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01.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2606.12423

The Challenges of Balancing AI Compliance and Technological Innovations in Critical Sectors: A Systematic Literature Review

作者:

Ayush Enkhtaivan↗Chinazunwa Uwaoma↗

arXiv:2606.12423v1 Announce Type: cross Abstract: The rapid integration of artificial intelligence (AI) into critical infrastructure including healthcare, finance, energy, and defense, offers transformative benefits but also conflicts with evolving regulatory and governance frameworks. This paper presents a systematic literature review (SLR) to examine the challenges of balancing AI compliance and technological innovation across critical infrastructure sectors. The review follows established SLR guidelines to extract and synthesize insights from peer-reviewed articles, report, and institutional sources published between 2020-2025. The study identifies three interrelated challenges: fragmented regulations, excessive compliance burdens for smaller to medium enterprises (SMEs), and misaligned governance models. To address these challenges, the study highlights practical governance strategies, including risk-tiered regulation, compliance by design, and explainable AI, to support scalable and trustworthy AI deployment in critical sectors. Key contributions include a concise mapping of core AI-governance challenges and a conceptual diagram illustrating their overlap, as well as actionable strategies for policymakers and practitioner to harmonize oversight with innovation.

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02.

arXiv (CS.CL) 2026-06-12 DOI: arXiv:2606.13121

NaturalFlow: Reducing Disruptive Pauses for Natural Speech Flow in Simultaneous Speech-to-Speech Translation

作者:

Dongwook Lee↗Youngho Cho↗Sangkwon Park↗Heeseung Kim↗Sungroh Yoon↗

Simultaneous speech-to-speech translation aims to enable near-real-time communication by minimizing latency, offering a compelling, real-time alternative to the high latency of consecutive translation. However, the excessive pursuit of low latency often results in fragmented chunk-wise speech. Consequently, listeners are subjected to an unnatural acoustic flow punctuated by frequent pauses, which could increase their cognitive load. To bridge this gap, we introduce a fluency-aware optimization framework designed to discover the sweet spot between the low-latency benefits of simultaneous translation and the natural flow of consecutive translation. Our framework minimizes inter-chunk silences by leveraging model-internal signals, including linguistic diversity and induced temporal variability in speech durations. Experiments on short- and long-form benchmarks show that our framework produces natural speech flow while maintaining competitive latency and translation quality.

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03.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2606.18283

Gaussian Mixture Attention: Linear-Time Sequence Mixing via Probabilistic Latent Routing

作者:

Yongchao Huang↗Hassan Raza↗

arXiv:2606.18283v1 Announce Type: new Abstract: The dense token-to-token interaction pattern of standard dot-product attention remains a central bottleneck in scaling Transformer architectures to long contexts. We introduce Gaussian Mixture Attention (GMA), a probabilistic attention-style sequence mixer that replaces explicit pairwise query–key comparison with routing through $K$ learned Gaussian mixture components. Queries and keys are mapped to posterior responsibility vectors over a shared latent routing space; their overlap defines an implicit responsibility-space affinity, while values are written into and read from a $K$-slot latent memory. By exploiting the associativity of matrix multiplication, GMA avoids materializing the induced $N\times N$ affinity matrix and instead uses two responsibility matrices whose dominant activation storage scales as $\mathcal{O}(NK)$ rather than $\mathcal{O}(N^2)$ for fixed $K$. We formulate bidirectional and causal variants of GMA, provide an end-to-end differentiable parameterization of the Gaussian mixture components, and analyze its responsibility-modulated gradient structure, constrained non-negative low-rank affinity interpretation, and local routing stability. Empirically, GMA exhibits the intended fixed-$K$ linear memory scaling and is competitive with attention-style baselines on long-context classification, while causal GMA improves over tested linear/random-feature attention variants on WikiText-103 but remains behind optimized causal SDPA and Mamba in the current implementation. Analysis of learned responsibilities further shows broad component usage and moderate alignment with surface-form token categories, supporting GMA as a probabilistic, interpretable, fixed-$K$ linear-time attention-style alternative rather than a universal replacement for optimized softmax attention or state-space models.

