Tootfinder

UniView: Enhancing Novel View Synthesis From A Single Image By Unifying Reference Features
Haowang Cui, Rui Chen, Tao Luo, Rui Li, Jiaze Wang
https://arxiv.org/abs/2509.04932 ht…

UniView: Enhancing Novel View Synthesis From A Single Image By Unifying Reference Features
The task of synthesizing novel views from a single image is highly ill-posed due to multiple explanations for unobserved areas. Most current methods tend to generate unseen regions from ambiguity priors and interpolation near input views, which often lead to severe distortions. To address this limitation, we propose a novel model dubbed as UniView, which can leverage reference images from a similar object to provide strong prior information during view synthesis. More specifically, we construct…

Eigenvoice Synthesis based on Model Editing for Speaker Generation
Masato Murata, Koichi Miyazaki, Tomoki Koriyama, Tomoki Toda
https://arxiv.org/abs/2507.03377

Eigenvoice Synthesis based on Model Editing for Speaker Generation
Speaker generation task aims to create unseen speaker voice without reference speech. The key to the task is defining a speaker space that represents diverse speakers to determine the generated speaker trait. However, the effective way to define this speaker space remains unclear. Eigenvoice synthesis is one of the promising approaches in the traditional parametric synthesis framework, such as HMM-based methods, which define a low-dimensional speaker space using pre-stored speaker features. Thi…

An Adaptive Port Technique for Synthesising Rotational Components in Component Modal Synthesis Approaches
Xiang Zhao, My Ha Dao
https://arxiv.org/abs/2507.03276

An Adaptive Port Technique for Synthesising Rotational Components in Component Modal Synthesis Approaches
Component Modal Synthesis (CMS) is a reduced order modelling method widely used for large-scale complex systems. It can effectively approximate system-level models through component synthesis, in which the repetitive geometrical components are modelled once and synthesised together. However, the conventional CMS only applies to systems with stationary components connected by strictly compatible ports, limiting it from modelling systems with moving components. This paper presents an adaptive por…

Distributionally Robust System Level Synthesis With Output Feedback Affine Control Policy
Yun Li, Jicheng Shi, Colin N. Jones, Neil Yorke-Smith, Tamas Keviczky
https://arxiv.org/abs/2508.05466

Distributionally Robust System Level Synthesis With Output Feedback Affine Control Policy
This paper studies the finite-horizon robust optimal control of linear systems subject to model mismatch and additive stochastic disturbances. Utilizing the system level synthesis (SLS) parameterization, we propose a novel SLS design using output-feedback affine control policy and extend it to a distributionally robust setting to improve system resilience by minimizing the cost function while ensuring constraint satisfaction against the worst-case uncertainty distribution. The scopes of model m…

HLStrans: Dataset for LLM-Driven C-to-HLS Hardware Code Synthesis
Qingyun Zou, Nuo Chen, Yao Chen, Bingsheng He, WengFei Wong
https://arxiv.org/abs/2507.04315

HLStrans: Dataset for LLM-Driven C-to-HLS Hardware Code Synthesis
High-level synthesis (HLS) enables software developers to describe and implement hardware at a higher level of abstraction by using C/C++ instead of traditional hardware description languages to automatically generate FPGA-ready designs. However, generating HLS code significantly differs from standard C/C++: it disallows certain coding idioms, relies on specialized libraries, and critically requires fine-grained transformations and the insertion of optimization directives (pragmas) to achieve h…

P-Aligner: Enabling Pre-Alignment of Language Models via Principled Instruction Synthesis
Feifan Song, Bofei Gao, Yifan Song, Yi Liu, Weimin Xiong, Yuyang Song, Tianyu Liu, Guoyin Wang, Houfeng Wang
https://arxiv.org/abs/2508.04626

P-Aligner: Enabling Pre-Alignment of Language Models via Principled Instruction Synthesis
Large Language Models (LLMs) are expected to produce safe, helpful, and honest content during interaction with human users, but they frequently fail to align with such values when given flawed instructions, e.g., missing context, ambiguous directives, or inappropriate tone, leaving substantial room for improvement along multiple dimensions. A cost-effective yet high-impact way is to pre-align instructions before the model begins decoding. Existing approaches either rely on prohibitive test-time…

Resource-Efficient Synthesis of Sparse Quantum States
Renaud Vilmart, Sunheang Ty, Chetra Mang
https://arxiv.org/abs/2508.05386 https://arxiv.org/pdf/2508.…

Resource-Efficient Synthesis of Sparse Quantum States
Preparing a quantum circuit that implements a given sparse state is an important building block that is necessary for many different quantum algorithms. In the context of fault-tolerant quantum computing, the so-called non-Clifford gates are much more expensive to perform than the Clifford ones. We hence provide an algorithm for synthesizing sparse quantum states with a special care for quantum resources. The circuit depth, ancilla count, and crucially non-Clifford count of the circuit produced…

Particle Swarm Optimization for Quantum Circuit Synthesis: Performance Analysis and Insights
Mirza Hizriyan Nubli Hidayat, Tan Chye Cheah
https://arxiv.org/abs/2507.02898

Particle Swarm Optimization for Quantum Circuit Synthesis: Performance Analysis and Insights
This paper discusses how particle swarm optimization (PSO) can be used to generate quantum circuits to solve an instance of the MaxOne problem. It then analyzes previous studies on evolutionary algorithms for circuit synthesis. With a brief introduction to PSO, including its parameters and algorithm flow, the paper focuses on a method of quantum circuit encoding and representation as PSO parameters. The fitness evaluation used in this paper is the MaxOne problem. The paper presents experimental…

SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model
Chun Xie, Yuichi Yoshii, Itaru Kitahara
https://arxiv.org/abs/2507.05148 https://

SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model
X-ray imaging is a rapid and cost-effective tool for visualizing internal human anatomy. While multi-view X-ray imaging provides complementary information that enhances diagnosis, intervention, and education, acquiring images from multiple angles increases radiation exposure and complicates clinical workflows. To address these challenges, we propose a novel view-conditioned diffusion model for synthesizing multi-view X-ray images from a single view. Unlike prior methods, which are limited in an…

SAGE-HLS: Syntax-Aware AST-Guided LLM for High-Level Synthesis Code Generation
M Zafir Sadik Khan, Nowfel Mashnoor, Mohammad Akyash, Kimia Azar, Hadi Kamali
https://arxiv.org/abs/2508.03558

SAGE-HLS: Syntax-Aware AST-Guided LLM for High-Level Synthesis Code Generation
In today's rapidly evolving field of electronic design automation (EDA), the complexity of hardware designs is increasing, necessitating more sophisticated automation solutions. High-level synthesis (HLS), as a pivotal solution, automates hardware designs from high-level abstractions (e.g., C/C++). However, it faces significant challenges, particularly in design space exploration and optimization. While large language models (LLMs) have shown notable capabilities in code generation, their appli…

Cross-Domain Image Synthesis: Generating H&E from Multiplex Biomarker Imaging
Jillur Rahman Saurav, Mohammad Sadegh Nasr, Jacob M. Luber
https://arxiv.org/abs/2508.04734 htt…

Cross-Domain Image Synthesis: Generating H&E from Multiplex Biomarker Imaging
While multiplex immunofluorescence (mIF) imaging provides deep, spatially-resolved molecular data, integrating this information with the morphological standard of Hematoxylin & Eosin (H&E) can be very important for obtaining complementary information about the underlying tissue. Generating a virtual H&E stain from mIF data offers a powerful solution, providing immediate morphological context. Crucially, this approach enables the application of the vast ecosystem of H&E-based computer-aided diag…

UniTalker: Conversational Speech-Visual Synthesis
Yifan Hu, Rui Liu, Yi Ren, Xiang Yin, Haizhou Li
https://arxiv.org/abs/2508.04585 https://arxiv.org/pdf/2…

UniTalker: Conversational Speech-Visual Synthesis
Conversational Speech Synthesis (CSS) is a key task in the user-agent interaction area, aiming to generate more expressive and empathetic speech for users. However, it is well-known that "listening" and "eye contact" play crucial roles in conveying emotions during real-world interpersonal communication. Existing CSS research is limited to perceiving only text and speech within the dialogue context, which restricts its effectiveness. Moreover, speech-only responses further constrain the interact…

3D PixBrush: Image-Guided Local Texture Synthesis
Dale Decatur, Itai Lang, Kfir Aberman, Rana Hanocka
https://arxiv.org/abs/2507.03731 https://

3D PixBrush: Image-Guided Local Texture Synthesis
We present 3D PixBrush, a method for performing image-driven edits of local regions on 3D meshes. 3D PixBrush predicts a localization mask and a synthesized texture that faithfully portray the object in the reference image. Our predicted localizations are both globally coherent and locally precise. Globally - our method contextualizes the object in the reference image and automatically positions it onto the input mesh. Locally - our method produces masks that conform to the geometry of the refe…

