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02.
medRxiv (Medicine) 2026-06-11

Long-term exposure to PM2.5 components and lipid profiles in WTC Health Program general responders

作者:
Krasnov↗knobel↗Hsiao-Hsien Hsu↗Teitelbaum↗Mclaughlin↗Just↗A. C↗Kloog↗Yitshak Sade↗

Fine particulate matter (PM2.5) was found to be associated with elevated blood lipids, but fewer studies have examined the associations with specific constituents of PM2.5. We studied the associations between exposure to annual PM2.5 and its 14 constituents, and repeated blood lipid measurements among general responders enrolled in the World Trade Center Health Program between 2003 and 2019 (n = 44,876). We used generalized additive mixed effect models to investigate the single-pollutant associations with repeated measures of blood total cholesterol (TC), high and low-density lipoprotein (HDL-C and LDL-C) levels. We then used linear generalized weighted quantile sum regression with a random intercept for participant ID to account for the clustering of repeated measures and evaluate the combined associations with the component mixture. A decile increase in the mixture of 14 PM2.5 chemical components was associated with 0.375 mg/dL increase in TC levels (95% confidence Interval (CI): 0.174-0.577) and 0.302 mg/dL increase in LDL-C (95% CI: 0.063, 0.540). Lead, organic carbon, and iron were major drivers of both associations. Component-specific models also show higher TC and LDL levels associated with interquartile range increases in organic carbon (0.472, 95% CI [0.027, 0.918] and 0.648 95% CI [0.136, 1.160]) and iron exposure (1.081, 95% CI [0.630, 1.532] and 0.748, 95% CI [0.318, 1.178]). In conclusion, we found PM2.5 exposure to be associated with elevated lipid levels. The associations differed by PM2.5 composition, highlighting organic carbon, lead, and iron and major drivers. These findings are highly significant for a population exposed to extreme air pollution event and susceptible to lipid alterations that might trigger cardiovascular events.

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

Benchmarking Instance-Dependent Label Noise with Controlled Corruptions

作者:
Shadman Islam↗Agustinus Kristiadi↗Mostafa Milani↗

arXiv:2606.14965v1 Announce Type: new Abstract: Synthetic instance-dependent label noise (IDN) benchmarks are widely used to evaluate noisy-label learning methods, yet existing approaches typically generate noise through imperfect annotators or classifier raters, leaving the source of ambiguity implicit. We introduce CILN, a benchmark generation framework that creates IDN through controlled input corruptions. A diverse voter pool labels corrupted instances, producing benchmark datasets in which both the source and severity of ambiguity are explicit and controllable. Using CIFAR10, MNIST, and Adult, we construct 90 benchmark settings spanning multiple corruption families and severity levels. Our experiments show that the resulting benchmarks exhibit genuine instance-dependent noise, provide diverse confusion structures, and, on CIFAR-10, can produce label distributions that are closer to human uncertainty than an existing synthetic IDN benchmark. We further demonstrate that corruption-mediated IDN can expose failure modes of popular noisy-label learning methods, including Co-Teaching and DivideMix, that are not observed under comparable levels of rater-fallibility noise. These findings suggest that noise structure, not only noise rate, plays an important role in benchmark difficulty and algorithm behavior. By making ambiguity generation explicit and controllable, CILN provides a complementary benchmarking framework for studying noisy-label learning under diverse sources of instance difficulty.

