arXiv subjects

Jian Li

Publications and source records attributed to Jian Li.

At least 163 records · Page 9Linked to original sources

arXiv2025

A Quantum Walk-Enabled Blockchain with Weighted Quantum Voting Consensus

Quantum blockchains provide inherent resilience against quantum adversaries and represent a promising alternative to classical blockchain systems in the quantum era. However, existing quantum blockchain architectures largely depend on entanglement to maintain inter-block connections, facing challenges in stability, consensus efficiency, and system verification. To address these issues, this work proposes a novel quantum blockchain framework based on quantum walks, which reduces reliance on entanglement while improving stability and connection efficiency. We further propose a quantum consensus mechanism based on a weighted quantum voting protocol, which enables a fairer voting process while reflecting the weights of different nodes. To validate the proposed framework, we conduct circuit simulations to evaluate the correctness and effectiveness of both the quantum walk-based block construction and the quantum voting consensus mechanism. Compared with existing entanglement-dependent approaches, our framework achieves stronger stability and enables simpler verification of block integrity, making it a practical candidate for quantum-era blockchain applications.

quant-ph↗

arXiv2025

Floquet composite Dirac semimetals

Dirac semimetals can be classified into types I, II, and III based on the topological charge of their Dirac points. If a three-dimensional (3D) system can be sliced into a family of kz-dependent normal and topological insulators, type I Dirac points separate a 2D normal insulator from a 2D first-order topological insulator, while type II (III) Dirac points separate a 2D normal (first-order) insulator from a 2D second-order topological insulator. To investigate the effects arising from the interplay of distinct Dirac points, one may wonder whether these Dirac points can coexist in a single system. Here, we propose a scheme to induce composite Dirac semimetals by a special Floquet driving that preserves time-reversal and space-inversion symmetries. A general description is established to characterize Dirac semimetals in Floquet systems. The results show that Dirac semimetals hosting coexisting type I, II, and III Dirac points can be induced by delta-function or harmonic driving. Our results provide a promising new avenue for exploring novel Dirac semimetals.

cond-mat.mes-hall↗

arXiv2025

SSFO: Self-Supervised Faithfulness Optimization for Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) systems require Large Language Models (LLMs) to generate responses that are faithful to the retrieved context. However, faithfulness hallucination remains a critical challenge, as existing methods often require costly supervision and post-training or significant inference burdens. To overcome these limitations, we introduce Self-Supervised Faithfulness Optimization (SSFO), the first self-supervised alignment approach for enhancing RAG faithfulness. SSFO constructs preference data pairs by contrasting the model's outputs generated with and without the context. Leveraging Direct Preference Optimization (DPO), SSFO aligns model faithfulness without incurring labeling costs or additional inference burden. We theoretically and empirically demonstrate that SSFO leverages a benign form of \emph{likelihood displacement}, transferring probability mass from parametric-based tokens to context-aligned tokens. Based on this insight, we propose a modified DPO loss function to encourage likelihood displacement. Comprehensive evaluations show that SSFO significantly outperforms existing methods, achieving state-of-the-art faithfulness on multiple context-based question-answering datasets. Notably, SSFO exhibits strong generalization, improving cross-lingual faithfulness and preserving general instruction-following capabilities. We release our code and model at the anonymous link: https://github.com/chkwy/SSFO

cs.CL↗

arXiv2025

Discerning minds or generic tutors? Evaluating instructional guidance capabilities in Socratic LLMs

The conversational capabilities of large language models hold significant promise for enabling scalable and interactive tutoring. While prior research has primarily examined their ability to generate Socratic questions, it often overlooks a critical aspect: adaptively guiding learners in accordance with their cognitive states. This study moves beyond question generation to emphasize instructional guidance capability. We ask: Can LLMs emulate expert tutors who dynamically adjust strategies in response to learners' states? To investigate this, we propose GuideEval, a benchmark grounded in authentic educational dialogues that evaluates pedagogical guidance through a three-phase behavioral framework: (1) Perception, inferring learner states; (2) Orchestration, adapting instructional strategies; and (3) Elicitation, stimulating proper reflections. Empirical results indicate that existing LLMs often fail to provide effective adaptive scaffolding when learners experience confusion or require redirection. To complement the quantitative evaluation, we conduct a detailed failure case analysis, providing an intuitive understanding of these shortcomings. Furthermore, we introduce a behavior-guided finetuning strategy that leverages behavior-prompted instructional dialogues, substantially enhancing guidance performance. By shifting the focus from isolated content evaluation to learner-centered state-aware interaction, our work advocates a more dialogic paradigm for evaluating Socratic LLMs.

