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01.
arXiv (quant-ph) 2026-06-11

TensorKit.jl: A Julia package for large-scale tensor computations, with a hint of category theory

Authors:
Lukas Devos↗Jutho Haegeman↗

arXiv:2508.10076v2 Announce Type: replace-cross Abstract: TensorKit$.$jl is a Julia-based software package for tensor computations, especially focusing on tensors with internal symmetries. This paper introduces the design philosophy, core functionalities, and distinctive features, including how to handle abelian, non-abelian, and anyonic symmetries through the ``TensorMap'' type. We highlight the software's flexibility, performance, and its capability to extend to new tensor types and symmetries, illustrating its practical applications through select case studies.

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02.
arXiv (CS.CL) 2026-06-11

Geometric Metrics and LLMs: What They Measure and When They Work

Authors:
Viacheslav Yusupov↗Anna Antipina↗Ameliia Alaeva↗Danil Maksimov↗Anna Vasileva↗Tatyana Zaitseva↗Alina Ermilova↗Evgeny Burnaev↗Egor Shvetsov↗

We present a systematic stress-test of geometric metrics for LLM evaluation. Rank-based geometric properties of internal representations have shown promise as reference-free quality signals, but the conditions under which they are reliable remain unclear. We evaluate eight commonly-used metrics: intrinsic-dimensionality estimators, spectral norms, and related quantities across six tester models (0.5-8B) and eight generators on contrasting tasks, separating genuine geometric signal from text-length effects and from what standard text statistics already capture. Three findings emerge. First, some metrics (notably Schatten Norm and MOM) mainly reflect output length, and their apparent discriminative power collapses once length is controlled. Second, geometric metrics add modest but real information beyond text statistics: combined with them, a classifier reaches 78% accuracy on 6-way generator identification versus 69% for text statistics alone. Third, rather than tracking a general notion of text quality, the metrics demonstrate only moderate association between the intrinsic-dimensionality and lexical diversity (RTTR). We give use-case-specific recommendations and identify failure detection as the most promising near-term application.

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03.
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

Authors:
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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04.
arXiv (quant-ph) 2026-06-24

Quantum-enabled active matter at the atomic scale

Authors:
Sabrina Burgardt↗Julian Fe{\ss}↗Alexander Guthmann↗Silvia Hiebel↗Aritra K. Mukhopadhyay↗Sangyun Lee↗Michael te Vrugt↗Benno Liebchen↗Hartmut L\"owen↗Raphael Wittkowski↗Artur Widera↗

arXiv:2606.24615v1 Announce Type: new Abstract: Active matter comprises particles that extract energy from their local environment and convert it into motion. Although active particles have been miniaturized down to the nanoscale, realizing activity at the fundamentally smaller scale of individual atoms remains an open challenge, where quantum effects become increasingly relevant. Here, we experimentally demonstrate that individual Cs-133 atoms confined in an optical dipole trap extract energy from an ultracold bath of Rb-87 atoms via quantum-mechanical spin interactions and convert it into active motion. We quantitatively reproduce the resulting dynamics using a parameter-free active Langevin model derived from kinetic theory and support it with event-driven Monte Carlo collision simulations. The microscopic origin of activity is identified as quantum spin exchange, which transfers discrete internal spin energy into kinetic motion. Our work establishes a quantum-enabled route to active matter at the fundamental size limit of single atoms and opens perspectives for exploring the interplay of activity, quantum physics, and mesoscopic non-equilibrium thermodynamics.

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05.
arXiv (CS.LG) 2026-06-16

Learning the Geometry of Data: A Mathematical Review of Shape Space Analysis

Authors:
Gary P. T. Choi↗Khanh Dao Duc↗Shira Faigenbaum-Golovin↗Karen Habermann↗Emmanuel Hartman↗Christoph von Tycowicz↗Chi Zhang↗Wenjun Zhao↗Felix Zhou↗

