arXiv subjects

Ying Yuan

Publications and source records attributed to Ying Yuan.

At least 19 recordsLinked to original sources

arXiv2026

A staggered seamless dose-optimization design for co-developing monotherapy and combination therapy

Contemporary oncology drug development increasingly requires efficient dose-optimization strategies that evaluate monotherapy (Mono) and combination therapy (Combo) while balancing activity, efficacy, and tolerability. We propose a staggered seamless phase I/II design for settings in which a novel agent is evaluated alone and in combination with an established therapy. In phase I, Mono dose finding begins first, and Combo subtrials can be opened adaptively once a prespecified combination-initiation signal based on early clinical or biological information is observed. Dose assignment uses a model-assisted rule based on toxicity and early activity, with backfilling at tolerable and potentially promising regimens. At the end of phase I, two candidate regimens are selected from the evaluated Mono and Combo regimens using an efficacy-toxicity utility based on accumulated toxicity and treatment-response data. Phase II seamlessly carries forward patients treated at the selected regimens, enrolls additional patients as needed, and applies Bayesian futility and efficacy stopping boundaries to identify a final recommended optimal biological dose (OBD). Simulation studies showed that the proposed design shortened phase I trial duration relative to the comparator designs while maintaining competitive OBD-selection performance and acceptable safety. The seamless phase II component further reduced the need for additional enrollment and supported efficient final OBD selection.

stat.ME↗

arXiv2026

Detecting an Effect Is Not Learning to Act on It: A Reward-SNR Floor for LLM Acquisition Agents

Many pipelines can pay a per-example cost to acquire an auxiliary, model-derived observation -- an LLM's structured reasoning, a slow oracle, an expensive measurement -- and then must decide when the acquired signal is worth using. Our thesis is a distinction that is easy to miss: detecting that such a signal helps on average is not the same as learning to act on it per instance, and a reward-SNR floor governs when the second is even possible. Even when the signal is faithful and an in-sample oracle picking the top-b examples by realized reward shows a sizable apparent gain, no deployable policy can learn when to acquire it: across per-impression, cluster, regime, and uplift-tree granularities, learned routing never beats random, and a matched-moment noise placebo reproduces >=100% of the oracle's apparent gain -- the apparent "learnable structure" is order statistics of noise. We explain this with one distinction, detecting a mean effect vs. learning a per-instance acquisition policy, and a reward-SNR detectability floor: routing is estimable offline only if the reward SNR rho clears rho*(N) ~= 2.8/sqrt(N), with a positive control confirming a true low-SNR limit rather than a broken pipeline. As a concrete instantiation we introduce Structured Hypothesis Embeddings (SHE): a frozen LLM turns a user history into ranked, confidence-scored, evidence-grounded intent hypotheses, fused into a recommender. On three public datasets (MIND, REES46, Amazon-Beauty), SHE is faithful and calibratable, yet its value is backbone- and regime-conditional (significant over an ordered GRU, +0.0114, 95% CI [+0.0030, +0.0209], but a global redundancy gap indistinguishable from zero), and learned acquisition collapses at every granularity because all three datasets sit below the floor. The realizable unit is a design-time regime gate, not a per-instance policy. We release code and a one-command reproduction.

cs.LG↗

arXiv2026

From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation

Large-scale dexterous grasp datasets encode rich priors over hand-object interaction, but their use has largely been confined to grasp generation and pick-and-place manipulation. We study whether such data can instead support functional dexterity in articulated tool use, where a robot must acquire a tool, maintain contact, and operate its functional moving parts. We adapt a hierarchical imitation learning framework that combines high-level hand sub-goal prediction with a low-level goal-conditioned controller. We construct a 355k-trajectory grasp-pretraining dataset from large-scale dexterous grasp annotations and use it to pretrain the low-level controller. The controller is then fine-tuned on downstream task demonstrations. To evaluate this setting, we introduce DexCraft, a simulation benchmark with six articulated tool-use tasks requiring coordinated finger motion. Across simulation and real-world experiments, our approach outperforms end-to-end diffusion policy baselines and hierarchical policies trained from scratch. In the real world, it improves full-task success by 33.3 percentage points over DP3. These results show that grasp datasets can serve not only as resources for grasp synthesis, but also as scalable pretraining data for contact-rich dexterous manipulation. Videos are shown on https://yingyuan0414.github.io/grasp2dexterity/ .

