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01.
arXiv (CS.CL) 2026-06-17

The Measurement Gap in the Automation of EU Law: Benchmarking Doctrinal Legal Reasoning under the EU AI Act

Authors:
Mich\`ele Finck↗

Large language models now produce legal text of at least median quality, yet no existing benchmark can evaluate whether they perform doctrinal legal reasoning, which forms the interpretive core of legal work, rather than the ancillary, paralegal tasks that most current legal-AI evaluations measure. This measurement gap is not only methodological but legal: the EU AI Act makes "appropriate accuracy" a binding requirement for high-risk AI used in the judicial domain, yet that requirement cannot acquire operational content without the very doctrinal-reasoning benchmark the field lacks.

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02.
medRxiv (Medicine) 2026-06-11

Malaria Risk among Internally Mobile Individuals and Heterogeneous Mobility Patterns in Two Hypoendemic Communities: Implications for Malaria Elimination in the Peruvian Amazon.

Authors:
Ramirez Saavedra↗Acosta↗Rodriguez↗Cabrera-Sosa↗Escalente↗A. A↗Vinetz↗J. M↗Torres↗Gamboa↗

Background: Human mobility is increasingly recognized as a key factor influencing malaria transmission dynamics, particularly in low-transmission settings approaching elimination. This study aimed to assess mobility patterns and their association with malaria risk in two hypoendemic communities in the Peruvian Amazon. Method: A longitudinal study was conducted in the communities of Libertad and Urcomirano (Mazan River basin). Monthly population screenings were combined with weekly active and passive case detection. A total of 678 individuals were enrolled. Mobility patterns were assessed through structured questionnaires, and social network analysis was used to characterize travel connections. Log-binomial regression analysis was applied to identify risk factors associated with malaria infection. Result: Internally, mobile individuals in Libertad showed a higher malaria incidence (>32.47 cases per 1,000 person-months) than those in Urcomirano (

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03.
arXiv (CS.CV) 2026-06-12

Efficient, Robust, and Anti-Collusion Fingerprinting of Image Diffusion Models

Authors:
Jianwei Fei↗Yunshu Dai↗Zhihua Xia↗Xiaochun Cao↗Jiantao Zhou↗Alessandro Piva↗Benedetta Tondi↗

Model fingerprinting, embedding user-specific identifiers (fingerprints) into generated outputs, has recently emerged as a popular solution to protect the intellectual property rights (IPR) of generative text-to-image (T2I) models and prevent unauthorized redistribution. In this work, we reveal a previously unexplored systematic vulnerability in existing generative model fingerprinting methods: they lack robustness against collusion attacks, where multiple attackers combine their models to remove or obscure the fingerprints. To address this issue, we take the first step towards a robust fingerprinting method for T2I models with anti-collusion capabilities. The proposed method encodes strings of bits, namely fingerprints, into the coefficients of a personalized normalization module (PNM) incorporated into T2I models, so that fingerprints can be reliably recovered from any generated image. To defend against collusion attacks and prevent unauthorized model redistribution, we introduce an anti-collusion mechanism based on lossless function-invariant parameter transformations. This mechanism significantly degrades the image generation quality of colluded models, making them effectively unusable. Moreover, our method allows developers to efficiently create multiple copies of fingerprinted T2I models by reparameterizing the PNM without the need for retraining. We also introduce a worst-case optimization strategy to improve robustness against model-level attacks. Our experiments demonstrate that the proposed method achieves high fidelity and robustness across multiple T2I image generation and editing tasks, with fingerprint extraction accuracy exceeding 99.5%. Compared with existing methods, our method demonstrates, for the first time, a notable proactive robustness to collusion attacks by significantly increasing the FID of colluded models.

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04.
arXiv (CS.AI) 2026-06-12

LLM-as-an-Investigator: Evidence-First Reasoning for Robust Interactive Problem Diagnosis

