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01.
medRxiv (Medicine) 2026-06-18

Empirical Validation and Predictive Utility of the Perinatal Grief Scale in Men after Perinatal Loss

Authors:
Ravaldi↗Mosconi↗Nespoli↗Fumagalli↗Vannacci↗

Background. The Perinatal Grief Scale (PGS) is a widely used instrument for assessing grief following pregnancy loss, yet no study has validated it specifically in men despite documented use in several studies. This gap is critical given fathers' persistent underrepresentation in perinatal bereavement research and the absence of empirically supported screening thresholds for this population. Methods. This cross-sectional validation study used data from the OPALE project (Observatory on PerinatAL hEalth) conducted by the CiaoLapo Foundation in Italy. Among 276 fathers who experienced stillbirth or miscarriage, we examined criterion validity by testing the association between PGS scores and trauma-related symptomatology assessed via three validated instruments: the Revised Impact of Event Scale (RIES, n=103), National Stressful Events Survey Short Scale (NSESSS, n=95), and SCL-90 (n=173). We systematically tested multiple threshold combinations to identify optimal discriminative performance. Results. The PGS demonstrated excellent criterion validity. The optimal threshold (PGS >=92) showed sensitivity 81.0%, specificity 81.8%, and Youden's J index 0.628. Fathers scoring >=92 had 19.12 times the odds of high trauma symptoms (95% CI: 9.35 to 39.14, p

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02.
arXiv (CS.AI) 2026-06-15

Korzhinskii-Net: Physics-Informed Neural Network for Sub-Surface Mineral Prospectivity Modelling

Authors:
Boris Kriuk↗

arXiv:2606.13695v1 Announce Type: cross Abstract: Mineral prospectivity modelling (MPM) underpins exploration economics, yet most operational pipelines reduce to data-driven classifiers trained on shallow surface proxies. Such models are blind to the subsurface physics that actually localises ore: heat advection, fluid flow, and lithology-dependent precipitation. We present Korzhinskii-Net, a 2-D radial physics-informed neural network (PINN) that couples Darcy flow, advective-diffusive heat transport, and a softplus-saturated reaction rate into a single differentiable forward model, weakly supervised by surface and remote-sensing proxies. The network is named after Dmitri S. Korzhinskii (1899-1985), whose theory of infiltration metasomatism provides the physical scaffold. We evaluate Korzhinskii-Net on five ore provinces spanning four commodity classes – Norilsk (Ni-Cu-PGE), Pechenga (Ni-Cu sulphide), Udokan (sandstone-hosted Cu), Sukhoi Log (orogenic Au), and Mirny (kimberlitic diamond) – under a fair, leakage-controlled 5-fold cross-validation protocol with hard ring-shaped negatives. Korzhinskii-Net attains a mean PR-AUC of 0.885 versus 0.281 for the strongest classical baseline (gradient boosting), and a mean fractional rank of 0.019 versus 0.413. The improvement is consistent across all five provinces and four commodity systems, suggesting that physics-informed differentiable simulators, even when constrained only by global open-data proxies, can recover localisation patterns that pure feature-based learners systematically miss. We release the full pipeline and evaluation harness as open source.

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03.
arXiv (CS.LG) 2026-06-18

Provable quantum speedups for computing persistence in topological data analysis

Authors:
Casper Gyurik↗Alexander Schmidhuber↗Robbie King↗Vedran Dunjko↗Ryu Hayakawa↗

arXiv:2410.21258v2 Announce Type: replace-cross Abstract: Topological data analysis (TDA) aims to extract noise-robust features from a data set by examining the number and persistence of holes in its topology. We provide an efficient quantum algorithm for a computational problem closely related to a core task in TDA – determining whether a given hole persists across different length scales. Further, we prove the problem itself is $\mathsf{BQP}_1$-hard, implying that a classical solution is extremely unlikely; this stands in contrast to all previous quantum approaches to TDA, where the problems were also intractable for quantum computers, or where a rigorous proof of classical hardness still remains open. This result implies an {exponential} quantum speedup for this problem under standard complexity-theoretic assumptions. Our approach relies on encoding the persistence of a hole in a variant of the guided sparse Hamiltonian problem, where the guiding state is constructed from a harmonic representative of the hole.