Hexagonal boron nitride thin film synthesis with a ns-pulsed MHCD: in-situ plasma diagnostics and post-growth film characterization
Belkacem Menacer (LSPM), Dimitrios Stefas (LSPM), Nikolaos Chazapis (LSPM), Claudia Lazzaroni (LSPM), Kristaq Gazeli (LSPM), Vianney Mille (LSPM)
https://arxiv.org/abs/2507.04759

Hexagonal boron nitride thin film synthesis with a ns-pulsed MHCD: in-situ plasma diagnostics and post-growth film characterization
Hexagonal boron nitride (h-BN) is deposited on Si <100> wafer ($\approx$20 cm2) via Plasma Enhanced Chemical Vapor Deposition (PECVD) using a ns-pulsed N2/Ar Micro Hollow Cathode Discharge (MHCD) as a microplasma source. For the first time, aluminum nitride (AIN) is employed as the dielectric material in the MHCD to mitigate film contamination by atomic oxygen, an issue previously observed with conventional Al 2O3 dielectrics. A comprehensive multi-diagnostic approach is followed to characteriz…

AI-Driven Cytomorphology Image Synthesis for Medical Diagnostics
Jan Carreras Boada, Rao Muhammad Umer, Carsten Marr
https://arxiv.org/abs/2507.05063 https…

AI-Driven Cytomorphology Image Synthesis for Medical Diagnostics
Biomedical datasets often contain a large sample imbalance and are subject to strict privacy constraints, which together hinder the development of accurate machine learning models. One potential solution is to generate synthetic images, as this can improve data availability while preserving patient privacy. However, it remains difficult to generate synthetic images of sufficient quality for training robust classifiers. In this work, we focus on the classification of single white blood cells, a …

LAPS-Diff: A Diffusion-Based Framework for Singing Voice Synthesis With Language Aware Prosody-Style Guided Learning
Sandipan Dhar, Mayank Gupta, Preeti Rao
https://arxiv.org/abs/2507.04966

LAPS-Diff: A Diffusion-Based Framework for Singing Voice Synthesis With Language Aware Prosody-Style Guided Learning
The field of Singing Voice Synthesis (SVS) has seen significant advancements in recent years due to the rapid progress of diffusion-based approaches. However, capturing vocal style, genre-specific pitch inflections, and language-dependent characteristics remains challenging, particularly in low-resource scenarios. To address this, we propose LAPS-Diff, a diffusion model integrated with language-aware embeddings and a vocal-style guided learning mechanism, specifically designed for Bollywood Hin…

Control Synthesis Along Uncertain Trajectories Using Integral Quadratic Constraints
Felix Biert\"umpfel, Peter Seiler, Harald Pfifer
https://arxiv.org/abs/2507.03101

Control Synthesis Along Uncertain Trajectories Using Integral Quadratic Constraints
The paper presents a novel approach to synthesize robust controllers for nonlinear systems along perturbed trajectories. The approach linearizes the system with respect to a reference trajectory. In contrast to existing methods rooted in robust linear time-varying synthesis, the approach accurately includes perturbations that drive the system away from the reference trajectory. Hence, the controller obtained in the linear framework provides a significantly more robust nonlinear performance. The…

Simplicity Lies in the Eye of the Beholder: A Strategic Perspective on Controllers in Reactive Synthesis
Mickael Randour
https://arxiv.org/abs/2509.04129 https://

Simplicity Lies in the Eye of the Beholder: A Strategic Perspective on Controllers in Reactive Synthesis
In the game-theoretic approach to controller synthesis, we model the interaction between a system to be controlled and its environment as a game between these entities, and we seek an appropriate (e.g., winning or optimal) strategy for the system. This strategy then serves as a formal blueprint for a real-world controller. A common belief is that simple (e.g., using limited memory) strategies are better: corresponding controllers are easier to conceive and understand, and cheaper to produce and…

The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Literature Review
Amr Mohamed, Maram Assi, Mariam Guizani
https://arxiv.org/abs/2507.03156

The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Literature Review
Large language model assistants (LLM-assistants) present new opportunities to transform software development. Developers are increasingly adopting these tools across tasks, including coding, testing, debugging, documentation, and design. Yet, despite growing interest, there is no synthesis of how LLM-assistants affect software developer productivity. In this paper, we present a systematic literature review of 37 peer-reviewed studies published between January 2014 and December 2024 that examine…

UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis
Zhuo Li, Xuhang Chen, Shuqiang Wang
https://arxiv.org/abs/2507.03341

UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis
Functional ultrasound (fUS) is a neuroimaging technique known for its high spatiotemporal resolution, enabling non-invasive observation of brain activity through neurovascular coupling. Despite its potential in clinical applications such as neonatal monitoring and intraoperative guidance, the development of fUS faces challenges related to data scarcity and limitations in generating realistic fUS images. This paper explores the use of a generative adversarial network (GAN) framework tailored for…

ForgeHLS: A Large-Scale, Open-Source Dataset for High-Level Synthesis
Zedong Peng, Zeju Li, Mingzhe Gao, Qiang Xu, Chen Zhang, Jieru Zhao
https://arxiv.org/abs/2507.03255

ForgeHLS: A Large-Scale, Open-Source Dataset for High-Level Synthesis
We introduce ForgeEDA, an open-source comprehensive circuit dataset across various categories. ForgeEDA includes diverse circuit representations such as Register Transfer Level (RTL) code, Post-mapping (PM) netlists, And-Inverter Graphs (AIGs), and placed netlists, enabling comprehensive analysis and development. We demonstrate ForgeEDA's utility by benchmarking state-of-the-art EDA algorithms on critical tasks such as Power, Performance, and Area (PPA) optimization, highlighting its ability to…

SegmentDreamer: Towards High-fidelity Text-to-3D Synthesis with Segmented Consistency Trajectory Distillation
Jiahao Zhu, Zixuan Chen, Guangcong Wang, Xiaohua Xie, Yi Zhou
https://arxiv.org/abs/2507.05256

SegmentDreamer: Towards High-fidelity Text-to-3D Synthesis with Segmented Consistency Trajectory Distillation
Recent advancements in text-to-3D generation improve the visual quality of Score Distillation Sampling (SDS) and its variants by directly connecting Consistency Distillation (CD) to score distillation. However, due to the imbalance between self-consistency and cross-consistency, these CD-based methods inherently suffer from improper conditional guidance, leading to sub-optimal generation results. To address this issue, we present SegmentDreamer, a novel framework designed to fully unleash the p…

GP and LLMs for Program Synthesis: No Clear Winners
Jose Guadalupe Hernandez, Anil Kumar Saini, Gabriel Ketron, Jason H. Moore
https://arxiv.org/abs/2508.03966 https://

GP and LLMs for Program Synthesis: No Clear Winners
Genetic programming (GP) and large language models (LLMs) differ in how program specifications are provided: GP uses input-output examples, and LLMs use text descriptions. In this work, we compared the ability of PushGP and GPT-4o to synthesize computer programs for tasks from the PSB2 benchmark suite. We used three prompt variants with GPT-4o: input-output examples (data-only), textual description of the task (text-only), and a combination of both textual descriptions and input-output examples…

Multi-Step Prediction and Control of Hierarchical Emotion Distribution in Text-to-Speech Synthesis
Sho Inoue, Kun Zhou, Shuai Wang, Haizhou Li
https://arxiv.org/abs/2507.04598

Multi-Step Prediction and Control of Hierarchical Emotion Distribution in Text-to-Speech Synthesis
We investigate hierarchical emotion distribution (ED) for achieving multi-level quantitative control of emotion rendering in text-to-speech synthesis (TTS). We introduce a novel multi-step hierarchical ED prediction module that quantifies emotion variance at the utterance, word, and phoneme levels. By predicting emotion variance in a multi-step manner, we leverage global emotional context to refine local emotional variations, thereby capturing the intrinsic hierarchical structure of speech emot…

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks
Xuan Hou, Shuhan Liu, Zhaohui Peng, Yaohui Chu, Yue Zhang, Yining Wang
https://arxiv.org/abs/2508.21340

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks
Time series synthesis is an effective approach to ensuring the secure circulation of time series data. Existing time series synthesis methods typically perform temporal modeling based on random sequences to generate target sequences, which often struggle to ensure the temporal dependencies in the generated time series. Additionally, directly modeling temporal features on random sequences makes it challenging to accurately capture the feature information of the original time series. To address t…

Fairness in Dysarthric Speech Synthesis: Understanding Intrinsic Bias in Dysarthric Speech Cloning using F5-TTS
Anuprabha M, Krishna Gurugubelli, Anil Kumar Vuppala
https://arxiv.org/abs/2508.05102