arXiv:2606.17022v1 Announce Type: cross Abstract: A central objective of machine learning is to identify structure and patterns in data. Advances in data acquisition have increasingly produced datasets whose observations possess rich geometric form, giving rise to shape spaces that encode variability in object geometry. Such datasets arise across a wide range of disciplines, including biology, medicine, anthropology, and computer vision, where subtle geometric differences often carry important scientific information. Traditional machine learning methods, however, are frequently ill-equipped to account for the nonlinear geometric structure underlying these data. This survey synthesizes a rapidly growing body of work on shape space analysis, which provides a mathematical and computational framework for the study of geometric data. Drawing on ideas from differential geometry, statistics, and machine learning, we organize the literature around a common analytical pipeline: shape representation and parameterization, the rigorous construction of robust geodesic metrics, statistical analysis on shape spaces, and geometry-aware learning methods. We discuss how these tools enable the characterization of shape variability, the comparison of geometric objects, and the analysis of structural trajectories across populations and time. To illustrate the breadth of the field, we highlight applications spanning multiple scales of biological organization, including studies of subcellular morphology and primate tooth evolution. Across these and many other domains, researchers face common challenges arising from complex, nonlinear, and often unaligned geometric variation. The review concludes by identifying key theoretical and computational challenges, as well as emerging opportunities driven by increasingly large and diverse geometric datasets.

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

From Uncertain Judgments to Calibrated Rankings: Conformal Elo Estimation for LLM Evaluation

Authors:
Bora Kargi↗David Salinas↗

arXiv:2606.13221v1 Announce Type: new Abstract: Evaluating new large language models typically requires costly human annotation campaigns at scale. LLM-as-a-judge offers a cheaper alternative, but judge scores carry systematic errors - such as position bias, self-preference, or intransitivity - that can strongly miscalibrate the resulting rankings. We quantify the resulting judge-human disagreement at two complementary levels. At the local level, we estimate per-battle uncertainty from the judge's own score differences by propagating calibrated win probabilities rather than hard labels into the Bradley-Terry procedure. This alone provides a drastic improvement to Elo estimation accuracy, bringing LLM-derived ratings within 17.9 Elo MAE of human-derived ones when averaged over 55 held-out models on LMArena. At the global level, we apply split conformal prediction to the residual gap between LLM-derived and human-derived Elo ratings across held-out models, producing prediction intervals with distribution-free marginal coverage guarantees that account for irreducible LLM-human disagreement. Together, these two layers yield a low-cost evaluation tool that provides developers with calibrated Elo estimates and honest uncertainty bounds, without access to large-scale human annotations.To facilitate reproducibility, we release our code at https://github.com/kargibora/SoftElo .

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

Variational Network with Wavelet-based UNET in Accelerated MRI Reconstruction from Under Sampled K-space Data

Authors:
Yasir Arafat Prodhan↗Shaikh Anowarul Fattah↗

Fully sampled MRI requires dense k-space acquisition, leading to long scan times, reduced clinical throughput, and increased sensitivity to patient motion. Accelerated MRI addresses this by acquiring undersampled k-space data and reconstructing the missing information computationally. However, reconstruction from undersampled measurements is highly ill-posed and can introduce aliasing artifacts, noise amplification, and loss of anatomical detail. Although conventional parallel imaging and compressed sensing methods mitigate these issues, and deep learning methods have further improved reconstruction quality, preserving high-frequency structures under aggressive undersampling remains challenging. In this work, we propose a Variational Network with a Wavelet-based U-Net (W-UNet) for accelerated MRI reconstruction. The framework combines physics-guided iterative reconstruction with learnable multi-scale frequency representations. Standard pooling operations are replaced with Discrete Wavelet Transform and Inverse Wavelet Transform modules, enabling lossless downsampling while preserving low-frequency structure and high-frequency edge details. Integrated into the refinement and sensitivity map estimation stages, the proposed design improves artifact suppression, feature preservation, and reconstruction fidelity in both single-coil and multi-coil settings. Experiments on fastMRI knee and M4Raw brain datasets show state-of-the-art performance. Ablation studies further confirm the effectiveness of wavelet-based feature decomposition for accelerated MRI reconstruction.

