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arXiv preprints from January 1, 2026 through September 21, 2026 — 21:19:19 EST

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Posted in q-bio.GN · 2026-09-14 · Rui Xiao, Yili Xu

Democratizing Clinical Tumor Whole Genome Sequencing: 18-hour End-to-end Analysis via Trillion-parameter Large Language Models Locally Deployed on Consumer-grade Hardware

Whole genome sequencing (WGS) is essential for precision oncology, yet its clinical adoption remains limited by prohibitive computational costs and multi-day turnaround times. This work presents a fully localized low-resource framework enabling stable deployment of a trillion-parameter biomedical LLM on a single consumer-grade RTX...

💬 0 commentsarXiv:2609.17620v1PDF
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Posted in q-fin.TR · 2026-09-16 · Vincent Maciejewski

Model-Free Passive Execution via Order-Level Shadowing

Automated execution algorithms are organized into schedule-based and liquidity-seeking families. This paper concerns the first, whose members -- Time-Weighted Average Price (TWAP), Volume-Weighted Average Price (VWAP), Percentage of Volume (POV) and Implementation Shortfall -- are all model-based: each derives its decisions from an...

💬 0 commentsarXiv:2609.18019v1PDF
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Posted in q-fin.MF · 2026-09-15 · Florian Bourgey, Jim Gatheral

Demystifying the Bergomi-Guyon expansion

Alòs, Gatheral and Radoičić derived the Bergomi-Guyon expansion of the implied variance smile from the forest expansion of the cumulant generating function. Its coefficients are sums of products of diamond trees, with prefactors that are polynomials in the log-strike $k$. Matching moments order by order produces, at order $ε^\ell$,...

💬 0 commentsarXiv:2609.17869v1PDF
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Posted in q-fin.TR · 2026-09-15 · Georgios Chionas, Charalampos Kleitsikas, Stefanos Leonardos, Leandro Sánchez-Betancourt, Carmine Ventre

SAiFE-gym: Model-based Environments for Automated Market Making with Concentrated Liquidity

We present SAiFE_gym, a Python module that provides a collection of simulation environments for studying trading problems in Constant Product Markets (CPMs) with Concentrated Liquidity (CL). These markets give Liquidity Providers (LPs) granular control over how their capital is allocated and enable them to adjust their range of...

💬 0 commentsarXiv:2609.17788v1PDF
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Posted in physics.soc-ph · 2026-09-14 · Maksym Nechepurenko

On-Demand Combinatorial Event Markets on Kalshi: Instantiation, Concentration, and Effective Market Breadth

Kalshi's multivariate-event architecture produces market objects on demand from exact selected legs. Across a registered seven-day interval, 190 independently validated temporal shards yield 7,611,594 unique REST MVE market tickers after excluding 5,777 boundary-overlap observations; the population was created at an average rate of...

💬 0 commentsarXiv:2609.17610v1PDF
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Posted in q-fin.PM · 2026-09-14 · Marc da Costa Nunes

Separated Signal Libraries: Packing, Saturation, and Joint Spectral Limits

We study libraries of cross-sectional signals: at each date, a forecast vector over $d$ assets intended to predict the next period's cross-sectional return. Demeaned and unit-normalized, a signal is a point on a sphere and its $T$-date history a point on a product of $T$ spheres. A pairwise correlation cap on histories is a minimum...

💬 0 commentsarXiv:2609.17609v1PDF
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Posted in cs.CE · 2026-09-16 · Romain Deloffre, Lorène Héraud, Julie Lartigau

From powder to part: influence of virgin and recovered Inconel 625 powders on the DED-LP processability, microstructure and mechanical properties

The reuse of metal powders in directed energy deposition using laser powder offers promising sustainability benefits for additive manufacturing, yet its impact on part quality remains unknown. This study investigates the influence of powder reuse on the directed energy deposition process of Inconel 625. Virgin powder was first...

💬 0 commentsarXiv:2609.18636v1PDF
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Posted in cs.DS · 2026-09-16 · Adithya Diddapur

Deterministic Streaming Lower Bounds for Approximate Maximum Clique and Maximum Independent Set

We study the canonical \textsf{Maximum Clique} and \textsf{Maximum Independent Set} problems in the one-pass edge-arrival graph streaming setting. Here, the edges of some input graph $G = (V,E)$ are presented one at a time (possibly including deletions), before an algorithm needs to produce either a large clique or independent set at...

💬 0 commentsarXiv:2609.18635v1PDF
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Posted in cs.CV · 2026-09-16 · Emanuele Artioli, Daniele Lorenzi, Shivi Vats, Farzad Tashtarian, Christian Timmerer

GenStream: Semantic Streaming Framework for Generative Reconstruction of Human-centric Media

Video streaming dominates global internet traffic, yet conventional pipelines remain inefficient for structured, human-centric content such as sports, performance, or interactive media. Standard codecs re-encode entire frames, foreground and background alike, treating all pixels uniformly and ignoring the semantic structure of the...