Authors:
Fabrizio Marozzo↗Pietro Li\`o↗

arXiv:2606.13220v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as interactive assistants for technical problem solving. However, when users provide incomplete descriptions or plausible but unverified explanations, LLMs may prematurely align with these assumptions and propose solutions before collecting sufficient evidence. We refer to this behavior as user-driven sycophancy: the tendency of an LLM to reinforce a user-provided hypothesis instead of testing alternative explanations. This paper introduces LLM-as-an-Investigator, an evidence-first agentic AI methodology for robust problem diagnosis. The approach is implemented through a Solution Investigator Agent, which estimates the ambiguity of an initial problem description, generates candidate hypotheses, asks targeted clarification questions, and updates hypothesis probabilities after each answer. Rather than producing an immediate response, the agent continues the investigation until the evidence makes one candidate explanation stronger than the alternatives. To evaluate the approach, we build a benchmark from solved technical forum threads in mechanical, electrical, and hydraulic domains. We use a three-agent evaluation pipeline in which a Problem-Solution Extractor Agent converts solved threads into structured cases, a Ground-Truth Evaluator Agent simulates the user while hiding the known solution, and the tested assistant attempts to recover the solution through dialogue. The experiments compare standard assistants, reasoning-oriented LLMs, and the proposed investigator-based model across LLM backbones. In addition to diagnostic accuracy, we analyze how standard assistants follow misleading user hypotheses in diagnostic cases. The results show that the proposed approach identifies the problem more accurately than direct prompting and reasoning-only baselines, while its evidence-first protocol helps reduce user-induced conversational bias.

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05.
arXiv (CS.CV) 2026-06-11

MLT-Dedup: Efficient Large-Scale Online Video Deduplication via Multi-Level Representations and Spatial-Temporal Matching

Authors:
David Yuchen Wang↗Haoying Li↗Hailun Xu↗Wei Chee Yew↗Zirui Zhu↗Sanjay Saha↗Hao Hei↗Kanchan Sarkar↗Kun Xu↗

The explosive growth of user-generated video content on online platforms is accompanied by the emergence of numerous near-duplicate videos–videos that are identical or highly similar but differ by partial edits. These duplicates degrade user experience and increase storage and bandwidth costs, making large-scale video deduplication a critical task. Existing video deduplication frameworks face a fundamental challenge in retrieving sufficient high-quality candidates under a limited index budget, as well as trade-offs between efficiency and precision. To address these issues, we propose MLT-Dedup, an efficient large-scale online video deduplication framework with Multi-Level representations and spatial-Temporal matching. Our approach employs a Multi-Level Video Encoder (ML-VE) to extract both fine-grained frame-level and sparse clip-level embeddings: sparse embeddings support efficient candidate retrieval, while fine-grained embeddings are loaded for precise pairwise matching. During matching, we introduce DiF-SiM, a Differential Feature-enhanced Similarity Module capable of locating duplicated temporal segments and providing reliable similarity evidence to support policy-driven deduplication decisions. Extensive experiments on a real-world large-scale platform demonstrate that MLT-Dedup reduces online repetition rates by 91% at 90% precision. Furthermore, our sparse retrieval design achieves a 5x increase in indexing capacity, enabling broader candidate coverage in real-world deployment.

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06.
arXiv (quant-ph) 2026-06-16

High-fidelity two-qubit gates in a 7-qubit register for quantum networks

Authors:
Margriet van Riggelen↗Jiwon Yun↗H. Benjamin van Ommen↗Tim H. Taminiau↗

arXiv:2606.14847v1 Announce Type: new Abstract: Quantum networks based on optically active solid-state spins may enable quantum technologies including long-range quantum communication and distributed quantum computing. Network nodes containing multiple high-fidelity qubits can facilitate large-scale fault-tolerant operation. However, the stringent error thresholds remain out of reach for multi-qubit registers. In this work, we demonstrate high-fidelity two-qubit gates in a 7-qubit register, based on nuclear spins coupled to a nitrogen-vacancy (NV) center in diamond. We analyze crosstalk in highly connected spin systems, develop an efficient optimization procedure, and characterize the gates using gate set tomography. The two-qubit gate fidelities (best: 99.61(5)%, average: 99.18(2)%) demonstrate a multi-qubit register at the threshold for distributed quantum computation. Finally, as an example application, we perform a variational quantum eigensolver (VQE) simulation of the ground-state energy of H2 and LiH molecules. These results demonstrate one of the key prerequisites for scalable quantum networks based on solid-state spins.