cs.RO↗

arXiv2026

GHOST: Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation

We present GHOST, a framework for learning visuomotor manipulation policies that generalize beyond the training distribution. GHOST factorizes control into (i) a high-level policy that predicts the next sub-goal as a distribution over 3D end-effector poses from multi-view RGB-D observations, and (ii) a low-level goal-conditioned controller that executes embodiment-specific actions. To condition image-based policies on 3D goals, we introduce a simple spatial interface that projects predicted goals into the image plane and represents them as end-effector heatmaps. Across a suite of manipulation tasks, this hierarchical factorization consistently improves performance and robustness compared to a flat Diffusion Policy. Further, we show that this hierarchical interface also makes it easy to incorporate human demonstrations without relying on (noisy) action retargeting. As sub-goals are largely embodiment-agnostic, we train the high-level policy on human video to specify how learned skills should be applied and composed, while keeping the low-level policy trained purely on robot data. This hierarchy enables adaptation to novel objects and task variations using a small number of human demonstrations.

cs.RO↗

arXiv2026

Externally Controlled Trials: A Review of Design and Borrowing Through a Causal Lens

Externally controlled trials (ECTs) are increasingly used when randomized controls are infeasible, unethical, or insufficient, including applications in rare diseases, oncology, pediatrics, and post-approval effectiveness research. Although methodological work has expanded rapidly across causal inference, Bayesian dynamic borrowing, and hybrid trial designs, the literature remains fragmented. We adopt a six-step scientific roadmap to organize modern ECT methodology in two primary settings: (i) single-arm trials that evaluate efficacy through comparison with external controls, and (ii) hybrid controlled trials that augment the internal control arm with external controls drawn from real-world data or historical studies. The roadmap clarifies causal estimands, identifiability assumptions, and how statistical parameters arise from identification, and shows how modeling and borrowing strategies trade off efficiency and robustness, especially under covariate shift and outcome drift. Within this framework, we synthesize and evaluate recent Bayesian and frequentist developments, compare their strengths, limitations, operating characteristics, and available software, and emphasize the role of sensitivity analysis. By re-framing ECT methodology through a causal lens, this work establishes a coherent foundation for integrating external data into regulatory and clinical decision-making and highlights core challenges and opportunities for future research.

stat.ME↗

arXiv2026

I can't recognize (yet): Delayed Rendering to Defeat Visual Phishing Detectors

Phishing webpages are continuously polluting the Web. Plenty of countermeasures have been proposed and the most advanced techniques leverage machine-learning methods that infer whether a webpage is benign or not by inspecting its visual representation. Yet, despite the demonstrated effectiveness of such detection methods, this class of defenses is, by design, susceptible to a kind of subtle-but-cheap timing-based attacks which -- worryingly, and perhaps surprisingly -- have never been investigated so far. Such an oversight questions the overall reliability of these defenses in the wild. First, we show that timing-based evasion attacks have not been accounted for by prior work on visual phishing websites detectors. Then, we elucidate the intrinsic vulnerability of these detectors: they can be bypassed by delaying the rendering of webpage elements. Practically, these detectors must compute the visual similarity between a target webpage and a known legitimate one. This requires taking a "snapshot" of the target webpage before the similarity computation. Attackers can deliberately delay the rendering of key elements, such as the logo, so that these elements appear fully only after the snapshot has been taken. This simple tactic misleads the visual-similarity module, leading the system to incorrectly classify the phishing page as benign. We empirically show that state-of-the-art detectors can be completely defeated (detection rate dropping from 100% to 0%) by employing easy-to-apply problem-space techniques such as curtain effects. We also carry out a user study, evaluating the effectiveness of these attacks against real humans, and find that end users are unable to reliably identify our "perturbations" (p<.05). Finally, we propose mitigations, including a browser-extension that, without making any call to remote services, warns users that they may have landed on a phishing webpage.

cs.CR↗

arXiv2026

On the Calibration of Bayesian Success Criteria and Operating Characteristics for Clinical Trials