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08.
arXiv (CS.AI) 2026-06-17

KANLib – An Modular, Extensible and Fast Kolmogorov-Arnold Network Implementation

Authors:
Julian Hoever↗Gregor Schiele↗

arXiv:2606.17927v1 Announce Type: cross Abstract: Kolmogorov-Arnold Networks (KANs) have recently emerged as a promising alternative to traditional multilayer perceptrons by replacing linear weights with learnable univariate functions. Despite their theoretical advantages in interpretability and expressiveness, practical research of KANs remains difficult due to high computational costs and inconsistent feature support across existing frameworks. This paper introduces KANLib, a modular, extensible, and computationally efficient framework for developing and evaluating KAN architectures. KANLib unifies core concepts from existing implementations, including PyKAN, EfficientKAN, and FastKAN, within a consistent software architecture that emphasizes flexibility, feature parity, and high performance. The framework supports two basis function types, adaptive grid rescaling, grid extension, and fine-grained architectural customization while maintaining compatibility with standard PyTorch workflows. Experimental evaluation on the California Housing benchmark demonstrates that KANLib reproduces the predictive behavior of established reference KAN implementations while achieving competitive computational efficiency. Furthermore, the framework enables the exploration of architectural variations beyond standard KAN formulations with only minor impacts on predictive performance. Overall, KANLib provides a robust foundation for future research on scalable and extensible KAN architectures.

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09.
bioRxiv (Bioinfo) 2026-06-18

Accounting for allelic diversity and multicopy gene detection improves the accuracy of antibiotic resistance genotypic determination

Authors:
Garcia Gonzalez↗Ferragud↗Blane↗Kim↗J. I↗Torok↗M. E↗Harrison↗E. M↗Gouliouris↗Coll↗

Background Genomic prediction of antimicrobial resistance (AMR) relies on the accurate detection of resistance genes or allelic variants of core genes from raw or assembled genomes sequences. For several bacterial species and antibiotics, AMR genotype-phenotype discrepancies are common, indicating that important sources of error remain unresolved. For Enterococcus faecium, we focused on identifying the sources of discrepancies for tetracycline resistance, for which genotypic detection had shown particularly low accuracy. We investigated the effect of structural variation in antibiotic resistance genes (ARGs), including gene duplications, truncations, interruptions, and mixed configurations of complete and partial gene copies, as a source of genotype-phenotype discrepancies from short-read data. We conduct further extended investigations to other antibiotic families and into another bacterial species: Escherichia coli. Methods We analyzed collections of E. faecium and E. coli genomes, integrating high-quality complete assemblies, simulated Illumina short reads, and matched AMR phenotypic data. The integrity, copy number, and allelic diversity of ARGs were examined for multiple antibiotic classes, and their impact on ARG detection and accuracy of AMR determination was assessed using several commonly used bioinformatic tools (SRST2, ARIBA and AMRFinderPlus). Results For E. faecium, after ruling out the effect of specific tet allelic variants on tetracycline susceptibility, we found that the integrity and copy number of tet(M) had a major effect on detection accuracy. Duplicated and incomplete ARGs are also common in E. faecium genomes, particularly for macrolides (erm(B)) and aminoglycosides (ant(6)-Ia and aph(3')-IIIa). In E. coli, similar patterns were observed for tet(A), erm(B) and aminoglycoside-associated genes (aph(3')-IIIa and ant(6)-Ia). Across ARGs in both species, short-read mapping methods wrongly reported interrupted genes as complete in some instances, while assembly-based methods often failed to resolve complete copies of duplicated genes. Detection accuracy improved when tools were adapted to account for gene integrity and when extended AMR databases incorporating species-specific alleles were included. Conclusions Our findings reveal that bioinformatic limitations in dealing with ARG copy number and completeness, and in accounting for allelic variation, underly a substantial source of genotype-phenotype errors, highlighting the need for improved AMR databases and bioinformatic tools that consider these factors to achieve reliable genomic prediction of AMR.

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10.
arXiv (CS.AI) 2026-06-11

Making Models Unmergeable via Scaling-Sensitive Loss Landscape

Authors:
Minwoo Jang↗Hoyoung Kim↗Jabin Koo↗Jungseul Ok↗

arXiv:2601.21898v2 Announce Type: replace Abstract: The rise of model hubs has made it easier to access reusable model components, making model merging a practical tool for combining capabilities. Yet, this modularity also creates a governance gap: downstream users can recompose released weights into unauthorized mixtures that bypass safety alignment or licensing terms. Because existing defenses are largely post-hoc and architecture-specific, they provide inconsistent protection across diverse architectures and release formats in practice. To close this gap, we propose Trap$^2$, an architecture-agnostic protection framework that encodes protection into updates during fine-tuning, regardless of whether they are released as adapters or full models. Instead of relying on architecture-dependent approaches, Trap$^2$ uses weight re-scaling as a simple proxy for the merging process. It keeps released weights effective in standalone use, but degrades them under re-scaling that often arises in merging, undermining unauthorized recomposition.

