March 2026 arXiv papers — page 80

Andreas Galanis, Daniel Stefankovic, Eric Vigoda

We study the complexity of approximating the partition function of dense Ising models in the critical regime. Recent work of Chen, Chen, Yin, and Zhang (FOCS 2025) established fast mixing at criticality, and even beyond criticality in a window of width $N^{-1/2}$. We complement these algorithmic results by proving nearly tight hardness bounds, thus yielding

Lucy S. Nathwani, Anne Ruperto, Ashvini Vallipuram, Abigail Y. Jiang

Perovskite oxides display correlated electrical, magnetic, and thermal properties that can be further tuned in the thin-film limit, making them contenders for next-generation electronics. Measuring thermal transport in thin films is challenging, because traditional techniques are dominated by the substrate. Here, frequency-domain thermoreflectance (FDTR) of

Navya Mehrotra, Adam Visokay, Kristina Gligorić

Large language models are increasingly used to annotate texts, but their outputs reflect some human perspectives better than others. Existing methods for correcting LLM annotation error assume a single ground truth. However, this assumption fails in subjective tasks where disagreement across demographic groups is meaningful. Here we introduce Perspective-Dri

Diomidis Spinellis, Zoe Kotti

Software engineering research benefited for decades from openly available tools, accessible systems, and problems that could be studied at modest scale. Today, many of the most relevant software systems are large, proprietary, and embedded in industrial contexts that are difficult to access or replicate in academia. We review how the field reached this point

Mohammad Abutoama

Thin films are preferred for high photocurrent conversion efficiency, but strong photon absorption at photon energies below the bandgap (near and shortwave infrared) typically requires thicker semiconductor layers. To address this tradeoff, various optical approaches have been proposed, including light scattering within the active layer, reducing surface ref

Khoi Duong

We introduce a fast, quasi-linear-time heuristic for the Close-Enough Traveling Salesman Problem (CETSP), a continuous generalization of the Euclidean TSP in which each target is a disk that must be intersected. The method adapts the pair-center clustering paradigm to circular neighborhoods: a hierarchical clustering phase merges nearby disks into proxy circ

Domenico Cafiero, Michele Correggi, Davide Fermi

We consider a non-relativistic quantum particle in $\mathbb{R}^d$, $d=2$ or $d = 3$, interacting with singular zero-range potentials concentrated on a large collection of points. We analyze the homogenization regime where the intensities of the singular potentials and the distances between the points simultaneously go to zero as their number grows, while the

Anthony T. Nixon

Any capacity-limited observer induces a canonical quotient on its environment: two situations that no bounded agent can distinguish are, for that agent, the same. We formalise this for finite POMDPs. A fixed probe family of finite-state controllers induces a closed-loop Wasserstein pseudometric on observation histories and a probe-exact quotient merging hist

Johnathan Sun, Andrew Zhang

Large language models (LLMs) are increasingly deployed as autonomous decision-makers in strategic settings, yet we have limited tools for understanding their high-level behavioral traits. We use activation steering methods in game-theoretic settings, constructing persona vectors for altruism, forgiveness, and expectations of others by contrastive activation

Alfred Shen, Aaron Shen

Current AI agent frameworks commit early to a single interaction protocol, a fixed tool integration strategy, and static user models, limiting their deployment across diverse interaction paradigms. To address these constraints, we introduce STEM Agent (Self-adapting, Tool-enabled, Extensible, Multi-agent), a modular architecture inspired by biological plurip

Bin Han, Michelle Michelle

The solution $u$ of an elliptic interface problem in a domain $Ω$ is often smooth away from the interface $Γ\subset Ω$, but its gradient is discontinuous across $Γ$. Consequently, $u$ has low global regularity and generally does not belong to $H^{3/2}(Ω)$. This paper studies 1D elliptic interface problems using wavelet methods. We propose a Galerkin method b

Long Teng, Zhiwei Wang, Jiuyi Zhu

We study quantitative unique continuation for second order elliptic equations with lower-order terms of H\"older regularity via a weighted frequency function method. We establish quantitative three-ball inequalities and corresponding vanishing-order bounds for Schr\"odinger equations with H\"older potentials and H\"older gradient terms, and corresponding res

Hiroki Suyari, Antonio M. Scarfone

The Large Deviation Principle (LDP) and the Central Limit Theorem (CLT) are central pillars of probability theory. While their formulations are established under the i.i.d. assumption, the probabilistic foundation for power-law distributions has primarily evolved through descriptive models or variational principles, rather than a constructive derivation comp