Recently, the U.S. Food and Drug Administration (FDA) released draft guidance \citep{FDA2026} signaling a paradigm shift that facilitates the use of Bayesian methodology as the primary analysis and decision framework for drug approval. The cornerstone and fundamental challenge of this framework is the specification and calibration of Bayesian success criteria to control decision errors, ensuring reliable clinical and regulatory outcomes. In this work, we systematically investigate various Bayesian decision-error metrics, their theoretical interrelationships, and their alignment with conventional Frequentist counterparts. This investigation provides critical theoretical insights and practical guidance on calibrating Bayesian success criteria and operating characteristics to ensure robust decision-making and the integrity of public health decisions. We illustrate this framework using a clinical trial evaluating revascularization strategies for cardiogenic shock. A Shiny application will be available at www.trialdesign.org to assist sponsors and regulators in evaluating calibration strategies consistent with recent regulatory perspectives.

stat.ME↗

arXiv2026

A Utility Score Framework for Dose Optimization Studies with Binary Efficacy-Safety Endpoints: Sample Size Determination and Bias Characterization

The FDA's Project Optimus initiative emphasizes patient-centered dose selection in oncology that balances efficacy and safety. We develop a framework for randomized dose optimization studies that uses clinically interpretable utility scores to integrate binary efficacy and safety endpoints and select the optimal dose for a follow-on confirmatory trial. The framework provides: (i) a systematic method for eliciting utility scores that reflect clinical priorities; (ii) closed-form sample size formulas to achieve prespecified Probabilities of Correct Selection (PCS) under clinically relevant scenarios; and (iii) analytical expressions characterizing the propagation of selection-induced bias to confirmatory trials, including time-to-event endpoints correlated with the selection endpoint. Extensive simulations (10^6 replications per scenario) confirm that the sample size methods achieve target PCS and that the bias and Type I error formulas closely match empirical estimates. An R package DoseOptDesign and an interactive Shiny application are publicly available.

stat.AP↗

arXiv2026

DualMap: Enabling Both Cache Affinity and Load Balancing for Distributed LLM Serving

In LLM serving, reusing the KV cache of prompts across requests is critical for reducing TTFT and serving costs. Cache-affinity scheduling, which co-locates requests with the same prompt prefix to maximize KV cache reuse, often conflicts with load-balancing scheduling that distributes requests evenly across compute instances. Existing schedulers fail to reconcile this trade-off as they operate within a single mapping space, typically applying cache-affinity routing to a subset of requests and load-balanced routing to the rest, without a unified solution to achieve both goals. To address this limitation, we propose DualMap, a dual-mapping scheduling strategy for distributed LLM serving that achieves both cache affinity and load balancing. Its key idea is to map each request to two candidate instances via two independent hash functions based on the request prompt, then intelligently select the better candidate based on current system states. This design increases the likelihood that requests with shared prefixes are co-located, while evenly dispersing distinct prefixes across the cluster via ``the power of two choices''. To make DualMap robust under dynamic and skewed real-world workloads, we incorporate three techniques: 1) SLO-aware request routing, which prioritizes cache affinity but switches to load-aware scheduling when TTFT exceeds the SLO, enhancing load balance without sacrificing cache reuse; 2) hotspot-aware rebalancing, which dynamically migrates requests from overloaded to underloaded instances, mitigating hotspots and rebalancing the system; 3) lightweight dual-hash-ring scaling, which leverages a dual-hash-ring mapping to support fast and low-overhead instance scaling without costly global remapping. Experiments on real-world workloads show that DualMap improves effective request capacity by up to 2.25$\times$ under the same TTFT SLO constraints compared with SOTA work.

cs.DC↗

arXiv2025

Precise $^{136}$Xe Double Beta Decay Measurement in PandaX-4T with Implications on the Nuclear Matrix Elements and Majorons

The continuous spectrum of double beta decay ($\beta\beta$) provides a sensitive probe to test the predictions of the standard model and to search for signatures of new physics beyond it. We present a comprehensive analysis of the $^{136}$Xe $\beta\beta$ spectrum utilizing $39.1 \pm 0.7~\textrm{kg}\cdot\textrm{yr}$ of $^{136}$Xe exposure from the PandaX-4T experiment. The analysis yields the most precise measurement to date of the $^{136}$Xe two-neutrino double beta decay ($2\nu\beta\beta$) half-life, $(2.14 \pm 0.05) \times 10^{21}$ years, the uncertainty of which is reduced by a factor of 2 compared to our previous result. We measure the parameter $\xi_{31}^{2\nu}$, defined as the ratio between the subleading and leading components of the $^{136}$Xe $2\nu\beta\beta$ nuclear matrix element, to be $0.59^{+0.41}_{-0.38}$, which is consistent with theoretical predictions. We also search for Majoron-emitting modes of $^{136}$Xe $\beta\beta$, establishing the most stringent limit for the spectral index $n=7$.

