arXiv subjects

Min Li

Publications and source records attributed to Min Li.

At least 55 records · Page 3Linked to original sources

arXiv2026

DexViTac: Collecting Human Visuo-Tactile-Kinematic Demonstrations for Contact-Rich Dexterous Manipulation

Large-scale, high-quality multimodal demonstrations are essential for robot learning of contact-rich dexterous manipulation. While human-centric data collection systems lower the barrier to scaling, they struggle to capture the tactile information during physical interactions. Motivated by this, we present DexViTac, a portable, human-centric data collection system tailored for contact-rich dexterous manipulation. The system enables the high-fidelity acquisition of first-person vision, high-density tactile sensing, end-effector poses, and hand kinematics within unstructured, in-the-wild environments. Building upon this hardware, we propose a kinematics-grounded tactile representation learning algorithm that effectively resolves semantic ambiguities within tactile signals. Leveraging the efficiency of DexViTac, we construct a multimodal dataset comprising over 2,400 visuo-tactile-kinematic demonstrations. Experiments demonstrate that DexViTac achieves a collection efficiency exceeding 248 demonstrations per hour and remains robust against complex visual occlusions. Real-world deployment confirms that policies trained with the proposed dataset and learning strategy achieve an average success rate exceeding 85% across four challenging tasks. This performance significantly outperforms baseline methods, thereby validating the substantial improvement the system provides for learning contact-rich dexterous manipulation. Project page: https://xitong-c.github.io/DexViTac/.

cs.RO↗

arXiv2026

Fundamental Limits of Bistatic Integrated Sensing and Communications over Memoryless Relay Channels

The problem of bistatic integrated sensing and communications over memoryless relay channels is considered, where destination concurrently decodes the message sent by the source and estimates unknown parameters from received signals with the help of a relay. A state-dependent discrete memoryless relay channel is considered to model this setup, and the fundamental limits of the communication-sensing performance tradeoff are characterized by the capacity-distortion function. An upper bound on the capacity-distortion function is derived, extending the cut-set bound results to address the sensing operation at the destination. A hybrid-partial-decode-and-compress-forward coding scheme is also proposed to facilitate source-relay cooperation for both message transmission and sensing, establishing a lower bound on the capacity-distortion function. It is found that the hybrid-partial-decode-and-compress-forward scheme achieves optimal sensing performance when the communication task is ignored. Furthermore, the upper and lower bounds are shown to coincide for three specific classes of relay channels. Numerical examples are provided to illustrate the communication-sensing tradeoff and demonstrate the benefits of integrated design.

cs.IT↗

arXiv2026

AssertLLM: Generating and Evaluating Hardware Verification Assertions from Design Specifications via Multi-LLMs

Assertion-based verification (ABV) is a critical method for ensuring design circuits comply with their architectural specifications, which are typically described in natural language. This process often requires human interpretation by verification engineers to convert these specifications into functional verification assertions. Existing methods for generating assertions from natural language specifications are limited to sentences extracted by engineers, discouraging its practical application. In this work, we present AssertLLM, an automatic assertion generation framework that processes complete specification files. AssertLLM breaks down the complex task into three phases, incorporating three customized Large Language Models (LLMs) for extracting structural specifications, mapping signal definitions, and generating assertions. Our evaluation of AssertLLM on a full design, encompassing 23 I/O signals, demonstrates that 89\% of the generated assertions are both syntactically and functionally accurate.

cs.AR↗

arXiv2026

Humanizing Robot Gaze Shifts: A Framework for Natural Gaze Shifts in Humanoid Robots

Leveraging auditory and visual feedback for attention reorientation is essential for natural gaze shifts in social interaction. However, enabling humanoid robots to perform natural and context-appropriate gaze shifts in unconstrained human--robot interaction (HRI) remains challenging, as it requires the coupling of cognitive attention mechanisms and biomimetic motion generation. In this work, we propose the Robot Gaze-Shift (RGS) framework, which integrates these two components into a unified pipeline. First, RGS employs a vision--language model (VLM)-based gaze reasoning pipeline to infer context-appropriate gaze targets from multimodal interaction cues, ensuring consistency with human gaze-orienting regularities. Second, RGS introduces a conditional Vector Quantized-Variational Autoencoder (VQ-VAE) model for eye--head coordinated gaze-shift motion generation, producing diverse and human-like gaze-shift behaviors. Experiments validate that RGS effectively replicates human-like target selection and generates realistic, diverse gaze-shift motions.