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07.
arXiv (CS.LG) 2026-06-16

Finite Resources False Discovery Rate Control in Structured Hypothesis Spaces

Authors:
Binyamin Perets↗Shie Mannor↗

arXiv:2606.15393v1 Announce Type: cross Abstract: Scientific discovery relies on large-scale hypothesis testing. However, the capacity to identify true discoveries while controlling false discovery faces major challenges: obtaining relevant reference data (the null distribution) is resource-intensive, leaving finite-data uncertainty, and the procedure should account for the inherent structure in the hypothesis space, when such structure exists. Here, we present a framework for controlling the false discovery rate both when each hypothesis is evidenced only by a finite count of null draws, leaving its p-value uncertain, and when the hypothesis space carries arbitrary structure, requiring only that the structure be represented through a suitable reproducing kernel. We present two decision rules that are both robust to structural mis-specification, yet offer a distinct trade-off between exact FDR control and statistical power. The first rule guarantees exact FDR control; the second maximizes power by adapting mirror-statistic control into count space, utilizing an analytical framework to assess FDR control when exact mirror symmetry is relaxed. Furthermore, the tractability gained by the RKHS framework allows us to directly investigate finite-data uncertainties, which we leverage to suggest a policy for the efficient allocation of null distribution samples.

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08.
arXiv (CS.CV) 2026-06-16

Physics-Driven Zero-Shot MRI Reconstruction with Non-local Image Priors

Authors:
Lingtong Zhang↗Wenlei Li↗Mu He↗Li Xiao↗Yang Ji↗

Zero-Shot Self-Supervised Learning (ZS-SSL) has emerged as a promising paradigm for accelerated Magnetic Resonance Imaging (MRI) reconstruction, eliminating the reliance on fully-sampled external datasets. However, learning solely from a single under-sampled scan suffers from supervision scarcity and optimization instability, often leading to overfitting or artifacts. To address these challenges, we propose a robust physics-driven ZS-SSL framework that synergizes physical consistency with image-domain non-local priors. Our method introduces three core innovations: (1) a Coil Sensitivity Map (CSM)-Guided Dynamic Repository, which stabilizes the training trajectory by filtering physically inconsistent artifacts based on coil sensitivity constraints; (2) a SPIRiT-based regularization, which enforces k-space self-consistency via a learned correlation kernel and stochastic masking; (3) a Non-Local Self-Similarity (NSS) Pixel Bank, which leverages the high-fidelity reference established by the former modules to explicitly mine non-local anatomical similarities, thereby augmenting supervision in the image domain. Extensive experiments on the FastMRI dataset demonstrate that our approach achieves state-of-the-art performance, particularly under high acceleration factors, effectively bridging the gap between zero-shot learning and supervised methods. The code is available at https://github.com/Zolento/NS-SSL.

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09.
bioRxiv (Bioinfo) 2026-06-14

Generative design of antigen-specific T-cell receptor sequences with a conditional diffusion model

Authors:
Zhang↗Liang↗Xu↗Witney↗Rossjohn↗Su↗Purcell↗A. W↗Wang↗Song↗

T cell receptor (TCR)-based immunotherapy holds immense potential for treating cancers and infectious diseases, where highly antigen-specific TCR recognition is crucial for adaptive immunity against tumors and pathogens. Engineering or de novo generation of the complementarity-determining region 3 (CDR3) loops of TCRs using artificial intelligence offers a powerful alternative to designing reactive TCRs rather than laborious experimental screening. However, current in silico approaches are constrained by weak conditional guidance, limited flexibility, and a lack of rigorous functional validation. To address these limitations, we introduce TCRDiff, a generative diffusion framework for designing antigen-specific TCRs conditioned on peptide-MHC (pMHC) targets and germline-encoded variable genes. By leveraging pre-trained knowledge from massive T-cell repertoires and TCR-pMHC recognition data, TCRDiff generates CDR3{beta} sequences with state-of-the-art fidelity to native binding TCRs through a denoising diffusion process. Furthermore, incorporating the interface geometry features generated TCR-pMHC complexes with superior structural plausibility. As a proof of concept, we deployed TCRDiff in a systematic pipeline to design candidate TCRs for immunotherapy. In vitro activation assays validated that TCRDiff-generated TCRs specifically recognize the MAGE-A3 epitope with minimized off-target cross-reactivity. Together, TCRDiff establishes a powerful, validated computational paradigm to accelerate the development of TCR-based immunotherapies.

