arXiv subjects

Shengyuan Xu

Publications and source records attributed to Shengyuan Xu.

At least 19 recordsLinked to original sources

arXiv2026

ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research

AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capability remains difficult to verify. We present ResearchClawBench, a benchmark for evaluating autonomous scientific research across 40 tasks from 10 scientific domains. Each task is grounded in a real published paper, provides related literature and raw data, and hides the target paper during evaluation. Expert-curated multimodal rubrics decompose the target scientific artifacts into weighted criteria, enabling evaluation of target-paper-level re-discovery while leaving room for new discovery. We evaluate seven autonomous research (auto-research) agents under a unified protocol and seventeen native LLMs through the lightweight ResearchHarness. Current systems remain far from reliable re-discovery: the strongest autonomous agent, Claude Code, averages 21.5, and the strongest ResearchHarness LLM, Claude-Opus-4.7, averages 20.7, with an LLM frontier mean of only 26.5. Error analysis shows that failures concentrate in experimental protocol mismatch, evidence mismatch, and missing scientific core. ResearchClawBench provides a reproducible evaluation frontier for measuring progress toward autonomous scientific research.

cs.LG↗

arXiv2026

ACE-Step 1.5: Pushing the Boundaries of Open-Source Music Generation

We present ACE-Step v1.5, a highly efficient open-source music foundation model that brings commercial-grade generation to consumer hardware. On commonly used evaluation metrics, ACE-Step v1.5 achieves quality beyond most commercial music models while remaining extremely fast -- under 2 seconds per full song on an A100 and under 10 seconds on an RTX 3090. The model runs locally with less than 4GB of VRAM, and supports lightweight personalization: users can train a LoRA from just a few songs to capture their own style. At its core lies a novel hybrid architecture where the Language Model (LM) functions as an omni-capable planner: it transforms simple user queries into comprehensive song blueprints -- scaling from short loops to 10-minute compositions -- while synthesizing metadata, lyrics, and captions via Chain-of-Thought to guide the Diffusion Transformer (DiT). Uniquely, this alignment is achieved through intrinsic reinforcement learning relying solely on the model's internal mechanisms, thereby eliminating the biases inherent in external reward models or human preferences. Beyond standard synthesis, ACE-Step v1.5 unifies precise stylistic control with versatile editing capabilities -- such as cover generation, repainting, and vocal-to-BGM conversion -- while maintaining strict adherence to prompts across 50+ languages. This paves the way for powerful tools that seamlessly integrate into the creative workflows of music artists, producers, and content creators. The code, the model weights and the demo are available at: https://ace-step.github.io/ace-step-v1.5.github.io/

cs.SD↗

arXiv2025

Cryptanalysis of Gleeok-128

Gleeok is a family of low latency keyed pseudorandom functions (PRFs) consisting of three parallel SPN based permutations whose outputs are XORed to form the final value. Both Gleeok-128 and Gleeok-256 use a 256 bit key, with block sizes of 128 and 256 bits, respectively. Owing to its multi branch structure, evaluating security margins and mounting effective key recovery attacks present nontrivial challenges. This paper provides the first comprehensive third party cryptanalysis of Gleeok-128. We introduce a two stage MILP based framework for constructing branch wise and full cipher differential linear (DL) distinguishers, together with an integral based key recovery framework tailored to multi branch designs. Our DL analysis yields 7, 7, 8, and 4 round distinguishers for Branch 1, Branch 2, Branch 3, and Gleeok-128, respectively, with squared correlations approximately 2 to the power minus 88.12, 2 to the power minus 88.12, 2 to the power minus 38.73, and 2 to the power minus 49.04, outperforming those in the design document except for the full PRF case. By tightening algebraic degree bounds, we further derive 9, 9, and 7 round integral distinguishers for the three branches and a 7 round distinguisher for the full PRF, extending the designers results by 3, 3, and 2 rounds and by 2 rounds, respectively. These integral properties enable 7 round and 8 round key recovery attacks in the non full codebook and full codebook settings. In addition, we identify a flaw in the original linear security evaluation of Branch 3, showing that it can be distinguished over all 12 rounds with data complexity about 2 to the power 48. We also propose optimized linear layer parameters that significantly improve linear resistance without sacrificing diffusion. Our results advance the understanding of Gleeok-128 and provide general methods for analyzing multi branch symmetric designs.