cs.CL↗

arXiv2025

AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning

Continual learning (CL) is essential for deploying large language models (LLMs) in dynamic real-world environments without the need for costly retraining. Recent model merging-based methods have attracted significant attention, but they still struggle to effectively manage the trade-off between learning new knowledge and preventing forgetting, a challenge largely stemming from suboptimal number of merges and merging frequency. In this paper, we introduce Adaptive Iterative Model Merging (AimMerging), a novel CL framework that utilizes learning and forgetting signals from the training trajectory to dynamically monitor the model's training status. Guided by dynamic monitoring, the training trajectory-guided merge controller adaptively determines the timing and frequency of iterative fusion, while the rehearsal-based knowledge fusion module computes the merging weights and executes the fusion. Comprehensive experiments on three CL benchmarks with various model sizes (from 770M to 13B) demonstrate that AimMerging achieves significant performance improvements over existing state-of-the-art methods, with an average relative improvement of 80% and 59% on FWT and BWT, respectively. The source code is provided for reproducibility.

cs.CL↗

arXiv2025

Constrained Feedback Learning for Non-Stationary Multi-Armed Bandits

Non-stationary multi-armed bandits enable agents to adapt to changing environments by incorporating mechanisms to detect and respond to shifts in reward distributions, making them well-suited for dynamic settings. However, existing approaches typically assume that reward feedback is available at every round - an assumption that overlooks many real-world scenarios where feedback is limited. In this paper, we take a significant step forward by introducing a new model of constrained feedback in non-stationary multi-armed bandits, where the availability of reward feedback is restricted. We propose the first prior-free algorithm - that is, one that does not require prior knowledge of the degree of non-stationarity - that achieves near-optimal dynamic regret in this setting. Specifically, our algorithm attains a dynamic regret of $\tilde{\mathcal{O}}({K^{1/3} V_T^{1/3} T }/{ B^{1/3}})$, where $T$ is the number of rounds, $K$ is the number of arms, $B$ is the query budget, and $V_T$ is the variation budget capturing the degree of non-stationarity.

cs.LG↗

arXiv2025

Dr.V: A Hierarchical Perception-Temporal-Cognition Framework to Diagnose Video Hallucination by Fine-grained Spatial-Temporal Grounding

Recent advancements in large video models (LVMs) have significantly enhance video understanding. However, these models continue to suffer from hallucinations, producing content that conflicts with input videos. To address this issue, we propose Dr.V, a hierarchical framework covering perceptive, temporal, and cognitive levels to diagnose video hallucination by fine-grained spatial-temporal grounding. Dr.V comprises of two key components: a benchmark dataset Dr.V-Bench and a satellite video agent Dr.V-Agent. Dr.V-Bench includes 10k instances drawn from 4,974 videos spanning diverse tasks, each enriched with detailed spatial-temporal annotation. Dr.V-Agent detects hallucinations in LVMs by systematically applying fine-grained spatial-temporal grounding at the perceptive and temporal levels, followed by cognitive level reasoning. This step-by-step pipeline mirrors human-like video comprehension and effectively identifies hallucinations. Extensive experiments demonstrate that Dr.V-Agent is effective in diagnosing hallucination while enhancing interpretability and reliability, offering a practical blueprint for robust video understanding in real-world scenarios. All our data and code are available at https://github.com/Eurekaleo/Dr.V.

cs.CV↗

arXiv2025

Sculpting Topological Modes on Photonic Chips by Artificial Gauge Fields

Significant efforts have been devoted to manipulating topological states, which often manifest as localized modes at interfaces between distinct topological phases. In this work, we demonstrate a versatile approach to sculpting topological modes (TMs) into any desired shapes by incorporating various artificial gauge fields (AGFs), including scalar, vector, and imaginary gauge potentials, and leveraging the power of artificial neural networks (ANNs). These AGFs enable precise tuning of the dissipation of the TMs across that of bulk modes, facilitating a transition from localized to fully delocalized states. Moreover, ANNs allow precise engineering of these eigenmodes to achieve tailored profiles of topological states, which remain spectrally isolated within the bandgap and exhibit minimal loss compared to other modes. Our theoretical results are experimentally validated on silicon photonic platforms, demonstrating flexible manipulation of TM profiles. This approach enables the design of topological states with customized properties, offering significant potential for diverse applications in photonics and beyond.

physics.optics↗

arXiv2025

Physics of Strong Magnetism with eXTP

In this paper we present the science potential of the enhanced X-ray Timing and Polarimetry (eXTP) mission, in its new configuration, for studies of strongly magnetized compact objects. We discuss the scientific potential of eXTP for quantum electrodynamic (QED) studies, especially leveraging on the recent observations made with the NASA IXPE mission. Given eXTP's unique combination of timing, spectroscopy, and polarimetry, we focus on the perspectives for physics and astrophysics studies of strongly magnetized compact objects, such as magnetars and accreting X-ray pulsars. Developed by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of Sciences, the eXTP mission is expected to launch in early 2030.

