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

Formalizing Numerical Analysis: An Agent Pipeline and Quality Audit Beyond Kernel Acceptance

Authors:
Theodore Meek↗Siyuan Ge↗Di Qiu Xiang↗Simon Chess↗Vasily Ilin↗

arXiv:2606.14000v1 Announce Type: new Abstract: Recent work has demonstrated that coding agents can formalize entire advanced mathematics textbooks in Lean 4, yet existing efforts concentrate on branches of mathematics already well-represented in mathlib and measure success solely through kernel acceptance. We address both limitations by applying a coding agent to formalize Numerical Methods for Ordinary Differential Equations, a textbook in numerical analysis that is largely absent from mathlib, stressing the agent's capacity to develop new theory from scratch. We further introduce a systematic, reproducible three-dimensional framework for evaluating the quality of agent-produced formalizations beyond compilation: semantic correctness, Mathlib reuse, and cross-file reuse via LLM-as-judge methods. Applying this framework to our own formalization and to the released outputs of RepoProver and M2F, we uncover recurring unfaithful formalization patterns, including incomplete multi-part statements, added weakening hypotheses, and parameter restrictions, that kernel acceptance entirely obscures. Our results suggest that compilation-based metrics substantially overstate formalization quality, and we provide a reproducible audit methodology to support more rigorous evaluation of future autoformalization systems.

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02.
arXiv (CS.CL) 2026-06-18

RECOM: A Validity Discrimination Tradeoff in Automatic Metrics for Open Ended Reddit Question Answering

Authors:
Pushwitha Krishnappa↗Amit Das↗Vinija Jain↗Aman Chadha↗Tathagata Mukherjee↗

Automatic metrics are the default for evaluating LLM-generated text, yet a metric is quietly asked to do two jobs: tell genuine content alignment from surface coincidence (validity), and tell a better system from a worse one (discriminative power). On open-ended, opinion-driven question answering, the two are in tension. We introduce RECOM (Reddit Evaluation for Correspondence of Models), a contamination-free evaluation dataset of 15,000 r/AskReddit questions (September 2025), each paired with its authentic community replies, which postdate every evaluated model's training cutoff. Scoring five open-source LLMs (7–10B) against every reply each metric paired with a random-derangement noise floor we find that no metric does both jobs well. Cosine similarity separates real from random answers (Cohen's $d \approx 2$) but cannot rank the five models ($|d| < 0.1$); BERTScore precision appears to rank the models (raw $|d|$ up to 0.63), but once response length is controlled this collapses to $|d| = 0.09$ and its validity is weak ($d \approx 0.8$, versus cosine's $\approx 2$). Because every metric scores the same outputs, this validity–discrimination tradeoff is a property of the metrics, not the models, and we argue it stems from representation design. Three independent LLM judges reproduce the validity gap and likewise separate the five models only weakly. We recommend reporting metrics on both axes, with an explicit random-baseline floor. RECOM is publicly available at https://anonymous.4open.science/r/recom-D4B0

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03.
arXiv (CS.CV) 2026-06-19

InfantFace: Detecting infant faces in neonatal clinical environments

Authors:
Abdullah Bin-Obaid↗Maria M. Cobo↗Rebeccah Slater↗Lionel Tarassenko↗Mauricio Villarroel↗

Reliable localisation of the neonatal face is the first step for several video-camera based non-contact assessments such as pain and distress related facial expression analysis, pain scoring, cardiorespiratory signal extraction and cessation of breathing alerts. However, major challenges persist in neonatal clinical environments. Cluttered backgrounds, illumination changes and poor lighting conditions can reduce the accuracy of face detection models. Clinical interventions, monitoring equipment and, in some cases, medical devices can obstruct the face, making visual assessment difficult. We propose a one-stage YOLOv11m-based model tailored for face detection of infants in neonatal clinical environments. We combined multiple publicly available datasets (VGGFace2, CelebA, FDDB, WIDER FACE) to train and evaluate our proposed model. We then fine-tuned our model on a neonatal research dataset involving 228 videos from 114 recording sessions of 113 independent infants. Before fine-tuning, our model achieved an AP50 of 0.87, surpassing the performance of three state-of-the-art general face detectors. Performance improved further to an AP50 of 0.96 after clinical-domain adaptation. Evaluating face detection performance across different datasets remains a challenge due to the lack of publicly available neonatal datasets. Prioritising the creation of such datasets, while upholding appropriate privacy safeguards and ethical standards in their creation and use, would greatly support further progress in this field.

