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01.

medRxiv (Medicine) 2026-06-15 DOI: HASH:564a1829cba45b6e6aa8aa30c071a112

Dysplasia-Stratified Management of Barrett's Esophagus: An Incidence-Based U.S. Cost-Effectiveness Analysis

作者:

Kowada↗

Background and Aims Barrett's esophagus (BE) is the principal precursor of esophageal adenocarcinoma (EAC), whose incidence has risen sharply in Western countries since the 1960s. Effective, dysplasia stratified surveillance strategies are needed to prevent progression. This study evaluated the cost effectiveness of dysplasia stratified surveillance intervals and endoscopic eradication therapy (EET) across the BE spectrum. Methods We developed an incidence-based Markov state transition model of BE progression calibrated to U.S. epidemiologic data from a healthcare sector perspective over a lifetime horizon. Four hypothetical cohorts of 50-year-old individuals with short segment BE (SSBE), nondysplastic BE (NDBE), low grade dysplasia (LGD), or high-grade dysplasia (HGD) were evaluated. Strategies included no surveillance; surveillance at 1-, 2-, 3-, 4-, 5-, or 10-year intervals; standard or AI assisted endoscopy; non endoscopic screening (sponge, breath, miRNA tests); and EET for LGD and HGD. Outcomes included costs, quality adjusted life years (QALYs), incremental cost effectiveness ratios (ICERs), net monetary benefits (NMBs), EAC cases, and EAC-related deaths. Sensitivity analyses used a willingness to pay threshold of US$100,000 per QALY. Results No surveillance was the most cost-effective strategy for SSBE and NDBE. For LGD, upfront EET was more cost effective than all surveillance strategies, with results sensitive to EAC incidence and recurrence. For HGD, EET was cost saving and yielded the greatest QALYs, with findings robust in 99.9% of simulations. EET prevented 12,614 and 44,295 EAC related deaths per 100,000 individuals with LGD and HGD, respectively. Conclusion Dysplasia-stratified management is essential for optimizing surveillance and treatment strategies in BE. Any degree of dysplasia should receive EET followed by targeted post-treatment monitoring, establishing EET as the central therapeutic pathway for dysplastic BE.

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02.

arXiv (CS.LG) 2026-06-24 DOI: arXiv:2606.24087

NeuroSonic: Conditional Flow Matching for EEG-to-Speech Reconstruction

作者:

Wenhao Gao↗Yifan Wang↗Yijia Ma↗Carl Yang↗Wen Li↗Chenyu You↗

arXiv:2606.24087v1 Announce Type: new Abstract: Reconstructing continuous speech from scalp electroencephalography (EEG) remains fundamentally challenging. EEG provides a weak, spatially diffuse, and highly variable measurement of distributed cortical activity, whereas speech is organized as a coherent acoustic trajectory with strong harmonic and temporal structure. The resulting mismatch makes waveform regression unstable and causes stochastic multi-step generation to be sensitive to artifact-dependent conditioning and subject variability. We introduce NeuroSonic, a conditional flow-matching framework for EEG-to-speech reconstruction. Instead of predicting waveforms directly or refining them through stochastic denoising, NeuroSonic learns a deterministic probability-flow velocity field that transports a noise-corrupted acoustic state toward clean speech under EEG conditioning. EEG and audio are embedded into a shared token space and processed by a time-conditioned gated Transformer that parameterizes the transport ordinary differential equation. This formulation models trajectory evolution explicitly while avoiding iterative stochastic sampling. We evaluate NeuroSonic on the CineBrain and EAV benchmarks under cross-subject evaluation. Across both datasets, the proposed method improves distributional realism, spectral fidelity, and perceptual quality over representative GAN-, diffusion-, and mean-flow baselines, with up to a 26.3\% gain in overall perceptual quality. The performance gap is most evident in artifact-heavy segments, where conditioning variability is strongest. These findings indicate that deterministic conditional transport provides a stable and effective formulation for EEG-driven speech reconstruction. Code is available at https://github.com/Y-Research-SBU/NeuroSonic/ .

