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01.

arXiv (math.PR) 2026-06-18 DOI: arXiv:2308.00805

Second-Order Approximation of Limit Order Books in a Single-Scale Regime

作者:

Ulrich Horst↗D\"orte Kreher↗Konstantins Starovoitovs↗

arXiv:2308.00805v3 Announce Type: replace-cross Abstract: We establish a first- and second-order approximation for an infinite dimensional limit order book model in a single (critical) scaling regime where market and limit orders arrive at a common time scale. With our choice of scaling we obtain non-degenerate first- and second-order approximations for the price and volume dynamics. While the first-order approximation is given by a coupled ODE-PDE system, the second-order approximation is described in terms of an infinite-dimensional stochastic evolution equation driven by a cylindrical Brownian motion. The driving noise processes exhibit a non-trivial correlation in terms of the model parameters. We prove that the evolution equation has a unique solution and that the sequence of standardized limit order book models converges weakly to the solution of the evolution equation. The proof uses a non-standard martingale problem. We calibrate a linearized model to market data and explain how our model can be used for deriving confidence intervals of portfolio liquidation values.

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02.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2606.20094

MakeupMirror: Improving Facial Attribute Preservation in Diffusion Models for Makeup Transfer

作者:

Nefeli Andreou↗Angel Mart\'inez-Gonz\'alez↗Sabine Sternig↗Matthieu Guillaumin↗Epameinondas Antonakos↗Michael Opitz↗

arXiv:2606.20094v1 Announce Type: cross Abstract: Makeup transfer models enable fun augmented reality (AR) experiences as well as virtual try-on (VTO) for online makeup shopping. While recent state-of-the-art diffusion based solutions such as Stable-Makeup dramatically improve the accuracy and realism of makeup transfer, they still face limitations in identity and skin color preservation, making production-level VTO for makeup shopping unrealistic. In this work, we propose MakeupMirror, a diffusion-based approach to makeup transfer that makes significant progress towards preserving facial features and skin tone. We introduce several technical innovations over Stable-Makeup: (1) integration of facial geometry conditioning with ControlNets to maintain facial fidelity; (2) region-specific makeup transfer control to enable precise makeup application across facial regions such as skin, eyes and lips; (3) skin tone-based makeup transfer modulation that prevent skin tone alteration in cross-subject transfer scenarios; and (4) integration of a Levenberg-Marquardt Langevin sampler to speed up inference while maintaining generation quality. Our experiments on CPM-Real, Makeup Wild, and (herein newly collected, more diverse) MakeupSelfies datasets show that MakeupMirror improves relative facial recognition similarity by +60%, reduces relative skin tone difference by -50% over Stable-Makeup, with a latency of 0.7s, while achieving expert acceptance rate of 94% across core facial identity preservation criteria.

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03.

arXiv (CS.CL) 2026-06-24 DOI: arXiv:2501.11790

Benchmarking LLMs' Mathematical Reasoning with Unseen Random Variables Questions

作者:

Zijin Hong↗Hao Wu↗Su Dong↗Junnan Dong↗Yilin Xiao↗Yujing Zhang↗Zhu Wang↗Feiran Huang↗Linyi Li↗Hongxia Yang↗Xiao Huang↗

Recent studies have raised significant concerns regarding the reliability of current mathematics benchmarks, highlighting issues such as simplistic design and potential data contamination. Consequently, developing a reliable benchmark that effectively evaluates large language models' (LLMs) genuine capabilities in mathematical reasoning remains a critical challenge. To address these concerns, we propose RV-Bench, a novel evaluation methodology for Benchmarking LLMs with Random Variables in mathematical reasoning. Specifically, we build question-generating functions to produce random variable questions (RVQs), whose background content mirrors original benchmark problems, but with randomized variable combinations, rendering them "unseen" to LLMs. Models must completely understand the inherent question pattern to correctly answer RVQs with diverse variable combinations. Thus, an LLM's genuine reasoning capability is reflected through its accuracy and robustness on RV-Bench. We conducted extensive experiments on over 30 representative LLMs across more than 1,000 RVQs. Our findings propose that LLMs exhibit a proficiency imbalance between encountered and ``unseen'' data distributions. Furthermore, RV-Bench reveals that proficiency generalization across similar mathematical reasoning tasks is limited, but we verified it can still be effectively elicited through test-time scaling.

