Institution profile

Cornell University

Academic institutionnorthamerica · us
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Research library1,731linked papers
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Selected work

Representative Papers

Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

Dec 29, 2023arXiv.org

The Circle of Willis (CoW) suffers from a scarcity of high-quality voxel-level annotations in CTA/MRA imaging, reliance on labor-intensive expert manual segmentation, and poor guarantee of topological consistency. Method: We introduce the first publicly available voxel-level multi-class CoW dataset—comprising 13 vascular structures with paired MRA/CTA volumes—and propose a topology-aware segmentation framework: (i) a novel VR-assisted annotation paradigm ensuring anatomical plausibility; (ii) a multimodal registration and topology-constrained segmentation network; and (iii) topology-sensitive metrics including branch F1 and topo-Dice. Contribution/Results: This benchmark has attracted >140 teams across four continents. State-of-the-art models achieve ≈90% Dice on most arterial branches, while exposing persistent topological matching bottlenecks—particularly for communicating arteries and anatomical variants.

24 citations2 influentialRead paper

Projection Inference for set-identified SVARs

Apr 18, 2025

This paper addresses inference challenges for structural vector autoregressive (SVAR) models under set identification. We propose a projection-based inferential method that simultaneously delivers asymptotic frequentist coverage and robust Bayesian credibility: the Wald ellipsoid for reduced-form parameters is projected onto the structural parameter space to construct joint confidence regions. We establish, for the first time in general stationary SVARs, that this projection method achieves asymptotic 1−α frequentist coverage and robust Bayesian credibility. Moreover, we introduce a posterior-calibrated radius adjustment algorithm that ensures exact robust credibility of 1−α while guaranteeing precise 1−α coverage over the identification set. Theoretically, our work unifies dual guarantees—frequentist and robust Bayesian—within a coherent framework; computationally, it remains efficient and implementable. Empirically, we replicate the Baumeister–Hamilton (2015) labor supply–demand model, demonstrating the method’s tightness and robustness.

15 citations1 influentialRead paper

Double Robustness of Local Projections and Some Unpleasant VARithmetic

May 01, 2024Social Science Research Network

This paper investigates coverage robustness of impulse response inference in locally misspecified vector autoregression (VAR) models. We find that conventional VAR confidence intervals exhibit severe undercoverage—even for statistically subtle, theoretically admissible misspecifications—when lag orders are short to moderate. In contrast, local projection (LP) confidence intervals demonstrate double robustness: they maintain nominal coverage under strong misspecification and asymptotically match or exceed VAR performance under weak misspecification. We establish, for the first time, that LP possesses a semilinear-regression–like double-robust structure and rigorously prove that VAR inference achieves asymptotic robustness only as the lag order diverges to infinity. Asymptotic expansions and Monte Carlo simulations confirm that LP consistently sustains nominal coverage across diverse misspecification regimes, whereas VAR exhibits substantially deflated coverage—and narrower, misleadingly precise intervals—under standard lag selections.

14 citationsRead paper

Rationalizing Dynamic Choices

Mar 29, 2019Social Science Research Network

This paper studies how an observer determines whether a sequence of observable actions can be rationalized by a Bayesian agent with endogenous information acquisition and updating, under a known utility function. Method: The authors derive the first necessary and sufficient condition for dynamic rationalizability: behavior is irrationalizable if and only if a universally dominant deviation exists—a deviation that strictly improves expected utility across all possible information structures. This condition is information-structure-free, greatly enhancing testability. Contribution/Results: The framework extends to stochastic choice, enabling monotonic rationalization under risk aversion, empirical falsification of Bayesian models, feasibility characterization in dynamic information design, and partial identification of utility parameters. Its core innovation is a verifiable dominance-based rationalizability criterion, which reveals that stronger risk aversion weakens predictive power of behavior and permits preference identification without assuming any specific information structure.

13 citations2 influentialRead paper

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Jan 17, 2026

This work addresses the challenge that existing AI agent benchmarks inadequately evaluate performance on real-world, complex, and long-horizon command-line tasks. To bridge this gap, the authors introduce a novel evaluation benchmark comprising 89 high-difficulty terminal tasks, all derived from authentic workflows and accompanied by isolated execution environments, human-authored reference solutions, and automated verification tests. The benchmark is designed to ensure realism, verifiability, and diversity, substantially narrowing the disparity between practical scenarios and current model evaluation paradigms. Experimental results demonstrate that even state-of-the-art agents achieve success rates below 65% on this benchmark. The paper further provides comprehensive error analysis and publicly releases the dataset and evaluation toolchain to support future research in this domain.

9 citations1 influentialRead paper
Recent publications

Latest Papers

MoRE: Mixture of Reused Experts

Sep 16, 2026

本文提出MoRE模型，通过在相邻层间共享专家池来解决Mixture-of-Experts架构中参数增加导致内存占用高的问题，同时引入深度嵌入以区分不同层。

0 citationsRead paper