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Fan Liu

Publications and source records attributed to Fan Liu.

At least 37 records · Page 2Linked to original sources
arXiv2026

An Operational Leakage Metric for Eavesdropping in Joint Sensing and Communications Systems

In upcoming integrated sensing and communications (ISAC) systems, security depends on whether an eavesdropper (Eve) can recover data symbols or infer sensing information from a common observation with a legitimate receiver. However, existing information and estimation measures do not quantify this dual risk under finite observation records. This letter introduces the novel metric of operational leakage, which signifies Eve's optimal probability, in the Bayesian sense, of reconstructing communication symbols or estimating a sensing parameter within prescribed distortions. By showing that prior adversarial success probability separates the effect of side information from that of the observation, we propose a conditional Laplace approximation for efficiently evaluating the proposed metric and jointly quantifying the eavesdropping risk to the sensing and communication functionalities. Numerical results showcase that the risk enabled by adversarial observation increases with Eve's signal-to-noise ratio (SNR), conventional metrics do not uniquely determine finite-record adversarial success probability, and that the proposed conditional Gaussian approximation closely follows numerical Bayes evaluation.

eess.SP↗
arXiv2026

Full-Frame OFDM-ISAC: Joint Constellation, Pilot, and Receive-Filter Design

Orthogonal frequency-division multiplexing (OFDM) integrated sensing and communication (ISAC) reuses the entire time-frequency frame, including pilots and data payloads, for radar sensing. The random data payload, however, reduces the sensing dynamic range and degrades the target detection and parameter estimation performance. Existing studies typically optimize the modulation constellation, the pilot pattern, the power allocation, and the receive filter in isolation, overlooking their coupled effects on the sensing ambiguity structure. To address this gap, we propose a joint design of the constellation, the pilot placement and power split, and the mismatched receive filter for full-frame OFDM-ISAC systems, which minimizes the residual interference inside declared regions of interest (ROIs) under a communication rate constraint. For a given transmit signaling scheme, the receive filter admits a closed-form solution. With this filter in place, we derive closed-form expressions for the receiver-specific interference floor and noise enhancement over the 2-D range-Doppler domain, which show that the floor is governed by a single effective statistic of the full-frame signaling. This law reduces the joint design to two steps. A balanced staggered pilot placement removes the deterministic grating lobes of pilot-power boosting, and the shaped constellation and the power split are then selected jointly on the boundary of the rate constraint under the channel estimation error, which attains the joint optimum over the constructed shaping levels to first order in the declared ROI size. Under a quality-of-service (QoS) constraint of $98\%$ of the rate of a conventional 16-QAM frame, the proposed design significantly lowers the interference floor, improves the target detection probability, and achieves better ranging accuracy compared to the conventional receive filter-aware design.

eess.SP↗
arXiv2026

Ambiguity Function Analysis of OFDM Signals With Pilots and Data Payloads

Practical orthogonal frequency division multiplexing (OFDM) communication frames contain both deterministic pilots and random data payloads, motivating the joint ambiguity function (AF) analysis of the two components when the entire frame is reused for integrated sensing and communication (ISAC). This paper characterizes two discrete AF formulations for different Doppler regimes, namely the discrete periodic AF (DP-AF) and fast-slow-time AF (FST-AF), and derives closed-form expressions for their expected squared values. For the FST-AF, the expected sidelobe level (ESL) is uniform over the delay-Doppler plane and depends only on the pilot count, constellation kurtosis and total number of time-frequency resources, but not on the pilot symbols or pattern. For the DP-AF, we establish attainable lower and upper ESL bounds and show that no pilot design can minimize all sidelobes simultaneously. We further prove that attaining the lower bound at non-zero Doppler requires a periodic pilot pattern, while equally spaced chirp pilots, including Zadoff-Chu (ZC) sequences, maximize the numbers of sidelobes attaining the lower and upper bounds simultaneously. Two representative ZC pilot patterns widely encountered in communication frames are then examined: contiguous placement produces delay-Doppler ridges described by squared Dirichlet kernels, whereas equally spaced placement generates periodic peak-and-notch structures. Both regular patterns exhibit pronounced high sidelobes, suggesting that communication-oriented pilot patterns should be re-designed for delay-Doppler estimation in the context of ISAC. Numerical results validate the analysis and show that irregular pilot placement can suppress high sidelobes and improve target estimation performance.

eess.SP↗
arXiv2026

Spatiotemporal programming via asymmetric dielectric engineering for nonvolatile 2D optoelectronics