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04.

arXiv (quant-ph) 2026-06-25 DOI: arXiv:2606.25795

Benchmarking Dark Matter Search using a Parity-Check Protocol with Machine-Learning Optimized Pulses

作者:

Yu-Han Chang↗Ilya Moskalenko↗Marko Kuzmanovi\'c↗Ognjen Stanisavljevi\'c↗Isak Bj\"orkman↗David D\'iez-Ib\'a\~nez↗Yikun Gu↗Akash V. Dixit↗Igor G. Irastorza↗Gheorghe Sorin Paraoanu↗

arXiv:2606.25795v1 Announce Type: new Abstract: We report on an improved microwave detection protocol for dark matter candidates such as the axion and the dark photon. We employ a superconducting transmon qubit dispersively coupled to a double-cavity system, enabling quantum non-demolition measurements of the photon occupation in a relatively short-lived storage cavity. To reduce the experimental cycle time and enhance sensitivity for axion and dark-photon searches, we operate this detector in a regime of increased qubit-cavity coupling, resulting in Stark shifts of 4.6 MHz. In this regime, conventional control pulses suffer from strong frequency-detuning sensitivity and photon-number-dependent errors. We address this limitation by implementing frequency-detuning-robust $\pi/2$ pulses (obtained by machine-learning optimization) that preserve high-fidelity qubit control over a bandwidth of approximately 20 MHz. We experimentally validate this protocol and demonstrate single-photon detection performance comparable to previous implementations, despite significantly reduced qubit coherence times and storage-cavity lifetimes. Using parity-based measurement sequences combined with a Hidden Markov Model (HMM) analysis, we achieve background rates on the order of $\mathcal{O}(20)$ Hz. In the absence of a magnetic field, we derive exclusion limits on the dark photon model for dark matter, reaching a sensitivity to the kinetic mixing angle of $\epsilon_{95\%} \sim 1\times10^{-14}$ at 5.051 GHz. These results establish machine-learning robust control as a key enabler for faster, more scalable microwave quantum sensors for dark-matter searches.

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05.

arXiv (CS.AI) 2026-06-25 DOI: arXiv:2606.25997

Variable Bound Tightening for Nash Equilibrium Computation in Multiplayer Imperfect-Information Games

作者:

Sam Ganzfried↗

arXiv:2606.25997v1 Announce Type: cross Abstract: There has been significant recent progress in algorithms for approximation of Nash equilibrium in large two-player zero-sum imperfect-information games and exact computation of Nash equilibrium in multiplayer strategic-form games. While counterfactual regret minimization and fictitious play are scalable to large games and have convergence guarantees in two-player zero-sum games, they do not guarantee convergence to Nash equilibrium in multiplayer games. Recently, an approach has been presented for exact computation of Nash equilibrium in multiplayer imperfect-information games that solves a quadratically constrained program based on a nonlinear complementarity problem formulation derived from the sequence-form game representation. This formulation was solved using Gurobi's nonconvex quadratic solver, which employs spatial branch-and-bound to iteratively refine variable bounds by solving convex relaxations of bilinear terms via McCormick envelopes. During presolve, Gurobi introduces auxiliary variables and, in some cases, binary variables, leading to an internal MIQCP reformulation. This approach was demonstrated to outperform prior algorithms from the Gambit software suite and quickly solve three-player Kuhn poker after removal of dominated actions; however, the algorithm was not able to solve the full version of the game within 24 hours. In this paper, we derive finite bounds on slack and multiplier variables in the nonlinear complementarity formulation. These bounds strengthen the convex relaxations used within spatial branch-and-bound and lead to substantial computational improvements. We demonstrate the impact of the proposed bounds on exact Nash equilibrium computation in three-player Kuhn poker.

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06.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2407.04884

Convex Approximation of Two-Layer ReLU Networks for Hidden State Differential Privacy

作者:

Rob Romijnders↗Antti Koskela↗

arXiv:2407.04884v4 Announce Type: replace Abstract: The hidden state threat model of differential privacy (DP) assumes that the adversary has access only to the final trained machine learning (ML) model, without seeing intermediate states during training. However, the current privacy analyses under this model are restricted to convex optimization problems, reducing their applicability to multi-layer neural networks, which are essential in modern deep learning applications. Notably, the most successful applications of the hidden state privacy analyses in classification tasks have only been for logistic regression models. We demonstrate that it is possible to privately train convex problems with privacy-utility trade-offs comparable to those of 2-layer ReLU networks trained with DP stochastic gradient descent (DP-SGD). This is achieved through a stochastic approximation of a dual formulation of the ReLU minimization problem, resulting in a strongly convex problem. This enables the use of existing hidden state privacy analyses and provides accurate privacy bounds also for the noisy cyclic mini-batch gradient descent (NoisyCGD) method with fixed disjoint mini-batches. Empirical results on benchmark classification tasks demonstrate that NoisyCGD can achieve privacy-utility trade-offs on par with DP-SGD applied to 2-layer ReLU networks.