David Ramirez, Elvis Cabrera, Jyrko Correa-Morris

Random linear network coding (RLNC) provides a powerful framework for non-coherent communication, where reliable transmission requires correcting errors and erasures induced by network mixing and motivates the use of subspace codes. In this work, we introduce an atomic perspective on subspace coding by formalizing the notion of minimal atomic decompositions

Jinghan Cao, Yu Ma, Xinjin Li, Qingyang Ren

Large Language Models achieve remarkable performance but incur substantial computational costs unsuitable for resource-constrained deployments. This paper presents the first comprehensive task-specific efficiency analysis comparing 16 language models across five diverse NLP tasks. We introduce the Performance-Efficiency Ratio (PER), a novel metric integratin

Christian Mancas, Diana Christina Mancas

This research paper mainly describes how to develop MS Blazor safe web applications with Claude AI, version Sonnet 4.5, starting from (Elementary) Mathematical Data Model schemas. In the sequel, it also provides a list of general software engineering best practice rules, as well as issues of the MS Blazor Server platform.

Feng Xu, Xun Li, Lars Petersson, Yulei Sui

Facial expression recognition relies on facial data that inherently expose identity and thus raise significant privacy concerns. Current privacy-preserving methods typically fail in realistic open-set video settings where identities are unknown, and identity labels are unavailable. We propose a two-stage framework for video-based privacy-preserving FER in ch

Hadush Hailu, Bruk Gebregziabher

Monocular visual-inertial odometry (VIO) cannot recover metric scale from vision alone; scale must be resolved through inertial measurements. We present a trajectory-dependent observability analysis showing that translational acceleration, produced by curvature, not constant-speed straight-line travel, is the fundamental source that couples scale to the iner

Nikolay Kormushev, Josip Šarić, Matej Kristan

Open-vocabulary panoptic segmentation remains hindered by two coupled issues: (i) mask selection bias, where objectness heads trained on closed vocabularies suppress masks of categories not observed in training, and (ii) limited regional understanding in vision-language models such as CLIP, which were optimized for global image classification rather than loc

Tobias Schneider, Stefan Schumacher, Xuekai Ma

All-optical logic gates have significantly advanced over a diverse range of photonic systems, boosted by intricate nonlinearities that facilitate the engineering of complex logic operations. Here, we demonstrate that in semiconductor microcavities, polariton condensates trapped in Ouroboros-shaped rings form specifically charged vortices, determined by the s

Didem Aydoğan, Korkut Kaan Tokgöz

This letter examines the impact of oscillator phase noise on sub-terahertz OFDM transceiver architectures, with a focus on the comparison between homodyne and heterodyne designs. Using a Hexa-X compliant phase noise model, we analytically show that heterodyne architectures reduce the total accumulated phase noise variance by distributing frequency translatio

Junkeun Yi, Damon Mosk-Aoyama, Baihe Huang, Ritu Gala

Post-training for long-horizon agentic tasks has a tension between compute efficiency and generalization. While supervised fine-tuning (SFT) is compute efficient, it often suffers from out-of-domain (OOD) degradation. Conversely, end-to-end reinforcement learning (E2E RL) preserves OOD capabilities, but incurs high compute costs due to many turns of on-polic

Annabel Goldman, Yuan Cui, Matthew Kay

Data literacy has become a key learning objective in K-12 education, but it remains an ambiguous concept as teachers interpret it differently. When creating assessments, teachers turn broad ideas about "working with data" into concrete decisions about what materials to include. Since working with data visualizations is a core component of data literacy, teac

Zaid Ahmed, Omar A. Khan, Hyeongil Nam, Kangsoo Kim

Extended Reality (XR) enables immersive capture and re-experience of personal memories, yet how interface representations shape these experiences remains underexplored. We examine how users relive and share XR memories through three interaction approaches: (1) physical memory-linked objects, (2) virtual memory-linked objects, and (3) a conventional virtual g

Mirco A. Mannucci

In 2006 we proposed Quantum Fuzzy Sets, observing that states of a quantum register could serve as characteristic functions of fuzzy subsets, embedding Zadeh's unit interval into the Bloch sphere. That paper was deliberately preliminary. In the two decades since, the idea has been taken up by researchers working on quantum annealers, intuitionistic fuzzy con

