中 EN
登录 注册账户

Academic Intelligence · Curated Daily

探索全球前沿学术脉络

AcademicHub 汇聚顶级期刊与预印本平台的实时文献。定制您的专属科研雷达，利用大语言模型自动生成交叉领域文献分析简报。

登录 注册账户
01.
medRxiv (Medicine) 2026-06-22

Hyperlipidemia Pharmacotherapy in Skilled Nursing Facilities: A Real-World Evidence Study

作者:
Ashraf↗Mathers↗K. E↗Wagner↗Saumur↗

Objectives: To estimate hyperlipidemia medication order prevalence and associated variables in U.S. skilled nursing facility (SNF) residents. Design: Retrospective, observational study. Setting and Participants: Electronic Health Record data from 447,080 SNF residents with a hyperlipidemia diagnosis identified in PointClickCare's Life Sciences clinical database (January-April 2025) were reviewed. Methods: The presence and absence of medication orders for hyperlipidemia treatments recommended by the American Heart Association were assessed. Descriptive analyses summarized demographic and clinical characteristics, and a modified Poisson regression model was used to estimate risk ratios for having a medication order, adjusting for demographic, clinical, and facility characteristics. Results: Overall, 83.3% of residents diagnosed with hyperlipidemia had at least one hyperlipidemia medication order. Statins were ordered by 96.2% of active order residents, while other medication classes i.e., omega-3 fatty acids, cholesterol absorption inhibitors, fibrates were less common (

阅读与讨论 → 访问原文 →
02.
arXiv (CS.CV) 2026-06-17

FATE: Pillar Encoding and Frequency-Aware Training for Event-Based Object Detection

作者:
Md Tawheedul Islam Bhuian↗Kyoung-Don Kang↗

Event cameras are bio-inspired sensors that asynchronously capture logarithmic intensity changes, offering inherent advantages in high-speed and high-dynamic-range scenarios. However, the sparse and asynchronous nature of event streams poses a fundamental challenge for modern deep learning architectures. To enable compatibility with standard models, most existing approaches partition the accumulation window into fixed temporal sub-bins. While effective for spatial processing, this internal discretization discards fine-grained temporal structure and constrains inference to the low temporal frequencies imposed by training supervision. To address this limitation, we propose FATE, a unified framework built upon a novel Pillar Encoding (PE). While operating over discrete macro-accumulation windows dictated by the target frequency, PE avoids internal temporal sub-binning. It organizes events into spatial pillars and approximates their intra-window evolution via projection onto a continuous-time orthogonal polynomial basis. This formulation yields an L2-optimal representation that retains rich temporal dynamics in a dense pseudo-image, mitigating information loss under sparse event conditions. To fully leverage this representation, we introduce Frequency-Aware Training (FAT), a soft mean-teacher curriculum that generates temporally dense pseudo-labels, effectively bridging the mismatch between low-frequency supervision and high-frequency inference. Extensive experiments demonstrate that FATE generalizes across architectural paradigms and consistently outperforms strong baselines. It enables robust object detection at high temporal resolutions up to 200 Hz, while incurring minimal overhead in parameter count and inference latency

阅读与讨论 → 访问原文 →
03.
bioRxiv (Bioinfo) 2026-06-16

RetroMol: Parsing a shared encoding from natural products and their biosynthetic gene clusters

作者:
Meijer↗Williams↗S. E↗Terlouw↗Charusanti↗Kok↗Skinnider↗M. A↗Weber↗van der Hooft↗J. J. J↗Healy↗…

Natural products such as polyketides and nonribosomal peptides (NRPs) are important sources of bioactive compounds, including many antibiotics. Many of them are assembled by modular enzyme complexes and further modified and diversified by tailoring reactions encoded by biosynthetic gene clusters (BGCs). Although natural products and their coding BGCs describe different data modalities of the same biochemical process, a unified language to jointly describe their biochemistry is lacking. Here we introduce a sequence-based representation of the core biosynthesis of modular natural products, which we call primary sequences, that bridges chemical structures and BGCs. We also present RetroMol, an algorithm that parses either natural product structures or their encoding BGCs into their primary sequences of natural product building blocks. RetroMol allows for similarity scoring between natural products and BGCs, enabling the retrieval of compounds, BGCs, and a combination of the two, based on their biosynthetic similarity. This can, for instance, be used to retrieve biosynthetically similar but structurally dissimilar compounds, or link natural products to candidate coding BGCs in large experimental datasets. We demonstrate the latter by rediscovering the nocardichelin B BGC as a proof of principle. We also exemplify the utility of biosynthetic similarity by showing various pairs of biosynthetically similar compounds with low structural similarity. Together, these results establish primary sequences as a shared biosynthetic encoding for natural product comparison and BGC prioritization.