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03.

arXiv (CS.CL) 2026-06-25 DOI: arXiv:2606.25935

Overview of HIPE-2026: Person-Place Relation Extraction from Multilingual Historical Texts

作者:

Juri Opitz↗Maud Ehrmann↗Corina Racl\'e↗Andrianos Michail↗Matteo Romanello↗Simon Clematide↗

Was this person ever at that place, and if so, when? Answering such questions from noisy, multilingual historical documents is the central challenge of HIPE-2026, the third edition of the HIPE evaluation series. Moving from named entity recognition and linking (HIPE-2020, HIPE-2022) to reasoning about relationships between entities, HIPE-2026 targets two temporally grounded relation types: $at$, indicating that a person was present at a location at some point prior to a document's publication date, and $isAt$, indicating presence contemporaneous with that date. This paper presents the results of the evaluation campaign, which confronted 17 participating teams with the challenges of historical language variation, OCR noise, and indirect contextual cues across three languages: French, German, and English. The datasets include historical newspaper text from the nineteenth and twentieth centuries, as well as a surprise-domain generalization set drawn from early modern French literary texts. A distinctive feature of HIPE-2026 is its three-fold evaluation framework, which assesses predictive accuracy, computational efficiency, and cross-domain generalization, reflecting the practical demands of large-scale historical document processing in the cultural heritage domain. Across more than 40 submitted runs, results reveal a wide range of strategies, from state-of-the-art large language models to lightweight task-specific classifiers, and highlight the trade-offs between accuracy, efficiency, and robustness inherent to historical relation extraction at corpus scale. System descriptions, datasets, and findings are presented and discussed, offering a detailed picture of the current state of temporally grounded relation extraction for historical documents.

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04.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2501.17015

UniMM: A Unified Mixture Model Framework for Multi-Agent Simulation

作者:

Longzhong Lin↗Xuewu Lin↗Kechun Xu↗Haojian Lu↗Lichao Huang↗Rong Xiong↗Yue Wang↗

arXiv:2501.17015v2 Announce Type: replace Abstract: Simulation plays a crucial role in assessing autonomous driving systems, where the generation of realistic multi-agent behaviors is a key aspect. In multi-agent simulation, the primary challenges include behavioral multimodality and closed-loop distributional shifts. In this study, we formulate a unified mixture model (UniMM) framework for generating multimodal agent behaviors, which can cover the mainstream methods including regression-based mixture models and discrete NTP models. Furthermore, we introduce a closed-loop sample generation approach tailored for mixture models to mitigate distributional shifts. Within the UniMM framework, we recognize critical configurations from both the model and data perspectives. We conduct a systematic examination of various model configurations, and comprehensively characterize their effects. Moreover, our investigation into the data configuration highlights the pivotal role of closed-loop samples in achieving realistic simulations. To extend the benefits of closed-loop samples across a broader range of mixture models, we further introduce a temporal disentanglement-and-alignment mechanism to address the shortcut learning and off-policy learning issues. Leveraging insights from our exploration, the distinct variants proposed within the UniMM framework, including discrete, anchor-free, and anchor-based models, all achieve state-of-the-art performance on the WOSAC benchmark.

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05.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2507.15104

AnalogFed: Privacy-Preserving Discovery of Analog Circuits at Scale with Federated Generative AI

作者:

Qiufeng Li↗Shu Hong↗Tian Lan↗Weidong Cao↗

arXiv:2507.15104v2 Announce Type: replace-cross Abstract: Recent advances in generative AI (GenAI) have shown transformative potential for modern hardware design. However, existing GenAI-driven approaches fall short of enabling large-scale electronic design automation (EDA) due to the proprietary and siloed nature of hardware datasets, which cannot be centralized for model training. Achieving at-scale GenAI-driven EDA, therefore, requires a novel privacy-preserving framework that can leverage distributed data without compromising confidentiality. This work introduces AnalogFed, the first privacy-preserving framework for large-scale analog circuit topology discovery using federated learning (FedL) and GenAI. AnalogFed establishes the feasibility of collaborative analog topology design while addressing key security challenges: it mitigates membership inference attacks (MIAs) through a novel input perturbation strategy based on dummy token injection, and defends against model inversion attacks with customized, efficient homomorphic encryption. Extensive experiments demonstrate AnalogFed's effectiveness and efficiency, achieving strong privacy protection without degrading model utility. This framework lays the foundation for scalable, multi-party collaboration in next-generation hardware design automation with GenAI.