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04.
Nature (Science) 2026-06-12

‘Student Geng’ ignites research-integrity scandal in China after calling out senior academics<b> </b>

Authors:
Xiaoying You↗

Video blogger’s viral accusations of data manipulation in Nature journals have sparked intense debate and speedy institutional investigations. Video blogger’s viral accusations of data manipulation in Nature journals have sparked intense debate and speedy institutional investigations.

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05.
arXiv (CS.CV) 2026-06-18

OneCanvas: 3D Scene Understanding via Panoramic Reprojection

Authors:
Bart{\l}omiej Baranowski↗Dave Zhenyu Chen↗Matthias Nie{\ss}ner↗

Existing approaches to 3D scene understanding in Vision-Language Models (VLMs) either rely on complex, model-specific geometry encoders or large training budgets in pursuit of spatial reasoning. Instead, OneCanvas aggregates patch features from all views onto a single equirectangular panoramic canvas. Namely, each patch is unprojected to a 3D world coordinate using its depth and camera pose, then placed on the canvas at the continuous longitude and latitude of that point as seen from the canvas origin, with no rasterization or aggregation across overlapping views. A 3D position embedding of the patch's metric coordinates is added to its feature, restoring the depth lost when collapsing the world position to an angular canvas coordinate. Patches from all frames thus share one spatial coordinate system with no fusion or major architectural modifications of the backbone. The pretrained VLM consumes this representation as if it were an ordinary image. Because the canvas can be centered on any pose of interest, the same representation directly supports situated reasoning from a specific viewpoint, a common requirement in robotics and embodied AI. Thanks to this representation, we can also introduce a spatial pretraining curriculum: by procedurally placing patch features of objects, drawn from real images, at chosen 3D world positions on an otherwise empty canvas, we generate on-the-fly supervision spanning a broad range of spatial reasoning tasks, with answer distributions controlled to reduce spatial reasoning shortcuts. OneCanvas achieves state-of-the-art accuracy on SQA3D and VSI-Bench, and generalizes to out-of-distribution data on SPBench, using an order of magnitude less training compute than the strongest competing methods.

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06.
arXiv (math.PR) 2026-06-11

Mean-field theory via dissociated arrays for particle systems interacting through noisy weights

Authors:
Nicolas Fournier↗Datong Zhou↗

arXiv:2606.12135v1 Announce Type: new Abstract: We study a mean-field limit for a $N$-particle system in which each particle follows a diffusion and interacts with other particles through a weight on each directed edge. Each weight evolves according to its own nonlinear SDE driven by a Brownian motion, with coefficients involving the states of the two endpoint particles of the edge. The initial vertex and edge variables are assumed to have a dissociated Aldous–Hoover form. We construct the limiting nonlinear SDE by averaging the interaction over an independent neighbor and an edge input, prove its well-posedness, and show that the dissociated vertex-edge structure is propagated by the dynamics. This propagation property is an analogue of propagation of chaos in the case where the weight of each edge may remain correlated with the states of the two endpoint particles. Under either a bounded-observable assumption or a sub-Gaussian edge-input condition, the finite system converges to this limit through quantitative coupling estimates for a typical particle and a typical edge. We also prove the convergence of the empirical measure of particle's state pairs and their interaction weights.

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07.
arXiv (quant-ph) 2026-06-16

Watching a Superconducting Coplanar Waveguide Heat Up with a Single Color Center

Authors:
Ioannis Karapatzakis↗Jeremias Resch↗David Hunger↗Wolfgang Wernsdorfer↗

arXiv:2606.15398v1 Announce Type: new Abstract: Single color centers in diamond offer a local probe of their cryogenic environment, providing a direct way to quantify heating in spin-control hardware. Here, we establish a single spectrally stable tin-vacancy (SnV) center as an on-chip thermometer for a diamond membrane and use it to characterize microwave- and radio-frequency-induced heating in a superconducting coplanar waveguide patterned on the same chip. We first calibrate the temperature dependence of the optical C-transition frequency and linewidth from $20\,\mathrm{K}$ down to the few-kelvin regime. At lower temperatures, where the optical response becomes weakly temperature dependent, we use the spin-lattice relaxation time $T_1$ as a complementary thermometer and tune its sensitivity with the transverse magnetic-field component. Applying this local thermometer to a niobium coplanar waveguide, we observe magnetic-field-dependent superconducting breakdown under GHz drive, accompanied by abrupt heating of the diamond. In contrast, at $20\,\mathrm{MHz}$ and $400\,\mathrm{mT}$, relevant for nuclear-spin control, we detect no measurable heating up to the breakdown threshold of $9.4\,\mathrm{dBm}$, corresponding to $B_\mathrm{ac}\sim1.2\,\mathrm{mT}$. These results define a safe operating window for superconducting microwave and RF control structures in diamond-based quantum nodes.