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

A Stationary (and Therefore Compatible) Representation is All You Need

作者:
Niccol\`o Biondi↗Federico Pernici↗Simone Ricci↗Alberto Del Bimbo↗

arXiv:2606.12488v1 Announce Type: new Abstract: Learning compatible representations aims to learn feature representations that can be used interchangeably over time whenever a model undergoes updates. In this paper, we demonstrate that stationary representations learned by d-Simplex fixed classifiers imply compatibility as in its formal definition. This result establishes a foundation for future works and can be directly exploited in practical learning scenarios. We address the challenge of learning compatibility using $d$-Simplex fixed classifiers when the model is sequentially fine-tuned. Learning according to a d-Simplex fixed classifier with the cross-entropy loss aligns feature distributions at the first-order statistics. Consequently, it may not fully capture higher-order dependencies in the representation between model updates. To address this issue, we demonstrate that training the model using a $d$-Simplex fixed classifier through a convex combination of the cross-entropy loss and a contrastive loss not only captures higher-order dependencies, but is also equivalent to learning with the cross-entropy under the compatibility constraints. We confirm our findings with extensive experiments also considering a new scenario where a pre-trained model is sequentially fine-tuned and occasionally replaced with an improved model. We show that stationary representations enable uninterrupted retrieval services (without reprocessing gallery images) while improving performance during model updates and replacements, achieving state-of-the-art. Code at https://github.com/miccunifi/iamcl2r.

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05.
arXiv (CS.CL) 2026-06-15

Dialogue SWE-Bench: A Benchmark for Dialogue-Driven Coding Agents

作者:
Brendan King↗Jeffrey Flanigan↗

AI coding agents have rapidly transformed software engineering, powering widely used interactive coding assistants. Despite their interactive real-world use, existing benchmarks evaluate them as fully-autonomous systems. In this work, we introduce Dialogue SWE-Bench, an automatic benchmark dataset for evaluating the ability of coding agents to resolve real-world software engineering problems through dialogue with a user. We design a novel, persona-grounded user simulator to support our task evaluation, and augment our task evaluation with automatic evaluations of dialogue quality. We also propose a new schema-guided agent, aimed at improving the dialogue capabilities of off-the-shelf coding agents, which improves over strong baselines by 3-14%. Our results indicate that better coding models do not always correspond to better dialogue models, suggesting that dialogue capability is a distinct and currently understudied dimension of coding agent performance.

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

Reinforcement Learning for Accelerated Aerodynamic Shape Optimisation

作者:
Florian Sobieczky↗Alfredo Lopez↗Erika Dudkin↗Christopher Lackner↗Matthias Hochsteger↗Bernhard Scheichl↗Helmut Sobieczky↗

arXiv:2507.17786v2 Announce Type: replace Abstract: We introduce a reinforcement learning (RL) based adaptive optimization algorithm for aerodynamic shape optimization focused on dimensionality reduction. The form in which RL is applied here is that of a surrogate-based, actor-critic policy evaluation MCMC approach allowing for temporal 'freezing' of some of the parameters to be optimized. The goals are to minimize computational effort, and to use the observed optimization results for interpretation of the discovered extrema in terms of their role in achieving the desired flow-field. By a sequence of local optimized parameter changes around intermediate CFD simulations acting as ground truth, it is possible to speed up the global optimization if (a) the local neighbourhoods of the parameters in which the changed parameters must reside are sufficiently large to compete with the grid-sized steps and its large number of simulations, and (b) the estimates of the rewards and costs on these neighbourhoods necessary for a good step-wise parameter adaption are sufficiently accurate. We give an example of a simple fluid-dynamical problem on which the method allows interpretation in the sense of a feature importance scoring.

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07.
arXiv (CS.AI) 2026-06-17

The Stanford EDGAR Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data

作者:
Nick Bettencourt↗Xiaowei Ding↗Kay Giesecke↗

arXiv:2606.18192v1 Announce Type: new Abstract: As high-quality public web corpora become increasingly exhausted, clean long-context documents have become a scarce and expensive source of training data for large language models (LLMs). Existing long-context corpora are often proprietary and costly to acquire, synthetically generated, or concentrated in narrow domains such as programming. We introduce the Stanford EDGAR Filings Dataset (SEFD), an open reconstruction of SEC filings into layout-faithful MultiMarkdown for financial language modeling and evaluation. SEFD makes audited financial statements, risk disclosures, ownership reports, accounting notes, and market-moving event filings usable as long-context pretraining data and as a basis for financial reasoning, forecasting, compliance, and document understanding. The resulting corpus is token-efficient, model-ready, and has less than 0.1% overlap with Common Crawl-derived corpora. We release SEFD-v1, a 152B-token initial public snapshot, and provide corpus-level analyses of a larger 18.5M-filing archive estimated at 550B tokens. We further introduce two SEFD-derived benchmarks: EDGAR-Forecast, which evaluates filing-grounded numerical forecasting after model knowledge cutoffs, and EDGAR-OCR, which evaluates transcription of complex financial tables.

