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01.
arXiv (CS.LG) 2026-06-15

More with LESS – Local Scene Representations for Tactile Imaging

Authors:
Zohar Rimon↗Elisei Shafer↗Tal Tepper↗Daniel Kozin↗Alon Malka↗Roy Holland↗Aviv Tamar↗

arXiv:2606.14344v1 Announce Type: new Abstract: Tactile imaging seeks to reconstruct the internal structure of soft objects through touch sensing, with applications in medical diagnosis and robotic manipulation. Recent self-supervised learning approaches have shown promising results, but rely on global, unstructured representations and robot-controlled sensing, limiting generalization and practical use. We propose Local Encoder for Spatial Sensing (LESS), an object-centric tactile representation that exploits the local nature of touch. The tactile scene is modeled as a grid of recurrent encoders with local receptive fields, whose states are fused to reconstruct 2D or 3D images of internal structure. This compositional design enables strong generalization: models trained on single-inclusion phantoms accurately image objects with multiple inclusions and varying sizes. The local structure further supports spatial uncertainty estimation. In addition, we enable hand-held tactile imaging via external pose tracking and human-like palpation data, and extend tactile imaging to full 3D reconstruction.

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02.
arXiv (CS.CL) 2026-06-12

DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems

Authors:
Maojun Sun↗Yifei Xie↗Yue Wu↗Ruijian Han↗Binyan Jiang↗Defeng Sun↗Yancheng Yuan↗Jian Huang↗

Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning. However, the open-ended nature of real-world data science problems, which often span multiple taxonomies and lack standard answers, poses a significant challenge for evaluation. To address this, we introduce DSAEval, a benchmark comprising 641 real-world data science problems grounded in 285 diverse datasets, covering both structured and unstructured data (e.g., image and text). DSAEval incorporates three distinctive features: (1) Multimodal Environment Perception, which enables agents to interpret observations from multiple modalities, including text and vision; (2) Multi-Query Interactions, which mirror the iterative and cumulative nature of real-world data science projects; and (3) Multi-Dimensional Evaluation, which provides a holistic assessment across reasoning, code, and results. We systematically evaluate 13 recent advanced agentic LLMs using DSAEval. Our results show that Claude-Sonnet-4.5 achieves the strongest overall performance, MiMo-V2-Pro and GPT-5.2 lead in duration and step efficiency, respectively, and MiMo-V2-Flash is the most cost-effective. We further demonstrate that multimodal perception consistently improves performance on vision-related tasks, with gains ranging from 2.04\% to 11.30\%. Overall, while current data science agents perform well on structured data and routine data analysis workflows, substantial challenges remain in unstructured domains. Finally, we offer critical insights and outline future research directions.

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03.
arXiv (math.PR) 2026-06-18

Finite free perpetuities

Authors:
Julia Le Bihan↗Bartosz Ko{\l}odziejek↗

arXiv:2606.19115v1 Announce Type: new Abstract: We introduce and study finite free perpetuities, defined as monic polynomial solutions of degree $n$ to the affine fixed-point equation \[ p(z) = \mathbb{E}\!\left[ A^{n}\,p\!\left(\frac{z-B}{A}\right)\mathbf{1}_{\{A\neq0\}} \right] + \mathbb{E}\!\left[ (z-B)^n\mathbf{1}_{\{A=0\}} \right], \] where $A$ and $B$ are complex-valued random variables with finite moments up to order $n$. Equivalently, if $p(z)=\mathbb{E}[(z-X)^n]$, then $p$ encodes a truncated moment version of the classical perpetuity equation $X\stackrel{d}{=}AX+B$ with $X$ and $(A,B)$ independent. This places finite free perpetuities between classical perpetuities and free-probabilistic fixed-point laws. We prove existence and uniqueness under weak conditions, and we identify a broad class of admissible pairs $(A,B)$ for which the resulting polynomial has only real, nonnegative zeros. Our approach uses finite free additive and multiplicative convolutions together with a probabilistic representation via the $U$-transform. As a motivating example, we exhibit an explicit family of finite free perpetuities expressed in terms of Jacobi polynomials and show that their empirical root distributions converge to a free-beta-prime law. More generally, for admissible sequences of parameters, we prove weak convergence of the empirical root distributions of finite free perpetuities to the law of a free perpetuity characterized by the corresponding free fixed-point equation. This yields a finite-degree polynomial model approximating free perpetuities and clarifies the connection between classical affine recursions, finite free convolutions, and free probability.

