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Chengyuan Yang

Publications and source records attributed to Chengyuan Yang.

11 recordsLinked to original sources
arXiv2026

Equidistribution of small points over finitely generated fields

We study the equidistribution of small points over finitely generated fields, in connection with a conjecture of Yuan--Zhang. We first give a counterexample showing that numerical smallness, defined using Moriwaki heights for all polarizations, does not imply equidistribution at every valuation. We then prove that numerically small points do equidistribute at every fully transcendental valuation. In particular, when \(F\) is the function field of a curve \(B/\mathbb{Q}\), these valuations correspond to points of types \(2\), \(3\), and \(4\) in the Berkovich analytification of \(B_{\mathbb{Q}_p}\).

math.NT↗
arXiv2026

Toward Effective and Reliable LLM Agents via Dynamic Ontology

Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks, this leaves important semantic connections implicit. This often results in incomplete evidence use and brittle multi-step decisions. Ontologies offer a way to externalize domain concepts and relations as machine-interpretable structures, but constructing task-usable ontologies traditionally requires substantial effort from domain experts and is difficult to scale. Automatic construction is also challenging: an ontology that appears semantically plausible may not contain the relational structures needed for actual decision making. We present OaK, an ontology-as-a-kernel framework that dynamically constructs and refines task-oriented ontologies for LLM agents. Given task requirements and training data, OaK constructs an ontology and its knowledge graph, generates task-adaptation functions for graph reasoning, and uses judge feedback to iteratively refine both. By making relevant concepts and relations explicit, the ontology grounds knowledge retrieval and multi-step decision making. We evaluate OaK on TravelPlanner, CRMArenaPro, and ToolQA. Results show that OaK improves standard LLM agents, strengthens evidence grounding, and boosts the reliability of multi-step reasoning.

cs.AI↗
arXiv2026

Bivariate Prior Specification for Bayesian Decision Making in Early Phase Clinical Trials

Bayesian Go/No-Go decisions with co-primary endpoints require specifying prior distributions under the Normal-Inverse-Wishart framework; however guidance on how prior hyperparameters influence trial decisions remains limited. We propose a calibrated prior specification framework for bivariate Go/No-Go decisions. Skeptical and enthusiastic priors are calibrated so that each assigns a target probability to a clinically relevant decision region. We prove that for any prior precision $\kappa > 0$, a unique scale parameter $\lambda_0$ achieves the target calibration. Operating characteristics are evaluated across different $\kappa $ via simulation and applied to a phase~3 telitacicept lupus trial.The simulation result indicates $\kappa$ is the primary driver of prior discrimination. At $\kappa = 1$, the go rate difference between priors was 0.07; at $\kappa = 10$ it reached 0.56, with false positive rates below 0.01. Operating characteristics were robust to the degrees of freedom parameter $\nu_0$ and prior correlation $\rho_0$, supporting a default of $\nu_0 = 2$. In the lupus application, prior sensitivity was negligible at $\kappa = 1$ but at $\kappa = 10$ the enthusiastic go rate was three times the skeptical rate at small sample sizes. The framework reduces prior specification to two choices: the prior center and the prior precision $\kappa$. The identification of $\kappa$ as the dominant parameter, together with the cautious choice of $\kappa$ before the trial, motivates adaptive approaches to prior precision.

stat.ME↗
arXiv2026

Beyond Heuristics: A Standardized Real2Sim Pipeline for Physical Human Robot Interaction in Human-in-the-Loop Simulation