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07.

arXiv (CS.LG) 2026-06-12 DOI: arXiv:2606.05860

GenAutoML: An Agentic Framework for Dynamic Architecture Generation and Optimization in Time-Series Analysis

作者:

Oleeviya Babu Poikarayil↗C\'edric Schockaert↗Abdulrahman Nahhas↗Christian Daase↗Mursal Dawodi↗Jawid Ahmad Baktash↗

arXiv:2606.05860v2 Announce Type: replace Abstract: Designing neural architectures for time-series forecasting and anomaly detection remains a resource-intensive task that often requires substantial domain expertise. Traditional Automated Machine Learning (AutoML) systems typically rely on static, predefined search spaces, limiting their ability to adapt to diverse data characteristics. We present GenAutoML, an agentic framework that leverages Large Language Models (LLMs) as neural architects to bridge natural-language requirements and executable PyTorch implementations. The framework incorporates a Sandboxed Reflection Loop for autonomous code refinement and a Signature-Aware Runtime that enforces architectural consistency and execution safety. To improve robustness under non-stationary conditions, we further introduce a Dynamic Reversible Instance Normalization (Dyn-RevIN) wrapper. Experiments on the ETTh1, ETTm1, and Weather benchmarks demonstrate that GenAutoML can dynamically generate task-specific neural architectures tailored to dataset characteristics. Among the generated models, WaveInterferenceNet achieves inference latency below 0.01 ms per sample while maintaining competitive predictive performance. By emphasizing computational efficiency, architectural adaptability, and stable optimization behavior, GenAutoML enables the creation of ultra-lightweight neural networks suitable for resource-constrained and latency-sensitive Edge AI deployments.

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08.

medRxiv (Medicine) 2026-06-15 DOI: HASH:57b4d43d3a4540adc54f1e38ab9f40d8

Genome-wide colocalization of body fat distribution GWAS and subcutaneous adipose eQTLs identifies SNX10, DGKQ, and CBX3 as candidate causal genes for cardiometabolic disease

作者:

Iqbal↗M. S↗

Background: Genome-wide association studies (GWAS) have identified hundreds of loci associated with body fat distribution, yet the causal genes and regulatory mechanisms through which these variants exert their effects remain largely unknown. Expression quantitative trait locus (eQTL) colocalization provides a powerful framework for identifying genes whose expression is genetically coregulated with complex traits. Methods: We performed a genome-wide colocalization analysis integrating waist-hip ratio adjusted for body mass index (WHRadjBMI) GWAS summary statistics from 694,649 individuals (Pulit et al., 2019) with subcutaneous adipose tissue eQTLs from the Genotype-Tissue Expression (GTEx) Project v8 (N = 581 donors). GWAS coordinates were lifted from GRCh37 to GRCh38 to enable direct alignment with GTEx data. We incorporated CAVIAR fine-mapping results to overcome the limitation of FDR-significant eQTL filtering. Colocalization was assessed using the approximate Bayes factor framework (coloc.abf) across 335 independent genome-wide significant loci. Results: Of 2,897 locus-gene pairs tested, 489 (16.9%) showed strong colocalization (PP.H4 > 0.8) and 618 (21.3%) showed moderate evidence (PP.H4 > 0.5). The strongest colocalization was observed for SNX10 (sorting nexin 10; PP.H4 = 1.000), a recently characterized regulator of adipocyte differentiation and female-specific diet-induced obesity. Other top hits included DGKQ (diacylglycerol kinase theta; PP.H4 = 0.9999999), an emerging pharmacological target for insulin resistance, and CBX3 (chromobox 3; PP.H4 = 0.9999974), an epigenetic regulator linked to cardiovascular disease. Established adiposity genes including GRB14 (PP.H4 = 0.681) and KLF14 (PP.H4 = 0.590) were recovered, validating our approach. Several loci exhibited extensive allelic heterogeneity, with 50 genes colocalizing at a single chromosome 3 locus. Conclusions: Our analysis provides a comprehensive map of adipose tissue gene regulatory mechanisms underlying genetic risk for body fat distribution. The identification of SNX10, DGKQ, and CBX3 as high-confidence candidate causal genes advances the translation of GWAS associations into mechanistic understanding and therapeutic targets for obesity-related cardiometabolic disease.

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09.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.15646

NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models

作者:

Deepa Tilwani↗Yash Saxena↗Ankur Padia↗Srinivasan Parthasarathy↗Manas Gaur↗

arXiv:2606.15646v1 Announce Type: new Abstract: Large Language Models (LLMs) have transformed natural language processing, but their lack of interpretable reasoning and tendency to hallucinate pose significant challenges for legal applications. While LLMs show promise for legal text analysis and generation, they struggle with accurate citation attribution and precedent verification. For example, in legal contexts, a single incorrect precedent can jeopardize a case. Current approaches to improve LLM reliability in legal domains suffer from two key limitations: inadequate integration of structured legal knowledge during training or fine-tuning, and insufficient verification mechanisms for generated legal content. To address these challenges, we propose the TRISM (Trustworthy, Reliable, Interpretable, Safe Models) framework, which integrates NeuroSymbolic AI principles with LLMs to leverage both neural learning capabilities and symbolic reasoning over structured legal knowledge. The TRISM approach addresses the above limitations while maintaining interpretable decision pathways. Our framework formalizes the extraction of symbolic knowledge from legal textual documents and incorporates Retrieval-Augmented Generation (RAG) as a core component for grounding LLM outputs in verified legal sources. In this position paper, we make the following contributions: (1) An analysis of the limitations of AI in law; (2) Introduce RASOR RAG which creates foundations for neurosymbolic RAG by generating explicit interpretable rationales that could be formalized into symbolic representations; (3) A formalized methodology for creating symbolic legal knowledge bases that support both interpretable reasoning and output verification in LLMs; and (4) The TRISM framework for integrating symbolic legal knowledge with LLMs.