阅读与讨论 → 访问原文 →
04.
arXiv (CS.CL) 2026-06-19

Learning to Prompt: Improving Student Engagement with Adaptive LLM-based High-School Tutoring

作者:
Po-Chin Chang↗Nicholas Hogan↗Aske Plaat↗Michiel T. van der Meer↗

LLMs can personalize education, although current static-prompt tutoring systems struggle to adapt to diverse academic disciplines. We develop and test a system with subject-aware prompting, based on 14 pedagogical features (e.g., tutor scaffolding, student understanding) extracted from raw transcripts. We first train a prompt routing model in a simulation environment, and then deploy it for online adaptation with actual high-school students. The simulation benchmark shows the router outperforming two static baselines ($0.694$ vs. $0.647$ and $0.64$, $p

阅读与讨论 → 访问原文 →
05.
arXiv (CS.CV) 2026-06-12

Cross-Modal Masked Compositional Concept Modeling for Enhancing Visio-Linguistic Compositionality

作者:
Wei Li↗Zhen Huang↗Xinmei Tian↗

Contrastively trained vision-language models like CLIP, have made remarkable progress in learning joint image-text representations, but still face challenges in compositional understanding. They often exhibit a "bag-of-words" behavior–struggling to capture the object relations, attribute-object bindings, and word order dependencies. This limitation arises not only from the reliance on global, single-vector representations for optimization, but also from the insufficient exploitation and modeling of the rich compositional information inherently present in paired image text data. In this work, we propose MACCO (MAsked Compositional Concept MOdeling), a framework that masks compositional concepts in one modality and reconstructs them conditioned on the full contextual information from the other, enabling the model to capture and align cross-modal compositional structures more effectively. To facilitate this process, we introduce two auxiliary objectives that jointly align and regularize masked features both inter-modally and intra-modally. Extensive experiments on five compositional benchmarks, along with in-depth analyses, demonstrate that our approach not only significantly enhances compositionality in VLMs but also improves their ability to capture syntactic structure and linguistic information. Additionally, the improved compositionality also benefits text-to-image generation and multimodal large language model. Code is available at https://github.com/hiker-lw/MACCO.

阅读与讨论 → 访问原文 →
06.
arXiv (quant-ph) 2026-06-12

Approximability limits for bounded-degree max-LINSAT and implications for decoded quantum interferometry

作者:
Maximilian J. Kramer↗Carsten Schubert↗Jens Eisert↗

arXiv:2606.13570v1 Announce Type: new Abstract: For general max-k-XORSAT with $k \geq 3$, no polynomial-time algorithm can do substantially better than random guessing on worst-case instances unless $\mathsf{P} = \mathsf{NP}$: approximating beyond the random-assignment value of $1/2$ is $\mathsf{NP}$-hard. The picture changes when each variable appears in at most $D$ constraints. In that bounded-degree setting, polynomial-time algorithms can provably beat the random baseline by an additive amount of order $1/\sqrt{D}$. For Boolean instances, this scaling is known to be optimal: the matching hardness result is due to Trevisan, while the corresponding algorithmic guarantee was established by Barak et al. Whether the same holds over general finite fields, and what it implies for quantum algorithms, has not been established. We make this connection explicit and extend the hardness to max-E$k$-LINSAT$(q,r)$ with bounded degree $D$ and over arbitrary finite fields $\mathbb{F}_q$, proving that it is $\mathsf{NP}$-hard to exceed $r/q + \mathcal{O}_{q,r}(1/\sqrt{D})$. These results provide the complexity-theoretic benchmark for the bounded-degree instances targeted by decoded quantum interferometry (DQI), QAOA, and classical heuristics. Any quantum advantage on bounded-degree instances is therefore confined to the constant prefactor. We further show that in the context of DQI and on $(k,D)$-regular instances, this prefactor is sensitive to the nature of the decoder: DQI with classical decoders faces an information-theoretic $1/\sqrt{D \log D}$ barrier that prevents it from matching the hardness scaling, while DQI with quantum decoders is compatible with the $1/\sqrt{D}$ scaling – identifying quantum decoding as the key ingredient for matching the complexity-theoretic scaling with DQI.

