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01.
arXiv (CS.LG) 2026-06-25

From Forecasting Leaderboards to Deployment Decisions: A Fail-Closed Certification Protocol

Authors:
Geumyoung Kim↗

arXiv:2606.24996v1 Announce Type: new Abstract: Forecasting leaderboards rank models by predictive quality, but their winners are often read as deployment-ready top-1 advice. That reading can fail when forecasts are passed through a fixed decision interface, such as an alert threshold, a top-k budget, or a switching-cost policy. We study when a forecast-side winner can be certified as deployment-actionable for a specified interface and deployed utility. We introduce a fail-closed certification protocol whose gates are sufficient evidential conditions for a strong claim: a friction-caused, non-tie, statistically supported, and recurrent deployment-side reversal. Traffic-Hourly provides a certified anchor: winners agree at zero friction, but positive switching friction makes the forecast winner deployed-suboptimal. A locked native audit tests overclaiming: across 22 verified candidates and 362 full-grid cells, 155 apparent forecast/deployment winner inversions are blocked before certification. The contribution is not a new forecaster, metric, or universal utility, but a conservative protocol for deciding when forecasting leaderboard winners should be read as deployment-actionable top-1 advice.

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02.
medRxiv (Medicine) 2026-06-17

LLM-Driven Extraction of NI-RADS and Imaging Tumor Characteristics to Enhance Oropharyngeal Cancer Survivorship Surveillance

Authors:
Song↗Shbita↗Jang↗I. J. H↗Starostina↗Lewis↗Sahli↗Floyd↗W. R↗Mahin↗Rinsurongkawong↗Barbon↗…

Abstract Purpose Radiologic surveillance is essential for oropharyngeal cancer (OPC) survivors, guiding recurrence detection and follow-up strategies. The Neck Imaging Reporting and Data System provides a standardized framework for post-treatment risk reporting at both the primary tumor site (pNI-RADs) and cervical lymph nodes (nNI-RADS). Comprehensive surveillance additionally requires assessment of disease status, including the primary tumor, nodal involvement, and distant metastases. These clinical results are often embedded as unstructured data within free-text radiology reports. We hypothesized that a large language model (LLM) can reliably extract NI-RADS score criteria and summarize key imaging features from unstructured radiology text, achieving high concordance with expert review. Methods Previously untreated OPC patients who received definitive cancer therapy were identified. Eligible imaging reports included post-treatment head and neck CT, MRI, or FDG PET/CT scans containing narrative and impression text. Examinations lacking narrative or impression text, containing pre-existing NI-RADS annotations, or involving non-surveillance imaging modalities were excluded. A total of 200 reports were randomly selected from 7,076 eligible examinations for manual abstraction using a three-reviewer consensus framework to establish a reference dataset. Using the Palantir Foundry Pipeline Builder, a GPT-5-based LLM was deployed to extract pNI-RADS and nNI-RADS scores, and key imaging features of disease status from these reports. Performance was evaluated using exact agreement and F1-based metrics. Results Agreement for no evidence of disease (score of 1) was 93.3% (126/135; F1 = 0.94) and 90.3% (130/144; F1 = 0.93) for pNI-RADS and nNI-RADS, respectively. For NI-RADS [≥]2, exact category agreement was 73.1% (38/52; macro-F1 = 0.75) for pNI-RADS and 64.3% (27/42; macro-F1 = 0.56) for nNI-RADS. Quadratic weighted {kappa} was 0.81 and 0.59, respectively. For post-treatment disease surveillance variables, agreement was 94.9% (149/157; F1 = 0.87) for primary tumor presence, 89.1% (164/184; F1 = 0.87) for nodal disease presence, and 94.7% (126/133; F1 = 0.70) for distant metastasis detection. Specificity was high across disease-status variables (0.95-0.99), with negative predictive values of 0.95 for primary tumor, 0.87 for nodal disease, and 0.99 for distant metastasis. Conclusions Our LLM-based information retrieval and classification approach for radiographic treatment response from unstructured, multidimensional imaging reports achieved high performance for disease exclusion and moderate performance for detecting suspected residual and/or new disease. This pipeline supports scalable and standardized surveillance data capture for longitudinal monitoring, clinical analytics, and survivorship research in head and neck oncology.