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10.
arXiv (CS.AI) 2026-06-25

ATMA: Length-Invariant Language Modeling via Polar Attention and Gated-Delta Compression Memory

Authors:
Habibullah Akbar↗

arXiv:2606.25156v1 Announce Type: cross Abstract: Modern large language models based on softmax scaled-dot-product attention are constrained by their training sequence length: as the key-value sequence grows, softmax probability mass can dilute across a wider distribution, inducing activation shift and long-context performance collapse. Moreover, long-context language modeling faces a structural tension: a sliding-window attention core maintains a bounded local representation and low perplexity but is blind to long-range dependencies, while full-context attention preserves global recall but suffers from out-of-distribution perplexity explosion. To resolve these limitations, we introduce ATMA, a hybrid convolutional-attention architecture that integrates a novel three-channel attention mechanism. ATMA factorizes the attention mixing step into: (1) a count-blind, unit-vector direction channel, (2) a bounded magnitude channel driven by the participation ratio of effective matches over an extreme-value-corrected null sink, and (3) a long-term recurrent compression memory optimized via a gated-delta fast-weights rule. Neither the Polar Attention core nor the recurrent memory is sufficient alone; their combination enables monotonic perplexity reduction and high-fidelity long-range retrieval simultaneously. We evaluate ATMA using a 100-run factorial ablation sweep, demonstrating that the combined Polar + memory model maintains induction needle-in-a-haystack retrieval accuracy above 90% out to 64K tokens (32 times the training length of 2K) while its document perplexity improves monotonically, outperforming softmax-based memory baselines which collapse at extreme context lengths. Code: https://github.com/kreasof-ai/atma

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11.
arXiv (quant-ph) 2026-06-16

Nonlinear cascaded quantum network with giant emitters

Authors:
Xin Wang↗Jia-Qi Li↗Zhihai Wang↗Anton Frisk Kockum↗Lei Du↗Tao Liu↗Franco Nori↗

arXiv:2404.09829v2 Announce Type: replace Abstract: Chiral quantum optics is central to developing scalable quantum networks, yet existing approaches rely predominantly on linear single-photon regimes. It remains unclear how to generate directional multiphotons. Here we show that giant emitters coupled to nonlinear quantum optical baths enable tunable directional correlated photons, revealing a mechanism for multiphoton directional emission. We demonstrate that the propagation phases of correlated photons, together with the coupling phases of giant emitters, can generate destructive interference in one direction while enhancing emission in the opposite direction, making directionality fully tunable. Building on this mechanism, we introduce a nonlinear cascaded quantum network paradigm mediated by correlated flying qubits, providing a configurable building block enabling distinct many-body applications beyond linear unidirectional setups. These results reveal a rich landscape for engineering multiphoton propagation and correlations through interference in giant emitter-nonlinear bath architectures, offering pathways for quantum networks and strongly correlated light-matter platforms.

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12.
bioRxiv (Bioinfo) 2026-06-11

An AI-Powered Trisomy 21 Research Assistant

Authors:
NANDI↗Sundararajan↗Subirana-Granes↗Espinosa↗J. M↗Pividori↗Sullivan↗K. D↗Galbraith↗M. D↗Costello↗

Down syndrome, caused by trisomy 21, increases the risk of diverse co-occurring conditions. With more than 34,000 related publications indexed in PubMed as of early 2026, keeping pace with this expanding literature is challenging. While general-purpose large language models are widely used for information retrieval, they often rely on broad training data rather than specific evidence. Retrieval-augmented generation (RAG) improves rigor and reliability of responses by linking model outputs to source texts. In research, source texts are peer-reviewed articles. Standard implementations treat all manuscript sections equally, allowing background text to rank as highly as experimental results. To focus model outputs on experimentally supported responses, we developed the T21 Research Assistant, a section-aware RAG system that prioritizes Results sections to ground responses in primary experimental evidence. The system draws exclusively from 1,789 open-access Down syndrome publications from PubMed Central, including 327 NIH INCLUDE-funded studies, and uses a multistage pipeline for query validation, retrieval, reranking, synthesis, and citation verification. Built on NVIDIA Nemotron models, it generates structured, cited responses. Evaluation using expert-curated questions demonstrated strong performance, achieving a BERTScore F1 of 0.712 and recall of 0.758, comparable to or exceeding leading proprietary and open-source models. T21 Research Assistant is available at: https://bioinformatics.cuanschutz.edu/t21-res-assi/

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13.
arXiv (quant-ph) 2026-06-16

Phase controlled spectral topology, dynamic stability and sensitivity in Non-Hermitian Cavity Magnonics

