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01.

medRxiv (Medicine) 2026-06-22 DOI: HASH:115569039a2361d41513c778ab8aa459

Impact of Antidiabetic Medications on IgG and Plasma Protein N-Glycosylation in Type 2 Diabetes Patients

作者:

Mraz↗Vuckovic↗Pribic↗Rados Kajic↗Matic↗Pape Medvidovic↗Kolaric↗Rahelic↗Lauc↗Stambuk↗

Introduction. Diabetes is a growing global health challenge, necessitating effective management strategies. Glycosylation, a highly regulated post-translational protein modification, has emerged as a pivotal factor in diabetes pathophysiology. However, the modulation of protein glycosylation by antidiabetic treatment is still largely unknown. This study explored the longitudinal effects of four distinct antidiabetic therapies - metformin, insulin, sodium-glucose cotransporter-2 (SGLT2) inhibitors, and glucagon-like peptide-1 receptor agonists (GLP-1RA) - on plasma protein and immunoglobulin G (IgG) glycosylation in patients with type 2 diabetes (T2D). Research Design and Methods. Plasma protein and IgG N-glycans were enzymatically released, purified and chromatographically profiled in a cohort of 124 patients, examined at four time points, to assess therapy-induced glycan alterations. Linear mixed models adjusting for covariates and multiple testing (FDR

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02.

arXiv (math.PR) 2026-06-16 DOI: arXiv:2604.00190

Stochastic control with dividend payments and capital injections for Markov additive processes

作者:

Kei Noba↗

arXiv:2604.00190v4 Announce Type: replace Abstract: Motivated by de Finetti's optimal dividend problem with capital injections, we study a stochastic control problem for the additive component of a Markov additive process (MAP). In contrast to previous studies, the modulating component is allowed to be a general right process on a Radon space, so the model is not restricted to finite-state regime switching and cannot in general be reduced to a finite collection of Lévy process control problems. Capital injections are allowed at arbitrary times. We first consider the case in which dividend payments are allowed only at prescribed discrete times and establish necessary and sufficient conditions for the optimality of a strategy. These conditions then yield the optimality of a class of Markov-modulated periodic–classical barrier strategies. Combining this optimality result with an approximation argument, we obtain insight into the possible form of optimal strategies in the case where dividend payments, like capital injections, may be made at arbitrary times. Because of the generality of the MAPs considered here, the proof techniques used in previous studies of similar problems are not directly applicable. We therefore develop an alternative argument based on the additive structure of MAPs and dynamic programming between dividend opportunities. The argument also suggests a possible approach to other stochastic control problems involving general MAPs.

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03.

arXiv (CS.CL) 2026-06-24 DOI: arXiv:2511.21056

Bilevel Data Curation for LLM Fine-tuning: Offline Selection and Online Self-Refining Generation

作者:

Quan Xiao↗Yutong Xuan↗Gaowen Liu↗Ramana Rao Kompella↗Tianyi Chen↗

Supervised fine-tuning (SFT) datasets are critical to the downstream performance of large language models, yet they often contain low-quality or harmful question-response pairs. To improve SFT data quality, we develop a unified bilevel framework that combines offline data selection with the online self-refining generation. In the offline setting, bilevel data selection (BDS) selects question-response pairs from the offline SFT dataset to maximize the validation performance. We theoretically show that the optimal model given by BDS outperforms direct data mixing approach in useful data coverage. Moreover, we provide a global convergence analysis for gradient-based BDS approach for one-layer Transformer, showing that the epsilon-global optimum of offline BDS is achievable in finite time. Although efficient, offline BDS discards potentially harmful questions together with responses, thereby reducing question diversity. We address this limitation by refining the responses to selected questions using online self-refining generation framework. However, BDS is inefficient to update the response weights when responses are regenerated online. To address this issue, we introduce bilevel multi-objective optimization (BMO) for response-level weighting. We show that BMO recovers the same validation-aligned solution as BDS, but admits a closed-form importance-ratio weight that adapts to regenerated responses. Experiments on LLM quality enhancement and safety-aware fine-tuning demonstrate that the proposed framework consistently improves both data quality and downstream fine-tuning performance.