💬 0 commentsarXiv:2609.18634v1PDF
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Posted in cs.OS · 2026-09-16 · Daniel Borkmann, Paul Chaignon

Netkit: Specializing Linux Packet Delivery for Container Networks

Cloud-native microservices architectures rely on network namespaces for isolation, with the overhead of container communications remaining a critical performance bottleneck. While colocating containers on the same host mitigates some of this overhead, it cannot match the performance of communication within a single network namespace....

💬 0 commentsarXiv:2609.18633v1PDF
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Posted in cs.CV · 2026-09-16 · Qilin Wang, Mingyu Li, Hao Tang

VibeAvatar: Aligning Phonetic Kinematics and Human Aesthetics for High-Fidelity Talking Avatar Synthesis

Multi-modal talking avatar synthesis aims to generate realistic talking videos from a reference portrait and speech. Despite rapid progress in diffusion-based methods, existing approaches still struggle to jointly achieve accurate lip articulation, human-preferred motion aesthetics, and efficient inference. We observe that phonetic...

💬 0 commentsarXiv:2609.18632v1PDF
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Posted in cs.RO · 2026-09-16 · Yueying Zhu, Xiang Li, Thien-Minh Nguyen, Xuehe Wang, Shenghai Yuan

Benchmarking Visual-Inertial Odometry in Subterranean Environments Under Sensor Degradation, Miscalibration, and Dynamic Occlusion

Visual-inertial odometry (VIO) is a core capability for autonomous operation in GPS-denied subterranean environments, yet its reliability can degrade sharply under sensor drift, calibration errors, and dynamic occlusion. Existing evaluations mainly emphasize nominal-condition accuracy, offering limited insight into when practical...

💬 0 commentsarXiv:2609.18628v1PDF
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Posted in cs.MM · 2026-09-16 · Emanuele Artioli, Mohammadreza Ghafari, Md Tariqul Islam, Farzad Tashtarian, Christian Rothenberg, Christian Timmerer

MoQSplat: Adaptive Progressive Streaming of 3D Gaussian Splatting via MoQ

3D Gaussian Splatting (3DGS) enables photorealistic novel view synthesis, but transmitting gigabyte-scale scene data remains challenging for immersive applications. Traditional HTTP Adaptive Streaming over TCP introduces Head-of-Line (HOL) blocking and coarse segmenting ill-suited to fine-grained 3DGS delivery. We propose MoQSplat,...

💬 0 commentsarXiv:2609.18624v1PDF
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Posted in cs.CV · 2026-09-16 · Kemal Oksuz, Alexandru Buburuzan, Yuhan Yao, Puneet K. Dokania

FIVE-VLA: Fast and EffectIVE Autonomous Driving with Recurrent Action Memory

State-of-the-art vision-language-action models (VLA) for autonomous driving face critical limitations: excessive parameter counts, inefficient high-resolution image processing, and lack of temporal memory. We introduce Fast and EffectIVE VLA (FIVE-VLA) to address these through two key contributions. First, we employ an efficient...

💬 0 commentsarXiv:2609.18623v1PDF
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Posted in cs.LG · 2026-09-16 · Tetsuji Kuboyama

How Many Labels Does Model Choice Need? Certificates and Budgets for Selective Prediction

Classifiers can make identical predictions yet require labels to compare their selective performance: confidence ranks weight the same errors differently. We quantify this requirement for the area under the generalized risk-coverage curve (AUGRC). A prelabel lower bound rules out insufficient budgets. With all labels known, a covering...

💬 0 commentsarXiv:2609.18622v1PDF
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Posted in cs.RO · 2026-09-16 · Can Li, Jie Gu, Zishun Deng, Jingmin Chen, Lei Sun

DeformSmith: Physics Harness-Guided Hierarchical Generation of Deformable Assets for Robot Manipulation

Creating deformable assets for robot manipulation requires jointly specifying their geometry, appearance, and physical properties. This is especially challenging for deformable objects, since text and images provide limited evidence about how they deform and respond to contact, yet these responses directly affect their suitability for...

💬 0 commentsarXiv:2609.18620v1PDF
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Posted in cs.LG · 2026-09-16 · Sebastian Gerstner, Hilal AlQuabeh, Kentaro Inui, Hinrich Schütze

Weakening Neurons: An Input-Output Functionality in Transformers with Outsize Influence

We analyze the learned input-output behavior of GLU-based neurons in large language models (LLMs). We propose a simple analysis method: For each neuron, we compute the cosine similarities between its input (reading) and output (writing) weight vectors. In this scheme, a strong negative cosine similarity indicates the neuron weakens...

