arXiv subjects

Jun Yang

Publications and source records attributed to Jun Yang.

At least 127 records · Page 7Linked to original sources

arXiv2025

Evidence for a brief appearance of gamma-ray periodicity after a compact star merger

The product of a compact star merger is usually hypothesized to be a hyperaccreting black hole, typically resulting in a gamma-ray burst (GRB) with a duration shorter than 2~s. However, recent observations of GRB~211211A and GRB~230307A, both arising from compact star mergers, challenge this model due to their minute-long durations. The data from both events are consistent with having a nascent, rapidly spinning highly magnetized neutron star (a millisecond magnetar) as the merger product and GRB engine, but a smoking gun signature is still missing. Here we report strong but not yet conclusive evidence for the detection of a 909-Hz gamma-ray periodic signal during a brief time window of GRB~230307A, which is consistent with the rotation frequency of such a millisecond magnetar. Notably, the periodic signal appeared for only 160~ms at an epoch coinciding with the transition epoch when the jet emission from the GRB central engine ceased and when the delayed emission from high latitudes started. If this signal is real, the temporal and spectral features of this gamma-ray periodicity can be consistently interpreted as asymmetric mini-jet emission from a dissipating Poynting-flux-dominated jet, as revealed by the energy-dependent light curve data of this burst.

astro-ph.HE↗

arXiv2025

SALM: Spatial Audio Language Model with Structured Embeddings for Understanding and Editing

Spatial audio understanding is essential for accurately perceiving and interpreting acoustic environments. However, existing audio-language models exhibit limitations in processing spatial audio and perceiving spatial acoustic scenes. To address this gap, we propose the Spatial Audio Language Model (SALM), a novel framework that bridges spatial audio and language through multi-modal contrastive learning. SALM integrates a text encoder with a dual-branch audio encoder that decomposes spatial sound into semantic and spatial components via structured audio embeddings. Key features of SALM include seamless alignment between spatial audio and natural language, both separate and joint extraction of spatial and semantic representations, zero-shot direction classification, and flexible support for spatial audio editing. Experimental results demonstrate that SALM effectively captures and aligns cross-modal representations, yielding well-structured audio embeddings. Furthermore, SALM enables advanced editing capabilities, such as modifying directional audio using text-based embeddings.

cs.SD↗

arXiv2025

Stabilizing Information Flow Entropy: Regularization for Safe and Interpretable Autonomous Driving Perception

Deep perception networks in autonomous driving traditionally rely on data-intensive training regimes and post-hoc anomaly detection, often disregarding fundamental information-theoretic constraints governing stable information processing. We reconceptualize deep neural encoders as hierarchical communication chains that incrementally compress raw sensory inputs into task-relevant latent features. Within this framework, we establish two theoretically justified design principles for robust perception: (D1) smooth variation of mutual information between consecutive layers, and (D2) monotonic decay of latent entropy with network depth. Our analysis shows that, under realistic architectural assumptions, particularly blocks comprising repeated layers of similar capacity, enforcing smooth information flow (D1) naturally encourages entropy decay (D2), thus ensuring stable compression. Guided by these insights, we propose Eloss, a novel entropy-based regularizer designed as a lightweight, plug-and-play training objective. Rather than marginal accuracy improvements, this approach represents a conceptual shift: it unifies information-theoretic stability with standard perception tasks, enabling explicit, principled detection of anomalous sensor inputs through entropy deviations. Experimental validation on large-scale 3D object detection benchmarks (KITTI and nuScenes) demonstrates that incorporating Eloss consistently achieves competitive or improved accuracy while dramatically enhancing sensitivity to anomalies, amplifying distribution-shift signals by up to two orders of magnitude. This stable information-compression perspective not only improves interpretability but also establishes a solid theoretical foundation for safer, more robust autonomous driving perception systems.

cs.LG↗

arXiv2025

Conceptual Design Report of Super Tau-Charm Facility: The Accelerator

Electron-positron colliders operating in the GeV region of center-of-mass energies or the Tau-Charm energy region, have been proven to enable competitive frontier research, due to its several unique features. With the progress of high energy physics in the last two decades, a new-generation Tau-Charm factory, Super Tau Charm Facility (STCF) has been actively promoting by the particle physics community in China. STCF holds great potential to address fundamental questions such as the essence of color confinement and the matter-antimatter asymmetry in the universe in the next decades. The main design goals of STCF are with a center-of-mass energy ranging from 2 to 7 GeV and a peak luminosity surpassing 5*10^34 cm^-2s^-1 that is optimized at a center-of-mass energy of 4 GeV, which is about 50 times that of the currently operating Tau-Charm factory - BEPCII. The STCF accelerator is composed of two main parts: a double-ring collider with the crab-waist collision scheme and an injector that provides top-up injections for both electron and positron beams. As a typical third-generation electron-positron circular collider, the STCF accelerator faces many challenges in both accelerator physics and technology. In this paper, the conceptual design of the STCF accelerator complex is presented, including the ongoing efforts and plans for technological R&D, as well as the required infrastructure. The STCF project aims to secure support from the Chinese central government for its construction during the 15th Five-Year Plan (2026-2030) in China.