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06.

arXiv (CS.LG) 2026-06-15 DOI: arXiv:2606.14195

Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators

作者:

Naichang Ke↗Pongpisit Thanasutives↗Yoshinobu Kawahara↗

arXiv:2606.14195v1 Announce Type: new Abstract: Kalman filters based on the Embedded Latent Transfer Operators (ELTO) emerge as novel statistical tools for sequential state estimation. However, a critical limitation stems from their use of simplified noise models, which fail to dynamically adapt to non-stationary processes. To address this limitation, we introduce an ELTO-based Bayesian filtering approach with a new structured parameterization for the filter's noise model. This parameterization enables structured noise adaptation, which couples the data-driven learning of an optimal time-invariant noise model with dynamic parameter adaptation that responds to changes in dynamics within non-stationary processes. Empirical results show that our structured noise adaptation improves the filter's dynamic state estimation performance in noisy, time-varying environments.

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07.

arXiv (CS.CV) 2026-06-25 DOI: arXiv:2606.25465

EchoStyle: Unlocking High-Fidelity Video Stylization with Reverse Data Synthesis

作者:

Huaqiu Li↗Jiahao Wang↗Sijia Cai↗Hualian Sheng↗Bing Deng↗Jieping Ye↗Wenhan Luo↗

While image stylization has been studied extensively, video stylization remains a critical and largely unsolved challenge in the field of intelligent content creation. Existing methods, usually utilizing a reference image as the style prior, suffer from content leakage, data scarcity and limited adaptability to long videos, leading to suboptimal results with severe style drift and motion distortion. For these issues, we present EchoStyle, a scalable text-driven framework to achieve high-quality stylization of videos with arbitrary lengths. To start with, we construct a video-to-video architecture to appropriately re-fuse the video content and the text style. To address data scarcity, we pioneer an automatic reverse-synthesis pipeline to establish V-Style20k, a large-scale stylization dataset of 20k high-quality video pairs. To facilitate long video stylization, we devise an init-follow-mode mechanism along with a sliding-window inference strategy. Extensive experiments demonstrate EchoStyle's excellent performance across a wide range of artistic styles, even comparable to leading closed-source solutions.

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08.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.15555

Service-Induced Congestion in Memory-Constrained LLM Serving

作者:

Ruicheng Ao↗Jing Dong↗Gan Luo↗David Simchi-Levi↗

arXiv:2606.15555v1 Announce Type: cross Abstract: In large language model (LLM) serving, each request accumulates persistent graphics processing unit (GPU) memory during service as its key-value cache grows with every generated token. Under high concurrency, aggregate memory usage therefore increases endogenously over time: the service process itself creates future capacity pressure. When memory capacity is exceeded, systems evict active requests, discarding cached state and restarting them later, which wastes computation and reduces throughput. We develop a discrete-time dynamical model of memory-constrained LLM inference that captures admission, memory growth, and eviction under continuous batching. In the saturated-input regime, the system admits both eviction-free fixed points and limit cycles with evictions. For homogeneous workloads, we show that the eviction-free equilibrium is unstable and that, except for a Lebesgue-measure-zero exact-capture set, the system converges to a unique worst-case limit cycle that is asymptotically stable outside this exceptional set, with throughput losses as large as 50%. For heterogeneous workloads, we prove a stability criterion in the two-class common-input setting and explain how the survival-polynomial mechanism generalizes to multiple classes and heterogeneous-input lengths. Under an input-dominated scaling regime, coprime decoding lengths stabilize the eviction-free equilibrium, while non-coprime lengths create synchronized modes that drive instability. These results characterize when workload heterogeneity desynchronizes completions and helps stabilize memory-constrained serving. More broadly, we identify service-induced congestion as a structural instability mechanism and derive scheduling design principles for sustaining high throughput.