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04.

bioRxiv (Bioinfo) 2026-06-20 DOI: HASH:b00f4529f8bcd43c3c015575f8cc2053

MIRATS framework: Normative multiscale characterization of brain regulatory systems across sex and age using multimodal MRI

作者:

Ge↗Dou↗

Deep brain systems involved in arousal, autonomic regulation, sensory integration, and homeostatic control remain underrepresented in conventional whole-brain neuroimaging frameworks. In particular, diencephalic and brainstem nuclei are often insufficiently represented in cortex-centered analyses, limiting the normative references needed to interpret systems-level variation in health and disease. To address this gap, we developed a unified multiscale framework with explicit representation of deep nuclei. By integrating cerebral, cerebellar, diencephalic, and brainstem atlases in standard space, we constructed a 220-region whole-brain parcellation and extracted complementary features at three analytical scales: nodal properties, edge-wise connectivity, and persistent-homology-based topological descriptors. We applied this framework to healthy adults from the Human Connectome Project-Aging cohort to characterize normative multiscale organization and test sex- and age-related variation. Applied to this cohort, our framework revealed pronounced heterogeneity across anatomical systems. Brainstem and diencephalic nuclei showed multiscale feature profiles distinct from those of cerebral and cerebellar regions across nodal, edge-wise, and higher-order topological scales. Sex comparisons identified selective differences across different scales, whereas age modeling revealed widespread but feature- and system-dependent variation across adulthood. Together, these findings show that normative whole-brain organization in this deep-system-aware space is structured by system-specific rather than globally uniform patterns. These findings establish a normative multiscale framework for characterizing brainstem-diencephalic-cerebellar-cerebral organization in healthy adults and provide a quantitative reference for future translational studies of disease-related abnormalities in deep regulatory systems.

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05.

arXiv (CS.CL) 2026-06-24 DOI: arXiv:2606.24530

NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers?

作者:

Yuru Wang↗Lejun Cheng↗Yuxin Zuo↗Sihang Zeng↗Bingxiang He↗Che Jiang↗Junlin Yang↗Yuchong Wang↗Kaikai Zhao↗Weifeng Huang↗Kai Tian↗Zhenzhao Yuan↗…

We introduce NatureBench, a cross-discipline benchmark of 90 tasks distilled from peer-reviewed Nature-family publications, designed to evaluate whether AI coding agents can move beyond reproduction toward discovery on real scientific problems. NatureBench is built on NatureGym, an automated pipeline that constructs a standardized, per-task containerized environment from a source paper, addressing the environment-fragmentation problem that has limited the credibility of prior agent-on-research benchmarks. Evaluating ten frontier agent configurations under a strict web-search-disabled protocol, we find that the strongest model surpasses SOTA on only 17.8% of tasks under the g>0.1 criterion. Analysis of method pathways reveals that agents succeed primarily through methodological translation, converting scientific tasks into familiar supervised prediction problems, rather than through genuine scientific invention. Failures are dominated by wrong method choice and insufficient compute budget, not by task misunderstanding. We release the benchmark, the NatureGym pipeline, and a public leaderboard with maintainer-side reproduction. Code: https://github.com/FrontisAI/NatureBench

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06.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.15055

Bridging Geographic Bias in Urban Streetscape Inference via Lifelong Learning with Visual-Semantic Pivoting

作者:

Xinze Zhang↗

Visual perception of urban streetscapes underpins evidence-based decisions in landscape planning, public health, and place-making. Yet models trained on a few well-photographed metropolises systematically misjudge underrepresented districts, propagating geographic bias into downstream policy. We address this gap with HVSP-LL, a lifelong learning framework that couples a stratified visual-semantic pivoting module with an equity-aware rehearsal mechanism. The pivoting module organises landscape concepts along a three-tier ontology (macro structure, meso composition, micro element) and aligns image features to learnable semantic anchors at each tier, providing transferable representations that resist distributional drift. The lifelong adaptation component sequentially absorbs new urban regions while constraining inter-region perception gaps through a worst-region sample-reweighting objective and a structurally-aware exemplar buffer. We evaluate HVSP-LL on a panoramic streetscape benchmark assembled from twelve cities across four continents and seven perceptual dimensions. The framework attains 0.834 Spearman correlation on the held-out city sequence, an absolute 6.1 point improvement over the strongest continual baseline, and shrinks the inter-city perception gap to 0.094 – a 38% reduction relative to the strongest continual baseline (0.151) and a 57% reduction relative to a representative regularisation baseline (0.218). Ablations confirm that each tier of the pivoting hierarchy contributes monotonically, and the equity-aware rehearsal converts mean backward transfer from -0.038 (without retention) to +0.013, eliminating catastrophic forgetting on the held-out sequence. Our results indicate that hierarchical anchoring is a practical pathway toward geographically equitable streetscape inference at city scale.