Authors:
Sachin Singh Bargahi↗Rajeev Singh↗Biswanath Bhoi↗

arXiv:2606.16522v1 Announce Type: new Abstract: We theoretically investigate a non-Hermitian cavity-magnon platform in which coherent photonmagnon interactions and reservoir-mediated dissipative coupling interfere through a single externally tunable phase. We show that this interference phase provides a universal control parameter that continuously rotates the effective coupling between Hermitian and anti-Hermitian regimes, enabling dynamic transitions between level repulsion and level attraction without modifying intrinsic system parameters. The resulting phase-controlled non-Hermitian topology gives rise to exceptional points, linewidth engineering, and zero-damping conditions. Owing to the propagation-direction dependence of the dissipative interaction, the system further exhibits strong nonreciprocal transport and phase-tunable isolation arising from asymmetric hybridization of the cavity and magnon modes. Beyond its spectral and transport properties, we establish a direct connection between nonHermitian spectral topology and nonequilibrium population dynamics. The interference phase governs the stability of the hybrid modes, driving transitions between stable relaxation, critical slowing down near exceptional points, oscillatory energy exchange, and exponentially amplified dynamics. We further demonstrate that the same phase-controlled exceptional topology can be exploited for enhanced sensing, where the eigenvalue response exhibits the characteristic square-root scaling associated with exceptional-point physics. Our results provide a unified framework linking spectral topology, directional transport, dynamical stability, and sensing functionality through reservoirengineered interference in cavity magnonic systems.

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14.
arXiv (CS.AI) 2026-06-19

When, Where, and How: Adaptive Binning for Tabular Self-Supervised Learning

Authors:
Daehwan Kim↗Haejun Chung↗Ikbeom Jang↗

arXiv:2606.19827v1 Announce Type: cross Abstract: Medical tabular data are ubiquitous in clinical research, but deep learning for tables remains underexplored because reliable labels often require costly expert adjudication, even though structured clinical variables are routinely available in tabular form. Self-supervised learning can leverage these unlabeled tables, and recent binning-based pretexts offer a promising inductive bias, but existing objectives fix a single global quantile discretization and apply feature-agnostic supervision. We propose Adaptive Binning, a training-adaptive discretization pretext for tabular SSL that couples discretization to learning through a feature-wise coarse-to-fine curriculum. Motivated by the spectral bias of neural networks and the principles of curriculum learning, our method progressively refines discretization per feature upon plateau detection and selects representation-aware splits to jointly improve value-space concentration and representation-space coherence. A heterogeneity-aware objective unifies categorical reconstruction with ordinal supervision for numerical features, and experiments on public medical tabular datasets under unified evaluation protocols show consistent gains for linear probing and fine-tuning without dataset-specific discretization tuning. We further introduce a medical tabular SSL benchmark with standardized protocols to support reproducible progress in this underexplored domain. Our code is available at https://github.com/labhai/Adaptive-Binning.

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15.
arXiv (CS.LG) 2026-06-19

Adversarial Bandit Optimization with Globally Bounded Perturbations to Convex Losses

Authors:
Zhuoyu Cheng↗Kohei Hatano↗Eiji Takimoto↗

arXiv:2606.19891v1 Announce Type: new Abstract: We study adversarial bandit optimization in which the loss functions may be non-convex and non-smooth. In each round, the learner selects an action and observes only the loss incurred at that action. The loss consists of an underlying convex and $\beta$-smooth component and an adversarial perturbation that may be chosen after observing the learner's action. The perturbations are subject to a global budget controlling their cumulative magnitude over time. This framework extends the globally budgeted, post-action perturbation model from underlying linear losses to general convex and $\beta$-smooth losses. For this broader class, we establish expected regret guarantees that explicitly characterize the effect of the perturbation budget. To establish these guarantees, we modify a standard bandit optimization algorithm and develop an analysis that controls the additional regret caused by the perturbations. In the absence of perturbations, our results reduce to regret guarantees for the standard bandit convex optimization setting with $\beta$-smooth losses.

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16.
arXiv (CS.CL) 2026-06-25

ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments

Authors:
Taicheng Guo↗Haomin Zhuang↗Kehan Guo↗Yujun Zhou↗Nitesh V. Chawla↗Olaf Wiest↗Xiangliang Zhang↗

Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding strong configurations within a limited budget. Because every HPO tool relies on a surrogate prior that imparts its own inductive bias, individual tools struggle once problems become sufficiently diverse and drift from these priors. Motivated by the reasoning and generalization capabilities of LLMs, recent work has explored using LLMs for HPO and reports improved per-iteration performance. Yet these methods share two limitations with a common origin: they use the LLM as a single-tool replacement evaluated by iteration count. (i) Deployed in place of prior tools, the LLM is itself constrained by its pretraining objective to one family of inductive-biased proposals; this single-source setup still fails to handle the full diversity of problems. (ii) Per-iteration evaluation ignores that, in real runs, LLM inference or tool execution is paid serially on top of model evaluation every round, so iteration-count gains do not translate into end-to-end wall-clock gains. We present ASAP, an agent-system co-design that addresses both limitations. On the agent side, ASAP uses the LLM to integrate a diverse pool of inductive-biased optimizers and to select among their proposals each round. On the system side, ASAP re-architects the loop to reduce end-to-end wall-clock while preserving regret quality: a prefix-stable prompt maximizes KV-cache reuse across rounds; speculation parallelism hides the remaining LLM and tool latency under model evaluation via a relative-error accept test; and a Self-Tuner adapts the speculation threshold from execution logs off the critical path. Extensive experiments on diverse modern HPO tasks show that ASAP consistently outperforms baselines, underscoring the value of tool integration and agent-system co-design.