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09.

arXiv (CS.LG) 2026-06-17 DOI: arXiv:2606.17756

A fairness-aware extension of Stochastic Multicriteria Acceptability Analysis for ranking

作者:

Guilherme Dean Pelegrina↗Renata Pelissari↗

arXiv:2606.17756v1 Announce Type: new Abstract: Fairness has become a central concern in ranking problems involving individuals or social groups, particularly under the Responsible Artificial Intelligence agenda. In Multi-Criteria Decision Analysis, Stochastic Multicriteria Acceptability Analysis (SMAA) provides a robust framework for handling uncertainty and incomplete preference information, but it does not explicitly address fairness in the resulting rankings. This paper proposes SMAA-Fair, a fairness-aware extension of SMAA for ranking problems. The approach reweights the simulated rankings generated by SMAA according to their level of group fairness, so that fairer rankings contribute more strongly to the acceptability indices and central weights vector. The framework is independent of the aggregation model and can incorporate different fairness metrics. In this study, Statistical Parity, normalized discounted Kullback–Leibler divergence (rKL) and normalized discounted cumulative Kullback–Leibler divergence (nDKL) are adopted. Rankings are derived from the fairness-adjusted acceptability matrix using expected ranking and maximum acceptability ranking. We also derive the central weight according to the degree of fairness in the obtained rankings. Numerical experiments with synthetic and real data show that SMAA-Fair improves the representation of protected groups among favourable ranking positions, while preserving robustness to preference uncertainty.

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10.

arXiv (CS.CL) 2026-06-15 DOI: arXiv:2606.04883

Optimizing the Cost-Quality Tradeoff of Agentic Theorem Provers in Lean

作者:

K\'ari R\"ognvaldsson↗Chenhao Sun↗Jasper Dekoninck↗Martin Vechev↗

Large language models (LLMs) are increasingly used in workflows for generating formal proofs in Lean. These workflows often decompose problems into smaller lemmas, sample many proof attempts, and use compiler feedback to guide search. However, they can be prohibitively expensive, often spending substantial compute on attempts that ultimately fail. In this work, we address this problem with an action routing agent that consists of a data plane and a control plane. The data plane generates natural-language lemma decompositions, formalizes them in Lean, and samples proof attempts for the resulting theorem and lemma targets. The control plane observes previous failed Lean attempts, estimates both the likelihood of success and cost of another attempt, and decides whether to continue proving the current target or restart from a new breakdown. On a subset of PutnamBench, our agent decreases the cost by $28.9\%$ over a fixed-step baseline on average, preserving performance while using substantially less compute. These results suggest that failed Lean trajectories provide actionable signals for cost-aware resource allocation in agentic theorem proving.

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11.

arXiv (CS.AI) 2026-06-11 DOI: arXiv:2606.11440

INFRAMIND: Infrastructure-Aware Multi-Agent Orchestration

作者:

Ahasan Kabir↗Jiaqi Xue↗Mengxin Zheng↗Qian Lou↗

arXiv:2606.11440v1 Announce Type: new Abstract: Existing multi-agent LLM orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and model features. However, these methods do not consider the runtime state of the serving infrastructure. On shared GPU clusters under concurrent load, this infrastructure blindness causes systematic resource underutilization: preferred models accumulate deep request queues while equally capable alternatives sit idle. In multi-agent pipelines, where each query triggers multiple sequential model calls, these delays then compound across every downstream step. Closing this gap is challenging because the relevant infrastructure signals (queue depths, KV-cache pressure, latencies) are dynamic and noisy, and they must drive three different decisions: planning, per-step routing, and scheduling. We introduce INFRAMIND, a framework that makes the entire multi-agent stack infrastructure-aware. An infra-aware planner conditions topology and role selection on real-time system load and remaining budget, biasing toward simpler graphs under congestion and richer ones at low load. An infra-aware executor then observes per-model queue depths, cache utilization, and response latencies at each agent step to decide which model to call and how deeply to reason; a budget-aware scheduler further reorders each model's queue so that urgent requests are served first. Cast as a hierarchical constrained MDP and solved end-to-end via reinforcement learning, the system learns to balance quality against latency automatically. Across five benchmarks, INFRAMIND delivers up to +7.6 pp accuracy over the prior baseline at low load with up to 7x lower latency, and sustains up to 99.9% SLO compliance under high load where every baseline drops below 50%.