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03.
arXiv (CS.AI) 2026-06-16

Synthetic Counteradaptation: A Principle of Human-AI Co-evolution

Authors:
Ivar Frisch↗Jackie Kay↗Philip Moreira Tomei↗

arXiv:2606.15503v1 Announce Type: new Abstract: In this paper, we introduce the concept of synthetic counteradaptation, a process where human and AI systems co-evolve by adapting to each other's strategies and behaviors. Synthetic counteradaptation occurs when AI systems develop novel strategies or social protocols, prompting humans to extract insights and adapt their own behaviors in response, leading to the emergence of new agent interaction dynamics. To illustrate these dynamics, we analyze examples from various contexts, including the game of Go, mixed-motive social interactions, and geopolitical simulations. By exploring these cases, we demonstrate how synthetic counteradaptation provides a framework for understanding the recursive and co-evolutionary nature of human-AI interactions in multi-agent environments.

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04.
arXiv (CS.CV) 2026-06-17

Complex Layout Classification in the Wild: A Low-Resource Approach with Layout-Preserving Augmentations

Authors:
Sharva Gogawale↗Iddo Hakim↗Gal Grudka↗Mohammad Suliman↗Omer Ventura↗Daria Vasyutinsky-Shapira↗Berat Kurar-Barakat↗Nachum Dershowitz↗

Many digitized corpora suffer from low resources because annotations may be scarce, page scans are noisy and of poor resolution, or layouts are structurally complex in ways that negatively affect the quality of automatic transcription. Developing robust classification models for low-resource languages is inhibited by the lack of large-scale annotated data and by the frequent semantic complexity of page layouts. To this end, we have curated a complex-layout dataset, manually classified into eight distinct layout types based on their separator regions. To overcome data scarcity, we propose a novel training strategy in the form of a CNN-based classifier that employs strong, domain-aware augmentations to improve generalization. We utilize narrow anisotropic Gaussian masking to suppress incidental textual details while preserving essential separations, compelling the model to learn global geometric arrangements. Additionally, we implement reflection-induced label transformations to enrich the training distribution while maintaining label consistency across asymmetric categories. The results demonstrate that layout-specific augmentations can substantially improve page-level layout classification under severe annotation scarcity.

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05.
arXiv (CS.LG) 2026-06-16

GRASP: Gradient-Aligned Sequential Parameter Transfer for Memory-Efficient Multi-Source Learning

Authors:
Mary Isabelle Wisell↗Nicholas Jacobs↗Aayush Manandhar↗Salimeh Yasaei Sekeh↗

arXiv:2606.14900v1 Announce Type: new Abstract: Multi-source transfer learning faces a fundamental scalability bottleneck: existing approaches require either loading all K source models into memory simultaneously during parameter fusion, requiring O(K) memory, or deploying all models at inference time, making production deployment infeasible. We propose GRASP (Gradient-Aligned Sequential Parameter Transfer), which achieves superior knowledge integration while maintaining O(1) memory consumption through three key innovations: (1) sequential processing that merges one source at a time into an evolving target model, (2) parameter-wise gradient alignment that selectively transfers only parameters whose optimization directions align with the target domain, avoiding negative transfer, and (3) iterative fine-tuning that adapts transferred knowledge before integrating the next source. Extensive experiments across three continual learning benchmarks (Yearbook, CLEAR-10, CLEAR-100) spanning 10 to 108-year temporal distribution shifts and four architectures (1.3M to 25.6M parameters) demonstrate that GRASP achieves 93.5% mean accuracy over all datasets and architectures compared to ensemble method's 71.7% accuracy while requiring only constant memory versus K models for standard multi-source fusion. Critically, GRASP's sequential previously merged models and scales to arbitrarily many sources without memory growth, making it uniquely suitable for resource-constrained deployment and continually evolving source domains.

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

Detecting Sensitive Personal Information in Japanese Pre-Training Corpora for Large Language Models

Authors:
Rei Minamoto↗Yusuke Oda↗Daisuke Kawahara↗

Sensitive personal information can appear in large-scale pre-training corpora for large language models (LLMs). Detecting and filtering such information is therefore essential to ensure compliance with privacy regulations and prevent unintended information leakage. However, in contrast to English and other languages, research into sensitive personal information has been limited in the Japanese language. In this study, we focus on sensitive personal data defined as special care-required personal information (SCPI) under Japan's Act on the Protection of Personal Information (APPI). We construct an SCPI dataset using LLM-based annotation and train machine learning models to rapidly detect SCPI in text. As a result, our SCPI classifier can effectively identify information related to SCPI. This study is the first to explore SCPI detection in Japanese text corpora, highlighting the challenges of accurate detection.