astro-ph.HE↗

arXiv2025

WindFM: An Open-Source Foundation Model for Zero-Shot Wind Power Forecasting

High-quality wind power forecasting is crucial for the operation of modern power grids. However, prevailing data-driven paradigms either train a site-specific model which cannot generalize to other locations or rely on fine-tuning of general-purpose time series foundation models which are difficult to incorporate domain-specific data in the energy sector. This paper introduces WindFM, a lightweight and generative Foundation Model designed specifically for probabilistic wind power forecasting. WindFM employs a discretize-and-generate framework. A specialized time-series tokenizer first converts continuous multivariate observations into discrete, hierarchical tokens. Subsequently, a decoder-only Transformer learns a universal representation of wind generation dynamics by autoregressively pre-training on these token sequences. Using the comprehensive WIND Toolkit dataset comprising approximately 150 billion time steps from more than 126,000 sites, WindFM develops a foundational understanding of the complex interplay between atmospheric conditions and power output. Extensive experiments demonstrate that our compact 8.1M parameter model achieves state-of-the-art zero-shot performance on both deterministic and probabilistic tasks, outperforming specialized models and larger foundation models without any fine-tuning. In particular, WindFM exhibits strong adaptiveness under out-of-distribution data from a different continent, demonstrating the robustness and transferability of its learned representations. Our pre-trained model is publicly available at https://github.com/shiyu-coder/WindFM.

cs.LG↗

arXiv2025

A note on logarithmic mean equicontinuity

We study the set of harmonic limits of empirical measures in topological dynamical systems. We obtain a characterization of unique ergodicity based of logarithmic (harmonic) mean convergence in place of Cesàro convergence. We introduce logarithmic mean equicontinuity and show that a topological dynamical system is logarithmically mean equicontinuous if and only if it is mean equicontinuous.

math.DS↗

arXiv2025

From Answers to Questions: EQGBench for Evaluating LLMs' Educational Question Generation

Large Language Models (LLMs) have demonstrated remarkable capabilities in mathematical problem-solving. However, the transition from providing answers to generating high-quality educational questions presents significant challenges that remain underexplored. To advance Educational Question Generation (EQG) and facilitate LLMs in generating pedagogically valuable and educationally effective questions, we introduce EQGBench, a comprehensive benchmark specifically designed for evaluating LLMs' performance in Chinese EQG. EQGBench establishes a five-dimensional evaluation framework supported by a dataset of 900 evaluation samples spanning three fundamental middle school disciplines: mathematics, physics, and chemistry. The dataset incorporates user queries with varying knowledge points, difficulty gradients, and question type specifications to simulate realistic educational scenarios. Through systematic evaluation of 46 mainstream large models, we reveal significant room for development in generating questions that reflect educational value and foster students' comprehensive abilities.

cs.CL↗

arXiv2025

Discovery and dynamics of a Sedna-like object with a perihelion of 66 au

Trans-Neptunian objects (TNOs) with large perihelion distances ($q > 60$ au) and semi-major axes ($a > 200$ au) provide insights into the early evolution of the solar system and the existence of a hypothetical distant planet. These objects are still rare and their detection is challenging, yet they play a crucial role in constraining models of solar system formation. Here we report the discovery of a Sedna-like TNO, 2023\,KQ$_{14}$, nicknamed `Ammonite', with $q = 66$ au, $a = 252$ au, and inclination $i=11^\circ$. Ammonite's orbit does not align with those of the other Sedna-like objects and fills the previously unexplained `$q$-gap' in the observed distribution of distant solar system objects. Simulations demonstrate that Ammonite is dynamically stable over 4.5 billion years. % with less than 1\% variation in its semi-major axis. Our analysis suggests that Ammonite and the other Sedna-like objects may have shared a primordial orbital clustering around 4.2 billion years ago. Furthermore, Ammonite's stable orbit favors larger orbits ($\sim$ 500 au) rather than closer ones for a large hypothetical planet in present-day trans-Neptunian space.

astro-ph.EP↗

arXiv2025

Kronos: A Foundation Model for the Language of Financial Markets

The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). However, their application to financial candlestick (K-line) data remains limited, often underperforming non-pre-trained architectures. Moreover, existing TSFMs often overlook crucial downstream tasks such as volatility prediction and synthetic data generation. To address these limitations, we propose Kronos, a unified, scalable pre-training framework tailored to financial K-line modeling. Kronos introduces a specialized tokenizer that discretizes continuous market information into token sequences, preserving both price dynamics and trade activity patterns. We pre-train Kronos using an autoregressive objective on a massive, multi-market corpus of over 12 billion K-line records from 45 global exchanges, enabling it to learn nuanced temporal and cross-asset representations. Kronos excels in a zero-shot setting across a diverse set of financial tasks. On benchmark datasets, Kronos boosts price series forecasting RankIC by 93% over the leading TSFM and 87% over the best non-pre-trained baseline. It also achieves a 9% lower MAE in volatility forecasting and a 22% improvement in generative fidelity for synthetic K-line sequences. These results establish Kronos as a robust, versatile foundation model for end-to-end financial time series analysis. Our pre-trained model is publicly available at https://github.com/shiyu-coder/Kronos.