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12.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2605.06738

Trust Without Trusting: A Recomputable Trust Protocol for Autonomous Agents

作者:

Lars Kersten Kroehl↗

arXiv:2605.06738v2 Announce Type: replace-cross Abstract: Autonomous AI agents already transact at production scale – 69,000 bots, 165 million transactions, $50 million in volume on a single marketplace – and any party can verify a signed credential without a central service. In an open agent world that covers most of what trust requires: there are no universal borders, and each party chooses for itself whom to deal with. Borders appear only where a closed space draws one – a marketplace, a platform, or a consortium sets house rules. Whoever draws the border holds the authority to apply it, and may apply it as they choose, behind closed doors. This paper addresses the gap that opens there: when you rely on someone else's border, how do you check that they applied their own published rules – taking no one's word for it, and handing the check to no new trusted party? Our answer is the Combined Evidence Protocol (CEP): a five-condition predicate any party recomputes from anchored data, turning "did the boundary-owner follow its own admission rules" into a fact anyone verifies rather than a claim anyone believes. The move that secures optimistic rollups secures this – correctness rests on recomputation, so the measurement belongs to everyone and the oracle problem dissolves. Its load-bearing setting is a consortium of co-equal, mutually distrusting peers under a shared charter, each able to verify, independently, that the rules they jointly agreed are the rules being applied. CEP belongs to the family of trustless systems – optimistic and zero-knowledge rollups, verifiable ML, self-sovereign-identity predicates. The infrastructure beneath it is live: a W3C VC + DID trust layer running since March 2026, anchored on Base L2, continuing arXiv:2605.06738 and standing on its own.

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13.

bioRxiv (Bioinfo) 2026-06-11 DOI: HASH:7fd28c982f3ffec78f74b5c0ad70fa87

PhyloZoo: a unified framework for phylogenetic network analysis in Python

作者:

Holtgrefe↗

Reticulate evolutionary processes (events in which lineages merge, such as hybridization, recombination, and horizontal gene transfer) are widespread across nature but cannot be represented by phylogenetic trees alone. Phylogenetic networks have therefore become an important modelling tool, yet existing software is typically tied to specific inference paradigms and provides limited support for working with multiple network representations in a unified and programmable environment. PhyloZoo is an open-source Python framework that lowers the barrier to developing practical, easy-to-use software for phylogenetic network analysis. It provides data structures and algorithms covering the main representations used in the field, together with dedicated visualization tools and robust I/O for all major phylogenetic file formats. A particular emphasis lies on semi-directed phylogenetic networks, which explicitly represent root uncertainty and have so far received limited support in existing software. By offering a shared foundation for developing interoperable tools and a combinatorial layer that supports computational proofs and theoretical exploration, PhyloZoo enables reproducible workflows for applied, methodological, and theoretical studies of reticulate evolution. Availability and implementation: PhyloZoo is implemented in Python and installable from PyPI, with source code, documentation, and examples available at https://github.com/nholtgrefe/phylozoo.

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14.

arXiv (CS.CV) 2026-06-19 DOI: arXiv:2606.20131

TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields

作者:

Haoxuan Li↗Ziya Erko\c{c}↗Daniele Sirigatti↗Vladislav Rosov↗Lei Li↗Angela Dai↗Matthias Nie{\ss}ner↗

We present TriFlow, a new generative approach for producing compact 3D meshes with artist-like triangle topology directly from input geometry conditions such as signed distance fields. Our key insight is to represent mesh topology as a nearest-vertex vector field (NVF) defined over the surface, where each point encodes its association to the nearest triangle vertex in the local barycentric frame. We train a latent flow-matching model to synthesize this field, enabling topology generation conditioned on the input geometry. To extract a coherent mesh, we cluster surface regions using the generated NVF and guide a constrained quadric error metric (QEM) mesh simplification with topology-aware optimization. This yields output meshes that closely match the input geometry while exhibiting structured, artist-like connectivity. Experiments demonstrate that TriFlow achieves stronger generalization and significantly improved topology quality compared to state-of-the-art learning-based approaches, alongside 90% lower Chamfer Distance and an 8x speedup.