Fairness in Dysarthric Speech Synthesis: Understanding Intrinsic Bias in Dysarthric Speech Cloning using F5-TTS
Dysarthric speech poses significant challenges in developing assistive technologies, primarily due to the limited availability of data. Recent advances in neural speech synthesis, especially zero-shot voice cloning, facilitate synthetic speech generation for data augmentation; however, they may introduce biases towards dysarthric speech. In this paper, we investigate the effectiveness of state-of-the-art F5-TTS in cloning dysarthric speech using TORGO dataset, focusing on intelligibility, speak…

Exploring Exponential Runge-Kutta Methods: A Survey
Alessia and\`o, Nicol\`o Cangiotti, Mattia Sensi
https://arxiv.org/abs/2507.04024 https://

Exploring Exponential Runge-Kutta Methods: A Survey
In this survey, we provide an in-depth investigation of exponential Runge-Kutta methods for the numerical integration of initial-value problems. These methods offer a valuable synthesis between classical Runge-Kutta methods, introduced more than a century ago, and exponential integrators, which date back to the 1960s. This manuscript presents both a historical analysis of the development of these methods up to the present day and several examples aimed at making the topic accessible to a broad …

Engineering an LTLf Synthesis Tool
Alexandre Duret-Lutz (LRE), Shufang Zhu (Chalmers), Nir Piterman (Chalmers), Giuseppe de Giacomo (UNIROMA), Moshe Y Vardi
https://arxiv.org/abs/2507.02491

Engineering an LTLf Synthesis Tool
The problem of LTLf reactive synthesis is to build a transducer, whose output is based on a history of inputs, such that, for every infinite sequence of inputs, the conjoint evolution of the inputs and outputs has a prefix that satisfies a given LTLf specification. We describe the implementation of an LTLf synthesizer that outperforms existing tools on our benchmark suite. This is based on a new, direct translation from LTLf to a DFA represented as an array of Binary Decision Diagrams (MTBDDs) …

From Provable Correctness to Probabilistic Generation: A Comparative Review of Program Synthesis Paradigms
Zurabi Kobaladze, Anna Arnania, Tamar Sanikidze
https://arxiv.org/abs/2508.00013

From Provable Correctness to Probabilistic Generation: A Comparative Review of Program Synthesis Paradigms
Program synthesis--the automated generation of executable code from high-level specifications--has been a central goal of computer science for over fifty years. This thesis provides a comparative literature review of the main paradigms that have shaped the field, tracing its evolution from formal logic based methods to recent advances using large scale neural models. We examine five key approaches: logic based (deductive) synthesis, inductive (example based) synthesis, sketch/schema based synth…

A colossal dielectric response of HfxZr1-xO2 nanoparticles
Oleksandr S. Pylypchuk, Victor V. Vainberg, Vladimir N. Poroshin, Oksana V. Leshchenko, Victor N. Pavlikov, Irina V. Kondakova, Serhii E. Ivanchenko, Lesya P. Yurchenko, Lesya Demchenko, Anna O. Diachenko, Myroslav V. Karpets, Mikhail P. Trubitsyn, Eugene A. Eliseev, Anna N. Morozovska
https://

A colossal dielectric response of HfxZr1-xO2 nanoparticles
We reveal a colossal dielectric response of small (5 - 10 nm) oxygen-deficient HfxZr1-xO2 nanoparticles (x = 1 - 0.4), prepared by the solid-state organonitrate synthesis. The effective dielectric permittivity of the pressed HfxZr1-xO2 nanopowders has a pronounced maximum at 38 - 88 C, which shape can be fitted by the Curie-Weiss type dependence modified for the diffuse ferroelectric-paraelectric phase transition. The maximal value of the dielectric permittivity increases from 1.5*10^3 (for x =…

Deep neutral hydrogen observations of the early-type galaxy NGC 2768: collided by a newly discovered satellite galaxy?
Nai-Ping Yu, Ming Zhu, Jin-Long Xu, Chuan-Peng Zhang, Xiao-Lan Liu, Peng Jiang
https://arxiv.org/abs/2507.04703

Deep neutral hydrogen observations of the early-type galaxy NGC 2768: collided by a newly discovered satellite galaxy?
We present the results of a deep neutral hydrogen (Hi) observation of the early-type galaxy NGC 2768 using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Leveraging the high sensitivity of FAST, we discover an extended gas envelope around NGC 2768. The total Hi mass is measured to be 8.1 x 10^8 M_sun , representing a magnitude increase compared to previous Westerbork Synthesis Radio Telescope (WSRT) studies. Position-velocity (PV) diagram indicates the envelope mainly involve…

Control Synthesis in Partially Observable Environments for Complex Perception-Related Objectives
Zetong Xuan, Yu Wang
https://arxiv.org/abs/2507.02942 http…

Control Synthesis in Partially Observable Environments for Complex Perception-Related Objectives
Perception-related tasks often arise in autonomous systems operating under partial observability. This work studies the problem of synthesizing optimal policies for complex perception-related objectives in environments modeled by partially observable Markov decision processes. To formally specify such objectives, we introduce \emph{co-safe linear inequality temporal logic} (sc-iLTL), which can define complex tasks that are formed by the logical concatenation of atomic propositions as linear ine…

Reducing Quantum Circuit Synthesis to #SAT
Dekel Zak, Jingyi Mei, Jean-Marie Lagniez, Alfons Laarman
https://arxiv.org/abs/2508.00416

Reducing Quantum Circuit Synthesis to #SAT
Quantum circuit synthesis is the task of decomposing a given quantum operator into a sequence of elementary quantum gates. Since the finite target gate set cannot exactly implement any given operator, approximation is often necessary. Model counting, or #SAT, has recently been demonstrated as a promising new approach for tackling core problems in quantum circuit analysis. In this work, we show for the first time that the universal quantum circuit synthesis problem can be reduced to maximum mode…

MathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy
Shaoxiong Zhan, Yanlin Lai, Ziyu Lu, Dahua Lin, Ziqing Yang, Fei Tang
https://arxiv.org/abs/2508.05592

MathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy
Large language models have achieved substantial progress in mathematical reasoning, yet their advancement is limited by the scarcity of high-quality, high-difficulty training data. Existing synthesis methods largely rely on transforming human-written templates, limiting both diversity and scalability. We propose MathSmith, a novel framework for synthesizing challenging mathematical problems to enhance LLM reasoning. Rather than modifying existing problems, MathSmith constructs new ones from scr…

ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data
Yu Zhang, Ruijie Yu, Jidong Tian, Feng Zhu, Jiapeng Liu, Xiaokang Yang, Yaohui Jin, Yanyan Xu
https://arxiv.org/abs/2506.23520

ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data
With the increasing interest in robotic synthesis in the context of organic chemistry, the automated extraction of chemical procedures from literature is critical. However, this task remains challenging due to the inherent ambiguity of chemical language and the high cost of human annotation required for developing reliable computer-aided extraction protocols. Here, we present ChemActor, a fully fine-tuned large language model (LLM), as a chemical executor to convert between unstructured experim…

Controller synthesis method for multi-agent system based on temporal logic specification
Ruohan Huang, Zining Cao
https://arxiv.org/abs/2509.00870 https://…

Controller synthesis method for multi-agent system based on temporal logic specification
Controller synthesis is a theoretical approach to the systematic design of discrete event systems. It constructs a controller to provide feedback and control to the system, ensuring it meets specified control specifications. Traditional controller synthesis methods often use formal languages to describe control specifications and are mainly oriented towards single-agent and non-probabilistic systems. With the increasing complexity of systems, the control requirements that need to be satisfied a…

Synthesis of higher-order Poincar\'e sphere THz beams
Yaqun Liu, Valdas Pasiskevicius
https://arxiv.org/abs/2509.02706 https://arxiv.org/pdf/2509.02706…

Synthesis of higher-order Poincaré sphere THz beams
A novel method to generate vector vortex THz beams with angular momentum by a pair of pumps was reported. The helicity of the vector THz beam, that from polarization to orbital angular momentum can be finely tuned by manipulating the pulse delay between two pump pulses and a S-waveplate. All the states of THz vector vortices on the higher-order Poincaré sphere are able to be generated. The synthesis was theoretically explored and experimentally verified. This approach paves the way to the sour…

Space Environment and Debris: A Review of Micro-Meteoroids and Orbital Debris Impact Protection
Binkal Kumar Sharma, Harshitha Baskar
https://arxiv.org/abs/2508.03957 https://…

Space Environment and Debris: A Review of Micro-Meteoroids and Orbital Debris Impact Protection
The threat posed by MMOD to spacecraft has escalated with the growing density of orbital objects, driven by the proliferation of satellite constellations such as Starlink and OneWeb. This paper reviews the current challenges and advancements in MMOD impact protection, emphasizing innovations in shielding technologies. The synthesis of recent developments highlights the role of hybrid materials, additive manufacturing, and international collaboration in ensuring spacecraft resilience while promo…