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10.

arXiv (CS.CV) 2026-06-11 DOI: arXiv:2506.03933

Diffusion-based Cumulative Adversarial Purification for Vision Language Models

作者:

Jia Fu↗Yongtao Wu↗Yihang Chen↗Kunyu Peng↗Xiao Zhang↗Volkan Cevher↗Sepideh Pashami↗Anders Holst↗

Vision Language Models (VLMs) have shown remarkable capabilities in multimodal understanding, yet their susceptibility to adversarial perturbations poses a significant threat to their reliability in real-world applications. Despite often being imperceptible to humans, these perturbations can drastically alter model outputs, leading to erroneous interpretations and decisions. This paper introduces DiffCAP, a novel diffusion-based purification strategy that can effectively neutralize adversarial corruptions in VLMs. We theoretically establish a provable recovery region in the forward diffusion process and meanwhile quantify the convergence rate of semantic variation with respect to VLMs. These findings manifest that adversarial effects monotonically fade as diffusion unfolds. Guided by this principle, DiffCAP leverages noise injection with a similarity threshold of VLM embeddings as an adaptive criterion, before reverse diffusion restores a clean and reliable representation for VLM inference. Through extensive experiments across six datasets with three VLMs under varying attack strengths in three task scenarios, we show that DiffCAP outperforms existing defense techniques by a substantial margin. Notably, DiffCAP significantly reduces both hyperparameter tuning complexity and the required diffusion time, thereby accelerating the denoising process. Equipped with theorems and empirical support, DiffCAP provides a robust and practical solution for securely deploying VLMs in adversarial environments. The source code is available at https://github.com/JasonFu1998/DiffCAP.

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11.

arXiv (CS.CV) 2026-06-17 DOI: arXiv:2605.09313

Attention Sinks in Diffusion Transformers: A Causal Analysis

作者:

Fangzheng Wu↗Brian Summa↗

Attention sinks – tokens that receive disproportionate attention mass – are assumed to be functionally important in autoregressive language models, but their role in diffusion transformers remains unclear. We present a causal analysis in text-to-image diffusion, dynamically identifying dominant attention recipients per timestep and suppressing them via paired, training-free interventions on the score and value paths. Across 553 GenEval prompts on Stable Diffusion~3 (with SDXL corroboration), removing these sinks does not degrade text-image alignment (CLIP-T) or preference proxies (ImageReward, HPS-v2) at $k{=}1$; only under stronger interventions ($k\!\geq\!10$) does HPS-v2 exhibit a metric-dependent boundary, while CLIP-T remains robust throughout. The perceptual shifts induced by suppression are nonetheless sink-specific – $\sim\!6\times$ larger than equal-budget random masking – revealing an empirical dissociation between trajectory-level perturbation and semantic alignment in diffusion transformers. \footnote{Code available at https://github.com/wfz666/ICML26-attention-sink.}

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12.

arXiv (math.PR) 2026-06-19 DOI: arXiv:2512.10686

Maximal rigidity of random measure and uniqueness pairs: stealthy processes, quasicrystals and periodicity

作者:

Rapha\"el Lachi\`eze-Rey↗

arXiv:2512.10686v2 Announce Type: replace Abstract: This article investigates the phenomenon of maximal rigidity in spatial processes, where perfect interpolation of the process is possible from partial information, specifically, from its restriction to a strict subdomain, often resulting in a trivial tail $\sigma$algebra. A classical example known since the 1930's is that a time series is fully determined by its values on the negative integers if its spectrum has a gap, or at least a sufficiently deep zero. We extend such results to higher dimensions and continuous settings by establishing a connection with the concept of uniqueness pairs, rooted in the uncertainty principle of harmonic analysis. We present several other manifestations of this principle, unify and strengthen seemingly unrelated results across different models: quasicrystals and stealthy processes are shown to be maximally rigid on cones, and discrete integer-valued processes are necessarily periodic when they have a simply connected spectrum. Finally, we identify a surprising class of continuous fields with seemingly standard behavior, such as linear variance and finite dependency range, that undergo a phase transition: they are perfectly interpolable on B(0, $\rho$) for $\rho$ ___ 2 $\pi$ but exhibit no rigidity for $\rho$ > 2.

