arXiv subjects

Zongxian Yang

Publications and source records attributed to Zongxian Yang.

6 recordsLinked to original sources

arXiv2026

One World, Dual Timeline: Decoupled Spatio-Temporal Gaussian Scene Graph for 4D Cooperative Driving Reconstruction

Reconstructing dynamic scenes from Vehicle-to-Infrastructure Cooperative Autonomous Driving (VICAD) data is fundamentally complicated by temporal asynchrony: vehicle and infrastructure cameras operate on independent clocks, capturing the same dynamic agent such as cars and pedestrians at different physical times. Existing Gaussian Scene Graph methods implicitly assume synchronized observations and assign a single pose per agent per frame, which is an assumption that breaks in cooperative settings, where the resulting gradient conflicts cause severe ghosting on dynamic agents. We identify this as a representation-level failure, not an optimization artifact: we prove that any single-timeline formulation incurs an irreducible photometric loss scaling quadratically with agent velocity and cross-source time offset. To resolve this, we propose Dust (DecoUpled Spatio-Temporal) Gaussian Scene Graph for 4D Cooperative Driving Reconstruction. DUST Gaussian Scene Graph shares a canonical Gaussian set per agent for appearance consistency, while maintaining decouple pose trajectories aligned to each source's true capture timestamps. We prove that this decoupling enables the pose-gradient kernel block-diagonal, eliminating cross-source interference entirely. To make Dust practical, we further introduce a static anchor-based pose correction pipeline that corrects spatio misalignment between vehicle and infrastructure annotations, and a pose-regularized joint optimization scheme that prevents trajectory jitter and drift during early training. On 26 sequences from V2X-Seq, DUST achieves state-of-the-art performance, improving dynamic-area PSNR by 3.2 dB over the strongest baseline and reducing Fréchet Video Distance by 37.7%, with keeping robustness under larger temporal asynchrony.

cs.CV↗

arXiv2026

QM-ToT: A Medical Tree of Thoughts Reasoning Framework for Quantized Model

Large language models (LLMs) face significant challenges in specialized biomedical tasks due to the inherent complexity of medical reasoning and the sensitive nature of clinical data. Existing LLMs often struggle with intricate medical terminology and the need for accurate clinical insights, leading to performance reduction when quantized for resource-constrained deployment. To address these issues, we propose Quantized Medical Tree of Thought (QM-ToT), a path-based reasoning framework. QM-ToT leverages a Tree of Thought (ToT) reasoning approach to decompose complex medical problems into manageable subtasks, coupled with evaluator assessment layers. This framework facilitates substantial performance improvements in INT4-quantized models on the challenging MedQAUSMLE dataset. Specifically, we demonstrate a remarkable accuracy increase from 34% to 50% for the LLaMA2-70b model and from 58.77% to 69.49% for LLaMA-3.1-8b. Besides, we also proposed an effect data distillation method based on ToT. Compared to the traditional distillation method, we achieved an improvement of 86. 27% while using only 3.9% of the data.This work, for the first time, showcases the potential of ToT to significantly enhance performance on complex biomedical tasks, establishing a crucial foundation for future advances in deploying high-performing quantized LLM in resource-limited medical settings.

cs.CL↗

arXiv2026

Uniform Inductive Spatio-Temporal Kriging

Inductive spatio-temporal kriging infers signals at unobserved locations from observed sensors, but real-world observations are often incomplete and exhibit block-wise missingness caused by failures, interruptions, or maintenance. A common impute-then-krige pipeline suffers from objective mismatch: better reconstruction on observed sensors does not necessarily improve downstream kriging, and value-dependent imputation bias can be propagated to unobserved nodes. We propose UniSTOK, a plug-and-play framework for inductive spatio-temporal kriging under incomplete observations. We first introduce Reliability-guided Signal Regulation (RSR), which estimates entry-wise reliability from temporal continuity and spatial support, and uses it to regulate the input signals so that reliable observations are emphasized while long-gap or weakly supported entries are suppressed before spatial propagation. We further introduce Residual Bias Calibration (RBC), which estimates value-conditioned residual prototypes after the main predictor converges and learns context-correction amplitudes to adaptively calibrate systematic over- or under-estimation in final kriging predictions. Extensive experiments on real-world datasets show that UniSTOK consistently improves multiple kriging backbones.