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07.
arXiv (CS.CV) 2026-06-12

Navigating Gigapixel Pathology Images with Large Multimodal Models

Authors:
Thomas A. Buckley↗Kian R. Weihrauch↗Katherine Latham↗Andrew Z. Zhou↗Padmini A. Manrai↗Arjun K. Manrai↗

Recent advances in large multimodal models have allowed for the development of interactive chat models that can converse and reason about pathology whole-slide images (WSIs). However, existing slide-level chat systems are often highly specialized, typically compressing WSIs into fixed slide-level embeddings or relying on multi-component pipelines, which can lose multi-scale detail and limit generalizability beyond the target task. We present GIANT (Gigapixel Image Agent for Navigating Tissue), a simple, training-free approach that lets general-purpose multimodal models navigate WSIs on their own, iteratively selecting multi-magnification crops and aggregating evidence over time. To evaluate generalizability in WSI question answering and to promote reproducibility, we introduce MultiPathQA, a benchmark suite spanning five clinical challenges and 934 questions over 868 unique WSIs. This includes a new set of 128 pathologist-authored multiple-choice questions designed to mirror real diagnostic search and multi-scale reasoning. Using GPT-5, GIANT outperforms models specialized for pathology question answering, achieving state-of-the-art performance on four out of five benchmarks.

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08.
arXiv (CS.LG) 2026-06-25

Closed-Loop Graph Algorithm Execution with Small Language Models: Step Accuracy and Rollout Reliability

Authors:
Michal Podstawski↗

arXiv:2606.24980v1 Announce Type: new Abstract: Small language models offer an efficient alternative to large-scale systems, but their ability to execute structured algorithms over multiple dependent decisions remains poorly understood. We study graph algorithm execution as a closed-loop prediction problem in which a model repeatedly selects the next action from the current graph and algorithmic state. Our evaluation framework covers several classical graph procedures, multiple synthetic graph families, and disjoint training, validation, and test partitions. It assesses both local decision quality and global execution behaviour using step accuracy, exact rollout accuracy, constraint validity, partial solution quality, prefix survival, and intervention-based diagnostics. The results show that adaptation can produce reliable policies for structural procedures such as traversal and coloring, while weighted algorithms remain substantially more sensitive to error accumulation. More broadly, the findings demonstrate that strong next-step prediction does not necessarily translate into reliable autonomous execution and motivate evaluating algorithmic language models through complete closed-loop rollouts rather than isolated decisions.

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09.
arXiv (CS.AI) 2026-06-16

Revisiting Chebyshev Polynomial and Anisotropic RBF Models for Tabular Regression

Authors:
Luciano Gerber↗Huw Lloyd↗

arXiv:2602.22422v2 Announce Type: replace-cross Abstract: Smooth-basis models such as Chebyshev polynomial regressors and radial basis function (RBF) networks are well established in numerical analysis. Their continuously differentiable prediction surfaces suit surrogate optimisation, sensitivity analysis, and other settings where the response varies gradually with inputs. Despite these properties, smooth models seldom appear in tabular regression, where tree ensembles dominate. We ask whether they can compete, benchmarking models across 55 regression datasets organised by application domain. We develop an anisotropic RBF network with data-driven centre placement and gradient-based width optimisation, a ridge-regularised Chebyshev polynomial regressor, and a smooth-tree hybrid (Chebyshev model tree); all three are released as scikit-learn-compatible packages. We benchmark these against tree ensembles, a pre-trained transformer, and standard baselines, evaluating accuracy alongside generalisation behaviour. The transformer ranks first on accuracy across a majority of datasets, but its GPU dependence, inference latency, and dataset-size limits constrain deployment in the CPU-based settings common across applied science and industry. Among CPU-viable models, smooth models and tree ensembles are statistically tied on accuracy, but the former tend to exhibit tighter generalisation gaps. We recommend routinely including smooth-basis models in the candidate pool, particularly when downstream use benefits from tighter generalisation and gradually varying predictions.

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10.
medRxiv (Medicine) 2026-06-23

Oxidative Stress Biomarker Profile Dynamics across Blood and Cerebrospinal Fluid

Authors:
Noriega de la Colina↗Skaperda↗Charisis↗Ntanasi↗Mamalaki↗Yannakoulia↗Papandreou↗Tekos↗Kouretas↗Scarmeas↗