Ambipolar two dimensional (2D) semiconductors integrated with floating-gate architectures offer a promising platform for nonvolatile, reconfigurable electronics. However, the switching between p-n and n-p junction polarities has conventionally required complex multi-gate designs, hindering the scalability and integration density. Here, we demonstrate a spatiotemporal programming strategy using a dual-floating-gate architecture with a symmetry broken tunneling dielectric. An asymmetric dielectric stack creates distinct tunneling thresholds for two floating gates, enabling a single input gate to encode spatial doping profiles in the 2D channel via defined voltage pulse sequences. We achieve on demand, nonvolatile, and reversible switching between p-n and n-p configurations with excellent retention and endurance. The reconfigurable homojunction serves as a multifunctional platform for logic encoding, rectification, photodetection, and in sensor computing. This work establishes a design paradigm that replaces spatial input complexity with spatiotemporal programming, paving the way for high-density, multifunctional intelligent hardware.

cond-mat.mes-hall↗
arXiv2026

Relations between the higher Hamiltonians of the trigonometric and the rational spin Calogero-Sutherland models

In this paper we study the relations between the Hamiltonian hierarchy generated by trigonometric Cherednik-Dunkl operators and the one generated by rational Dunkl operators. We develop two nested structures relating these two hierarchies. The first nested relation reconstructs the higher rational spin Calogero-Sutherland Hamiltonians exactly from the trigonometric ones. The second nested relation reconstructs the higher trigonometric spin Calogero-Sutherland Hamiltonians as leading terms from the rational ones. In addition, we provide an explanation of the eigenvalues of the trigonometric spin Calogero-Sutherland Hamiltonians in terms of $N$-colored Young diagrams and Maya diagrams.

hep-th↗
arXiv2026

Exact Degrees of Freedom of Spatially Sparse MIMO Channels Without Prior CSI

We characterize the degree of freedom (DoF) of a point-to-point blockwise memoryless channel without prior channel state information (CSI), with a fixed number $K$ of propagation paths, where the transmitter (Tx) and the receiver (Rx) are equipped with nonuniform linear arrays (NULAs) of $N_t$ and $N_r$ antennas, respectively. The positions of array elements are fixed, known, pairwise distinct, and need not be equally spaced. The uniform linear array (ULA) is a special case. In each block of length $T$, the continuous angles of arrival (AoAs), angles of departure (AoDs), and independent complex Gaussian path gains are redrawn. Both Tx and Rx know the state distributions but are not given the current realizations before transmission. The receiver may estimate the channel from reference signals or decode without explicit channel estimation, with reference symbols counted in $T$ and their energy counted against the power constraint. Under the aforementioned model, we show that the DoF is $1-\frac{1}{T}$ for $K=1$, and $K(1-\frac{3}{2T})$ for $K \geq 2$, when $N_r\ge K+1$, $N_t\ge\max\{K,2\}$, and $T\ge K$. The analytical results are further demonstrated by their applications to the DoF tradeoff analysis in integrated sensing and communication (ISAC). For more general array structures, an achievability result is established, while the converse remains open in general.

cs.IT↗
arXiv2026

Mobility Information Capacity in the Sky: A Gaussian Channel Perspective

Existing airspace capacity metrics mainly quantify occupancy or flow, although the same number of aerial vehicles may result in different motion alternatives. This letter establishes \emph{mobility information capacity} as an information-theoretic measure for low-altitude wireless networks. It quantifies the maximum information that trajectory observations reveal about intentional maneuver inputs under a given maneuver-resource budget and environmental uncertainty. For a common fixed feedback architecture, we formulate a lifted linear-Gaussian mobility channel and derive its finite-horizon log-determinant capacity. Cost and uncertainty whitening gives the spatiotemporal mobility eigenmodes, whose optimal maneuver-resource allocation follows water-filling. When the number of nondegenerate modes grows linearly with time and their efficiencies become asymptotically symmetric, we arrive at the Shannon-like law $R_M^{\rm G}=\frac{B_M}{2}\log_2(1+\mathrm{MNR})$, where MNR is the mobility-to-noise ratio. The proposed measure opens a motion-centric capacity perspective for the sky, while remaining a distinguishability baseline rather than a collision- or geometry-constrained airspace capacity.

cs.IT↗
arXiv2026

The Sun's chemical peculiarity: disentangling Galactic chemical evolution and planetary engulfment in solar twins