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04.
arXiv (CS.AI) 2026-06-16

Reward Hacking in Language Model Agents: Revisiting AI Safety Gridworlds

Authors:
\"Omer Veysel \c{C}a\u{g}atan↗Xuandong Zhao↗

arXiv:2606.15385v1 Announce Type: new Abstract: Reward hacking, where AI systems exploit misspecified objectives to achieve high reward without satisfying intended goals, remains a central challenge in AI safety. Yet most known instances have been discovered post hoc in frontier systems where controlled study is impractical. We adapt the AI Safety Gridworlds framework into a text-based evaluation suite that reformulates classic reinforcement learning safety tasks for language-based agents. Across frontier and mid-scale models, we find that specification gaming emerges zero-shot: models systematically achieve high observed reward while underperforming on hidden safety objectives, and even apparently safe behaviors can reflect misunderstanding rather than principled safety. Reinforcement learning does not correct these failures: direct reward optimization widens the gap between observed and hidden reward, as the model's initial competence causes it to lock into locally rewarding strategies before discovering safer alternatives. This pattern persists across model scales (1.5B–14B) and is not resolved by finer credit assignment, exploration prompts, or entropy regularization. Our results show that reward hacking arises naturally when optimizing proxy objectives with capable language model agents and resists standard mitigations, suggesting that proxy-reward failures in agentic settings may require approaches beyond standard exploration and credit-assignment fixes. To facilitate reproducibility, the code for this work is available at \href{https://github.com/asparius/verl-agent-safety}{our public repository}.

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05.
arXiv (CS.AI) 2026-06-19

A Comparative Study of Pretrained Transformer Models for Quranic ASR: Speech Representations, Label Formats, and Dataset Composition

Authors:
Nabil Mosharraf Hossain↗Riasat Islam↗Unaizah Obaidellah↗

arXiv:2606.19747v1 Announce Type: new Abstract: Quran Automatic Speech Recognition (ASR) aims to convert Quranic recitation into text, enabling applications such as aided memorisation tools and Quranic search engines. However, existing ASR models often exhibit high Word Error Rates (WER) on user-recited verses and lack full coverage of the Quranic corpus. This paper presents a systematic empirical study of domain-specific fine-tuning of pretrained Transformer-based models for Quranic ASR, using advanced speech feature extraction methods: Wav2Vec2.0, HuBERT, and XLS-R. These models apply self-supervised learning by masking portions of input audio and using Transformer architectures to learn context-aware speech features. The pretrained models are fine-tuned on a filtered Quranic dataset exceeding 870 hours of professional and user recitations. Through comprehensive ablation studies across feature extractors, output label formats, training strategies, and clip durations, we identify the key factors that affect transcription accuracy in this domain. Our best-performing configuration achieves a WER of 0.08 on the EveryAyah subset and 0.11 on the combined EveryAyah+Tarteel setting, representing roughly a five-percentage-point gain over the Citrinet baseline (WER = 0.163) while reducing combined-model training time from 140 hours to 40 hours. Arabic text without diacritics yields the best fine-tuning results, and Wav2Vec2-XLSR-53 provides the strongest overall representation. Future work includes improving dataset quality and developing phoneme-aware models to extract deeper speech feature representations for Tajweed-sensitive applications.

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

Simulating Students' Java Programming Errors with Large Language Models

Authors:
Ali Keramati↗Jie Cao↗Iman Mohammadi↗Mark Warschauer↗Yang Shi↗

Understanding student errors in the programming is a cornerstone of programming education, yet obtaining a representative set of student errors for any newly designed task remains slow and costly, since authentic submissions only accumulate after extensive classroom deployment. This paper explores whether large language models (LLMs) can serve as scalable proxies for students by simulating realistic logical errors in code submissions. Using the CodeWorkout dataset of 74,000+ unique student Java submissions across 37 problems, we evaluate five LLMs under three mainstream prompting strategies: Input-Output (IO), Chain-of-Thought (CoT), and iterative Self-Refine. We assess performance along two key dimensions: diversity (the range of distinct error patterns) and alignment (alignment with authentic student mistakes), and examine how these vary by struggling level of programming tasks. Our quantitative findings reveal that while all models generate diverse errors, their alignment to human submissions diverges: Claude Sonnet 4 achieves the most balanced performance. In addition, we conducted a blinded expert annotation study (N = 401) comparing synthetic and authentic errors. This qualitative analysis confirms that the generated errors are functionally indistinguishable from authentic student errors. Moreover, higher-struggling-level problems elicit more diverse but less student-like errors. These results highlight trade-offs in using LLMs to simulate human learners and suggest design considerations for integrating synthetic errors into teachable agents, intelligent tutoring systems, and large-scale learning analytics.