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

MedSynth: Realistic, Synthetic Medical Dialogue-Note Pairs

作者:
Ahmad Rezaie Mianroodi↗Amirali Rezaie↗Niko Grisel Todorov↗Nadine A. Friedrich↗Maria P Mogollon↗Alexander Hernandez-Tirado↗Guillermo Lopez Garcia↗Cyril Rakovski↗Frank Rudzicz↗

Physicians spend significant time documenting clinical encounters, a burden that contributes to professional burnout. To address this, robust automation tools for medical documentation are crucial. We introduce MedSynth – a novel dataset of synthetic medical dialogues and notes designed to advance the Dialogue-to-Note (Dial-2-Note) and Note-to-Dialogue (Note-2-Dial) tasks. Informed by an extensive analysis of disease distributions, this dataset includes over 10,000 dialogue-note pairs covering over 2000 ICD-10 codes. We demonstrate that our dataset markedly enhances the performance of models in generating medical notes from dialogues, and dialogues from medical notes. The dataset provides a valuable resource in a field where open-access, privacy-compliant, and diverse training data are scarce. Code is available at https://github.com/ahmadrezarm/MedSynth/tree/main and the dataset is available at https://huggingface.co/datasets/Ahmad0067/MedSynth.

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09.
arXiv (CS.LG) 2026-06-18

Wasserstein Policy Learning for Distributional Outcomes

作者:
Yiyan Huang↗Cheuk Hang Leung↗Qi Wu↗Zhiheng Zhang↗

arXiv:2606.19117v1 Announce Type: cross Abstract: Offline policy learning has received growing attention in causal inference. The primary objective is to learn a policy (individualized treatment rule) as a mapping from covariates to treatment that maximizes the empirical welfare defined as the mean of scalar-valued potential outcomes. In this paper, we study offline policy learning with distribution-valued outcomes, where each potential outcome is a probability measure on $\mathbb{R}$ and the reward is defined through a utility functional applied to the Wasserstein barycenter of induced outcome distributions. We establish statistical guarantees for the policy learning framework based on both Inverse Probability Weighting (IPW) and Doubly Robust (DR) estimators. By handling the challenging uniform deviation over the product of the combinatorial policy class and the infinite-dimensional quantile domain, we prove that the finite-sample regret has leading dependence $\widetilde{\mathcal{O}}(\sqrt{\mathrm{N-dim}(\Pi)/N})$. In the one-dimensional Wasserstein setting and under the stated regularity conditions, the leading regret rate is still governed by the policy-class complexity. Moreover, we provide a minimax lower bound establishing the sharpness of the leading dependence on $N$ and $\mathrm{N-dim}(\Pi)$.

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10.
arXiv (CS.LG) 2026-06-17

Public transit gains and spatially uneven travel demand changes after NYC congestion pricing

作者:
Donghang Li↗Dingyi Zhuang↗Yunlin Li↗Chenan Shen↗Nina Cao↗Yunhan Zheng↗Shenhao Wang↗Jinhua Zhao↗

arXiv:2606.17530v1 Announce Type: cross Abstract: New York City implemented the nation's first cordon-based congestion pricing program in January 2025, providing an opportunity to evaluate how system-wide urban mobility responds to large-scale pricing interventions. Because such policies generate spillovers across modes and locations, credible control groups are difficult to construct. We address this challenge using time series foundation models to generate probabilistic counterfactual demand forecasts with calibrated uncertainty. Applying this framework to bus, subway, and aggregate trip volume data, we find that post-policy bus and subway ridership increased significantly relative to expected no-policy demand, while overall travel demand decreased modestly. The effects are spatially heterogeneous: while reductions in overall travel demand are concentrated within the Congestion Relief Zone, transit gains extend beyond Manhattan's core. Socio-demographic analyses further reveal uneven adaptation across neighborhoods, highlighting spatial equity implications. Our framework provides a scalable approach for the uncertainty-aware evaluation of system-wide urban interventions when clean control groups are unavailable.

