arXiv subjects

Yang liu

Publications and source records attributed to Yang liu.

3 recordsLinked to original sources

arXiv2026

Interpretations of the $10\%$ polarization observed in the early forward-shock afterglow of GRB 091208

The $\sim10\%$ optical polarization observed at the early stage of GRB 091208B comes from the forward shock emission, which is higher than the conventionally predicted value. Polarizations of the forward shock radiation would depend on the observational geometry and the post-shock magnetic field structure. This magnetic field could arise either from the compression of a pre-existing magnetic field (i.e., the magnetic field in the outer medium) or from the shock-generated instabilities. In this paper, we use a synchrotron radiation model to fit the light curve and polarization observations of GRB 091208B. Two scenarios are considered: one is the case of a slightly off-axis observer, and the other is with a large-scale ordered magnetic field component in the burst environment. We found both scenarios could interpret the observations of GRB 091208B. For the slightly off-axis observation scenario, the observational angle is restricted to be within the range of (1.02, 1.05) times the jet half-opening angle. For the large-scale ordered magnetic field component scenario, the ratio between the ordered component to the random component is constrained to be around 1.

astro-ph.HE↗

arXiv2025

TRKT: Weakly Supervised Dynamic Scene Graph Generation with Temporal-enhanced Relation-aware Knowledge Transferring

Dynamic Scene Graph Generation (DSGG) aims to create a scene graph for each video frame by detecting objects and predicting their relationships. Weakly Supervised DSGG (WS-DSGG) reduces annotation workload by using an unlocalized scene graph from a single frame per video for training. Existing WS-DSGG methods depend on an off-the-shelf external object detector to generate pseudo labels for subsequent DSGG training. However, detectors trained on static, object-centric images struggle in dynamic, relation-aware scenarios required for DSGG, leading to inaccurate localization and low-confidence proposals. To address the challenges posed by external object detectors in WS-DSGG, we propose a Temporal-enhanced Relation-aware Knowledge Transferring (TRKT) method, which leverages knowledge to enhance detection in relation-aware dynamic scenarios. TRKT is built on two key components:(1)Relation-aware knowledge mining: we first employ object and relation class decoders that generate category-specific attention maps to highlight both object regions and interactive areas. Then we propose an Inter-frame Attention Augmentation strategy that exploits optical flow for neighboring frames to enhance the attention maps, making them motion-aware and robust to motion blur. This step yields relation- and motion-aware knowledge mining for WS-DSGG. (2) we introduce a Dual-stream Fusion Module that integrates category-specific attention maps into external detections to refine object localization and boost confidence scores for object proposals. Extensive experiments demonstrate that TRKT achieves state-of-the-art performance on Action Genome dataset. Our code is avaliable at https://github.com/XZPKU/TRKT.git.

cs.CV↗

arXiv2025

Importance Sampling is All You Need: Predict LLM's performance on new benchmark by reusing existing benchmark

With the rapid advancement of large language models , code generation has become a key benchmark for evaluating LLM capabilities. However, existing benchmarks face two major challenges: (1) the escalating cost of constructing high-quality test suites and reference solutions, and (2) the increasing risk of data contamination, which undermines the reliability of benchmark-based evaluations. In this paper, we propose BIS, a prompt-centric evaluation framework that enables ground-truth-free prediction of LLM performance on code generation tasks. Rather than executing generated code, BIS estimates performance metrics by analyzing the prompt distribution alone. Built on importance sampling theory and implemented using Importance Weighted Autoencoders, our method reweights samples from existing annotated benchmarks to estimate performance on new, unseen benchmarks. To stabilize the estimation, we introduce weight truncation strategies and compute marginal expectations across the fitted distributions. BIS serves as a complementary tool that supports benchmark development and validation under constrained resources, offering actionable and quick feedback for prompt selection and contamination assessment. We conduct extensive experiments involving 8,000 evaluation points across 4 CodeLlama models and 9 diverse benchmarks. Our framework achieves an average absolute prediction error of 1.1% for code correctness scores, with best- and worst-case errors of 0.3% and 1.9%, respectively. It also generalizes well to other metrics, attaining average absolute errors of 2.15% for pass@1. These results demonstrate the reliability and broad applicability of BIS, which can significantly reduce the cost and effort of benchmarking LLMs in code-related tasks.

cs.AI↗