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07.
Nature Medicine 2026-06-22

Biological aging and generational shifts in early-onset cancer risk

Authors:
Ruiyi Tian↗

Incidence of early-onset cancer is rising globally in recent generations, which underscores the need to elucidate the influence of emerging generational risk factors. Systemic and organ-specific aging reflects the cumulative impact of exposures and may provide an integrative and complementary approach to understand early-onset cancer risk. Here among 154,169 young adults from the United Kingdom Biobank, systemic aging measured by PhenoAge increased across birth cohorts, with 23% s.d. increase for those born 1965–1974 versus 1950–1954, and was associated with early-onset solid cancer risk (hazard ratio (HR)per s.d. 1.08; 95% confidence interval (CI), 1.03–1.13), driven by lung, gastrointestinal and uterine cancers, independent of genetic risks of aging and cancer. Patterns were consistent using alternative systemic aging measures, including the Klemera–Doubal method-defined age gap and metabolomic-based age gap. These findings were validated partially among 10,262 participants in the United States All of Us Research Program. Proteomics-based organ-specific aging analyses linked immune aging with early-onset lung cancer (HRper s.d. 1.89; CI, 1.20–2.97) and adipose tissue aging to early-onset colorectal cancer (HR 1.60; CI, 1.11–2.32). Greater age gap, reflecting more advanced biological aging relative to chronological age, may serve as a driver associated with risk of early-onset solid cancers, highlighting the importance of uncovering underlying mechanisms to guide effective prevention strategies. Analyses of population cohorts found that young adults exhibited earlier systemic and organ-specific aging, which was associated with increased risk of early-onset cancer compared with older adults born decades earlier.

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08.
arXiv (quant-ph) 2026-06-24

Auxiliary Schmidt Rank as a Resource for Photonic Bell Measurements

Authors:
Pradip Laha↗Peter van Loock↗

arXiv:2606.24591v1 Announce Type: new Abstract: In quantum communication and fusion-based quantum computation, photonic Bell measurements are fundamentally limited when only passive linear optics is employed. While for qubits, some Bell states can be unambiguously identified with static beam splitters and no extra photons or entanglement, additional auxiliary photons or at least additional auxiliary degrees of freedom with a certain level of additional entanglement are needed to approach or attain a complete, deterministic Bell measurement. Here, we prove an exact resource threshold when the same two photons carry system qudits of dimension $d$ and a fixed auxiliary entangled state $\Phi$, possibly distributed over several additional degrees of freedom, with total Schmidt rank $r_\Phi$. We show that a single conclusive Bell-label functional can occur for $r_\Phi\geqslant\lceil d/2\rceil$, but deterministic discrimination of all $d^2$ Bell-state labels requires $r_\Phi\geqslant d$. A maximally entangled rank-$d$ auxiliary state achieves the bound by local Bell-basis sorting between each photon's system and auxiliary degrees of freedom. Thus, the auxiliary Schmidt rank is a certified resource for ancilla-photon-free, embedded photonic Bell measurements.

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09.
arXiv (quant-ph) 2026-06-19

QMCtwin: Master-Equation Simulation of Syndrome Statistics Beyond Pauli Noise

Authors:
Tong Shen↗Huo Chen↗Benchen Huang↗Tyler Takeshita↗Arian Vezvaee↗Izhar Medalsy↗Daniel A. Lidar↗

arXiv:2606.19848v1 Announce Type: new Abstract: As quantum error correction moves toward large-scale experimental implementations, decoder performance increasingly depends on how faithfully hardware noise is translated into syndrome statistics. Standard stabilizer workflows achieve scalability by replacing device dynamics with stochastic Pauli or detector-error models, but this compression can discard coherent phase information, nonunital drift, continuous-time effects of always-on couplings, and correlations generated by simultaneous Hamiltonian and dissipative evolution. Here we present QMCtwin, a sign-problem-suppressed quantum Monte Carlo framework for master-equation simulation of QEC circuits, and apply it to a full syndrome-extraction round of a distance-$7$ rotated surface code with $97$ physical qubits. The open-system model includes realistic superconducting-device noise mechanisms such as relaxation, pure dephasing, coherent gate miscalibration, residual $ZZ$ crosstalk, and drive-qubit detuning. By directly estimating syndrome observables from the QMC-generated stochastic density matrix estimator, we compare the master-equation dynamics with their Pauli-twirled Clifford simulation counterparts. QMCtwin predicts syndrome-extraction biases and correlations between syndromes and proxies of logical-string-parity that are absent or strongly suppressed in the stochastic Pauli description. We introduce information-theoretic diagnostics that further quantify how information concerning syndromes versus string-parity proxies differs between the realistic master-equation simulation and the corresponding Pauli-twirled model. These results show that QMC-based master-equation digital twins can expose noise features hidden by conventional Pauli/Clifford noise models and provide a practical path toward more accurate decoder-facing syndrome models.