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11.
arXiv (CS.LG) 2026-06-19

Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET

作者:
Loukas Ilias↗Anthi-Maria Vozinaki↗Christos Ntanos↗Dimitris Askounis↗

arXiv:2606.20037v1 Announce Type: new Abstract: Alzheimer's disease (AD) is an irreversible neurodegenerative disorder and a leading cause of death worldwide. Early diagnosis plays an important part especially at the Mild Cognitive Impairment stage, where timely intervention can help slow its progression before it advances to AD. Neuroimaging data, like Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scans, can help detect brain changes early by providing structural and functional brain changes related to the disease. Yet, many multimodal models still fuse MRI and PET with static concatenation and apply identical computation to all subjects, which limits robustness to patient/site heterogeneity and can waste computation. To address these limitations, we present the first study of combining 3D convolutional feature extractors with three fusion strategies - concatenation, Gated Multimodal Unit (GMU), and gated self-attention - and a sparsely gated Mixture-of-Experts (MoE) classifier that performs input-adaptive routing, activating only the most informative experts per case. Finally, we utilize Grad-CAM to visualize disease-related regions, ensuring model interpretability. Experiments are performed across three binary classification tasks (NC vs. MCI, MCI vs. AD, and NC vs. AD). Results show that GMU achieves accuracies of 80.46 % (NC vs. MCI) and 95.47 % (NC vs. AD), while gated self-attention attains 82.08 % on MCI vs. AD. Ablations show that removing the MoE consistently degrades accuracy across all tasks. These findings underscore the value of input-adaptive, multimodal modeling for AD diagnosis by leveraging the complementary nature of MRI and PET.

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12.
arXiv (quant-ph) 2026-06-15

The Bilateral Efficiency of Ethernet: Recalibrating Metcalfe and Boggs After Fifty Years

作者:
Paul Borrill↗

arXiv:2603.19406v2 Announce Type: replace-cross Abstract: In July 1976, Metcalfe and Boggs published their foundational paper on Ethernet in Communications of the ACM. Their efficiency model – E = (P/C)/(P/C + W*T) – measures the fraction of Ether time carrying good forward packets under contention. For fifty years this model has framed how the community thinks about Ethernet performance. We argue it is silent on the question that matters for modern intra-rack interconnect: bilateral transaction efficiency – the fraction of link time that produces committed agreements between sender and receiver. Metcalfe and Boggs themselves planted the seed in their EFTP "end-dally" protocol (Section 7.2.2), and the deeper anchor is older still: Abramson's Alohanet carried positive acknowledgments at the link layer – a bilateral mechanism Metcalfe consciously removed in 1973 to obtain Ethernet's simple, ACK-free packet format. The result is a fifty-year bilateral zigzag: Aloha (bilateral) to Ethernet (unilateral) to the EFTP end-dally (bilateral) to TCP (unilateral-with-bilateral-above). We formalize bilateral efficiency, connect it to the back-to-back Shannon channel with Perfect Information Feedback, and – scoping the claim explicitly to intra-rack distances of one meter or less – describe how the Open Aethernet link recovers mutual knowledge at the link layer. The correction to Table 1 is not a different set of numbers. It is a different question.

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

How Much Can We Trust LLM Search Agents? Measuring Endorsement Vulnerability to Web Content Manipulation

作者:
Yimeng Chen↗Zhe Ren↗Firas Laakom↗Yu Li↗Dandan Guo↗J\"urgen Schmidhuber↗

Large language model (LLM)-based search agents synthesize open-web content into actionable recommendations on behalf of users, creating a risk that attacker-published pages are transformed into endorsed claims. We introduce SearchGEO, a controlled evaluation framework for measuring endorsement corruption in LLM-based web-search agents, combining a web-evidence manipulation pipeline, a five-mode attack taxonomy, and multiple output-level metrics. We evaluate 13 LLM backends on 308 cases each. Results show that vulnerability patterns vary across backends: overall attack success rate (ASR) ranges from 0.0% on Claude-Sonnet-4.6 to 31.4% on Gemini-3-Flash, the strongest attack mode differs by model family, and the same deployment scaffold could amplify or decrease ASR on different backends. An auxiliary agent-skill probe, where endorsement becomes an install command, exposes a sharp split among otherwise robust backends: Claude over-rejects while GPT over-trusts. These findings argue for treating recommendation reliability under adversarial search content as a first-class dimension of backend safety evaluation.

