arXiv subjects

Jingyang Liu

Publications and source records attributed to Jingyang Liu.

8 recordsLinked to original sources

arXiv2026

NavProbe: Evidence-Grounded Reasoning with Active Memory Retrieval for Zero-Shot Navigation

Long-horizon navigation requires an agent to revise its intermediate objectives as evidence accumulates. Full visual histories are costly to process, while compact summaries may omit details needed to reconsider earlier decisions. We introduce NavProbe, a hierarchical zero-shot navigation agent that couples a dynamic subgoal agenda with active evidence retrieval. A compact index links summaries of visited places, transitions, and landmarks to their visual and geometric records. When the current context is insufficient, a task executive retrieves targeted evidence to generate, revise, or resolve subgoals. Reusable conclusions are used to update the index, and a skill policy converts the revised task state into parameterized navigation actions. NavProbe achieves 71.7% SR and 55.8% SPL on R2R-CE and 55.3% SR and 38.6% SPL on RxR-CE, outperforming strong zero-shot baselines. It also achieves 79.3% SR on HM3D-v2 ObjectNav, with qualitative real-robot demonstrations illustrating physical deployment.

cs.RO↗

arXiv2026

XDen-1K: A Density Field Dataset of Real-World Objects

A deep understanding of the physical world is essential for robotic manipulation and physically realistic simulation. While current methods, including VLM-based and other learning-based approaches, have shown promise in physical property inference, their evaluation is often hindered by the lack of physically grounded reference data. To address this gap, we introduce XDen-1K, the first large-scale multimodal dataset that provides physically grounded density field for real-world objects. XDen-1K comprises 1,000 real-world objects spanning 137 categories, with comprehensive data for each object, including a high-resolution, carefully curated 3D geometric model with part-level annotations and paired real-world biplanar X-ray scans. In addition, XDen-1K includes high-fidelity volumetric density field reconstructed from sparse biplanar X-ray views via a novel optimization framework. XDen-1K also provides a benchmark for density estimation and enables X-ray-conditioned volumetric segmentation. Experiments further demonstrate that the center-of-mass prior derived by XDen-1K can improve robotic manipulation performance. By providing real-world X-ray scans and physics-consistent density field, XDen-1K establishes a foundation for advancing physical property inference and embodied AI.

cs.CV↗

arXiv2026

Visual Semantic Decoding of Electrocorticography from Video Stimuli using End-to-End Deep Learning

ECoG-based visual semantic decoding enables inference of semantic interpretation of visual perception from complex, noisy brain activity. This study examines the feasibility of visual semantic decoding using an end-to-end deep learning framework using electrocorticography (ECoG). Specifically, the decoding task is to predict visual categories from video stimuli using time-series neural inputs. A previously collected ECoG dataset from participants ($n=17$) with drug-resistant epilepsy is used for analysis. With fewer than 50 training samples per visual category, this study evaluates multiple deep learning approaches, artificial neural network architectures, and frequency-band filtered inputs. The best-performing approach is analyzed to shed light on the discriminative information it relies on across spectral, temporal, and cortical dimensions. The selected decoding system uses mixup augmentation, a Transformer-based encoder, and high-gamma (80-150 Hz) inputs with a 900 ms post-stimulus window. Further analysis shows that early visual cortex (V2-V4), ventral stream visual cortex, MT+ complex with neighbouring visual areas, and lateral temporal cortex contributed substantially to decoding performance. This study demonstrates that an end-to-end deep learning framework can yield promising decoding performance from dynamic visual stimuli without handcrafted features, while the model behavior remains interpretable through spectral, temporal, and cortical dimensions, which are broadly consistent with established neuroscience knowledge.

cs.LG↗

arXiv2025

See it. Say it. Sorted: Agentic System for Compositional Diagram Generation

We study sketch-to-diagram generation: converting rough hand sketches into precise, compositional diagrams. Diffusion models excel at photorealism but struggle with the spatial precision, alignment, and symbolic structure required for flowcharts. We introduce See it. Say it. Sorted., a training-free agentic system that couples a Vision-Language Model (VLM) with Large Language Models (LLMs) to produce editable Scalable Vector Graphics (SVG) programs. The system runs an iterative loop in which a Critic VLM proposes a small set of qualitative, relational edits; multiple candidate LLMs synthesize SVG updates with diverse strategies (conservative->aggressive, alternative, focused); and a Judge VLM selects the best candidate, ensuring stable improvement. This design prioritizes qualitative reasoning over brittle numerical estimates, preserves global constraints (e.g., alignment, connectivity), and naturally supports human-in-the-loop corrections. On 10 sketches derived from flowcharts in published papers, our method more faithfully reconstructs layout and structure than two frontier closed-source image generation LLMs (GPT-5 and Gemini-2.5-Pro), accurately composing primitives (e.g., multi-headed arrows) without inserting unwanted text. Because outputs are programmatic SVGs, the approach is readily extensible to presentation tools (e.g., PowerPoint) via APIs and can be specialized with improved prompts and task-specific tools. The codebase is open-sourced at https://github.com/hantaoZhangrichard/see_it_say_it_sorted.git.

