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Yijie Shi

Publications and source records attributed to Yijie Shi.

7 recordsLinked to original sources

Contract-Coding: Towards Repo-Level Generation via Structured Symbolic Paradigm

The shift toward intent-driven software engineering (often termed "Vibe Coding") exposes a critical Context-Fidelity Trade-off: vague user intents overwhelm linear reasoning chains, leading to architectural collapse in complex repo-level generation. We propose Contract-Coding, a structured symbolic paradigm that bridges unstructured intent and executable code via Autonomous Symbolic Grounding. By projecting ambiguous intents into a formal Language Contract, our framework serves as a Single Source of Truth (SSOT) that enforces topological independence, effectively isolating inter-module implementation details, decreasing topological execution depth and unlocking Architectural Parallelism. Empirically, while state-of-the-art agents suffer from different hallucinations on the Greenfield-5 benchmark, Contract-Coding achieves 47\% functional success while maintaining near-perfect structural integrity. Our work marks a critical step towards repository-scale autonomous engineering: transitioning from strict "specification-following" to robust, intent-driven architecture synthesis. Our code is available at https://github.com/imliinyi/Contract-Coding.

cs.SE

(P)rior(D)yna(F)low: A Priori Dynamic Workflow Construction via Multi-Agent Collaboration

Recent studies have shown that carefully designed workflows coordinating large language models(LLMs) significantly enhance task-solving capabilities compared to using a single model. While an increasing number of works focus on autonomous workflow construction, most existing approaches rely solely on historical experience, leading to limitations in efficiency and adaptability. We argue that while historical experience is valuable, workflow construction should also flexibly respond to the unique characteristics of each task. To this end, we propose an a priori dynamic framework for automated workflow construction. Our framework first leverages Q-table learning to optimize the decision space, guiding agent decisions and enabling effective use of historical experience. At the same time, agents evaluate the current task progress and make a priori decisions regarding the next executing agent, allowing the system to proactively select the more suitable workflow structure for each given task. Additionally, we incorporate mechanisms such as cold-start initialization, early stopping, and pruning to further improve system efficiency. Experimental evaluations on four benchmark datasets demonstrate the feasibility and effectiveness of our approach. Compared to state-of-the-art baselines, our method achieves an average improvement of 4.05%, while reducing workflow construction and inference costs to only 30.68%-48.31% of those required by existing methods.

cs.AI

Valley resolved dynamics of phonon bottleneck in semiconductor molybdenum ditelluride

Semiconductor molybdenum ditelluride (2H-MoTe2) possess multiple valleys in the band structure, enriching its physical properties and potentials in applications. The understanding of its multivalley nature of fundamental processes involving population and relaxation of carriers and phonons is still evolving; particularly, the possible phonon bottleneck has not yet been addressed. Here, we investigate the carrier intra- and intervalley scattering and the phonon dynamics in different valleys in photoexcited few-layer 2H-MoTe2, by using the time resolved measurements of optical absorption and electron diffraction, together with the density functional theory calculation and molecular dynamics simulation. The pathways and timescales of carrier relaxation, accompanied with the emissions of optical phonons at the Brillouin zone center and acoustic phonons at the zone border are revealed. We present a couple of approaches to estimate the population of different phonon modes based on the results of optical and electron diffraction measurements, hence quantitatively identify the occurrences of phonon bottleneck located in different valleys. Our findings make possible to construct a comprehensive picture of the complex interactions between carriers and phonons in 2H-MoTe2 with the valley degree of freedom resolved.

cond-mat.mtrl-sci

Formation of Anisotropic Polarons in Antimony Selenide

Antimony Selenide (Sb$_2$Se$_3$) is an attractive candidate of photovoltaics with not yet satisfying efficiency. Beside defects, polaron formation originated from lattice distortion was proposed to account for trapping free carriers, and the subsequent photoexcitation dynamics and optoelectronic properties, but such a mechanism is still lack of structural observations. Here we directly track the pathways of carrier and lattice evolutions after photoexcitation through optical and electron diffraction pump-probe methods, revealing the temporal correlations between dynamics of both degrees of freedom. The observed opposite separation changes of Se2-Sb2 and Sb2-Sb1 atom pairs in a few picoseconds, and the intermediate state induced by local structural distortions lasting several tens of picoseconds, coinciding with the optical phonons population and coupling, and the trapping process of carriers, respectively, together with the analyses of modulation on diffuse scattering by the atomic displacement fields of polaron model, indicate the formation of anisotropic polarons with large size. Our findings provide carrier and structural information for helping the elucidation of polaron scenario in Sb2Se3, and probably in materials with anisotropic structure and soft lattice which are popular in developing novel optoelectronics.

cond-mat.mtrl-sci

Time-resolved optical assessment of exciton formation in mixed two-dimensional perovskite films

We report the observation of exciton formation from the cooled band-edge carriers in mixed two-dimensional hybrid organic-inorganic perovskites using femtosecond transient absorption spectroscopy. By monitoring the changes of bleach signal upon excitations with various photon energy, we are able to extract the values of exciton binding energy and the occupancies of carriers of free and bound states for each two-dimensional phase. We also confirm the existence of Mahan exciton when injected carrier density is above the Mott criterion.

physics.optics

MKL-$L_{0/1}$-SVM

This paper presents a Multiple Kernel Learning (abbreviated as MKL) framework for the Support Vector Machine (SVM) with the $(0, 1)$ loss function. Some KKT-like first-order optimality conditions are provided and then exploited to develop a fast ADMM algorithm to solve the nonsmooth nonconvex optimization problem. Numerical experiments on real data sets show that the performance of our MKL-$L_{0/1}$-SVM is comparable with the one of the leading approaches called SimpleMKL developed by Rakotomamonjy, Bach, Canu, and Grandvalet [Journal of Machine Learning Research, vol. 9, pp. 2491-2521, 2008].

stat.ML

An ADMM Solver for the MKL-$L_{0/1}$-SVM

We formulate the Multiple Kernel Learning (abbreviated as MKL) problem for the support vector machine with the infamous $(0,1)$-loss function. Some first-order optimality conditions are given and then exploited to develop a fast ADMM solver for the nonconvex and nonsmooth optimization problem. A simple numerical experiment on synthetic planar data shows that our MKL-$L_{0/1}$-SVM framework could be promising.

stat.ML