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07.

arXiv (CS.AI) 2026-06-11 DOI: arXiv:2606.11828

Feature-Aligned Speech Watermarking for Robustness to Reconstruction Distortions

作者:

Haiyun Li↗Shuhai Peng↗Zhisheng Zhang↗Jingran Xie↗Xiaofeng Xie↗Hanyang Peng↗Zhiyong Wu↗

arXiv:2606.11828v1 Announce Type: cross Abstract: Audio watermarking aims to embed identifiable information into audio while remaining imperceptible. Existing methods adopt high-fidelity, low-energy designs to preserve perceptual quality, but the resulting watermarks lack robustness under suppression by speech reconstruction models. Improving robustness is challenging due to the inherent robustness-fidelity trade-off in existing designs, where increasing watermark energy improves robustness but reduces fidelity. To address this problem, we propose a feature-aligned watermarking method that aligns the watermark with the original speech feature distribution, allowing higher watermark energy to improve robustness while preserving imperceptibility. We use a pretrained speech codec to generate a pseudo-speech watermark and fuse it into the spectrogram of the input audio, with VAD loss and perceptual losses guiding embedding within voiced regions. Experiments show that our method maintains imperceptibility comparable to existing approaches while substantially improving robustness under both seen and unseen speech reconstruction models.

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08.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.16028

The Information-Theoretic Benefit of Shared Representations under Orthogonality Constraints

作者:

Thomas Dittrich↗Oliver Potocki↗Philipp Grohs↗

arXiv:2606.16028v1 Announce Type: new Abstract: Modern deep learning architectures are increasingly multi-task and multi-modal, using a pretrained foundation model combined with task-specific, fine-tuned models. Empirically, exploiting similarity across different problems, instead of solving them individually, can significantly improve overall performance. While the generalization and sample complexity properties of multitask learning have been widely studied, the parametric complexity of joint approximation in comparison to separate approximation remains less well understood. The question is particularly relevant in modern deep learning, where models are increasingly required to satisfy structural constraints such as equivariance, conservation laws, or orthogonality. We prove lower and upper bounds on the description-length for separate and joint approximation classes, respectively, in uniform norm. We build a class of orthogonal functions by composing a shared hard feature, realized by a Rademacher-Haar wavelet series, with Sawtooth-Walsh readouts to enforce orthogonality of output coordinates. The dyadic tree structure of the Rademacher-Haar wavelet concentrates the approximation hardness in the common feature component, while the readouts act as task-specific heads. Using an information-theoretic framework, we obtain a sharp gap between the optimal approximation rates achievable by joint and separate coding. Finally, we realize this separation in a neural network model using Heaviside activations via reduction to triangle-wave approximation. Our results show that even under an orthogonality constraint joint approximation requires strictly fewer bits in compositional architectures, provided the tasks share a latent hard feature. This provides theoretical insight into the description-length-efficiency of compositional multi-output architectures and clarifies how neural networks can retain expressivity under geometric constraints.

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09.

arXiv (quant-ph) 2026-06-12 DOI: arXiv:2606.12724

Block algebra for morphing circuits

作者:

Rui Chao↗

arXiv:2606.12724v1 Announce Type: new Abstract: Morphing circuits are a new paradigm for quantum error correction that relaxes hardware requirements. We present four constructions for CNOT-based CSS morphing circuits with explicit qubit connectivity degrees. All four constructions are specified in block algebra notation, with entries in algebras generated by permutation matrices. The first three are obtained by rewriting existing surface- and color-code morphing circuits; the fourth is a new three-round construction modeled on the 6.6.6 color code. The surface-code construction recovers the morphing circuit of Ref. [ST25] for two-block group algebra codes. Numerical search then instantiates these permutation matrices using regular representations of finite groups. [ST25] M. H. Shaw and B. M. Terhal, Phys. Rev. Lett. 134(9), 090602 (2025).

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10.

arXiv (CS.CV) 2026-06-17 DOI: arXiv:2606.17343

Bayesian Magnetic Resonance Joint Image Reconstruction and Uncertainty Quantification using Sparsity Prior Models and Markov Chain Monte Carlo Sampling

作者:

Ahmed Karam Eldaly↗Matteo Figini↗Daniel C. Alexander↗

We propose a novel framework for uncertainty quantification using compressed sensing magnetic resonance image reconstruction. The problem is formulated within a Bayesian framework as a linear inverse problem, with prior distributions assigned to the unknown model parameters. Specifically, the image to be reconstructed is assumed to be sparse in a given basis. We develop a general framework applicable to any basis and as examples, we test the sparsity of the image in its (1) spatial gradients using a total variation prior model, and in its (2) wavelet transform. A Markov chain Monte Carlo (MCMC) method, based on a split-and-augmented Gibbs sampler, is then employed to sample from the posterior distribution of the unknown parameters. The non-differentiable conditional distributions are efficiently sampled using a proximal MCMC method. The proposed algorithms are validated on both single-coil and multi-coil datasets using various k-space sub-sampling patterns and ratios. The results demonstrate the superior performance of each proposed approach in reconstructing images compared to its counterpart optimisation-based method. Moreover, our framework effectively quantifies uncertainty, showing a notable correlation between estimated uncertainty maps and error maps computed using ground truth and reconstructed images, compared with existing deep learning-based methods.