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15.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2606.18119

First Proof Second Batch

作者:

Mohammed Abouzaid↗Nikhil Srivastava↗Rachel Ward↗Lauren Williams↗

arXiv:2606.18119v1 Announce Type: new Abstract: To assess the ability of current AI systems to correctly solve research-level mathematics problems, we tested several AI systems on a set of ten problems in a broad range of mathematical fields; these problems arose naturally in the research process of the contributors. This document includes the problems, our methodology, and the results of our testing. We provide links to supplementary documents including the human solutions, the AI-generated solutions, and the referee reports and logs for the AI-generated solutions. The ten problems were contributed by the following mathematicians: (1) Dariusz Kaloci\'nski and Theodore A. Slaman, (2) Richard Schwartz, (3) Aleksa Milojevic and Benny Sudakov, (4) Larry Guth, (5) Oleg Butkovsky, Jonathan Mattingly, and Lorenzo Zambotti, (6) Joshua Evan Greene and Duncan McCoy, (7) Sucharit Sarkar, (8) Sam Payne and Jidong (Jayden) Wang, (9) Sylvie Corteel and John Lentfer, (10) Srivatsav Kunnawalkam Elayavalli.

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16.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.15024

Resilient Consensus in Agentic AI

作者:

Sribalaji C. Anand↗George J. Pappas↗

arXiv:2606.15024v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly deployed in multi-agent systems where they must coordinate and agree on shared decisions. We ask whether classical resilient consensus theory, developed for deterministic agents, transfers to LLM agents that may behave adversarially. Framing LLM agreement as a Byzantine consensus game, we run controlled experiments on complete and general communication graphs. We find that prompted LLM agents fail to reach agreement that is achievable in principle: consensus can fail even in settings where classical theory guarantees that a convergent algorithm exists, and this failure persists across temperatures and horizons. At the same time, wrapping the agents with classical resilient consensus filters improves agreement. The benefit of filtering depends on how much robustness the underlying topology already provides. Our results suggest that classical resilient consensus theory is a useful lens for the safety of agentic AI.

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17.

arXiv (CS.LG) 2026-06-15 DOI: arXiv:2606.13867

Muon$^p$: Muon with Fractional Spectral Powers

作者:

Yihe Dong↗Will Sawin↗

arXiv:2606.13867v1 Announce Type: new Abstract: Muon is an increasingly widely used optimizer that replaces a gradient $G=USV^\top$ with its polar factor $UV^\top$, thereby flattening the singular spectrum. However, full flattening discards singular-value information that may matter for adaptation. We introduce Muon$^p$, a Muon-style optimizer that instead uses fractional spectral-power updates $US^pV^\top$ for rational $p\in(0,1)$, interpolating between Muon and gradient descent. To make it practical, we prove that fractional spectral powers cannot be computed by any fixed univariate polynomial iteration, and furthermore derive low-degree odd bivariate recurrences that approximate $US^pV^\top$ using only matrix multiplications, preserving Muon's matrix-multiplication-only structure and compute complexity. We show that Muon$^p$ maximizes the linear improvement in loss under the Schatten $q$-norm for $q=1+\frac{1}{p}$. Empirically, Muon$^p$ is especially effective for finetuning: on billion-scale models, Muon$^p$ improves validation perplexity and downstream task performance. We further analyze when Muon$^p$ is less suitable, through the lens of spectral geometry. Our results reveal important insights on when preserving the singular spectrum can bring significant gains, and introduce a principled way to achieve them.

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18.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.15301

Discovering Lattice Reduction Strategies via Self-Play

作者:

Mohamed Malhou↗Kristin Lauter↗Ludovic Perret↗

arXiv:2606.15301v1 Announce Type: cross Abstract: The Lenstra-Lenstra-Lovász (LLL) algorithm is a seminal contribution to computer science used for lattice basis reduction, yet its polynomial-time outputs produce bases that are far from optimal as the dimension grows. We show that deep reinforcement learning can discover strictly superior, generalizable reduction strategies by interacting with the primitive action space of LLL. We formulate lattice reduction as a single-player Markov Decision Process (MDP) and train a deep residual network using an AlphaZero-style self-play pipeline augmented with adaptive-horizon MCTS (Monte Carlo Tree Search), which couples multi-step network predictions with an entropy-gated expansion mechanism. The resulting policy, DeltaStar, is trained exclusively on small $8$-dimensional $q$-ary lattices and requires fewer primitive row operations than LLL. Crucially, it generalizes zero-shot to unseen moduli and higher dimensions up to $n=32$ without retraining.