阅读与讨论 → 访问原文 →
07.
arXiv (math.PR) 2026-06-24

Autoregressive Processes on Riemannian Manifolds

作者:
Meshal Abuqrais↗Davide Pigoli↗

arXiv:2606.24771v1 Announce Type: cross Abstract: This paper introduces a Riemannian autoregressive (R-AR) model of order one, generalising classical discrete-time stochastic processes to manifold-valued data. The model is based on two parameters, a parameter $\mu$ representing the intrinsic central tendency as the Fréchet mean and an autoregressive parameter $\phi$ controlling the stationarity and ergodic properties. Due to the inherent dependence structure of the R-AR process, the estimation procedure for these parameters necessitates new asymptotic results for dependent processes on manifolds. Thus, we establish a strong law of large numbers for the sample Fréchet mean set of ergodic Markov chains in proper metric spaces. By proving this general consistency result, we move beyond the limitations of classical i.i.d. theory to provide the mathematical foundation required for the strong consistency of our proposed estimators. The framework is validated through numerical simulations in the hyperbolic plane and an application to aerosol size distributions on the Fisher-Rao manifold, demonstrating how the proposed model can characterise mean-reverting dynamics in nonlinear geometries.

阅读与讨论 → 访问原文 →
08.
arXiv (CS.AI) 2026-06-24

Context-Aware Prediction of Student Quiz Performance with Multimodal Textbook Features

作者:
Samin Khan↗

arXiv:2606.24770v1 Announce Type: cross Abstract: Educational platforms often predict student performance from prior interactions, but the assessment content itself also varies in linguistic and visual complexity. This paper studies whether lightweight content features extracted from CourseKata chapter-review questions improve prediction of end-of-chapter quiz scores beyond a student's average prior exercise performance. The study combines 2023 CourseKata student response data with chapter-level text features from review-question wording and image features from textbook visuals. Across 4,742 student-chapter observations from 562 class-student IDs, adding content features improves student-grouped five-fold quiz prediction performance by 9.1% relative to a prior-performance baseline. In leave-chapter-out validation, text features reduce prediction error relative to the baseline, while image-containing models have higher error. This paper suggests that a context-aware model adds useful signal about the text and visual features of questions to better predict student quiz performance compared with using past student performance alone.

阅读与讨论 → 访问原文 →
09.
arXiv (CS.LG) 2026-06-24

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach

作者:
Sre\'cko {\DJ}ura\v{s}inovi\'c↗Jean-Bernard Lasserre↗Victor Magron↗

arXiv:2509.22879v2 Announce Type: replace-cross Abstract: Mixture models, such as Gaussian mixture models, are widely used in machine learning to represent complex data distributions. A key challenge, especially in high-dimensional settings, is to determine the mixture order and estimate the mixture parameters. We study the problem of approximating a target measure, available only through finitely many of its moments, by a mixture of distributions from a parametric family (e.g., Gaussian, exponential, Poisson), with approximation quality measured by the 2-Wasserstein or the total variation distance. Unlike many existing approaches, the parameter set is not assumed to be finite; it is modeled as a compact basic semi-algebraic set. We introduce a hierarchy of semidefinite relaxations with asymptotic convergence to the desired optimal value. In addition, when a certain rank condition is satisfied, the convergence is even finite and recovery of an optimal mixing measure is obtained. We also present an application to clustering, where our framework serves either as a stand-alone method or as a preprocessing step that yields both the number of clusters and strong initial parameter estimates, thereby accelerating convergence of standard (local) clustering algorithms.

阅读与讨论 → 访问原文 →
10.
medRxiv (Medicine) 2026-06-10

Towards the Virtual Amyotrophic Lateral Sclerosis Patient: Inferring Cortical Excitability through Whole-Brain Dynamical Modeling

作者:
Angiolelli↗Demuru↗Lopez↗E. T↗Hashemi↗Ziaeemeh↗Rabuffo↗Trojsi↗Granata↗Tafuri↗De Luca↗Gallo↗…