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11.

arXiv (CS.AI) 2026-06-11 DOI: arXiv:2606.12018

MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning

作者:

Shang Ma↗Jisheng Dang↗Wencan Zhang↗Yifan Zhang↗Bimei Wang↗Hong Peng↗Bin Hu↗Qi Tian↗Tat-Seng Chua↗

arXiv:2606.12018v1 Announce Type: new Abstract: We propose a multi-agent collaborative framework built upon a lightweight Multimodal Large Language Model (MLLM), specifically designed for social intelligence reasoning. A key feature of our approach is that both the training and inference phases are augmented via knowledge distillation. Within this architecture, multi-modal data pertinent to social intelligence is precisely localized. Furthermore, relevant long-tail events are identified, extracted, and rendered as formatted, explicit text. This formatting strategy prevents critical long-tail information from being overshadowed by head events and environmental noise during the tokenization process. Specifically, we integrate Test-Time Adaptation (TTA) across the entire reasoning pipeline, encompassing the extraction and representation of long-tail events, Chain-of-Thought (CoT) prompting, and self-reflection. This TTA mechanism is also distillation-enhanced, utilizing Low-Rank Adaptation (LoRA) to fine-tune the foundation model exclusively for instance-level reasoning. Extensive evaluations against various open-source and proprietary AI models across multiple benchmarks demonstrate the effectiveness of the proposed framework. With around 30% of training data from IntentTrain, we achieve state-of-the-art results. Codes are available at https://github.com/eeee-sys/MODF-SIR, demo is available at https://huggingface.co/spaces/Harry-1234/MODF-SIR, LoRA is available at https://huggingface.co/Harry-1234/MODF-SIR and the dataset for training router is available at https://huggingface.co/datasets/Harry-1234/IntentRouterTrain.

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12.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2602.12670

SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

作者:

Xiangyi Li↗Yimin Liu↗Wenbo Chen↗Bingran You↗Zonglin Di↗Yifeng He↗Shenghan Zheng↗Kyoung Whan Choe↗Jiankai Sun↗Shuyi Wang↗Chujun Tao↗Binxu Li↗…

arXiv:2602.12670v4 Announce Type: replace Abstract: Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time. Despite rapid adoption, there is no standard way to measure whether they actually help. We present SkillsBench, a benchmark whose current inventory contains 87 tasks across 8 domains paired with curated Skills and deterministic verifiers. Our latest aggregate evaluation runs the 87-task benchmark under matched no-Skills and curated-Skills conditions for 18 model-harness configurations. Curated Skills raise the average pass rate from 33.9% to 50.5% (+16.6 percentage points; 25.5% normalized gain), with configuration-level gains ranging from +4.1 to +25.7 pp. Focused Skills with at most three modules outperform larger or exhaustive bundles, and smaller models with Skills can match larger models without them. SkillsBench establishes paired evaluation as the foundation for rigorous measurement of Skill efficacy on agentic, expertise-heavy work.

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13.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.16501

Post-Hoc Merging is Not Enough: Many-Shot Model Merging with Loss-Gap Balancing

作者:

Kyungjin Im↗Miru Kim↗Chanin Eom↗Minhae Kwon↗

arXiv:2606.16501v1 Announce Type: new Abstract: Model merging has become a practical post-training strategy for building a single multi-task large language model (LLM) by combining multiple task-specialized models. However, most existing approaches rely on post-hoc merging, in which task-specific models are merged only once after training. This one-shot aggregation often suffers from task interference, leading to information erasure across individual tasks. In this work, we show that replacing post-hoc merging with an iterative many-shot merging protocol is effective in improving multi-task performance. Building on this insight, we propose METIS, Mitigating Erasure from Task Interference for Stable many-shot merging. METIS is a loss-aware many-shot merging method that addresses information erasure in post-hoc merging through task-wise loss-gap weighting and consensus-based masking. Notably, METIS exhibits significant performance improvement on the worst-performing task, effectively mitigating information erasure. (Project page: https://imkyungjin.github.io/METIS/)