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08.
arXiv (math.PR) 2026-06-12

Dimension-free Markov–Bernstein inequalities for product measures

Authors:
Egor Kosov↗

arXiv:2606.13575v1 Announce Type: cross Abstract: We study dimension-free Markov–Bernstein inequalities for polynomials with respect to product probability measures. In the Gaussian case, for $p\ge4$, we prove that \[ \|\nabla f\|_{L^p(\gamma^n)} \le C(p)d^{\frac12+\theta_p} \|f\|_{L^p(\gamma^n)} \] for every polynomial $f$ of degree at most $d$, where $\theta_p\le \frac{2}{3p}$ and $\theta_p=0$ whenever $p$ is an even integer. Thus, for even integer exponents, we establish the sharp dependence on the degree conjectured by Eskenazis–Ivanisvili. For general $p\ge4$, the estimate improves upon their dimension-free inequality. We also obtain dimension-free Markov–Bernstein inequalities with sharp dependence on the degree for even integer exponents beyond the Gaussian setting. We first prove such estimates for the uniform distribution on the unit cube and then extend them to products of absolutely continuous measures with unimodal densities. Finally, we treat products of one-dimensional Freud measures with densities proportional to $e^{-|t|^{2m}}$.

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09.
arXiv (quant-ph) 2026-06-11

Lowest order Carleman linearization for low Reynolds long-term behaviour of fluid flow simulations

Authors:
Luca Cappelli↗Sauro Succi↗

arXiv:2605.23380v2 Announce Type: replace Abstract: It is shown that the lowest (second) order truncation of the Carleman linearization of the fluid equations (C2) recovers the late stage of the evolution, namely the steady-state solution, although to a decreasing degree of accuracy at increasing Reynolds number. This asymptotic property is first proved analytically for the decaying logistic with external forcing and then shown to hold to a significant degree of accuracy also for the more complex case of two-dimensional Kolmogorov-like fluid flow at low Reynolds numbers, below $Re \sim 10$. This time-asymptotic property may open interesting prospects for the quantum simulation of low-Reynolds steady-state fluid flows.

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10.
arXiv (CS.CL) 2026-06-17

Dynamic Rollout Editing for Reducing Overthinking in RL-Trained Reasoning Models

Authors:
Zihao Wei↗Wenjie Shi↗Liang Pang↗Jingcheng Deng↗Shicheng Xu↗Shasha Guo↗Zenghao Duan↗Jiahao Liu↗Jingang Wang↗Huawei Shen↗Xueqi Cheng↗

Long-form chain-of-thought reasoning can improve LLM performance on complex tasks, but models often continue generating unnecessary reasoning after a correct answer has emerged. We refer to this behavior as overthinking. We study this phenomenon from the perspective of GRPO-style reinforcement learning (RL) post-training, framing it as a training-time credit-assignment problem rather than merely a decoding-time stopping problem. In rollouts sampled at the onset of GRPO training, we observe that successful trajectories can exhibit a slightly higher degree of overthinking than unsuccessful trajectories for the same prompts. This early imbalance provides a starting point for an undesirable feedback loop: because GRPO assigns sequence-level credit, it cannot distinguish the solution-reaching prefix from the unnecessary continuation that lengthens a successful trajectory. Both receive positive update signal, allowing the initial imbalance to grow into more severe overthinking during training. To address this issue, we introduce Dynamic Rollout Editing (DRE), a training-time intervention for successful trajectories that continue thinking after answer emergence. DRE preserves the accepted verified prefix, edits the remaining thinking, and prefers the edited trajectory within the same RL group, weakening the preference signal for unnecessary thinking without penalizing the reasoning needed to reach the answer. Experiments across diverse tasks show the effectiveness of DRE.