physics.acc-ph↗

arXiv2025

A Dynamic Knowledge Update-Driven Model with Large Language Models for Fake News Detection

As the Internet and social media evolve rapidly, distinguishing credible news from a vast amount of complex information poses a significant challenge. Due to the suddenness and instability of news events, the authenticity labels of news can potentially shift as events develop, making it crucial for fake news detection to obtain the latest event updates. Existing methods employ retrieval-augmented generation to fill knowledge gaps, but they suffer from issues such as insufficient credibility of retrieved content and interference from noisy information. We propose a dynamic knowledge update-driven model for fake news detection (DYNAMO), which leverages knowledge graphs to achieve continuous updating of new knowledge and integrates with large language models to fulfill dual functions: news authenticity detection and verification of new knowledge correctness, solving the two key problems of ensuring the authenticity of new knowledge and deeply mining news semantics. Specifically, we first construct a news-domain-specific knowledge graph. Then, we use Monte Carlo Tree Search to decompose complex news and verify them step by step. Finally, we extract and update new knowledge from verified real news texts and reasoning paths. Experimental results demonstrate that DYNAMO achieves the best performance on two real-world datasets.

cs.CL↗

arXiv2025

Integrating Diffusion-based Multi-task Learning with Online Reinforcement Learning for Robust Quadruped Robot Control

Recent research has highlighted the powerful capabilities of imitation learning in robotics. Leveraging generative models, particularly diffusion models, these approaches offer notable advantages such as strong multi-task generalization, effective language conditioning, and high sample efficiency. While their application has been successful in manipulation tasks, their use in legged locomotion remains relatively underexplored, mainly due to compounding errors that affect stability and difficulties in task transition under limited data. Online reinforcement learning (RL) has demonstrated promising results in legged robot control in the past years, providing valuable insights to address these challenges. In this work, we propose DMLoco, a diffusion-based framework for quadruped robots that integrates multi-task pretraining with online PPO finetuning to enable language-conditioned control and robust task transitions. Our approach first pretrains the policy on a diverse multi-task dataset using diffusion models, enabling language-guided execution of various skills. Then, it finetunes the policy in simulation to ensure robustness and stable task transition during real-world deployment. By utilizing Denoising Diffusion Implicit Models (DDIM) for efficient sampling and TensorRT for optimized deployment, our policy runs onboard at 50Hz, offering a scalable and efficient solution for adaptive, language-guided locomotion on resource-constrained robotic platforms.

cs.RO↗

arXiv2025

ASCMamba: Multimodal Time-Frequency Mamba for Acoustic Scene Classification

Acoustic Scene Classification (ASC) is a fundamental problem in computational audition, which seeks to classify environments based on the distinctive acoustic features. In the ASC task of the APSIPA ASC 2025 Grand Challenge, the organizers introduce a multimodal ASC task. Unlike traditional ASC systems that rely solely on audio inputs, this challenge provides additional textual information as inputs, including the location where the audio is recorded and the time of recording. In this paper, we present our proposed system for the ASC task in the APSIPA ASC 2025 Grand Challenge. Specifically, we propose a multimodal network, ASCMamba, which integrates audio and textual information for fine-grained acoustic scene understanding and effective multimodal ASC. The proposed ASCMamba employs a DenseEncoder to extract hierarchical spectral features from spectrograms, followed by a dual-path Mamba blocks that capture long-range temporal and frequency dependencies using Mamba-based state space models. In addition, we present a two-step pseudo-labeling mechanism to generate more reliable pseudo-labels. Results show that the proposed system outperforms all the participating teams and achieves a 6.2% improvement over the baseline. Code, model and pre-trained checkpoints are available at https://github.com/S-Orion/ASCMamba.git.