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11.
arXiv (CS.AI) 2026-06-16

Visualizing Uncertainty: Spatial Maps of Missing and Conflicting Evidence in Deep Learning

Authors:
Dong Hyun Jeong↗Feng Chen↗Jin-Hee Cho↗Lance M. Kaplan↗Audun J{\o}sang↗Soo-Yeon Ji↗

arXiv:2606.15767v1 Announce Type: cross Abstract: Understanding when and why deep neural networks are uncertain is crucial for deploying reliable machine learning systems in safety-critical domains. While existing uncertainty quantification methods provide scalar measures of model confidence, they offer limited insight into which spatial regions of an input contribute to different types of uncertainty. We propose a novel visualization framework, Uncertainty Activation Map (UAM), that combines Evidential Deep Learning (EDL) with Full-Gradient Class Activation Mapping (FullGrad) to generate interpretable spatial uncertainty activation maps. Our approach distinguishes between two fundamental types of uncertainty: vacuity, representing lack of evidence, and dissonance, capturing conflicting evidence between competing hypotheses. By leveraging the complete gradient decomposition property of FullGrad and the principled uncertainty quantification of Subjective Logic, our method produces theoretically grounded visualizations that highlight specific image regions responsible for model uncertainty. With this framework, vacuity and dissonance activation maps are generated by computing belief-weighted attributions, enabling identification of where models lack knowledge versus where they encounter ambiguous evidence. Extensive evaluations across multiple benchmark datasets demonstrate that the proposed framework effectively addresses the critical gap between uncertainty quantification and explainability, providing intuitive visual feedback to assess model reliability in complex visual recognition tasks.

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12.
arXiv (CS.AI) 2026-06-24

MGI: Member vs Generated Inference

Authors:
Bihe Zhao↗Michel Meintz↗Juangui Xu↗Franziska Boenisch↗Adam Dziedzic↗

arXiv:2606.23872v1 Announce Type: cross Abstract: As generative models increasingly produce samples that are indistinguishable from human-created content, it becomes difficult to determine whether a given data point was part of a model's natural training set or was generated by the model itself, especially when models memorize and reproduce training data. We formalize this challenge as Member vs Generated Inference (MGI): given a sample and a target generative model, infer whether the sample is a true training member or a generated output of that model. Focusing on image generation, we show that existing membership inference methods systematically misclassify generated samples as training members, while attribution-based methods often misclassify true members as generated. This failure arises because both approaches rely on likelihood-related signals that are similarly elevated for training examples and for the model's own outputs. To address MGI, we propose Data Circuit Breaker (DCB), a three-stage method that combines complementary signals from a generative model's autoencoder and latent generator to distinguish training members from generated samples. Across multiple generative models, including image autoregressive and diffusion models, DCB consistently addresses the shortcomings of membership inference and attribution methods, remains effective even when models reproduce near-duplicates of training samples, and generalizes to challenging model derivative settings in which new models are trained on generated data.

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

EChO-Agent: Evidence Chain Orchestration Agent for Audio Reasoning

Authors:
Siyuan Zhang↗Jian Zong↗Junyu Wang↗Peiyuan Jiang↗Jiahao Yan↗Jingyu Zhang↗Tianrui Wang↗Xiaobao Wang↗Longbiao Wang↗Jianwu Dang↗

arXiv:2606.15141v1 Announce Type: cross Abstract: While LALMs show promise on audio question answering, they fail to focus on question-relevant segments of audio and provide a clear, checkable reasoning process when dealing with complex audio reasoning. Reinforcement learning and tool-augmented prompting can help models better relate questions to audio but lack a reliable way to understand, integrate, and self-verify audio segments. To address this gap, we present EChO-Agent, a modular agent framework that reformulates complex audio QA as a planning, tool execution, evidence integration, and answer verification workflow. Experiments on MMAR benchmark show EChO-Agent improves both accuracy and rubric scores over baseline and ablation studies show evidence integration is the key factor.