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13.

arXiv (CS.CL) 2026-06-19 DOI: arXiv:2606.19558

Displacement Is Not Direction: Evaluating Fidelity Metrics for Quantized LLM Deployment

作者:

Milo\v{s} Nikoli\'c↗Ali Hadi Zadeh↗Enrique Torres Sanchez↗Andreas Moshovos↗

Fidelity metrics, such as per-token KL divergence (KLD) against a high-precision reference, are often used in practice as low-cost proxies for benchmark quality. We test this practice on a 28-quant cohort of Qwen3.6-35B-A3B and a 41-quant cohort of Devstral-Small-2-24B, evaluated across a suite of downstream benchmarks. We find that KLD is strongly correlated with benchmark score over the full cohort ($\rho=-0.72$ on Qwen and $\rho=-0.86$ on Devstral, both with $p

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14.

medRxiv (Medicine) 2026-06-18 DOI: HASH:fb11076d96d21564ba39a31feeb60958

A Brain-Aging Transcriptomic Signature Reclassifies WHO Glioma Grade and Predicts Survival Independently of IDH Status: A Multi-Cohort Study

作者:

Saadawy↗Khatan↗

Background Despite WHO grade and IDH status, significant survival differences remain in diffuse gliomas. We hypothesized that a brain-aging transcriptomic signature, reflecting neuroinflammation, myeloid infiltration, and synaptic loss, would independently predict survival and allow for molecular reclassification. Methods A neurodegeneration score was derived via PCA of brain MRI volumes from 1,057 OASIS-3 subjects and projected onto 888 TCGA-LGG/GBM (discovery) and 693 CGGA gliomas (validation). A 14-gene signature of glial/myeloid (GFAP, AQP4, TYROBP, TREM2, C1QA, CD68, ITGAM) and neuronal (SYP, DLG4, GRIN1, GRIA1, SNAP25, SYN1, RBFOX3) genes were computed. Elastic-net Cox regression identified a 3-gene panel (C1QA, CD68, GRIA1). Kaplan-Meier, multivariate Cox, decision curve, and single-cell RNA-seq analyses were performed. Results High brain-aging scores predicted poorer overall survival (p < 0.0001) and remained an independent prognostic factor after adjusting for WHO grade and IDH status (z = 4.72, p < 0.001); chronological age was non-significant (p = 0.231). In IDH-mutant gliomas, significance was confirmed in both cohorts (TCGA p = 0.027; CGGA p < 0.0001). Bidirectional reclassification showed high-risk Grade 2 tumors with Grade 3-like survival (p = 0.00089), and indolent Grade 3 tumors resembling Grade 2 by Ki-67. Single-cell RNA-seq confirmed macrophage localization of signature genes; DCA demonstrated net benefit over grade alone at 5-30% probability thresholds. Conclusions A brain-aging transcriptomic signature independently predicts glioma survival beyond WHO grade and IDH status, validated in an independent Chinese cohort, with clinical utility for identifying high-risk Grade 2 and sparing over-treatment of indolent Grade 3 tumors.

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15.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2606.20076

Variable-Length Tokenization via Learnable Global Merging for Diffusion Transformers

作者:

Dong Hoon Lee↗Seunghoon Hong↗

arXiv:2606.20076v1 Announce Type: cross Abstract: Latent Diffusion Models (LDMs) have become dominant in visual synthesis, but their quality-compute trade-off is largely constrained by the tokenizer's fixed compression ratio. Variable-length tokenizers (VLTs) promise adaptive compression by varying token counts, allowing diffusion models to flexibly balance quality and compute. However, conventional VLTs modulate length by truncating ordered token sequences, which makes token semantics depend on token position and breaks representational alignment across lengths. This leads to a cross-length shift in the latent distribution that hinders a single variable-length diffusion model from operating effectively. To address this, we propose a novel variable-length tokenizer that modulates length by merging tokens. We show that encouraging similar tokens to merge enables direct cross-length representation alignment when the diffusion transformer operates according to the merging pattern. Since conventional merging methods are data-dependent, making the merging pattern inaccessible during generation, we introduce learnable global merging, which is data-independent, to ensure compatibility with diffusion transformers. On ImageNet 256$\times$256 generation, our merging-based variable-length tokenizer integrated with a diffusion transformer achieves a superior gFID-compute trade-off compared to prior VLT methods. Code is available at [this https URL](https://github.com/movinghoon/lgm)

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16.

arXiv (CS.CL) 2026-06-25 DOI: arXiv:2604.05738

MedLayBench-V: A Large-Scale Benchmark for Expert-Lay Semantic Alignment in Medical Vision Language Models

作者:

Han Jang↗Junhyeok Lee↗Heeseong Eum↗Kyu Sung Choi↗

Medical Vision-Language Models (Med-VLMs) have achieved expert-level proficiency in interpreting diagnostic imaging. However, current models are predominantly trained on professional literature, limiting their ability to communicate findings in the lay register required for patient-centered care. While text-centric research has actively developed resources for simplifying medical jargon, there is a critical absence of large-scale multimodal benchmarks designed to facilitate lay-accessible medical image understanding. To bridge this resource gap, we introduce MedLayBench-V, the first large-scale multimodal benchmark dedicated to expert-lay semantic alignment. Unlike naive simplification approaches that risk hallucination, our dataset is constructed via a Structured Concept-Grounded Refinement (SCGR) pipeline. This method enforces strict semantic equivalence by integrating Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs) with micro-level entity constraints. MedLayBench-V provides a verified foundation for training and evaluating next-generation Med-VLMs capable of bridging the communication divide between clinical experts and patients.

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17.

arXiv (CS.CV) 2026-06-17 DOI: arXiv:2606.17389

Visuals Lie, Consistency Speaks: Disentangling Spatial Attention from Reliability in Vision-Language Models

作者:

Logan Mann↗Yi Xia↗Ajit Saravanan↗Ishan Dave↗Saadullah Ismail↗Shikhar Shiromani↗Emily Huang↗Ruizhe Li↗Kevin Zhu↗

Multimodal Foundation Models are increasingly used as reasoning agents, making reliability, knowing when a model may hallucinate, critical. A common intuition, which we call the Attention-Confidence Assumption, holds that reliability follows from "structural" visual perception: tight attention on relevant regions should signal a trustworthy answer, while scattered attention signals confusion. We challenge this through the VLM Reliability Probe (VRP), a systematic cross-family study of reliability signals in contemporary Vision-Language Models (VLMs). We introduce structural-attention metrics, cluster counts (C_k) and spatial entropy (H_s), to quantify the visual encoder's gaze, and track its evolution (Delta H_s) across layers. This reveals a "Symbolic Detachment": models often "Early Lock" visual features only to diffuse attention later, severing early perception from final generation. Contrary to the grounding hypothesis, we find a "Cluster Failure": spatial attention has near-zero correlation (R approx 0.001) with accuracy. Instead, reliability is a phenomenon of generation dynamics and internal-state distributions. Self-Consistency, the agreement rate across sampled reasoning paths, is the dominant predictor of truth (R = 0.429). Scaling causal interventions exposes a sharp architectural divergence: LLaVA locks its prediction in a fragile late-stage bottleneck, whereas PaliGemma and Qwen2-VL distribute reliability globally, staying resilient even when ~50% or more of their most predictive layer is destroyed. For current VLMs, reliability signals are detached from visual grounding maps and are best inferred from generation-time dynamics and hidden-state probes.

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18.

arXiv (CS.LG) 2026-06-24 DOI: arXiv:2606.23444

SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors

作者:

Pratyaksh Rao↗Wancong Zhang↗Randall Balestriero↗Yann LeCun↗Giuseppe Loianno↗

arXiv:2606.23444v2 Announce Type: replace-cross Abstract: Accurate dynamics models are critical for informed decision-making in robotic systems, particularly for agile aerial vehicles operating under uncertainty. Neural network dynamics models are attractive for capturing complex nonlinear effects, but existing predictive approaches struggle with long-horizon forecasting because their autoregressive rollout mechanism amplifies errors over time. Joint Embedding Predictive Architectures (JEPAs) offer a compelling alternative by modeling dynamics in latent space, yet prior JEPA-style methods for robot navigation have been studied primarily for kinematic-level planning, with limited investigation in high-frequency control. In this work, we introduce the JEPA-style model for real-time quadrotor control. The proposed approach combines a latent dynamics model with a novel physics-inspired prober that maps frozen latents to interpretable state, enabling physically grounded long-horizon prediction. Additionally, we combine the learned model with a sampling-based optimal control solution to take advantage of its predictive capabilities for real-time control on embedded hardware. Finally, to reduce the dependence on expensive and unsafe real-world data collection, we develop a structured pipeline for automated dataset generation. Extensive open-loop and outdoor closed-loop experiments demonstrate accurate prediction, robust zero-shot sim-to-real transfer, and strong generalization across diverse operating conditions.