Chan Roh, Geunhee Gwak, Young-Do Yoon, Young-Sik Ra

Multimode Gaussian states are a versatile resource for quantum information technologies and have been realized across a wide range of physical platforms. Recent progress in the large-scale generation of such states provides a key ingredient for scalable quantum technologies. Despite the importance of accurately characterizing these states, conventional tomog

Martina Iannacito, Sascha Portaro, Davide Palitta, Claudio Arlandini

In the last decades, tensors have emerged as the right tool to represent multidimensional data in a compact yet informative manner. Moreover, it is well-known that by performing low-rank factorizations of such tensors one is often able to effectively unveil possible hidden structure in data, mainly due to unexpected dependencies among the different variables

Yijia Song, Juliet Biggs, Alin Achim, Robert Popescu

Phase unwrapping remains a critical and challenging problem in InSAR processing, particularly in scenarios involving complex deformation patterns. In earthquake-related deformation, shallow sources can generate surface-breaking faults and abrupt displacement discontinuities, which severely disrupt phase continuity and often cause conventional unwrapping algo

Mohamed A Mabrok

We present HamVision, a framework for medical image analysis that uses the damped harmonic oscillator, a fundamental building block of signal processing, as a structured inductive bias for both segmentation and classification tasks. The oscillator's phase-space decomposition yields three functionally distinct representations: position~$q$ (feature content),

Elizabeth Pavlova, Mariia Koroliuk, Karthik Viswanathan, Cameron Tice

We propose a new architectural change, and post-training pipeline, for making LLMs more verbose reasoners by teaching a model to truncate forward passes early. We augment an existing transformer architecture with an early-exit mechanism at intermediate layers and train the model to exit at shallower layers when the next token can be predicted without deep co

Mohammed Abdullah, George Iosifidis, Salah Eddine Elayoubi, Tijani Chahed

We study Constrained Online Convex Optimization with Memory (COCO-M), where both the loss and the constraints depend on a finite window of past decisions made by the learner. This setting extends the previously studied unconstrained online optimization with memory framework and captures practical problems such as the control of constrained dynamical systems

Peng Sun, Liang Zhong, Qing-Guo Zeng, Li Wang

The rapid deployment of electric vehicles (EVs) in public parking facilities and fleet operations raises challenging intra-day charging scheduling problems under tight charger capacity and limited dwell times. We model this problem as a variant of the Partition Coloring Problem (PCP), where each vehicle defines a partition, its candidate charging intervals a

Pawel Batorski, Paul Swoboda

In-context learning (ICL) adapts large language models by conditioning on a small set of ICL examples, avoiding costly parameter updates. Among other factors, performance is often highly sensitive to the ordering of the examples. However, exhaustive search over the $n!$ possible orderings is infeasible. Therefore more efficient ordering methods use model con

Adrian Celestino, Franz Lehner, Kamil Szpojankowski

We study distributions of polynomials in conditionally free (c-free) random variables, a notion of independence for two-state noncommutative probability spaces introduced by Bozejko, Leinert and Speicher. To this end we establish recursive relations between the joint Boolean cumulants of c-free random variables, analogous to previously found recursions for B

Krzysztof Fonal

Speculative decoding accelerates LLM inference by using a small draft model to propose k candidate tokens for a target model to verify. While effective for same-tokenizer pairs on high-bandwidth GPUs, its applicability to cross-family pairs with mismatched tokenizers and consumer-grade unified memory remains underexplored. We extend the MLX-LM framework with

Saud Čindrak, Lara Giebeler, Niclas Götting, Christopher Gies

Quantum reservoir computing (QRC) harnesses driven quantum dynamics for time-series processing, yet the mechanisms behind the differing performance levels across its many implementations remain unclear. We show that apparently unrelated approaches-including memory restriction, weak measurements, operation near the edge of quantum chaos, and dissipative dynam

Arno Strouwen, Bart M. Nicolaï, Peter Goos

Current experimental design techniques for dynamical systems often only incorporate measurement noise, while dynamical systems also involve process noise. To construct experimental designs we need to quantify their information content. The Fisher information matrix is a popular tool to do so. Calculating the Fisher information matrix for linear dynamical sys

Sagnik Seth, Adway Kumar Das, Anandamohan Ghosh

The prevalence of sparsity in interacting many-body systems motivates an investigation into the spectral statistics of sparse random matrices with on-site disorder. We numerically demonstrate that the Anderson transition can be identified through the statistical properties of the ground state. By analytically deriving the energy moments and calculating the s