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14.

arXiv (quant-ph) 2026-06-16 DOI: arXiv:2602.21350

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

作者:

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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15.

arXiv (CS.AI) 2026-06-18 DOI: arXiv:2602.23092

Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design

作者:

Zhuoliang Xie↗Fei Liu↗Zhenkun Wang↗Qingfu Zhang↗

arXiv:2602.23092v2 Announce Type: replace Abstract: The Capacitated Vehicle Routing Problem (CVRP), a fundamental combinatorial optimization challenge, focuses on optimizing fleet operations under vehicle capacity constraints. While extensively studied in operational research, the NP-hard nature of CVRP continues to pose significant computational challenges, particularly for large-scale instances. This study presents AILS-AHD (Adaptive Iterated Local Search with Automatic Heuristic Design), a novel approach that leverages Large Language Models (LLMs) to revolutionize CVRP solving. Our methodology integrates an evolutionary search framework with LLMs to dynamically generate and optimize ruin heuristics within the AILS method. Additionally, we introduce an LLM-based acceleration mechanism to enhance computational efficiency. Comprehensive experimental evaluations against state-of-the-art solvers, including AILS-II and HGS, demonstrate the superior performance of AILS-AHD across both moderate and large-scale instances. Notably, our approach establishes new best-known solutions for 8 out of 10 instances in the CVRPLib large-scale benchmark, underscoring the potential of LLM-driven heuristic design in advancing the field of vehicle routing optimization.

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16.

medRxiv (Medicine) 2026-06-17 DOI: HASH:53ff6afa91deea4280552d9ef6b18054

Investigating shared genetic overlap of immune-mediated inflammatory diseases and cardiometabolic diseases

作者:

Alduhayhi↗S. S↗Morris↗A. P↗Zhao↗Bowes↗

Abstract Background: Immune-mediated inflammatory diseases (IMIDs) are associated with increased risk of cardiometabolic diseases. Investigating genetic overlap among these conditions can provide insights into their clinical management. Methods: Genetic correlation was assessed using linkage disequilibrium score regression (LDSC). Then, a meta-analysis was conducted using Association Analysis Based on SubSETs (ASSET) to pinpoint independent single nucleotide polymorphisms (SNPs) shared across the diseases. Each independent SNP was then used to define a genomic window (+/-500KB) for colocalisation analysis and Local Analysis of [co]Variant Association (LAVA) to offer multiple layers of regional pleiotropic evidence. Over-representation analysis was then run to identify enriched biological pathways, which then were used for drug target analysis. Results: The LDSC analysis showed a significant global genetic correlation for rheumatoid arthritis (RA) and cardiometabolic diseases including hypertension, coronary artery disease (CAD), heart failure (HF), stroke, atrial fibrillation (AF), and type two diabetes mellitus (T2DM) ranging from rg = 0.09 to 0.24. ASSET meta-analysis identified 164 independent SNPs shared across RA and the cardiometabolic diseases with P < 5 x 10- in the overall one-sided meta-analysis P-value, FDR < 0.05 in both individual GWASs, and TRUE phenotype matrix. Colocalisation analysis revealed multiple loci with strong evidence (Posterior probabilities [&ge;] 80) of single causal SNPs between the trait pairs. LAVA analysis was then used as an additional layer of confirmation for the findings generated by ASSET and colocalisation and thus several loci were highlighted. Over-representation analysis showed significant enriched immune-related pathways across RA-hypertension, RA-CAD, RA-AF, and RA-T2DM trait pairs. Drug target analysis highlighted several drugs which could be further tested for their effectiveness in RA and its common comorbidities. Conclusion: The findings revealed a shared genetic architecture and key immune-related biological pathways underlying RA and its associated cardiometabolic comorbidities. The identified genes and drugs provide opportunities for further therapeutic assessment which could improve clinical management strategies.