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14.
arXiv (quant-ph) 2026-06-16

Diagonal-Budgeted Trotterization for Efficient Quantum Hamiltonian Simulation

Authors:
Srikar Chundury↗Blake Burgstahler↗Jiajia Li↗In-Saeng Suh↗Frank Mueller↗

arXiv:2606.16959v1 Announce Type: new Abstract: Efficient classical simulation of quantum Hamiltonian dynamics is often bottlenecked by exponential state growth and the overhead of generic sparse linear algebra. We introduce diagonal-budgeted Trotterization, a structure-aware strategy that decomposes Hamiltonians into factors preserving diagonal sparsity while tightly controlling fidelity loss. Our implementation, HamSim, utilizes a compact diagonal-sparse data layout and specialized C++/CUDA kernels to bypass the overheads of generic formats like CSR. By leveraging SIMD vectorization, multithreading, and GPU acceleration, HamSim achieves high performance across heterogeneous architectures. Benchmarks on the HamLib suite show that HamSim significantly outperforms Qiskit-Aer. On CPUs, HamSim attains speedups of $182$–$1,269\times$ on optimization instances (TSP, MaxCut) and $4.8$–$841\times$ on physical models (TFIM, Heisenberg). On GPUs, it achieves up to $178\times$ speedup for $12$–$16$ qubit problems. Unlike traditional Trotterization, HamSim maintains near-perfect fidelity without requiring exponential steps. This demonstrates that diagonal-aware numerical kernels provide a scalable foundation for high-fidelity classical Hamiltonian simulation.

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

Structure-Semantic Co-optimized Latent Diffusion Model for Fast Visual Anagram Synthesis

Authors:
Xiang Gao↗Yunpeng Jia↗

Visual anagram is an intriguing form of art creation wherein a single image presents different conceptual interpretations under transformations such as flipping or rotation. Recent work has achieved visual anagram synthesis by leveraging pretrained text-to-image (T2I) diffusion models, yet still suffers from several key limitations including computational inefficiency, suboptimal aesthetic quality, and weak semantic fidelity and expressiveness. This work focuses on generating visual anagrams with substantially improved visual quality at minimal computational cost, thereby advancing intelligent creation of illusionary digital art. To increase image resolution while reducing time overhead, we adapt the cutting-edge parallel denoising algorithm from pixel-based T2I model to the adversarially distilled latent-based one, and accordingly propose a structure-semantic co-optimization (S2CO) framework to counteract the consequent visual degradation. As the core of our approach, S2CO framework comprises three key innovations: (\romannumeral1) null-text structure alignment optimization; (\romannumeral2) semantic enhancement optimization; (\romannumeral3) attention-guided noise fusion. Building upon these components, our method dubbed S2CO-Anagram is able to generate higher-resolution anagram images with noticeably superior visual harmony and semantic faithfulness than related SOTA approaches, all while achieving substantially faster inference speed. Code will be publicly available.

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16.
arXiv (CS.AI) 2026-06-24

FP8 is All You Need (Part 2): Efficient Ozaki-Bailey Style FFT Through Tensor-core Garner Reformulation and Kulisch Escape Route

Authors:
Satoshi Matsuoka↗

arXiv:2606.23698v1 Announce Type: cross Abstract: NVIDIA's Blackwell Ultra (B300) cuts FP64 vector throughput to ~1.3 TFLOPS per GPU, roughly 30x below B200 and well below the level at which bandwidth-limited FP64 workloads stay memory-bound. The Ozaki Scheme II framework recovers FP64-equivalent throughput by routing dense matrix multiply through FP8 tensor cores with a mantissa-sliced Chinese-remainder reconstruction. A companion Part (1) paper covers dense GEMM, batched GEMV, stencils, and SpMV; this paper adds the fifth canonical primitive, the 3-D FFT. We present Ozaki-Bailey FFT, an emulated 3-D FFT via the Bailey six-step decomposition with both 1-D FFT GEMMs on FP8 tensor cores. Bailey's small inner factor k ~ sqrt(N) (k=32 for N=1024) puts the kernel in the regime k

