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Yiqing Xu

Publications and source records attributed to Yiqing Xu.

At least 19 recordsLinked to original sources
arXiv2026

Replication Certification at Publication: Checkable by Anyone, Anywhere, in Seconds, Forever

Authors increasingly deposit replication datasets---the data and code behind publications---in archives, and journals increasingly require them to, under progressively onerous rules. But a deposit alone proves little, so authors keep paying: months for the journal's reanalyses, years answering readers' questions, and rancor as failed replications become accusations because no record establishes what actually ran. We introduce a replication certificate: a cryptographically signed record of the deposited files and logged output. It replaces the journal's reanalyses with evidence anyone can check in seconds, which no one, including the archive, can backdate, and no one can fake without risking detection. Certification asks little of anyone and benefits everyone: The author deposits, then reruns the analysis once under a random seed the archive computes from the deposit's hash and a blockchain block; the archive signs, timestamps (in that blockchain), and stores the record---confidential data included---without rerunning anything; the journal merely checks a website for the certificate; and anyone can check or replay any step decades later.

cs.DL↗
arXiv2026

StackGen3D: Stable 3D Block Stack Generation from Multiview Silhouettes

Generating physically stable 3D structures from visual specifications requires resolving geometric ambiguity while accounting for physical interactions among objects. We present StackGen3D, a framework for generating stable block assemblies conditioned on a given block inventory and three orthographic silhouettes. Our approach consists of two stages. First, we develop a physics-informed graph neural network (PI-GNN) that models support relations between blocks and serves as a fast stability predictor for curating a diverse dataset of stable structures. Second, we train a conditional flow-matching model on this dataset to capture the multimodal distribution of block configurations consistent with the target silhouettes. At inference time, lightweight geometric selection and refinement reduce inter-block penetration without physics simulation. Preliminary experiments show that the PI-GNN generalizes from simple stacks to complex structures substantially better than a parameter-matched multilayer perceptron. For structure generation, Best-of-10 selection improves physical stability from 45.3% to 72.4% while achieving a silhouette IoU of 0.970. These results highlight the potential of combining relational physical reasoning, scalable data curation, and conditional generative modeling for creative 3D assembly. Project website: [https://ripl.github.io/StackGen3D-web/](https://ripl.github.io/StackGen3D-web/)

cs.CV↗
arXiv2026

The Human--VLA Communication Gap: What Humans Leave Implicit and What Policies Cannot Recover

A robot may know how to perform a task yet fail when a human asks for it naturally. We study this human--VLA communication gap by separating an \emph{information gap}, where task semantics, grounding, or event structure remain unresolved, from a residual \emph{execution gap}, where even a fully specified task depends on how it is organized and invoked. To do this, we characterize how people naturally express demonstrated objectives and evaluate controlled variants of the same physical tasks, with detailed analysis on $π_{0.5}$ and consistent trends on $π_0$-FAST and Cosmos3-Edge. Our study reveals that humans increasingly compress instructions as tasks grow, relying more on contextual grounding and implicit execution structure. Current policies are misaligned with this behavior: they tolerate grounding dependence relatively well, but struggle when task semantics or event structure must be reconstructed. Even after the information gap is resolved, execution remains sensitive to command granularity and invocation state. These findings identify concrete targets for human-centered policy learning and interfaces that better bridge natural human objectives to robot behavior. Project website: https://commgap.github.io/.

cs.RO↗
arXiv2026

BrickBench: Evaluating Agentic Brick Design

We propose BrickBench, a benchmark for agentic text-conditioned LEGO-set design. Given a prompt, an agent is tasked with producing an assembly that not only satisfies semantic and design criteria, but that can also be physically built. To do so, it must select parts from a discrete library and reason jointly about local and global constraints. We score validity, alignment, and design across three settings that vary in scale and part availability. We provide BrickAgent, an environment for coding agents to construct, inspect, and validate their designs. We find that leading agents largely satisfy verifiable physical and semantic requirements, but fall short of human designs. We release our benchmark and environment at http://www.brickben.ch

cs.AI↗
arXiv2026

ECoMEM: Explicit Concept Memory for Memory-Dependent Robot Control

A robot may lose sight of an object it must later retrieve, need to recall what a person demonstrated earlier, or track which steps of a task it has already completed. Current vision-language-action (VLA) policies often fail once the information needed for action disappears from the current observation, making memory critical for long-horizon robot behavior. Existing approaches typically provide longer histories or learn implicit memory from observation-action trajectories. But action supervision tells a policy how to act, not what to remember: it does not specify which past facts should persist or how they should change as new evidence arrives. We therefore separate maintaining an evidence-grounded account of the past from learning how to act on it. This insight motivates Explicit Concept Memory (ECoMEM), which represents task-relevant history with a reusable library of grounded concepts. An evidence-based Writer selects and updates these records, while a learned Reader turns them into memory tokens that directly condition the VLA. Across 16 RoboMME tasks, ECoMEM leads the evaluated robot policies on 15 tasks. On two new real-robot tasks, the same memory library either transfers directly or requires only one new concept, achieving 86.1% success versus 8.6% for a no-memory VLA. These results show that explicit concepts provide a reusable and extensible memory interface for robot control. Project website: https://ecomem.github.io/

cs.RO↗
arXiv2026

Tapered Fibre Fabry-Pérot Cavities for the Generation of Near-Visible, Phase-locked, and Broadband Kerr Frequency Combs