Heidi Campana Piva, Shaina Ashraf, Maziar Kianimoghadam Jouneghani, Arianna Longo

Conspiracy theories are anti-authoritarian narratives that lead to social conflict, impacting how people perceive political information. To help in understanding this issue, we introduce the Conspiracy Frame: a fine-grained semantic representation of conspiratorial narratives derived from frame-semantics and semiotics, which spawned the Conspiracy Frames (Co

Oliver Knill

We define a deformation of the exterior derivative that is a bounded operator and preserves the symmetries of the geometry. It satisfies a modified wave equation that honors the strong Huygens principle in all dimensions.

Zengqun Zhao, Yanzuo Lu, Ziquan Liu, Jifei Song

Autoregressive video diffusion has recently emerged as a promising paradigm for long-video generation, enabling causal synthesis beyond the temporal limits of bidirectional models. Existing forcing-based training strategies reduce exposure bias by conditioning models on their own predictions during rollout, yet minute-scale generation remains challenging due

Jaber Jaber, Osama Jaber

Large language models run every token through every layer, regardless of difficulty. We present TIDE, a post-training system that attaches tiny learned routers at periodic checkpoint layers and, at inference time, selects the earliest layer whose hidden state has converged for each token. TIDE requires no model retraining, works with any HuggingFace causal L

Haifa I. Alrebdi, Muhammad Ajaz

We present a sequential nine-parameter retune of PYTHIA 8.316, built on the Monash 2013 baseline, for soft-QCD observables in proton--proton collisions at $\sqrt{s}=0.9$--$13$ TeV. The tune is constructed with direct generator evaluation and developed in stages: an initial five-parameter localization in the hadronization-sensitive sector, successive extensio

Yunfan Zhou, Qiming Shi, Zhongsu Luo, Xiwen Cai

LLM-driven tools have significantly lowered barriers to writing SQL queries. However, user instructions are often underspecified, assuming the model understands implicit knowledge, such as dataset schemas, domain conventions, and task-specific requirements, that isn't explicitly provided. This results in frequently erroneous scripts that require users to rep

Liang Ding

Evaluating LLM agent trajectories is fundamentally task-specific: a code-debugging agent should be judged on Correctness and Error Handling, not on Fluency or Safety. Yet the dominant paradigm -- LLM-as-Judge with a fixed rubric -- applies the same static dimensions regardless of task, producing systematic mis-evaluation. We present AdaRubric, a framework th

Arno Strouwen, Peter Goos

The coordinate-exchange algorithm is commonly used to construct optimal experimental designs. Every execution of the coordinate-exchange algorithm produces a new, seemingly random, order of the selected design points. In this short communication, we study the order of the design points produced by the algorithm and conclude that certain orders appear much mo

Timo Hopp, Shengyu Tian, Thorsten Kleine

Understanding the origin of the Earth requires determining the original formation location of its building material. Based on the similar Fe isotopic composition of Earth's mantle and Ivuna-type (CI) chondrites, a prior study has argued that Earth formed by accretion of sunward-drifting pebbles from the outer Solar System. Here, using new high-precision Fe i

K. M. Jubair Sami, Dipto Sumit, Ariyan Hossain, Farig Sadeque

Large language models (LLMs) frequently exhibit performance biases against regional dialects of low-resource languages. However, frameworks to quantify these disparities remain scarce. We propose a two-phase framework to evaluate dialectal bias in LLM question-answering across nine Bengali dialects. First, we translate and gold-label standard Bengali questio

Bushi Xiao, Qian Shen

It is crucial to explore the impact of different teaching methods on student learning in educational research. However, real-person experiments face significant ethical constraints, and we cannot conduct repeated teaching experiments on the same student. LLM-based generative agents offer a promising avenue for simulating student behavior. Before large-scale

Liang Ding

LLM-agent training pipelines routinely discard failed trajectories even though GPT-4o achieves only 14-20% on WebArena and below 55% pass@1 on ToolBench; even specialised systems at 50-65% leave the majority of trajectories unused. We introduce AgentHER, which recovers this lost signal by adapting Hindsight Experience Replay (HER) to natural-language agent t

Yuqiu Liu, Jialin Song, Marissa Ramirez de Chanlatte, Rochishnu Chowdhury

Real objects that inhabit the physical world follow physical laws and thus behave plausibly during interaction with other physical objects. However, current methods that perform 3D reconstructions of real-world scenes from multi-view 2D images optimize primarily for visual fidelity, i.e., they train with photometric losses and reason about uncertainty in the