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04.
arXiv (CS.CV) 2026-06-18

iTryOn: Mastering Interactive Video Virtual Try-On with Spatial-Semantic Guidance

Authors:
Jun Zheng↗Zhengze Xu↗Mengting Chen↗Jing Wang↗Jinsong Lan↗Xiaoyong Zhu↗Kaifu Zhang↗Bo Zheng↗Xiaodan Liang↗

Video Virtual Try-On (VVT) aims to seamlessly replace a garment on a person in a video with a new one. While existing methods have made significant strides in maintaining temporal consistency, they are predominantly confined to non-interactive scenarios where models merely showcase garments. This limitation overlooks a crucial aspect of real-world apparel presentation: active human-garment interaction. To bridge this gap, we introduce and formalize a new challenging task: Interactive Video Virtual Try-On (Interactive VVT), where subjects in the video actively engage with their clothing. This task introduces unique challenges beyond simple texture preservation, including: (1) resolving the semantic ambiguity of interactions from standard pose information, and (2) learning complex garment deformations from video where interactive moments are sparse and brief. To address these challenges, we propose iTryOn, a novel framework built upon a large-scale video diffusion Transformer. iTryOn pioneers a multi-level interaction injection mechanism to guide the generation of complex dynamics. At the spatial level, we introduce a garment-agnostic 3D hand prior to provide fine-grained guidance for precise hand-garment contact, effectively resolving spatial ambiguity. At the semantic level, iTryOn leverages global captions for overall context and time-stamped action captions for localized interactions, synchronized via our novel Action-aware Rotational Position Embedding (A-RoPE). Extensive experiments demonstrate that iTryOn not only achieves state-of-the-art performance on traditional VVT benchmarks but also establishes a commanding lead in the new interactive setting, marking a significant step towards more dynamic and controllable virtual try-on experiences.

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05.
arXiv (CS.LG) 2026-06-12

Individual Control Barrier Functions-Guided Diffusion Model for Safe Offline Multi-Agent Reinforcement Learning

Authors:
Qingyun Guo↗Junyi Shi↗Jianuo Huang↗Tianyu Shi↗

arXiv:2606.12640v1 Announce Type: new Abstract: Offline reinforcement learning allows control policies to be learned directly from data without online interaction, making it suitable for safety-critical tasks. Recent studies have applied diffusion models to offline reinforcement learning to leverage their strong capacity for modeling complex data distributions. However, existing approaches primarily focus on single-agent settings, leaving the safety challenges in multi-agent environments largely unexplored. In this work, we propose a safe offline multi-agent reinforcement learning algorithm that embeds neural individual control barrier functions into the diffusion model to enhance safety during trajectory generation, with control policies recovered through inverse dynamics. We evaluate our algorithm across diverse benchmarks, demonstrating substantial safety improvements while maintaining competitive rewards.

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06.
arXiv (CS.LG) 2026-06-12

EPM-JEPA: Operator-Side Experience Modulation in JEPA-Family World Models

Authors:
Vedant Pandya↗

arXiv:2606.12979v1 Announce Type: new Abstract: JEPA-family world models use a static predictor whose weights do not adapt when test-time dynamics diverge from training. We compare two mechanisms for incorporating accumulated experience into a JEPA predictor under distribution shift: operand-side injection, where a compressed experience representation is added as a residual to the predictor's hidden state (EI-JEPA), and operator-side modulation, where the same representation generates low-rank weight deltas via LoRA applied to the predictor's weights (EPM-JEPA). On a pre-registered comparison (Moving MNIST, gravity shift), EPM-JEPA (D_shift^{n=50} = 0.7848 +/- 0.0078, three seeds) differs from EI-JEPA (0.8238) by delta = 4.74% - Outcome C: a null result - by our stated criterion, a valid outcome. As a secondary, non-pre-registered observation, EPM-JEPA improves 1.90% over a no-memory baseline (0.8000), consistently across seeds, while EI-JEPA underperforms the baseline, indicating the benefit is specific to weight-level modulation. Our primary contribution is a mechanism analysis: the D_shift^{n=50} trajectory reflects three independent dynamical processes - buffer cycling, EMA target drift, and an intrinsic LoRA settling transient of +0.021 - rather than convergence to equilibrium. These findings motivate PEM-JEPA, a physics-grounded successor addressing this dynamical-peak limitation.