Peripheral blood measurements dominate oxidative stress research, yet whether they reflect central nervous system (CNS) redox status remains untested in humans. We simultaneously profiled five biomarkers, total antioxidant capacity (TAC), glutathione (GSH), thiobarbituric acid-reactive substances (TBARS), ferric reducing antioxidant power (FRAP), and hydroxyl radical scavenging activity (HRSA), in paired blood and cerebrospinal fluid (CSF) from 140 adults in the ALBION cohort. Only FRAP showed a significant positive cross-compartment correlation ({rho} = +0.49, FDR-p < 0.001), supporting its role as a systemic antioxidant signal. TBARS showed a significant inverse cross-compartment association ({rho} = -0.20, FDR-p = 0.042), suggesting compartmental compensation in lipid peroxidation regulation rather than parallel dynamics. TAC and GSH showed no meaningful intercompartmental alignment. Individual biomarker levels were largely stable across the 40-85 year age range in both compartments, suggesting that age effects operate through coordinated latent networks rather than single-marker trajectories. Principal component extraction with varimax rotation identified four latent factors explaining 66.6% of total variance, dominated by a coherent CSF-centred redox axis alongside multiple partially opposing peripheral components. Age stratification revealed progressive fragmentation: middle-aged adults retained four coherent cross-compartment factors, whereas older adults exhibited five more dispersed components. Sex-stratified analyses showed that females exhibited four-factor modular organisation centred on glutathione, while males showed a simpler three-factor structure with tighter cross-compartment coupling anchored by FRAP. Blood and CSF oxidative stress biomarkers are not interchangeable, a finding with direct implications for biomarker selection in clinical trials targeting neurological conditions.

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11.
arXiv (CS.CL) 2026-06-12

EvoBrowseComp: Benchmarking Search Agents on Evolving Knowledge

Authors:
Yunhan Wang↗Jiaan Wang↗Lianzhe Huang↗Xianfeng Zeng↗Fandong Meng↗

Search Agents – large language models augmented with search tools – have intensified the need for future-proof evaluation benchmarks. Existing benchmarks such as BrowseComp rely on static knowledge, making them vulnerable to test-set contamination and parametric memorization. Consequently, models can achieve high scores through fact recall rather than genuine retrieval, obscuring true browsing competence via reasoning shortcuts. In this paper, we introduce EvoBrowseComp, an evolving benchmark of 400 English and 400 Chinese contamination-free complex questions synthesized via live-web traversal. To collect these questions, we design a three-agent collaborative framework: (1) a QA synthesis agent that retrieves fresh knowledge from the live web to synthesize QA pairs; (2) an information filtering agent that filters retrieved knowledge in terms of credibility and popularity to block parametric shortcuts; and (3) a high-level guidance agent that formalizes questions into reasoning graphs to reduce logical redundancy and shortcuts in synthesized QA pairs. Because the framework supports fully automated synthesis, EvoBrowseComp can be regularly updated to prevent data contamination and maintain temporal freshness. Extensive experiments confirm its great difficulty, requiring broad horizontal search. It establishes a scalable paradigm for auto-updatable, high-difficulty benchmarking that keeps pace with both evolving world knowledge and advancing agent capabilities.

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12.
arXiv (CS.CL) 2026-06-17

Security and Privacy Prompts in the Wild: What Users Ask LLMs and How LLMs Respond

Authors:
Hobin Kim↗Xiaoyuan Wu↗Omer Akgul↗Lujo Bauer↗Nicolas Christin↗

Large language models (LLMs) are widely used to fulfill users' information needs; users ask LLMs about the weather, pose educational questions, and consult them for legal assistance. One particularly understudied area is digital security and privacy (S&P), where users may seek LLMs' help on how to secure their online accounts or protect their computers from cyber attacks. To the best of our knowledge, no prior study has collected or analyzed the S&P questions users ask LLMs; prior research on LLM response quality relied on expert-authored S&P misconceptions or FAQs rather than user queries. Drawing from WildChat, a dataset of 3.2M user-LLM conversations collected in the wild, our study identifies 14,727 S&P prompts and categorizes them into nine categories covering a wide range of S&P topics. From the S&P prompts, we sampled 450 and performed a thematic analysis to characterize the S&P questions users ask LLMs. Separate from the thematic analysis, we curated 270 advice-seeking S&P prompts, where users ask for recommendations, guidance, or specific S&P information. We measured LLM response quality and consistency when posing the prompt to LLMs 10 times. We found that commercial LLMs outperform open-weight models (GPT 5.5 provided "good enough" responses on 98% of prompts; Llama 4 on 47%). However, among prompts that received high-quality responses on average, commercial models sometimes produce contradictory responses across runs, risking confusing or misleading users.

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

MBABench: Evaluating LLM Agents on End-to-End Spreadsheet Tasks in Finance

Authors:
Thomson Yen↗Julian Poeltl↗Harshith Srinivas Gear↗Yilin Meng↗Joshua Fan↗Adam Shen↗Yili Liu↗Ali Bauyrzhan↗Siri Du↗Haoyang Liu↗Daniel Guetta↗Hongseok Namkoong↗…