cs.SD↗

arXiv2025

A Jetted Wandering Massive Black Hole Candidate in a Dwarf Galaxy

Wandering massive black holes (MBHs) are thought to form through gravitational recoil or galaxy mergers, but observational confirmation of their displacement in dwarf galaxies, critical laboratories for early-universe SMBH seeding, remains scarce. Using multi-epoch very long baseline interferometry (VLBI), we identify a displaced MBH in the dwarf galaxy MaNGA 12772-12704, located 0.94 kilo-parsec from its optical center. The source exhibits unambiguous signatures of an accreting MBH: a brightness temperature exceeding $10^9$K, a parsec-scale jet, and flux density variability over a 30-year baseline. This system provides the first robust evidence that dynamical black hole interactions predicted in hierarchical galaxy evolution occur even in low-mass hosts. The discovery challenges models requiring centralized gas reservoirs for MBH growth and directly informs high-redshift seeding scenarios.

astro-ph.GA↗

arXiv2025

Efficient Agent: Optimizing Planning Capability for Multimodal Retrieval Augmented Generation

Multimodal Retrieval-Augmented Generation (mRAG) has emerged as a promising solution to address the temporal limitations of Multimodal Large Language Models (MLLMs) in real-world scenarios like news analysis and trending topics. However, existing approaches often suffer from rigid retrieval strategies and under-utilization of visual information. To bridge this gap, we propose E-Agent, an agent framework featuring two key innovations: a mRAG planner trained to dynamically orchestrate multimodal tools based on contextual reasoning, and a task executor employing tool-aware execution sequencing to implement optimized mRAG workflows. E-Agent adopts a one-time mRAG planning strategy that enables efficient information retrieval while minimizing redundant tool invocations. To rigorously assess the planning capabilities of mRAG systems, we introduce the Real-World mRAG Planning (RemPlan) benchmark. This novel benchmark contains both retrieval-dependent and retrieval-independent question types, systematically annotated with essential retrieval tools required for each instance. The benchmark's explicit mRAG planning annotations and diverse question design enhance its practical relevance by simulating real-world scenarios requiring dynamic mRAG decisions. Experiments across RemPlan and three established benchmarks demonstrate E-Agent's superiority: 13% accuracy gain over state-of-the-art mRAG methods while reducing redundant searches by 37%.

cs.AI↗

arXiv2025

Spectral Hardening Reveals Afterglow Emergence in Long-Duration Fast X-ray Transients: A Case Study of GRB 250404A/EP250404a

The prompt emission and afterglow phases of gamma-ray bursts (GRBs) have been extensively studied, yet the transition between these two phases remains inadequately characterized due to limited multiwavelength observational coverage. Among the recent growing samples of fast X-ray transients observed by Einstein Probe (EP), a subgroup of GRBs are captured with long-duration X-ray emission, potentially containing featured evolution from prompt emission to the afterglow phase. In this Letter, we present a detailed analysis of GRB 250404A/EP250404a, a bright fast X-ray transient detected simultaneously by EP and the Fermi Gamma-ray Burst Monitor in X-rays and gamma rays. Its continuous X-ray emission reveals a long-duration tail, accompanied by distinct spectral evolution manifested by the spectral index $α_{\rm X}$ with an initial softening, followed by an evident hardening, eventually reaching a plateau at the value of $\sim$ -2. Early optical and near-infrared observations enable broadband modeling with forward- and reverse-shock components, confirming that the X-ray hardening signals the emergence of the external-shock afterglow. From this spectral hardening we infer that the prompt phase in soft X-rays lasted $\sim300\;\mathrm{s}$, which is more than 3 times longer than the gamma-ray $T_{90}$. This well-tracked soft-hard-flat spectral pattern provides a clear indication of afterglow emergence from the fading prompt emission and offers a practical criterion for identifying a distinct population of GRBs among fast X-ray transients, even when the detection of the gamma-ray counterpart or obvious temporal break is absent.

astro-ph.HE↗

arXiv2025

LLM-based Multi-Agent Copilot for Quantum Sensor

Large language models (LLM) exhibit broad utility but face limitations in quantum sensor development, stemming from interdisciplinary knowledge barriers and involving complex optimization processes. Here we present QCopilot, an LLM-based multi-agent framework integrating external knowledge access, active learning, and uncertainty quantification for quantum sensor design and diagnosis. Comprising commercial LLMs with few-shot prompt engineering and vector knowledge base, QCopilot employs specialized agents to adaptively select optimization methods, automate modeling analysis, and independently perform problem diagnosis. Applying QCopilot to atom cooling experiments, we generated 10${}^{\rm{8}}$ sub-$\rmμ$K atoms without any human intervention within a few hours, representing $\sim$100$\times$ speedup over manual experimentation. Notably, by continuously accumulating prior knowledge and enabling dynamic modeling, QCopilot can autonomously identify anomalous parameters in multi-parameter experimental settings. Our work reduces barriers to large-scale quantum sensor deployment and readily extends to other quantum information systems.