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

Transformer-Based Warm-Starting for Feasible and Optimal Terminal Approach to Tumbling Objects with Space Manipulators

Authors:
Yuji Takubo↗Maximilian Adang↗Mac Schwager↗Simone D'Amico↗

arXiv:2606.17317v1 Announce Type: cross Abstract: Real-time trajectory generation for on-orbit robotic servicing is challenging due to the nonlinear coupling between spacecraft bus motion, manipulator dynamics, visibility cone, and trajectory-level safety constraints. This paper studies learning-based warm-starting for sequential convex programming (SCP) in the terminal approach of a space manipulator toward a tumbling target. The proposed framework decomposes the problem into a system center-of-mass translational planning stage and a coupled attitude–manipulator torque-allocation stage, and applies a causal transformer warm-start to the latter, which constitutes the dominant computational bottleneck. Linear and flow matching action decoders are compared under different action-chunking and training dataset sizes, and the resulting warm-starts are evaluated under both cost-optimal and feasibility projection using SCP. Across 300 held-out scenarios, the learned warm-start reduces the second-stage SCP iteration count by up to 28% and the runtime by 23% while preserving the final control-cost distribution. When the learned warm-starts are used for nonconvex feasibility projection, they nearly halve the runtime relative to cost-optimal SCP, while avoiding the catastrophic high-cost tail behavior observed when initialized heuristically. These results indicate that sequence-model warm-starts can improve both the computational efficiency and trajectory robustness of optimization-based terminal guidance for space manipulation.

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18.
arXiv (CS.LG) 2026-06-17

Regularized Machine Learning for System Identification of Ship Free-Running Manoeuvres from CFD-Based Synthetic Data: A Comparative Study

Authors:
R. F. Su\'arez↗J. C. Berndt↗M. Abdel-Maksoud↗

arXiv:2606.17121v1 Announce Type: cross Abstract: This study investigates supervised machine learning techniques for identifying ship hydrodynamic coefficients from CFD-generated data from free-running simulations. Specifically, ordinary least squares and regularized regression methods are applied to Abkowitz-type manoeuvring models. Training and validation datasets are derived from URANS simulations of zig-zag and turning circle manoeuvres, which are validated against experimental benchmark data. The analysis evaluates the effects of coefficient set size, minimum training length required for predictive model training, and manoeuvre combinations on model performance. Results demonstrate the suitability of large-angle zig-zag manoeuvres for hydrodynamic system identification, provided that multicollinearity is addressed through appropriate coefficient selection, regression models, or input data variability. Larger coefficient sets offer greater model flexibility for variable conditions but are more prone to multicollinearity. Regularized regression techniques effectively mitigate multicollinearity and notably enhance prediction accuracy, as does incorporating more diverse manoeuvring data. Among tested models, Ridge regression provided the best compromise between computational efficiency and prediction accuracy.

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19.
arXiv (CS.CV) 2026-06-18

HeatKV: Head-tuned KV-cache Compression for Visual Autoregressive Modeling

Authors:
Jonathan Cederlund↗Axel Berg↗William Isaksson↗Durmus Alp Emre Acar↗Chuteng Zhou↗Pontus Giselsson↗

Visual Autoregressive (VAR) models have recently demonstrated impressive image generation quality while maintaining low latency. However, they suffer from severe KV-cache memory constraints, often requiring gigabytes of memory per generated image. We introduce HeatKV, a novel compression method that adapts cache allocation in each head based on its attention to previously generated scales. Using a small offline calibration set, the attention heads are ranked according to their attention scores over prior scales. Based on this ranking, we construct a static pruning schedule tailored to a given memory budget. Applied to the Infinity-2B model, HeatKV achieves $2 \times$ higher compression ratio in memory allocation for KV cache compared to existing methods, while maintaining similar or better image fidelity, prompt alignment and human perception score. Our method achieves a new state-of-the-art (SOTA) for VAR model KV-cache compression, showcasing the effectiveness of fine-grained, head-specific cache allocation. Code and calibration script available at https://github.com/arm-research/heatkv.