nucl-ex↗

arXiv2025

GPS-Synchronized Monitoring of Core-collapse Supernova Bursts with PandaX-4T via Coherent Elastic Neutrino Nuclear Scattering

The landmark detection of neutrinos from SN1987A marked the dawn of neutrino astrophysics. The neutrino burst provided essential insights into fundamental properties of neutrinos, and served as key probes of stellar evolution and supernova dynamics. The recent advancement in coherent elastic neutrino-nucleus scattering enables the detection of core-collapse supernova burst neutrinos using tonne-scale liquid xenon detectors originally designed for dark matter direct detection. Leveraging this capability, we developed and deployed an online supernova monitoring system for the PandaX-4T experiment. This system features a GPS module with millisecond-level timing precision, a low false-alarm rate, and high sensitivity to galactic core-collapse supernova explosion events. The methodology is robust, directly scalable, and planned for implementation in the next-generation PandaX-20T experiment.

hep-ex↗

arXiv2025

Probing scalar-neutrino and scalar-dark-matter interactions with PandaX-4T

Scalar-mediated interactions may exist among neutrinos, dark matter particles, or between the two. Double $\beta$-decay experiments provide a powerful tool to probe such exotic interactions. Using $^{136}$Xe double $\beta$-decay data from PandaX-4T, we perform the first direct spectral search in the energy range of 20 to 2800~keV, setting the most stringent limits to date on scalar-mediated neutrino self-interactions for mediator masses below 2~MeV$/c^2$. These results place significant constraints on models invoking such interactions to alleviate the Hubble Tension. Assuming the same scalar also mediates dark matter self-interactions, constraints on the dark matter-scalar interactions can be placed in conjunction with cosmological constraints.

hep-ex↗

arXiv2025

Tomographic beta-gamma spectroscopy of nuclear beta decay

Nuclear $\beta$ decay, a sensitive probe of nuclear structure and weak interactions, has become a precision test bed for physics beyond the Standard Model, driven by recent advances in spectrometric techniques. Here we introduce tomographic $\beta$-$\gamma$ spectroscopy (TBGS) of nuclear $\beta$ decay, a method that detects the energies of $\beta$, $\gamma$, and internal conversion electrons while simultaneously reconstructing the energy deposition vertices. Using the PandaX-4T detector operated as a TBGS, we obtain a precise and unbiased decay scheme of $^{214}$Pb, a key background isotope in searches for dark matter and Majorana neutrinos. For the first time, transitions of $^{214}$Pb to both the ground and excited states of $^{214}$Bi are measured concurrently, revealing discrepancies in branching ratios of up to 4.7$\sigma$ relative to previous evaluations. Combined with state-of-the-art theoretical spectral-shape calculations, these results establish a new benchmark for background modelling in rare-event searches and highlight the potential of TBGS as a versatile tool for fundamental physics and nuclear applications.

nucl-ex↗

arXiv2025

Kinetic Freeze-Out Properties from Transverse Momentum Spectra of kaon, Pion, and (anti-)proton production in U+U collisions at $\sqrt{s_{NN}}$ = 193 GeV

In the framework of the multi-source thermal model employing the Tsallis distribution, the transverse momentum distributions of kaon, pion, and (anti -)proton production in U+U collisions at $\sqrt{s_{NN}}$ = 193 GeV with varying centrality are investigated. The transverse momentum spectra are appropriately characterized. The dependencies of parameters (average transverse momenta, effective temperature, and entropy index) on event centrality are determined. It is observed that the q parameters increase as the average number of particles participating in the collisions rises, which implies that the nuclear stopping degree elevates with the increase of collision centrality. The T value remains fundamentally consistent for the same particle under different collision parameters, suggesting that the kinetic freezing temperature of particle ejection in this collision system is independent of the collision parameters.

hep-ph↗

arXiv2025

BE-BOIN: A Dose Optimization Design Accommodating Backfill and Late-Onset Toxicity