quant-ph↗

arXiv2025

Enhancing Robustness of Control Barrier Function: A Reciprocal Resistance-based Approach

In this note, a new reciprocal resistance-based control barrier function (RRCBF) is developed to enhance the robustness of control barrier functions for disturbed affine nonlinear systems, without requiring explicit knowledge of disturbance bounds. By integrating a reciprocal resistance-like term into the conventional zeroing barrier function framework, we formally establish the concept of the reciprocal resistance-based barrier function (RRBF), rigorously proving the forward invariance of its associated safe set and its robustness against bounded disturbances. The RRBF inherently generates a buffer zone near the boundary of the safe set, effectively dominating the influence of uncertainties and external disturbances. This foundational concept is extended to formulate RRCBFs, including their high-order variants. To alleviate conservatism in the presence of complex, time-varying disturbances, we further introduce a disturbance observer-based RRCBF (DO-RRCBF), which exploits disturbance estimates to enhance safety guarantees and recover nominal control performance. The effectiveness of the proposed framework is validated through two simulation studies: a second-order linear system illustrating forward invariance in the phase plane, and an adaptive cruise control scenario demonstrating robustness in systems with high relative degree.

eess.SY↗

arXiv2025

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs

The emergence of multimodal large language models (MLLMs) presents promising opportunities for automation and enhancement in Electronic Design Automation (EDA). However, comprehensively evaluating these models in circuit design remains challenging due to the narrow scope of existing benchmarks. To bridge this gap, we introduce MMCircuitEval, the first multimodal benchmark specifically designed to assess MLLM performance comprehensively across diverse EDA tasks. MMCircuitEval comprises 3614 meticulously curated question-answer (QA) pairs spanning digital and analog circuits across critical EDA stages - ranging from general knowledge and specifications to front-end and back-end design. Derived from textbooks, technical question banks, datasheets, and real-world documentation, each QA pair undergoes rigorous expert review for accuracy and relevance. Our benchmark uniquely categorizes questions by design stage, circuit type, tested abilities (knowledge, comprehension, reasoning, computation), and difficulty level, enabling detailed analysis of model capabilities and limitations. Extensive evaluations reveal significant performance gaps among existing LLMs, particularly in back-end design and complex computations, highlighting the critical need for targeted training datasets and modeling approaches. MMCircuitEval provides a foundational resource for advancing MLLMs in EDA, facilitating their integration into real-world circuit design workflows. Our benchmark is available at https://github.com/cure-lab/MMCircuitEval.

cs.LG↗

arXiv2025

DiffCkt: A Diffusion Model-Based Hybrid Neural Network Framework for Automatic Transistor-Level Generation of Analog Circuits

Analog circuit design consists of the pre-layout and layout phases. Among them, the pre-layout phase directly decides the final circuit performance, but heavily depends on experienced engineers to do manual design according to specific application scenarios. To overcome these challenges and automate the analog circuit pre-layout design phase, we introduce DiffCkt: a diffusion model-based hybrid neural network framework for the automatic transistor-level generation of analog circuits, which can directly generate corresponding circuit structures and device parameters tailored to specific performance requirements. To more accurately quantify the efficiency of circuits generated by DiffCkt, we introduce the Circuit Generation Efficiency Index (CGEI), which is determined by both the figure of merit (FOM) of a single generated circuit and the time consumed. Compared with relative research, DiffCkt has improved CGEI by a factor of $2.21 \sim 8365\times$, reaching a state-of-the-art (SOTA) level. In conclusion, this work shows that the diffusion model has the remarkable ability to learn and generate analog circuit structures and device parameters, providing a revolutionary method for automating the pre-layout design of analog circuits. The circuit dataset will be open source, its preview version is available at https://github.com/CjLiu-NJU/DiffCkt.

cs.ET↗

arXiv2025

AnalogTester: A Large Language Model-Based Framework for Automatic Testbench Generation in Analog Circuit Design

Recent advancements have demonstrated the significant potential of large language models (LLMs) in analog circuit design. Nevertheless, testbench construction for analog circuits remains manual, creating a critical bottleneck in achieving fully automated design processes. Particularly when replicating circuit designs from academic papers, manual Testbench construction demands time-intensive implementation and frequent adjustments, which fails to address the dynamic diversity and flexibility requirements for automation. AnalogTester tackles automated analog design challenges through an LLM-powered pipeline: a) domain-knowledge integration, b) paper information extraction, c) simulation scheme synthesis, and d) testbench code generation with Tsinghua Electronic Design (TED). AnalogTester has demonstrated automated Testbench generation capabilities for three fundamental analog circuit types: operational amplifiers (op-amps), bandgap references (BGRs), and low-dropout regulators (LDOs), while maintaining a scalable framework for adaptation to broader circuit topologies. Furthermore, AnalogTester can generate circuit knowledge data and TED code corpus, establishing fundamental training datasets for LLM specialization in analog circuit design automation.