Amyotrophic lateral sclerosis (ALS) is increasingly recognized as a multisystem neurodegenerative disorder in which motor-neuron degeneration is accompanied by widespread alterations in cortical dynamics. Among its most reproducible neurophysiological signatures is cortical hyperexcitability, yet how this local excitability imbalance shapes distributed whole-brain activity remains poorly understood. Here, we combined source-reconstructed resting-state MEG data, tractography-informed whole-brain modeling, and simulation-based inference to investigate whether ALS-related alterations in large-scale brain dynamics can be mechanistically explained by changes in cortical excitability. First, we characterized empirical brain dynamics using complementary features spanning regional activity amplitude and variability, functional connectivity, and avalanche-based metrics. These analyses revealed significant alterations in ALS patients relative to healthy controls, as well as associations with clinical impairment and disease staging. To mechanistically interpret these changes, we employed a reduced Wong-Wang whole-brain model in which local recurrent excitation modulates emergent large-scale neural dynamics. Simulations showed that increasing excitability systematically reproduced the empirical dynamical signatures observed in ALS. We then applied a simulation-based inference framework to estimate latent excitability parameters directly from empirical observations. Whole-brain model inversion revealed increased excitability in ALS patients compared with controls. The recovered excitability parameter was associated with disease staging, supporting its clinical relevance as a model-derived descriptor of ALS progression. Finally, by extending the model to estimate frontal and non-frontal excitability separately, we found that ALS-related alterations were predominantly associated with increased frontal excitability, whereas non-frontal regions appeared comparatively less affected. The recovered parameters related to disease staging. Together, these findings provide a mechanistic framework linking altered large-scale brain dynamics in ALS to selective cortical hyperexcitability, explaining how local excitability changes can give rise to global network reorganization. More broadly, they show how computational model inversion can recover latent multiscale pathophysiological processes from empirical neural recordings, offering a non-perturbative alternative to complex experimental paradigms typically required to causally probe local-to-global mechanisms.

阅读与讨论 → 访问原文 →
11.
arXiv (CS.CL) 2026-06-16

SkillWiki: A Living Knowledge Infrastructure for Agent Skills

作者:
Dingcheng Huang↗Yuda Ding↗Bingshuo Liu↗Qingbin Liu↗Xi Chen↗Jiang Bian↗Hongliang Sun↗Zhiying Tu↗Dianhui Chu↗Xiaoyan Yu↗Dianbo Sui↗

While knowledge is managed through Wikipedia and software through GitHub, agent skills still lack an infrastructure for large-scale production, governance, and evolution. SkillWiki is a living knowledge infrastructure that supports the organization, grounding, and continuous evolution of agent skills by transforming heterogeneous knowledge into reusable skill assets linked to their originating evidence. Our demonstration presents the complete skill lifecycle, from knowledge ingestion and skill production to provenance-aware exploration, governance, and execution-driven evolution. SkillWiki highlights a future in which knowledge, skills, and execution experience co-evolve within a shared infrastructure. The live demonstration and source code are publicly available at https://github.com/Huangdingcheng/SkillWiki.

阅读与讨论 → 访问原文 →
12.
arXiv (CS.CL) 2026-06-11

Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite

作者:
Zhiyuan Cheng↗Longying Lai↗

Retrieval-Augmented Generation (RAG) pipelines are compute-intensive, combining embedding, retrieval, reranking, and large language model (LLM) generation. Running them entirely on-device benefits privacy, latency, and offline use, but the energy cost of CPU inference is a major barrier. We present what is, to our knowledge, the first end-to-end RAG pipeline that runs all neural stages – embedding, reranking, and LLM generation – on the Qualcomm Hexagon NPU of the Snapdragon X Elite. Profiling on a Dell XPS 13 laptop, we compare NPU-accelerated RAG against CPU and OpenCL/Adreno GPU baselines on indexing and query workloads. On indexing, the NPU achieves 9.1x higher embedding throughput and 12.3x less system energy. On a 120-query Wikipedia-passage benchmark, it delivers 18.1x faster LLM prefilling, 4.0x lower end-to-end query latency, and 4.0x less system energy than the CPU baseline; the same workload on the integrated GPU is 1.7x slower than CPU and uses 6.5x more energy than the NPU. A GPT-4.1 LLM-as-judge evaluation finds NPU answer quality on par with CPU and GPU within evaluator noise (mean 9.32 vs. 8.95 vs. 9.03 on a 1-10 rubric), with 86.7% of queries scoring identically across all three backends. On the Snapdragon X Elite / Hexagon class of laptop SoC, the NPU thus enables practical, energy-efficient on-device RAG without quality regression – a sustainable path toward green edge intelligence that we expect to generalize to comparable mobile NPUs (Apple Neural Engine, Intel NPU, MediaTek APU) as their software stacks mature.