The US Food and Drug Administration (FDA) launched Project Optimus and issued guidance to reform dose-finding and selection trials, shifting the paradigm from identifying the maximum tolerable dose (MTD) to determining the optimal biological dose (OBD), which optimizes the risk and benefit of treatments. The FDA's guidance emphasizes the importance of collecting sufficient toxicity and efficacy data across multiple doses and considering late-onset cumulative toxicity that often results in tolerability issues. To address these challenges, we propose the BE-BOIN (Backfill time-to-Event Bayesian Optimal INterval) design, which allows backfilling patients into safe and effective doses during dose escalation and accommodates late-onset toxicities. BE-BOIN enables the collection of additional safety and efficacy data to enhance the accuracy and reliability of OBD selection and supports real-time dose decisions for new patients. Our simulation studies show that BE-BOIN accurately identifies the MTD and OBD while significantly reducing trial duration.

stat.ME↗

arXiv2025

HypoGeneAgent: A Hypothesis Language Agent for Gene-Set Cluster Resolution Selection Using Perturb-seq Datasets

Large-scale single-cell and Perturb-seq investigations routinely involve clustering cells and subsequently annotating each cluster with Gene-Ontology (GO) terms to elucidate the underlying biological programs. However, both stages, resolution selection and functional annotation, are inherently subjective, relying on heuristics and expert curation. We present HYPOGENEAGENT, a large language model (LLM)-driven framework, transforming cluster annotation into a quantitatively optimizable task. Initially, an LLM functioning as a gene-set analyst analyzes the content of each gene program or perturbation module and generates a ranked list of GO-based hypotheses, accompanied by calibrated confidence scores. Subsequently, we embed every predicted description with a sentence-embedding model, compute pair-wise cosine similarities, and let the agent referee panel score (i) the internal consistency of the predictions, high average similarity within the same cluster, termed intra-cluster agreement (ii) their external distinctiveness, low similarity between clusters, termed inter-cluster separation. These two quantities are combined to produce an agent-derived resolution score, which is maximized when clusters exhibit simultaneous coherence and mutual exclusivity. When applied to a public K562 CRISPRi Perturb-seq dataset as a preliminary test, our Resolution Score selects clustering granularities that exhibit alignment with known pathway compared to classical metrics such silhouette score, modularity score for gene functional enrichment summary. These findings establish LLM agents as objective adjudicators of cluster resolution and functional annotation, thereby paving the way for fully automated, context-aware interpretation pipelines in single-cell multi-omics studies.

q-bio.QM↗

arXiv2025

Learn from What We HAVE: History-Aware VErifier that Reasons about Past Interactions Online

We introduce a novel History-Aware VErifier (HAVE) to disambiguate uncertain scenarios online by leveraging past interactions. Robots frequently encounter visually ambiguous objects whose manipulation outcomes remain uncertain until physically interacted with. While generative models alone could theoretically adapt to such ambiguity, in practice they obtain suboptimal performance in ambiguous cases, even when conditioned on action history. To address this, we propose explicitly decoupling action generation from verification: we use an unconditional diffusion-based generator to propose multiple candidate actions and employ our history-aware verifier to select the most promising action by reasoning about past interactions. Through theoretical analysis, we demonstrate that employing a verifier significantly improves expected action quality. Empirical evaluations and analysis across multiple simulated and real-world environments including articulated objects, multi-modal doors, and uneven object pick-up confirm the effectiveness of our method and improvements over baselines. Our project website is available at: https://liy1shu.github.io/HAVE_CoRL25/

cs.RO↗

arXiv2025

Is Repeated Bayesian Interim Analysis Consequence-Free?

Interim analyses are vital in clinical trials for early decision-making. While frequentist implications are well-established, the consequences of repeated Bayesian interim monitoring for efficacy, specifically regarding multiplicity, remain contentious. This article provides theoretical justification and numerical evidence evaluating the impact of such designs on bias, mean squared error (MSE), credible interval coverage, false discovery rate (FDR), and average Type I error (ATIE). Our findings show that when the inferential prior matches the data-generating prior, sequential efficacy stopping does not bias the posterior mean or degrade credible interval coverage. However, even under this ``matched" condition, the FDR, ATIE, and MSE are significantly altered. In the more practically relevant scenario where the inferential and data-generating priors differ, all aforementioned operating characteristics, including estimation bias and coverage, are substantially impacted. These results reconcile long-standing conflicting arguments regarding Bayesian multiplicity. We demonstrate that while some Bayesian properties are invariant to sequential looks, others are not. Our work underscores the necessity of thoughtful prior specification and comprehensive evaluation of frequentist-Bayesian operating characteristics to ensure reliable inference in adaptive trial designs.

stat.ME↗