cs.MA↗

arXiv2025

Ocean Tides on Asynchronously Rotating Planets Orbiting Low-mass Stars

Planets in the liquid-water habitable zone of low-mass stars experience large tidal forces, $10^3$ to $10^4$ times those on Earth, due to the small distance between the habitable zone and the host stars. Therefore, interior solid tides, ocean tides and atmospheric tides on these planets could be much stronger than that on Earth, but rare work has been done to explicitly simulate the ocean tides. Here, for the first time, we perform global ocean tide simulations and show that ocean tides on asynchronously rotating planets with large eccentricities can reach $\mathcal{O}(1000)\,\mathrm{m}$ in height and $\mathcal{O}(10)\,\mathrm{m\,s^{-1}}$ in flow speed. Interactions between tide and bottom topography can induce large energy dissipation, $\sim\mathcal{O}(100)\,\mathrm{W\,m^{-2}}$ in global mean. This tidal energy dissipation can strongly accelerate orbital evolution by 1-2 orders of magnitude. However, for planets with small eccentricities, the ocean tides are much weaker but still comparable to that on modern Earth. Our results suggest that ocean tides on eccentric planets orbiting low-mass stars are orders of magnitude more powerful than those on Earth and can dramatically influence surface geography and orbital evolution.

astro-ph.EP↗

arXiv2025

ReviewInstruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models

The effectiveness of large language models (LLMs) in conversational AI is hindered by their reliance on single-turn supervised fine-tuning (SFT) data, which limits contextual coherence in multi-turn dialogues. Existing methods for generating multi-turn dialogue data struggle to ensure both diversity and quality in instructions. To address this, we propose Review-Instruct, a novel framework that synthesizes multi-turn conversations through an iterative "Ask-Respond-Review" process involving three agent roles: a Candidate, multiple Reviewers, and a Chairman. The framework iteratively refines instructions by incorporating Reviewer feedback, enhancing dialogue diversity and difficulty. We construct a multi-turn dataset using the Alpaca dataset and fine-tune the LLaMA2-13B model. Evaluations on MT-Bench, MMLU-Pro, and Auto-Arena demonstrate significant improvements, achieving absolute gains of 2.9\% on MMLU-Pro and 2\% on MT-Bench compared to prior state-of-the-art models based on LLaMA2-13B. Ablation studies confirm the critical role of the Review stage and the use of multiple Reviewers in boosting instruction diversity and difficulty. Our work highlights the potential of review-driven, multi-agent frameworks for generating high-quality conversational data at scale.

cs.CL↗

arXiv2025

Three-dimensional Transport-induced Chemistry on Temperate sub-Neptune K2-18b, Part I: the Effects of Atmospheric Dynamics

The low equilibrium temperatures of temperate sub-Neptunes lead to extremely long chemical timescales in their upper atmospheres, causing the abundances of chemical species to be strongly shaped by atmospheric transport. Here, we used a three-dimensional (3D) general circulation model involving a passive tracer to investigate the atmospheric circulation and 3D transport of temperate gas-rich sub-Neptunes, using K2-18b as an example. We model K2-18b as a synchronous or asynchronous rotator, exploring spin-orbit resonances (SOR) of 2:1, 6:1, and 10:1. We find that the strong absorption of CO$_2$ and CH$_4$ induces a detached convective zone between 1 and 5 bar, resulting in strong vertical mixing at these levels. The upper atmosphere is dominated by eastward winds (an equatorial superrotating jet present in all simulations), leading to warmer evening terminators and approximately 20% higher passive tracer mass mixing ratios compared to the morning terminators. Rotation rates have minimal impact on the strength of global mean vertical mixing, but significantly influence the latitudinal distribution of passive tracers. For synchronous, 2:1 SOR, and 6:1 SOR simulations, passive tracers are more abundant in the upwelling branches at latitudes within 60$^\circ$, while for the 10:1 SOR simulation, strong transient eddies at high latitudes (>70$^\circ$) between 0.1 to 1 bar can transport passive tracers upward from the deep atmosphere, making them more abundant there, despite their alignment with the downwelling branch of the large-scale circulation. This study focuses on the atmospheric dynamics and its influence on passive tracer transport, while a follow-up paper will incorporate active chemical species.

astro-ph.EP↗