Parameterized Infinite-State Reactive Synthesis
Benedikt Maderbacher, Roderick Bloem
https://arxiv.org/abs/2508.00613 https://arxiv.org/pdf/2508.00613

Parameterized Infinite-State Reactive Synthesis
We propose a method to synthesize a parameterized infinite-state systems that can be instantiated for different parameter values. The specification is given in a parameterized temporal logic that allows for data variables as well as parameter variables that encode properties of the environment. Our synthesis method runs in a counterexample-guided loop consisting of four main steps: First, we use existing techniques to synthesize concrete systems for some small parameter instantiations. Second, …

SPGrasp: Spatiotemporal Prompt-driven Grasp Synthesis in Dynamic Scenes
Yunpeng Mei, Hongjie Cao, Yinqiu Xia, Wei Xiao, Zhaohan Feng, Gang Wang, Jie Chen
https://arxiv.org/abs/2508.20547

SPGrasp: Spatiotemporal Prompt-driven Grasp Synthesis in Dynamic Scenes
Real-time interactive grasp synthesis for dynamic objects remains challenging as existing methods fail to achieve low-latency inference while maintaining promptability. To bridge this gap, we propose SPGrasp (spatiotemporal prompt-driven dynamic grasp synthesis), a novel framework extending segment anything model v2 (SAMv2) for video stream grasp estimation. Our core innovation integrates user prompts with spatiotemporal context, enabling real-time interaction with end-to-end latency as low as …

A Keynesian Intertemporal Synthesis (KIS) Model: Towards a unified and empirically grounded framework for fiscal policy
Ricardo Alonzo Fern\'andez Salguero
https://arxiv.org/abs/2508.00224

A Keynesian Intertemporal Synthesis (KIS) Model: Towards a unified and empirically grounded framework for fiscal policy
This paper develops a new generation of the Keynesian Intertemporal Synthesis (KIS) Model, a macroeconomic framework designed to reconcile the empirical strengths of the Post-Keynesian (PK) and New Keynesian (NK) traditions. The central innovation of this work is the abandonment of the traditional Cobb-Douglas production function in favor of a Constant Elasticity of Substitution (CES) specification. This modification is directly motivated by the compelling evidence from the meta-analysis by Gec…

The new USGS Cooperative National Geologic Map web tool was designed to also be mobile friendly,
allowing users to explore geologic data from smartphones and tablets, in addition to larger devices
https://ngmdb.usgs.gov/nationalgeology

Cooperative National Geologic Map | NGMDB
Explore the Cooperative National Geologic Map, a unified view of our Nation's geology, with interactive layers and geologic data.

A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning
Nirhoshan Sivaroopan, Kaushitha Silva, Chamara Madarasingha, Thilini Dahanayaka, Guillaume Jourjon, Anura Jayasumana, Kanchana Thilakarathna
https://arxiv.org/abs/2507.01976

A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning
Synthetic network traffic generation has emerged as a promising alternative for various data-driven applications in the networking domain. It enables the creation of synthetic data that preserves real-world characteristics while addressing key challenges such as data scarcity, privacy concerns, and purity constraints associated with real data. In this survey, we provide a comprehensive review of synthetic network traffic generation approaches, covering essential aspects such as data types, gene…

Modelling Program Spaces in Program Synthesis with Constraints
Tilman Hinnerichs, Bart Swinkels, Jaap de Jong, Reuben Gardos Reid, Tudor Magirescu, Neil Yorke-Smith, Sebastijan Dumancic
https://arxiv.org/abs/2508.00005

Modelling Program Spaces in Program Synthesis with Constraints
A core challenge in program synthesis is taming the large space of possible programs. Since program synthesis is essentially a combinatorial search, the community has sought to leverage powerful combinatorial constraint solvers. Here, constraints are used to express the program semantics, but not as a potentially potent tool to remove unwanted programs. Recent inductive logic programming approaches introduce constraints on the program's syntax to be synthesized. These syntactic constraints allo…

Influence of Density Distribution on Synchrotron Polarization Dispersion in Magnetized Interstellar Medium
Yawen Xiao, Jianfu Zhang, Alex Lazarian, Dmitri Pogosyan
https://arxiv.org/abs/2508.02938

Influence of Density Distribution on Synchrotron Polarization Dispersion in Magnetized Interstellar Medium
Faraday rotation measure synthesis is a well-known approach originated in Burn (1966) and later developed by Sokoloff et al. (1998) and Brentjens \& de Bruyn (2005) for studying magnetic fields. This work presents a complementary approach--the polarization frequency analysis (PFA)--allowing the properties of the turbulent magnetic field, which are difficult for Burn's original approach. Based on synthetic polarization observation of MHD turbulence simulation data, we study the influence of the …

Spectropolarimetric synthesis of forbidden lines in MHD models of coronal bright points
Ernest Alsina Ballester, Daniel N\'obrega-Siverio, Fernando Moreno-Insertis, Supriya Hebbur Dayananda
https://arxiv.org/abs/2506.23884

Spectropolarimetric synthesis of forbidden lines in MHD models of coronal bright points
The inference of the magnetic field vector from spectropolarimetric observations is crucial for understanding the physical processes governing the solar corona. We investigate which information on the magnetic fields of coronal bright points (CBP) can be gained from the intensity and polarization of the Fe XIII 10747 A, Fe XIV 5303 A, Si X 14301 A, and Si IX 39343 A forbidden lines. We apply the P-CORONA synthesis code to a CBP model in the very low corona, obtained with the Bifrost code, and t…

GPT-5 review: GPT-5-Thinking is a substantial upgrade over o3, Auto is only useful for free tier users, picking the right model still matters, and more (Zvi Mowshowitz/Don't Worry About the Vase)
https://thezvi.substack.com/p/gpt-5s-are-alive-synthesis

GPT-5s Are Alive: Synthesis
What do I ultimately make of all the new versions of GPT-5?

Zero-disparity Distribution Synthesis: Fast Exact Calculation of Chi-Squared Statistic Distribution for Discrete Uniform Histograms
Nikola Bani\'c, Neven Elezovi\'c
https://arxiv.org/abs/2506.23416

Zero-disparity Distribution Synthesis: Fast Exact Calculation of Chi-Squared Statistic Distribution for Discrete Uniform Histograms
Pearson's chi-squared test is widely used to assess the uniformity of discrete histograms, typically relying on a continuous chi-squared distribution to approximate the test statistic, since computing the exact distribution is computationally too costly. While effective in many cases, this approximation allegedly fails when expected bin counts are low or tail probabilities are needed. Here, Zero-disparity Distribution Synthesis is presented, a fast dynamic programming approach for computing the…

Neural translation for Stokes inversion and synthesis
A. Asensio Ramos, J. de la Cruz Rodriguez
https://arxiv.org/abs/2507.00594 https://

Neural translation for Stokes inversion and synthesis
[Abridged] The physical conditions in stellar atmospheres can be obtained from the interpretation of solar spectro-polarimetric observations. However, traditional inversion codes are computationally demanding, especially for lines whose formation is complex. The necessity of faster alternatives has motivated the emergence of machine learning solutions. This paper introduces an approach to the inversion and synthesis of Stokes profiles inspired by neural machine translation. Our aim is to develo…

CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection
Qiyu Chen, Zhen Qu, Wei Luo, Haiming Yao, Yunkang Cao, Yuxin Jiang, Yinan Duan, Huiyuan Luo, Chengkan Lv, Zhengtao Zhang
https://arxiv.org/abs/2508.03447

CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection
Recently, large pre-trained vision-language models have shown remarkable performance in zero-shot anomaly detection (ZSAD). With fine-tuning on a single auxiliary dataset, the model enables cross-category anomaly detection on diverse datasets covering industrial defects and medical lesions. Compared to manually designed prompts, prompt learning eliminates the need for expert knowledge and trial-and-error. However, it still faces the following challenges: (i) static learnable tokens struggle to …

Refining Gaussian Splatting: A Volumetric Densification Approach
Mohamed Abdul Gafoor, Marius Preda, Titus Zaharia
https://arxiv.org/abs/2508.05187 https://

Refining Gaussian Splatting: A Volumetric Densification Approach
Achieving high-quality novel view synthesis in 3D Gaussian Splatting (3DGS) often depends on effective point primitive management. The underlying Adaptive Density Control (ADC) process addresses this issue by automating densification and pruning. Yet, the vanilla 3DGS densification strategy shows key shortcomings. To address this issue, in this paper we introduce a novel density control method, which exploits the volumes of inertia associated to each Gaussian function to guide the refinement pr…