arXiv:2605.22664v3 Announce Type: replace Abstract: LLM agents are increasingly expected to carry out end-to-end workflows, producing complete artifacts from high-level user instructions. To meet enterprise needs, frontier AI labs have developed agents that can construct entire spreadsheets from scratch. This is especially relevant in finance, where core workflows such as financial modeling, forecasting, and scenario analysis are commonly conducted through spreadsheets. Yet, existing spreadsheet benchmarks do not measure this advanced capability, focusing instead on question-answering or single-formula edits. To address this gap, we provide one of the first evaluations of agents on end-to-end spreadsheet tasks, focusing on economically critical financial workflows such as modeling and scenario analysis. Since deliverables therein are routinely reviewed and revised by multiple stakeholders, judging their quality necessarily involves high-level criteria such as readability or ease of modification. To reflect the multidimensional nature of solution quality, we develop an evaluation taxonomy comprising three dimensions: Accuracy, Formula, and Format, each comprising fine-grained criteria that reflect professional standards. The Claude family leads the benchmark and produces the most professional-looking outputs in our qualitative review, but even the strongest agents frequently fall short of professional finance standards and degrade sharply as the difficulty increases beyond a few chained calculations. This suggests that current agents are not yet able to reliably produce professional-quality spreadsheets at the level of complexity real-world workflows demand.

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14.
arXiv (CS.CV) 2026-06-17

When LLMs Analyze Scars: From Images to Clinically-Meaningful Features

Authors:
Ruman Wang↗Hangting Ye↗

Medical image classification faces a fundamental dilemma: while deep learning models achieve remarkable performance at scale, real-world clinical scenarios often suffer from severe data scarcity due to annotation costs, privacy constraints, and disease rarity. This challenge is particularly pronounced in pathological scar classification, where differentiating keloids from hypertrophic scars requires subtle expert knowledge and labeled images are extremely limited. We propose a novel paradigm that repositions large language models (LLMs) as knowledge-driven feature engineers rather than end-to-end classifiers. We call this framework ScaFE (Scar Feature Engineering). Our key insight is that LLMs encode rich medical knowledge that can be externalized as executable feature extraction code, enabling the transformation of high-dimensional images into low-dimensional, clinically interpretable representations. Specifically, we prompt an LLM with established scar assessment criteria to generate deterministic Python code that extracts features aligned with clinical scoring systems such as the Vancouver Scar Scale. Our approach offers three key advantages: (1) data efficiency, achieving robust performance with limited training samples by decoupling knowledge acquisition from statistical learning; (2) privacy preservation, as raw images are processed locally without exposure to external LLMs; and (3) interpretability, through explicit features grounded in clinical reasoning. Extensive experiments on scar classification demonstrate that our method consistently outperforms end-to-end deep learning baselines or using LLMs as black-box classifiers under limited data conditions, establishing a promising direction for integrating LLMs into data-efficient and clinically transparent medical AI systems.

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15.
arXiv (CS.CV) 2026-06-25

Homogeneity Bias in Open-Weight LLMs Is Robust to Decoding Hyperparameters

Authors:
Messi H. J. Lee↗

Large language models (LLMs) reproduce homogeneity bias – the tendency to portray marginalized groups as more internally similar than dominant groups – but whether this bias is stable or an artifact of inference settings has only been studied in single proprietary models. We map homogeneity bias across a 5x5 temperature-by-top-p grid in seven open-weight instruction-tuned LLMs (7-20B parameters). Hispanic and Asian Americans are portrayed as more homogeneous than White Americans in at least 18 of 20 hyperparameter configurations across six of seven models, including at extreme sampling settings. African American and gender bias show model-specific variation in direction. A conservative cell-level re-analysis confirms Hispanic and Asian homogeneity as robust, while weaker African American and gender signals largely do not survive, establishing group-specific robustness. We also apply the same grid to a names-based paradigm in which group identity is signaled via racially distinctive surnames rather than explicit labels. The names paradigm corroborates Hispanic and Asian homogeneity bias, but Black-coded surnames elicit robustly less homogeneous outputs than White-coded names in every model tested – a reversal absent from the label paradigm – showing that how group identity is operationalized shapes which biases surface and in which direction.

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16.
arXiv (CS.AI) 2026-06-11

When Researchers Say Mental Model/Theory of Mind of AI, What Are They Really Talking About?

Authors:
Xiaoyun Yin↗Elmira Zahmat Doost↗Shiwen Zhou↗Garima Arya Yadav↗Jamie C. Gorman↗

arXiv:2510.02660v2 Announce Type: replace-cross Abstract: When researchers claim AI systems possess ToM or mental models, they are fundamentally discussing behavioral predictions and bias corrections rather than genuine mental states. This position paper argues that the current discourse conflates sophisticated pattern matching with authentic cognition, missing a crucial distinction between simulation and experience. While recent studies show LLMs achieving human-level performance on ToM laboratory tasks, these results are based only on behavioral mimicry. More importantly, the entire testing paradigm may be flawed in applying individual human cognitive tests to AI systems, but assessing human cognition directly in the moment of human-AI interaction. I suggest shifting focus toward mutual ToM frameworks that acknowledge the simultaneous contributions of human cognition and AI algorithms, emphasizing the interaction dynamics, instead of testing AI in isolation.