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11.
arXiv (quant-ph) 2026-06-11

Shadow Engineering of Quantum Processes

Authors:
Tian-Ci Tian↗De-Tao Jiang↗Wei-Ming Zhu↗Wei-You Liao↗Hong-Wei Li↗He-Liang Huang↗

arXiv:2606.12035v1 Announce Type: new Abstract: Characterizing quantum processes is essential for hardware benchmarking, error diagnosis, and algorithm verification. While recent work [PRX QUANTUM 4, 040337 (2023)] extended classical shadows from quantum state to quantum process, enabling efficient single-channel $\mathcal{E}$ property prediction, its applicability to composite processes $f(\mathcal{E}_1, \mathcal{E}_2,\cdots, \mathcal{E}_k)$ remains unexplored. We introduce shadow engineering, a framework encoding the classical shadows of processes into sparse transfer matrices to predict $f(\mathcal{E}_1, \mathcal{E}_2,\cdots, \mathcal{E}_k)$ properties with proven polynomial sample complexity, matching single-channel efficiency while exponentially lower than quantum process tomography. Crucially, this approach repurposes existing $\mathcal{E}_m$-shadow data without physical execution of $f(\mathcal{E}_1, \mathcal{E}_2,\cdots, \mathcal{E}_k)$, enabling flexible quantum process characterization with minimal hardware overhead. We demonstrate the framework's effectiveness and practicality on a superconducting quantum processor for typical applications such as error mitigation and Hamiltonian dynamical simulation. This framework unlocks new capabilities for predicting complex quantum behaviors without physical re-execution, with immediate applications in near-term device calibration and quantum simulation.

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12.
arXiv (CS.CL) 2026-06-18

SenFlow: Inter-Sentence Flow Modeling for AI-Generated Text Detection in Hybrid Documents

Authors:
Jingkun Luo↗Yifan Sun↗Da-Tian Peng↗Guanxiong Pei↗

Sentence-level AI-generated text detection (S-AGTD) for hybrid documents, where humans and LLMs co-author one text, faces two gaps: existing methods classify each sentence in isolation, discarding inter-sentence dependencies, and existing benchmarks omit the newest generation of generators. We construct MOSAIC, a benchmark of 16,000 hybrid documents over PubMed and XSum, generated by DeepSeek-V3.2 and Kimi K2 under stringent quality controls including a perplexity-consistency filter absent from prior benchmarks. We recast S-AGTD as structured prediction over the document sentence sequence and instantiate it as SenFlow, integrating graph-based inter-sentence propagation with linear-chain CRF decoding in a single document-level pass over a sentence graph. SenFlow reaches state-of-the-art performance on MOSAIC, with a +4.15 pp average Macro-F1 margin on cross-domain transfer, the hardest of three protocols of increasing difficulty. We further find that even after the perplexity filter equalizes overt cues, AI insertions retain a generator-dependent sentence-length gap that sentence-level detectors still exploit. Code and data: https://github.com/luojingkun22/SenFlow

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13.
arXiv (CS.LG) 2026-06-16

Graph Learning Should Move Beyond Restrictive Views of Spectral and Message-Passing GNNs

Authors:
Antonis Vasileiou↗Juan Cervino↗Pascal Frossard↗Charilaos I. Kanatsoulis↗Christopher Morris↗Michael T. Schaub↗Pierre Vandergheynst↗Zhiyang Wang↗Guy Wolf↗Ron Levie↗

arXiv:2602.10031v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are commonly divided into message-passing neural networks (MPNNs) and spectral GNNs, reflecting two largely separate research traditions in machine learning and signal processing. While MPNNs have a precise definition, there is no widely accepted criterion for what makes a mapping a spectral GNN. Most existing work restricts spectral GNNs to layered architectures based on linear spectral filters. Under this restriction, we show that spectral and spatial GNNs have largely equivalent expressive power. To promote progress in the field, we propose a precise definition of spectral GNNs based on eigenbasis symmetries, in contrast to the definition of MPNNs via neighborhood permutation symmetries. We further argue that the two perspectives offer complementary strengths. MPNNs provide a natural language for discrete structure and expressivity analysis through tools from logic and graph isomorphism, while the spectral perspective offers principled tools for understanding smoothing, bottlenecks, stability, and community structure. Overall, we argue that progress in graph learning will be accelerated by clarifying the similarities and differences between these perspectives and by moving toward a unified theoretical framework.