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17.

arXiv (CS.AI) 2026-06-15 DOI: arXiv:2606.14579

VISTA: View-Consistent Self-Verified Training for GUI Grounding

作者:

Xinyu Qiu↗Yunzhu Zhang↗Heng Jia↗Shuheng Shen↗Changhua Meng↗Linchao Zhu↗

arXiv:2606.14579v1 Announce Type: new Abstract: When applying Group Relative Policy Optimization (GRPO) for GUI Grounding, rollouts are sampled from a single screenshot view; groups often become either all failures on difficult instances or all successes on easy ones, yielding no useful relative advantage. We propose VISTA (View-Consistent Self-Verified Training), a GRPO-based training framework that constructs each comparison group from multiple target-preserving views of the same GUI instance.Each view is generated by a crop that keeps the target element visible and remaps its box exactly, so model rollouts are compared across semantically equivalent but geometrically different inputs. To stabilize short coordinate generation without turning reinforcement learning into unconditional imitation, VISTA further adds a self-verified cross-view anchor: an oracle answer optimized with an advantage-weighted loss, excluded from the group baseline and activated only when the model has produced a maximum-reward rollout. Across five GUI-grounding benchmarks and multiple Qwen backbones, VISTA consistently improves grounding accuracy.On ScreenSpot-Pro, it raises Qwen3-VL 4B/8B/30B-A3B from 55.5/52.7/53.7 to 63.4/65.8/67.0. Robustness analyses further show higher worst-view accuracy and lower prediction flip rates.

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18.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.16638

MVM-IOD: An Industrial Object-Centric Benchmark Dataset for the Evaluation of 3D Reconstruction Methods

作者:

Robert Langend\"orfer↗Markus Hillemann↗Markus Ulrich↗

3D object reconstruction, and camera pose estimation in industrial applications are challenging tasks, as errors are costly while the computation time is often limited. The complexity of typical industrial objects further complicates these tasks. Most of the existing datasets in this context do not depict realistic industrial scenarios. Therefore, we introduce the Machine Vision Metrology Industrial Object Dataset (MVM-IOD). Images of typical industrial objects are captured systematically, by moving a camera, mounted at the end effector of an industrial robot arm, on a hemisphere around the objects. MVM-IOD contains reference camera poses and reference 3D point clouds, the acquired RGB images of 9 objects and 2 background choices resulting in 18 scenes, which allows evaluation of all image based methods that compute a 3D reconstruction, camera poses, or novel views of a scene. Based on MVM-IOD, we extensively evaluate current SOTA 3D reconstruction and camera pose estimation methods, such as Structure from Motion, Multi-View Stereo, recent feed forward methods (Visual Geometry Grounded Transformer, {\pi}3), and 2D Gaussian Splatting and report our findings as a baseline for future research. The experiments show that capture setups like ours generate out-of distribution images for feed forward methods, leading to suboptimal point clouds and camera poses. However, these out-of-distribution images can be shifted closer to the training distribution by applying simple preprocessing steps. Consequently, in certain industrial applications, feed forward methods should be used with caution.

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19.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2606.19419

Playful Agentic Robot Learning

作者:

Junyi Zhang↗Jiaxin Ge↗Hanjun Yoo↗Letian Fu↗Zihan Yang↗Yaowei Liu↗Raj Saravanan↗Shaofeng Yin↗Justin Yu↗Dantong Niu↗Zirui Wang↗Roei Herzig↗…

arXiv:2606.19419v1 Announce Type: cross Abstract: Current agentic robot systems can write executable Code-as-Policy programs, observe feedback, and revise behavior across multiple attempts, but they remain largely task-driven: reusable skills are acquired only after explicit instructions. We study Playful Agentic Robot Learning, where an embodied coding agent uses self-directed play as a continual skill-learning stage before downstream tasks arrive. We introduce RATs, Robotics Agent Teams designed for play-time skill acquisition. During play, RATs proposes novel yet learnable exploratory tasks, plans and executes robot-code policies, verifies intermediate progress, diagnoses failures, retries with dense, step-level feedback, and distills successful executions into a persistent code skill library. At test time, the agent reuses relevant skills from this frozen library to help solve new tasks. Experiments in LIBERO-PRO and MolmoSpaces show that play-learned skills improve held-out downstream tasks over no-play and random-play baselines, with 20.6 and 17.0 percentage-point gains over CaP-Agent0 on LIBERO-PRO and MolmoSpaces, respectively. Moreover, the learned skills can be plugged into other inference-time Code-as-Policy agents by simply retrieving them into the context, improving RoboSuite and real-world transfer by 8.9 and 8.8 points, respectively, without finetuning the underlying model.