阅读与讨论 → 访问原文 →
13.
arXiv (CS.AI) 2026-06-15

The Weight Norm Sets the Grokking Timescale: A Causal Delay Law

作者:
Truong Xuan Khanh↗Doan Hoang Viet↗Luu Duc Trung↗Phan Thanh Duc↗

arXiv:2606.13753v1 Announce Type: cross Abstract: Grokking is the delayed onset of generalization in neural networks, arising long after they fit the training data. Whether the weight norm causes this delay is disputed: some studies report a critical norm at the transition, others observe grokking with no fixed norm at all. We settle this by intervening on the norm during training rather than only observing it. Under free training with weight decay, networks grok when the weight norm reaches a value Wc that varies little across seeds and learning rates (CV 1 to 2 percent) and grows with the modular base as a power law. When we instead clamp the norm to a fixed multiple rho of Wc and hold it there, the network still groks, but the delay follows T_grok proportional to exp(alpha rho). One exponent, alpha near 7.5, fits this delay across four moduli (R^2 = 0.996). Over the swept ranges the held norm moves the delay by about 19x and the learning rate by only about 2x, and holding the norm above Wc slows grokking rather than preventing it. A final LayerNorm removes the dependence by decoupling weight scale from the network function; without it the exponential law returns. This pinned-norm delay is the exponential counterpart to the logarithmic delay predicted for a freely contracting norm.

阅读与讨论 → 访问原文 →
14.
arXiv (CS.LG) 2026-06-24

Low-rank Updates in Slowly Time-varying Graphs for Spatial-Temporal Signal Interpolation

作者:
Saghar Bagheri↗Gene Cheung↗Tim Eadie↗Antonio Ortega↗

arXiv:2606.24011v1 Announce Type: cross Abstract: A crucial assumption in graph signal processing (GSP) is the existence of an underlying graph that captures the pairwise similarities between nodes, allowing filters to be designed based on this graph for tasks such as denoising. For spatial-temporal data in which node-to-node similarities evolve over time, a static spatial graph is insufficient. In this paper, to represent slowly time-varying pairwise relationships, we model the graph changes in two consecutive adjacency matrices $P = W^{(2)} - W^{(1)}$ across time as a low-rank matrix. % Specifically, given an initial adjacency matrix $W^{(1)}$ at time $t=1$, we jointly interpolate a signal $x_2$ and estimate $W^{(2)}$ at $t=2$ using both a graph signal smoothness prior for $x_2$ and a low-rank prior on $\P$. We alternate optimization steps. With $W^{(2)}$ fixed, $x_2$ is interpolated by solving a linear system. Alternatively, holding $x_2$ fixed, $W^{(2)}$ is updated via proximal gradient descent (PGD). The proximal mapping of the rank term $Gamma(W^{(2)} - W^{(1)})$ is approximated in linear time using a fast orthogonal matching pursuit (OMP) algorithm that selects a sparse combination of atoms from a dictionary $cR$ formed by the outer products of $W^{(1)}$'s eigenvectors. We unroll iterations of our algorithm into layers to build a lightweight neural network for limited data-driven parameter tuning. Experiments show that our joint optimization achieves better signal interpolation compared to existing time-varying graph models.

阅读与讨论 → 访问原文 →
15.
arXiv (CS.LG) 2026-06-17

Instrumental and Proximal Causal Inference with Gaussian Processes

作者:
Yuqi Zhang↗Krikamol Muandet↗Dino Sejdinovic↗Edwin Fong↗Siu Lun Chau↗

arXiv:2603.02159v2 Announce Type: replace-cross Abstract: Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding. Despite substantial methodological advances, existing approaches rarely provide reliable epistemic uncertainty (EU) quantification. We address this gap through a Deconditional Gaussian Process (DGP) framework for uncertainty-aware causal learning. Our formulation recovers popular kernel estimators as the posterior mean, ensuring predictive precision, while the posterior variance yields principled and well-calibrated EU. Moreover, the probabilistic structure enables systematic model selection via marginal log-likelihood optimization. Empirical results demonstrate strong predictive performance alongside informative EU quantification, evaluated via empirical coverage frequencies and decision-aware accuracy rejection curves. Together, our approach provides a unified, practical solution for causal inference under unobserved confounding with reliable uncertainty.

阅读与讨论 → 访问原文 →
16.
arXiv (CS.CV) 2026-06-24

Stochastic Signed Distance Processes

作者:
Hiroki Sakuma↗Masatoshi Okutomi↗

Multi-view surface reconstruction is a core problem in computer vision. One prominent line of work represents the surface implicitly as a signed distance field (SDF), optimizing it based on the photometric loss between rendered and observed pixel colors. These approaches typically employ SDF-based volume rendering to obtain a differentiable relaxation of discontinuous visibility along rays, thereby reducing reliance on silhouette supervision. In this paper, we reformulate SDF-based volume rendering as probabilistic surface rendering, where each pixel color is modeled as a mixture distribution induced by the random first ray-surface intersection. To this end, we introduce Stochastic Signed Distance Processes (SSDP), which model the SDF along each ray as a stochastic process, inducing a first-passage-time distribution for each ray. We then derive the first-passage probability for each sampling interval based on Bayesian filtering, together with its practical approximation for parallel rendering. We further show that NeuS, an existing SDF-based volume rendering method, arises as a special case of our formulation. Experiments on the DTU and MobileBrick datasets demonstrate that our method outperforms baselines in both surface reconstruction and uncertainty quantification, supporting the effectiveness of our first-passage formulation. Our code is available at https://github.com/skmhrk1209/SSDP.