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14.
medRxiv (Medicine) 2026-06-15

Nocturnal Respiratory Rate and Variability Predict Long-term Mortality in Stable Outpatients with Cardiovascular Disease

作者:
Pedros-Valls↗Gupta↗K. S↗Harrington↗Yu↗J. D↗Orr↗Owens↗R. L↗Torres Barba↗King↗K. R↗…

Background: Respiratory rate (RR) predicts short-term mortality in acute care settings, yet its prognostic significance in clinically stable outpatients remains poorly defined. Objectives: To determine whether the median and variability of nocturnal respiratory rate (NRR) are independently associated with long-term cardiovascular and all-cause mortality in outpatients with cardiovascular disease. Methods: We analyzed overnight chest belt waveforms from elective polysomnography in 5,679 older adults with cardiovascular disease enrolled in the Sleep Heart Health Study (SHHS). NRR was quantified at 30-second resolution, and per-subject median NRR and within-night variability (standard deviation) were derived. Kaplan-Meier survival analysis and Cox proportional hazards models were used to evaluate associations with cardiovascular and all-cause mortality over 3-year and 15-year follow-up periods, adjusting for demographic characteristics, cardiopulmonary comorbidities, and sleep apnea severity. Results: Higher median NRR and greater NRR variability were each associated with increased cardiovascular and all-cause mortality. Combining these metrics identified a high-risk group characterized by elevated median and high variability of NRR, with approximately five-fold higher 3-year all-cause mortality compared with a low-risk group; this association remained significant in Cox models (unadjusted HR: 2.61; 95% CI: 1.65, 4.14; p

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15.
arXiv (CS.LG) 2026-06-17

HeteRo-Select: Informativeness as the Participation Driver in Heterogeneous Federated Learning

作者:
Md. Akmol Masud↗Md Abrar Jahin↗Mahmud Hasan↗

arXiv:2508.06692v2 Announce Type: replace Abstract: Federated learning systems typically allocate gradient compression by link speed. This is sensible when bandwidth and data informativeness align. However, under non-IID data, these signals often decorrelate or invert. A bandwidth-driven allocator then risks compressing the most informative gradients hardest. We propose HeteRo-Select, a framework that replaces bandwidth with a per-client informativeness score as the primary driver of compression. The score jointly governs three decisions per round: client selection, compression ratio, and server aggregation weight, with bandwidth retained only as a hard ceiling. Score-proportional selection provably reduces the effective heterogeneity of the chosen subset; score-proportional compression provably lowers aggregate top-$k$ error at fixed traffic. Under the exact FedCG simulation protocol, HeteRo-Select delivers a $1.78\times$ speedup and an $18.2\%$ reduction in traffic on CIFAR-10. The same configuration, unchanged, scales from a $7{,}850$-parameter logistic regression to an $11.27$M-parameter ResNet-18, hitting the accuracy target on three of four benchmarks. When bandwidth and informativeness are deliberately anti-correlated, the method still achieves the target accuracy with less traffic than the normal-bandwidth run.

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

Self-Evolving Visual Questioner

作者:
Yijun Liang↗Hengguang Zhou↗Ming Li↗Lichen Li↗Cho-Jui Hsieh↗Tianyi Zhou↗

Vision-language models (VLMs) are typically trained as passive answerers, while their ability to actively ask diverse, non-trivial, visual-centric and grounded questions remains underexplored. Existing visual questioners' performance is bottlenecked by the availability of high-quality training data or the cost of curating them. We show that a VLM can continuously improve itself as a visual questioner without any external supervision. We propose a self-evolving framework that uses a VLM itself as both a proposer and a filter to produce harder, more informative, and visual-centric questions, while maintaining their exploration diversity to avoid training collapse. These questions are then used to train the VLM in both questioner and answerer modes. To evaluate the questioner, we introduce an agentic protocol that assesses questions along perception, reasoning, and diversity dimensions. Experiments across various backbone VLMs show that our method substantially enhances the quality and substantially expands the difficulty boundary of autonomous question generation. Under the same budget, our self-supervision is more effective than training on the static source data. Moreover, the self-evolving questioner remains a competitive or even better answerer.