cs.CR↗

arXiv2025

Ultracompact Wide-FOV Near-infrared Camera with Wafer-level Manufactured Meta-Aspheric Lens

Overcoming the trade-off between wide field of view (FOV) and compactness remains a central challenge for integrating near-infrared (NIR) imaging into smartphones and AR glasses. Existing refractive NIR optics cannot simultaneously achieve ultra-wide angles above 100° and ultrathin total track length (TTL) below 5 mm, limiting their use in portable devices. Here, we present a wafer-level-manufactured meta-aspheric lens (MAL) that achieves a 101.5° FOV, 3.39 mm TTL, and F/1.64 aperture within a compact volume of 0.02 cubic centimeters. Unlike previous hybrid lenses with separate refractive and diffractive components, our MAL features a fully integrated structure, which enables a compact form factor. This integration also simplifies fabrication, allowing high-throughput production via micrometer-level precision alignment and bonding on a single wafer, with only one dicing step and no need for additional mechanical fixtures. Furthermore, the design process explicitly considers manufacturability and accurately models metalens dispersion, ensuring that experimental performance matches simulated results. We validate our MAL through both direct and computational imaging experiments. Despite its small form factor, our scalable MAL demonstrates strong NIR imaging performance in blood vessel imaging, eye tracking, and computational pixel super-resolution tasks. This scalable MAL technology establishes a new benchmark for high-performance, miniaturized NIR imaging and opens the door to next-generation smartphone and AR optical systems.

physics.optics↗

arXiv2025

Distributed Online Stochastic Convex-Concave Optimization: Dynamic Regret Analyses under Single and Multiple Consensus Steps

This paper considers the distributed online convex-concave optimization with constraint sets over a multiagent network, in which each agent autonomously generates a series of decision pairs through a designable mechanism to cooperatively minimize the global loss function. To this end, under no-Euclidean distance metrics, we propose a distributed online stochastic mirror descent convex-concave optimization algorithm with time-varying predictive mappings. Taking dynamic saddle point regret as a performance metric, it is proved that the proposed algorithm achieves the regret upper-bound in $\mathcal{O}(\max \{T^{θ_1}, T^{θ_2} (1+V_T ) \})$ for the general convex-concave loss function, where $θ_1, θ_2 \in(0,1)$ are the tuning parameters, $T$ is the total iteration time, and $V_T$ is the path-variation. Surely, this algorithm guarantees the sublinear convergence, provided that $V_T$ is sublinear. Moreover, aiming to achieve better convergence, we further investigate a variant of this algorithm by employing the multiple consensus technique. The obtained results show that the appropriate setting can effectively tighten the regret bound to a certain extent. Finally, the efficacy of the proposed algorithms is validated and compared through the simulation example of a target tracking problem.

math.OC↗

arXiv2025

ACE-Step: A Step Towards Music Generation Foundation Model

We introduce ACE-Step, a novel open-source foundation model for music generation that overcomes key limitations of existing approaches and achieves state-of-the-art performance through a holistic architectural design. Current methods face inherent trade-offs between generation speed, musical coherence, and controllability. For example, LLM-based models (e.g. Yue, SongGen) excel at lyric alignment but suffer from slow inference and structural artifacts. Diffusion models (e.g. DiffRhythm), on the other hand, enable faster synthesis but often lack long-range structural coherence. ACE-Step bridges this gap by integrating diffusion-based generation with Sana's Deep Compression AutoEncoder (DCAE) and a lightweight linear transformer. It also leverages MERT and m-hubert to align semantic representations (REPA) during training, allowing rapid convergence. As a result, our model synthesizes up to 4 minutes of music in just 20 seconds on an A100 GPU-15x faster than LLM-based baselines-while achieving superior musical coherence and lyric alignment across melody, harmony, and rhythm metrics. Moreover, ACE-Step preserves fine-grained acoustic details, enabling advanced control mechanisms such as voice cloning, lyric editing, remixing, and track generation (e.g. lyric2vocal, singing2accompaniment). Rather than building yet another end-to-end text-to-music pipeline, our vision is to establish a foundation model for music AI: a fast, general-purpose, efficient yet flexible architecture that makes it easy to train subtasks on top of it. This paves the way for the development of powerful tools that seamlessly integrate into the creative workflows of music artists, producers, and content creators. In short, our goal is to build a stable diffusion moment for music. The code, the model weights and the demo are available at: https://ace-step.github.io/.