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19.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2606.18691

Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning

作者:

Youngwoo Cho↗Seunghoon Yi↗Wooil Yang↗Sungmo Kang↗Young-woo Son↗Jaegul Choo↗Joonseok Lee↗Soo Kyung Kim↗Hongkee Yoon↗

arXiv:2606.18691v1 Announce Type: new Abstract: Pre-trained materials foundation models, or machine learning interatomic potentials, leverage general physicochemical knowledge to effectively approximate potential energy surfaces. However, they often require domain-specific calibration due to physicochemical diversity as well as mismatches between practical computational settings and those used in constructing the pre-training data. To address this, we propose a sparsity-promoting fine-tuning method that selectively updates model parameters by exploiting the structural properties of E(3)-equivariant materials foundation models. On energy and force prediction tasks across molecular and crystalline benchmarks, our method matches or surpasses full fine-tuning and equivariant low-rank adaptation while updating only $\sim$3~\% of parameters, and in some cases as little as $\sim$0.5~\%. Beyond energy and force calibration, we further demonstrate task generalizability by applying our method to magnetic moment prediction and magnetism-aware total energy modeling. Finally, analysis of sparsity patterns reveals physically interpretable signatures, such as enhanced $d$-orbital contributions in transition metal systems. Overall, our results establish sparsity-promoting fine-tuning as a flexible and interpretable method for domain specialization of equivariant materials foundation models.

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20.

arXiv (CS.AI) 2026-06-25 DOI: arXiv:2606.25762

OncoSynth: Synthetic data generation for treatment effect estimation in oncology

作者:

Octavia-Andreea Ciora↗Julian Welzel↗Dennis Frauen↗Maresa Schr\"oder↗Marie Brockschmidt↗Harry Amad↗Thomas Callender↗Mihaela van der Schaar↗Stefan Feuerriegel↗

arXiv:2606.25762v1 Announce Type: cross Abstract: In oncology, access to patient-level data is often restricted. Synthetic data provides an alternative for analyzing treatment effectiveness, but existing methods for synthetic data generation fail to preserve the causal relationships between covariates, treatments, and outcomes, thereby leading to biased estimates of treatment effects. Here, we introduce OncoSynth, a generative, causally-aware machine learning framework designed to produce synthetic cohorts that enable accurate estimation of population- and patient-level treatment effects. OncoSynth uses a diffusion-based sequential approach to model how covariates influence treatment assignment and how treatment affects survival. We evaluate OncoSynth using large lung (N = 37,128) and breast cancer (N = 17,046) cohorts. Our results show that OncoSynth generates high-fidelity synthetic patient cohorts that preserve real-world patient, treatment, and outcome distributions. Notably, OncoSynth improves treatment effect estimation over existing approaches, by reducing population-level treatment effect error by up to 66%, and patient-level treatment effect error by up to 58%. Thereby, OncoSynth supports reliable evidence generation for precision oncology in settings where data sharing is restricted.

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21.

arXiv (math.PR) 2026-06-11 DOI: arXiv:2606.12381

Markov property and path regularity for the solutions to SPDEs driven by cylindrical-martingale valued measures

作者:

Santiago Cambronero↗David Campos↗C. A. Fonseca-Mora↗Dar\'io Mena↗

arXiv:2606.12381v1 Announce Type: new Abstract: In this paper we prove the Markov property for the solution to stochastic partial differential equations driven by a cylindrical orthogonal martingale-valued measure. We assume our coefficients are time-dependent and satisfy some growth and Lipschitz conditions. We also prove that for time-independent coefficients and under mild assumptions on the cylindrical orthogonal martingale-valued measure, the solutions to our stochastic partial differential equations are Feller. Finally, in the case that the $C_{0}$-semigroup is quasi-contraction, we show that the solution to our stochastic partial differential equation possesses a càdlàg version.

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22.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2508.10967

Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis

作者:

Xinyi Li↗Sai Wang↗Yutian Lin↗Yu Wu↗

arXiv:2508.10967v3 Announce Type: replace-cross Abstract: Retrosynthesis prediction aims to infer the reactant molecules based on a given product molecule, which is a fundamental task in chemical synthesis. However, existing methods rely on a static pattern-matching paradigm, which limits their ability to perform effective logical decision-making from chemical data, leading to a black-box process. We propose Retro-Expert, an interpretable retrosynthesis framework that performs collaborative reasoning by combining the complementary strengths of Large Language Models and specialized models via pure reinforcement learning. It outputs natural language explanations grounded in chemical logic through three components: (1) specialized models provide chemical knowledge that is distilled into a high-quality chemical decision space, (2) LLM-driven critical reasoning to generate predictions with an interpretable reasoning path, and (3) knowledge-grounded policy optimization refines the interpretable decision policy. Experiments show that Retro-Expert surpasses both LLM-based and specialized models across different metrics, while generating chemically grounded explanations that enhance chemists' trust in practice. The source code for this paper is available at https://github.com/MagixRab-ll/Retro-Expert.