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10.
arXiv (CS.CV) 2026-06-24

DLTPose: 6DoF Pose Estimation From Accurate Dense Surface Point Estimates

Authors:
Akash Jadhav↗Michael Greenspan↗

We propose DLTPose, a novel method for 6DoF object pose estimation from RGBD images that combines the accuracy of sparse keypoint methods with the robustness of dense pixel-wise predictions. DLTPose predicts per-pixel radial distances to a set of minimally four keypoints, which are then fed into our novel Direct Linear Transform (DLT) formulation to produce accurate 3D object frame surface estimates, leading to better 6DoF pose estimation. Additionally, we introduce a novel symmetry-aware keypoint ordering approach, designed to handle object symmetries that otherwise cause inconsistencies in keypoint assignments. Previous keypoint-based methods relied on fixed keypoint orderings, which failed to account for the multiple valid configurations exhibited by symmetric objects, which our ordering approach exploits to enhance the model's ability to learn stable keypoint representations. Extensive experiments on the benchmark LINEMOD, Occlusion LINEMOD and YCB-Video datasets show that DLTPose outperforms existing methods, especially for symmetric and occluded objects. The code is available at https://anonymous.4open.science/r/DLTPose_/ .

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11.
arXiv (CS.CV) 2026-06-24

3D Masked Autoencoders are Robust Learners of Volumetric and Multimodal Cellular Representations for Microscopy

Authors:
Amirhossein Kardoost↗Lion Gleiter↗Tingying Peng↗Carsten Marr↗

Self-supervised learning in fluorescence microscopy often relies on 2D projections, despite the inherently three-dimensional nature of cells. We present a systematic comparison of 2D and 3D masked autoencoders (MAE-2D vs. MAE-3D) on volumetric microscopy data. Under matched architectures and training protocols, MAE-3D consistently outperforms 2D max-projection and slice-based variants on downstream single-cell tasks. We further align visual representations with a pretrained protein language model (ESM2) and show that cross-modal supervision yields larger gains for volumetric models. Channel cross-attention and frequency-domain regularization are critical for leveraging 3D spatial context. On a protein–protein interaction task, MAE-3D achieves a ROC–AUC of 0.865, outperforming prior methods by up to +0.025. For protein localization, our best 3D model attains state-of-the-art AUC$_{micro}$ (0.952) and F1$_{micro}$ (0.742), improving over previous approaches by +0.003 and +0.010 absolute, respectively. Overall, these results demonstrate the advantages of native 3D modeling and multimodal alignment for representation learning in single-cell microscopy.

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12.
arXiv (quant-ph) 2026-06-19

Optimizing resource allocation for accuracy in noisy variational quantum algorithms

Authors:
Harshit Verma↗Thomas Ayral↗Alexia Auff\`eves↗Robert Whitney↗

arXiv:2606.20153v1 Announce Type: new Abstract: For quantum algorithms to achieve their full potential, we need methodologies to optimize them, such as reaching a given output accuracy with minimal resource costs. Here, we develop such a methodology for a class of Noisy Intermediate-Scale Quantum (NISQ) algorithms. We leverage simulations of a Variational Quantum Eigensolver (VQE) to propose a phenomenological model of such algorithms that captures the complex relationship between algorithmic accuracy, algorithmic resource costs, and the noise that exists in realistic quantum hardware. For this, we take the algorithmic resource cost to be the total number of quantum gate-operations in the algorithm; minimizing this cost typically makes the algorithm faster and more energy-efficient. We consider the subtle trade-off between quantum circuit size (small circuits are too imprecise, but large ones are too noisy), and the number of iterations of that quantum circuit for the full algorithm to sufficiently converge. Using a noise-metric-resource methodology, we identify the sweet spot (of circuit size versus iterations) that minimizes the algorithmic resource costs for a desired algorithm accuracy. It also gives the circuit size that maximizes algorithm accuracy for a fixed resource cost. Our methodology provides a practical guideline for near-term deployment of variational algorithms on realistic noisy hardware, including hardware that uses error mitigation.