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07.
arXiv (math.PR) 2026-06-12

On McDiarmid's Inequality under Dependence via Approximate Tensorization of Entropy

Authors:
Valentin Roth↗

arXiv:2606.12720v1 Announce Type: new Abstract: We argue that dependent versions of McDiarmid's inequality are a useful but underutilized tool in mathematical statistics, learning theory and theoretical computer science. To make this point, we first highlight that approximate tensorization of entropy (ATE) implies McDiarmid's via the Entropy Method. Second, we derive McDiarmid's inequality for non-isotropic Gaussian random vectors $X \sim \mathcal N(\mu, \Sigma)$ through ATE with a constant of the order of the condition number of $\Sigma$. We both independently obtain this ATE through a simple application of stochastic localization and also discuss how a more general ATE for the Gibbs sampler due to Ascolani et al., 2026 generalizes McDiarmid's-like concentration to strongly log-concave and log-smooth probability measures. We then apply the resulting concentration inequalities to resolve a question on the concentration of $\operatorname{sign}(X)$ posed by Simone Bombari, investigate Erdős-Rényi graphs under dependence and prove a Dvoretzky-Kiefer-Wolfowitz-type inequality for observations from a joint measure fulfilling ATE and continuous marginal CDFs. For the class of strongly log-concave and log-smooth measures, this result improves upon a prior Dvoretzky-Kiefer-Wolfowitz-type inequality for non-i.i.d. observations due to Bobkov and Götze, 2010, by establishing the expected $1/\sqrt{n}$-rate of convergence under weak dependence instead of $n^{-1/3}$.

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08.
arXiv (CS.LG) 2026-06-12

ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization

Authors:
Aitor S\'anchez-Ferrera↗Elisabeth Wetzer↗Kristoffer Wickstr{\o}m↗Michael Kampffmeyer↗Robert Jenssen↗

arXiv:2606.13277v1 Announce Type: cross Abstract: Recent advances in time series anomaly detection (TSAD) have highlighted the effectiveness of self-supervised classification-based approaches. These methods apply transformations to normal training samples, training a classifier to recognize transformation-specific patterns that help identify anomalies through increased classification errors. Despite their strong performance, a significant challenge is their lack of explainability, as they provide limited insight into the characteristics of flagged anomalies. To address this limitation, we propose ProtoX-AD, a prototype-based self-explainable framework for self-supervised TSAD. ProtoX-AD learns transformation-aware latent representations alongside interpretable prototypes, enabling both accurate anomaly detection and the identification of distinct anomalous profiles through prototype-based explanations. Additionally, it allows for systematic analysis of how transformation design impacts detection performance and explainability. Experimental results on synthetic and real-world datasets demonstrate that ProtoX-AD achieves detection performance comparable to its black-box counterparts while offering more consistent and semantically meaningful explanations than existing explainable baselines. Our code is publicly available at https://github.com/Aitorzan3/ProtoX-AD.

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

Rel-Zero: Harnessing Patch-Pair Invariance for Robust Zero-Watermarking Against AI Editing

Authors:
Pengzhen Chen↗Yanwei Liu↗Xiaoyan Gu↗Xiaojun Chen↗Wu Liu↗Weiping Wang↗

Recent advancements in diffusion-based image editing pose a significant threat to the authenticity of digital visual content. Traditional embedding-based watermarking methods often introduce perceptible perturbations to maintain robustness, inevitably compromising visual fidelity. Meanwhile, existing zero-watermarking approaches, typically relying on global image features, struggle to withstand sophisticated manipulations. In this work, we uncover a key observation: while individual image patches undergo substantial alterations during AI-based editing, the relational distance between patch pairs remains relatively invariant. Leveraging this property, we propose Relational Zero-Watermarking (Rel-Zero), a novel framework that requires no modification to the original image but derives a unique zero-watermark from these editing-invariant patch relations. By grounding the watermark in intrinsic structural consistency rather than absolute appearance, Rel-Zero provides a non-invasive yet resilient mechanism for content authentication. Extensive experiments demonstrate that Rel-Zero achieves substantially improved robustness across diverse editing models and manipulations compared to prior zero-watermarking approaches.