阅读与讨论 → 访问原文 →
17.
arXiv (CS.AI) 2026-06-16

MA-SBI: Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance

作者:
Arunkumar V↗Manoranjan Gandhudi↗Gangadharan G. R↗Arun Prakash↗S. Senthilkumar↗

arXiv:2606.16923v1 Announce Type: new Abstract: Simulation-based inference (SBI) of latent parameters is often hindered by simulator misspecification, the mismatch between simulated and real-world observations caused by inherent modeling simplifications. RoPE, the recent state-of-the-art for robust SBI, addresses this through optimal transport between learned representations of real and simulated observations, but requires ground-truth parameter calibration pairs that are typically unavailable in the very settings where SBI is needed. What practitioners do have is unstructured side-information such as regime labels, instruction text, and policy bulletins. We propose Misspecification-Aware Simulation-Based Inference (MA-SBI), a calibration-free framework that turns this side-channel into a posterior correction. A learned corrector maps side-channel text to an observation-space shift applied before any pre-trained amortized posterior, requiring no retraining and no parameter ground-truth. Our main theorem bounds achievable bias reduction by the mutual information between misspecification and side-channel, with a non-vacuous constant that extends to all sub-Gaussian noise via Donsker-Varadhan. On hide-the-calibration benchmarks, MA-SBI with text alone matches the oracle posterior across 10 seeds and two backbones (TOST equivalence), while RoPE given more data does not. The two approaches are complementary: where misspecification is structural and recoverable from parameter pairs, RoPE dominates, as the theory predicts. A stochastic variant improves posterior-predictive log-likelihood on real COVID and OxCGRT epidemiological data, and correctly leaves the posterior unchanged on a well-specified cognitive-science corpus.

阅读与讨论 → 访问原文 →
18.
arXiv (CS.AI) 2026-06-18

DeepInflation: an AI agent for research and model discovery of inflation

作者:
Ze-Yu Peng↗Hao-Shi Yuan↗Qi Lai↗Jun-Qian Jiang↗Gen Ye↗Jun Zhang↗Yun-Song Piao↗

arXiv:2601.14288v2 Announce Type: replace-cross Abstract: We present DeepInflation, an AI agent designed for research and model discovery in inflationary cosmology. Built upon a multi-agent architecture, DeepInflation integrates Large Language Models (LLMs) with a symbolic regression (SR) engine and a retrieval-augmented generation (RAG) knowledge base. This framework enables the agent to automatically explore and verify the vast landscape of inflationary potentials while grounding its outputs in established theoretical literature. We demonstrate that DeepInflation can successfully discover simple and viable single-field slow-roll inflationary potentials consistent with the latest observations (with the ACT DR6 results taken as an example) or any given $n_s$ and $r$, and provide accurate theoretical context for obscure inflationary scenarios. DeepInflation serves as a prototype for a new generation of autonomous scientific discovery engines in cosmology, which enables researchers and non-experts alike to explore the inflationary landscape using natural language. This agent is available at https://github.com/pengzy-cosmo/DeepInflation.

阅读与讨论 → 访问原文 →
19.
arXiv (CS.LG) 2026-06-18

Not Just How Much, But Where: Decomposing Epistemic Uncertainty into Per-Class Contributions

作者:
Mame Diarra Toure↗David A. Stephens↗

arXiv:2602.21160v3 Announce Type: replace-cross Abstract: In safety-critical classification, the cost of failure is often asymmetric, yet Bayesian deep learning summarises epistemic uncertainty with a single scalar, mutual information (MI), that cannot distinguish whether a model's ignorance involves a benign or safety-critical class. We decompose MI into a per-class vector $C_k(x)=\sigma_k^{2}/(2\mu_k)$, with $\mu_k{=}\mathbb{E}[p_k]$ and $\sigma_k^2{=}\mathrm{Var}[p_k]$ across posterior samples. The decomposition follows from a second-order Taylor expansion of the entropy; the $1/\mu_k$ weighting corrects boundary suppression and makes $C_k$ comparable across rare and common classes. By construction $\sum_k C_k \approx \mathrm{MI}$, and a companion skewness diagnostic flags inputs where the approximation degrades. After characterising the axiomatic properties of $C_k$, we validate it on three tasks: (i) selective prediction for diabetic retinopathy, where critical-class $C_k$ reduces selective risk by 34.7\% over MI and 56.2\% over variance baselines; (ii) out-of-distribution detection on clinical and image benchmarks, where $\sum_k C_k$ achieves the highest AUROC and the per-class view exposes asymmetric shifts invisible to MI; and (iii) a controlled label-noise study in which $\sum_k C_k$ shows less sensitivity to injected aleatoric noise than MI under end-to-end Bayesian training, while both metrics degrade under transfer learning. Across all tasks, the quality of the posterior approximation shapes uncertainty at least as strongly as the choice of metric, suggesting that how uncertainty is propagated through the network matters as much as how it is measured.