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04.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2507.05019

Meta-Learning Transformers to Improve In-Context Generalization

作者:

Lorenzo Braccaioli↗Anna Vettoruzzo↗Prabhant Singh↗Joaquin Vanschoren↗Mohamed-Rafik Bouguelia↗Nicola Conci↗

arXiv:2507.05019v2 Announce Type: replace-cross Abstract: In-context learning enables transformer models to generalize to new tasks based solely on input prompts, without any need for weight updates. However, existing training paradigms typically rely on large, unstructured datasets that are costly to store, difficult to evaluate for quality and balance, and pose privacy and ethical concerns due to the inclusion of sensitive information. Motivated by these limitations and risks, we propose an alternative training strategy where we leverage a collection of multiple, small-scale, and domain-specific datasets. We empirically demonstrate that the increased quality and diversity of such data improve the generalization abilities of in-context learners beyond their training domain, while achieving comparable performance with models trained on a single large-scale dataset. We investigate this paradigm by leveraging meta-learning to train an in-context learner on the Meta-Album collection under several settings. Firstly, we show the performance in a controlled environment, where the test domain is completely excluded from the training knowledge. Secondly, we explore the robustness of these models to forgetting in a continual scenario where the information is accessible for a limited time. Finally, we explore the more challenging unsupervised scenario. Our findings demonstrate that transformers still generalize for in-context prediction when trained on a curated dataset collection while offering advantages in modularity and replaceability.

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05.

arXiv (CS.AI) 2026-06-11 DOI: arXiv:2606.07001

DataEvolver: Automatic Data Preparation for Large Language Models through Multi-Level Self-Evolving

作者:

Chao Deng↗Shaolei Zhang↗Ju Fan↗Xiaoyong Du↗

arXiv:2606.07001v2 Announce Type: replace-cross Abstract: High-quality training data is essential to large language models (LLMs) and typically requires extensive and costly manual curation. Existing automatic data preparation methods rely on predefined pipelines or customized human instructions, which limits their adaptability to diverse data distributions and lacks principled guidance from high-quality examples. In this paper, we introduce DataEvolver, the first self-evolving data preparation system that automatically constructs pipelines to transform raw data into high-quality data. DataEvolver employs a multi-level mechanism to ensure both pipeline executability and effectiveness. At the operator level, it incrementally expands the operator set to construct a logical plan while resolving dependency conflicts. At the pipeline level, it instantiates logical plans into executable code and iteratively refines pipeline orchestration through a feedback loop that reduces the distribution gap between prepared data and high-quality examples. Experiments on seven benchmarks show that DataEvolver substantially improves data quality and achieves an average 10\% gain in downstream LLM performance compared with training on original data, highlighting new opportunities for the iterative co-evolution of LLMs and data.

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06.

bioRxiv (Bioinfo) 2026-06-11 DOI: HASH:6b1c5a1e0ab3ae8cf93f99c641584c19

EditorForge: An Active-Site-Aware Framework for Inverse-Folding-Based Protein Redesign

作者:

Chen↗Siddiqui↗Taucar↗Tiralongo↗Tkachenko↗Xu↗Bawa↗Guo↗Pinska↗Rim↗Shi↗Wang↗…

Inverse-folding models can rapidly generate protein sequences compatible with a supplied backbone, but unconstrained redesign is poorly suited to enzyme and genome-editor-associated domains, where catalytic, substrate-proximal, and conserved structural regions must remain protected. In this paper, we present EditorForge, a modular constraint-and-audit suite for editor-domain protein redesign that wraps fixed-backbone inverse folding with explicit design masks, fixed-position enforcement, active-site-proximity auditing, active-site-shielded regeneration, and downstream structural quality control. Using full-length Moloney murine leukemia virus reverse transcriptase structure 4MH8 (MMLV RT 4MH8) as a demonstration target, EditorForge first restricted redesign to a bounded 25-position envelope while fixing 428 residues. An initial audit detected active-site-proximal failure modes despite fixed-position integrity. Later, the Active Site Shield module then removed five unsafe design positions, replaced them with lower-contact alternatives, and regenerated candidates under stricter constraints. Post Shield Audit evaluated 24 regenerated candidates, all of which satisfied the hard sequence/mask and active-site-shield constraints. For the eight candidates that were selected or returned for structure-prediction/refolding quality control. Enhanced RefoldQC found that all 8 evaluated predicted structures passed the computational structure-QC screen. That said, the selected 8 candidates passed the computational structure-QC screen, with global C RMSD values of 1.2061–1.5555~[A], active-site C RMSD values of 0.4098–1.8397~[A], mutation-neighborhood C RMSD values of 1.3155-1.6848~[A], and average pLDDT-like confidence values of 94.87-95.11. In short, EditorForge provides a reproducible triage layer that converts general inverse-folding output into constrained and editor-specific candidate sets for downstream structural and biological review on top of existing structural prediction tools.

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07.

arXiv (CS.LG) 2026-06-11 DOI: arXiv:2606.12280

Holding the FP8 Quality Ceiling at 8-Bit Weights and Activations: INT8 and GGUF Post-Training Quantization of Ideogram 4.0 for Consumer GPUs

作者:

Deep Gandhi↗Ali Asaria↗Tony Salomone↗

arXiv:2606.12280v1 Announce Type: new Abstract: Post-training quantization lets large text-to-image diffusion transformers run on consumer GPUs, yet the hardware-specific trade-offs are seldom measured directly. We quantize Ideogram 4.0 - a 9.3B flow-matching diffusion transformer (DiT), shipped as two separate-weight copies of a single-stream 34-layer backbone for classifier-free guidance and conditioned by a Qwen3-VL-8B encoder - for Ampere RTX 3090 GPUs, which lack FP8 tensor cores. Our INT8 W8A8 recipe (per-channel weights, per-token dynamic activations, SmoothQuant, and mixed-precision protection of a small high-fragility layer set) holds the FP8 quality ceiling: on a 200-prompt benchmark the paired same-seed bootstrap CI for INT8-FP8 includes zero on both Pick and CLIP, while INT8 improves on NF4 by $+1.9$ CLIP (95% CI $[+1.21,+2.64]$, excluding zero). A per-category OCR analysis, to our knowledge unreported for this model class, confirms text legibility is preserved, and an ablation isolates protection of the FFN down-projections as the dominant quality lever. Our GGUF Q4_K quantization beats NF4 at equal on-disk size and is the Pareto winner on the quality-memory frontier, with paired confidence intervals excluding zero (Q8_0 is quality neutral). Finally, we characterize where 8-bit quantization helps and where it does not: INT8's weights match FP8's footprint rather than shrink it, so a speed gain on Ampere awaits a fused INT8 kernel.

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08.

arXiv (CS.LG) 2026-06-19 DOI: arXiv:2501.18322

A Unified Perspective on the Dynamics of Deep Transformers

作者:

Val\'erie Castin↗Pierre Ablin↗Jos\'e Antonio Carrillo↗Gabriel Peyr\'e↗

arXiv:2501.18322v2 Announce Type: replace Abstract: Transformers, which are state-of-the-art in most machine learning tasks, represent the data as sequences of vectors called tokens. This representation is then exploited by the attention function, which learns dependencies between tokens and is key to the success of Transformers. However, the iterative application of attention across layers induces complex dynamics that remain to be fully understood. To analyze these dynamics, we identify each input sequence with a probability measure and model its evolution as a Vlasov equation called Transformer PDE, whose velocity field is non-linear in the probability measure. Our first set of contributions focuses on compactly supported initial data. We show the Transformer PDE is well-posed and is the mean-field limit of an interacting particle system, thus generalizing and extending previous analysis to several variants of self-attention: multi-head attention, L2 attention, Sinkhorn attention, Sigmoid attention, and masked attention–leveraging a conditional Wasserstein framework. In a second set of contributions, we are the first to study non-compactly supported initial conditions, by focusing on Gaussian initial data. Again for different types of attention, we show that the Transformer PDE preserves the space of Gaussian measures, which allows us to analyze the Gaussian case theoretically and numerically to identify typical behaviors. This Gaussian analysis captures the evolution of data anisotropy through a deep Transformer. In particular, we highlight a clustering phenomenon that parallels previous results in the non-normalized discrete case.

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09.

arXiv (CS.CV) 2026-06-17 DOI: arXiv:2604.24696

NeuroClaw Technical Report

作者:

Cheng Wang↗Zhibin He↗Zhihao Peng↗Shengyuan Liu↗Yufan Hu↗Carl Yang↗Lifang He↗Lichao Sun↗Xiang Li↗Yixuan Yuan↗

Agentic artificial intelligence systems promise to accelerate scientific workflows, but neuroimaging poses unique challenges: heterogeneous modalities (sMRI, fMRI, dMRI, EEG), long multi-stage pipelines, and persistent reproducibility risks. To address this gap, we present NeuroClaw, a domain-specialized multi-agent research assistant for executable and reproducible neuroimaging research. NeuroClaw operates directly on raw neuroimaging data across formats and modalities, grounding decisions in dataset semantics and BIDS metadata so users need not prepare curated inputs or bespoke model code. The platform combines harness engineering with end-to-end environment management, including pinned Python environments, Docker support, automated installers for common neuroimaging tools, and GPU configuration. In practice, this layer emphasizes checkpointing, post-execution verification, structured audit traces, and controlled runtime setup, making toolchains more transparent while improving reproducibility and auditability. A three-tier skill/agent hierarchy separates user-facing interaction, high-level orchestration, and low-level tool skills to decompose complex workflows into safe, reusable units. Alongside the NeuroClaw framework, we introduce NeuroBench, a system-level benchmark for executability, artifact validity, and reproducibility readiness. Across multiple multimodal LLMs, NeuroClaw-enabled runs yield consistent and substantial score improvements compared with direct agent invocation. Project homepage: https://cuhk-aim-group.github.io/NeuroClaw/index.html

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10.

arXiv (CS.CL) 2026-06-12 DOI: arXiv:2606.10403

KCSAT-ML: Probing Reasoning Models with Nationwide-Cohort Human Difficulty

作者:

Sanghee Park↗Geewook Kim↗Kee-Eung Kim↗

Math reasoning benchmarks have proliferated, yet most lack a per-item difficulty signal grounded in actual human performance. We introduce KCSAT-ML, a decade (2014-2025) of Korean College Scholastic Ability Test (KCSAT; Suneung) mathematics: 664 problems with a 339-item core set carrying official per-item error rates from nationwide cohorts of hundreds of thousands of examinees. We pair the benchmark with Difficulty-aligned Reasoning Gain (DRG): a score-orthogonal metric that asks whether a model's mistakes concentrate on the items humans found hard, or on items humans found easy. Together they expose, across a wide range of VLMs (and LLMs via OCR), three patterns: (i) low-budget accuracy collapses on the high-human-error tail at every model size; (ii) test-time scaling (TTS) raises token use roughly linearly with cohort error rate, while accuracy gains follow a non-monotonic curve; (iii) within a single family, TTS flips between anti-scaling on the hardest items and overthinking on easier ones – two faces of the same alignment failure. On DRG, models with near-identical accuracy can sit at near-opposite values: one model gets wrong what humans also find hard, while another solves the hardest items yet fails on items humans find easy – a contrast that aggregate accuracy hides. Our code and dataset builder will be open-sourced at https://github.com/naver-ai/KCSAT-ML.

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11.

arXiv (CS.CL) 2026-06-15 DOI: arXiv:2606.13715

WorkBench Revisited: Workplace Agents Two Years On

作者:

Olly Styles↗

The best agent on WorkBench in March 2024, GPT-4, completed 43% of tasks and took an unintended harmful action, such as emailing the wrong person, on 26% of them. We re-visit the benchmark in June 2026 and find that the best agent to date, Claude Opus 4.8, completes 89% and takes an unintended harmful action on 2.5%. Aside from this considerable progress in frontier agent performance, three things stand out. First, capability and safety go together on WorkBench rather than trade off, so the models that finish the most tasks also do the least unintended damage. Second, while several classes of error have been totally eliminated, frontier models still make some basic mistakes that occasionally result in irreversible harm, such as sending an email to the wrong person. Third, the rise of open-weight models has drastically lowered costs for a performance level that was previously only accessible to proprietary models, while frontier costs have stayed relatively stable. We release an updated version of the benchmark with data and code quality improvements, new model scores, and analysis of agent progress on WorkBench since 2024.

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12.

arXiv (CS.CL) 2026-06-12 DOI: arXiv:2604.24079

The Pragmatic Persona: Discovering LLM Persona through Bridging Inference

作者:

Jisoo Yang↗Jongwon Ryu↗Minuk Ma↗Trung X. Pham↗Junyeong Kim↗

Large Language Models (LLMs) reveal inherent and distinctive personas through dialogue. However, most existing persona discovery approaches rely on surface-level lexical or stylistic cues, treating dialogue as a flat sequence of tokens and failing to capture the deeper discourse-level structures that sustain persona consistency. To address this limitation, we propose a novel analytical framework that interprets LLM dialogue through bridging inference – implicit conceptual relations that connect utterances via shared world knowledge and discourse coherence. By modeling these relations as structured knowledge graphs, our approach captures latent semantic links that govern how LLMs organize meaning across turns, enabling persona discovery at the level of discourse coherence rather than surface realizations. Experimental results across multiple reasoning backbones and target LLMs, ranging from small-scale models to 80B-parameter systems, demonstrate that bridging-inference graphs yield significantly stronger semantic coherence and more stable persona identification than frequency or style-based baselines. These results show that persona traits are consistently encoded in the structural organization of discourse rather than isolated lexical patterns. This work presents a systematic framework for probing, extracting, and visualizing latent LLM personas through the lens of Cognitive Discourse Theory, bridging computational linguistics, cognitive semantics, and persona reasoning in large language models. Codes are available at https://github.com/JiSoo-Yang/Persona_Bridging.git

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13.

arXiv (CS.CV) 2026-06-12 DOI: arXiv:2606.13673

SpatialClaw: Rethinking Action Interface for Agentic Spatial Reasoning

作者:

Seokju Cho↗Ryo Hachiuma↗Abhishek Badki↗Hang Su↗Byung-Kwan Lee↗Chan Hee Song↗Sifei Liu↗Subhashree Radhakrishnan↗Seungryong Kim↗Yu-Chiang Frank Wang↗Min-Hung Chen↗

Spatial reasoning, the ability to determine where objects are, how they relate, and how they move in 3D, remains a fundamental challenge for vision-language models (VLMs). Tool-augmented agents attempt to address this by augmenting VLMs with specialist perception modules, yet their effectiveness is bounded by the action interface through which those tools are invoked. In this work, we study how the design of this interface shapes the agent's capacity for open-ended spatial reasoning. Existing spatial agents either employ single-pass code execution, which commits to a full analysis strategy before any intermediate result is observed, or rely on a structured tool-call interface that often offers less flexibility for freely composing operations or tailoring the analysis to each task. Both designs offer limited flexibility for open-ended, complex 3D/4D spatial reasoning. We therefore propose SpatialClaw, a training-free framework for spatial reasoning that adopts code as the action interface. SpatialClaw maintains a stateful Python kernel pre-loaded with input frames and a suite of perception and geometry primitives, letting a VLM-backed agent write one executable cell per step conditioned on all prior outputs, enabling the agent to flexibly compose and manipulate perception results and adapt its analysis to both intermediate text and visual observations and the demands of each problem. Evaluated across 20 spatial reasoning benchmarks spanning a broad range of static and dynamic 3D/4D spatial reasoning tasks, SpatialClaw achieves 59.9% average accuracy, outperforming the recent spatial agent by +11.2 points, with consistent gains across six VLM backbones from two model families without any benchmark- or model-specific adaptation.