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17.
arXiv (CS.CL) 2026-06-24

Holistic Data Scheduler for LLM Pre-training via Multi-Objective Reinforcement Learning

Authors:
Chenhao Dang↗Jing Ma↗Mingjie Liao↗

The composition of training data, governed by the diversity of sources and their mixing strategy, is a cornerstone of Large Language Model (LLM) pre-training. Online Data Mixing (ODM), the technique of adaptively adjusting data mixtures during training, has emerged as a promising direction to improve efficiency. However, existing methods are constrained by their reliance on a singular optimization perspective, which fundamentally overlooks the need for complex LLM pre-training to consider the dynamic data composition from multiple dimensions. To overcome this limitation, we introduce the Holistic Data Scheduler (HDS), a novel online data mixing framework. HDS formulates the data scheduling challenge as a reinforcement learning problem in a continuous control space and leverages the Soft Actor-Critic (SAC) algorithm for its stability and sample efficiency in exploring the high-dimensional policy space. At the core of HDS lies a novel multi-objective, holistic reward function that integrates three critical perspectives: a data-driven reward for quality, a loss-driven reward capturing inter-domain influence, and a model-driven reward based on weight norms. To validate our design and determine its optimal configuration, we conducted systematic experiments on LLMs of various sizes. On The Pile benchmark, HDS reaches the final validation perplexity of the next best method with 44% fewer training iterations. Furthermore, it achieves a 7.2% improvement on the MMLU 0-shot task along with consistent gains on other benchmarks, showcasing its ability to enhance both training efficiency and final model capability.

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18.
arXiv (CS.CV) 2026-06-24

Differential Unfolding: Efficient Unfolding Reconstruction for Video Snapshot Compressive Imaging

Authors:
Muyuan Zhang↗Jiancheng Zhang↗Haijin Zeng↗Yin-ping Zhao↗

While Deep Unfolding Networks (DUNs) dominate video Snapshot Compressive Imaging (SCI), they remain constrained by a uniform design philosophy. Existing methods repeatedly stack high-complexity priors with identical structures, ignoring the fact that optimization trajectories converge toward static states. This results in representation stagnation, where high-cost computations are wasted on minimal feature updates. To address this inefficiency, we present Differential Unfolding (DU), a heterogeneous framework that replaces uniform repetition with dynamic evolution. Central to DU is the Differential Evolutionary Framework (DEF), which partitions the unfolding process into two complementary roles: structural anchoring and differential evolution. In this scheme, high-parameter general stages are sparsely deployed to generate high-fidelity feature foundations. Complementing these, lightweight differential stages employ a Differential Representation Prior (DRP) to propagate and refine these foundational features through a differential mechanism. By integrating Differential Representation Attention (DRA) for evolving attention maps and a Differential Modulated FFN (DM-FFN) for feature rectification, DRP effectively models cross-stage variations with minimal overhead. By focusing computational resources on dynamic evolution rather than static redundancy, DU achieves a superior trade-off between accuracy and efficiency. Extensive experiments verify that our method establishes new state-of-the-art results while significantly slashing computational overhead. https://github.com/Muyuan-Zhang/DU

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19.
arXiv (math.PR) 2026-06-18

A random recursive tree model with doubling events

Authors:
Jakob E. Bj\"ornberg↗C\'ecile Mailler↗

arXiv:2501.18466v3 Announce Type: replace Abstract: We introduce a new model of random tree that grows like a random recursive tree, except at some exceptional "doubling events" when the tree is replaced by two copies of itself attached to a new root. We prove asymptotic results for the size of this tree at large times, its degree distribution, and its height profile. We also prove a lower bound for its height. Because of the doubling events that affect the tree globally, the proofs are all much more intricate than in the case of the random recursive tree in which the growing operation is always local.