We demonstrate the generation of near-visible, broadband Kerr frequency combs using a tapered fibre Fabry-Pérot resonator. By incorporating micron-diameter tapered silica fibre into a pulsed-driven Fabry-Pérot configuration, we exploit geometric dispersion engineering to overcome the strong normal dispersion intrinsic to silica (and nearly all materials) at short wavelengths, achieving net anomalous dispersion in the near-visible regime. The strong mode confinement of the tapered fibre simultaneously enhances the Kerr nonlinearity by over an order of magnitude relative to standard fibre, substantially reducing the threshold drive power for nonlinear structure formation. Through precise control of the cavity fabrication, we experimentally demonstrate and numerically confirm a rich suite of coherent, phase-locked nonlinear structures, including conventional anomalous dispersion cavity solitons, zero dispersion solitons with spectral bandwidths exceeding 16 THz between 750 nm and 800 nm, as well as switching wave combs and broadband Raman solitons, the latter also reported here for the first time in the near-visible regime. Our results establish the tapered fibre Fabry-Pérot resonator as a versatile platform for near-visible and potentially visible coherent frequency comb generation.

physics.optics↗
arXiv2026

The Credibility Revolution in Political Science

How far has the credibility revolution spread through political science? We address this question by classifying 91,632 articles published between 2003 and 2023 across 156 political science journals using large language models, focusing on research design, credibility-enhancing practices, and citation patterns. We find that design-based studies, those leveraging plausibly exogenous variation to justify causal claims, have become increasingly common. In contrast, model-based approaches that rely more heavily on modeling assumptions have declined. Yet the rise of design-based work is uneven: it is concentrated in top journals and among authors at highly ranked institutions, and it is driven primarily by the growth of survey experiments. Other credibility-enhancing practices that help reduce false positives and false negatives, such as placebo tests and power calculations, remain rare. Taken together, our findings point to substantial but selective change, more consistent with a partial reform than a revolution.

cs.DL↗
arXiv2026

APIVOT: Adaptive Planning with Interleaved Vision-Language Thoughts

Long-horizon robot planning requires jointly reasoning over semantic task structure and geometric feasibility. To successfully execute a task, a robot must decompose goals, select task-relevant objects, and sequence actions, while ensuring that plans satisfy spatial constraints such as limited free space and object collisions. In this work, we propose APIVOT, a VLM-based planner that adaptively interleaves language and visual thoughts for long-horizon planning. APIVOT learns to leverage language for semantic reasoning, while using visual thoughts as imagined future states for internal verification of geometric feasibility. On long-horizon kitchen tasks, APIVOT outperforms general-purpose VLMs and prior planning frameworks, achieving the largest gains in spatially constrained settings. We find that APIVOT learns meaningful modality selection behavior, demonstrating that adaptive interleaving of vision-language thoughts improves both planning success and reasoning efficiency.

cs.CV↗
arXiv2026

Learning Preferences from Conjoint Data: A Hybrid Structural Deep Learning Approach

Conjoint experiments randomize multidimensional profiles, yet political science applications typically report only nonparametric averages that do not recover counterfactual choices or individual tradeoffs. We develop a hybrid structural approach for recovering individual preferences from conjoint data. The estimator combines a flexible machine-learning mean preference function, via a deep netural network in our applications, with respondent-level empirical-Bayes updating in a logistic random utility model, allowing preferences to vary with observed characteristics while learning residual heterogeneity from repeated choices. Double/debiased machine learning delivers valid inference for population-average preference parameters with any sufficiently accurate first-stage learner. Across three applications, the method reveals heterogeneity reduced-form averages obscure: opposition to undemocratic behavior is broad but uneven in intensity, progressive tax preferences are widespread across partisan subgroups, and partisan polarization offsets the average gender effect in candidate choice. The framework opens the door to core theoretical questions in political science by recovering substantively interpretable structural parameters.

stat.ME↗
arXiv2026

Spectral tailoring of Raman soliton generation via a dispersion-managed fibre Fabry-Pérot resonator