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20.
arXiv (CS.CL) 2026-06-15

Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling

Authors:
Yiran Guo↗Zhongjian Qiao↗Yingqi Xie↗Jie Liu↗Dan Ye↗Ruiqing Zhang↗Shuang Qiu↗Lijie Xu↗

Effective exploration is a key challenge in reinforcement learning for large language models: discovering high-quality trajectories within a limited sampling budget from the vast natural language sequence space. Existing methods face notable limitations: GRPO samples exclusively from the root, saturating high-probability trajectories while leaving deep, error-prone states under-explored. Tree-based methods blindly disperse budgets across trivial or unrecoverable states, causing sampling dilution that fails to uncover rare correct suffixes and destabilizes local baselines. To address this, we propose Deep Dense Exploration (DDE), a strategy that focuses exploration on $pivots$-deep, recoverable states within unsuccessful trajectories. We instantiate DDE with DEEP-GRPO, which introduces three key innovations: (1) a lightweight data-driven utility function that automatically balances recoverability and depth bias to identify pivot states; (2) local dense resampling at each pivot to increase the probability of discovering correct subsequent trajectories; and (3) a dual-stream optimization objective that decouples global policy learning from local corrective updates. Experiments on mathematical reasoning benchmarks demonstrate that our method consistently outperforms GRPO, tree-based methods, and other strong baselines. Code is available at https://github.com/AgentCombo/DEEP-GRPO

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21.
bioRxiv (Bioinfo) 2026-06-23

EnrichViz: An Interactive R Shiny Application for Visualization of Pathway Enrichment Results from Omics Data

Authors:
Garcia-Milian↗

Pathway and functional enrichment analysis is a cornerstone of omics data interpretation, enabling researchers to map differentially expressed proteins or genes onto curated biological processes, signaling cascades, and molecular functions. While tools such as Ingenuity Pathway Analysis (IPA), g:Profiler, and Enrichr are widely used to generate ranked enrichment results, translating these tabular outputs into clear, publication-ready figures remains a time-consuming step that typically requires custom scripting and familiarity with visualization libraries, a significant barrier for researchers without a computational background. Here we present EnrichViz, a self-contained, browser-based R Shiny application that enables interactive, code-free visualization of pathway and functional enrichment results from quantitative proteomics, transcriptomics, and metabolomics experiments. EnrichViz accepts three standard CSV files as input, a normalized abundance matrix, a sample annotation or metadata file, and enrichment results from any platform that exports tabular output, and produces six complementary, publication-ready visualizations: bar and bubble plots for ranking enriched terms by significance, chord diagrams for exploring pathway-molecule connectivity, clustered heatmaps for displaying Z-score normalized expression patterns across experimental groups, and boxplots or violin plots for examining the abundance distribution of individual proteins, genes, or metabolites. The application supports both raw p-values and pre-transformed -log10(p) values through automatic detection, and all plot parameters are adjustable in real time through a graphical sidebar. Every figure can be exported as a high-resolution PNG file at 300 dpi. EnrichViz is implemented in R using the Shiny, ggplot2, pheatmap, and circlize packages, and is freely available at https://rgmilian.shinyapps.io/EnrichViz/

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

Recognizing and Reconstructing a Multi-Unit Floor Plan

Authors:
Lukas Kratochvila↗Gijs de Jong↗Monique Arkesteijn↗Simon Bilik↗Tomas Zemcik↗Karel Horak↗Jan S. Rellermeyer↗

Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and faster rescue intervention. Nevertheless, creating the twins still remains a largely manual effort, due to a lack of 3D-representations, which are available only in limited amounts for some new buildings. Thus, in this paper we aim to synthesize 3D information from commonly available 2D architectural floor plans. We propose two novel pixel-wise segmentation methods based on the MDA-Unet and MACU-Net architectures with improved skip connections, an attention mechanism, and a training objective together with a reconstruction part of the pipeline, which vectorizes the segmented plans to create a 3D model. The proposed methods are compared with two other state-of-the-art techniques and several benchmark datasets. On the commonly used CubiCasa benchmark dataset, our methods have achieved the mean F1 score of 0.86 over five examined classes, outperforming the other pixel-wise approaches tested. We have also made our code publicly available to support research in the field.