MiSTR: Multi-Modal iEEG-to-Speech Synthesis with Transformer-Based Prosody Prediction and Neural Phase Reconstruction
Mohammed Salah Al-Radhi, G\'eza N\'emeth, Branislav Gerazov
https://arxiv.org/abs/2508.03166

MiSTR: Multi-Modal iEEG-to-Speech Synthesis with Transformer-Based Prosody Prediction and Neural Phase Reconstruction
Speech synthesis from intracranial EEG (iEEG) signals offers a promising avenue for restoring communication in individuals with severe speech impairments. However, achieving intelligible and natural speech remains challenging due to limitations in feature representation, prosody modeling, and phase reconstruction. We introduce MiSTR, a deep-learning framework that integrates: 1) Wavelet-based feature extraction to capture fine-grained temporal, spectral, and neurophysiological representations o…

Compiler Bugs Detection in Logic Synthesis Tools via Linear Upper Confidence Bound
Hui Zeng, Zhihao Xu, Hui Li, Siwen Wang, Qian Ma
https://arxiv.org/abs/2509.01149 https://

Compiler Bugs Detection in Logic Synthesis Tools via Linear Upper Confidence Bound
Field-Programmable Gate Arrays (FPGAs) play an indispensable role in Electronic Design Automation (EDA), translating Register-Transfer Level (RTL) designs into gate-level netlists. The correctness and reliability of FPGA logic synthesis tools are critically important, as unnoticed bugs in these tools may infect the final hardware implementations. However, recent approaches often rely heavily on random selection strategies, limiting the structural diversity of the generated HDL test cases and re…

Prediction of synthesis parameters for N, Si, Ge and Sn diamond vacancy centers using machine learning
Zhi Jiang, Marco Peres, Carlo Bradac, Gil Gon\c{c}alves
https://arxiv.org/abs/2507.02808

Prediction of synthesis parameters for N, Si, Ge and Sn diamond vacancy centers using machine learning
Diamond and diamond color centers have become prime hardware candidates for solid state-based technologies in quantum information and computing, optics, photonics and (bio)sensing. The synthesis of diamond materials with specific characteristics and the precise control of the hosted color centers is thus essential to meet the demands of advanced applications. Yet, challenges remain in improving the concentration, uniform distribution and quality of these centers. Here we perform a review and me…

Leveraging Equivariances and Symmetries in the Control Barrier Function Synthesis
Adrian Wiltz, Dimos V. Dimarogonas
https://arxiv.org/abs/2509.04399 https://

Leveraging Equivariances and Symmetries in the Control Barrier Function Synthesis
The synthesis of Control Barrier Functions (CBFs) often involves demanding computations or a meticulous construction. However, structural properties of the system dynamics and constraints have the potential to mitigate these challenges. In this paper, we explore how equivariances in the dynamics, loosely speaking a form of symmetry, can be leveraged in the CBF synthesis. Although CBFs are generally not inherently symmetric, we show how equivariances in the dynamics and symmetries in the constra…

Beyond Memorization: Reasoning-Driven Synthesis as a Mitigation Strategy Against Benchmark Contamination
Terry Jingchen Zhang, Gopal Dev, Ning Wang, Nicole Ni, Wenyuan Jiang, Yinya Huang, Bernhard Sch\"olkopf, Mrinmaya Sachan, Zhijing Jin
https://arxiv.org/abs/2509.00072

Beyond Memorization: Reasoning-Driven Synthesis as a Mitigation Strategy Against Benchmark Contamination
Capability evaluation of large language models (LLMs) is increasingly shadowed by rising concerns of data contamination that cast doubts on whether static benchmarks measure genuine reasoning or mere memorization. We present an empirical study using an infinitely scalable framework to synthesize research-level QA directly from arXiv papers, harnessing the natural temporal structure of research publications where performance decay after knowledge cutoffs may indicate potential contamination. We …

Symbolic Graphics Programming with Large Language Models
Yamei Chen, Haoquan Zhang, Yangyi Huang, Zeju Qiu, Kaipeng Zhang, Yandong Wen, Weiyang Liu
https://arxiv.org/abs/2509.05208

Symbolic Graphics Programming with Large Language Models
Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise visual content remains underexplored. We study symbolic graphics programming, where the goal is to generate an SGP from a natural-language description. This task also serves as a lens into how LLMs understand the visual world by prompting them to generate images rendered from SGPs. Among various SGPs, our paper sticks to scalable vector graphics (SVGs)…

Synthesis Benchmarks for Automated Reasoning
M\'arton Hajdu, Petra Hozzov\'a, Laura Kov\'acs, Andrei Voronkov, Eva Maria Wagner, Richard Steven \v{Z}ilin\v{c}\'ik
https://arxiv.org/abs/2507.19827

Synthesis Benchmarks for Automated Reasoning
Program synthesis is the task of constructing a program conforming to a given specification. We focus on deductive synthesis, and in particular on synthesis problems with specifications given as $\forall\exists$-formulas, expressing the existence of an output corresponding to any input. So far there has been no canonical benchmark set for deductive synthesis using the $\forall\exists$-format and supporting the so-called uncomputable symbol restriction. This work presents such a data set, compos…

Compiling Prompts, Not Crafting Them: A Reproducible Workflow for AI-Assisted Evidence Synthesis
Teo Susnjak
https://arxiv.org/abs/2509.00038 https://arxiv…

Compiling Prompts, Not Crafting Them: A Reproducible Workflow for AI-Assisted Evidence Synthesis
Large language models (LLMs) offer significant potential to accelerate systematic literature reviews (SLRs), yet current approaches often rely on brittle, manually crafted prompts that compromise reliability and reproducibility. This fragility undermines scientific confidence in LLM-assisted evidence synthesis. In response, this work adapts recent advances in declarative prompt optimisation, developed for general-purpose LLM applications, and demonstrates their applicability to the domain of SL…

CNOT Oriented Synthesis for Small-Scale Boolean Functions Using Spatial Structures of Parallelotopes
Qiang Zheng, Yongzhen Xu, Jiaxi Zhang, Zhaofeng Su, Shenggen Zheng
https://arxiv.org/abs/2509.01912 …

CNOT Oriented Synthesis for Small-Scale Boolean Functions Using Spatial Structures of Parallelotopes
Quantum computing has garnered significant interest for its potential to achieve exponential speedups over classical approaches. However, in the Noisy Intermediate-Scale Quantum (NISQ) era, quantum circuit scalability remains limited by gate fidelity and qubit counts, restricting physical implementations to small-scale circuits. While prior work has explored logic network structures for quantum circuit synthesis, these methods often neglect the spatial structure intrinsic to Boolean functions. …

FLAMES: Improving LLM Math Reasoning via a Fine-Grained Analysis of the Data Synthesis Pipeline
Parker Seegmiller, Kartik Mehta, Soumya Saha, Chenyang Tao, Shereen Oraby, Arpit Gupta, Tagyoung Chung, Mohit Bansal, Nanyun Peng
https://arxiv.org/abs/2508.16514

FLAMES: Improving LLM Math Reasoning via a Fine-Grained Analysis of the Data Synthesis Pipeline
Recent works improving LLM math reasoning with synthetic data have used unique setups, making comparison of data synthesis strategies impractical. This leaves many unanswered questions about the roles of different factors in the synthetic data pipeline, such as the impact of filtering low-quality problems. To address this gap, we introduce FLAMES, a Framework for LLM Assessment of Math rEasoning Data Synthesis, and perform a systematic study of 10 existing data synthesis strategies and multiple…

CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis
Alec Sargood, Lemuel Puglisi, James H. Cole, Neil P. Oxtoby, Daniele Rav\`i, Daniel C. Alexander
https://arxiv.org/abs/2508.01292

CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis
Synthesizing amyloid PET scans from the more widely available and accessible structural MRI modality offers a promising, cost-effective approach for large-scale Alzheimer's Disease (AD) screening. This is motivated by evidence that, while MRI does not directly detect amyloid pathology, it may nonetheless encode information correlated with amyloid deposition that can be uncovered through advanced modeling. However, the high dimensionality and structural complexity of 3D neuroimaging data pose si…

Perceive-Sample-Compress: Towards Real-Time 3D Gaussian Splatting
Zijian Wang, Beizhen Zhao, Hao Wang
https://arxiv.org/abs/2508.04965 https://arxiv.org/pd…

Perceive-Sample-Compress: Towards Real-Time 3D Gaussian Splatting
Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated remarkable capabilities in real-time and photorealistic novel view synthesis. However, traditional 3DGS representations often struggle with large-scale scene management and efficient storage, particularly when dealing with complex environments or limited computational resources. To address these limitations, we introduce a novel perceive-sample-compress framework for 3D Gaussian Splatting. Specifically, we propose a scene percept…