cs.AI↗

arXiv2025

Research progress of artificial intelligence empowered quantum communication and quantum sensing systems

Quantum communication and quantum sensing, which leverage the unique characteristics of quantum systems, enable information-theoretically secure communication and high-precision measurement of physical quantities. However, they both face numerous challenges on the path to practical application. Artificial intelligence (AI), as a major technological advancement in current scientific landscape, offers powerful data processing and analytical capabilities, providing new ideas and methods for optimizing and enhancing quantum communication and sensing systems. In quantum communication, AI techniques have greatly improved the performance and security of quantum key distribution, quantum memory, and quantum networks through parameter optimization, real-time feedback control, and attack detection. In quantum sensing, quantum sensing technology enables ultra-high sensitivity detection of physical quantities such as time and magnetic fields. The introduction of AI has opened up new avenues for achieving high-precision and high-sensitivity quantum measurements. With AI, sensor performance is optimized, and measurement accuracy is further enhanced through data analysis. This paper also analyzes the current challenges in using AI to empower quantum communication and sensing systems, such as implementing efficient algorithm deployment and system feedback control under limited computational resources, and addressing complex task environments, dynamically changing scenarios, and multi-task coordination requirements. Finally, this paper discusses and envisions future development prospects in this field.

quant-ph↗

arXiv2025

Hybrid Implementation for Untrusted-node-based Quantum Key Distribution Network

Quantum key distribution (QKD) serves as a cornerstone of secure quantum communication, providing unconditional security grounded in quantum mechanics. While trusted-node networks have facilitated early QKD deployment, their vulnerability to node compromise underscores the need for untrusted-node architectures. Measurement-device-independent QKD (MDI-QKD) and twin-field QKD (TF-QKD) have emerged as leading candidates, addressing security vulnerabilities and extending transmission distances. Despite the wide adoptions in various fiber scaling, no integrated implementation of these two protocols has been demonstrated to date. Here, we present a hybrid system that seamlessly integrates TF-QKD and MDI-QKD into one untrusted-node-based architecture. Utilizing an efficient phase estimation method based on asymmetric interferometers, we convert twin-field global phase tracking to relative phase calibration, allowing near continuous running of both protocols. Experiments demonstrate secure finite-size key rates for sending-or-not-sending QKD and MDI-QKD over fiber distances of 150 to 431 km. The results align with theoretical simulations and show the ability to surpass the absolute repeaterless key capacity. Our work offers an unified framework for deploying multi-protocol QKD networks, laying the foundation for adaptable and scalable quantum infrastructures that can meet a wide range of security and performance needs.

quant-ph↗

arXiv2024

Low-Energy Line Codes for On-Chip Networks

Energy is a primary constraint in processor design, and much of that energy is consumed in on-chip communication. Communication can be intra-core (e.g., from a register file to an ALU) or inter-core (e.g., over the on-chip network). In this paper, we use the on-chip network (OCN) as a case study for saving on-chip communication energy. We have identified a new way to reduce the OCN's link energy consumption by using line coding, a longstanding technique in information theory. Our line codes, called Low-Energy Line Codes (LELCs), reduce energy by reducing the frequency of voltage transitions of the links, and they achieve a range of energy/performance trade-offs.

cs.HC↗

arXiv2024

Human Detection in Realistic Through-the-Wall Environments using Raw Radar ADC Data and Parametric Neural Networks

The radar signal processing algorithm is one of the core components in through-wall radar human detection technology. Traditional algorithms (e.g., DFT and matched filtering) struggle to adaptively handle low signal-to-noise ratio echo signals in challenging and dynamic real-world through-wall application environments, which becomes a major bottleneck in the system. In this paper, we introduce an end-to-end through-wall radar human detection network (TWP-CNN), which takes raw radar Analog-to-Digital Converter (ADC) signals without any preprocessing as input. We replace the conventional radar signal processing flow with the proposed DFT-based adaptive feature extraction (DAFE) module. This module employs learnable parameterized 3D complex convolution layers to extract superior feature representations from ADC signals, which is beyond the limitation of traditional preprocessing methods. Additionally, by embedding phase information from radar data within the network and employing multi-task learning, a more accurate detection is achieved. Finally, due to the absence of through-wall radar datasets containing raw ADC data, we gathered a realistic through-wall (RTW) dataset using our in-house developed through-wall radar system. We trained and validated our proposed method on this dataset to confirm its effectiveness and superiority in real through-wall detection scenarios.

eess.SP↗