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

EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering

Authors:
Fengchen Gu↗Xiaotian Ren↗Zhengyong Jiang↗Zhilu Zhang↗\'Angel F. Garc\'ia-Fern\'andez↗Angelos Stefanidis↗Mian Zhou↗Huakang Li↗Jionglong Su↗

Large language models are increasingly used to answer questions over annual reports, earnings decks, and analyst notes, yet their outputs remain difficult to verify in high-stakes financial workflows. A fluent answer can blend directly grounded statements, weak synthesis, and unsupported claims across narrative text, tables, and charts. We present EvidenceLens, a visual analytics prototype that treats financial question answering as a claim-evidence alignment problem. The system decomposes an answer into atomic claims, summarizes support composition and confidence, support gaps, and coordinates claim-level inspection with source passages, table cells, and chart regions. Its core visual representation is a multimodal claim-evidence matrix that makes coverage, contradiction, and modality imbalance immediately visible. To support reproducibility, we also specify a JSON-based artifact schema, a lightweight multimodal alignment pipeline, and a deterministic review-priority ranking that maps backend signals into an auditable visual structure. Through representative report-auditing scenarios, we show how EvidenceLens helps analysts distinguish grounded claims from overconfident synthesis that conventional chat interfaces flatten.

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18.
arXiv (math.PR) 2026-06-16

Layerwise Terminal Discrepancy in Chen's Reverse-Heat Coupling on the Boolean Cube

Authors:
Yanjin Xiang↗Zhihua Zhang↗

arXiv:2606.04573v2 Announce Type: replace-cross Abstract: Recently, Chen [Chen2026] proved that Talagrand's Boolean convolution conjecture holds up to the dimension-free factor \((\log\log\eta)^{3/2}\), namely for every fixed \(\tau>0\), \[ \mu\{P_\tau f>\eta\|f\|_1\} \le C_\tau \frac{(\log\log\eta)^{3/2}}{\eta\sqrt{\log\eta}}, \qquad \eta>e^3. \] We revisit the terminal testing-discrepancy step in Chen's perturbed reverse-heat coupling. Chen estimates this discrepancy globally in terms of the remaining gap to the terminal level. We keep the same coupling and the same reverse-heat formulations, but localize the terminal discrepancy on each remaining-gap layer before summing the layers. This changes the fixed-time anti-concentration cost from order \((\log L)^{3/2}/\sqrt L\) to order \((\log L)/\sqrt L\), where \(L=\log\eta\). Consequently, we obtain a \((\log\log\eta)^{1/2}\) improvement as \[ \mu\{P_\tau f>\eta\|f\|_1\} \le C_\tau \frac{\log\log\eta}{\eta\sqrt{\log\eta}}, \qquad \eta>e^3. \]

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19.
arXiv (CS.CV) 2026-06-24

Latent Visual States for Efficient Multimodal Reasoning

Authors:
Xiuwei Chen↗Wentao Hu↗Yongxin Wang↗Zisheng Chen↗Likui Zhang↗Kun Xiang↗Jianhua Han↗Hui-Ling Zhen↗Jingyuan Zou↗Hang Xu↗Xiaodan Liang↗

The integration of visual evidence has significantly enhanced the capabilities of large multimodal models. However, this integration predominantly relies on generating discrete outputs (etc., code or box coordinates) to invoke external tools, a process that introduces rigid dependencies and substantial latency. To overcome these limitations, we propose {EVA} (LatEnt Visual StAtes), a novel framework that natively generates continuous latent visual representations. These internal representations manifest as an adaptive sequence of Latent\_slot tokens, serving as intermediate visual thoughts during the reasoning process. These Latent\_slot tokens are then trained end-to-end with the discrete text tokens. This co-optimization, notably, causes extreme policy deviation in the 'transition window' following the Latent\_slot tokens. We develop D-GSPO (Decouple-GSPO) to target this root cause by decoupling the optimization of latent and discrete components. To support SFT, we construct EVA-230K, a high-quality text-image interleaved CoT dataset encompassing a diverse range of real-world scenes, documents, charts and OCR tasks. Extensive experiments across multiple benchmarks confirm that EVA achieves significant performance gains while enhancing inference efficiency.