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20.
arXiv (quant-ph) 2026-06-17

Many-body spectral transitions through the lens of the variable-range SYK2 model

Authors:
Andrea Legramandi↗Soumik Bandyopadhyay↗Philipp Hauke↗

arXiv:2412.14280v2 Announce Type: replace-cross Abstract: The Sachdev-Ye-Kitaev (SYK) model is a cornerstone in the study of quantum chaos and holographic quantum matter. Real-world implementations, however, deviate from the idealized all-to-all connectivity, raising questions about the robustness of its chaotic properties. In this work, we investigate a quadratic SYK model with distance-dependent interactions governed by a power-law decay. By analytically and numerically studying the spectral form factor (SFF), we uncover how transitions present in the single-particle limit carry over to the many-body system. Non-trivial cancellations in the one-loop contributions lead to a robustness of the SFF under a considerable reduction of the interaction range. Further suppression leads to a breakdown of perturbation theory around the infinite-range path-integral saddle and the appearance of new spectral regimes, marked by a higher dip and the emergence of a secondary plateau. Our results highlight the interplay between single-particle criticality and many-body dynamics, offering new insights into the quantum chaos-to-localization transition and its reflection in spectral statistics.

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21.
medRxiv (Medicine) 2026-06-22

Integration of lung tissue proteomics and genome-wide association data to identify lung cancer susceptibility proteins and potential drug targets

Authors:
Xu↗Shi↗Shu↗X.-O↗Tao↗Dou↗Guo↗Wen↗Yang↗Zhang↗Wu↗Deppen↗…

Background: Proteins directly impact disease development and act as drug targets. Therefore, we integrated genomic and lung tissue proteomics data to identify lung cancer susceptibility proteins, elucidating genetic mechanisms and candidate drug targets. Method: We profiled the proteome and genome in non-neoplastic lung tissue from 200 lung cancer patients. Using this data, we constructed genetic models to predict abundance across the proteome in lung tissue. We applied these models to genome-wide association study (GWAS) data from 55,174 lung cancer cases and 1,294,174 controls to evaluate their associations with the risk of lung cancer, overall and by major histological subtypes. Bayesian colocalization and Mendelian randomization (MR) analyses were used to prioritize putative causal proteins, which were cross-referenced with three main drug-protein databases to identify potential therapeutic targets. Results: We identified 29 proteins associated with lung cancer risk at a false discovery rate < 5%, including 25 for overall lung cancer, two (AQP3 and IL18) specifically for adenocarcinoma, and another two (HMGN2 and HLA-DMB) for squamous cell carcinoma. Of them, genes encoding 17 proteins reside at least 2Mb away from any known GWAS risk loci, including 14 for overall lung cancer (HYI, GPX1, GMPPB, DSP, HDDC2, MTCH2, SUOX, JMJD7, PDIA3, IL16, IQGAP1, SULT1A2, ARHGAP27, and TYMP) and three for subtypes (AQP3, IL18, and HMGN2). Among the 12 proteins located within the known risk loci, EPHX2, CLDN18, PSMD5, and CYP2S1 proteins showed an association independent of the proximal GWAS-identified lead variant. Colocalization and/or MR analysis suggested 11 potential causal proteins. Five of these candidate causal proteins (DSP, CLDN18, IQGAP1, IL18 and TYMP) are targeted by nine drugs already approved by the FDA or in phase III trials. Conclusion: Our study identified novel lung cancer susceptibility proteins and potential drug targets, offering valuable insights into lung cancer biology and future translational utilities.

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22.
arXiv (CS.CV) 2026-06-16

KGEdit: Ambiguity-Aware Knowledge Graphs for Training-Free Precise Video Generation and Editing

Authors:
Mingshu Cai↗Miao Zhang↗Chenghe Yang↗Yixuan Li↗Osamu Yoshie↗Yuya Ieiri↗

In recent years, training-free video generation has progressed remarkably. However, when handling complex textual instructions, existing methods still suffer from semantic ambiguity, incorrect concept binding, and cross-frame inconsistency. To address these issues, we propose KGEdit, a structured semantic control framework for text-to-video (T2V) diffusion models. Specifically, we first construct an ambiguity-aware knowledge graph (AAKG) to disentangle and disambiguate the input prompt, converting it into four types of structured semantics: identity, relation, attribute, and negative constraints. We then design a structured semantic injection module (SSIM) to inject these semantic signals into key layers of the diffusion Transformer, enabling fine-grained semantic control. In addition, we introduce a temporal-aware semantic control (TASC) module that dynamically schedules semantic objectives according to the stage-wise characteristics of the denoising process, further improving semantic alignment and temporal consistency. Experiments show that KGEdit outperforms existing methods in editing precision and temporal stability, while offering higher efficiency and controllability in text-driven interaction scenarios.