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10.
arXiv (CS.LG) 2026-06-16

A spectral audit framework reveals task-dependent aperiodic reliance across EEG and ECG deep learning

Authors:
Jasmeet Singh Bindra↗Siddharth Panwar↗

arXiv:2606.08583v2 Announce Type: replace Abstract: Deep learning on physiological time series is interpreted through domain-specific features – oscillatory rhythms in EEG, morphological complexes in ECG – yet these signals sit atop a broadband aperiodic 1/f-like envelope that covaries with arousal, age, and pathology. We introduce a spectral audit framework combining aperiodic/periodic decomposition, phase-preserving Fourier interventions, sham controls, and simulation validation. Aperiodic reliance was task-dependent and architecture-general: across six neural architectures, flattening drops exceeded 0.42 balanced-accuracy points for sleep-wake classification, reached 0.07-0.13 for clinical abnormality detection, and remained minimal for motor imagery. Six of seven EEG foundation models showed FDR-significant aperiodic reliance on clinical EEG; age/sex and recording-era controls reduced but did not eliminate the effect. Applying the audit to PTB-XL ECG revealed neural drops of 0.32–0.36 persisting after demographic matching, confirming this confound class extends beyond EEG. Aperiodic controls should become standard for interpretable physiological time-series deep learning.

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

Initiation of Superradiance from Different Collective Spin States

Authors:
Adnan Alabbar↗Zhenghao Zhang↗Girish S. Agarwal↗

arXiv:2606.14949v1 Announce Type: new Abstract: Superradiance is an extensive cooperative spontaneous emission phenomenon. Some atomic collective spin states exhibit it. However, distinct initial states differ in their decay dynamics. Dicke states with different numbers of excitations have their peak emission intensity shifted in time depending on the number of excitations. Emission intensity in atomic coherent states depends on their polarization. Some specific states undergo a squeezing controlled crossover, making the emission character dependent on the amount of squeezing in the state. We present detailed results on the superradiant dynamics of a representative selection of Dicke states. For large N, we are able to predict fairly accurately the pulse profile in each case using the mean field approximation, an approach based on the Fokker Planck Equation. We also present results on the intensity correlation function of the emission.

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12.
arXiv (CS.CL) 2026-06-16

Replay What Matters: Off-Policy Replay for Efficient LLM Reinforcement Unlearning

Authors:
Zirui Pang↗Chenlong Zhang↗Haosheng Tan↗Zhuoran Jin↗Jiaheng Wei↗Zixin Zhong↗

LLM unlearning has emerged as a cost-effective alternative to full retraining for removing hazardous knowledge from pretrained models while preserving general utility. Recent RL-based methods such as RULE reformulate unlearning as learning a refusal behavior, but their on-policy optimization repeatedly samples from the same forget and retain/boundary prompts throughout training. We identify a critical inefficiency in this process: easy cases quickly converge and provide little useful gradient signal, while hard cases near the forget/retain boundary continue to produce low-reward rollouts that are discarded after a single use. To address this issue, we propose ReRULE, an off-policy replay enhancement for reinforcement unlearning. ReRULE stores low-reward hard-case rollout groups in a replay buffer during early GRPO training and reuses them in later stages through importance-sampled off-policy updates, redirecting computation toward boundary cases that still require learning. Theoretically, we show that ReRULE yields a tighter hard-case convergence bound than pure on-policy RULE. Empirically, ReRULE improves MUSE-Books Retain Quality from 46.3 to 56.2 while adding only 5–11% training time across benchmarks. Its limited improvement on the simpler TOFU setting further supports the intended conditional behavior: replay is most beneficial when the hard/easy disparity is pronounced.

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

Information Gap and Feasibility-Aware Inference in Binomial Logistic Mixtures

Authors:
Yuta Hayashida↗Shonosuke Sugasawa↗

arXiv:2606.15665v1 Announce Type: cross Abstract: This paper studies the information gap between mixture detection and label recovery in binomial logistic mixtures. Standard likelihood-based criteria such as the Bayesian information criterion (BIC) can detect the presence of two components, but this does not guarantee that the corresponding labels are recoverable. We show that this gap is intrinsic to binomial logistic mixtures with a fixed number of trials: observed-data evidence for mixture structure and per-observation information for label recovery have different local orders in the component separation, and only the former accumulates with the sample size. As a result, there exists a detectable-but-unrecoverable regime in which BIC selects two components while the posterior labels remain essentially uninformative. To address this issue, we propose two feasibility-aware inference procedures: a recoverability-aware BIC with a posterior-entropy penalty and an entropy-regularized estimator that mitigates the tendency of the maximum likelihood estimator to produce overly separated components and overly concentrated posterior responsibilities. Numerical experiments confirm the predicted gap and demonstrate that the proposed methods avoid misleading component selections and improve the calibration of posterior label probabilities.