ICAS: Detecting Training Data from Autoregressive Image Generative Models
Hongyao Yu, Yixiang Qiu, Yiheng Yang, Hao Fang, Tianqu Zhuang, Jiaxin Hong, Bin Chen, Hao Wu, Shu-Tao Xia
https://arxiv.org/abs/2507.05068

ICAS: Detecting Training Data from Autoregressive Image Generative Models
Autoregressive image generation has witnessed rapid advancements, with prominent models such as scale-wise visual auto-regression pushing the boundaries of visual synthesis. However, these developments also raise significant concerns regarding data privacy and copyright. In response, training data detection has emerged as a critical task for identifying unauthorized data usage in model training. To better understand the vulnerability of autoregressive image generative models to such detection, …

TTS-CtrlNet: Time varying emotion aligned text-to-speech generation with ControlNet
Jaeseok Jeong, Yuna Lee, Mingi Kwon, Youngjung Uh
https://arxiv.org/abs/2507.04349

TTS-CtrlNet: Time varying emotion aligned text-to-speech generation with ControlNet
Recent advances in text-to-speech (TTS) have enabled natural speech synthesis, but fine-grained, time-varying emotion control remains challenging. Existing methods often allow only utterance-level control and require full model fine-tuning with a large emotion speech dataset, which can degrade performance. Inspired by adding conditional control to the existing model in ControlNet (Zhang et al, 2023), we propose the first ControlNet-based approach for controllable flow-matching TTS (TTS-CtrlNet)…

LibriQuote: A Speech Dataset of Fictional Character Utterances for Expressive Zero-Shot Speech Synthesis
Gaspard Michel, Elena V. Epure, Christophe Cerisara
https://arxiv.org/abs/2509.04072

LibriQuote: A Speech Dataset of Fictional Character Utterances for Expressive Zero-Shot Speech Synthesis
Text-to-speech (TTS) systems have recently achieved more expressive and natural speech synthesis by scaling to large speech datasets. However, the proportion of expressive speech in such large-scale corpora is often unclear. Besides, existing expressive speech corpora are typically smaller in scale and primarily used for benchmarking TTS systems. In this paper, we introduce the LibriQuote dataset, an English corpus derived from read audiobooks, designed for both fine-tuning and benchmarking exp…

HyCodePolicy: Hybrid Language Controllers for Multimodal Monitoring and Decision in Embodied Agents
Yibin Liu, Zhixuan Liang, Zanxin Chen, Tianxing Chen, Mengkang Hu, Wanxi Dong, Congsheng Xu, Zhaoming Han, Yusen Qin, Yao Mu
https://arxiv.org/abs/2508.02629

HyCodePolicy: Hybrid Language Controllers for Multimodal Monitoring and Decision in Embodied Agents
Recent advances in multimodal large language models (MLLMs) have enabled richer perceptual grounding for code policy generation in embodied agents. However, most existing systems lack effective mechanisms to adaptively monitor policy execution and repair codes during task completion. In this work, we introduce HyCodePolicy, a hybrid language-based control framework that systematically integrates code synthesis, geometric grounding, perceptual monitoring, and iterative repair into a closed-loop …

Radiance Fields in XR: A Survey on How Radiance Fields are Envisioned and Addressed for XR Research
Ke Li, Mana Masuda, Susanne Schmidt, Shohei Mori
https://arxiv.org/abs/2508.04326

Radiance Fields in XR: A Survey on How Radiance Fields are Envisioned and Addressed for XR Research
The development of radiance fields (RF), such as 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF), has revolutionized interactive photorealistic view synthesis and presents enormous opportunities for XR research and applications. However, despite the exponential growth of RF research, RF-related contributions to the XR community remain sparse. To better understand this research gap, we performed a systematic survey of current RF literature to analyze (i) how RF is envisioned for X…

Passive nonlinear FIR filters for data-driven control
Zixing Wang, Fulvio Forni
https://arxiv.org/abs/2508.05279 https://arxiv.org/pdf/2508.05279

Passive nonlinear FIR filters for data-driven control
We propose a new class of passive nonlinear finite impulse response operators. This class is constructed by the action of finite impulse response filters in a lifted space. This allows for efficient control synthesis through constrained optimization. Closed-loop performance is taken into account through least-squares fitting, based on the theory of virtual reference feedback tuning. Passivity is established through efficient linear constraints, based on sampling in the frequency domain. Because…

ChatHLS: Towards Systematic Design Automation and Optimization for High-Level Synthesis
Runkai Li, Jia Xiong, Xiuyuan He, Jieru Zhao, Qiang Xu, Xi Wang
https://arxiv.org/abs/2507.00642

ChatHLS: Towards Systematic Design Automation and Optimization for High-Level Synthesis
The increasing complexity of computational demands has accelerated the adoption of domain-specific accelerators, yet traditional hardware design methodologies remain constrained by prolonged development and verification cycles. High-Level Synthesis (HLS) bridges the gap between software and hardware by enabling hardware design from high-level programming languages. However, its widespread adoption is hindered by strict coding constraints and intricate hardware-specific optimizations, creating s…

CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback
Qiushi Sun, Jinyang Gong, Lei Li, Qipeng Guo, Fei Yuan
https://arxiv.org/abs/2507.22080

CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback
Acquiring high-quality instruction-code pairs is essential for training Large Language Models (LLMs) for code generation. Manually curated data is expensive and inherently limited in scale, motivating the development of code-centric synthesis methods. Yet, current approaches either focus on augmenting existing code or rely on predefined heuristics, both lacking rigorous data validation, which results in synthetic data that is ungrounded, repetitive, or overly simplistic. Inspired by collaborati…

High pressure synthesis and structural study of AuGa2 intermetallic compound
Azkar Saeed Ahmad, Zhuoyang Yu, Sizhe Wang, Tong Weng, Wenting Lu, Baihong Sun, Jiewu Song, Qian Zhang, Martin Kunz, Bihan Wang, Elissaios Stavrou
https://arxiv.org/abs/2507.00592

High pressure synthesis and structural study of AuGa2 intermetallic compound
We report the synthesis of the AuGa2 intermetallic compound, using a direct reaction of the relevant elements at room temperature and at very low pressure. The pressure needed to synthesize the AuGa2 compound is below 1 GPa, that is at the lower limit of modern large volume presses, routinely used to synthesize other commercially available materials. This study presents a new method of synthesizing AuGa2, which is much more cost efficient and environmentally friendly than the previously used hi…

TauGenNet: Plasma-Driven Tau PET Image Synthesis via Text-Guided 3D Diffusion Models
Yuxin Gong (for the Alzheimer's Disease Neuroimaging Initiative), Se-in Jang (for the Alzheimer's Disease Neuroimaging Initiative), Wei Shao (for the Alzheimer's Disease Neuroimaging Initiative), Yi Su (for the Alzheimer's Disease Neuroimaging Initiative), Kuang Gong (for the Alzheimer's Disease Neuroimaging Initiative)

TauGenNet: Plasma-Driven Tau PET Image Synthesis via Text-Guided 3D Diffusion Models
Accurate quantification of tau pathology via tau positron emission tomography (PET) scan is crucial for diagnosing and monitoring Alzheimer's disease (AD). However, the high cost and limited availability of tau PET restrict its widespread use. In contrast, structural magnetic resonance imaging (MRI) and plasma-based biomarkers provide non-invasive and widely available complementary information related to brain anatomy and disease progression. In this work, we propose a text-guided 3D diffusion …

A virtual sensor fusion approach for state of charge estimation of lithium-ion cells
Davide Previtali, Daniele Masti, Mirko Mazzoleni, Fabio Previdi
https://arxiv.org/abs/2508.04268

A virtual sensor fusion approach for state of charge estimation of lithium-ion cells
This paper addresses the estimation of the State Of Charge (SOC) of lithium-ion cells via the combination of two widely used paradigms: Kalman Filters (KFs) equipped with Equivalent Circuit Models (ECMs) and machine-learning approaches. In particular, a recent Virtual Sensor (VS) synthesis technique is considered, which operates as follows: (i) learn an Affine Parameter-Varying (APV) model of the cell directly from data, (ii) derive a bank of linear observers from the APV model, (iii) train a m…

NVSpeech: An Integrated and Scalable Pipeline for Human-Like Speech Modeling with Paralinguistic Vocalizations
Huan Liao, Qinke Ni, Yuancheng Wang, Yiheng Lu, Haoyue Zhan, Pengyuan Xie, Qiang Zhang, Zhizheng Wu
https://arxiv.org/abs/2508.04195

NVSpeech: An Integrated and Scalable Pipeline for Human-Like Speech Modeling with Paralinguistic Vocalizations
Paralinguistic vocalizations-including non-verbal sounds like laughter and breathing, as well as lexicalized interjections such as "uhm" and "oh"-are integral to natural spoken communication. Despite their importance in conveying affect, intent, and interactional cues, such cues remain largely overlooked in conventional automatic speech recognition (ASR) and text-to-speech (TTS) systems. We present NVSpeech, an integrated and scalable pipeline that bridges the recognition and synthesis of paral…

