arXiv subjects

Chengyao Tang

Publications and source records attributed to Chengyao Tang.

5 recordsLinked to original sources

arXiv2026

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments

Pretraining scaling laws reveal that model capability improves predictably with data and compute. But learning from real world environments after deployment remains far less understood. Analyzing roughly 38,000 hours of agent interaction with the environment across 134 real world tasks, we find, to the best of our knowledge, the first evidence that overall performance during environment learning follows a log-sigmoid scaling law with remarkably high precision, reaching R^2 = 0.998. Across model generations, we also find that agent learning speed roughly doubles every three months. This discovery stems from EdgeBench, a suite of 134 real world tasks with ultra-long horizons, spanning scientific discovery, software engineering, combinatorial optimization, professional knowledge work, formal mathematics, and interactive games. Each task sustains at least 12 hours of continuous agent operation under rich, multilevel feedback, and is built through substantial expert effort. We publicly release 51 tasks and our full evaluation framework to accelerate the study of how agents learn from real world experience.

cs.CL↗

arXiv2026

Ocean Circulation on Tide-locked Lava Worlds: 3D Modeling with a Simple Boundary Iteration Method

Tide-locked lava worlds are surface-melted rocky planets under 1:1 tidally locked orbit (i.e., synchronously rotating) with orbital period being equal to rotation period and with permanent hot dayside and cold nightside. Previous studies on this type of planets employed scaling analyses and two-dimensional (2D) simulations. This work is a continuation of the previous researches but including the effect of the Coriolis force and the simulation domain is extended to a 3D global sphere. We find that under the condition with thermal-only forcing (without surface wind stresses), the area-mean ocean depth is about 50--300 m (depending on vertical diffusivity) and the area-mean effect of horizontal ocean heat transport (in the order of 10$^{3}$ to 10$^{4}$ W m$^{-2}$) is significantly smaller than stellar radiation (in the order of 10$^{6}$ W m$^{-2}$ at the substellar region), being consistent with previous results. Different from 2D results, due to the effect of the Coriolis force, large-scale horizontal gyres form on the dayside, ocean currents near the west boundaries are much stronger than that near the east boundaries (called as ``western intensification''), the deepest ocean is not right at the substellar point but in the middle latitudes as the vertical diffusivity is moderate or large, and meanwhile there exists significant asymmetry between the west and the east of the substellar point. These results establish a first picture for the 3D thermal-driven ocean circulation and confirm that the lava ocean should be shallow on tide-locked lava worlds.

astro-ph.EP↗

arXiv2024

Using Geometrical information to Measure the Vibration of A Swaying Millimeter-wave Radar

This paper presents two new, simple yet effective approaches to measure the vibration of a swaying millimeter-wave radar (mmRadar) utilizing geometrical information. Specifically, for the planar vibrations, we firstly establish an equation based on the area difference between the swaying mmRadar and the reference objects at different moments, which enables the quantification of planar displacement. Secondly, volume differences are also utilized with the same idea, achieving the self-vibration measurement of a swaying mmRadar for spatial vibrations. Experimental results confirm the effectiveness of our methods, demonstrating its capability to estimate both the amplitude and a crude direction of the mmRadar's self-vibration.

eess.SP↗

arXiv2024

BERT: Accelerating Vital Signs Measurement for Bioradar with An Efficient Recursive Technique

Recent years have witnessed the great advance of bioradar system in smart sensing of vital signs (VS) for human healthcare monitoring. As an important part of VS sensing process, VS measurement aims to capture the chest wall micromotion induced by the human respiratory and cardiac activities. Unfortunately, the existing VS measurement methods using bioradar have encountered bottlenecks in making a trade-off between time cost and measurement accuracy. To break this bottleneck, this letter proposes an efficient recursive technique (BERT) heuristically, based on the observation that the features of bioradar VS meet the conditions of Markov model. Extensive experimental results validate that BERT measurement yields lower time costs, competitive estimates of heart rate, breathing rate, and heart rate variability. Our BERT method is promising us a new and superior option to measure VS for bioradar. This work seeks not only to solve the current issue of how to accelerate VS measurement with an acceptable accuracy, but also to inspire creative new ideas that spur further advances in this promising field in the future.

eess.SP↗

arXiv2023

A simple sensitivity analysis method for unmeasured confounders via linear programming with estimating equation constraints

In estimating the average treatment effect in observational studies, the influence of confounders should be appropriately addressed. To this end, the propensity score is widely used. If the propensity scores are known for all the subjects, bias due to confounders can be adjusted by using the inverse probability weighting (IPW) by the propensity score. Since the propensity score is unknown in general, it is usually estimated by the parametric logistic regression model with unknown parameters estimated by solving the score equation under the strongly ignorable treatment assignment (SITA) assumption. Violation of the SITA assumption and/or misspecification of the propensity score model can cause serious bias in estimating the average treatment effect. To relax the SITA assumption, the IPW estimator based on the outcome-dependent propensity score has been successfully introduced. However, it still depends on the correctly specified parametric model and its identification. In this paper, we propose a simple sensitivity analysis method for unmeasured confounders. In the standard practice, the estimating equation is used to estimate the unknown parameters in the parametric propensity score model. Our idea is to make inference on the average causal effect by removing restrictive parametric model assumptions while still utilizing the estimating equation. Using estimating equations as constraints, which the true propensity scores asymptotically satisfy, we construct the worst-case bounds for the average treatment effect with linear programming. Different from the existing sensitivity analysis methods, we construct the worst-case bounds with minimal assumptions. We illustrate our proposal by simulation studies and a real-world example.

stat.ME↗