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

AgentFinVQA: A Deployable Multi-Agent Pipeline for Auditable Financial Chart QA

Authors:
Aravind Narayanan↗Shaina Raza↗

Financial chart question answering in regulated settings demands more than accuracy: practitioners must know which answers to trust before acting on them, and many institutions cannot send client data to external model providers. Yet existing chart-QA agents are accuracy-focused and opaque, and most assume proprietary API access; to our knowledge, none combines auditability with on-premise deployability without significant accuracy compromise. We present AgentFinVQA, a multi-agent pipeline that decomposes each query into planning, OCR, legend grounding, visual inspection, and verification, recording every step in a traceable Model Evaluation Packet (MEP) per sample. On FinMME, AgentFinVQA improves $+7.68$ pp over a primary-backbone matched zero-shot baseline with a proprietary backbone (Gemini-3 Flash; 71.24% vs. 63.56%, McNemar $p \approx 1.1 \times 10^{-16}$), and $+4.84$ pp with open-weights Qwen3.6-27B-FP8 served locally. The verifier's verdict also serves as a useful confidence signal (68.2% vs. 55.6% exact accuracy on confirmed vs. revised answers), enabling human-in-the-loop review routing. Error analysis shows that question misunderstanding, legend confusion and extraction error account for nearly two-thirds of failures and are the categories least detected by the verifier, identifying clear directions for future work. Together these results show that auditable, on-premise financial chart QA is practical and that the open-weights system keeps most of the accuracy gains while enabling full data residency. We release our code to support reproducible evaluation.

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15.
bioRxiv (Bioinfo) 2026-06-14

TopoMIL: Topology Improves Multiple Instance Learning in Diagnostic Microscopic Images

Authors:
Kazeminia↗Dasdelen↗M. F↗Rieck↗Marr↗

Microscopic images of cells and tissues are central to disease diagnosis. In computational pathology, multiple instance learning (MIL) has emerged as a key paradigm for analyzing numerous images within a single patient sample. While the representative distribution of cells in a sample is important for diagnosis, existing MIL frameworks largely overlook it. We introduce TopoMIL, a framework that extracts the representative topological structure of the sample and integrates it into the MIL classifier. Three topological representations are assessed, each with distinct advantages and computational costs. We evaluate TopoMIL on four histopathology and cytomorphology datasets, each presenting unique challenges. Integrating the sample's topological information into MIL enhances classification across average, max, attention-based, and transformer pooling, yielding AUCROC gains of 3.3%, 4.2%, 5.9%, and 0.5%, respectively, with moderate computational cost. Our work underscores the potential of TopoMIL as a scalable extension to existing morphology-based models in computational pathology.

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16.
arXiv (CS.AI) 2026-06-15

The Accountability Paradox: How Platform API Restrictions Undermine AI Transparency Mandates

Authors:
Florian A. D. Burnat↗Brittany I. Davidson↗

arXiv:2505.11577v5 Announce Type: replace-cross Abstract: Recent application programming interface (API) restrictions on major social media platforms challenge compliance with the EU Digital Services Act [20], which mandates data access for algorithmic transparency. We develop a structured audit framework to assess the growing misalignment between regulatory requirements and platform implementations. Our comparative analysis of X/Twitter, Reddit, TikTok, and Meta identifies critical ``audit blind-spots'' where platform content moderation and algorithmic amplification remain inaccessible to independent verification. Our findings reveal an ``accountability paradox'': as platforms increasingly rely on AI systems, they simultaneously restrict the capacity for independent oversight. We propose targeted policy interventions aligned with the AI Risk Management Framework of the National Institute of Standards and Technology [80], emphasizing federated access models and enhanced regulatory enforcement.

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17.
arXiv (quant-ph) 2026-06-16

Adiabatically-induced Kawaguchi geometry and jerk in quantum-classical systems

Authors:
Athanasios Chatzistavrakidis↗Larisa Jonke↗Ryan Requist↗

arXiv:2606.16037v1 Announce Type: new Abstract: Adiabatically eliminating the quantum degrees of freedom in a mixed quantum-classical system produces an effective force in the classical equation of motion. The elimination can be made to any order in the adiabatic parameter, generating a series of higher order forces. By applying a sequence of near-identity unitary transformations to the quantum state, we derive a hierarchy of increasingly accurate effective actions for the classical variables. The third order Euler-Lagrange equation is non-Newtonian as the force depends on the jerk, the third order time derivative of position. We find that the third order terms induce a special kind of Kawaguchi geometry on the space of classical variables. This geometry is characterized by an almost symplectic structure and a differential line element that depends on the acceleration in addition to the velocity. Our results can be used to efficiently capture higher order nonadiabatic effects in molecular dynamics simulations.