Efficient Strategy Synthesis for MDPs via Hierarchical Block Decomposition
Alexandros Evangelidis, Gricel V\'azquez, Simos Gerasimou
https://arxiv.org/abs/2506.17792

Efficient Strategy Synthesis for MDPs via Hierarchical Block Decomposition
Software-intensive systems, such as software product lines and robotics, utilise Markov decision processes (MDPs) to capture uncertainty and analyse sequential decision-making problems. Despite the usefulness of conventional policy synthesis methods, they fail to scale to large state spaces. Our approach addresses this issue and accelerates policy synthesis in large MDPs by dynamically refining the MDP and iteratively selecting the most fragile MDP regions for refinement. This iterative procedu…

Neural Autoregressive Modeling of Brain Aging
Ridvan Yesiloglu, Wei Peng, Md Tauhidul Islam, Ehsan Adeli
https://arxiv.org/abs/2507.22954 https://arxiv.org…

Neural Autoregressive Modeling of Brain Aging
Brain aging synthesis is a critical task with broad applications in clinical and computational neuroscience. The ability to predict the future structural evolution of a subject's brain from an earlier MRI scan provides valuable insights into aging trajectories. Yet, the high-dimensionality of data, subtle changes of structure across ages, and subject-specific patterns constitute challenges in the synthesis of the aging brain. To overcome these challenges, we propose NeuroAR, a novel brain aging…

LesionGen: A Concept-Guided Diffusion Model for Dermatology Image Synthesis
Jamil Fayyad, Nourhan Bayasi, Ziyang Yu, Homayoun Najjaran
https://arxiv.org/abs/2507.23001 https://

LesionGen: A Concept-Guided Diffusion Model for Dermatology Image Synthesis
Deep learning models for skin disease classification require large, diverse, and well-annotated datasets. However, such resources are often limited due to privacy concerns, high annotation costs, and insufficient demographic representation. While text-to-image diffusion probabilistic models (T2I-DPMs) offer promise for medical data synthesis, their use in dermatology remains underexplored, largely due to the scarcity of rich textual descriptions in existing skin image datasets. In this work, we…

First-Order LTLf Synthesis with Lookback (Extended Version)
Sarah Winkler
https://arxiv.org/abs/2508.18149 https://arxiv.org/pdf/2508.18149

First-Order LTLf Synthesis with Lookback (Extended Version)
Reactive synthesis addresses the problem of generating a controller for a temporal specification in an adversarial environment; it was typically studied for LTL. Driven by applications ranging from AI to business process management, LTL modulo first order-theories over finite traces (LTLfMT) has recently gained traction, where propositional variables in properties are replaced by first-order constraints. Though reactive synthesis for LTLf with some first-order features has been addressed, exist…

Approximate Logic Synthesis Using BLASYS
Jingxiao Ma, Soheil Hashemi, Sherief Reda
https://arxiv.org/abs/2506.22772 https://arxiv.org…

Approximate Logic Synthesis Using BLASYS
Approximate computing is an emerging paradigm where design accuracy can be traded for improvements in design metrics such as design area and power consumption. In this work, we overview our open-source tool, BLASYS, for synthesis of approximate circuits using Boolean Matrix Factorization (BMF). In our methodology the truth table of a given circuit is approximated using BMF to a controllable approximation degree, and the results of the factorization are used to synthesize the approximate circuit…

An AST-guided LLM Approach for SVRF Code Synthesis
Abanoub E. Abdelmalak, Mohamed A. Elsayed, David Abercrombie, Ilhami Torunoglu
https://arxiv.org/abs/2507.00352

An AST-guided LLM Approach for SVRF Code Synthesis
Standard Verification Rule Format (SVRF) is essential for semiconductor applications like Design Rule Check (DRC), Layout Versus Schematic (LVS), and Optical Proximity Correction (OPC) and it faces challenges as advancing nodes create complex design rules that renders traditional SVRF development ineffective and highlight an expertise gap. This paper introduces a novel methodology integrating Abstract Syntax Tree (AST) embedding and Retrieval-Augmented Generation (RAG) for enhanced SVRF code sy…

Spray flame synthesis of Y2O3-MgO nanoparticles for mid-infrared transparent nanocomposite ceramics
Shuting Lei, Yiyang Zhang, Xing Jin, Yanan Li, Zhu Fang, Shuiqing Li
https://arxiv.org/abs/2507.22443

Spray flame synthesis of Y2O3-MgO nanoparticles for mid-infrared transparent nanocomposite ceramics
Spray flame synthesis offers a promising method for scalable production of homogeneously mixed Y2O3-MgO nanopowders as next-generation infrared-transparent window material, which has attracted significant attention owing to its excellent optical properties at high temperatures. However, systematic understanding of how flame synthesis parameters influence particle morphology, crystal phase, solid solubility, and subsequent ceramic performance remains insufficiently understood. In this study, we …

Enhancing Spectrogram Realism in Singing Voice Synthesis via Explicit Bandwidth Extension Prior to Vocoder
Runxuan Yang, Kai Li, Guo Chen, Xiaolin Hu
https://arxiv.org/abs/2508.01796

Enhancing Spectrogram Realism in Singing Voice Synthesis via Explicit Bandwidth Extension Prior to Vocoder
This paper addresses the challenge of enhancing the realism of vocoder-generated singing voice audio by mitigating the distinguishable disparities between synthetic and real-life recordings, particularly in high-frequency spectrogram components. Our proposed approach combines two innovations: an explicit linear spectrogram estimation step using denoising diffusion process with DiT-based neural network architecture optimized for time-frequency data, and a redesigned vocoder based on Vocos specia…

DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis
Liana Patel, Negar Arabzadeh, Harshit Gupta, Ankita Sundar, Ion Stoica, Matei Zaharia, Carlos Guestrin
https://arxiv.org/abs/2508.20033

DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis
The ability to research and synthesize knowledge is central to human expertise and progress. An emerging class of systems promises these exciting capabilities through generative research synthesis, performing retrieval over the live web and synthesizing discovered sources into long-form, cited summaries. However, evaluating such systems remains an open challenge: existing question-answering benchmarks focus on short-form factual responses, while expert-curated datasets risk staleness and data c…

RAAG: Ratio Aware Adaptive Guidance
Shangwen Zhu, Qianyu Peng, Yuting Hu, Zhantao Yang, Han Zhang, Zhao Pu, Ruili Feng, Fan Cheng
https://arxiv.org/abs/2508.03442 https://

RAAG: Ratio Aware Adaptive Guidance
Flow-based generative models have recently achieved remarkable progress in image and video synthesis, with classifier-free guidance (CFG) becoming the standard tool for high-fidelity, controllable generation. However, despite their practical success, little is known about how guidance interacts with different stages of the sampling process-especially in the fast, low-step regimes typical of modern flow-based pipelines. In this work, we uncover and analyze a fundamental instability: the earliest…

On the MIA Vulnerability Gap Between Private GANs and Diffusion Models
Ilana Sebag, Jean-Yves Franceschi, Alain Rakotomamonjy, Alexandre Allauzen, Jamal Atif
https://arxiv.org/abs/2509.03341

On the MIA Vulnerability Gap Between Private GANs and Diffusion Models
Generative Adversarial Networks (GANs) and diffusion models have emerged as leading approaches for high-quality image synthesis. While both can be trained under differential privacy (DP) to protect sensitive data, their sensitivity to membership inference attacks (MIAs), a key threat to data confidentiality, remains poorly understood. In this work, we present the first unified theoretical and empirical analysis of the privacy risks faced by differentially private generative models. We begin by …

Open-Source Full-Duplex Conversational Datasets for Natural and Interactive Speech Synthesis
Zhitong Zhou, Qingqing Zhang, Lei Luo, Jiechen Liu, Ruohua Zhou
https://arxiv.org/abs/2509.04093

Open-Source Full-Duplex Conversational Datasets for Natural and Interactive Speech Synthesis
Full-duplex, spontaneous conversational data are essential for enhancing the naturalness and interactivity of synthesized speech in conversational TTS systems. We present two open-source dual-track conversational speech datasets, one in Chinese and one in English, designed to enhance the naturalness of synthesized speech by providing more realistic conversational data. The two datasets contain a total of 15 hours of natural, spontaneous conversations recorded in isolated rooms, which produces s…

Scaling GR(1) Synthesis via a Compositional Framework for LTL Discrete Event Control
Hern\'an Gagliardi, Victor Braberman, Sebastian Uchitel
https://arxiv.org/abs/2506.16557