q-fin.ST↗

arXiv2025

Harmonic maps and 2D Boussinesq equations

Within the framework of Lagrangian variables, we develop a method for deriving explicit solutions to the 2D Boussinesq equations using harmonic mapping theory. By reformulating the characterization of flow solutions described by harmonic functions, we reduce the problem to solving a particular nonlinear differential system in complex space. To solve this nonlinear differential system, we introduce the Schwarzian and pre-Schwarzian derivatives, and derive the properties of the sense-preserving harmonic mappings with equal Schwarzian and pre-Schwarzian derivatives. Our method yields explicit solutions in Lagrangian coordinates that contain two fundamental classes of classical solutions.: Kirchhoff's elliptical vortex (1876) and Gerstner's gravity wave (1809, rediscovered by Rankine in 1863).

math.AP↗

arXiv2025

CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards

Role-Playing Language Agents (RPLAs) have emerged as a significant application direction for Large Language Models (LLMs). Existing approaches typically rely on prompt engineering or supervised fine-tuning to enable models to imitate character behaviors in specific scenarios, but often neglect the underlying \emph{cognitive} mechanisms driving these behaviors. Inspired by cognitive psychology, we introduce \textbf{CogDual}, a novel RPLA adopting a \textit{cognize-then-respond } reasoning paradigm. By jointly modeling external situational awareness and internal self-awareness, CogDual generates responses with improved character consistency and contextual alignment. To further optimize the performance, we employ reinforcement learning with two general-purpose reward schemes designed for open-domain text generation. Extensive experiments on the CoSER benchmark, as well as Cross-MR and LifeChoice, demonstrate that CogDual consistently outperforms existing baselines and generalizes effectively across diverse role-playing tasks.

cs.CL↗

arXiv2025

DOPL: Direct Online Preference Learning for Restless Bandits with Preference Feedback

Restless multi-armed bandits (RMAB) has been widely used to model constrained sequential decision making problems, where the state of each restless arm evolves according to a Markov chain and each state transition generates a scalar reward. However, the success of RMAB crucially relies on the availability and quality of reward signals. Unfortunately, specifying an exact reward function in practice can be challenging and even infeasible. In this paper, we introduce Pref-RMAB, a new RMAB model in the presence of \textit{preference} signals, where the decision maker only observes pairwise preference feedback rather than scalar reward from the activated arms at each decision epoch. Preference feedback, however, arguably contains less information than the scalar reward, which makes Pref-RMAB seemingly more difficult. To address this challenge, we present a direct online preference learning (DOPL) algorithm for Pref-RMAB to efficiently explore the unknown environments, adaptively collect preference data in an online manner, and directly leverage the preference feedback for decision-makings. We prove that DOPL yields a sublinear regret. To our best knowledge, this is the first algorithm to ensure $\tilde{\mathcal{O}}(\sqrt{T\ln T})$ regret for RMAB with preference feedback. Experimental results further demonstrate the effectiveness of DOPL.

cs.LG↗

arXiv2025

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations

Federated reinforcement learning (FedRL) enables multiple agents to collaboratively learn a policy without sharing their local trajectories collected during agent-environment interactions. However, in practice, the environments faced by different agents are often heterogeneous, leading to poor performance by the single policy learned by existing FedRL algorithms on individual agents. In this paper, we take a further step and introduce a \emph{personalized} FedRL framework (PFedRL) by taking advantage of possibly shared common structure among agents in heterogeneous environments. Specifically, we develop a class of PFedRL algorithms named PFedRL-Rep that learns (1) a shared feature representation collaboratively among all agents, and (2) an agent-specific weight vector personalized to its local environment. We analyze the convergence of PFedTD-Rep, a particular instance of the framework with temporal difference (TD) learning and linear representations. To the best of our knowledge, we are the first to prove a linear convergence speedup with respect to the number of agents in the PFedRL setting. To achieve this, we show that PFedTD-Rep is an example of the federated two-timescale stochastic approximation with Markovian noise. Experimental results demonstrate that PFedTD-Rep, along with an extension to the control setting based on deep Q-networks (DQN), not only improve learning in heterogeneous settings, but also provide better generalization to new environments.

cs.LG↗