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20.
medRxiv (Medicine) 2026-06-10

Developing a Unified Criminal Justice Pathway into Drug and Alcohol Treatment from Police Custody: A Public Health Service Evaluation and Pathway-Design Project in Blackpool, United Kingdom

Authors:
Badmos↗A. O↗AbdulKareem↗Mills↗Gawne↗Idris↗

Introduction: Blackpool, England's most deprived local authority, has the highest drug-related death rate in the country. People in police custody with problem substance use are a key Core20PLUS5 inclusion-health group, yet referral from the police into structured drug and alcohol treatment is fragmented and relies heavily on self-report. We evaluated the current police-to-treatment route in Blackpool and designed an evidence-informed unified pathway. Materials and Methods: A mixed-methods service evaluation and pathway-design project was conducted during a six-month General Practice / Public Health rotation. Routinely collected referral data from Horizon (the local specialist drug and alcohol service) covering the 47-month period from December 2019 to October 2023 were analysed. Findings were triangulated with national policy, the Project ADDER and Liaison and Diversion evaluations, and the international evidence on police-led pre-arrest diversion. Results: Of 5,900 total referrals into Horizon over 47 months, only 269 (4.56%) originated from the police. Police referrals accounted for fewer than 5% of monthly referrals in 30 of 47 months, for 5 to 9.9% in 16 months, and for >/= 10% in only one month (10.8%, December 2022). Blackpool recorded 76 drug-misuse deaths in 2019-21 (19.4 per 100,000, approximately four times the England rate). A six-step unified pathway is proposed: Initiate Referral (opt-out, from ADDER Police and Liaison and Diversion); Initial Assessment; Tailored Treatment Plan; Continuous Support; Collaboration and Monitoring; and Evaluation and Adjustment. Conclusions: Police contact is markedly under-used as a gateway to treatment despite Blackpool having the highest drug-related mortality in England. An opt-out, multi-agency pathway anchored in Core20PLUS5 has the potential to narrow the treatment gap, reduce re-offending, and address the structural health inequalities that drive premature mortality.

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21.
arXiv (quant-ph) 2026-06-15

Dissipation-induced superradiance in matter coupled to a self-interacting cavity

Authors:
Sebastian Schmid↗Matteo Soriente↗Oded Zilberberg↗Javier del Pino↗

arXiv:2606.14526v1 Announce Type: new Abstract: Light-matter interactions are often modeled via the Dicke model, namely, by two-level systems coupled to a cavity mode. Alas, the threshold for superradiance is often experimentally inaccessible or hindered by light's diamagnetic term. Here, within the Dicke setting, we consider self-interacting light in a cavity, modeled by a photonic Kerr nonlinearity. We show that negative Kerr nonlinearity gives rise to a low-threshold superradiant phase with spin inversion. While unstable in a closed system, cavity dissipation stabilizes this lit phase, opening avenues for lasing and bath-engineered phases.

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22.
arXiv (CS.LG) 2026-06-19

Weighted Bayesian Conformal Prediction

Authors:
Xiayin Lou↗Peng Luo↗

arXiv:2604.06464v2 Announce Type: replace Abstract: Conformal prediction provides distribution-free prediction intervals with finite-sample coverage guarantees, and recent work by Snell \& Griffiths reframes it as Bayesian Quadrature (BQ-CP), yielding powerful data-conditional guarantees via Dirichlet posteriors over thresholds. However, BQ-CP fundamentally requires the i.i.d. assumption. Meanwhile, weighted conformal prediction handles distribution shift via importance weights but remains frequentist, producing only point-estimate thresholds. We propose Weighted Bayesian Conformal Prediction (WBCP), which generalizes BQ-CP to arbitrary importance-weighted settings by replacing the uniform Dirichlet $\Dir(1,\ldots,1)$ with a weighted Dirichlet $\Dir(\neff \cdot \tilde{w}_1, \ldots, \neff \cdot \tilde{w}_n)$, where $\neff$ is Kish's effective sample size. We prove four theoretical results: (1)~$\neff$ is the unique concentration parameter matching frequentist and Bayesian variances; (2)~posterior standard deviation decays as $O(1/\sqrt{\neff})$; (3)~BQ-CP's stochastic dominance guarantee extends to per-weight-profile data-conditional guarantees; (4)~the HPD threshold provides $O(1/\sqrt{\neff})$ improvement in conditional coverage. We instantiate WBCP for spatial prediction as Geographical BQ-CP, where kernel-based spatial weights yield per-location posteriors with interpretable diagnostics. Experiments on synthetic and real-world spatial datasets demonstrate that WBCP maintains coverage guarantees while providing substantially richer uncertainty information.