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14.

arXiv (CS.AI) 2026-06-11 DOI: arXiv:2606.11637

TouchThinker: Scaling Tactile Commonsense Reasoning to the Open World with Large-scale Data and Action-aware Representation

作者:

Kailin Lyu↗Di Wu↗Pengwei Zhang↗Yuhang Zheng↗Yingxin Lai↗Long Xiao↗Kangyi Wu↗Pengna Li↗Chen Gao↗Lianyu Hu↗Xiaobin Hu↗Jie Hao↗…

arXiv:2606.11637v1 Announce Type: new Abstract: Touch is a key modality for embodied agents to understand the physical world. Although recent work has incorporated tactile signals into language systems for tactile commonsense reasoning, scaling such systems to realistic open-world settings remains challenging due to two key bottlenecks: (1) current tactile reasoning datasets remain limited in format and scale, providing insufficient supervision for reasoning from tactile observations to physical commonsense and hindering the learning of transferable tactile commonsense; (2) Tactile signals are inherently redundant and action-specific, yet existing methods often overlook these properties, resulting in inefficient representations with limited semantic expressiveness. To address these limitations, we propose TouchThinker, a tactile-language framework that scales tactile commonsense reasoning to the open world from both data and representation perspectives. First, we construct TouchThinker-1M, a million-scale, multi-source tactile reasoning dataset covering 415 objects, 8 scenarios, and 7 sensor types, providing a solid data foundation for open-world generalization. We further introduce TouchThinker-Bench, an open-world benchmark with more realistic and diverse tasks. Then, we propose action-aware modeling mechanism to improve tactile representation efficiency and enable efficient reasoning. Experimental results demonstrate that TouchThinker achieves competitive performance against state-of-the-art models across multiple datasets. Our code and dataset will be made available at: https://github.com/lvkailin0118/TouchThinker.

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15.

arXiv (CS.CL) 2026-06-24 DOI: arXiv:2507.13563

Balalaika: Data-Centric, Prosody-Aware Annotation Pipeline for Russian Speech

作者:

Kirill Borodin↗Nikita Vasiliev↗Vasiliy Kudryavtsev↗Maxim Maslov↗Mikhail Gorodnichev↗Grach Mkrtchian↗

We introduce Balalaika, an open-source, data-centric pipeline for processing audio and producing prosody-aware annotations. It combines semantic VAD for context-preserving segmentation, multi-ASR ensembling with ROVER consensus decoding, while retaining optional word-level timestamps, followed by automatic quality and speaker-purity filtering. The text is further enriched with punctuation restoration, lexical stress and "\textipa{e}/\textipa{\H{e}}" normalization, and IPA phonemes. Using Balalaika, we build a 5.1k-hour multi-source Russian corpus with rich annotations, and show consistent gains under equalized training budgets for both speech denoising and TTS; ablations confirm complementary benefits of stress and punctuation and improved synthesis with stricter MOS filtering. The datasets are publicly available at \href{https://huggingface.co/collections/lab260/balalaika-dataset}{\underline{HuggingFace}}

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16.

arXiv (quant-ph) 2026-06-15 DOI: arXiv:2606.14577

Trap-Quenched Matter-Wave Optics for Dual Species Lensing

作者:

Gabriel M\"uller↗Timoth\'e Estrampes↗Claudia Puertas Gonz\'alez↗Jannik Str\"ohle↗David B. Reinhardt↗Dana Codruta Marinica↗Ethan R. Elliott↗Jason R. Williams↗Nathan Lundblad↗Eric Charron↗Ernst M. Rasel↗Matthias Meister↗…

arXiv:2606.14577v1 Announce Type: cross Abstract: Dual-species atom interferometry in space promises precise tests of the Universality of Free Fall (UFF), with a sensitivity that grows quadratically with the extended interrogation time accessible in weightlessness. These tests demand exquisite control over the expansion energies of both condensed sources as well as over their differential center-of-mass dynamics. We propose a trap-quenched collimation technique featuring in-trap excitations of collective modes compatible with state-of-the-art atom-chip setups. Using NASA's Cold Atom Laboratory aboard the International Space Station, we demonstrate it on a single-species $^{87}$Rb condensate. By controlling the center-of-mass release dynamics we observe free expansion times up to 700 ms and measure a two-dimensional expansion energy of $k_B \cdot 78\pm 9 \;\mathrm{pK}$ in the imaging plane. A detailed model of the magnetically-induced dynamics indicates that this corresponds to a two-dimensional expansion energy of about $k_B \cdot 15^{+12}_{-5}\; \mathrm{pK}$ along two of the condensate's eigenaxes. Finally, we theoretically study this trap-quenched collimation scheme for a $^{41}$K-$^{87}$Rb mixture, predicting a simultaneous collimation that meets the expansion energy requirements for a state-of-the-art UFF test at the $10^{-15}$ accuracy level.