Huamin Chen, Xunzhuo Liu, Bowei He, Fuyuan Lyu

Over the past year, the vLLM Semantic Router project has released a series of work spanning: (1) core routing mechanisms -- signal-driven routing, context-length pool routing, router performance engineering, policy conflict detection, low-latency embedding models, category-aware semantic caching, user-feedback-driven routing adaptation, hallucination detecti

Joshua Brakensiek, Venkatesan Guruswami, Aaron Putterman

Given a constraint satisfaction problem (CSP) predicate $P \subseteq D^r$, the non-redundancy (NRD) of $P$ is maximum-sized instance on $n$ variables such that for every clause of the instance, there is an assignment which satisfies all but that clause. The study of NRD for various CSPs is an active area of research which combines ideas from extremal combina

A. B. Aleksandrov, V. V. Peller

We study the behaviour of functions of pairs of commuting self-adjoint operators under perturbations by relatively bounded operators. We obtain analogs of our earlier results for functions of a single self-adjoint operator under relatively bounded perturbations. The main tool is double operator integrals

Thomas Savage, Evan Madill

Recognition of respiratory distress through visual inspection is a life saving clinical skill. Clinicians can detect early signs of respiratory deterioration, creating a valuable window for earlier intervention. In this study, we evaluate whether recent advances in video transformers can enable Artificial Intelligence systems to recognize the signs of respir

Yu-Shan Tai, An-Yeu, Wu

Recently, diffusion models (DMs) have made significant strides in high-quality image generation. However, the multi-step denoising process often results in considerable computational overhead, impeding deployment on resource-constrained edge devices. Existing methods mitigate this issue by compressing models and adjusting the time step sequence. However, the

Lionel J. Dmello, David Gross

A long-standing problem in the foundations of quantum mechanics is to identify a physical principle that explains why algebraically maximal violations of Bell inequalities can generally not be achieved in Nature. One recently proposed approach considers iterated Bell tests, where a Bell test is performed on a state that has undergone several rounds of entang

Amílcar Branquinho, Ana Foulquié-Moreno, Manuel Mañas

Given a banded matrix $\mathscr{T}_N$ with $p$ subdiagonals and $q$ superdiagonals arising from the Gauss--Borel factorization $\mathscr{M}_N = \mathscr{L}_N^{-1}\mathscr{U}_N^{-1}$ of a moment matrix, this paper constructs explicitly its bidiagonal factorization \[ \mathscr{T}_N = L_1 \cdots L_p\, U_q \cdots U_1. \] Bidiagonal factorizations of this type ar

Ulisses Braga-Neto

In this short note we propose using agentic swarms of virtual labs as a model of an AI Science Community. In this paradigm, each particle in the swarm represents a complete virtual laboratory instance, enabling collective scientific exploration that mirrors real-world research communities. The framework leverages the inherent properties of swarm intelligence

Nora Gavrea, Derek Harland, Martin Speight

In this paper we investigate the existence of internal modes of vortices in the gauged $\mathbb{C}P^1$ sigma model. We develop a clean geometric formalism that highlights the symmetries of the Jacobi operator, obtained from the second variation of the energy functional. The formalism and subsequent results fundamentally rely on the Bogomol'nyi decomposition

Oussama Zekri, Théo Uscidda, Nicolas Boullé, Anna Korba

We introduce Generalized Discrete Diffusion from Snapshots (GDDS), a unified framework for discrete diffusion modeling that supports arbitrary noising processes over large discrete state spaces. Our formulation encompasses all existing discrete diffusion approaches, while allowing significantly greater flexibility in the choice of corruption dynamics. The fo

Dongyoung Kim, Sumin Park, Woomin Song, Seungku Kim

Improving embodied reasoning in multimodal-large-language models (MLLMs) is essential for building vision-language-action models (VLAs) on top of them to readily translate multimodal understanding into low-level actions. Accordingly, recent work has explored enhancing embodied reasoning in MLLMs through supervision of vision-question-answering type. However,

Seth Dobrin, Lukasz Chmiel

This paper presents ARYA, a composable, physics-constrained, deterministic world model architecture built on five foundational principles: nano models, composability, causal reasoning, determinism, and architectural AI safety. We demonstrate that ARYA satisfies all canonical world model requirements, including state representation, dynamic prediction, causal

Chenxi Zhu

Channel capacity lies at the core of wireless communication, yet determining it typically requires detailed channel information between the transmitter and receiver. For near field MIMO systems, obtaining the detailed native channel is often difficult or expensive. This paper develops a scheme to approximate the near field channel in a Gaussian beam domain.