Scaling GR(1) Synthesis via a Compositional Framework for LTL Discrete Event Control
We present a compositional approach to controller synthesis of discrete event system controllers with linear temporal logic (LTL) goals. We exploit the modular structure of the plant to be controlled, given as a set of labelled transition systems (LTS), to mitigate state explosion that monolithic approaches to synthesis are prone to. Maximally permissive safe controllers are iteratively built for subsets of the plant LTSs by solving weaker control problems. Observational synthesis equivalence i…

Towards 3D Semantic Image Synthesis for Medical Imaging
Wenwu Tang, Khaled Seyam, Bin Yang
https://arxiv.org/abs/2507.00206 https://a…

Towards 3D Semantic Image Synthesis for Medical Imaging
In the medical domain, acquiring large datasets is challenging due to both accessibility issues and stringent privacy regulations. Consequently, data availability and privacy protection are major obstacles to applying machine learning in medical imaging. To address this, our study proposes the Med-LSDM (Latent Semantic Diffusion Model), which operates directly in the 3D domain and leverages de-identified semantic maps to generate synthetic data as a method of privacy preservation and data augme…

Veila: Panoramic LiDAR Generation from a Monocular RGB Image
Youquan Liu, Lingdong Kong, Weidong Yang, Ao Liang, Jianxiong Gao, Yang Wu, Xiang Xu, Xin Li, Linfeng Li, Runnan Chen, Ben Fei
https://arxiv.org/abs/2508.03690

Veila: Panoramic LiDAR Generation from a Monocular RGB Image
Realistic and controllable panoramic LiDAR data generation is critical for scalable 3D perception in autonomous driving and robotics. Existing methods either perform unconditional generation with poor controllability or adopt text-guided synthesis, which lacks fine-grained spatial control. Leveraging a monocular RGB image as a spatial control signal offers a scalable and low-cost alternative, which remains an open problem. However, it faces three core challenges: (i) semantic and depth cues fro…

Arrows of Math Reasoning Data Synthesis for Large Language Models: Diversity, Complexity and Correctness
Sirui Chen, Changxin Tian, Binbin Hu, Kunlong Chen, Ziqi Liu, Zhiqiang Zhang, Jun Zhou
https://arxiv.org/abs/2508.18824

Arrows of Math Reasoning Data Synthesis for Large Language Models: Diversity, Complexity and Correctness
Enhancing the mathematical reasoning of large language models (LLMs) demands high-quality training data, yet conventional methods face critical challenges in scalability, cost, and data reliability. To address these limitations, we propose a novel program-assisted synthesis framework that systematically generates a high-quality mathematical corpus with guaranteed diversity, complexity, and correctness. This framework integrates mathematical knowledge systems and domain-specific tools to create …

OmniSim: Simulating Hardware with C Speed and RTL Accuracy for High-Level Synthesis Designs
Rishov Sarkar, Cong Hao
https://arxiv.org/abs/2508.19299 https://

OmniSim: Simulating Hardware with C Speed and RTL Accuracy for High-Level Synthesis Designs
High-Level Synthesis (HLS) is increasingly popular for hardware design using C/C++ instead of Register-Transfer Level (RTL). To express concurrent hardware behavior in a sequential language like C/C++, HLS tools introduce constructs such as infinite loops and dataflow modules connected by FIFOs. However, efficiently and accurately simulating these constructs at C level remains challenging. First, without hardware timing information, functional verification typically requires slow RTL synthesis …

Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generation
Xunzhi Xiang, Yabo Chen, Guiyu Zhang, Zhongyu Wang, Zhe Gao, Quanming Xiang, Gonghu Shang, Junqi Liu, Haibin Huang, Yang Gao, Chi Zhang, Qi Fan, Xuelong Li
https://arxiv.org/abs/2508.03334

Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generation
Current autoregressive diffusion models excel at video generation but are generally limited to short temporal durations. Our theoretical analysis indicates that the autoregressive modeling typically suffers from temporal drift caused by error accumulation and hinders parallelization in long video synthesis. To address these limitations, we propose a novel planning-then-populating framework centered on Macro-from-Micro Planning (MMPL) for long video generation. MMPL sketches a global storyline f…

Robust and Efficient Autoregressive Speech Synthesis with Dynamic Chunk-wise Prediction Policy
Bohan Li, Zhihan Li, Haoran Wang, Hanglei Zhang, Yiwei Guo, Hankun Wang, Xie Chen, Kai Yu
https://arxiv.org/abs/2506.22023

Robust and Efficient Autoregressive Speech Synthesis with Dynamic Chunk-wise Prediction Policy
Recently, autoregressive (AR) language models have emerged as a dominant approach in speech synthesis, offering expressive generation and scalable training. However, conventional AR speech synthesis models relying on the next-token prediction paradigm often encounter significant challenges when handling long speech sequences. These models often struggle to construct stable frame-to-frame attention, leading to increased latency and degraded synthesis quality, thereby limiting their feasibility f…

Dr. Boot: Bootstrapping Program Synthesis Language Models to Perform Repairing
Noah van der Vleuten
https://arxiv.org/abs/2507.15889 https://

Dr. Boot: Bootstrapping Program Synthesis Language Models to Perform Repairing
Language models for program synthesis are usually trained and evaluated on programming competition datasets (MBPP, APPS). However, these datasets are limited in size and quality, while these language models are extremely data hungry. Additionally, the language models have a misaligned program synthesis process compared to humans. While humans iteratively develop code with the help of a compiler, most program synthesis models currently produce code in one go. To solve these issues, we introduce …

Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis
Marc-Andr\'e Carbonneau, Benjamin van Niekerk, Hugo Seut\'e, Jean-Philippe Letendre, Herman Kamper, Julian Za\"idi
https://arxiv.org/abs/2507.02176

Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis
Modeling voice identity is challenging due to its multifaceted nature. In generative speech systems, identity is often assessed using automatic speaker verification (ASV) embeddings, designed for discrimination rather than characterizing identity. This paper investigates which aspects of a voice are captured in such representations. We find that widely used ASV embeddings focus mainly on static features like timbre and pitch range, while neglecting dynamic elements such as rhythm. We also ident…

Video Perception Models for 3D Scene Synthesis
Rui Huang, Guangyao Zhai, Zuria Bauer, Marc Pollefeys, Federico Tombari, Leonidas Guibas, Gao Huang, Francis Engelmann
https://arxiv.org/abs/2506.20601

Video Perception Models for 3D Scene Synthesis
Traditionally, 3D scene synthesis requires expert knowledge and significant manual effort. Automating this process could greatly benefit fields such as architectural design, robotics simulation, virtual reality, and gaming. Recent approaches to 3D scene synthesis often rely on the commonsense reasoning of large language models (LLMs) or strong visual priors of modern image generation models. However, current LLMs demonstrate limited 3D spatial reasoning ability, which restricts their ability to…

Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis
Bowen Zhang, Sicheng Xu, Chuxin Wang, Jiaolong Yang, Feng Zhao, Dong Chen, Baining Guo
https://arxiv.org/abs/2507.23785

Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis
In this paper, we present a novel framework for video-to-4D generation that creates high-quality dynamic 3D content from single video inputs. Direct 4D diffusion modeling is extremely challenging due to costly data construction and the high-dimensional nature of jointly representing 3D shape, appearance, and motion. We address these challenges by introducing a Direct 4DMesh-to-GS Variation Field VAE that directly encodes canonical Gaussian Splats (GS) and their temporal variations from 3D anima…

FireRedTTS-2: Towards Long Conversational Speech Generation for Podcast and Chatbot
Kun Xie, Feiyu Shen, Junjie Li, Fenglong Xie, Xu Tang, Yao Hu
https://arxiv.org/abs/2509.02020

FireRedTTS-2: Towards Long Conversational Speech Generation for Podcast and Chatbot
Current dialogue generation approaches typically require the complete dialogue text before synthesis and produce a single, inseparable speech containing all voices, making them unsuitable for interactive chat; moreover, they suffer from unstable synthesis, inaccurate speaker transitions, and incoherent prosody. In this work, we present FireRedTTS-2, a long-form streaming TTS system for multi-speaker dialogue generation, delivering stable, natural speech with reliable speaker switching and conte…

AnyI2V: Animating Any Conditional Image with Motion Control
Ziye Li, Hao Luo, Xincheng Shuai, Henghui Ding
https://arxiv.org/abs/2507.02857 https://…

AnyI2V: Animating Any Conditional Image with Motion Control
Recent advancements in video generation, particularly in diffusion models, have driven notable progress in text-to-video (T2V) and image-to-video (I2V) synthesis. However, challenges remain in effectively integrating dynamic motion signals and flexible spatial constraints. Existing T2V methods typically rely on text prompts, which inherently lack precise control over the spatial layout of generated content. In contrast, I2V methods are limited by their dependence on real images, which restricts…