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17.

arXiv (CS.LG) 2026-06-17 DOI: arXiv:2606.17294

VISTA: Scale-Aware Visual Navigation via Action History Conditioning

作者:

Maeva Guerrier↗Koki Kobayashi↗Simon Roy↗Jana Pavlasek↗Giovanni Beltrame↗

arXiv:2606.17294v1 Announce Type: cross Abstract: Vision Navigation Foundation Models (VNMs) promise end-to-end learned navigation policies capable of zero-shot deployment across diverse embodiments and environments. To maintain generality, many vision-based navigation models predict normalized actions. However, this normalization introduces a critical deployment vulnerability: applying different scaling factors to the same normalized trajectory alters its physical geometry, which degrades navigation performance and increases collision risks. We address this vulnerability by conditioning the model on normalized action histories alongside image observations, providing explicit context on the relationship between the model's predictions and the robot's actual physical displacement. Furthermore, current VNMs often struggle in visually repetitive environments that lack distinct features. To resolve this issue, we integrate a DINOv3 encoder, whose richer representations enable our model to capture both spatial and geometric dimensions between observations. VISTA generalizes robustly to out-of-distribution environments, achieving 100% goal prediction accuracy in zero-shot, real-world deployment in Outdoor, Forest and Office settings, and an average of 95% checkpoints crossed, demonstrating consistent path following in unseen environments.

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18.

medRxiv (Medicine) 2026-06-18 DOI: HASH:0102be157fe057bbcb06da21920c5c72

Diabetes is associated with increased nocturnal respiratory rate

作者:

Gupta↗K. S↗Pedros-Valls↗Harrington↗Torres Barba↗King↗K. R↗

Background and Objective: Diabetes mellitus (DM) causes autonomic neuropathy, which may alter nocturnal respiratory rate (NRR). To test the association between DM and NRR, we analyzed elective polysomnograms of four large observational cohorts. Research Design and Methods: We performed cross-sectional analysis of over 25,000 individuals with polysomnograms (PSGs) from the Sleep Heart Health Study (SHHS), Hispanic Community Health Study/Study of Latinos (HCHS/SOL), Osteoporotic Fractures in Men Study (MrOS), and Wisconsin Sleep Cohort (WSC). Patient-level NRRs were derived from inductance plethysmography waveforms. DM status was determined by self-report, physician diagnosis, medication use, or laboratory values, depending on the cohort. We related DM and NRR (continuous and dichotomized) using logistic regression models and adjusted for potential confounders. Cohort-specific results were combined using random-effects meta-analysis. Results: Meta-analysis of unadjusted models showed a pooled odds ratio (OR) of 1.10 (95% CI:1.04-1.17) for each breath-per-minute (brpm) increase in NRR. This association remained significant after multivariable adjustment (OR:1.06, 95% CI:1.02-1.11). Dichotomized analyses similarly showed higher odds of DM across dichotomization thresholds ranging from 15 to 21 brpm. At a threshold of 18 brpm, the unadjusted pooled OR was 1.77 (95% CI:1.23-2.55, P=0.0022), and the adjusted OR was 1.49 (95% CI:1.10-2.02, P=0.0098). Conclusions: Clinically stable outpatients with elevated NRR have an increased prevalence of DM. Additional studies are needed to investigate whether the mechanism is autonomic neuropathy and whether monitoring NRR can detect early complications of DM.

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19.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2606.18734

Point-Cloud-Assistant Localized Statistical Channel Prediction by Tangent Gaussian Splatting

作者:

Ye Xue↗Yiheng Wang↗Xinhua Shao↗Qi Yan↗Shutao Zhang↗Tsung-Hui Chang↗

arXiv:2606.18734v1 Announce Type: cross Abstract: Accurate, site-specific channel information is crucial for optimizing next-generation wireless networks. Among various approaches, localized statistical channel modeling (LSCM), which models the channel multipath angular power spectrum (APS) from the reference signal received power (RSRP) measurement, has emerged as a state-of-the-art method tailored for efficient network optimization. However, despite its effectiveness, LSCM cannot predict APS at the vast majority of locations where no measurements are available, which significantly restricts its applicability in large-scale, real-world scenarios. To address this challenge, we present point-cloud-assisted tangent Gaussian splatting (PC-TGS), the first framework to extrapolate APS to unmeasured outdoor grids by integrating sparse radio measurements with dense LiDAR-based geometry. PC-TGS represents environmental scatterers as anisotropic 3D Gaussians, initialized and refined through a relaxed-mean reparameterization of the raw point cloud. A tangent-plane projection accurately maps each Gaussian into the local angular domain, while a depth-aware electromagnetic splatting process aggregates their contributions. To ensure practical deployment, we derive a closed-form Gaussian-weighted average (GWA) for APS bin integration and provide a provable error bound. { Evaluations on a LiDAR-scanned city-scale dataset (5M points, 6,310 RSRP samples) demonstrate that PC-TGS achieves better APS and RSRP prediction performance compared to state-of-the-art baselines and faster inference time for APS extrapolation task. These results highlight the potential of PC-TGS to enable geometry-aware and data-efficient channel prediction in large-scale wireless digital twins.