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23.
arXiv (CS.CL) 2026-06-16

MemBoost: A Memory-Boosted Framework for Cost-Aware LLM Inference

Authors:
Joris K\"oster↗Zixuan Liu↗Siavash Khajavi↗Zizhan Zheng↗

Large Language Models (LLMs) deliver strong performance but incur high inference cost in real-world services, especially under workloads with repeated or near-duplicate queries across users and sessions. In this work, we propose MemBoost, a memory-boosted LLM serving framework that enables a lightweight model to reuse previously generated answers and retrieve relevant supporting information for cheap inference, while selectively escalating difficult or uncertain queries to a stronger model. Unlike standard retrieval-augmented generation, which primarily grounds a single response, MemBoost is designed for interactive settings by supporting answer reuse, continual memory growth, and cost-aware routing. Experiments across multiple models under simulated workloads show that MemBoost substantially reduces expensive large-model invocations and overall inference cost, while maintaining high answer quality comparable to the strong model baseline.

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

DREAM: Extending Vision-Language Models with Dual-Objective Encoding for Cross-Modal Retrieval

Authors:
Kaleem Ullah↗Altaf Hussain↗Muhammad Munsif↗Sung Wook Baik↗

In today's media-driven world, the exponential growth of video content across domains such as surveillance, education, and entertainment has made retrieving semantically relevant videos via natural language queries increasingly critical. Early video retrieval systems relied on handcrafted features or shallow cross-modal mappings, limiting their ability to capture complex semantics and temporal dynamics. While large-scale vision-language models have improved cross-modal alignment, challenges remain in modeling fine-grained temporal dependencies and nuanced linguistic structures. In this paper, we introduce DREAM: Dual-path Representation Enhancement and Alignment Model, a novel multimodal framework that addresses these limitations through enhanced visual and textual encoding. DREAM incorporates a hybrid language modeling strategy that combines masked and permuted language modeling objectives to capture both local and global linguistic semantics. On the visual side, we design a hierarchical vision encoder with cascaded group attention, which integrates spatial and temporal information through multi-stage token interaction and coarse-to-fine attention refinement. We validate DREAM through comprehensive evaluations on the widely-used MSRVTT, MSVD and LSMDC benchmark datasets, where it achieves new state-of-the-art R1 scores of 49.4%, 49.7% and 27.3%, respectively. Qualitative analyses further show the model's ability to maintain coherent attention across frames and align complex queries with dynamic video content. These findings underscore the effectiveness of hierarchical attention and dual-objective textual modeling in enabling robust, context-aware video retrieval, and pave the way for future research in advancing cross-modal representation learning.

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25.
medRxiv (Medicine) 2026-06-15

Fanconi Anemia as a Window into Premalignant Field Cancerization of the Oral Mucosa

Authors:
Berger↗Donovan↗F. X↗Lin↗Y.-C↗Soma↗May↗Bhadresha↗Krieg↗Giri↗McReynolds↗L. J↗…

Head and neck squamous cell carcinoma (HNSCC) evolves through stepwise clonal expansion within genetically altered mucosa fields, yet actionable biomarkers remain undefined. Leveraging Fanconi anemia (FA), a cancer predisposition syndrome with extreme HNSCC risk due to defective DNA interstrand crosslink repair, we profiled premalignant changes in the oral cavity using noninvasive brush biopsies. Consistent with our prior demonstration of genomic instability in FA-associated SCCs, we detected pathogenic TP53 variants in 26% and copy number alterations in 60.5% in clinically normal-appearing oral mucosa of individuals with FA. These subclinical clonal expansions define candidate biomarkers of early clonal evolution amenable to serial sampling for risk stratification and prevention studies. Since FA-associated SCCs share genomic features with sporadic HNSCC, these findings may extend to the broader population. We also identify somatic reversion of a pathogenic FANCB variant, providing evidence of genomic self-correction and suggesting a potential avenue for gene-based cancer prevention in FA.

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