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17.
arXiv (CS.LG) 2026-06-16

Understanding Latent Diffusability via Fisher Geometry

作者:
Jing Gu↗Morteza Mardani↗Wonjun Lee↗Dongmian Zou↗Gilad Lerman↗

arXiv:2604.02751v2 Announce Type: replace Abstract: Diffusion models often degrade in latent spaces, yet the formal causes remain poorly understood. We quantify latent-space diffusability via the rate of change of the Minimum Mean Squared Error (MMSE) along the diffusion trajectory. Our framework decomposes this MMSE rate into contributions from Fisher Information (FI) and Fisher Information Rate (FIR). We demonstrate that while global isometry ensures FI alignment, FIR is governed by the interplay between encoder and data geometries. Our analysis decouples diffusion degradation into four penalties: dimensional compression, tangential distortion, high-frequency encoder curvature, and intrinsic data curvature. We derive theoretical conditions for FIR preservation to ensure stable diffusability. Experiments across diverse autoencoding architectures demonstrate the implications of our theoretical bounds. We establish FI and FIR as a comprehensive analytical framework for understanding latent diffusability.

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

StyleShield: Exposing the Fragility of AIGC Detectors through Continuous Controllable Style Transfer

作者:
Guantian Zheng↗

arXiv:2605.00924v2 Announce Type: replace-cross Abstract: AI-generated content (AIGC) detectors are increasingly deployed in high-stakes settings such as academic integrity screening, yet their reliability rests on a fundamental paradox: as language models are trained on human-written corpora, the statistical boundary between AI and human writing will inevitably dissolve as models improve. Commercial incentives have further distorted this landscape – detection services and "de-AIification" tools often operate within the same supply chain, replacing evaluation of content quality with judgment of content origin. We present StyleShield, the first flow matching framework for conditional text style transfer, operating directly in continuous token embedding space via a DiT backbone with zero-initialized cross-attention adapters conditioned on frozen Qwen-7B representations. At inference, we adapt the SDEdit paradigm from image synthesis to text embeddings, with a single parameter gamma providing smooth continuous control over the evasion-preservation trade-off. On a multi-domain Chinese benchmark, StyleShield achieves 94.6% evasion against the training detector and >=99% against three unseen detectors, maintaining 0.928 semantic similarity. We further introduce RateAudit, a document-level scheduling algorithm that demonstrates detection-rate verdicts can be set to arbitrary values, directly questioning the reliability of score-based evaluation.

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

AgenticRec: A Recommendation-Oriented Agentic Framework with Progressive Tool-Integrated Reasoning Optimization

作者:
Tianyi Li↗Zixuan Wang↗Guidong Lei↗Xiaodong Li↗Hui Li↗

arXiv:2603.21613v2 Announce Type: replace-cross Abstract: Recommender agents built on Large Language Models offer a promising paradigm for personalized recommendation. However, existing agents typically suffer from a misalignment between their tool-integrated reasoning trajectories and recommendation feedback, limiting their ability to distinguish fine-grained user preferences. To address these challenges, we propose AgenticRec, an agentic recommendation framework that formulates recommendation as a tool-integrated reasoning process over a recommendation-oriented tool suite. Built upon this framework, we further develop a dedicated two-stage training paradigm tailored for recommender agents. In the first stage, we introduce Recommendation-Oriented Trajectory Activation, optimize the agentic recommendation ability under implicit feedback. In the second stage, Progressive Preference Refinement further refines the agent through bidirectional preference reasoning over self-bootstrapped hard pairs, progressively sharpening preference boundaries. Theoretical analysis and extensive experiments demonstrate the effectiveness of AgenticRec. Our code is available at https://anonymous.4open.science/r/AgenticRec-FB16.