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18.
arXiv (CS.CV) 2026-06-16

An Extensive Benchmark for Single-round and Multi-round Instruction-based Image Editing

Authors:
Yiwei Ma↗Ke Ye↗Weihuang Lin↗Jiayi Ji↗Xiaoshuai Sun↗Tat-Seng Chua↗Rongrong Ji↗

In recent years, there have been notable advancements in the area of instruction-based image editing (IIE), which focuses on the automatic alteration of input images using a model. Nevertheless, assessing the effectiveness of these editing models poses a considerable challenge due to the intricate nature of instructions and the wide variety of edits. To tackle this problem, one urgent task in this domain is the development of a robust evaluation framework that can precisely gauge the quality of editing outcomes and offer valuable benchmarks to guide future improvements. To address this challenge, we present a comprehensive evaluation benchmark named I2EBench2.0, designed for single-round and multi-round assessment of IIE models. I2EBench2.0 has four key features: 1) Evaluation Across Single and Multi-rounds: I2EBench2.0 simultaneously evaluates both single-round and multi-round instruction-based edits, assessing the precision and consistency of the edits. 2) Extensive Evaluation Criteria: I2EBench2.0 encompasses a broad range of criteria, evaluating both high-level and low-level aspects of each IIE model. Specifically, it incorporates 16 dimensions for single-round evaluations and 7 for multi-round evaluations. 3) Alignment with Human Judgment: To ensure our benchmark aligns with human evaluation, we conducted a comprehensive user study for each criterion. 4) Research-driven Insights: By analyzing the strengths and weaknesses of current IIE models across all 16 single-round and 7 multi-round dimensions, we provide critical insights aimed at directing future research in this area. We tested eight recently developed IIE models using I2EBench2.0 and derived academic insights through meticulous comparison and analysis. The related code, dataset, and images generated by all IIE models are available on GitHub: https://github.com/cocoshe/I2EBench.

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19.
arXiv (CS.LG) 2026-06-16

Branching Flows: Discrete, Continuous, and Manifold Flow Matching with Splits and Deletions

Authors:
Lukas Billera↗Hedwig Nora Nordlinder↗Jack Collier Ryder↗Anton Oresten↗Aron St{\aa}lmarck↗Theodor Mosetti Bj\"ork↗Ben Murrell↗

arXiv:2511.09465v4 Announce Type: replace-cross Abstract: Diffusion and flow matching approaches to generative modeling have shown promise in domains where the state space is continuous, such as image generation or protein folding & design, and discrete, exemplified by diffusion large language models. They offer a natural fit when the number of elements in a state is fixed in advance (e.g. images), but require ad hoc solutions when, for example, the length of a response from a large language model, or the number of amino acids in a protein chain is not known a priori. Here we propose Branching Flows, a generative modeling framework that, like diffusion and flow matching approaches, transports a simple distribution to the data distribution. But in Branching Flows, the elements in the state evolve over a forest of binary trees, branching and dying stochastically with rates that are learned by the model. This allows the model to control, during generation, the number of elements in the sequence. We also show that Branching Flows can compose with any flow matching base process on discrete sets, continuous Euclidean spaces, smooth manifolds, and `multimodal' product spaces that mix these components. We demonstrate this in three domains: small molecule generation (multimodal), antibody sequence generation (discrete), and protein backbone generation (multimodal), and show that Branching Flows is a capable distribution learner with a stable learning objective, and that it enables new capabilities.

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20.
arXiv (CS.CL) 2026-06-12

PRISM: Prosody-Integrated Multi-Agent Reasoning Framework for Empathetic Spoken Dialogue

Authors:
Wen Zhang↗Xiaocui Yang↗Zhuoyue Gao↗Shi Feng↗Daling Wang↗Yifei Zhang↗

Empathetic spoken dialogue systems require not only semantically appropriate responses but also emotionally aligned prosodic expression. However, cascade pipelines often discard acoustic cues during speech-to-text conversion, while end-to-end speech models lack interpretable control over emotion and knowledge integration. To address these challenges, we propose PRISM, a multi-agent framework for empathetic spoken dialogue that decouples speech perception, response generation, and speech synthesis into coordinated components. PRISM introduces a prosody-to-language translation mechanism to stabilize large language model reasoning and enables on-demand invocation of external knowledge tools for empathetic dialogue generation. Experimental results demonstrate that PRISM achieves consistent improvements in empathy, prosodic appropriateness, and text response generation quality across objective and subjective metrics. Our code is available at: https://github.com/Bxzfrm/PRISM.