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23.
arXiv (CS.CL) 2026-06-18

TurnGuide: Enhancing Meaningful Full Duplex Spoken Interactions via Dynamic Turn-Level Text-Speech Interleaving

Authors:
Wenqian Cui↗Lei Zhu↗Xiaohui Li↗Zhihan Guo↗Haoli Bai↗Lu Hou↗Irwin King↗

Full-Duplex Speech Language Models (FD-SLMs) are specialized foundation models designed to enable natural, real-time spoken interactions by modeling complex conversational turn-taking such as interruptions, backchannels, and overlapping speech. End-to-end (e2e) FD-SLMs leverage real-world double-channel conversational data to capture nuanced two-speaker dialogue patterns for human-like interactions, but their conversational abilities often degrade compared to pure-text conversation due to prolonged speech sequences and limited high-quality spoken dialogue data. Although interleaved text-speech generation could mitigate this degradation, integrating discrete text tokens into continuous double-channel audio streams could disrupt the precise time alignment required for fluid interaction. To address this, we propose TurnGuide, a novel text-speech interleaved generation approach for e2e FD-SLMs that dynamically segments assistant speech into dialogue turns and interleaves turn-level text and speech generation. This approach allows FD-SLMs to integrate the semantic intelligence of LLMs without compromising the natural acoustic flow. Extensive experiments show that TurnGuide not only significantly improves e2e FD-SLMs to produce semantically meaningful, coherent speech but also achieves state-of-the-art performance on various turn-taking events. Demos are available at https://dreamtheater123.github.io/TurnGuide-Demo/. Code is available at https://github.com/dreamtheater123/TurnGuide.

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

Beyond Reward Engineering: A Data Recipe for Long-Context Reinforcement Learning

Authors:
Xiaoyue Xu↗Sikui Zhang↗Xiaorong Wang↗Xu Han↗Chaojun Xiao↗

Long-context reasoning is an essential capability for large language models, particularly when they are deployed as autonomous agents that must reason over lengthy trajectories. Reinforcement learning (RL) has recently emerged as a dominant paradigm for improving this ability, yet existing work largely focuses on reward engineering while diverse training data remains scarce. We revisit this problem from a data-centric perspective and show that a simple yet effective data recipe alone, paired with a minimal outcome-based GRPO setup, suffices to substantially improve long-context reasoning. Our recipe targets three complementary task families – retrieval, multi-evidence synthesis, and reasoning – for which we construct and curate eight datasets totaling ~14K examples. Experiments on three models (Qwen3-4B/8B/30B-A3B) yield average gains of +7.2/+3.2/+6.4 points across seven long-context benchmarks, surpassing prior RL training sets. We further demonstrate that these gains transfer to agentic tasks, where continuing RL training on an agent-tuned model with our data recipe improves GAIA by +4.8 and BrowseComp by +7.0 points. We will release our datasets to facilitate future research.

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25.
arXiv (CS.AI) 2026-06-12

Real-Time Execution with Autoregressive Policies

Authors:
Sangkyu Lee↗Seohyeon Park↗Tackgeun You↗Avi Caciularu↗Idan Szpektor↗Hwasup Lim↗Youngjae Yu↗

arXiv:2606.13355v1 Announce Type: cross Abstract: Real-time execution, enabled by asynchronous inference that ensures both smooth action trajectories and fast reactivity, is critical for realistic deployments of large-scale Vision-Language-Action models. However, recent work on real-time execution primarily focuses on variants of diffusion policies, even though it is more critical for autoregressive policies given their slower rollout speed in synchronous inference. In contrast, we demonstrate that autoregressive policies can achieve real-time execution by adjusting the tokenization horizon and applying constrained decoding, thereby guaranteeing strict latency bounds that enable multi-trajectory decoding to maximize performance. Across simulated and real-world environments, we find that the autoregressive policy consistently outperforms its equivalent-level flow-matching policy counterpart while achieving significantly improved task completion speeds from synchronous inference. Coupled with the inherent advantages of autoregressive policies, such as faster convergence and better generalizability in instruction-following, these results confirm that autoregressive policies can remain a competitive policy type supporting real-time execution.

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