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13.
arXiv (CS.LG) 2026-06-17

Damage Adaptation in Seconds for Architected Materials

Authors:
James Avtges↗Jake Ketchum↗Helena Young↗Taekyoung Kim↗Ryan Truby↗Todd Murphey↗

arXiv:2606.17394v1 Announce Type: cross Abstract: Adaptation to damages and in-situ physical repairs is essential for long-term robot autonomy, yet challenging outside of narrowly defined and well-anticipated bounds. In this work we proprioceptively adapt to catastrophic damage in soft-actuated systems in under one minute. Architected materials are well equipped for adaptation: actuator failure occurs gradually rather than acutely, and damage can be described in a low-dimensional, discrete coordinate space. Surprisingly, latent damage representations plus a simple yet robust ensemble method is sufficient for adapting to unseen damage in real-time. Moreover, we identify conditions under which exponential sample complexity collapses to linear sample complexity for learned representations of architected materials, a concrete advantage over rigid components or continuum soft mechanisms. We demonstrate LEAP, our method for adaptive proprioception, via a tracing task for a 6DoF soft wrist based on Handed Shearing Auxetic (HSA) actuators. Our algorithm is able to adapt to cuts, burns, and actuator repairs, enabling simulation-free real-time adaptation that is critical for realizing the promise of soft robots outside the lab. Videos and more information are available at https://murpheylab.github.io/leap.

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14.
arXiv (quant-ph) 2026-06-19

Variational Polaron Theory for Ground States of Strongly Coupled Light-Matter and Electron-Phonon Systems

Authors:
Nguyen Thanh Phuc↗

arXiv:2606.19748v1 Announce Type: cross Abstract: Strong light-matter and electron-phonon coupling generate ground states dressed by virtual bosonic excitations, making bare-state truncations and perturbative treatments unreliable in the ultrastrong-coupling regime. We introduce a nonperturbative variational ground-state framework based on a state-dependent polaron transformation, combined with a product-state ansatz and a second-order perturbative correction for residual matter-boson entanglement. We show that the optimized transformed frame becomes asymptotically decoupled at infinite coupling, because the leading linear coupling is canceled while off-diagonal matter transitions are suppressed by displaced-oscillator overlaps. The approach is asymptotically correct in both weak- and strong-coupling limits and remains accurate in the intermediate regime, where fixed polaron transformations are least reliable. Dicke-model benchmarks reproduce ground-state energies, fidelities, and the superradiant transition, with second-order energy errors below 0.2%. Holstein-model benchmarks yield errors below 0.5% and clarify how translational symmetry affects wave-function quality. This dressed-basis framework enables nonperturbative modeling of strongly coupled light-matter and electron-phonon systems.

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

Whose hotel does the AI recommend? An algorithm audit of reputation signals in LLM-assisted hotel selection

Authors:
Mirza Samad Ahmed Baig↗Syeda Anshrah Gillani↗Asher Ali↗

Travelers increasingly ask large language model (LLM) assistants which hotel to book, making these systems gatekeepers of property visibility – yet what moves their recommendations is undocumented. We conduct a pre-specified algorithm audit using a randomized choice-based conjoint: across personas, prompt templates, and twelve open-weight and proprietary models, assistants choose among five hotels whose guest rating, review volume and recency, management response, chain affiliation, price, eco-certification, and list position are independently randomized. We estimate the average marginal component effect of each signal on the probability of recommendation. Guest rating and price dominate (a top rating raises selection by 31.6 percentage points; a high price lowers it by 30.0), reproducing human valence-and-price primacy but over-weighting eco-certification and ignoring management response. List position – a content-free artifact – shifts recommendations causally, worth about \$12 per night. Stated reasons track revealed weights imperfectly. The findings ground generative engine optimization and the accountability of AI infomediaries in causal evidence.

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16.
arXiv (quant-ph) 2026-06-19

Efficient upsampling for tensor-network and quantum-state encoded functions

Authors:
Siddhartha E. Guzman↗Egor Tiunov↗Leandro Aolita↗

arXiv:2601.03885v2 Announce Type: cross Abstract: Both tensor trains (TTs) and quantum states provide compressed representations of grid-structured data with potentially exponential compression power. We present a unified framework for upsampling data encoded in vector amplitudes, with efficient realizations in both classical TT and quantum settings. Starting from an \(n\)-core TT or an \(n\)-qubit state on a coarse grid with \(2^n\) points, the construction produces an \((n+m)\)-core TT or \((n+m)\)-qubit state on a finer grid with \(2^{n+m}\) points. In the TT setting, it supports interpolation, quasi-interpolation, augmentation, and synthesis through efficient low-rank contractions, with the added \(m\) cores retaining constant rank. For function-value encodings, the resulting interpolation satisfies an \(\ell^2\)-error bound independent of the number of added grid points, achieves exponential compression at fixed accuracy, and has a logarithmic complexity in the number of grid points. In the quantum setting, the refined state is prepared by a \(\mathrm{poly}(n,m)\)-size circuit using \(\log(p+1)\) ancillas, where \(p\) controls the smoothness of the quasi-interpolant; the corresponding error scales quadratically with the initial grid spacing. We validate our framework for tensor networks in one-, two-, and three-dimensional examples, including functions, derivatives, airfoil masks, and synthetic random fields such as three-dimensional turbulence. In particular, fractal fields can be generated directly in TT format with logarithmic memory and runtime. These results open a practical route to multiscale solvers, generative models, and geometry-aware algorithms on tensor-network and quantum platforms, with potential applications in scientific simulation, imaging, and real-time graphics.