Oleg D. Lavrentovich

Ground states of materials with orientational order ranging from solid ferromagnets and ferroelectrics to liquid crystals often contain spatially varying vector-like order parameter caused by inner factors such as the shape of building units or by the geometry of confinement. This review presents examples of how the shapes, chirality, and polarity of molecul

Yakov Nekrich, Saladi Rahul

Shallow cuttings are a fundamental tool in computational geometry and spatial databases for solving offline and online range searching problems. For a set $P$ of $N$ points in 3-D, at SODA'14, Afshani and Tsakalidis designed an optimal $O(N\log_2N)$ time algorithm that constructs shallow cuttings for 3-D dominance ranges in internal memory. Even though shall

Bingfei Yan, Eric Handy-Cardenas, Kenny Breuer, Jennifer A. Franck

This study introduces a vortex gust generation method for isolated vortices impacting a downstream airfoil that is applicable to both numerical simulations and experiments. The vortex gust is generated by a symmetric airfoil undergoing a rapid pitching maneuver during a prescribed heaving motion. The resulting vortices propagate along trajectories nearly par

Saketh Vinjamuri, Marielle Fis Loperena, Marie C. Spezia, Ramez Kouzy

Time toxicity, the cumulative healthcare contact days from clinical trial participation, is an important but labor-intensive metric to extract from protocol documents. We developed TimeTox, an LLM-based pipeline for automated extraction of time toxicity from Schedule of Assessments tables. TimeTox uses Google's Gemini models in three stages: summary extracti

Mulong Xie, Yang Xie

Chat-based natural language interfaces have emerged as the dominant paradigm for human-agent interaction, yet they fundamentally constrain engagement with structured information and complex tasks. We identify three inherent limitations: the mismatch between structured data and linear text, the high entropy of unconstrained natural language input, and the lac

Haolan Xu, Keli Cheng, Lei Wang, Ning Bi

Audio-driven 3D talking head synthesis has advanced rapidly with Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). By leveraging rich pre-trained priors, few-shot methods enable instant personalization from just a few seconds of video. However, under expressive facial motion, existing few-shot approaches often suffer from geometric instability

Jaber Jaber, Osama Jaber

Writing high-performance GPU kernels is among the most labor-intensive tasks in machine learning systems engineering. We present AutoKernel, an open-source framework that applies an autonomous agent loop to GPU kernel optimization for arbitrary PyTorch models. Given a model, AutoKernel profiles it to identify computational bottlenecks, ranks them by Amdahl's

Hongyang Yang, Boyu Zhang, Yang She, Xinyu Liao

We present FinRL-X, a modular and deployment-consistent trading architecture that unifies data processing, strategy construction, backtesting, and broker execution under a weight-centric interface. While existing open-source platforms are often backtesting- or model-centric, they rarely provide system-level consistency between research evaluation and live de

Xiaozhe Li, Tianyi Lyu, Siyi Yang, Yizhao Yang

Understanding human intent is a high-level cognitive challenge for Large Language Models (LLMs), requiring sophisticated reasoning over noisy, conflicting, and non-linear discourse. While LLMs excel at following individual instructions, their ability to distill Collective Intent - the process of extracting consensus, resolving contradictions, and inferring l

Basudeb Datta

We present some natural crystallizations of the generalized lens spaces $L(p, q_1, \dots, q_n)$ for integers $p\geq 2$, $n\geq 1$ and integers $q_1, \dots, q_n$ relatively prime to $p$. These crystallizations are quotients of triangulations of the sphere $S^{2n+1}$.

