uses Act-VAE to learn an executable action-token vocabulary from robot trajectories and then aligns human visual transitions with this vocabulary through contrastive learning. This alignment maps unlabeled human videos into a physically grounded latent action space rather than reconstructing appearance. Building on the aligned tokens, we train CLAP-NTP as an autoregressive VLA using robot demonstrations and pseudo-labeled human videos, preserving instruction following and object generalization. For deployment and target-domain adaptation, we further introduce a post-training strategy that combines CLAP-RF, a Rectified Flow action head for low-latency continuous action chunk prediction, with Knowledge Matching regularization to preserve pretrained semantic knowledge during fine-tuning. Extensive experiments show that CLAP achieves strong performance against competitive baselines while enabling effective skill transfer from human videos to robotic execution.

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25.
arXiv (CS.CL) 2026-06-15

Deja Vu at Scale: Paraphrase-Robust Detection of Duplicate Gherkin Steps in Behaviour-Driven Software Testing with Sentence-Transformer Embeddings and a 1.1M-Step Open Benchmark

作者:
Ali Hassaan Mughal↗Noor Fatima↗Muhammad Bilal↗

Context. Behaviour-Driven Development (BDD) suites in Gherkin accumulate step-text duplication with documented maintenance cost. Prior detectors either require runnable tests or are single-organisation, leaving a gap: a static, paraphrase-robust, step-level detector and a public benchmark to calibrate it. Objective. We release (i) the largest cross-organisational BDD step corpus to date, (ii) a labelled pair-level calibration benchmark, and (iii) a four-strategy detector with a consolidation-savings model linking clusters to ISO/IEC 25010 maintainability sub-characteristics. Method. The corpus contains 347 public GitHub repositories, 23,667 .feature files, and 1,113,616 Gherkin steps, SPDX-tagged. The detector layers exact hashing, normalised Levenshtein, sentence-transformer cosine, and a Levenshtein-banded hybrid. Calibration uses 1,020 manually labelled step pairs under a released rubric (60-pair overlap, Fleiss kappa = 0.84). We report precision, recall, and F1 with bootstrap 95% CIs under the primary rubric and a score-free relabelling, and benchmark against SourcererCC-style and NiCad-style lexical baselines. Results. Step-weighted exact-duplicate rate is 80.2%; median-repository rate is 58.6% (Spearman rho = 0.51). The top hybrid cluster has 20,737 occurrences across 2,245 files. Near-exact reaches F1 = 0.822 on score-free labels; semantic F1 = 0.906 under the primary rubric reflects a disclosed stratification artefact. Lexical baselines reach F1 = 0.761 and 0.799. The savings model estimates 893,357 corpus-wide eliminable step occurrences; on the median repository 62.5% of step lines are eliminable.

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