cs.RO↗

arXiv2026

FormalRTL: Verified RTL Synthesis at Scale

Large language models (LLMs) have demonstrated significant potential in automating hardware synthesis, yet substantial barriers remain for industrial-scale, datapath-centric designs due to ambiguous specifications and a lack of formal correctness guarantees. In this work, we present FormalRTL, a novel end-to-end multi-agent framework that systematically integrates software reference models as formal, executable specifications to guide register-transfer level (RTL) code generation and verification. By tightly coupling planning, synthesis, and formal equivalence checking, FormalRTL achieves scalable and reliable hardware code generation that addresses the critical challenges faced in industrial contexts. The comprehensive evaluation of a new suite of complex industrial-grade benchmarks demonstrates the effectiveness and robustness of our approach. We will open-source the FormalRTL framework and the benchmark suite to facilitate future research in this area.

cs.AR↗

arXiv2026

Rethinking ANN-based Retrieval: Multifaceted Learnable Index for Large-scale Recommendation System

Approximate nearest neighbor (ANN) search is widely used in the retrieval stage of large-scale recommendation systems. In this stage, candidate items are indexed using their learned embedding vectors, and ANN search is executed for each user (or item) query to retrieve a set of relevant items. However, ANN-based retrieval has two key limitations. First, item embeddings and their indices are typically learned in separate stages: indexing is often performed offline after embeddings are trained, which can yield suboptimal retrieval quality-especially for newly created items. Second, although ANN offers sublinear query time, it must still be run for every request, incurring substantial computation cost at industry scale. In this paper, we propose MultiFaceted Learnable Index (MFLI), a scalable, real-time retrieval paradigm that learns multifaceted item embeddings and indices within a unified framework and eliminates ANN search at serving time. Specifically, we construct a multifaceted hierarchical codebook via residual quantization of item embeddings and co-train the codebook with the embeddings. We further introduce an efficient multifaceted indexing structure and mechanisms that support real-time updates. At serving time, the learned hierarchical indices are used directly to identify relevant items, avoiding ANN search altogether. Extensive experiments on real-world data with billions of users show that MFLI improves recall on engagement tasks by up to 11.8\%, cold-content delivery by up to 57.29\%, and semantic relevance by 13.5\% compared with prior state-of-the-art methods. We also deploy MFLI in the system and report online experimental results demonstrating improved engagement, less popularity bias, and higher serving efficiency.

cs.IR↗

arXiv2026

Broadband terahertz comb with sub-Hz comb linewidth

Terahertz (THz) frequency combs are increasingly essential for spectroscopy, metrology, and quantum science. However, generating a dense array of evenly spaced ultra-narrow THz comb lines is challenging. Here, we demonstrate broadband THz comb generation using a photoconductive antenna that transfers a noise-suppressed near-infrared electro-optical (EO) comb into the THz domain. Our noise-suppression strategy, leveraging soliton self-frequency shift and spectral filtering, effectively suppresses EO comb phase noise without requiring active stabilization. The resulting THz comb exhibits broad spectral coverage (0.05-4 THz), narrow comb linewidths (0.3 Hz at the Fourier-transform limit), and excellent frequency stability (8.6*10^-14 at 1-second integration). We further demonstrate asynchronous THz time-domain spectroscopy, resolving ~36,000 comb lines with 50 MHz spacing. Crucially, the inherent frequency agility of the EO comb enables rapid and wide-range tuning of the THz comb line spacing. These attributes position our THz comb as a versatile tool for high-resolution molecular spectroscopy and precision THz metrology.

physics.optics↗

arXiv2026

SCAN-BEST: Sub-6GHz-Aided Near-field Beam Selection with Formal Reliability Guarantees

