arXiv subjects

Jiaqi Yuan

Publications and source records attributed to Jiaqi Yuan.

7 recordsLinked to original sources

arXiv2026

Floquet-sideband-enhanced shortwave electrometry with Rydberg atoms

Rydberg atomic electric-field sensors, under the framework of optical excitation and readout, can overcome the size-to-wavelength constraint imposed by the Chu limit. However, their sensitivity for decametric-wavelength shortwave electric fields is substantially lower than that for microwave ones, stemming from the off-resonant nature of low-frequency signals with Rydberg transitions. Here, we demonstrate a high-sensitivity heterodyne shortwave sensor based on microwave-dressed Rydberg atoms, leveraging precisely modulated Floquet sidebands. Around local- and microwave-field-engineered Floquet sidebands, the steep response gradient, arising from the enhanced atom-shortwave interaction through additionally created coherent channels, induces pronounced amplification of the heterodyne intermediate-frequency signal. As a result, compared to the same atomic heterodyne setup without microwave modulation, such Floquet-sideband-enhanced shortwave measurement boosts the sensitivity by four orders of magnitude, yielding a sensitivity of -122.7 dBm/Hz for shortwave at 30 MHz. This work offers a potential route to high-sensitive portable shortwave receivers in radio astronomy, radar and long-distance communications.

physics.atom-ph↗

arXiv2026

Retrieving Minimal and Sufficient Reasoning Subgraphs with Graph Foundation Models for Path-aware GraphRAG

Graph-based retrieval-augmented generation (GraphRAG) exploits structured knowledge to support knowledge-intensive reasoning. However, most existing methods treat graphs as intermediate artifacts, and the few subgraph-based retrieval methods depend on heuristic rules coupled with domain-specific distributions. They fail in typical cold-start scenarios where data in target domains is scarce, thus yielding reasoning contexts that are either informationally incomplete or structurally redundant. In this work, we revisit retrieval from a structural perspective, and propose GFM-Retriever that directly responds to user queries with a subgraph, where a pre-trained Graph Foundation Model acts as a cross-domain Retriever for multi-hop path-aware reasoning. Building on this perspective, we repurpose a pre-trained GFM from an entity ranking function into a generalized retriever to support cross-domain retrieval. On top of the retrieved graph, we further derive a label-free subgraph selector optimized by a principled Information Bottleneck objective to identify the query-conditioned subgraph, which contains informationally sufficient and structurally minimal golden evidence in a self-contained "core set". To connect structure with generation, we explicitly extract and reorganize relational paths as in-context prompts, enabling interpretable reasoning. Extensive experiments on multi-hop question answering benchmarks demonstrate that GFM-Retriever achieves state-of-the-art performance in both retrieval quality and answer generation, while maintaining efficiency.

cs.IR↗

arXiv2026

AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization

Generative search engines represent a transition from traditional ranking-based retrieval to Large Language Model (LLM)-based synthesis, transforming optimization goals from ranking prominence towards content inclusion. Generative Engine Optimization (GEO), specifically, aims to maximize visibility and attribution in black-box summarized outputs by strategically manipulating source content. However, existing methods rely on static heuristics, single-prompt optimization, or engine preference rule distillation that is prone to overfitting. They cannot flexibly adapt to diverse content or the changing behaviors of generative engines. Moreover, effectively optimizing these strategies requires an impractical amount of interaction feedback from the engines. To address these challenges, we propose AgenticGEO, a self-evolving agentic framework formulating optimization as a content-conditioned control problem, which enhances intrinsic content quality to robustly adapt to the unpredictable behaviors of black-box engines. Unlike fixed-strategy methods, AgenticGEO employs a MAP-Elites archive to evolve diverse, compositional strategies. To mitigate interaction costs, we introduce a Co-Evolving Critic, a lightweight surrogate that approximates engine feedback for content-specific strategy selection and refinement, efficiently guiding both evolutionary search and inference-time planning. Through extensive in-domain and cross-domain experiments on two representative engines, AgenticGEO achieves state-of-the-art performance and demonstrates robust transferability, outperforming 14 baselines across 3 datasets. Our code and model are available at: https://github.com/AIcling/agentic_geo.