阅读与讨论 → 访问原文 →
20.
arXiv (CS.CL) 2026-06-16

Does Traversal Order Matter? A Systematic Study of Tree Traversal Methods in Transformer Grammars

作者:
Zongru Liu↗Pengyu Ji↗Pengcheng Wang↗Kewei Tu↗

Transformer Grammars (TGs) enhance language modeling by incorporating syntactic tree structures. Despite the potentially significant impact on model performance of how syntactic trees are linearized in TGs, existing studies rely solely on Depth-First Traversal (DFT) for linearization. In this paper, we expand the traversal design space by exploring Breadth-First Traversal (BFT) and a novel hybrid traversal strategy, Production-Rule Traversal (PRT), which combines the structural lookahead of BFT with the early lexical generation of DFT. We integrate these traversal methods with varying tree configurations and masking strategies, and empirically evaluate their performance on language modeling, syntactic generalization and summarization. We reveal the inherent trade-offs between nested composition and global lookahead, providing actionable recommendations for designing task-aware Transformer Grammars.

阅读与讨论 → 访问原文 →
21.
arXiv (quant-ph) 2026-06-11

Generating function and Bloch representation for quantum Fisher tensor

作者:
Felipe P. Abreu↗Wei Chen↗

arXiv:2603.04615v2 Announce Type: replace Abstract: The Uhlmann relative amplitude between two density matrices is shown to be a generating function, through which the quantum Fisher tensor that contains both the quantum Fisher information matrix and the mean Uhlmann curvature can be obtained via differentiation over system parameters. In the pure state limit, our generating function recovers that of the quantum geometric tensor proposed by Het\'{e}nyi and L\'{e}vay, and also clarifies the fidelity and phase between two quantum states as the generating functions of the quantum metric and Berry curvature, respectively. A generic expression for the quantum Fisher tensor in terms of the Bloch representation of density matrices is derived, which facilitates the calculation of the tensor, mean Uhlmann curvature, and geometric properties derived from the quantum Fisher information matrix. Canonical ensembles of spins are adopted to demonstrate our formalism, which reveals a constant Ricci scalar, a vacuum Einstein equation, and a cosmological constant on the 3D Euclidean manifold of the magnetic field.

阅读与讨论 → 访问原文 →
22.
medRxiv (Medicine) 2026-06-15

Population-scale genomics reveals divergent pathogenicity of variant classes across paralogous collagen IV genes

作者:
Tzoumkas↗Doctor↗G. T↗Sadeghi-Alavijeh↗Gale↗D. P↗

Monoallelic pathogenic or likely pathogenic variants in COL4A3 and COL4A4 occur in approximately 1 in 106 individuals, yet whether these paralogous genes confer equivalent pathogenicity for the same variant classes has not been tested at population scale. Using whole-genome sequencing data from the UK Biobank (UKB; n = 500,000), with replication in the All of Us Research Program (n = 414,000), we performed per-variant association testing, gene-based collapsing analyses and phenome-wide association studies (PheWAS) across haematuria, proteinuria and chronic kidney disease. We identified 64 COL4A3 and 92 COL4A4 rare variants significantly associated with haematuria or proteinuria, generating a quantitative allelic series for clinical variant interpretation. Glycine substitutions within collagenous domains conferred similar risks in both genes. In contrast, truncating and non-collagenous domain (NC1) missense variants were strongly associated with haematuria and proteinuria in COL4A4 carriers but showed substantially attenuated or absent associations in COL4A3 carriers despite comparable carrier frequencies and predicted pathogenicity scores. These findings were independently replicated in All of Us. Genome-wide association analysis identified the COL4A3/COL4A4 locus as the dominant genetic determinant of haematuria, with the signal attributable to the aggregate effects of rare coding variants and no evidence of independent common variant or trans-acting modifier effects. These findings demonstrate substantial gene-specific differences in tolerance to truncating and NC1 variants between COL4A3 and COL4A4, challenging assumptions of equivalent pathogenicity across paralogous collagen IV genes. Gene identity and not variant class alone, should inform risk stratification, variant interpretation and genetic counselling in individuals carrying collagen IV risk genotypes.