Dissipative Raman solitons in passive Kerr resonators have emerged as a promising route to broadband coherent frequency comb generation. Yet, their centre frequency has so far been mostly fixed near the Raman gain peak (13~THz downshifted from the pump centre frequency in silica-based fibres), constraining spectral coverage and compatibility with standard optical amplifiers. This limitation arises because the frequency shift of Raman solitons that fulfills phase-matching and group-velocity-matching conditions has to fall within the Raman gain band, leaving little room for spectral tuning when using a single conventional optical fibre. Here, we demonstrate that dispersion management of the fibre Fabry-Pérot resonator which allows us to directly shift the soliton centre frequency. By combining two fibres with complementary dispersion profiles, we tailor the resonator's average dispersion to satisfy the phase-matching conditions for soliton formation at a target frequency downshifted by 7.8~THz from a pulsed pump, which is well outside the conventional 13~THz Raman gain band. This allows us to optically amplify the dissipative Raman soliton with a commercial L-band erbium-doped fibre amplifier, and fully characterise its temporal profile via the frequency-resolved optical gating technique.

physics.optics↗
arXiv2026

Scaling Reproducibility: An AI-Assisted Workflow for Large-Scale Replication and Reanalysis

Computational reproducibility is central to scientific credibility, yet verifying published results at scale remains costly. We develop an AI-assisted workflow for automated full-paper replication -- retrieving materials, reconstructing environments, executing code, and matching outputs to point estimates reported in regression tables. We define a universe of all empirical and quantitative papers from the three top political science journals (2010--2025) and measure stated data availability using automated extraction. For a stratified sample of 384 studies, we apply the workflow to conduct full-paper replication, totaling 3,523 empirical models. We find that journal verification requirements, combined with data archiving mandates, drive reproducibility: the share of fully or largely reproducible papers rises from 20.8% before DA-RT adoption to 82.5% after, and conditional on accessible replication packages, 92.1% of papers are fully or largely reproducible (234/254). As a secondary application, we apply standardized IV diagnostics to 84 studies (597 IV specifications among 1,910 replicated models), illustrating how automated execution enables systematic reanalysis across heterogeneous empirical settings.

econ.EM↗
arXiv2026

Interpretable Discriminative Text Representations via Agreement and Label Disentanglement

Interpretable text representations should expose coordinates that are not only predictive, but also meaningful enough for independent auditors to apply. Existing discriminative representations often use anonymous embedding directions, while concept-bottleneck and LLM-assisted methods attach natural-language names to features without ensuring that those definitions are reproducible or distinct from the target label. We propose an operational criterion for interpretable discriminative text representations: each coordinate should satisfy conceptual clarity, measured by chance-adjusted agreement between independent annotators applying the feature definition, and label disentanglement, meaning the feature should not merely paraphrase the prediction target. We instantiate this criterion in LLM-assisted Feature Discovery (LFD), an iterative method that proposes lexical and semantic features from contrastive outcome-opposed text pairs, screens candidates using cross-LLM Cohen's $κ$, and selects features by residual held-out predictive gain. A stylized analysis connects the $κ$ screen to a per-feature annotation-noise bound, formalizing agreement as a reliability check. Across ten text-classification tasks spanning seven corpora, LFD matches the predictive performance of a strong text bottleneck baseline while producing substantially clearer and less label-entangled features. Human audits with 232 raters show that LFD features achieve higher human--human and human--LLM agreement than baseline concepts, and raters consistently judge them as less label-leaking. These results suggest that agreement-tested, label-disentangled coordinates provide a practical auditability standard for interpretable text classification.

cs.CL↗
arXiv2026

A Practical Guide to Estimating Conditional Marginal Effects: Modern Approaches

This Element offers a practical guide to estimating conditional marginal effects-how treatment effects vary with a moderating variable-using modern statistical methods. Commonly used approaches, such as linear interaction models, often suffer from unclarified estimands, limited overlap, and restrictive functional forms. This guide begins by clearly defining the estimand and presenting the main identification results. It then reviews and improves upon existing solutions, such as the semiparametric kernel estimator, and introduces robust estimation strategies, including augmented inverse propensity score weighting with Lasso selection (AIPW-Lasso) and double machine learning (DML) with modern algorithms. Each method is evaluated through simulations and empirical examples, with practical recommendations tailored to sample size and research context. All tools are implemented in the accompanying \texttt{interflex} package for \texttt{R}.

stat.ME↗
arXiv2026

The Harmonic Synthetic Control Method

Synthetic control methods can produce misleading counterfactual predictions when outcome series contain unit-specific stochastic trends, a common feature of nonstationary macroeconomic data. Existing remedies, such as pre-filtering or differencing, reduce spurious matching but may discard shared nonstationary variation that helps estimate donor weights. We propose Harmonic Synthetic Control (HSC), which replaces this binary choice with a soft allocation mechanism. HSC jointly estimates donor weights and a treated-unit-specific smooth residual component, then extrapolates this component into post-treatment periods using a time-series forecaster. A tuning parameter, selected by rolling-origin cross-validation, governs the division between donor matching and forecasting. As it varies, HSC continuously interpolates between synthetic control applied to differenced outcomes and synthetic control applied to raw outcomes with an intercept or trend. We provide a spectral interpretation showing how HSC downweights low-frequency residual components in donor matching and assigns them to the forecasting branch. A prediction-error decomposition separates weight-estimation distortion from residual-forecasting error. Monte Carlo exercises show that HSC adapts across regimes, performing well when stochastic trends are predominantly common or idiosyncratic, while estimators fixed to one regime can fail in the other.