As millimeter-wave (mmWave) MIMO systems adopt larger antenna arrays, near-field propagation becomes increasingly prominent, especially for users close to the transmitter. Traditional far-field beam training methods become inadequate, while near-field training faces the challenge of large codebooks due to the need to resolve both angular and distance domains. To reduce in-band training overhead, prior work has proposed to leverage the spatial-temporal congruence between sub-6 GHz (sub-6G) and mmWave channels to predict the best mmWave beam within a near-field codebook from sub-6G channel estimates. To cope with the uncertainty caused by sub-6G/mmWave differences, we introduce a novel Sub-6G Channel Aided Near-field BEam SelecTion (SCAN-BEST) framework that wraps around any beam predictor to produce candidate beam subset with formal suboptimality guarantees. The proposed SCAN-BEST builds on conformal risk control (CRC), and is calibrated offline using limited calibration data. Its performance guarantees apply even in the presence of statistical shifts between calibration and deployment. Numerical results validate the theoretical properties and efficiency of SCAN-BEST.

cs.IT↗

arXiv2026

StellarF: A Physics-Informed LoRA Framework for Stellar Flare Forecasting with Historical & Statistical Data

Stellar flare forecasting represents a critical frontier in astrophysics, offering profound insights into stellar activity mechanisms and exoplanetary habitability assessments. Yet the inherent unpredictability of flare activity, rooted in stellar diversity and evolutionary stages, underpins the field's core challenges: (1) sparse, incomplete, noisy lightcurve data from traditional observations; (2) ineffective multi-scale flare evolution capture via single representations; (3) poor physical interpretability in data-driven models lacking physics-informed priors. To address these challenges, we propose StellarF, a physics-informed framework synergizing general Al with astrophysical domain knowledge via three core components: a unified preprocessing pipeline for lightcurve refinement (missing-value imputation, temporal patch partitioning, adaptive sample filtering); a Low-Rank Adaptation (LoRA)-finetuned large language model (LLM) backbone enhanced by first-order difference augmentation, flare statistical information, and flare historical record modules for multimodal fusion instead of only simple representations; and a novel physics-informed loss embedding a minimum rising rate prior, appended to the cross-entropy loss, to align with flare physics. Extensive experiments on Kepler and TESS datasets show StellarF achieves state-of-the-art performance across key metrics, setting new benchmarks for flare forecasting. This work bridges general AI with astrophysics, offering a practical, physically interpretable paradigm for transient event forecasting in time-domain astronomy.

cs.LG↗

arXiv2026

An efficient solver based on low-rank approximation and Neumann matrix series for unsteady diffusion-type partial differential equations with random coefficients

In this paper, we develop an efficient numerical solver for unsteady diffusion-type partial differential equations with random coefficients. A major computational challenge in such problems lies in repeatedly handling large-scale linear systems arising from spatial and temporal discretizations under uncertainty. To address this issue, we propose a novel generalized low-rank matrix approximation to represent the stochastic stiffness matrices, and approximate their inverses using the Neumann matrix series expansion. This approach transforms high-dimensional matrix inversion into a sequence of low-dimensional matrix multiplications. Therefore, the solver significantly reduces the computational cost and storage requirements while maintaining high numerical accuracy. The error analysis of the proposed solver is also provided. Finally, we apply the method to two classic uncertainty quantification problems: unsteady stochastic diffusion equations and the associated distributed optimal control problems. Numerical results demonstrate the feasibility and effectiveness of the proposed solver.

math.NA↗

arXiv2026

Network Integrated Sensing and Communication

Integrated sensing and communication (ISAC) is a cornerstone technology for 6G networks, offering unified support for high-rate communication and high-accuracy sensing. While existing literature extensively covers link-level designs, the transition toward large-scale deployment necessitates a fundamental understanding of network-level performance. This paper investigates a network ISAC model where a source node communicates with a destination via a relay network, while intermediate nodes concurrently perform cooperative sensing over specific spatial regions. We formulate a novel optimization framework that captures the interplay between multi-node routing and sensing coverage. For a one-dimensional path network, we provide an analytical characterization of the complete sensing-throughput region. Extending this to general network topologies, we establish that the sensing-throughput Pareto boundary is piecewise linear and provide physical interpretations for each segment. Our results reveal the fundamental trade-offs between sensing coverage and communication routing, offering key insights for the design of future 6G heterogeneous networks.

cs.IT↗

arXiv2026

LIDL: LLM Integration Defect Localization via Knowledge Graph-Enhanced Multi-Agent Analysis