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21.
arXiv (CS.LG) 2026-06-19

On the Variance of Temporal Difference Learning and its Reduction Using Control Variates

Authors:
Hsiao-Ru Pan↗Bernhard Sch\"olkopf↗

arXiv:2606.20357v1 Announce Type: new Abstract: We analyze the variance of temporal difference (TD) learning using the phased setting with tabular representation, and show that one of the mechanisms behind its ability to reduce variance is by effectively aggregating over a larger number of independent trajectories. Based on this insight, we demonstrate that (1) the variance of TD is asymptotically bounded from above by Monte Carlo (MC) estimators, and (2) shorter horizon updates incurs less variance for a fixed number of samples. Beyond TD, we show that Direct Advantage Estimation (DAE), a method for estimating the advantage function, can be seen as a type of regression-adjusted control variate, which achieves a tighter bound on the variance compared to TD in the large-sample limit. Finally, we numerically illustrate the behaviors of these estimators with carefully designed environments.

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22.
arXiv (CS.LG) 2026-06-18

Unlocking air traffic flow prediction through microscopic aircraft-state modeling

Authors:
Bin Wang↗Anqi Liu↗Jiangtao Zhao↗Hina Birahmani↗Yanyong Huang↗Peilan He↗Guiyuan Jiang↗Feng Hong↗Yanwei Yu↗Yuanyuan Hou↗Tianrui Li↗

arXiv:2605.10083v2 Announce Type: replace Abstract: Short-term air traffic flow prediction in terminal airspace is essential for proactive air traffic management. Existing approaches predominantly model traffic flow as aggregated time series. However, traffic dynamics are governed by aircraft states and their interactions in continuous airspace. Such aggregation obscures fine-grained information, including aircraft kinematics, boundary interactions, and control intent. Here we present AeroSense, a state-to-flow modeling paradigm that predicts future traffic flow directly from instantaneous airspace situations represented as dynamic sets of aircraft states derived from ADS-B trajectories. By establishing an end-to-end mapping from microscopic aircraft states to future regional traffic flow, AeroSense preserves aircraft-level dynamics while naturally accommodating varying traffic density without relying on historical look-back windows. Experiments on a large-scale real-world dataset show that AeroSense exhibits admirable predictive accuracy and robustness over aggregation-based forecasting approaches, particularly during high-density traffic periods. These findings suggest that aircraft-state situation modeling provides a promising alternative to conventional time-series forecasting in air traffic flow management.

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23.
medRxiv (Medicine) 2026-06-15

Reaching out-of-school girls with HPV vaccination: A qualitative evaluation in six low- and middle-income countries using the RE-AIM framework

Authors:
Zhang↗Rosser↗Wysong↗M. D↗Surkan↗P. J↗Rosen↗J. G↗Limaye↗R. J↗Park↗

Background Infection with human papillomavirus (HPV), the primary cause of cervical cancer, disproportionately affects women in low- and middle-income countries (LMICs). While school-based vaccination of adolescent girls against HPV is highly effective, this strategy systematically excludes out-of-school (OOS) girls. Using the RE-AIM framework, we explored strategies to reach OOS girls with HPV vaccination across six African and Asian LMICs. Methods We conducted semi-structured key informant interviews with 32 vaccination program stakeholders from Cambodia, Cameroon, Kenya, Malawi, Mozambique, and Uganda between May and September 2024. Interviews explored countries implementation successes, challenges, and strategies to reach OOS girls with HPV vaccination and sustainability considerations. Data were analyzed using a hybrid team-based thematic analysis approach guided by the RE-AIM framework. Results Community outreach-based strategies, typically integrated into routine immunization outreach, were identified as the most effective approach to reach OOS girls with HPV vaccination. Targeted strategies, such as locating outreach clinics in community venues frequented by OOS girls (e.g., churches, markets) enhanced implementation. Perceived effectiveness of these strategies varied across participants, and formal assessment of effectiveness was constrained by the absence of disaggregated vaccination coverage data by school enrollment status. Some subpopulations of OOS girls (i.e., girls in nomadic or migrant communities, urban OOS girls) were not readily reached through standard outreach approaches, prompting implementation of adapted and tailored strategies for these subpopulations. Costs associated with conducting outreach in harder-to-reach areas were major barriers to reaching OOS girls, presenting challenges to the sustainability and cost-effectiveness of these approaches. Conclusions Routine community outreach platforms were widely perceived as most effective for reaching OOS girls. Strengthening disaggregated monitoring systems, adapting outreach for harder-to-reach subpopulations of OOS girls, and financing delivery models for tailored outreach strategies will be critical to improving equitable HPV vaccine coverage among OOS girls.