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19.

arXiv (CS.CV) 2026-06-24 DOI: arXiv:2508.17728

Segmentation and Classification of Pap Smear Images for Cervical Cancer Detection Using Deep Learning

作者:

Nisreen Albzour↗Sarah S. Lam↗

Cervical cancer remains a significant global health concern and a leading cause of cancer-related deaths among women. Early detection through Pap smear tests is essential to reduce mortality rates; however, the manual examination is time consuming and prone to human error. This study proposes a deep learning framework that integrates U-Net for segmentation and a classification model to enhance diagnostic performance. The Herlev Pap Smear Dataset, a publicly available cervical cell dataset, was utilized for training and evaluation. The impact of segmentation on classification performance was evaluated by comparing the model trained on segmented images and another trained on non-segmented images. Experimental results showed that the use of segmented images marginally improved the model performance on precision (about 0.41 percent higher) and F1-score (about 1.30 percent higher), which suggests a slightly more balanced classification performance. While segmentation helps in feature extraction, the results showed that its impact on classification performance appears to be limited. The proposed framework offers a supplemental tool for clinical applications, which may aid pathologists in early diagnosis.

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20.

arXiv (CS.CV) 2026-06-19 DOI: arXiv:2603.29924

Abstraction in Style: Beyond Texture and Color

作者:

Min Lu↗Yuanfeng He↗Anthony Chen↗Jianhuang He↗Pu Wang↗Daniel Cohen-Or↗Hui Huang↗

Artistic styles often embed abstraction beyond surface appearance, involving deliberate reinterpretation of structure rather than mere changes in texture or color. Conventional style transfer methods typically preserve the input geometry and therefore struggle to capture this deeper abstraction behavior, especially for illustrative and nonphotorealistic styles. In this work, we introduce Abstraction in Style (AiS), a generative framework that separates structural abstraction from visual stylization. Given a target image and a small set of style exemplars, AiS first derives an intermediate abstraction proxy that reinterprets the target's structure in accordance with the abstraction logic exhibited by the style. The proxy captures semantic structure while relaxing geometric fidelity, enabling subsequent stylization to operate on an abstracted representation rather than the original image. In a second stage, the abstraction proxy is rendered to produce the final stylized output, preserving visual coherence with the reference style. Both stages are implemented using a shared image space analogy, enabling transformations to be learned from visual exemplars without explicit geometric supervision. By decoupling abstraction from appearance and treating abstraction as an explicit, transferable process, AiS supports a wider range of stylistic transformations, improves controllability, and enables more expressive stylization.

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21.

arXiv (CS.LG) 2026-06-19 DOI: arXiv:2606.19993

Activation- and Influence-Aware Ranks (AIR): Function-Preserving SVD Compression for LLMs

作者:

Nico Harder↗Daniel Becking↗Karsten Mueller↗Wojciech Samek↗

arXiv:2606.19993v1 Announce Type: new Abstract: We present Activation- and Influence-Aware Ranks (AIR), an SVD-based LLM compression framework that guides each weight matrix's low-rank approximation with a backward-signal influence metric. Starting from the activation-aware optimum of SVD-LLM(W), AIR runs a single closed-form alternating least squares (ALS) sweep that integrates influence element-wise under a monotone-descent guarantee. AIR is layer-local and composes orthogonally with end-to-end methods: alone it exceeds ACIP, and AIR+LoRA outperforms it further. AIR improves perplexity over SVD-LLM(W) by >18% at

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22.

arXiv (CS.CL) 2026-06-24 DOI: arXiv:2606.24783

Paying to Know: Micro-Transaction Markets for Verified Product Information in Agentic E-Commerce

作者:

Filippos Ventirozos↗Matthew Shardlow↗

Commercial NLP treats the shopping chatbot as a recommender or a conversion tool: its job is to match a user to a catalogue entry and close a sale. We argue that the arrival of agent-native micro-payment rails (e.g., x402, AP2) changes what is scarce. When the buyer is an autonomous agent that can investigate exhaustively, the bottleneck is no longer matching products but acquiring trustworthy, decision-relevant information about them. We envision agentic e-commerce as a micro-transaction market for verified information: buyer agents spend fractions of a cent to progressively unlock seller- and reviewer-supplied data – service histories, third-party test reports, bills of materials, audited sales and support metrics – paid for a la carte under a freemium model, with reviewer trust scored reputationally. We sketch the architecture of such a market and argue that it rewards genuine product quality and yields truer competition than ranking-based storefronts. We then translate the vision into concrete NLP problems – cost-optimal information acquisition, data pricing and negotiation, real-time entity resolution, grounded value exchange, and privacy-preserving persona modelling – and argue that these, not chat fluency, deserve the field's attention.