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17.
medRxiv (Medicine) 2026-06-15

Long-read sequencing enables high-accuracy mitochondrial heteroplasmy detection in Parkinson's disease

Authors:
Lüth↗Schaake↗Much↗Belyea↗M. M↗Seibler↗Grünewald↗May↗Klein↗Weissensteiner↗Trinh↗

Background: Low-frequency heteroplasmic mitochondrial DNA (mtDNA) variants are associated with aging and neurological diseases, including Parkinson's disease (PD). Targeted deep mtDNA sequencing using PacBio HiFi long reads has the potential to resolve heteroplasmy across the full mitochondrial genome with high accuracy. Methods: To validate Vega PacBio sequencing for detecting mtDNA heteroplasmy, we analyzed four predefined mixtures of two mtDNA haplotypes. We generated a single long-range PCR amplicon covering the entire mitochondrial genome. These amplicons were mixed at predefined ratios (minor mixture haplotype component: 5%, 2%, 1%, and 0.1%). Variant calling was performed using Mutserve2, and accuracy was assessed by calculating the F1 score from comparisons between expected and detected variants. Full-length mtDNA PacBio sequencing was applied to investigate heteroplasmy across fibroblast passages derived from five LRRK2 p.Gly2019Ser variant carriers (n=3 affected with PD and n=2 unaffected carriers). Changes in mtDNA heteroplasmy level and variant load were assessed longitudinally using a linear mixed model. Results: The single-amplicon approach enabled full-length haplotype resolution without amplification bias associated with overlapping PCR strategies. The F1 score of the predefined mixtures was 1.0 for heteroplasmy levels between 5% and 1% and remained high (0.91) at 0.1%. We detected n=10/62 variants discordant with the Illumina reference at the 0.1% mixture, but sensitivity remained very high at 1.00 in that mixture. Detected minor variants closely matched expected heteroplasmy levels, with average variant levels of 0.057 (5%), 0.022 (2%), 0.011 (1%), and 0.001 (0.1%). Across twelve fibroblast passages, we observed fewer mtDNA heteroplasmic variants ({beta}=-3.2, p=0.026). Increased heteroplasmic variant load over time was also associated with older age ({beta}=1.50, p=0.001) and PD affection status ({beta}=5.0, p=1.0 x 10-4) in LRRK2 variant carriers. Notably, we observed distinct patterns of heteroplasmic variants that either increased or decreased in heteroplasmy level across passages. Conclusion: PacBio HiFi sequencing, combined with a single-amplicon strategy, enables accurate full-length mtDNA heteroplasmy detection and longitudinal analysis, providing a valuable tool for studying mitochondrial variation and dynamics in disease.

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18.
arXiv (quant-ph) 2026-06-16

The Inverse Born Rule Equivalence. On the Informational Limits of Real-Valued Amplitude Encodings and the Measurement of Quantum Advantage in Data Embeddings

Authors:
Sebastian Zaj\k{a}c↗Jacob L. Cybulski↗Bartosz Dziewit↗Tomasz Kulpa↗

arXiv:2602.21350v2 Announce Type: replace Abstract: When does quantum data encoding provide genuine quantum advantage, and when does it merely rephrase a classically solvable problem? We prove an Equivalence Theorem demonstrating that any encoding mapping classical data to real-valued amplitudes, $\vert\psi_c\rangle = \sum_i c_i \vert i\rangle$ with $c_i \in \mathbb{R}$ and $\sum_i c_i^2 = 1$, composed with a data-independent parameterised unitary and computational-basis measurement, yields exactly the class of classical quadratic forms. We identify the geometric mechanism driving this collapse: the restriction to $\mathbb{R}$ forces a vanishing Berry connection, removing the complex phases required for data-dependent quantum interference. To operationalize this boundary, we introduce encoding diagnostics – phase complexity $C[\Phi]$ and mode-wise von Neumann mutual information $I[\Phi]$ – and link them to the information-geometric excess $\Delta g$. We show that for all real-valued encodings, $\Delta g = 0$ identically. We term the misidentification of such models as evidence of quantum computational power the Inverse Born Rule Fallacy. Supported by numerical experiments, our results establish that complex-phase structure is a strictly necessary condition for data-driven (Type~B) quantum advantage.