Recent observational studies have suggested that the Sun may be chemically peculiar relative to the majority of solar twins. Here, we re-analyse high-resolution, high signal-to-noise spectra of 79 nearby solar twins using a differential spectroscopic approach and Bayesian framework to test whether the Sun's chemical peculiarity arises from Galactic chemical evolution (GCE) or planetary ingestion. Using the spectroscopic tool Korg, we obtain highly precise, validated atmospheric parameters and abundances for 18 elements, with an average abundance precision of 0.015dex (3.5%). Employing an independent Bayesian indicator, we disentangle GCE and planetary engulfment signatures from other processes influencing stellar composition, including intrinsic abundance scatter. Our results indicate that the chemical peculiarity of the Sun relative to the average solar twin is largely driven by GCE effects, with 62.3$\pm$5.8% of our sample exhibiting abundance patterns well-described by GCE trends. We further identify 2--6 solar twin candidates exhibiting chemical signatures consistent with planetary engulfment that warrant further investigation. These findings reinforce the importance of accounting for GCE effects when interpreting solar twin abundance patterns, and suggest that the Sun may not be chemically peculiar relative to the majority of solar twins.

astro-ph.SR↗
arXiv2026

Exploiting LLM Agents for Trustworthy AutoResearch in Wireless Communications

Large language model (LLM) agent-enabled AutoResearch is attracting growing interest across scientific disciplines, in which LLMs are leveraged for knowledge synthesis, multistep planning, code generation, tool invocation, and iterative refinement, thus automating the research lifecycle, from hypothesis generation and experimentation to analysis and manuscript preparation. Wireless communications is particularly suitable to this paradigm. This is due to the fact that advances in this field often rely on fundamental-limit analysis, system optimization, and protocol design, all supported by mature mathematical, simulation, and optimization toolchains, as well as standards, measurement, and digital-twin platforms. However, integrating autonomous agents into rigorous wireless research workflows requires traceable processes and verifiable evidence. This article presents a general trustworthy wireless AutoResearch framework. This framework employs typed research contracts, version-controlled artifacts, independent validators, and bounded agent authority to connect hypothesis generation with system modeling, mathematical formu lation, algorithm design, code generation, simulation, reproducible claims, and manuscript preparation. We conduct a case study on integrated sensing and communication (ISAC) for unmanned aerial vehicles (UAVs). It is shown that the proposed framework can generate innovative research ideas and, with appropriate expert intervention, produce a manuscript whose evaluation score is comparable to or higher than those of related IEEE conference and letter papers. These findings demonstrate the effectiveness of LLM agents in orchestrating authoritative scientific tools and versioned research artifacts, while underscoring the importance of human-AI collaboration.

eess.SP↗
arXiv2026

Relativistic Cramér-Rao Bound Scaling for Device-Based and Device-Free Sensing

This letter investigates range and velocity estimation under relativistic motion for device-based (DB) and device-free (DF) sensing. By deriving the exact time-scaling and time-shift relations induced by one-way and two-way propagation, both sensing modes are cast into a unified affine signal model. Closed-form Cramér--Rao bounds (CRBs) are obtained as explicit functions of normalized velocity, root-mean-squared (RMS) bandwidth, and RMS duration. The bounds recover the classical low-speed results but exhibit distinct velocity scaling in the ultrarelativistic regime. For rapidly receding motion, the range CRB diverges while the velocity CRB vanishes. For rapidly approaching motion, both CRBs vanish. The DB and DF modes further exhibit different asymptotic orders in the two directions, showing that relativistic motion changes not only the signal model but also the fundamental scaling laws governing sensing accuracy.

eess.SP↗
arXiv2026

RISE: Adaptive Imagination for World Action Models

World Action Models (WAMs) improve planning by incorporating future world evolution into action generation, yet existing methods allocate a fixed imagination budget to every scene. We propose RISE (\textbf{R}efining \textbf{I}magination through \textbf{SE}lective Rollout), a system-level adaptive imagination framework that makes sequential \textsc{Roll}/\textsc{Stop} decisions according to the expected planning benefit of continued rollout. At each step, a Latent Evaluator estimates the risk revealed by the current prefix and how much planning could improve if imagination continues, while a Rollout Gate weighs this expected benefit against additional computation cost. Since factual driving logs expose only one realized future, we further construct \textbf{CounterDrive}, a counterfactual dataset with diverse outcomes and risk levels, to enrich future dynamics and provide localized risk supervision. Each retained sample undergoes expert verification and annotation of trajectory validity, incident onset, and causal category, providing a reusable resource for safety-critical world-modeling research. Experiments on NAVSIM and nuScenes show that RISE achieves the best overall planning performance while reducing unnecessary rollout, with additional transfer results supporting its plug-in generality across WAM architectures.

cs.CV↗
arXiv2026

Gradient Mirage: Trainable yet Label-Unidentifiable Gradients in Large Language Model Split Learning