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23.

medRxiv (Medicine) 2026-06-15 DOI: HASH:1c5213bf0cfd87aeba8d994cb60b38aa

Instrumental Activities of Daily Living in Older Adults with Epilepsy: A Cross-Sectional and Longitudinal Multicenter Study

作者:

Zawar↗Reyes↗Arrotta↗Hermann↗B. P↗Busch↗R. M↗Quigg↗Kapur↗Struck↗A. F↗Punia↗…

Objective: Instrumental activities of daily living (IADLs) represent a critical but understudied measure of day-to-day function in persons with epilepsy(PWE). In the multicenter Brain Aging and Cognition in Epilepsy (BrACE) study of PWE aged greater than or equal to 55 years, we examined the proportion, clinical correlates, epilepsy-related predictors, and longitudinal trajectory of IADL impairment. Methods: IADLs were assessed using the Functional Activities Questionnaire (FAQ; range=0 to 30; higher=more impaired); a FAQ greater than or equal to 2 defines MCI-level impairment, and a FAQ greater than or equal to 5 defines dementia-level functional impairment. Multivariable logistic regression identified predictors of baseline function. Global cognition (Montreal Cognitive Assessment [MoCA]), individual cognitive measures, and quality of life (QOL) were compared between the impaired and unimpaired groups. Linear regression evaluated predictors of longitudinal functional decline. Results: Of 57 participants (mean age=66.6 years; female=52.6%), 38.6% (n=22) had MCI-level functional impairment and 17.5% (n=10) had dementia-level functional impairment. In univariate analyses, worse FAQ scores were associated with lower education, higher area deprivation index, early-onset epilepsy (EOE less than 60 years), antiseizure medication polytherapy, and epilepsy localization. In multivariable analysis, temporal lobe epilepsy (OR=4.46, 95% CI=1.09, 21.83,p=0.047), EOE(OR=7.14, 95% CI=1.16, 59.97, p=0.046), and lower education(OR=0.70,95% CI=0.49, 0.93, p=0.025) remained independently associated with baseline MCI-level functional-impairment. Lower education (OR=0.55,95% CI=0.29, 0.84, p=0.021) was the only factor associated with dementia-level IADL-impairment. IADL-impaired participants demonstrated lower verbal memory scores (adjusted p=0.041) and MoCA scores (adjusted p

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24.

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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25.

arXiv (CS.LG) 2026-06-25 DOI: arXiv:2606.04066

SC-TauPath: A Structural Connectivity Attribution Framework for Mapping Tau Propagation Pathways in Alzheimer's Disease

作者:

Jing Zhang↗Norman Scheel↗Minheng Chen↗Tong Chen↗Yanjun Lyu↗David C. Zhu↗Rong Zhang↗Dajiang Zhu↗

arXiv:2606.04066v2 Announce Type: replace-cross Abstract: Understanding how structural connections are associated with tau propagation in Alzheimer's disease (AD) remains a central open question, yet existing computational models either rely heavily on biophysical assumptions or lack neurobiologically interpretable pathway maps. We present SC-TauPath, a structural connectivity (SC) attribution framework that maps tau propagation pathways from in vivo neuroimaging data. SC-TauPath combines a Network Diffusion Model (NDM)-augmented multilayer perceptron with gradient $\times$ input attribution to score each SC edge's contribution to tau prediction, then translates these attribution scores into multi-scale pathway maps (backbone edges, high-traffic routes, and hub ROIs), which validates established Braak staging anatomy. Applied to 234 ADNI participants with paired DTI SC and 18F-Flortaucipir PET, SC-TauPath achieves strong cross-validated tau prediction and yields attribution-based pathway maps consistent with established Braak staging anatomy, demonstrating that SC encode spatially specific information about regional tau distribution in AD.

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