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23.

arXiv (CS.AI) 2026-06-15 DOI: arXiv:2606.14589

When Errors Become Narratives: A Longitudinal Taxonomy of Silent Failures in a Production LLM Agent Runtime

作者:

Wei Wu↗

arXiv:2606.14589v1 Announce Type: cross Abstract: LLM agent systems increasingly run as long-lived autonomous runtimes: scheduling jobs, calling tools, maintaining memory, and pushing results to humans. We present a longitudinal study of silent failures in one such system: a personal-assistant agent runtime in continuous production since March 2026, with roughly 40 scheduled jobs, 8 LLM providers, a tool-governance proxy, and a knowledge-base memory plane, defended by 4,286 unit tests and 827 governance checks. Over eight weeks we documented 22 incidents with full root-cause postmortems, in which one meta-pattern – a failure whose error signal never reaches a human in actionable form – manifested at least 28 times. We derive a five-class, mechanism-oriented taxonomy: (A) environment and platform quirks, (B) design-assumption mismatches, (C) error swallowing and dilution, (D) chained hallucination and fabrication, (E) operational omission and forensic blind spots. Class D is unique to LLM systems and the most dangerous: the system does not merely fail to report an error – the LLM transforms it into fluent, plausible narrative delivered to the user. We term this fail-plausible: gray failure's differential observability escalated – the observer is not just blind, it is convincingly lied to by the failure itself. Three findings: about 70% of silent failures were caught by human user-view observation, not tests or audits; a retrospective audit of 15 incidents found 0% ex-ante prevention but 87% regression blocking – audits are regression engines, not prediction engines; incident latency (13 hours to 60 days) tracks failure mechanism, not code complexity – the longest-lived failures lived in the seams between components, where no test runs. We describe the resulting defense framework and distill design principles for agent systems whose failures are loud, attributable, and boring. All postmortems and artifacts are public.

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24.

arXiv (CS.CL) 2026-06-12 DOI: arXiv:2606.12881

Direct Preference Optimization for Chatbot Fine-Tuning: An Empirical Study

作者:

Yvonne Qiu↗Dezhi Yu↗ShuoJia Fu↗

We present an approach to fine-tuning large language models using Direct Preference Optimization (DPO), a reinforcement learning technique. Our experimental results demonstrate that DPO simplifies the training pipeline, improves computational efficiency, and achieves competitive performance. The evaluation using BLEU, ROUGE, and cosine similarity metrics indicates effective learning and convergence, though further investigation is needed to address observed training instability.

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25.

medRxiv (Medicine) 2026-06-24 DOI: HASH:67f07f69ef826dfd60453926698611b1

Study protocol and statistical analysis plan for a randomized controlled trial evaluating the safety and feasibility of the recombinant human platelet-derived growth factor B (rhPDGF-BB)-enhanced collagen plug for complex perianal fistula healing

作者:

Kuo↗M. C↗Younan↗S. A↗Lempicki↗M. D↗Hawkins↗A. T↗Smith↗Shirey-Rice↗J. K↗Pulley↗…

Background A drug-repurposing-specific phenome-wide association study (PheWAS) demonstrated that patients with a single nucleotide variant that decreases expression of platelet-derived growth factor receptor beta (PDGFR{beta}) have a higher prevalence of fistulas, suggesting that PDGFR{beta} signaling is important for tissue repair. Recombinant human platelet derived growth factor B (rhPDGF) is an FDA-approved protein-based therapeutic that signals through PDGFR{beta} to heal and regenerate cutaneous skin wounds, periodontal tissue, and orthopedic bone with a strong safety profile. We hypothesize that rhPDGF will benefit other conditions identified by PheWAS with a similar physiological mechanism as the existing indications, such as complex perianal fistulas that are ineligible for a fistulotomy. Methods and analysis This prospective, blinded, single-site study aims to enroll 12 participants, randomized at a ratio of 2:1, comparing implantation of rhPDGF-enhanced collagen to routine care procedures, and stratified by fistula etiology, idiopathic versus Crohns disease (CD)-related. The primary outcome of this study will evaluate the technical performance of the rhPDGF-enhanced collagen implant for treatment of complex perianal fistulas as measured by the proportion of participants with successful implantation of the intervention without any intervention-related serious adverse events. The secondary outcomes will assess the preliminary safety and efficacy of the intervention based on all intervention-related adverse events, total fistulas healed, rate of fistula recurrence, and change in patient-reported symptoms. Complex perianal fistulas, idiopathic or CD-related, remain a major clinical challenge in need of new multimodal treatments aimed at tissue repair and regeneration. Pharmaceutical rhPDGF stimulation of PDGFR{beta} signaling promotes healing of skin, bone, and soft tissue. PheWAS revealed fistulas as a novel indication for repurposing rhPDGF. This protocol aims to evaluate the technical performance, preliminary safety and efficacy, and feasibility of rhPDGF-enhanced collagen for healing and remission of complex perianal fistulas. Ethics and dissemination This trial was approved by the Vanderbilt University Medical Center institutional review board (IRB#240585). Results will be submitted for publication in a peer-reviewed journal.

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