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20.

arXiv (CS.CL) 2026-06-17 DOI: arXiv:2606.17838

Environment-Grounded Automated Prompt Optimization for LLM Game Agents

作者:

Rean Clive Fernandes↗Lukas Fehring↗Theresa Eimer↗Marius Lindauer↗Matthias Feurer↗

LLM agents in interactive environments are highly sensitive to their prompts, yet prompt engineering remains a manual, task-specific process. We introduce an automated prompt optimization framework for LLM agents that decomposes the observation-to-action pipeline into a goal-conditioned descriptor agent and an action selection agent, and iteratively refines each module's prompt through an LLM-driven evolutionary loop guided by environment returns. We propose a behavior analyzer to attribute episode outcomes to specific prompt components, and a mutator to propose targeted revisions to the prompt, before validating them through environment rollouts. We evaluate on all five BabyAI tasks in the BALROG benchmark, comparing our pipeline against BALROG's RobustCoTAgent under both plain and guided prompt initializations. Optimization improves performance consistently across tasks and conditions, without requiring updates to the model weights. On PutNext, a multi-step coordination task where the RobustCoTAgent achieves 0% success, our framework reaches up to 72.5% success rate using the same underlying LLM with optimized prompts. These results suggest that a multi-agent framework, combined with automatic prompt optimization, enhances LLMs without the need for fine-tuning or extensive human supervision.

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21.

PLOS Medicine 2026-05-20 DOI: HASH:5b47d66d09bc5b131ec3f326f79cad8f

Prescribed hormonal contraceptive use trends in the Estonian Biobank: A longitudinal observational study

作者:

Jelisaveta Džigurski↗

by Jelisaveta Džigurski, Märt Möls, Kristi Läll, Hannah Currant, Mall Eltermaa, Estonian Biobank Research Team , Reedik Mägi, Lili Milani, Triin Laisk Background Hormonal contraceptives (HCs) are widely used and have well-documented population-level statistics. Previous studies with short follow-ups have focussed on individual HC use and side effects. However, the same aspects over longer periods, HC formulation switching, and the impact of genetic factors on HC side effects remain understudied due to the limited availability of suitable datasets. We investigated whether the Estonian Biobank (EstBB) is suitable for studying genetic risk for HC side effects. Methods and findings This is a longitudinal descriptive study combining prescribed HC purchase data collected from 2004 to 2022 with genetic and health data from 73,071 female EstBB HC users aged 15–55 at the time of purchase. HC usage was defined by the Anatomical Therapeutic Chemical (ATC) codes G02B, G03A, and G03HB01. Methods included calculating age-stratified annual user prevalence, inferring usage periods from purchases, assessing formulation switching, identifying the International Classification of Diseases, Tenth Revision (ICD-10)-based side effect-related diagnoses and thromboembolism risk factors, and assessing carrier status for Factor V Leiden (FVL, rs6025) and prothrombin G20210A (PTM, rs1799963) genetic variants as proof-of-concept. Over 19 years, 20 HC formulations with five administration routes (oral pills, transdermal patches, vaginal rings, subdermal implants, intrauterine devices) were used. In the EstBB, combined HCs were the most commonly used among users aged 15–29, while progestin-only HC use increased with age and over time, comparable to the Estonian population. Overall, 64.2% (n = 46,920) of users switched formulations at least once, with 17.7% (n = 12,929) being rapid switchers. Side effect-related diagnoses were observed in 23.1% (n = 2,982) of rapid switchers, with excessive/irregular menstrual bleeding being the most common. Genetic analysis revealed that 5.3% (n = 3,886) of users carried at least one variant previously associated with increased thrombosis risk (3.5% (n = 2,556) carried FVL only, 1.8% (n = 1,276) PTM only, and 0.07% (n = 54) both). Carriers of thrombosis-associated variants had a significantly higher percentage of thrombosis (6.5%) than non-carriers (4.2%; OR = 1.61, 95% CI [1.40, 1.84], p 

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22.

arXiv (math.PR) 2026-06-18 DOI: arXiv:2510.17629

Formation of clusters and coarsening in weakly interacting diffusions

作者:

Nicolai Gerber↗Rishabh S. Gvalani↗Martin Hairer↗Grigorios A. Pavliotis↗Andr\'e Schlichting↗

arXiv:2510.17629v3 Announce Type: replace-cross Abstract: This paper studies the clustering behavior of weakly interacting diffusions under the influence of sufficiently localized attractive interaction potentials on the one-dimensional torus. We describe how this clustering behavior is closely related to the presence of discontinuous phase transitions in the mean-field PDE. For local attractive interactions, we employ a new variant of the strict Riesz rearrangement inequality to prove that all global minimizers of the free energy are either uniform or single-cluster states, in the sense that they are symmetrically decreasing. We analyze different timescales for the particle system and the mean-field (McKean-Vlasov) PDE, arguing that while the particle system can exhibit coarsening by both coalescence and diffusive mass exchange between clusters, the clusters in the mean-field PDE are unable to move and coarsening occurs via the mass exchange of clusters. By introducing a new model for this mass exchange, we argue that the PDE exhibits dynamical metastability. We conclude by presenting careful numerical experiments that demonstrate the validity of our model.