Gradient matching attacks (GMAs) in LLM split learning (SL) rely on a critical yet underexplored assumption: the gradient exposed at the split interface is a faithful derivative of the client's full-label training objective. This gradient-objective consistency allows a curious server to recover private labels by searching for a sequence whose induced gradient explains the observation. We propose Gradient Mirage, a defense that breaks this consistency without discarding the optimization utility of the backward signal. Our key idea is to induce the adversary to solve a misspecified inverse problem, in which no plausible label sequence in the sequence space can explain the observed gradients. Concretely, Gradient Mirage achieves this by inducing inconsistency across three dimensions: objective, direction, and scale. Selective Autoregressive Supervision derives the exposed gradient from a masked surrogate loss rather than the full-label objective assumed by the attacker; Scale Blinding then applies randomized multiplicative rescaling, obscuring the gradient's natural magnitude; and Directional Privatization further randomizes the gradient direction while preserving its magnitude through the von Mises-Fisher (vMF) mechanism under a directional metric differential privacy guarantee. Crucially, utility is preserved: the Top segment still learns from all target tokens via Dual-Track Backpropagation, the exposed gradient remains informative since each supervised token retains its complete autoregressive context, and Bottom-Gradient Recovery restores the effective gradient for Bottom-segment optimization. Extensive experiments show that Gradient Mirage provides substantially stronger protection than existing defenses under comparable fine-tuning performance, achieving a better privacy-utility trade-off.

cs.CL↗
arXiv2026

How Much Sensing Information Is Needed to Control an Unstable Linear System?

Modern control systems increasingly rely on sensing to infer the system state before control actions can be taken. Yet a given observation mechanism may fail to preserve sufficient information about the unstable modes, regardless of the downstream estimator or controller. This paper asks how much sensing information is needed to estimate and control an unstable linear system, whose measurements are generated by a prescribed, possibly nonlinear and non-Gaussian, observation law p(y_t|x_t). To address this question, we first quantify sensing information using directed information, thereby accounting for causal feedback. We then establish necessary and sufficient information rate conditions for estimating and controlling this linear system. For necessity, keeping either the estimation error or the closed-loop state bounded in mean square requires a directed information rate of at least the open-loop expansion rate R_exp. This lower bound remains valid under additive process noise. Since this rate is difficult to evaluate, we derive computable bounds for nonlinear observations with additive noise. An upper bound below R_exp certifies infeasibility, whereas a lower bound above R_exp + R_NG certifies sufficiency under posterior covariance regularity. For linear Gaussian observations, the tight upper bound is determined by the steady-state Riccati equation. For sufficiency, the posterior non-Gaussianity rate R_NG measures the divergence rate from the covariance-matched Gaussian. Under uniform posterior covariance regularity, a rate above R_exp + R_NG guarantees mean-square convergence of the estimation error. For a stabilizable plant, certainty-equivalence feedback also guarantees mean-square convergence of the closed-loop state. Finally, verifiable curvature conditions on the likelihood and prior make R_NG vanish, so the sufficient threshold equals R_exp.

eess.SY↗
arXiv2026

RemoteZero: Geospatial Reasoning with Zero Labels

Geospatial reasoning requires models to identify image regions that satisfy complex and often implicit user intents. Recent reinforcement learning approaches improve reasoning without manually annotated reasoning traces, but still require human-provided target labels to construct rewards, limiting their use on large-scale unlabeled Earth observation data. We introduce RemoteZero, a label-free framework for reinforcement-based geospatial reasoning. Our key observation is twofold: MLLMs are often more reliable at evaluating candidates than generating solutions, while aerial imagery reduces interference in region-level verification. Therefore, RemoteZero converts each predicted region into a visual crop and uses its semantic consistency with the query as an intrinsic reward for GRPO optimization. This formulation removes the need for human-provided solution labels and further supports iterative self-evolution by reusing previous-round models as verifiers. Experiments show that RemoteZero outperforms strong supervised baselines, applies effectively to other Earth observation tasks, and continues to improve through self-evolution as the training data expand. We hope this direction can broadly benefit the Earth observation community.