cs.SD↗

arXiv2025

Asymptotic stabilization under homomorphic encryption: A re-encryption free method

In this paper, we propose methods to encrypted a pre-given dynamic controller with homomorphic encryption, without re-encrypting the control inputs. We first present a preliminary result showing that the coefficients in a pre-given dynamic controller can be scaled up into integers by the zooming-in factor in dynamic quantization, without utilizing re-encryption. However, a sufficiently small zooming-in factor may not always exist because it requires that the convergence speed of the pre-given closed-loop system should be sufficiently fast. Then, as the main result, we design a new controller approximating the pre-given dynamic controller, in which the zooming-in factor is decoupled from the convergence rate of the pre-given closed-loop system. Therefore, there always exist a (sufficiently small) zooming-in factor of dynamic quantization scaling up all the controller's coefficients to integers, and a finite modulus preventing overflow in cryptosystems. The process is asymptotically stable and the quantizer is not saturated.

eess.SY↗

arXiv2025

Quantized distributed Nash equilibrium seeking under DoS attacks

This paper studies distributed Nash equilibrium (NE) seeking under Denial-of-Service (DoS) attacks and quantization. The players can only exchange information with their own direct neighbors. The transmitted information is subject to quantization and packet losses induced by malicious DoS attacks. We propose a quantized distributed NE seeking strategy based on the approach of dynamic quantized consensus. To solve the quantizer saturation problem caused by DoS attacks, the quantization mechanism is equipped to have zooming-in and holding capabilities, in which the holding capability is consistent with the results in quantized consensus under DoS. A sufficient condition on the number of quantizer levels is provided, under which the quantizers are free from saturation under DoS attacks. The proposed distributed quantized NE seeking strategy is shown to have the so-called maximum resilience to DoS attacks. Namely, if the bound characterizing the maximum resilience is violated, an attacker can deny all the transmissions and hence distributed NE seeking is impossible.

eess.SY↗

arXiv2024

An evolutionary approach for discovering non-Gaussian stochastic dynamical systems based on nonlocal Kramers-Moyal formulas

Discovering explicit governing equations of stochastic dynamical systems with both (Gaussian) Brownian noise and (non-Gaussian) Lévy noise from data is chanllenging due to possible intricate functional forms and the inherent complexity of Lévy motion. This present research endeavors to develop an evolutionary symbol sparse regression (ESSR) approach to extract non-Gaussian stochastic dynamical systems from sample path data, based on nonlocal Kramers-Moyal formulas, genetic programming, and sparse regression. More specifically, the genetic programming is employed to generate a diverse array of candidate functions, the sparse regression technique aims at learning the coefficients associated with these candidates, and the nonlocal Kramers-Moyal formulas serve as the foundation for constructing the fitness measure in genetic programming and the loss function in sparse regression. The efficacy and capabilities of this approach are showcased through its application to several illustrative models. This approach stands out as a potent instrument for deciphering non-Gaussian stochastic dynamics from available datasets, indicating a wide range of applications across different fields.

stat.ML↗

arXiv2024

CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake Detection

Recent singing voice synthesis and conversion advancements necessitate robust singing voice deepfake detection (SVDD) models. Current SVDD datasets face challenges due to limited controllability, diversity in deepfake methods, and licensing restrictions. Addressing these gaps, we introduce CtrSVDD, a large-scale, diverse collection of bonafide and deepfake singing vocals. These vocals are synthesized using state-of-the-art methods from publicly accessible singing voice datasets. CtrSVDD includes 47.64 hours of bonafide and 260.34 hours of deepfake singing vocals, spanning 14 deepfake methods and involving 164 singer identities. We also present a baseline system with flexible front-end features, evaluated against a structured train/dev/eval split. The experiments show the importance of feature selection and highlight a need for generalization towards deepfake methods that deviate further from training distribution. The CtrSVDD dataset and baselines are publicly accessible.