The aging global population drives demand for assistive robots, yet the safety risks and costs of physical testing make Human-in-the-Loop (HITL) simulation an attractive alternative. Its fidelity for coupled systems, however, is limited by interaction models whose impedance parameters are tuned heuristically rather than identified from data. We present a Real2Sim pipeline that identifies the coupled Physical Human-Robot Interaction (pHRI) dynamics of a pelvis--strap interface on an overground mobile balance assistant. The interface is modeled as a 6-DoF viscoelastic mechanism whose 12 directional stiffness and damping parameters are identified per subject via Covariance Matrix Adaptation Evolution Strategy (CMA-ES), using the user's ``Safe \& Comfortable'' feedback as a reproducible operating point that resolves harness-tightness ambiguity across anthropometrics. An intraclass-correlation analysis over a five-subject cohort separates shareable from subject-specific parameters, yielding a set of prior parameters derived from the existing data. Deploying this prior configures a previously unseen subject by refining only 5 of the 12 parameters. The calibrated model then reproduces the real interaction envelope and induces biomechanically accurate gait adaptations in the Human Digital Twin (HDT). Overly compliant and overly stiff settings, by contrast, fail as extreme settings, confirming a correct operating point that no heuristic tuning procedure can reliably select. The pipeline thus improves HITL simulation fidelity and supports the Human Digital Twin as a predictive tool for pre-clinical verification of personalized controllers.

cs.RO↗
arXiv2026

DRBA: Dynamic Robotic Balance Assistant -- An assist-as-needed gait and balance rehabilitation robot for versatile training

The decline of human balance control due to aging and pathological conditions increases fall risk, a major concern in geriatric care and rehabilitation. Gait training is essential for balance recovery, enhancing walking ability and postural control. However, existing overground robotic gait trainers have limitations: body weight support systems are bulky and impractical for daily use, while end-effector-based systems often compromise transparency, altering natural gait dynamics. This paper presents the Dynamic Robotic Balance Assistant (DRBA), a novel gait trainer providing assist-as-needed body weight and balance support for various training scenarios. DRBA integrates a 3-degree-of-freedom (3-DoF) robotic arm for pelvic support with flexible motion, a compact sit-to-stand assistance module, and user-following and fall detection algorithms to ensure minimal interference and responsive support. Experimental results demonstrated high transparency, with minimal impact on natural gait dynamics. A patient trial with nine elderly patients with varying medical conditions and balance impairments (ranging from severe to mild) further validated DRBA's effectiveness. The results showed that DRBA-assisted training increased step length and walking speed compared to therapist-assisted gait training. Additionally, DRBA enabled users to perform tasks beyond their unaided ability, expanding rehabilitation possibilities. These findings highlight DRBA's potential to enhance rehabilitation outcomes by facilitating higher training intensity and enabling task-oriented exercises.

cs.RO↗
arXiv2026

AgenticDataBench: A Comprehensive Benchmark for Data Agents

Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society. Automating this process is essential to reducing labor-intensive efforts for data scientists and enabling scalable data-driven applications. Recently, large language model (LLM)-based data agents have emerged as a promising solution to automate data science workflows. However, the field lacks comprehensive benchmarks to rigorously evaluate these agents across diverse scenarios with fine-grained granularity. To address this gap, we propose AgenticDataBench, a comprehensive benchmark featuring realistic tasks spanning diverse domains with fine-grained ground-truth labels. This enables evaluations to capture the diversity and complexity of data science workflows and the detailed performance of agents. First, to cover diverse domains, we collect real datasets and tasks from 15 vertical domains, including 5 real-world B2B use cases from a leading fintech company. Second, to remove redundancy in real-world tasks and generate high-quality tasks for domains lacking real data, we introduce data science skills, recurring data-centric operational patterns, and quantify benchmark coverage by the number of skills included. Representative skills are extracted from large-scale task solutions on Stack Overflow using skill-aligned hierarchical clustering. Third, for real-world business tasks, we select task-solution pairs that maximize diversity in skill composition, ensuring broad coverage of practical scenarios. Fourth, to generate realistic tasks for devise domains without real tasks, we propose a systematic LLM-based task generation approach to create workflows and tasks based on these skills. Finally, we evaluate state-of-the-art data agents using our annotated benchmark and open-sourced testbed, providing detailed skill-level insights.

