arXiv subjects

Jieting Long

Publications and source records attributed to Jieting Long.

5 recordsLinked to original sources

arXiv2026

Porosity Effects on Cyclic Gas Invasion and Trapping in Deformable Porous Media

Fluid transport in deformable porous media is central to many biophysical and geophysical processes. While extensive studies exist, how porosity governs fluid behaviour in deformable systems during cyclic injection remains elusive. Here, we investigate gas-liquid multiphase flow in a quasi-2D Hele-Shaw cell packed with soft hydrogel particles at different initial porosities. Alternative gas and water injection experiments, combined with high-resolution imaging and continuous pressure monitoring, are used to quantify gas dynamics and pressure evolution. Results show that the gas entry pressure increases as porosity decreases, consistent with a Young-Laplace estimation based on effective pore-throat width. After entry, invasion shifts from cavity-dominated expansion in high porosity packings to localised pore invasion in low porosity packings, with a mixed cavity-fingering regime at intermediate porosity. Pressure fluctuations are linked to pore-scale gas escape and internal gas redistribution. Low porosity packings produce frequent small-amplitude pressure drops, whereas higher porosity packings produce more discrete pressure relaxations. Across cycles, the decreasing mean pressure suggests preferential-pathway reuse and reduced local capillary constraints. Residual gas saturation increases systematically with injection cycles and reaches higher terminal values as porosity decreases. Specific interfacial length increases as available pore space decreases and follows a power-law relationship with gas cluster size, with scaling exponent decreases as porosity decreases and cycling progresses. Together, these results demonstrate that gas trapping in deformable porous media depends on both initial packing structure and cyclically evolving gas-solid interactions. This study provides insights for interpreting porosity-dependent trapping and reinvasion during repeated gas injection.

cond-mat.soft↗

arXiv2026

Constraining Streaming Flow Models for Adapting Learned Robot Trajectory Distributions

Robot motion distributions often exhibit multi-modality and require flexible generative models for accurate representation. Streaming Flow Policies (SFPs) have recently emerged as a powerful paradigm for generating robot trajectories by integrating learned velocity fields directly in action space, enabling smooth and reactive control. However, existing formulations lack mechanisms for adapting trajectories post-training to enforce safety and task-specific constraints. We propose Constraint-Aware Streaming Flow (CASF), a framework that augments streaming flow policies with constraint-dependent metrics that reshape the learned velocity field during execution. CASF models each constraint, defined in either the robot's workspace or configuration space, as a differentiable distance function that is converted into a local metric and pulled back into the robot's control space. Far from restricted regions, the resulting metric reduces to the identity; near constraint boundaries, it smoothly attenuates or redirects motion, effectively deforming the underlying flow to maintain safety. This allows trajectories to be adapted in real time, ensuring that robot actions respect joint limits, avoid collisions, and remain within feasible workspaces, while preserving the multi-modal and reactive properties of streaming flow policies. We demonstrate CASF in simulated and real-world manipulation tasks, showing that it produces constraint-satisfying trajectories that remain smooth, feasible, and dynamically consistent, outperforming standard post-hoc projection baselines.

cs.RO↗

arXiv2025

Ctrl-Z Sampling: Scaling Diffusion Sampling with Controlled Random Zigzag Explorations

Diffusion models generate conditional samples by progressively denoising Gaussian noise, yet the denoising trajectory can stall at visually plausible but low-quality outcomes with conditional misalignment or structural artifacts. We interpret this behavior as local optima in a surrogate quality landscape: Once early denoising commits to a suboptimal global structure, later steps mainly sharpen details and seldom correct the underlying mistake. While existing inference-time approaches explore alternative diffusion states via re-noising with fixed strength or direction, they exhibit limited capacity to escape steep quality plateaus. We propose Controlled Random Zigzag Sampling (Ctrl-Z Sampling),a scalable sampling strategy that detects plateaus in quality landscape via a surrogate score, and allocates exploration only when a plateau is detected. Upon detection, Ctrl-Z Sampling rolls back to noisier states, samples a set of alternative continuations, and updates the trajectory when a candidate improves the score, otherwise escalating the exploration depth to escape the current plateau. The proposed method is model-agnostic and broadly compatible with existing diffusion frameworks. Experiments show that Ctrl-Z Sampling consistently improves generation quality over other inference-time scaling samplers across different NFE budgets, offering a scalable compute-quality trade-off. Code available at: https://github.com/ShunqiM/Ctrl-Z-Sampling.

cs.CV↗

arXiv2024

Multimodal Causal Reasoning Benchmark: Challenging Vision Large Language Models to Discern Causal Links Across Modalities

Multimodal Large Language Models (MLLMs) have showcased exceptional Chain-of-Thought (CoT) reasoning ability in complex textual inference tasks including causal reasoning. However, will these causalities remain straightforward when crucial hints hide in visual details? If not, what factors might influence cross-modal generalization? Whether we can effectively enhance their capacity for robust causal inference across both text and vision? Motivated by these, we introduce MuCR - a novel Multimodal Causal Reasoning benchmark that leverages synthetic siamese images and text pairs to challenge MLLMs. Additionally, we develop tailored metrics from multiple perspectives, including image-level match, phrase-level understanding, and sentence-level explanation, to comprehensively assess MLLMs' comprehension abilities. Our experiments reveal that current MLLMs fall short in multimodal causal reasoning compared to their performance in purely textual settings. Additionally, we find that identifying visual cues across images is key to effective cross-modal generalization. Finally, we propose a VcCoT strategy that better highlights visual cues, and our results confirm its efficacy in enhancing multimodal causal reasoning. The project is available at: https://github.com/Zhiyuan-Li-John/MuCR

cs.CV↗

arXiv2023

Jaeger: A Concatenation-Based Multi-Transformer VQA Model

Document-based Visual Question Answering poses a challenging task between linguistic sense disambiguation and fine-grained multimodal retrieval. Although there has been encouraging progress in document-based question answering due to the utilization of large language and open-world prior models\cite{1}, several challenges persist, including prolonged response times, extended inference durations, and imprecision in matching. In order to overcome these challenges, we propose Jaegar, a concatenation-based multi-transformer VQA model. To derive question features, we leverage the exceptional capabilities of RoBERTa large\cite{2} and GPT2-xl\cite{3} as feature extractors. Subsequently, we subject the outputs from both models to a concatenation process. This operation allows the model to consider information from diverse sources concurrently, strengthening its representational capability. By leveraging pre-trained models for feature extraction, our approach has the potential to amplify the performance of these models through concatenation. After concatenation, we apply dimensionality reduction to the output features, reducing the model's computational effectiveness and inference time. Empirical results demonstrate that our proposed model achieves competitive performance on Task C of the PDF-VQA Dataset. If the user adds any new data, they should make sure to style it as per the instructions provided in previous sections.

cs.CL↗