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14.
arXiv (CS.CL) 2026-06-19

Vero: An Open RL Recipe for General Visual Reasoning

Authors:
Gabriel Sarch↗Linrong Cai↗Qunzhong Wang↗Haoyang Wu↗Danqi Chen↗Zhuang Liu↗

What does it take to build a visual reasoner that works across charts, science, spatial understanding, and open-ended tasks? The strongest vision-language models (VLMs) suggest that broad visual reasoning is within reach, yet their closed data and reinforcement learning (RL) pipelines make their gains difficult to study, reproduce, or extend. We introduce Vero, a family of fully open VLMs that match or exceed existing open-weight models across diverse visual reasoning tasks. We scale RL data and rewards across six broad task categories, constructing Vero-600K, a 600K-sample dataset from 59 datasets, and designing task-routed rewards that handle heterogeneous answers. Across VeroEval, our 30-benchmark suite, Vero-600K outperforms existing RL datasets under controlled comparisons. Applied to five starting models, Vero variants gain 2.9-5.4 points on average over their initial models. Notably, Vero-Qwen3I-8B, trained on the Instruct model, surpasses Qwen3-VL-8B-Thinking by 3.8 points on average without additional distillation. Systematic ablations reveal that different task categories elicit distinct reasoning patterns and that broad gains depend on learning them jointly rather than in isolation. All data, code, and models are publicly available.

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15.
arXiv (CS.CV) 2026-06-18

GUMP-Net: An interpretable model-data-driven intelligent algorithm for multi-class pelvic segmentation

Authors:
Liheng Wang↗Yinghui Zhang↗Licheng Zhang↗Hailin Xu↗Qiyong Cao↗Chong Chen↗

Pelvic segmentation is one of the most important and fundamental research problems in precise and intelligent diagnosis and treatment, as well as surgical planning and navigation for pelvic fractures. By combining an improved geodesic active contour model with deep neural networks, we propose GUMP-Net, an interpretable model-data-driven intelligent algorithm for multi-class pelvic segmentation, in which three network modules are designed to constitute the overall segmentation framework together: the object detection module for automatic level set initialization, the edge detector module for learning an anatomy-aware edge detector function and the iteration module for deep level set evolution. Leveraging the advantages of level set representation and deep learning, GUMP-Net shows more accurate, robust and consistent segmentation performance, especially in small training data situation, compared to the state-of-the-art methods. Extensive experiments on pelvic datasets demonstrate the rationality and effectiveness of the proposed algorithm. Further experiments extended to ankle dataset indicate broader applications to other anatomies. The proposed algorithm not only provides an efficient segmentation method for complex fracture reduction, but also gives an interpretable geometric perspective for understanding deep learning segmentation.

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

Intrinsic Gradient Suppression for Label-Noise Prompt Tuning in Vision-Language Models

Authors:
Jiayu Li↗Jiaxin Qi↗Sheng Zhou↗Jiaqiang Huang↗Xiansheng Hua↗

Contrastive vision-language models like CLIP exhibit remarkable zero-shot generalization. However, prompt tuning remains highly sensitive to label noise, as mislabeled samples generate disproportionately large gradients that can overwhelm pre-trained priors. We argue that because CLIP already provides a near-optimal initialization, adaptation should be inherently conservative, particularly against the extreme gradient updates common in noisy settings. To this end, we propose Double-Softmax Prompt Tuning (DSPT), a hyperparameter-free method for intrinsic gradient suppression. By applying a sequential probabilistic normalization, DSPT induces a self-adaptive saturation zone that suppresses gradients from high-error noisy samples while maintaining informative updates. We also provide both theoretical analysis and empirical evidence about how this mechanism achieves adaptive suppression. This design transforms ``gradient vanishing'', traditionally a training bottleneck, into a principled noise-filtering shield for label-noise prompt tuning. Extensive experiments confirm that this simple, drop-in design achieves state-of-the-art robustness across various noisy benchmarks, outperforming methods with complex architectures and handcrafted hyperparameters.