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20.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2606.19212

Generalised Eigenvalue Geometry of Semantic Adversarial Attacks

作者:

Martin Anthony↗Kaveh Salehzadeh Nobari↗

arXiv:2606.19212v1 Announce Type: cross Abstract: Recent empirical work shows that semantically equivalent paraphrases can fool financial sentiment classifiers: although a paraphrase remains close to the original under a strong reference embedding, it may shift the target model's representation enough to change the predicted class. Existing robustness theory either assumes a single-model threat model or focuses mainly on empirical attack algorithms. We develop a continuous local model of semantic paraphrase perturbations that captures this two-model structure. We show that the worst-case local displacement of the target representation, subject to a proxy-model budget, is governed by the largest generalised eigenvalue of a matrix pencil $(A,B)$ constructed from the Jacobians of the two embedding maps. The resulting attackability index $\lambda^*(x)$ is intrinsic to the local paraphrase geometry and the chosen embedders, yields a closed-form prediction-flip condition for affine readouts, and supports conservative population and finite-sample attackability certificates. For uniform control over classes of affine readouts, we derive a distribution-free VC bound for binary attackability indicators and a scale-sensitive margin bound based on an attackability-adjusted margin that subtracts a local geometric penalty from the standard classifier margin. We also connect the continuous theory to discrete paraphrase search, identify an asymmetry between successful and unsuccessful finite searches, and give a covering condition under which the discrete and continuous settings agree. Finally, we propose an empirical verification framework using soft-token relaxations and generated paraphrase sets to assess the local eigenvalue geometry, prediction-flip condition, and finite-search approximation on a deployed financial-text classifier.

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21.

arXiv (CS.AI) 2026-06-24 DOI: arXiv:2510.04033

A global log for medical AI

作者:

Ayush Noori↗Aaron E. Boussina↗Hai Ho Bich↗James Anibal↗Julia Maslinski↗Manuel Burger↗Martin Faltys↗Adam Rodman↗Alan Karthikesalingam↗Alessandro Blasimme↗Annelia Itwaru↗Ben Kaplan↗…

arXiv:2510.04033v2 Announce Type: replace Abstract: Modern computer systems rely on syslog, a universal protocol that records critical events across heterogeneous infrastructure. Medicine's rapidly growing AI stack has no equivalent. As medicine deploys AI tools at scale, there is no standard way to record how, when, by whom, and for whom these models are used. Without such records, it is difficult to measure real-world performance and outcomes, detect adverse events, or identify bias and dataset drift. Here we introduce MedLog, a protocol for event-level logging of medical AI. Each time an AI model interacts with a human, another algorithm, or an automated workflow, MedLog creates a record. Each record contains nine core fields: header, model, user, target, inputs, artifacts, outputs, outcomes, and feedback. We apply MedLog across four deployments in the US, Switzerland, and Vietnam: ICU deterioration prediction, tetanus progression monitoring from wearable signals, automated sepsis quality reporting, and patient attendance prediction. MedLog records capture model behavior, workflow interactions, and downstream outcomes, including AI performance degradation during severe weather events in patient attendance prediction and increased laboratory testing after ICU deterioration alerts. MedLog limits the data footprint through risk-based sampling, lifecycle-aware retention policies, and write-behind caching, enabling deployment in low-resource settings. It also supports detailed traces for complex, agentic, or multi-stage workflows, creating a foundation for continuous monitoring, auditing, and improvement of medical AI.

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22.

arXiv (CS.CV) 2026-06-11 DOI: arXiv:2606.11740

UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA

作者:

Mengzhuo Chen↗Yan Shu↗Chi Liu↗Hongming Piao↗Xidong Wang↗Derek Li↗Bryan Dai↗

We study whether grounded reasoning supervision from abundant 2D medical images can improve 3D medical VQA when both input types are aligned through a common reasoning interface. We introduce UniReason-Med, a single-checkpoint framework that processes either a 2D image or a slice-serialized 3D volume at inference time, generating interleaved textual reasoning and localized visual evidence through shared box syntax, region-token injection, and a common grounded reasoning policy. To train this interface, we construct UniMed-CoT, a 220K instruction-tuning dataset with interleaved textual reasoning and grounded visual evidence, including 170K 2D and 50K 3D samples. Through supervised fine-tuning followed by outcome-level reinforcement learning, UniReason-Med learns to generate grounded reasoning traces without IoU/Dice-based localization rewards during RL. Data-mixture and component ablations show that joint 2D+3D grounded supervision substantially improves 3D reasoning over 3D-only training, while grounding and region-token injection consistently benefit both 2D and 3D tasks. These results suggest that a shared grounded reasoning interface can transfer reasoning structure from 2D images to slice-serialized volumetric medical understanding. The code and data are publicly available at https://github.com/IQuestLab/unireason-med.