eess.AS↗

arXiv2024

Rare events in a stochastic vegetation-water dynamical system based on machine learning

Stochastic vegetation-water dynamical systems play a pivotal role in ecological stability, biodiversity, water resource management, and adaptation to climate change. This research proposes a machine learning-based method for analyzing rare events in stochastic vegetation-water dynamical systems with multiplicative Gaussian noise. Utilizing the Freidlin-Wentzell large deviation theory, we derive the asymptotic expressions for the quasipotential and the mean first exit time. Based on the decomposition of vector field, we design a neural network architecture to compute the most probable transition paths and the mean first exit time for both non-characteristic and characteristic boundary scenarios. The results indicate that this method can effectively predict early warnings of vegetation degradation, providing new theoretical foundations and mathematical tools for ecological management and conservation. Moreover, the method offers new possibilities for exploring more complex and higher-dimensional stochastic dynamical systems.

math.DS↗

arXiv2023

The bottleneck and ceiling effects in quantized tracking control of heterogeneous multi-agent systems under DoS attacks

In this paper, we investigate tracking control of heterogeneous multi-agent systems under Denial-of-Service (DoS) attacks and state quantization. Dynamic quantized mechanisms are designed for inter-follower communication and leader-follower communication. Zooming-in and out factors, and data rates of both mechanisms for preventing quantizer saturation are provided. Our results show that by tuning the inter-follower quantized controller, one cannot improve the resilience beyond a level determined by the data rate of leader-follower quantized communication, i.e., the ceiling effect. Otherwise, overflow of followers' state quantizer can occur. On the other hand, if one selects a "large" data rate for leader-follower quantized communication, then the inter-follower quantized communication determines the resilience, and further increasing the data rate for leader-follower quantized communication cannot improve the resilience, i.e., the bottleneck effect. Simulation examples are provided to justify the results of our paper.

eess.SY↗

arXiv2023

Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network

Modeling human mobility helps to understand how people are accessing resources and physically contacting with each other in cities, and thus contributes to various applications such as urban planning, epidemic control, and location-based advertisement. Next location prediction is one decisive task in individual human mobility modeling and is usually viewed as sequence modeling, solved with Markov or RNN-based methods. However, the existing models paid little attention to the logic of individual travel decisions and the reproducibility of the collective behavior of population. To this end, we propose a Causal and Spatial-constrained Long and Short-term Learner (CSLSL) for next location prediction. CSLSL utilizes a causal structure based on multi-task learning to explicitly model the "\textit{when$\rightarrow$what$\rightarrow$where}", a.k.a. "\textit{time$\rightarrow$activity$\rightarrow$location}" decision logic. We next propose a spatial-constrained loss function as an auxiliary task, to ensure the consistency between the predicted and actual spatial distribution of travelers' destinations. Moreover, CSLSL adopts modules named Long and Short-term Capturer (LSC) to learn the transition regularities across different time spans. Extensive experiments on three real-world datasets show promising performance improvements of CSLSL over baselines and confirm the effectiveness of introducing the causality and consistency constraints. The implementation is available at https://github.com/urbanmobility/CSLSL.

cs.AI↗

arXiv2023

Dynamic quantized consensus under DoS attacks: Towards a tight zooming-out factor