Wenhan Wu, Zhishuai Guo, Chen Chen, Srijan Das

Stochastic human motion prediction aims to generate diverse, plausible futures from observed sequences. Despite advances in generative modeling, existing methods often produce predictions corrupted by high-frequency jitter and temporal discontinuities. To address these challenges, we introduce KHMP, a novel framework featuring an adaptiveKalman filter applie

Diego F. Vasquez Plaza, Vidya Manian

Jet flavor tagging plays an important role in precise Standard Model measurement enabling the extraction of mass dependence in jet-quark interaction and quark-gluon plasma (QGP) interactions. They also enable inferring the nature of particles produced in high-energy particle collisions that contain heavy quarks. The classification of bottom jets is vital for

Victor M. Pergamenshchik, Taras Bryk, Andrij Trokhymchuk

We turn the long time puzzle of the free volume, known for its highly irregular form, into exact analytical formulae and develop statistical mechanics of the hard disk model. The free volume is exactly expressed in terms of the intersection areas of up to five exclusion circles, which can be computed analytically as functions of disk coordinates. In turn, th

Guanchen Wu, Chung-Yueh Yuan, Kareem Hegazy, Samuel M. Blau

The reliable determination of transition states (TSs) benefits from second-order information for robust convergence and validation, but the computational expense of Hessians prohibits their routine use in TS optimization. Here, we present a machine-learning-driven TS optimizer that directly predicts the leftmost Hessian eigenvector (LMHE), the critical mode

Idan Amit

Context: Static analysis captures software engineering knowledge and alerts on possibly problematic patterns. Previous work showed that they indeed have predictive power for various problems. However, the impact of removing the alerts is unclear. Aim: We would like to evaluate the impact of alert removals on code complexity and the tendency to bugs. Method:

Pantea Karimi, Kimia Noorbakhsh, Mohammad Alizadeh, Hari Balakrishnan

Designing high-performance system heuristics is a creative, iterative process requiring experts to form hypotheses and execute multi-step conceptual shifts. While Large Language Models (LLMs) show promise in automating this loop, they struggle with complex system problems due to two critical failure modes: evolutionary neighborhood bias and the coherence cei

Jun-Kun Zhao, Enze Lv, Wei Li, Li Li

Quantum criticality is a hallmark of strongly correlated electron systems, as seen in heavy-fermion materials and high-temperature superconductors. Holographic duality provides a powerful framework to investigate these systems by translating them into weakly coupled classical gravity living in one higher dimension. Here, we harness this approach to study a f

Eduard Kapelko

This paper addresses the critical challenge of mesa-optimization in AI safety by providing a formal definition of agency and a framework for its analysis. Agency is conceptualized as a Continuous Representation of accumulated experience that achieves autopoiesis through a dynamic balance between curiosity (minimizing prediction error to ensure non-computabil

Yaiza Asensio, Lucía Olano-Vegas, Samuele Mattioni, Marco Gobbi

The chemical and structural flexibility of hybrid organic-inorganic metal halide perovskites (HOIPs) provides an ideal platform for engineering not only their well-studied optical properties, but also their magnetic ones. In this review we present HOIPs from a new perspective, turning the attention to their magnetic properties and their potential as new clas

James Clayton Kerce

Linear methods for steering transformer representations, including probing, activation engineering, and concept erasure, implicitly assume the geometry of representation space is Euclidean. Park et al. [Park et al., 2026] showed that softmax induces a curved Bregman geometry whose metric tensor is the Hessian of the log-normalizer, $H({\lambda}) = Cov[{\gamm

Andreea I. Niculescu, Jochen Ehnes, Minghui Dong

Aphasia, a language impairment primarily resulting from stroke or brain injury, profoundly disrupts communication and everyday functioning. Despite advances in speech therapy, barriers such as limited therapist availability and the scarcity of personalized, culturally relevant tools continue to hinder optimal rehabilitation outcomes. This paper reviews recen

Khushiyant, Param Thakkar

Audio representation learning typically evaluates design choices such as input frontend, sequence backbone, and sequence length in isolation. We show that these axes are coupled, and conclusions from one setting often do not transfer to others. We introduce HELIX, a controlled framework comparing pure Mamba, pure attention, and a minimal hybrid with a single

Ruocan Wei, Shufeng Wang, Ziwei Shi

Large language model (LLM) agents often suffer from high reasoning overhead, excessive token consumption, unstable execution, and inability to reuse past experiences in complex tasks like business queries, tool use, and workflow orchestration. Traditional methods generate workflows from scratch for every query, leading to high cost, slow response, and poor r

Fabien Polly

World models learn to predict future states of an environment, enabling planning and mental simulation. Current approaches default to Transformer-based predictors operating in learned latent spaces. This comes at a cost: O(N^2) computation and no explicit spatial inductive bias. This paper asks a foundational question: is self-attention necessary for predict