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17.
arXiv (CS.CV) 2026-06-19

Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation

Authors:
Hyun-Kurl Jang↗Jihun Kim↗Hyeokjun Kweon↗Kuk-Jin Yoon↗

Continual Test-Time Adaptation (CTTA) aims to maintain model performance under evolving target domains by adapting online without labeled data. However, practical deployments often cannot retain the source dataset due to privacy or licensing constraints, and purely source-free CTTA methods tend to become unstable under long-term distribution shift, suffering from compounding self-training errors and catastrophic forgetting. We introduce DO-ALL (Distill Once, Adapt Life-Long), a plug-and-play framework that revisits source information in a compact and privacy-conscious form via Dataset Distillation (DD). Before deployment, DO-ALL performs DD to produce a small set of synthetic distilled anchors that summarize the source distribution. During adaptation, each target sample is matched with its most semantically aligned anchor, which provides a stable reference for various CTTA via source replay, representation alignment, and manifold-smoothing regularization. DO-ALL can be seamlessly integrated into existing CTTA algorithms, consistently improving long-term robustness across CIFAR100-C, ImageNet-C, and the CCC benchmark. This demonstrates the potential of leveraging DD to enable stable and continuous adaptation without retaining raw source data. The code is available at https://github.com/blue-531/DOALL.

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18.
arXiv (quant-ph) 2026-06-17

Coupled-Mode Equations with Arbitrary Mode Combinations for Kinetic-Inductance Superconducting Traveling-Wave Parametric Devices: Theory and Experimental Validation

Authors:
F. Patricio Mena↗Camilo Espinoza↗Ryan O. Berriel↗Ricardo Finger↗David J. Thoen↗

arXiv:2606.17264v1 Announce Type: cross Abstract: The coupled-mode equations (CMEs) have proven very successful in describing parametric processes in nonlinear optics. More recently, the same formulation has been used to model microwave superconducting parametric amplifiers and frequency multipliers. However, when applied to the microwave regime, not all assumptions remain valid and losses play a more dramatic role. Here, we revisit the CMEs applied to traveling-wave superconducting amplifiers to include losses and provide a formulation that enables their systematic derivation for any combination of traveling waves. As examples, we discuss the impact of unwanted harmonics and intermodulation products on parametric amplification, as well as harmonic generation. We verify that, if not properly accounted for, device performance can deviate considerably from the ideal case. Furthermore, using a superconducting CPW-based artificial transmission line and combining an independent experimental determination of its nonlinear parameter $I'_*$ with simulations of its linear properties, we obtain a parameter-free validation of this formulation. The nonlinear parameter was determined to be $I'_* \approx 27$ mA which, surprisingly, scales with the theoretical depairing current and not with the much smaller critical current of the device. For the validation, we measured multiple-harmonic generation and found excellent agreement between theory and experiment. The fact that $I'_* \gg I_C$ has direct implications for device design.

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

Flux magnetism in a strongly interacting dipolar lattice supersolid under tunable gauge fields

Authors:
Michele Miotto↗Pietro Lombardi↗Giovanni Ferioli↗Joana Fraxanet↗Maciej Lewenstein↗Luca Tanzi↗Luca Barbiero↗

arXiv:2509.05058v2 Announce Type: replace-cross Abstract: Supersolidity and magnetism are fundamental phenomena characterizing strongly correlated matter. Here we unveil a mechanism that directly connects these two regimes and can be experimentally accessed in ultracold atomic systems. Specifically, we exploit the distinctive properties of magnetic lanthanide atoms trapped in a one-dimensional anti-magic wavelength optical lattice. This platform enables a realistic implementation of a triangular Bose-Hubbard ladder featuring two key ingredients: strong long-range interactions and tunable gauge fields. Owing to these properties, our numerical analysis reveals a robust lattice supersolid regime with finite fluxes in each triangular plaquette. Remarkably, we show that the density modulation of the supersolid phase and a finite gauge field induce magnetic ordering of the fluxes, forming ferromagnetic and ferrimagnetic patterns. Our results thus reveal a fascinating quantum effect that bridges supersolidity and magnetism.

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20.
bioRxiv (Bioinfo) 2026-06-13