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23.
arXiv (CS.CV) 2026-06-24

Are Text-to-Image Models Inductivist Turkeys? A Counterfactual Benchmark for Causal Reasoning

Authors:
Jiayi Lei↗Yuandong Pu↗Xingyu Han↗Rongpeng Zhu↗Jing Xu↗Jinyao Wang↗Zijian Zhou↗Bin Fu↗Yuewen Cao↗Yihao Liu↗Yongsheng Li↗

Text-to-image (T2I) generation models have achieved remarkable progress in producing visually realistic images from natural language prompts. Yet it remains unclear whether their success reflects genuine causal understanding or sophisticated pattern matching over visual-textual correlations. Inspired by Russell's inductivist turkey, we introduce Counterfactual-World (CF-World), a counterfactual benchmark designed to investigate whether text-to-image models can generate images under rules that systematically contradict real-world priors. CF-World organizes each scenario into three progressive levels: factual generation under ordinary world knowledge, explicit counterfactual generation with direct visual instructions, and implicit counterfactual generation requiring causal deduction from altered rules. We evaluate both open-source and closed-source T2I models using a Vision Language Model (VLM)-based evaluator (CF-Eval). Furthermore, we introduce two metrics: Prior Resistance Rate (PRR), which measures a model's ability to overcome entrenched real-world priors, and Reasoning Retention Rate (RRR), which assesses whether models can maintain reasoning-dependent counterfactual generation without explicit visual cues. Experiments show that all models exhibit sharp degradation from factual to counterfactual settings. Further analyses suggest that these failures arise because current T2I models encode world knowledge and visual appearances as tightly coupled patterns. Consequently, their heavy reliance on frequent visual co-occurrences within the training data forces them to default to familiar commonsense priors when tasked with rendering counterfactual worlds.

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

SLUM-i: Semi-supervised Learning for Urban Mapping of Informal Settlements and Data Quality Benchmarking

Authors:
Muhammad Taha Mukhtar↗Syed Musa Ali Kazmi↗Khola Naseem↗Muhammad Ali Chattha↗Andreas Dengel↗Sheraz Ahmed↗Muhammad Naseer Bajwa↗Muhammad Imran Malik↗

Rapid urban expansion has fueled the growth of informal settlements in major cities of low- and middle-income countries, with Lahore and Karachi in Pakistan and Mumbai in India serving as prominent examples. However, large-scale mapping of these settlements is severely constrained not only by the scarcity of annotations but by inherent data quality challenges, specifically high spectral ambiguity between formal and informal structures and significant annotation noise. We address this by introducing a benchmark dataset for Lahore, constructed from scratch, along with companion datasets for Karachi and Mumbai, which were derived from verified administrative boundaries, totaling approximately 900 $km^2$ of urban area. This collection is supplemented by four cities from prior literature across Sub-Saharan Africa and Latin America, with comprehensive data quality assessments provided for each city. We also propose a semi-supervised segmentation framework designed to mitigate the class imbalance and distribution mismatch inherent in standard semi-supervised learning pipelines. Our method integrates a Class-Aware Adaptive Thresholding mechanism that dynamically adjusts confidence thresholds to prevent minority class suppression, and a DINOv2-based unlabeled pool filter that removes out-of-distribution tiles prior to training to reduce covariate shift. Extensive experiments across seven cities spanning three continents, repeated over five random seeds, demonstrate gains of up to +5.9 pp mIoU over state-of-the-art semi-supervised baselines, with both components being architecture-agnostic and adding no inference overhead.

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25.
arXiv (CS.CV) 2026-06-17

Phys4D: Fine-Grained Physics-Consistent 4D Modeling from Video Diffusion

Authors:
Haoran Lu↗Shang Wu↗Songling Liu↗Jianshu Zhang↗Maojiang Su↗Guo Ye↗Chenwei Xu↗Lie Lu↗Pranav Maneriker↗Fan Du↗Manling Li↗Zhaoran Wang↗…

Recent video diffusion models have achieved impressive capabilities as large-scale generative world models. However, these models often struggle with fine-grained physical consistency, exhibiting physically implausible dynamics over time. In this work, we present Phys4D, a pipeline for learning physics-consistent 4D world representations from video diffusion models. Phys4D adopts a three-stage training paradigm that progressively lifts appearance-driven video diffusion models into physics-consistent 4D world representations. We first bootstrap robust geometry and motion representations through large-scale pseudo-supervised pretraining, establishing a foundation for 4D scene modeling. We then perform physics-grounded supervised fine-tuning using simulation-generated data, enforcing temporally consistent 4D dynamics. Finally, we apply simulation-grounded reinforcement learning to correct residual physical violations that are difficult to capture through explicit supervision. To evaluate fine-grained physical consistency beyond appearance-based metrics, we introduce a set of 4D world consistency evaluation that probe geometric coherence, motion stability, and long-horizon physical plausibility. Experimental results demonstrate that Phys4D substantially improves fine-grained spatiotemporal and physical consistency compared to appearance-driven baselines, while maintaining strong generative performance. Our project page is available at https://sensational-brioche-7657e7.netlify.app/

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