Emmanuel Apaaboah, Bernard Opoku, the GhanaHousePlanner Research Team

Ghana faces a residential housing deficit of two million units. A key driver of project failure is the "completeness gap", a systematic discrepancy between informal contractor quotes and actual costs. Informal estimates often use flat per-square-metre pricing that omits essential structural and finishing components, leading to project abandonment mid-constru

Yasha Yi, Danny Wilkerson

The rapid growth of AI and accelerator-driven workloads is forcing a fundamental rethinking of optical interconnect architectures in datacenters. Co-packaged optics and three-dimensional photonic integration have emerged as promising solutions to overcome the energy and bandwidth limitations of electrical I/O. Yet, as optics move closer to compute, packaging

Cheng-Tai Lee, Tomer Markovich

Chiral surface waves are surface-localized modes that propagate unidirectionally along a boundary, enabling directed transport and minimal back-scattering. While first identified in quantum systems, they were recently shown to emerge in classical metamaterials in the presence of `odd elasticity'. Owing to the non-reciprocality of odd elasticity, these waves

Monika, Tattwamasi Amrutam, Priyadarshi Dey

The numerical index of a Banach space is a geometric constant relating the numerical radius of bounded linear operators to their standard operator norm. In this paper, we study the continuity of the numerical index under two distinct notions of subspace convergence. First, we establish a full limit theorem in the operator opening topology: if $\{X_n\}_{n \in

Anirudh Kamath, Dustin Maas, Jacobus Van der Merwe, Anna Fariha

Data wrangling continues to be the most time-consuming task in the data science pipeline and wireless network data is no exception. Prior approaches for automatic or assisted data-wrangling primarily target unordered, single-table data. However, unlike traditional datasets where rows in a table are unordered and assumed to be independent of each other, wirel

Ghifari Adam Faza, Jolan Wauters, Fabio Cuzzolin, Hans Hallez

In engineering, uncertainty propagation aims to characterise system outputs under uncertain inputs. For interval uncertainty, the goal is to determine output bounds given interval-valued inputs, which is critical for robust design optimisation and reliability analysis. However, standard interval propagation relies on solving optimisation problems that become

Ojima Abraham, Onyinye Okoli

We present the first systematic cross-vendor analysis of GPU instruction set architectures spanning all four major GPU vendors: NVIDIA (PTX ISA v1.0 through v9.2, Fermi through Blackwell), AMD (RDNA 1 to 4 and CDNA 1 to 4), Intel (Gen11, Xe-LP, Xe-HPG, Xe-HPC), and Apple (G13, reverse-engineered). Drawing on official ISA reference manuals, architecture white

Maha Daoud

In this work, we study one-dimensional nonlocal elliptic transmission problems with piecewise constant coefficients that may change sign across an interface. In the local setting, we recall the T-coercive structure of the problem and characterize the critical contrast case. In the nonlocal setting, we focus on a simplified configuration in which the cross-in

Aleksei Klimov, Dmitry Shishkin, Noam Soker

We identify an S-shaped morphological feature in the enigmatic supernova remnant (SNR) 3C 397, which we attribute to the shaping by a precessing pair of jets during the explosion. We identify an S-shaped, faint region composed of two bubbles, located to the north and south of the center, between two X-ray-bright sides. We attribute the S-shape to a pair of p

Jiang-Tao Li

We develop an analytic framework for the evolution of feedback-driven bubbles expanding into a hot, volume-filling circumgalactic medium (CGM), where the ambient pressure and sound speed are non-negligible and radiative cooling is often inefficient. The evolution is organized into four stages -- free expansion, Sedov--Taylor expansion, pressure-modified/tran

Idris Zakariyya, Pai Chet Ng, Kaushik Bhargav Sivangi, S. Mohammad Sheikholeslami

Federated video action recognition enables collaborative model training without sharing raw video data, yet remains vulnerable to two key challenges: \textit{model exposure} and \textit{communication overhead}. Gradients exchanged between clients and the server can leak private motion patterns, while full-model synchronization of high-dimensional video netwo

Injae Kim, Chaehyeon Kim, Minseong Bae, Minseok Joo

Feed-forward 3D Gaussian Splatting methods enable single-pass reconstruction and real-time rendering. However, they typically adopt rigid pixel-to-Gaussian or voxel-to-Gaussian pipelines that uniformly allocate Gaussians, leading to redundant Gaussians across views. Moreover, they lack an effective mechanism to control the total number of Gaussians while mai