ProtAff: Protein Binding Affinity Prediction via LoRA-Finetuned ESM-2

Authors:
Chu↗L.-S↗Vogt↗Chungyoun↗Gray↗J. J↗

Predicting the binding affinity of protein–protein interactions remains a central challenge in computational biology. Structure prediction models such as AlphaFold3 (AF3) and Boltz-2 can produce high-quality docking poses, and their confidence scores indicate structure quality, but these same scores fail to rank binding affinity among confirmed binders. Here we present ProtAff, a sequence-only affinity prediction model built on ESM-2 (650M parameters) with low-rank adaptation (LoRA) fine-tuning and a cross-attention module. ProtAff is trained using a margin ranking loss on 362,567 affinity measurements spanning 20 heterogeneous data sources, and we removed all training samples whose target sequence exceeds 50% similarity to the test target EGFR. On the AdaptyvBio EGFR benchmark (N = 55), ProtAff achieves a Spearman correlation coefficient {rho} = 0.413, outperforming the best AF3 metric ({rho} = 0.054), the best Boltz-2 metric ({rho} = -0.046), and ML-based predictors MINT ({rho} = 0.242) and CrossAffinity ({rho} = 0.216). Applied to the AdaptyvBio Nipah virus binder design competition, a pipeline incorporating ProtAff for affinity ranking produced a design with KD = 0.132 nM (2 of 5 designs confirmed binding), a 2.8-fold improvement over the competition winner. On a cross-target discrimination benchmark of 91 VHH-antigen crystal structures, ProtAff underperforms structural methods for distinguishing cognate from non-cognate pairings, indicating that sequence-based affinity models are effective for within-target ranking but not for cross-target specificity.

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21.
medRxiv (Medicine) 2026-06-22

Pump-Free Patient-Derived Human Proximal Tubule Microphysiological System for Modeling Flow-Dependent Epithelial Maturation and Cisplatin Injury

Authors:
Sekiguchi↗Suzuki↗Nakao↗Hori↗Mori↗Mirza↗A. F↗Shindoh↗Morita↗Mandai↗Fujiki↗Kikuchi↗…

Recent initiatives by the U.S. Food and Drug Administration and the National Institutes of Health to reduce animal testing in drug development have highlighted the need for in vitro platforms that better recapitulate human biology for preclinical safety assessment. Drug-induced nephrotoxicity remains a major cause of drug attrition, underscoring the need for human-relevant kidney models. To address this, a pump-free human patient-derived proximal tubule microphysiological system was developed by integrating human renal proximal tubular epithelial cells (hRPTECs), isolated from non-tumorous nephrectomy cortex, with a porous membrane-based microfluidic device. Expanded hRPTECs were cultured for 10 days under static conditions or rocker-driven shear stress approximating physiological proximal tubular flow. Shear stress increased epithelial density, enhanced proximal tubule marker expression (Na+/K+-ATPase and aquaporin-1), and improved Zonula occludens-1 and occludin localization. Bulk RNA sequencing demonstrated transcriptomic changes associated with enhanced apical maturation and epithelial signature. In cisplatin-induced injury assays, shear-conditioned epithelia exhibited reduced cell density and increased {gamma}H2AX staining, indicating greater sensitivity to nephrotoxicity. These findings demonstrate that rocker-driven shear stress promotes epithelial maturation in patient-derived hRPTECs. The pump-free human patient-derived proximal tubule microphysiological system offers a practical, scalable, and physiologically relevant platform for modeling flow-dependent proximal tubule biology and assessing human-relevant nephrotoxicity.

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22.
arXiv (CS.AI) 2026-06-12

FinSTaR: Towards Financial Reasoning with Time Series Reasoning Models

Authors:
Seunghan Lee↗Jun Seo↗Jaehoon Lee↗Sungdong Yoo↗Minjae Kim↗Tae Yoon Lim↗Dongwan Kang↗Hwanil Choi↗Soonyoung Lee↗Wonbin Ahn↗

arXiv:2605.03460v3 Announce Type: replace Abstract: Time series (TS) reasoning models (TSRMs) have shown promising capabilities in general domains, yet they consistently fail in the financial domain, which exhibits unique characteristics. We propose a general 2 x 2 capability taxonomy for TSRMs by crossing 1) single-entity vs. multi-entity analysis with 2) assessment of the current state vs. prediction of future behavior. We instantiate this taxonomy in the financial domain-where the distinction between deterministic assessment and stochastic prediction is particularly critical-as ten financial reasoning tasks, forming the FinTSR-Bench benchmark based on S&P stocks. To this end, we propose FinSTaR (Financial Time Series Thinking and Reasoning), trained on FinTSR-Bench with distinct chain-of-thought (CoT) strategies tailored to each category. For assessment, which is deterministic (i.e., computable from observable data), we employ Compute-in-CoT, a programmatic CoT that enables models to derive answers directly from raw prices. For prediction, which is inherently stochastic (i.e., subject to unobservable factors), we adopt Scenario-Aware CoT, which generates diverse scenarios before making a judgment, mirroring how financial analysts reason under uncertainty. The proposed method achieves 78.9% average accuracy on FinTSR-Bench, substantially outperforming LLM and TSRM baselines. Furthermore, we show that the four capability categories are complementary and mutually reinforcing through joint training, and that Scenario-Aware CoT consistently improves prediction accuracy over standard CoT. Code is available at https://github.com/seunghan96/FinSTaR.