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19.
arXiv (quant-ph) 2026-06-24

Interaction-Enhanced Ergotropy in Phase-Driven Andreev Bound State Quantum Batteries

Authors:
Disha Verma↗B. Vigneshwar↗R. Sankaranarayanan↗

arXiv:2606.24456v1 Announce Type: new Abstract: We investigate a phase-driven quantum battery composed of two interacting Andreev bound state (ABS) units, providing a minimal superconducting platform for coherent energy storage. By analyzing the ergotropy dynamics under a superconducting phase ramp, we show that the interplay between avoided-crossing excitation and interaction-induced hybridization strongly modifies the charging process. In the high-transparency regime relevant for graphene SNS junctions, the interaction enhances the stored extractable work and generates pronounced oscillatory charging dynamics associated with coherent redistribution between coupled ABS sectors. The phase-resolved evolution further reveals optimal charging windows during the Josephson cycle, indicating the possibility of phase-programmable energy extraction through partial-cycle operation. Overall, our results identify interaction-assisted avoided-crossing dynamics as a microscopic mechanism for controllable energy storage in superconducting quantum batteries.

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20.
arXiv (CS.AI) 2026-06-17

CyberEvolver: Structured Self-Evolution for Cybersecurity Agents On the Fly

Authors:
Yihe Fan↗Changyi Li↗Lichen Xu↗Xudong Pan↗Jiarun Dai↗Hong Geng↗Min Yang↗

arXiv:2605.26195v2 Announce Type: replace-cross Abstract: LLM-based agents are increasingly used for cybersecurity tasks, but most existing systems rely on fixed, human-designed scaffolds that struggle to adapt across diverse targets and failure modes. We introduce \textsc{CyberEvolver}, a self-evolving cybersecurity agent framework that iteratively revises its own scaffold based on experience from failed execution attempts. Self-evolution in cybersecurity is challenging because the space of possible scaffold changes is largely unstructured, execution feedback is sparse and often obscured by the environment, and low-diversity updates can cause errors to compound over repeated iterations. \textsc{CyberEvolver} addresses these challenges with a four-layer evolvable agent architecture that decomposes scaffold optimization into structured components, a trace-to-diagnosis mechanism that converts noisy execution logs into actionable revision signals, and a population-based beam search strategy that preserves diverse agent variants during evolution. We evaluate \textsc{CyberEvolver} on CTF challenges, vulnerability exploitation, and penetration-testing tasks using four open-source LLMs. Across these settings, \textsc{CyberEvolver} improves the seed agent's success rate by $13.6$\,\% on average, and outperforms six human-designed cybersecurity agents as well as two self-improvement methods adapted from other domains. These results suggest that scaffold self-evolution is a promising direction for building adaptive LLM agents for security testing.

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21.
arXiv (CS.AI) 2026-06-16

Minimal Oversight: Uncertainty-Aware Governance for Delegated AI Systems

Authors:
Carlos R. B. Azevedo↗

arXiv:2606.15563v1 Announce Type: new Abstract: AI systems increasingly delegate decisions to specialized models, evaluators, tools, and supervisory controllers. The central AI problem is no longer only model accuracy, but uncertainty-aware governance: how much autonomy to grant, which evidence should calibrate trust, what performance ceiling a delegated AI system can sustain, and when human intervention becomes necessary. We propose the Minimum Sufficient Oversight Principle (MSO), a variational principle for principled autonomy delegation: minimize governance burden on the Fisher information manifold subject to a delivery constraint. The resulting Euler-Lagrange solution yields a water-filling allocation of governed delegation across the task space. Building on a revealed-action governed delegation channel model, we prove a capacity theorem for stationary symbolwise review policies, derive a local first-order approximation relating workflow complexity to quality degradation, and give a drift-dominated autonomy-time scaling law linking intervention timing to effective capacity, complexity, and drift. Within this framework, masking appears as a structural AI-governance pathology: corrected performance can hide the competence signal needed to calibrate trust. Synthetic simulations and a semi-real reconstructed workflow support design prescriptions including upstream-first correction, sensitivity-based intervention, and explicit feasibility checks before autonomy is expanded. The result is a computable framework for uncertainty, planning, and oversight in delegated AI systems. A companion Python package is available at https://github.com/crbazevedo/delegation-lab.