阅读与讨论 → 访问原文 →
23.
arXiv (CS.LG) 2026-06-17

Statistical Learning from Attribution Sets

作者:
Lorne Applebaum↗Robert Busa-Fekete↗August Y. Chen↗Claudio Gentile↗Tomer Koren↗Aryan Mokhtari↗

arXiv:2602.06276v2 Announce Type: replace Abstract: We address the problem of training conversion prediction models in advertising domains under privacy constraints, where direct links between ad clicks and conversions are unavailable. Motivated by privacy-preserving browser APIs and the deprecation of third-party cookies, we study a setting where the learner observes a sequence of clicks and a sequence of conversions, but can only link a conversion to a set of candidate clicks (an attribution set) rather than a unique source. We formalize this as learning from attribution sets generated by an oblivious adversary equipped with a prior distribution over the candidates. Despite the lack of explicit labels, we construct an unbiased estimator of the population loss from these coarse signals via a novel approach. Leveraging this estimator, we show that Empirical Risk Minimization achieves generalization guarantees that scale with the informativeness of the prior and is also robust against estimation errors in the prior, despite complex dependencies among attribution sets. Simple empirical evaluations on standard datasets suggest our unbiased approach significantly outperforms common industry heuristics, particularly in regimes where attribution sets are large or overlapping.

阅读与讨论 → 访问原文 →
24.
arXiv (CS.AI) 2026-06-17

Vulcan: Instance-specialized, Verifiable Systems Heuristics Through LLM-driven Search

作者:
Rohit Dwivedula↗Divyanshu Saxena↗Sujay Yadalam↗Eric Hayden Campbell↗Daehyeok Kim↗Aditya Akella↗

arXiv:2512.25065v2 Announce Type: replace-cross Abstract: Systems resource management tasks rely primarily on hand-designed heuristics. However, growing hardware heterogeneity and workload diversity require heuristics specialized to particular deployment instances, making manual design expensive and difficult to scale. In this paper, we explore how to synthesize systems heuristics using LLMs. The main challenge is ensuring that generated heuristics execute safely, integrate correctly with the surrounding system, and still achieve strong performance. We propose Vulcan, a framework that identifies LLM-friendly interfaces that isolate core decision logic from the rest of the implementation. With Vulcan, LLM-generated code is restricted to simple stateless decision functions, while trusted runtime abstractions provide rich derived statistics for meaningful policy exploration without system-integration bugs. To ensure execution safety, LLMs synthesize heuristics in a restricted language, Anvil, that guarantees important properties by construction. We evaluate Vulcan across three well-studied domains and demonstrate up to 4.9x higher savings for spot-VM scheduling, up to 2x lower miss ratios for cache eviction, and up to 10% higher application performance for tiered-memory systems, while ensuring execution safety throughout.

阅读与讨论 → 访问原文 →
25.
arXiv (CS.CL) 2026-06-19

From Texts to Scores: Tracing the Emergence of Essay Quality Representations in Large Language Models

作者:
Jiaxu Zuo↗Mu You↗Kaixin Lan↗Tao Fang↗Yujia Huo↗Henghua Shen↗Lidia S. Chao↗Derek F. Wong↗

Recent advances in Large Language Models (LLMs) have substantially transformed Automated Essay Scoring (AES), yet the internal mechanisms underlying LLM-based scoring remain poorly understood. In this work, we systematically analyze the hidden representations of eight LLMs across two English essay datasets (ASAP++, CSEE) and one Portuguese dataset (ENEM). Using linear probing, cross-prompt generalization, dimensionality reduction, and neuron-level analyses, we find consistent evidence that essay quality information is encoded in a linearly accessible form within LLM representations. These representations emerge progressively across layers, remain robust across prompting strategies, and partially transfer across essay prompts despite differences in scoring rubrics. In addition, nonlinear probes provide only marginal and inconsistent improvements over linear probes, suggesting that most essay quality information is already linearly decodable. We further identify individual ``essay scoring neurons'' whose activations strongly correlate with essay scores and whose behavior is sensitive to targeted intervention. Moreover, the layer-wise distribution of these neurons systematically shifts with essay length, with longer essays relying more heavily on deeper layers. Overall, our findings provide evidence that LLMs encode structured representations related to essay quality and offer new insights into the interpretability of LLM-based AES systems.

阅读与讨论 → 访问原文 →