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23.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2606.07489

How AI Agents Reshape Knowledge Work: Autonomy, Efficiency, and Scope

作者:

Jeremy Yang↗Kate Zyskowski↗Noah Yonack↗Jerry Ma↗

arXiv:2606.07489v2 Announce Type: replace Abstract: Frontier AI systems are bridging the gap between intelligence and utility by shifting from conversational assistants to autonomous agents that execute tasks end to end. Using production data from Perplexity's Search and Computer products, we study this transition by examining how AI agents accelerate and reshape knowledge work. Three key empirical findings emerge. First, using sessions with near-identical initial query pairs as natural experiments for the same underlying task attempted with both products, Computer performs 26 minutes of autonomous work per user session, versus 33 seconds for Search. Computer automates task decomposition and execution that Search users might otherwise manually orchestrate and implement. As a result, Computer shifts follow-up query distribution toward higher-order work such as verification and extension. Autonomy also increases execution quality, with per-query dissatisfaction rates 55% lower on Computer than on Search. Second, due to its autonomy advantage, Computer reduces completion time from 269 to 36 minutes on matched tasks, lowering estimated time and cost by 87% and 94%, respectively, compared to humans equipped with Search alone. Third, Computer changes the scope of work that users attempt: Computer queries more often cross occupational boundaries, require higher-order cognition, draw on broader expertise, take the form of composite tasks that bundle interdependent subtasks into a single query, and unlock work activities that are essentially absent from Search usage among the same users. Together, the evidence indicates that AI agents accelerate workflows, enhance output quality, reduce costs, and expand the breadth and depth of automated work.

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24.

medRxiv (Medicine) 2026-06-17 DOI: HASH:e0de9ec2b3150cc8e212b0feed31a113

Preserved Medial Temporal Lobe Flexibility Predicts Memory Generalization Only in the Context of Good Sleep Quality among Older African Americans

作者:

White↗P. G↗Budak↗Moallemian↗Fausto↗Gluck↗

Objectives: Poor sleep quality is a risk factor for Alzheimer's disease (AD). Older African Americans experience disproportionately high rates of sleep disturbance and AD. Medial temporal lobe (MTL) flexibility reflects dynamic neural reorganization and may be a marker of generalization performance. This study examined whether sleep quality moderates the association between MTL flexibility and memory generalization. Methods: Fifty older African Americans (MeanAge=69.7{+/-}6.21 years; 80% women) underwent rs-fMRI to quantify MTL flexibility, Rutgers Acquired Equivalence Task for memory generalization, and Pittsburgh Sleep Quality Index for sleep quality. Results: Greater MTL flexibility was associated with better generalization (r=0.367, p=.017). Good sleepers showed higher MTL flexibility (F(1,44)=8.11, p2=.156, p=.007) and superior generalization (F(1,46)= 12.33, p2=.211, p=.001). Sleep quality significantly moderated the MTL flexibility and generalization relationship ({beta}=-1.519, p=.012). Conclusions: Preserved MTL flexibility may confer generalization only in good sleepers, suggesting that sleep disturbance may disrupt the MTL neural resilience among older African Americans.

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25.

arXiv (CS.CL) 2026-06-17 DOI: arXiv:2606.18019

Reading between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews

作者:

Franziska Braun↗Alea R\"uggeberg↗Thomas Ranzenberger↗Hartmut Lehfeld↗Thomas Hillemacher↗Tobias Bocklet↗Korbinian Riedhammer↗

Dementia and depression are the most prevalent neuropsychiatric disorders in geriatric populations, and their overlapping symptoms pose major challenges for differential diagnosis. In this study, we investigate open-weights Large Language Models (LLMs) for predicting dementia and depression severity from speech samples collected during standardized history taking interviews with 154 German-speaking subjects. We introduce an observer-based Global Depression Scale (GDS-D) aligned with the established Global Deterioration Scale (GDS), enabling parallel global staging of affective and cognitive symptoms. We compare three LLMs (Mistral 3.1, DeepHermes, Qwen3) in two settings: (1) zero-shot prediction and (2) LLM-based feature extraction for Support Vector Regression, using human and pause-enriched transcripts. Results show that LLMs effectively predict depression severity in zero-shot settings (best MAE of 0.60), while dementia assessment benefits substantially from structured feature extraction (best MAE of 0.78), reducing errors by up to 35% over zero-shot baselines. Pause-enriched transcripts achieve competitive performance with human transcriptions, demonstrating the viability of fully automatic screening pipelines for differential neuropsychiatric assessment.

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