cs.AI↗

arXiv2026

Better Eyes, Better Thoughts: Why Vision Chain-of-Thought Fails in Medicine

Large vision-language models (VLMs) often benefit from chain-of-thought (CoT) prompting in general domains, yet its efficacy in medical vision-language tasks remains underexplored. We report a counter-intuitive trend: on medical visual question answering, CoT frequently underperforms direct answering (DirA) across general-purpose and medical-specific models. We attribute this to a \emph{medical perception bottleneck}: subtle, domain-specific cues can weaken visual grounding, and CoT may compound early perceptual uncertainty rather than correct it. To probe this hypothesis, we introduce two training-free, inference-time grounding interventions: (i) \emph{perception anchoring} via region-of-interest cues and (ii) \emph{description grounding} via high-quality textual guidance. Across multiple benchmarks and model families, these interventions improve accuracy, mitigate CoT degradation, and in several settings reverse the CoT--DirA inversion. Our findings suggest that reliable clinical VLMs require robust visual grounding and cross-modal alignment, beyond extending text-driven reasoning chains. Code is available \href{https://github.com/TianYin123/Better_Eyes_Better_Thoughts}{here}.

cs.CV↗

arXiv2026

Med-REFL: Medical Reasoning Enhancement via Self-Corrected Fine-grained Reflection

Large reasoning models excel in domains like mathematics where intermediate reasoning is straightforward to verify, but struggle to self-correct in medicine fields where evaluating intermediate reasoning is cumbersome and expensive. This verification bottleneck hinders the development of reliable AI reasoners for high-stakes application. Here we propose Med-REFL, a novel framework that learns fine-grained reflection without human labels or model distillation. Med-REFL introduces a deterministic structural assessment of the reasoning space to automatically generate preference data for reflection. By globally evaluating all explored reasoning paths in a tree-of-thoughts, our method quantifies the value of corrective actions, enabling the automated construction of direct preference optimization pairs. This trains the model to recognize and amend its own reasoning fallacies. Extensive experiments show Med-REFL delivers robust gains across diverse models architectures and medical benchmarks, boosting a general-purpose Llama3.1-8B by +5.82% and the state-of-the-art Huatuo-o1 by +4.13% on the MedQA benchmark. Our Med-REFL-8B achieves state-of-the-art performance among 7-8B models while even competing with models twice its size. Crucially, targeted ablations prove its success generalizes to other domains such as logical reasoning and mitigates the `fake reflection' phenomenon in LRMs. Ultimately, our framework provides a scalable solution to the verification bottleneck, paving the way for more reliable AI reasoners in high-stakes domains like medicine. Med-REFL has been made publicly available in https://github.com/TianYin123/Med-REFL.

cs.AI↗

arXiv2017

Theoretical insight into the interaction of O2 and H2O molecules with the perfect and the defective InSe monolayers

By using first-principles calculation, the interaction of O2 and H2O molecules with the pristine and the defective InSe monolayers is studied. It is predicted that the single Se and In vacancies exhibit significantly enhanced chemical activity toward the adsorbates compared with the perfect InSe lattice site, and the Se vacancies have a much higher chemical activity than the In vacancies. H2O molecule should be only physisorbed on the various InSe monolayers at ambient conditions, according to the calculated energies. The doping of the various InSe monolayers is discussed by the physisorbed H2O. The vacancies show a much higher chemical activity toward O2 than H2O. Although O2 molecules are still physisorbed on the pristine InSe monolayer, they will be chemisorbed on the defective InSe monolayers. Especially, our calculated energies suggest that the surface oxidation of the 2D InSe semiconductor should be dominated by the defects that expose under-coordinated host atoms, especially In atoms. Our theoretical results can help better understanding the doping and the oxidation of the 2D InSe semiconductor under ambient conditions.

cond-mat.mtrl-sci↗