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22.
medRxiv (Medicine) 2026-06-22

Knowledge, Attitudes, and Practices Regarding Maternal Nutrition Counselling Among Frontline Health Workers in Udupi, Karnataka, India: A Sequential Explanatory Mixed-Methods Study

Authors:
Amrutha↗M. S↗Rao↗C. R↗Hebbar↗Vennila↗Maiya↗G. A↗Bhat↗Kotebagilu↗N. P↗Bhagavath↗…

Background Indias maternal nutrition profile is undergoing a dual-direction shift, with persistent undernutrition coexisting alongside rising overweight and micronutrient deficiencies. Despite national efforts through Integrated Child Development Services (ICDS) and the National Health Mission (NHM), maternal dietary diversity remains suboptimal in India. Frontline health workers (FLWs) play a central role in delivering nutrition counselling; however, gaps remain between knowledge and its translation into practice, highlighting the need to strengthen training, applied competencies, and health system support within primary care settings. Objective To assess knowledge, attitudes, and practices (KAP) regarding maternal nutrition counselling among FLWs and to explore contextual factors influencing counselling delivery. Methods A sequential explanatory mixed-methods study was conducted in Udupi, Karnataka, India. In phase one, 46 FLWs- Accredited Social Health Activists (ASHA), Community Health Officers (CHO), and Primary Health Care Officers (PHCO) completed a validated Knowledge, Attitudes, and Practices (KAP) questionnaire. Data were analysed using descriptive statistics, Kruskal-Wallis test, Spearman correlation, and exploratory multiple linear regression. In phase two, one focus group discussion with 21 participants was conducted and analysed using reflexive thematic analysis. Results FLWs demonstrated moderate KAP scores (37.50 {+/-} 5.09), with lower scores observed in dietary diversity knowledge and counselling practices. CHOs and PHCOs had significantly higher knowledge (p < 0.001) and practice scores (p = 0.002) compared to ASHAs, while attitudes were similar across cadres. Knowledge was positively associated with practice ({rho} = 0.389, p = 0.008). Exploratory regression indicated that cadre and knowledge were associated with practice, while attitude was not statistically significant. Qualitative findings suggested that counselling was largely protocol-based and constrained by workload, limited counselling tools, economic barriers, and cultural food practices. Conclusion Despite positive attitudes towards maternal nutrition counselling, frontline health workers demonstrated gaps in knowledge and counselling practices. Mixed-methods findings suggest that counselling delivery is shaped by both provider competencies and health-system constraints, highlighting the need for implementation-focused strategies to strengthen maternal nutrition counselling in routine antenatal care.

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

Dummy Backdoor as a Defense: Removing Unknown Backdoors via Shared Internal Mechanisms for Generative LLMs

Authors:
Kazuki Iwahana↗Masaru Matsubayashi↗Takuma Koyama↗Toshiki Shibahara↗Kenichiro Omintato↗Akira Ito↗

Backdoor attacks pose a serious threat to the safety and reliability of Large Language Models (LLMs), as they cause models to behave normally on clean inputs while producing attacker-specified responses when hidden triggers are present. Removing such unknown backdoors is particularly challenging when the defender does not know the backdoor attack types or the internal mechanisms formed through backdoor training. In this work, we propose a simple but effective backdoor removal method based on shared internal mechanisms across different backdoors. First, we show that different backdoors with the same task (attack objective) induce similar trigger-activated changes in the internal activations. Motivated by this observation, our method intentionally embeds a backdoor with a known trigger (dummy backdoor) and then removes it through further fine-tuning on dummy-triggered inputs paired with clean responses. Since the dummy backdoor and the unknown backdoor can rely on shared internal mechanisms, removing the dummy backdoor also reduces the effect of the unknown backdoor. We evaluate our method on three backdoor attack types across multiple model families. Experimental results show that our method substantially reduces the attack success rate of the unknown backdoor while preserving model utility, outperforming representative existing defense methods in both backdoor removal effectiveness and utility preservation. These findings suggest that a defender-controllable backdoor can serve as a helpful proxy for mitigating unknown backdoors in generative LLMs.

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24.
arXiv (CS.CV) 2026-06-19

Vision-Reasoning-Guided Occlusion Removal from Light Fields

Authors:
Mohamed Youssef↗Oliver Bimber↗

Occlusion-robust scene recovery remains a major challenge in computational imaging, particularly in natural environments where dense foreground vegetation severely limits visibility. We propose a vision-reasoning-guided light field occlusion removal framework that combines the visibility recovery capability of light field integration (LFI) with the semantic reasoning capacity of vision-language models (VLMs). Multi-view observations are first integrated via LFI to suppress foreground occlusions and produce an initial visibility-enhanced representation. A VLM is then incorporated as a conditional semantic prior to restore degraded structures and recover fine details, guided by the observed measurements. To improve recovery consistency and reduce hallucination artifacts, we introduce a multi-sample fusion strategy that aggregates multiple generated hypotheses into a unified estimate. Experimental results on synthetic and real-world datasets demonstrate state-of-the-art performance, achieving the highest average SSIM across four synthetic light field benchmark scenes (4-Syn) and strong generalization across structured and unstructured acquisition settings. These results highlight the effectiveness of combining physical imaging constraints with vision-language reasoning for robust perception under severe occlusion, with applicability to search-and-rescue and exploratory robotic navigation.

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