cs.CV↗
arXiv2026

SelfWAM: A Self-Grounded Unified World Action Model for Fast Robot Control

World Action Models (WAMs) improve robot policy learning by jointly modeling actions and future observations. However, conditioning future prediction only on the task prompt and observation context risks capturing generic task progression rather than the action-specific consequences of the executed action. We introduce SelfWAM, a unified self-grounded WAM built on a modality-specialized Mixture-of-Transformers (MoT) architecture that jointly predicts actions, action-conditioned future RGB frames, and robot self-masks, thereby grounding future prediction in the robot's visible body and its action-induced motion. During joint training, SelfWAM allows future visual queries to attend to a clean copy of the demonstrated action, turning the video branch into an action-specific consequence model while leaving the fast action-only inference path unchanged. To focus video learning on action-relevant visual changes, we use prompt-specific objectives for future robot self-mask prediction, which removes appearance details and provides a target whose temporal evolution is tightly coupled with the conditioning action. Together, clean-action conditioning and future self-mask supervision make future predictions more directly reflect how the executed action changes the robot's visible motion and the surrounding scene. Experiments on RoboTwin 2.0 and real-world manipulation tasks show that SelfWAM produces more action-sensitive futures and preserves fast policy inference, while improving policy performance.

cs.RO↗
arXiv2026

SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents

Early warning of extreme weather is essential for mitigating the societal, economic, and environmental risks posed by hazardous weather events. However, expert-centered warning workflows are costly, labor-intensive, and difficult to scale throughout the warning-to-action process. Although recent advances in Large Language Model (LLM) agents have enabled the automation of weather-related tasks, existing studies remain centered on isolated scientific tasks and overlook the chain of interdependent processes required for operational extreme-weather early warning. To bridge this gap, this study investigates automated end-to-end extreme-weather early warning through LLM agents. We first develop SIREN-Bench, a comprehensive benchmark comprising 600 question-answer instances across 19 tasks, and covering four individual warning procedures and an end-to-end warning chain. Evaluation on SIREN-Bench reveals substantial capability gaps in existing weather agent frameworks. This motivates us to develop SIREN, an experience-grounded agent framework inspired by experts' use of historical cases, which combines an agentic execution environment integrating heterogeneous weather evidence and tools with a family of agent harnesses that exploit historical cases through retrieval, skill distillation, and predictive modeling. Extensive experiments demonstrate that SIREN outperforms weather-agent baselines on both individual warning procedures and end-to-end warning chains.

cs.AI↗
arXiv2026

Label-Noise Resistant Learning via Optimal Brain Damage Masking

Noisy labels are inevitable in real-world multimedia applications. Due to the strong memorization capacity of deep neural networks, these noisy labels cause significant performance degradation. Existing noise-robust methods have mainly focused on robust loss functions and sample selection strategies, with comparatively limited exploration of dynamic architectural adaptation. In this paper, we rethink the role of classifier connectivity under label noise. Intuitively, performance degradation stems from the backpropagation of noisy gradients. Since the final classifier layer acts as the primary gateway for this error propagation, selectively discarding redundant connections can restrict the backpropagation pathways of noisy gradients. Consequently, to identify redundant connections, we leverage the seminal Optimal Brain Damage (OBD) theory from model compression, which posits that parameters causing negligible loss perturbation can be removed. Guided by this principle, we show that masking low-activation edges limits the estimated loss perturbation to preserve the model's fitting capacity, while tightening a derived upper bound on noise-induced gradient error. To bridge this theoretical insight with practical training, we propose a novel Selective Edge Masking (SEM) mechanism for the fully connected (FC) layer to enhance noise robustness. It can adaptively retain critical edges for information propagation while suppressing gradient errors caused by noisy labels. As a plug-and-play component, SEM can be seamlessly integrated into various noise-robust methods. Additionally, we validate SEM's applicability by applying it to the newly emerged Kolmogorov-Arnold Network (KAN) employed as a classifier. Extensive evaluations on synthetic and real-world benchmarks demonstrate that our approach achieves state-of-the-art performance.

cs.LG↗
arXiv2026

SNR-Dependent Mismatched Filtering for Bistatic OFDM Ranging

This paper investigates the ranging performance of a bistatic integrated sensing and communications (ISAC) system employing orthogonal frequency-division multiplexing (OFDM), in which an ISAC transmitter emits a communication waveform carrying random data symbols, and a separate receiver performs ranging by correlating the received signal with a locally demodulated symbol sequence. Owing to inevitable demodulation errors, the ranging processor operates under mismatched filtering rather than ideal matched filtering, resulting in a delay-domain correlation response whose sidelobe structure explicitly depends on the signal-to-noise ratio (SNR). Focusing on frequency-flat fading channels, we derive closed-form expressions for the expected sidelobe level (ESL) and the average mainlobe level of the resulting mismatched ranging response for BPSK, QPSK, and general square QAM constellations. The analysis quantitatively characterizes how SNR-driven symbol decision errors reshape the delay-domain sidelobe behavior, thereby providing analytical insight into the SNR-dependent scaling behavior of ranging performance in bistatic OFDM-based ISAC systems. Simulation results validate the theoretical derivations and confirm the accuracy of the proposed analysis.

eess.SP↗