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

Adaptive Turn-Taking for Real-time Multi-Party Voice Agents

作者:
Soumyajit Mitra↗Prabhat Pandey↗Abhinav Jain↗Shanmukha Sahith↗K V Vijay Girish↗

Turn-taking in multi-party spoken conversations remains a fundamental challenge for voice-based agents, particularly under dynamic floor competition and varying user expectations. We propose ModeratorLM, a role-playing voice agent that conditions turn-taking behavior on an explicitly assigned role in multi-party settings. The system is built on a speech large language model operating in chunk-wise streaming manner. We further introduce a reasoning-augmented variant that incorporates chain-of-thought reasoning over conversational context and the assigned role. We construct RolePlayConv, a large-scale synthetic dataset of spoken multi-party conversations with diverse assistant roles. Experiments on real-world meeting data and RolePlayConv show improved turn-taking precision by over 40% and recall by more than 70%, while substantially reducing false-positive interruptions compared to non-role-conditioned baselines.

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21.
arXiv (quant-ph) 2026-06-17

On the entanglement induced by the deformation of phase-space

作者:
Shilpa Nandi↗Shatarupa Maity↗Pinaki Patra↗

arXiv:2606.17587v1 Announce Type: new Abstract: Most quantum gravity theories propose that the fundamental concept of space-time is mostly compatible with quantum theory in noncommutative (NC) space. In the present paper, we revisit the notion of entanglement induced by NC deformations of phase space. The positive partial transpose (PPT) criterion for separability of bipartite Gaussian states is extended to a general class of Bopp's shift. In particular, we have considered both the position-position and momentum-momentum noncommutativity, with deformation parameters $\theta$ and $\eta$, respectively. It turns out that $\theta$ and $\eta$ induce the entanglement. We have directly applied the formalism for an anisotropic two-dimensional harmonic oscillator. Peres-Horodecki separability condition leads to a constraint equation for the parameter values of the oscillator in NC space. It turns out that the bipartite Gaussian state is almost always entangled in deformed space. To implement the theoretical idea, we provide an outline for a gedankenexperiment to identify the signature of phase-space noncommutativity, i.e., quantum gravity. In particular, the gedankenexperiment is devised to test the separability of supposedly separable Gaussian states in the usual commutative space, through the covariance matrix, which is constructed via measured output photocurrents after interaction of input Gaussian states and reference states. If the experiment shows that the supposedly separable states are actually entangled, then the entanglement is created through the intermediate background noncommutative space, which is a signature of the quantum nature of gravity.

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22.
arXiv (CS.CL) 2026-06-24

A P\={a}ninian Foundation for Indic Language Processing

作者:
Ritwik Banerjee↗Lav R. Varshney↗

More than a billion people communicate in Indic languages, yet the natural language processing infrastructure serving them remains fragmented and underdeveloped. The cause is structural: the field organizes its tools and benchmarks around individual languages or small subsets of genealogical language families, building separate analyzers, parsers, and datasets for each language and starting over for the next. This overlooks a deep regularity. Through more than two millennia of convergence around Sanskrit, Indic languages came to share a morphosyntactic architecture formalized in P\={a}nini's grammar, the Ast\={a}dhy\={a}y\={i}. This cuts across genealogical lines, uniting languages through a common framework. We argue that this P\={a}ninian framework supplies a unifying computational architecture the field has lacked, and that benchmarks grounded explicitly in it would make Indic language systems more accurate, more data-efficient, and more transferable, effectively merging many apparently disparate and sparse Indic language resources into a single high-resource metalanguage bedrock. We propose a four-part benchmark suite to render this shared architecture explicit, measurable, and ready to be leveraged for practical applications. Moreover, we underscore the question it raises for interpretability research: whether neural models trained on these languages come to represent P\={a}nini's categories on their own.