econ.EM↗
arXiv2026

StatsClaw: An AI-Collaborative Workflow for Statistical Software Development

Translating statistical methods into reliable software is a persistent bottleneck in quantitative research. Existing AI code-generation tools produce code quickly but cannot guarantee faithful implementation -- a critical requirement for statistical software. We introduce StatsClaw, a multi-agent architecture for Claude Code that enforces information barriers between code generation and validation. A planning agent produces independent specifications for implementation, simulation, and testing, dispatching them to separate agents that cannot see each other's instructions: the builder implements without knowing the ground-truth parameters, the simulator generates data without knowing the algorithm, and the tester validates using deterministic criteria. We describe the approach, demonstrate it end-to-end on a probit estimation package, and evaluate it across three applications to the authors' own R and Python packages. The results show that structured AI-assisted workflows can absorb the engineering overhead of the software lifecycle while preserving researcher control over every substantive methodological decision.

cs.SE↗
arXiv2026

Factorial Difference-in-Differences

We formulate factorial difference-in-differences (FDID), a research design that extends canonical difference-in-differences (DID) to settings in which an event affects all units. In many panel data applications, researchers exploit cross-sectional variation in a baseline factor alongside temporal variation in the event, but the corresponding estimand is often implicit and the justification for applying the DID estimator remains unclear. We frame FDID as a factorial design with two factors, the baseline factor $G$ and the exposure level $Z$, and define effect modification and causal moderation as the associative and causal effects of $G$ on the effect of $Z$, respectively. Under standard DID assumptions of no anticipation and parallel trends, the DID estimator identifies effect modification but not causal moderation. Identifying the latter requires an additional \emph{factorial parallel trends} assumption, that is, mean independence between $G$ and potential outcome trends. We extend the framework to conditionally valid assumptions and regression-based implementations, and further to repeated cross-sectional data and continuous $G$. We demonstrate the framework with an empirical application on the role of social capital in famine relief in China.

stat.ME↗
arXiv2026

VLS: Steering Pretrained Robot Policies via Vision-Language Models

Why do pretrained diffusion or flow-matching policies fail when the same task is performed near an obstacle, on a shifted support surface, or amid mild clutter? Such failures rarely reflect missing motor skills; instead, they expose a limitation of imitation learning under train-test shifts, where action generation is tightly coupled to training-specific spatial configurations and task specifications. Retraining or fine-tuning to address these failures is costly and conceptually misaligned, as the required behaviors already exist but cannot be selectively adapted at test time. We propose Vision-Language Steering (VLS), a training-free framework for inference-time adaptation of frozen generative robot policies. VLS treats adaptation as an inference-time control problem, steering the sampling process of a pretrained diffusion or flow-matching policy in response to out-of-distribution observation-language inputs without modifying policy parameters. By leveraging vision-language models to synthesize trajectory-differentiable reward functions, VLS guides denoising toward action trajectories that satisfy test-time spatial and task requirements. Across simulation and real-world evaluations, VLS consistently outperforms prior steering methods, achieving a 31% improvement on CALVIN and a 13% gain on LIBERO-PRO. Real-world deployment on a Franka robot further demonstrates robust inference-time adaptation under test-time spatial and semantic shifts. Project page: https://vision-language-steering.github.io/webpage/

cs.RO↗
arXiv2026

Causal Panel Analysis under Parallel Trends: Lessons from a Large Reanalysis Study

Two-way fixed effects (TWFE) models are widely used in political science to establish causality, but recent methodological discussions highlight their limitations under heterogeneous treatment effects (HTE) and violations of the parallel trends (PT) assumption. This growing literature has introduced numerous new estimators and procedures, causing confusion among researchers about the reliability of existing results and best practices. To address these concerns, we replicated and reanalyzed 49 studies from leading journals that employ TWFE models for causal inference using observational panel data with binary treatments. Using six HTE-robust estimators, diagnostic tests, and sensitivity analyses, we find: (i) HTE-robust estimators yield qualitatively similar but highly variable results; (ii) while a few studies show clear signs of PT violations, many lack evidence to support this assumption; and (iii) many studies are underpowered when accounting for HTE and potential PT violations. We emphasize the importance of strong research designs and rigorous validation of key identifying assumptions.

stat.ME↗