arXiv subjects

Wang Song

Publications and source records attributed to Wang Song.

4 recordsLinked to original sources

arXiv2026

Timage: A Generative Text-in-Image Paradigm for Fine-Tuning Vision-Language Models

Multimodal Large Language Models (MLLMs) often lose track of the right image regions during fine-grained spatial reasoning, because a textual query rarely carries any explicit geometric anchor into the pixel domain. Prevailing remedies either rewire the model's weights or pad the prompt with verbose instructions, yet neither reliably pins the language to the correct visual coordinates without eroding the backbone's general competence. We introduce Timage, a paradigm that recasts multimodal understanding as an alignment problem solved at the input: the query is drawn, as a typeset overlay, onto the image itself. The placement and appearance of this overlay are produced by a Constrained Schr\"odinger Bridge (cSB), an entropic optimal-transport sampler that factorizes layout synthesis into two coupled stochastic stages. The first stage, Region Search, transports noise toward query-aligned image zones while obeying a hard occlusion barrier that protects salient foreground content; the second stage, Appearance Shaping, sizes the glyphs through an ``ink-budget'' regularizer so that the rendered text stays legible and visually balanced. The resulting overlay behaves as an explicit attention beacon that channels the model's focus along spatial semantics. On the VMCBench suite, Timage paired with a modest 7B backbone clearly overtakes far larger proprietary systems as well as parameter-tuned baselines. The study positions deliberate input reconstruction as a powerful, architecture-neutral lever for strengthening multimodal reasoning.

cs.CV↗

arXiv2024

A potential mass-gap black hole in a wide binary with a circular orbit

Mass distribution of black holes identified through X-ray emission suggests a paucity of black holes in the mass range of 3 to 5 solar masses. Modified theories have been devised to explain this mass gap, and it is suggested that natal kicks during supernova explosion can more easily disrupt binaries with lower mass black holes. Although recent LIGO observations reveal the existence of compact remnants within this mass gap, the question of whether low-mass black holes can exist in binaries remains a matter of debate. Such a system is expected to be noninteracting without X-ray emission, and can be searched for using radial velocity and astrometric methods. Here we report Gaia DR3 3425577610762832384, a wide binary system including a red giant star and an unseen object, exhibiting an orbital period of approximately 880 days and near-zero eccentricity. Through the combination of radial velocity measurements from LAMOST and astrometric data from Gaia DR2 and DR3 catalogs, we determine a mass of $3.6^{+0.8}_{-0.5}$ $M_{\odot}$ of the unseen component. This places the unseen companion within the mass gap, strongly suggesting the existence of binary systems containing low-mass black holes. More notably, the formation of its surprisingly wide circular orbit challenges current binary evolution and supernova explosion theories.

astro-ph.SR↗

arXiv2022

Binaries with possible compact components discovered from the LAMOST Time-Domain Survey of four $K$2 plates

Time-domain (TD) spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) can provide accurate and high-cadence radial velocities (RVs). In this work, we search for binaries with compact components with RV monitoring method by using the LAMOST TD survey of four $K$2 plates. Three binary systems including an unseen white dwarf or neutron star are found. For each binary system, we estimate the stellar parameters of the visible star and orbital parameters, and finally calculate the binary mass function and the minimum mass of the unseen star. No obvious double-lined feature is seen from the LAMOST medium-resolution spectra of the three sources. In addition, we found no X-ray counterpart for all these sources but UV companions for two of them. Spectral disentangling also shows no additional component with optical absorption spectra, supporting that these systems contain compact objects.

astro-ph.SR↗

arXiv2021

Yet Another Combination of IR- and Neural-based Comment Generation

Code comment generation techniques aim to generate natural language descriptions for source code. There are two orthogonal approaches for this task, i.e., information retrieval (IR) based and neural-based methods. Recent studies have focused on combining their strengths by feeding the input code and its similar code snippets retrieved by the IR-based approach to the neural-based approach, which can enhance the neural-based approach's ability to output low-frequency words and further improve the performance. However, despite the tremendous progress, our pilot study reveals that the current combination is not generalizable and can lead to performance degradation. In this paper, we propose a straightforward but effective approach to tackle the issue of existing combinations of these two comment generation approaches. Instead of binding IR- and neural-based approaches statically, we combine them in a dynamic manner. Specifically, given an input code snippet, we first use an IR-based technique to retrieve a similar code snippet from the corpus. Then we use a Cross-Encoder based classifier to decide the comment generation method to be used dynamically, i.e., if the retrieved similar code snippet is a true positive (i.e., is semantically similar to the input), we directly use the IR-based technique. Otherwise, we pass the input to the neural-based model to generate the comment. We evaluate our approach on a large-scale dataset of Java projects. Experiment results show that our approach can achieve 25.45 BLEU score, which improves the state-of-the-art IR-based approach, neural-based approach, and their combination by 41%, 26%, and 7%, respectively. We propose a straightforward but effective dynamic combination of IR-based and neural-based comment generation, which outperforms state-of-the-art approaches by a substantial margin.

cs.SE↗