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23.
arXiv (CS.AI) 2026-06-17

When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval

作者:
Mingxu Tao↗Jiawei Hu↗Xian Zhou↗Wenpeng Hu↗Jiajun Cheng↗Yunbo Cao↗Zhunchen Luo↗Guotong Geng↗

arXiv:2606.17220v1 Announce Type: new Abstract: Legal case retrieval remains challenging due to the complexity of legal language and the need for precise lexical alignment between queries and relevant cases. Although dense retrieval models have achieved notable progress, empirical studies show that BM25 continues to serve as a strong baseline in this domain. It motivates us to propose a self-evolving framework for rule-driven query rewriting that enhances BM25 without any parameter training. The framework equips an LLM-based agent with an automatic evaluation environment, enabling it to iteratively create rewriting rules, plan validation experiments over rule combinations, and eliminate ineffective rules based on historical feedbacks. We evaluate our method on the Chinese legal case retrieval benchmark LeCaRD-v2. Experimental results demonstrate that the proposed framework outperforms non-evolutionary baselines, including human-designed rules and greedy rule selection, particularly when powered by a highcapacity core LLM. We also conduct detailed analyses to investigate the mechanisms underlying self-evolution. Our findings reveal that LLM's capabilities to leverage previous experimental results and its intrinsic knowledge of rule elimination play critical roles in refining the rule set via self-evolution.

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24.
medRxiv (Medicine) 2026-06-16

Prevalence and Correlates of Ideal Cardiovascular Health among Ugandan Adolescents: A Cross-Sectional Study

作者:
Mugenyi↗Ouma↗Namakoola↗Namara↗Kintu↗I. S↗Namugera↗F. X↗Nalubega↗Nanozi↗Tumuhairwe↗Mirembe↗…

Introduction: Cardiovascular disease (CVD) risk factors often emerge during adolescence and track into adulthood, yet data on cardiovascular health (CVH) in sub-Saharan Africa remain limited. We assessed the prevalence and correlates of ideal CVH among Ugandan adolescents. Methods: We analysed baseline data of adolescents enrolled in a cluster-randomised controlled trial being conducted in urban (Kampala) and rural (Jinja) districts of Uganda. In this study, Ideal CVH was defined as meeting "ideal" status of 5-7 of the American Heart Association's Life's Simple 7 metrics. Random-effects logistic regression was used to identify factors associated with ideal CVH, accounting for village-level clustering. Results: We recruited 1316 participants with a mean age of 13.2 years, of whom 58.1% were female. Overall, the prevalence of ideal CVH was 66.8% (95% CI: 64.2% - 69.3%). The prevalence was higher in Jinja (74.4%, 95%CI: 70.9% - 77.7%) than Kampala (59.6%, 95%CI: 55.8%-63.2%) and the difference was evident (p

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

JE-IRT: A Geometric Lens on LLM Abilities through Joint Embedding Item Response Theory

作者:
Louie Hong Yao↗Nicholas Jarvis↗Tiffany Zhan↗Saptarshi Ghosh↗Linfeng Liu↗Tianyu Jiang↗

Standard LLM evaluation practices compress diverse abilities into single scores, obscuring their inherently multidimensional nature. We present JE-IRT, a geometric item-response framework that embeds both LLMs and questions in a shared space. For question embeddings, the direction encodes semantics and the norm encodes difficulty, while correctness on each question is determined by the geometric interaction between the model and question embeddings. This geometry replaces a global ranking of LLMs with topical specialization and enables smooth variation across related questions. Building on this framework, our experimental results reveal that out-of-distribution behavior can be explained through directional alignment, and that larger norms consistently indicate harder questions. Moreover, JE-IRT naturally supports generalization: once the space is learned, new LLMs are added by fitting a single embedding. The learned space further reveals an LLM-internal taxonomy that only partially aligns with human-defined subject categories. We also show that simple linear probes of the embedding space recover cross-subject ability directions, such as an arithmetic axis that highlights quantitatively demanding questions in seemingly distant subjects like virology and global facts. JE-IRT thus establishes a unified and interpretable geometric lens that connects LLM abilities with the structure of questions, offering a distinctive perspective on model evaluation and generalization.

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