cs.AI↗

arXiv2025

SDA-PLANNER: State-Dependency Aware Adaptive Planner for Embodied Task Planning

Embodied task planning requires agents to produce executable actions in a close-loop manner within the environment. With progressively improving capabilities of LLMs in task decomposition, planning, and generalization, current embodied task planning methods adopt LLM-based architecture.However, existing LLM-based planners remain limited in three aspects, i.e., fixed planning paradigms, lack of action sequence constraints, and error-agnostic. In this work, we propose SDA-PLANNER, enabling an adaptive planning paradigm, state-dependency aware and error-aware mechanisms for comprehensive embodied task planning. Specifically, SDA-PLANNER introduces a State-Dependency Graph to explicitly model action preconditions and effects, guiding the dynamic revision. To handle execution error, it employs an error-adaptive replanning strategy consisting of Error Backtrack and Diagnosis and Adaptive Action SubTree Generation, which locally reconstructs the affected portion of the plan based on the current environment state. Experiments demonstrate that SDA-PLANNER consistently outperforms baselines in success rate and goal completion, particularly under diverse error conditions.

cs.RO↗

arXiv2025

DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning

Graph Retrieval-Augmented Generation has emerged as a powerful paradigm for grounding large language models with external structured knowledge. However, existing Graph RAG methods struggle with temporal reasoning, due to their inability to model the evolving structure and order of real-world events. In this work, we introduce DyG-RAG, a novel event-centric dynamic graph retrieval-augmented generation framework designed to capture and reason over temporal knowledge embedded in unstructured text. To eliminate temporal ambiguity in traditional retrieval units, DyG-RAG proposes Dynamic Event Units (DEUs) that explicitly encode both semantic content and precise temporal anchors, enabling accurate and interpretable time-aware retrieval. To capture temporal and causal dependencies across events, DyG-RAG constructs an event graph by linking DEUs that share entities and occur close in time, supporting efficient and meaningful multi-hop reasoning. To ensure temporally consistent generation, DyG-RAG introduces an event timeline retrieval pipeline that retrieves event sequences via time-aware traversal, and proposes a Time Chain-of-Thought strategy for temporally grounded answer generation. This unified pipeline enables DyG-RAG to retrieve coherent, temporally ordered event sequences and to answer complex, time-sensitive queries that standard RAG systems cannot resolve. Extensive experiments on temporal QA benchmarks demonstrate that DyG-RAG significantly improves the accuracy and recall of three typical types of temporal reasoning questions, paving the way for more faithful and temporal-aware generation. DyG-RAG is available at https://github.com/RingBDStack/DyG-RAG.

cs.IR↗

arXiv2025

IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning

Deep graph learning has gained grand popularity over the past years due to its versatility and success in representing graph data across a wide range of domains. However, the pervasive issue of imbalanced graph data distributions, where certain parts exhibit disproportionally abundant data while others remain sparse, undermines the efficacy of conventional graph learning algorithms, leading to biased outcomes. To address this challenge, Imbalanced Graph Learning (IGL) has garnered substantial attention, enabling more balanced data distributions and better task performance. Despite the proliferation of IGL algorithms, the absence of consistent experimental protocols and fair performance comparisons pose a significant barrier to comprehending advancements in this field. To bridge this gap, we introduce IGL-Bench, a foundational comprehensive benchmark for imbalanced graph learning, embarking on 16 diverse graph datasets and 24 distinct IGL algorithms with uniform data processing and splitting strategies. Specifically, IGL-Bench systematically investigates state-of-the-art IGL algorithms in terms of effectiveness, robustness, and efficiency on node-level and graph-level tasks, with the scope of class-imbalance and topology-imbalance. Extensive experiments demonstrate the potential benefits of IGL algorithms on various imbalanced conditions, offering insights and opportunities in the IGL field. Further, we have developed an open-sourced and unified package to facilitate reproducible evaluation and inspire further innovative research, which is available at https://github.com/RingBDStack/IGL-Bench.

cs.LG↗

arXiv2024

Nonreciprocal spontaneous parametric process

Mediated by the interaction with quantum vacuum fields, a laser field propagating in a nonlinear optical medium can generate new light fields via spontaneous parametric process. Such process is inherent independent of the propagation direction of light and reciprocal thus far, due to the direction-independent field-vacuum interaction. In this work, we experimentally demonstrate a nonreciprocal spontaneous parametric four-wave mixing process in sodium atomic vapors with dispersive nonlinearity and further broadband optical isolation by unidirectionally coupling the probe field to an auxiliary quantum vacuum field in another four-wave mixing process. Thanks to the broad bandwidth of the spontaneous parametric process, in combination with the Doppler and power-induced broadening of atomic energy levels, we achieve optical isolation with a bandwidth larger than 100 GHz for isolation ratio >25 dB. Considering that both spontaneous parametric processes and wave mixing in nonlinear medium have been realized in diverse on-chip photonic platforms, our work paves the way for integrated broadband optical isolations and thus can boost scalability and function of photonic chips.

physics.optics↗