LLM-integrated software, which embeds or interacts with large language models (LLMs) as functional components, exhibits probabilistic and context-dependent behaviors that fundamentally differ from those of traditional software. This shift introduces a new category of integration defects that arise not only from code errors but also from misaligned interactions among LLM-specific artifacts, including prompts, API calls, configurations, and model outputs. However, existing defect localization techniques are ineffective at identifying these LLM-specific integration defects because they fail to capture cross-layer dependencies across heterogeneous artifacts, cannot exploit incomplete or misleading error traces, and lack semantic reasoning capabilities for identifying root causes. To address these challenges, we propose LIDL, a multi-agent framework for defect localization in LLM-integrated software. LIDL (1) constructs a code knowledge graph enriched with LLM-aware annotations that represent interaction boundaries across source code, prompts, and configuration files, (2) fuses three complementary sources of error evidence inferred by LLMs to surface candidate defect locations, and (3) applies context-aware validation that uses counterfactual reasoning to distinguish true root causes from propagated symptoms. We evaluate LIDL on 146 real-world defect instances collected from 105 GitHub repositories and 16 agent-based systems. The results show that LIDL significantly outperforms five state-of-the-art baselines across all metrics, achieving a Top-3 accuracy of 0.64 and a MAP of 0.48, which represents a 64.1% improvement over the best-performing baseline. Notably, LIDL achieves these gains while reducing cost by 92.5%, demonstrating both high accuracy and cost efficiency.

cs.SE↗

arXiv2025

TextEditBench: Evaluating Reasoning-aware Text Editing Beyond Rendering

Text rendering has recently emerged as one of the most challenging frontiers in visual generation, drawing significant attention from large-scale diffusion and multimodal models. However, text editing within images remains largely unexplored, as it requires generating legible characters while preserving semantic, geometric, and contextual coherence. To fill this gap, we introduce TextEditBench, a comprehensive evaluation benchmark that explicitly focuses on text-centric regions in images. Beyond basic pixel manipulations, our benchmark emphasizes reasoning-intensive editing scenarios that require models to understand physical plausibility, linguistic meaning, and cross-modal dependencies. We further propose a novel evaluation dimension, Semantic Expectation (SE), which measures reasoning ability of model to maintain semantic consistency, contextual coherence, and cross-modal alignment during text editing. Extensive experiments on state-of-the-art editing systems reveal that while current models can follow simple textual instructions, they still struggle with context-dependent reasoning, physical consistency, and layout-aware integration. By focusing evaluation on this long-overlooked yet fundamental capability, TextEditBench establishes a new testing ground for advancing text-guided image editing and reasoning in multimodal generation.

cs.CV↗

arXiv2025

Quantum relaxometry for detecting biomolecular interactions with single NV centers

The investigation of biomolecular interactions at the single-molecule level has emerged as a pivotal research area in life science, particularly through optical, mechanical, and electrochemical approaches. Spins existing widely in biological systems, offer a unique degree of freedom for detecting such interactions. However, most previous studies have been largely confined to ensemble-level detection in the spin degree. Here, we developed a molecular interaction analysis method approaching single-molecule level based on relaxometry using the quantum sensor, nitrogen-vacancy (NV) center in diamond. Experiments utilized an optimized diamond surface functionalized with a polyethylenimine nanogel layer, achieving $\sim$10 nm average protein distance and mitigating interfacial steric hindrance. Then we measured the strong interaction between streptavidin and spin-labeled biotin complexes, as well as the weak interaction between bovine serum albumin and biotin complexes, at both the micrometer scale and nanoscale. For the micrometer-scale measurements using ensemble NV centers, we re-examined the often-neglected fast relaxation component and proposed a relaxation rate evaluation method, substantially enhancing the measurement sensitivity. Furthermore, we achieved nanoscale detection approaching single-molecule level using single NV centers. This methodology holds promise for applications in molecular screening, identification and kinetic studies at the single-molecule level, offering critical insights into molecular function and activity mechanisms.

quant-ph↗

arXiv2025

Sensing-Then-Beamforming: Robust Transmission Design for RIS-Empowered Integrated Sensing and Covert Communication