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

No-Free-Fairness: Fundamental Limits and Trade-offs in Learning Systems

Authors:
Khoat Than↗

arXiv:2606.17810v1 Announce Type: cross Abstract: In this paper, we establish a set of theoretical impossibility results, termed the No-Free-Fairness theorems, that identify three fundamental sources of disparity in learning systems. First, we show that when a task exhibits irreducible cost on a subgroup, any decision rule must trade off overall performance with disparity, yielding an inherent fairness–cost frontier. Second, we prove that even in ideal, noise-free settings where a perfectly fair and accurate solution exists, finite-sample learning alone induces nontrivial subgroup disparity, ruling out distribution-free fairness guarantees. More seriously, enforcing strict relative fairness creates a statistical bottleneck: achieving low cost may require exponentially many samples. Third, we show that limitations of the model class can independently induce disparity: if the model cannot represent accurate solutions for a subgroup, fairness remains unattainable regardless of data or training procedure. Overall, these results demonstrate that unfairness is not solely a consequence of biased data or suboptimal optimization, but arises from the intrinsic structure of decision problems, the constraints of finite data, and the expressivity of models. Our framework applies broadly beyond standard supervised learning, and suggests that achieving fairness requires explicit trade-offs and should be treated as a core design consideration.

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25.
medRxiv (Medicine) 2026-06-11

Corticospinal tract risk modifies motor recovery after minimally invasive surgery for intracerebral hemorrhage: a secondary analysis of MISTIE-III

Authors:
Murray↗O. N↗Jenkins↗Walborn↗Patel↗H. C↗Harston↗G. W↗Cootes↗T. F↗Klijn↗C. J. M↗…

Objective: Outcome after surgical hematoma evacuation for intracerebral hemorrhage (ICH) depends on hematoma location. As corticospinal tract (CST) integrity affects motor recovery after stroke, we hypothesized that CST integrity drives heterogeneity in surgical outcomes and investigated this in a secondary analysis of MISTIE-III participants. Methods: Risk of CST injury was categorized into four levels, based on the interaction between the CST, the hematoma, and perihematomal edema (PHE) on automatically segmented stability CT: no risk, PHE infiltration, hematoma infiltration, and complete interruption of the CST. Associations with outcome were tested using multivariable linear regression for motor National Institutes of Health Stroke Scale (NIHSS) at day 180 and ordinal regression for modified Rankin Scale (mRS) at day 365, introducing an interaction term between CST risk and treatment group. Results: Day 180 motor NIHSS was significantly lower for 'no risk' ({beta}:-3.77, [95% confidence interval [CI]: -5.8 to -1.70], p=0.0003) and 'PHE infiltration' ({beta}:-2.3, [95%CI: -3.5 to -1.1]; p=0.0002) vs. 'complete interruption'. Surgery was associated with lower Day 180 motor NIHSS in participants with hematoma infiltration ({beta}:-2.07, [95%CI: -3.8 to -0.4], p=0.016). Compared to complete interruption, 'no risk' (adjusted odds ratio [aOR]:0.27, [95%CI: 0.10 to 0.74], p=0.01) and 'PHE infiltration' (aOR:0.41, [95%CI: 0.23 to 0.74]; p=0.003) were associated with lower odds of unfavorable day 365 mRS. Surgery was associated with lower mRS in participants with no risk (aOR:0.23, [95%CI: 0.05 to 0.97, p=0.045). Interpretation: Increasing CST risk is associated with worse motor recovery (day 180) and disability (day 365). CST risk modifies the effect of the MISTIE-III procedure on motor recovery and disability.

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