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

Geometric bias in eigenspace perturbation under random heterogeneous noise

Authors:
Fengkai Liu↗Ke Wang↗Wanjie Wang↗

arXiv:2606.11263v1 Announce Type: cross Abstract: Spectral methods rely fundamentally on the stability of principal eigenspaces under random perturbations. Classically, this stability is quantified by the Davis-Kahan and Wedin theorems, which bound the eigenspace error using the operator norm of the noise and the relevant spectral gaps. While these worst-case bounds are sharp for arbitrary deterministic perturbations, they can be wasteful in the low-rank signal-plus-random-noise setting, as they fail to capture the fine-grained interaction between the signal geometry and the noise distribution. In this paper, we study the spectral perturbation of signal-plus-noise matrices corrupted by sparse, random noise with an arbitrary, inhomogeneous variance profile. We demonstrate that under heterogeneous noise variances, the empirical eigenvectors suffer a systematic, deterministic geometric bias that is entirely invisible to classical perturbation bounds. By leveraging the Quadratic Vector Equation (QVE) and establishing fine-grained isotropic local laws, we derive near-optimal, non-asymptotic perturbation bounds for the leading eigenspaces in the operator and $2\to\infty$ norms. The bounds separate the usual signal-to-noise contribution, stochastic fluctuations, and structured geometric bias terms determined by the alignment between the signal eigenspaces and the row-wise variance profile.

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

SDVDiag: Multimodal Causal Discovery for Online Diagnosis in Software-defined Vehicles

Authors:
Matthias Wei{\ss}↗Athreya Hosahalli Prakash↗Falk Dettinger↗Nasser Jazdi↗Michael Weyrich↗

arXiv:2606.15559v1 Announce Type: cross Abstract: The transition toward software-defined vehicles concentrates an increasing share of vehicle functionality into distributed software services, where failures propagate through service dependencies and the surface symptom is often several causal hops away from the underlying defect. Existing approaches to causal root-cause analysis in such systems address this only partially: they typically reason over a single observability modality and operate in an offline, operator-driven mode that does not match the demands of continuous vehicle operation. This paper presents SDVDiag, a multimodal causal-discovery pipeline that fuses log-based and metric-based service representations into a shared embedding space before graph construction, coupled with an anomaly-driven trigger that converts the diagnostic platform from a manually operated batch tool into a continuously running online system. Evaluation on an Autonomous Valet Parking testbed shows that the multimodal pipeline produces sparser causal graphs than a metrics-only baseline (134 vs. 182 edges on average) and consistently outperforms it in edge-weighted reward against an expert knowledge graph at every stage of human-feedback refinement, showing a 2.4-fold improvement over the baseline after 60 feedback queries. An end-to-end fault-injection scenario further demonstrates that the integrated trigger correctly recovers a true root cause located two causal hops upstream of the observable symptom.

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25.
arXiv (CS.LG) 2026-06-18

Exponentially many initializations to avoid barren plateaus

Authors:
Ankit Kulshrestha↗Ricard Puig↗Diego Garc\'ia-Mart\'in↗Lukasz Cincio↗Ilya Safro↗Zo\"e Holmes↗M. Cerezo↗

arXiv:2606.18515v1 Announce Type: cross Abstract: Barren plateaus are stated as an average-case phenomenon: pick an ansatz, initialize it naively, and concentration follows. This has led to the common view that a potential cure for barren plateaus is simply to initialize the parameters more carefully. Here we show that the situation is subtler. We introduce a first-moment framework that gives a simple operator-level diagnostic for when an initialization may escape the fully concentrated barren-plateau fixed point, and for comparing the biases induced by different initialization strategies. Our framework recovers several known initialization schemes such as identity and Gaussian initialization, but also shows that barren-plateau avoidance is highly non-unique. Indeed, many shifted, biased, and non-symmetric parameter distributions can avoid concentration, and these choices need not be equivalent. In fact, our results show that one can generate exponentially many families of inequivalent initialization strategies. Then, our numerics indicate that different first-moment-distinct initializations can lead to different attained minima, suggesting that avoiding barren plateaus via smart initializations can trade the exponential concentration problem for the challenge of selecting the right trainable pocket amongst many options.

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