💬 0 commentsarXiv:2609.18612v1PDF
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Posted in cs.LG · 2026-09-16 · Fredy Pokou

A Geometric Theory of Decision Boundaries in Structured Markov Decision Processes

Classical dynamic programming represents optimal sequential decisions through value functions and policies. While this functional representation is natural for computing optimal decisions, it does not directly identify the mathematical object governing policy reconstruction, representation complexity, or oracle-query complexity once...

💬 0 commentsarXiv:2609.18610v1PDF
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Posted in cs.CL · 2026-09-16 · Mika Okamoto, Ansel Kaplan Erol

PACT: Can Enterprise AI Assistants Be Trusted Under Pressure?

As corporate AI adoption continues to grow, enterprise-grade LLM agents are being deployed into sensitive contexts such as hiring, healthcare, and finance. In these contexts, compliance with rules specified in an agent's system context is a first-order legal concern. Currently, no evaluation framework systematically measures which LLM...

💬 0 commentsarXiv:2609.18605v1PDF
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Posted in cs.CV · 2026-09-16 · Zhaoyang Jia, Tianyu Zhang, Zihan Zheng, Wenxuan Xie, Jiahao Li, Bin Li, Houqiang Li, Yan Lu

PULSE: Unlocking Practical Image Compression on Single-Thread CPU

Despite recent progress in learned image compression, existing methods remain computationally expensive on resource-constrained hardware, particularly CPUs. We introduce PULSE, a practical codec that enables (1) low-latency decoding on diverse hardware platforms with an ultra-low-complexity 5.2 kMAC/pixel neural receiver, and (2)...

💬 0 commentsarXiv:2609.18602v1PDF
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Posted in q-bio.QM · 2026-09-16 · Uros Sutulovic, Daniele Proverbio, Rami Katz, Giulia Giordano

Automatic denoising and differentiation based on Savitzky-Golay filtering and Homogeneous Differentiators for attractor reconstruction via differential embedding

Differential embedding methods aim to reconstruct attractors of dynamical systems from noisy measured time series, but require accurate estimates of signal derivatives. We introduce SHADED (Savitzky-Golay and Homogeneous-differentiator based Automatic DEnoising and Differentiation), a novel methodology for denoising and estimation of...

💬 0 commentsarXiv:2609.18631v1PDF
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Posted in eess.SP · 2026-09-16 · Julian P. Merkofer, Vincent van de Schaft, Ruud J. G. van Sloun

Learning Array Signal Topologies as Conditional Neural Manifolds

Subspace methods such as multiple signal classification (MUSIC) achieve super-resolution direction of arrival (DoA) estimation by exploiting the orthogonality between the array manifold and the noise subspace of the measurements. Their accuracy therefore depends on the assumed manifold and degrades under model mismatch, while...

💬 0 commentsarXiv:2609.18616v1PDF
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Posted in eess.IV · 2026-09-16 · Natascha Niessen, Ana Beatriz Solana, Carolin M. Pirkl, Tim Sprenger, Hannah Eichhorn, Veronika Spieker, Wenqi Huang, Rolf F. Schulte, Florian Wiesinger, Tobias C. Wood, Marion I. Menzel, Julia A. Schnabel on behalf of the PREDICTOM consortium

Highly accelerated 3D Cartesian MPnRAGE with implicit neural representation reconstruction

MPnRAGE enables multiple inversion contrast images in a single scan, allowing quantitative T1 mapping, tissue nulled contrasts, and standard MPRAGE synthesis. However, current 3D scan times remain clinically impractical, motivating accelerated 3D MPnRAGE. This work provides a highly accelerated Cartesian 3D MPnRAGE sequence with joint...

💬 0 commentsarXiv:2609.18589v1PDF
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Posted in cs.SD · 2026-09-16 · Giorgia Adorni, Michela Papandrea, Battista Rimoldi, Tiziano Leidi

TTM-Bench: A Framework for Text-to-Music System Performance Benchmarking

Text-to-music (TTM) systems are increasingly used to generate musical audio from natural-language descriptions. Robust evaluation is therefore essential, yet reliable performance comparison remains challenging. This difficulty stems from differences in system architecture, supported conditioning information, and access mode, as well...

💬 0 commentsarXiv:2609.18585v1PDF
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Posted in eess.SP · 2026-09-16 · Olli Apilo, Jorma Kilpi

QUBO Formulations of the Downlink MIMO Scheduling Problem in 5G Base Stations

Quantum computers can potentially solve large-scale combinatorial problems very efficiently when the problems are first converted into the quadratic unconstrained binary optimization (QUBO) format. Scheduling in fifth generation (5G) base stations is a practical combinatorial problem that cannot be solved optimally in real-time using...

💬 0 commentsarXiv:2609.18580v1PDF