cs.DB↗
arXiv2026

ActMem: Bridging the Gap Between Memory Retrieval and Reasoning in LLM Agents

Memory management is essential for LLM agents in long-term interactions. Current memory frameworks typically treat agents as passive ``recorders'' and retrieve information without understanding its deeper implications. They may fail in scenarios requiring reasoning and complex decision-making. To bridge this critical gap, we propose a novel actionable memory framework called ActMem that integrates memory retrieval with active causal reasoning. ActMem transforms unstructured dialogue history into a structured causal and semantic graph. By leveraging counterfactual reasoning and commonsense completion, it enables agents to deduce implicit constraints and resolve potential conflicts between past states and current intentions. Furthermore, we introduce a comprehensive dataset ActMemEval to evaluate agent reasoning capabilities in logic-driven scenarios, moving beyond the fact-retrieval focus of existing memory benchmarks. Experiments demonstrate that ActMem significantly outperforms baselines in handling complex, memory-dependent tasks, paving the way for more consistent and reliable intelligent assistants.

cs.CL↗
arXiv2026

Beyond Static Summarization: Proactive Memory Extraction for LLM Agents

Memory management is vital for LLM agents in long-term and personalized interactions. Most previous work studies how to retrieve and use memory, but pays less attention to how memory is extracted. We find two main limitations in existing methods. First, extraction is "ahead-of-time": the agent saves information before it knows future tasks. A single summary prompt often mixes details, events, and relations, so useful information is lost. Second, extraction is usually one-off. Without verification, errors and hallucinations may stay in memory for a long time. To address these limitations, we propose ProMem, a proactive memory extraction framework. It separates details, events, and relations, and uses different extraction strategies for each type. It also checks completeness to recover missed events and verifies facts at the atomic level to reduce hallucinations. Experiments show that ProMem improves memory completeness and QA accuracy, while keeping a good balance between quality and token cost.

cs.CL↗
arXiv2023

DASICS White Paper: Enhancing Memory Protection with Dynamic Compartmentalization

In the existing software development ecosystem, security issues introduced by third-party code cannot be overlooked. Among these security concerns, memory access vulnerabilities stand out prominently, leading to risks such as the theft or tampering of sensitive data. To address this issue, software-based defense mechanisms have been established at the programming language, compiler, and operating system levels. However, as a trade-off, these mechanisms significantly reduce software execution efficiency. Hardware-software co-design approaches have sought to either construct entirely isolated trusted execution environments or attempt to partition security domains within the same address space. While such approaches enhance efficiency compared to pure software methods, they also encounter challenges related to granularity of protection, performance overhead, and portability. In response to these challenges, we present the DASICS (Dynamic in-Address-Space Isolation by Code Segments) secure processor design, which offers dynamic and flexible security protection across multiple privilege levels, addressing data flow protection, control flow protection, and secure system calls. We have implemented hardware FPGA prototypes and software QEMU simulator prototypes based on DASICS, along with necessary modifications to system software for adaptability. We illustrate the protective mechanisms and effectiveness of DASICS with two practical examples and provide potential real-world use cases where DASICS could be applied.

cs.CR↗
arXiv2021

Large-scale Fabrication of High-Density Silicon-vacancy Centers via Helium-ion Implantation of Diamond Nucleation Surface

Silicon-vacancy (SiV) color centers in diamond have great potential for optical sensing and bio-imaging applications. However, the fabrication of large-scale high-density SiV centers in diamond remains difficult. Here, we report a promising method for the fabrication of high-density SiV- centers in a low-cost polycrystalline diamond film grown on an inches-scale Si wafer. Our method utilizes the nucleation surface of the diamond film which initially interfaces with the Si wafer. Benefited from the diamond seeding substrate of silicon, the nucleation surface has originally been incorporated with high-density Si atoms. Upon helium-ion implantation and subsequent thermal annealing, we demonstrate by performing PL mapping that these Si atoms can be efficiently converted to SiV- centers. The SiV- centers exhibit bright emission and a relatively long fluorescence lifetime (~1.08 ns) that is comparable to the SiV- lifetime reported in single-crystal diamonds. Furthermore, by using a focused helium beam and varying the helium fluence, we demonstrate the feasible density control and patterning of the SiV- centers. These results show that our method can produce high-density SiV- centers in low-cost wafer-scale polycrystalline diamonds, which could facilitate the commercialization of SiV- centers-based optical devices.

physics.app-ph↗