This paper deals with dynamic quantized consensus of dynamical agents in a general form under packet losses induced by Denial-of-Service (DoS) attacks. The communication channel has limited bandwidth and hence the transmitted signals over the network are subject to quantization. To deal with agent's output, an observer is implemented at each node. The state of the observer is quantized by a finite-level quantizer and then transmitted over the network. To solve the problem of quantizer overflow under malicious packet losses, a zooming-in and out dynamic quantization mechanism is designed. By the new quantized controller proposed in the paper, the zooming-out factor is lower bounded by the spectral radius of the agent's dynamic matrix. A sufficient condition of quantization range is provided under which the finite-level quantizer is free of overflow. A sufficient condition of tolerable DoS attacks for achieving consensus is also provided. At last, we study scalar dynamical agents as a special case and further tighten the zooming-out factor to a value smaller than the agent's dynamic parameter. Under such a zooming-out factor, it is possible to recover the level of tolerable DoS attacks to that of unquantized consensus, and the quantizer is free of overflow.

eess.SY↗

arXiv2022

A Two-phase On-line Joint Scheduling for Welfare Maximization of Charging Station

The large adoption of EVs brings practical interest to the operation optimization of the charging station. The joint scheduling of pricing and charging control will achieve a win-win situation both for the charging station and EV drivers, thus enhancing the operational capability of the station. We consider this important problem in this paper and make the following contributions. First, a joint scheduling model of pricing and charging control is developed to maximize the expected social welfare of the charging station considering the Quality of Service and the price fluctuation sensitivity of EV drivers. It is formulated as a Markov decision process with variance criterion to capture uncertainties during operation. Second, a two-phase on-line policy learning algorithm is proposed to solve this joint scheduling problem. In the first phase, it implements event-based policy iteration to find the optimal pricing scheme, while in the second phase, it implements scenario-based model predictive control for smart charging under the updated pricing scheme. Third, by leveraging the performance difference theory, the optimality of the proposed algorithm is theoretically analyzed. Numerical experiments for a charging station with distributed generation and energy storage demonstrate the effectiveness of the proposed method and the improved social welfare of the charging station.

eess.SY↗

arXiv2022

Size optimization of CNOT circuits on NISQ

Quantum computers in practice today require strict memory constraints, where 2-qubit operations can only be performed between the qubits closest to each other in a graph structure. So a quantum circuit must undergo a transformation to the graph before it can be implemented. In this paper, we study the optimization of the CNOT circuits on some noisy intermediate-scale quantum(NISQ) devices. Compared with previous works, we decompose it into two sub-problems: optimization with a given initial qubit distribution and optimization without limitations of initial qubit distribution. We find that most of the previous researches focused on the first sub-problem, and ignored the influence of different distribution of qubits in the same topology structure on the optimization results. In this paper, We take both sub-problems into account and give some new optimization algorithms. In short, our method is divided into two steps: matrix optimization and routing optimization. We implement matrix optimization with the algorithm in [XZL+20] and put forward a new heuristic algorithm with MILP method which can solve the second step. We implement our algorithm on IBM20 and some other NISQ devices, the results are better than most other methods in our experiment.

quant-ph↗

arXiv2022

Controlling mean exit time of stochastic dynamical systems based on quasipotential and machine learning

The mean exit time escaping basin of attraction in the presence of white noise is of practical importance in various scientific fields. In this work, we propose a strategy to control mean exit time of general stochastic dynamical systems to achieve a desired value based on the quasipotential concept and machine learning. Specifically, we develop a neural network architecture to compute the global quasipotential function. Then we design a systematic iterated numerical algorithm to calculate the controller for a given mean exit time. Moreover, we identify the most probable path between metastable attractors with help of the effective Hamilton-Jacobi scheme and the trained neural network. Numerical experiments demonstrate that our control strategy is effective and sufficiently accurate.

stat.ML↗

arXiv2022

Predefined-time Stabilization for Nonlinear Stochastic Systems

In this paper, a control scheme for stochastic predefined-time stabilization is proposed, which improves the control effect compared with stochastic finite-time or fixed-time stabilization. The stochastic predefined-time stabilization allows the upper bound of the mathematical expectation of the settling-time function below any given positive value. Some Lyapunov-type results for predefined-time stabilization of general stochastic Itô systems are presented. Moreover, a state feedback control scheme is designed for a class of stochastic nonlinear systems in strict-feedback form. Two simulation examples are supplied to show the usefulness of the proposed stochastic predefined-time stabilization.

math.OC↗