Traditional covert communication often relies on the knowledge of the warden's channel state information, which is inherently challenging to obtain due to the non-cooperative nature and potential mobility of the warden. The integration of sensing and communication technology provides a promising solution by enabling the legitimate transmitter to sense and track the warden, thereby enhancing transmission covertness. In this paper, we develop a framework for sensing-then-beamforming in reconfigurable intelligent surface (RIS)-empowered integrated sensing and covert communication (ISACC) systems, where the transmitter (Alice) estimates and tracks the mobile aerial warden's channel using sensing echo signals while simultaneously sending covert information to multiple legitimate users (Bobs) with the assistance of RIS, under the surveillance of the warden (Willie). Considering channel estimation errors, we formulate a robust non-convex optimization problem that jointly designs the communication beamformers, the sensing signal covariance matrix at Alice, and the phase shifts at the RIS to maximize the covert sum rate of Bobs while satisfying the constraints related to covert communication, sensing, transmitter power, and the unit modulus of the RIS elements. To solve this complex problem, we develop an efficient algorithm using alternating optimization, successive convex approximation, S-procedure, sequential rank-one constraint relaxation, and semidefinite relaxation techniques. Numerical results confirm the convergence of the proposed algorithm and demonstrate its effectiveness in tracking the warden's channel while ensuring robust covert transmission. Furthermore, the results highlight the advantages of using RIS to enhance the covert transmission rate compared to baseline schemes, and also illustrate the intricate trade-off between communication and sensing in ISACC systems.

cs.IT↗

arXiv2025

Dual-comb correlation spectroscopy reveals laser dynamics

Laser dynamics underpin a broad range of modern photonic technologies and continue to reveal rich nonlinear behaviors. However, existing spectroscopic tools, most notably time-stretched dispersive Fourier transform spectroscopy (TS-DFT), remain limited in spectral resolution, accuracy, and their ability to capture continuous waveforms and complex field dynamics. Here, we introduce dual-comb correlation spectroscopy (DCCS) as a powerful approach for resolving fast and intricate laser behaviors that are inaccessible to TS-DFT and conventional spectrometers. By correlating two sequences of heterodyne spectra produced by mixing a test laser with a pair of optical combs, DCCS enables rapid (e.g., 1 us) and high-resolution (0.08 pm) spectral retrieval over broad optical bandwidths. Leveraging these capabilities, we reveal mode-hopping and mode-competition dynamics in continuous-wave lasers, as well as the buildup process of a mode-locked laser. These results establish DCCS as a versatile and complementary tool to TS-DFT for exploring transient, broadband, and previously unresolvable behaviors in lasers and other time-evolving optical systems.

physics.optics↗

arXiv2025

Gradient-Driven Natural Selection for Compact 3D Gaussian Splatting

3DGS employs a large number of Gaussian primitives to fit scenes, resulting in substantial storage and computational overhead. Existing pruning methods rely on manually designed criteria or introduce additional learnable parameters, yielding suboptimal results. To address this, we propose an natural selection inspired pruning framework that models survival pressure as a regularization gradient field applied to opacity, allowing the optimization gradients--driven by the goal of maximizing rendering quality--to autonomously determine which Gaussians to retain or prune. This process is fully learnable and requires no human intervention. We further introduce an opacity decay technique with a finite opacity prior, which accelerates the selection process without compromising pruning effectiveness. Compared to 3DGS, our method achieves over 0.6 dB PSNR gain under 15\% budgets, establishing state-of-the-art performance for compact 3DGS. Project page https://xiaobin2001.github.io/GNS-web.

cs.CV↗

arXiv2025

Optimizing In-Context Learning for Efficient Full Conformal Prediction

Reliable uncertainty quantification is critical for trustworthy AI. Conformal Prediction (CP) provides prediction sets with distribution-free coverage guarantees, but its two main variants face complementary limitations. Split CP (SCP) suffers from data inefficiency due to dataset partitioning, while full CP (FCP) improves data efficiency at the cost of prohibitive retraining complexity. Recent approaches based on meta-learning or in-context learning (ICL) partially mitigate these drawbacks. However, they rely on training procedures not specifically tailored to CP, which may yield large prediction sets. We introduce an efficient FCP framework, termed enhanced ICL-based FCP (E-ICL+FCP), which employs a permutation-invariant Transformer-based ICL model trained with a CP-aware loss. By simulating the multiple retrained models required by FCP without actual retraining, E-ICL+FCP preserves coverage while markedly reducing both inefficiency and computational overhead. Experiments on synthetic and real tasks demonstrate that E-ICL+FCP attains superior efficiency-coverage trade-offs compared to existing SCP and FCP baselines.

cs.LG↗