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23.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.16806

LLM-based Visual Code Completion for Aerospace Geometric Design

作者:

Hau Kit Yong↗Robert Marsh↗Edmar A. Silva↗Andr\'as S\'obester↗Stuart E. Middleton↗

Recent advances in both Large Language Models (LLMs) and Vision Language Models (VLMs) have seen a step change in their ability to perform visual code completion, but the aerospace industry, which prioritizes safety and explainabilty over rapid LLM adoption, currently has no publicly announced LLM-based geometric design copilot systems in commercial use by aerospace Original Equipment Manufacturers (OEMs). This paper presents a LLM-based visual programming copilot application for aerospace engineering design tasks, using a visual programming variant of the ReAct methodology and GPT 5.4. In addition to the copilot, we describe Wingbuilder, a new Grasshopper plugin library with custom components for aerospace-specific geometry abstraction, and an associated Aerospace Visual Programming Dataset (AVPD) with 18 aerospace expert designed tasks at different levels of difficulty alongside ground truth solutions. We evaluate our copilot application with a user trial involving two experienced aerospace engineers from a large aircraft manufacturing company. We find our copilot visual programming ReAct methodology was successful in generating suggestions that participants found helpful, but slow ReAct inference times limit its usefulness to more complex time-consuming tasks where waiting for good copilot solution suggestion was worthwhile. Participants reported they liked the tool and would be willing to use it in the future.

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24.

arXiv (CS.CV) 2026-06-24 DOI: arXiv:2606.24479

MambaRaw: Selective State Space Modeling for Efficient 4K Raw Image Reconstruction

作者:

Peize Li↗Fanhu Zeng↗Tongda Xu↗Xingguo Xu↗Xinjie Zhang↗Xingtong Ge↗Haotian Zhang↗Yan Wang↗

In-camera JPEG previews are ubiquitous in raw image formats and provide an sRGB reference at negligible storage cost. Although existing metadata-based reconstruction frameworks can exploit this side information when recovering raw images, their context models often become computationally expensive especially at high resolution, eg, 4K raw image, given that attention mechanisms scale quadratically with feature maps, hindering its practical application. To address these limitations, we propose MambaRaw, a JPEG-conditioned metadata-based raw image reconstruction framework that uses State Space Models (SSMs) to estimate entropy parameters efficiently. Our key contribution comprises a Spatial-Energy Coupled Context Modeling mechanism with two lightweight modules: (1) TileMambaBlock, which performs Mamba-style selective scanning only on information-dense tiles to improve the efficiency; and (2) Energy-Aware Refinement (EAR), an identity-initialized residual module that enhance feature representation to match the long-tail energy distribution of raw signals. Extensive experiments on three camera datasets (Sony, Olympus, Samsung) show consistent improvements over strong metadata-based baselines and set a new state of the art for JPEG-guided raw reconstruction with great efficiency. Notably, at low metadata bitrates, MambaRaw increases PSNR by 1.2–1.4 dB and reduces end-to-end coding latency by about 9%. Code is released at https://github.com/Peizeli1/MambaRaw.

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25.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.14805

Knowledge-Based Zero-Replay Debugging of Multi-Agent LLM Traces

作者:

Dong Ho Kang↗Hyeonjeong Cha↗Daein Weon↗

arXiv:2606.14805v1 Announce Type: cross Abstract: Reliable operation of multi-agent large language model (LLM) systems depends on debugging long execution traces, where the few causally decisive events are buried in unstructured logs of messages, routes, memory writes, and tool calls. The standard tool is counterfactual replay (rewind, edit, and re-run the trajectory to measure each event's effect), but its cost grows linearly with the number of candidate events, making exhaustive replay infeasible at scale. We frame trace debugging as a knowledge-based decision-support problem. Each trace is compiled into a structured event knowledge graph over routing, memory, tool-use, uncertainty, and latent evidence, and a calibrated predictor decides where a scarce replay budget should be spent. We do not propose a new replay oracle; we propose a method to predict its results without paying the replay cost. We formulate zero-replay counterfactual-effect prediction: given a trace under a fixed budget, predict which events the oracle would mark high-effect before any replay is performed. BranchPoint-Latent is a lightweight predictor over observable, structural, uncertainty, and latent features of the knowledge graph. Calibrated against a deterministic replay oracle across 37 trace families, a single learning-to-rank gradient-boosted predictor raises per-trace localization (Branch Recall@5) from 0.73 to 0.93 on held-out families at zero oracle-replay cost. Rather than claiming universal dominance, we characterize when cheap graph centrality suffices and when learned evidence is necessary. The result is an auditable, cost-efficient decision-support system for AI-reliability debugging, positioned explicitly on the cost-accuracy frontier with reproducible artifacts.

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