arXiv subjects

Yifan Guo

Publications and source records attributed to Yifan Guo.

At least 19 recordsLinked to original sources

arXiv2026

Learning with Bilevel-Minimax Optimization for Efficient and Reliable Transfer Attacks

Transfer-based adversarial attacks craft adversarial examples using surrogate models to mislead black-box victim models. Beyond perturbation generation, transferability is fundamentally governed by the coupling of initialization, surrogate adaptation, and gradient dynamics. We revisit this challenge from a bilevel-minimax perspective and propose BMAT (Bilevel-Minimax Adversarial Transfer). The bilevel formulation captures the dependency between initialization and perturbation, while the inner minimax problem promotes surrogate robustness for cross-architecture generalization. Algorithmically, we develop an integrated bottom-up solver that combines a Soft Weight Modulator and an Implicit Gradient Approximator to enable ternary coupling among initialization, surrogate adaptation, and perturbation optimization. We further provide theoretical insights into the optimization dynamics of the proposed bilevel-minimax framework. Extensive experiments on classification and segmentation benchmarks show that BMAT outperforms more than 10 strong baselines across more than 30 victim models, improving both intra- and cross-architecture transfer and yielding up to a 2x reduction in mIoU. Code is available at https://github.com/callous-youth/BMAT.

cs.LG↗

arXiv2026

FSTC-Encoder: Feature--Spatial--Temporal Correlation Learning for Generalizable RF Sensing

Heterogeneous RF sensing differs substantially in feature structure, spatial layout, and temporal scale, making existing models difficult to reuse across devices, environments, and RF modalities. We propose FSTC-Encoder, which unifies heterogeneous RF representation learning through feature, spatial, and temporal correlation modeling. Structure-aware feature encoding accommodates different signal structures, set-based spatial encoding aggregates variable observations, and hierarchical temporal encoding jointly captures local variations and long-range dependencies. Across sensing tasks and modalities, FSTC-Encoder retains the same spatial--temporal backbone architecture while varying only the feature configuration and task head. Across Widar3.0, CSI-Bench, and XRF55, FSTC-Encoder achieves 92.15% mean Accuracy under multi-factor cross-domain protocols, ranks first on three of four additional sensing tasks, remains consistently strong across WiFi, millimeter-wave radar, and RFID, and reduces the cross-modality performance gap from 18.85% to 12.93% through cross-RF learning. These results demonstrate that FSTC-Encoder achieves high domain robustness, task generality, and modality extensibility.

cs.LG↗

arXiv2026

TRACE-Memory: Public-Conditioned Retrieval and Utility-Aware Evidence Admission for Personalized Generation

Personalized generation systems retrieve user history by request--memory relevance and inject it into the model context. Yet relevant history may concern the wrong preference aspect, duplicate public information, or provide insufficient support. We argue that personal memory should be used only when it adds utility beyond a public-only response. We propose TRACE-Memory, a two-stage framework for selective personalization. Stage 1 queries for user-specific information missing from the request and public context, then retrieves a coverage-oriented candidate pool. Stage 2 admits a compact subset of source-traceable evidence units, or the empty set, according to response-level incremental utility. We progressively train the query-generation and evidence-admission policies through structured SFT initialization, reduced-space stage-wise GRPO warm-up, and nested multi-sample Joint GRPO. Across 4,500 Controlled and Natural tasks from Goodreads, Amazon Reviews, and Reddit, TRACE-Memory consistently outperforms random and lexical memory use, improves over semantic retrieval, remains competitive with frontier-LLM memory pipelines as local generator capacity increases, and conditions evidence admission on public-context sufficiency, supporting selective rather than default personalization.

cs.AI↗

arXiv2026

XGait: A Multi-Modality Wireless Sensing Dataset for Indoor Human Tracking and Identification

Wireless sensing has emerged as a promising approach for tracking and identification using commodity Internet of Things devices. However, the features derived from a single wireless modality are often fragile to variations in environmental layouts and walking trajectories. Furthermore, most existing studies are based on datasets collected in specific scenarios with limited trajectory diversity and sensing modalities, preventing a robust evaluation of system generalization. \textcolor{blue}{To address this gap, we introduce \textbf{XGait}, a multi-modality wireless sensing dataset that synchronously captures human walking using Wi-Fi and acoustic transceivers across three indoor scenarios, with vision-based measurements serving as ground truth. Specifically, XGait contains more than 22K walking samples from 27 participants, covering diverse directions and trajectories to support both indoor tracking and identity recognition. To bridge the heterogeneity of wireless sensing modalities, we propose a unified Doppler spectrogram representation that maps Wi-Fi and acoustic signals into a shared time--frequency space, along with a standardized benchmark pipeline for pre-processing, temporal alignment, and feature construction, enabling reproducible evaluation and systematic cross-modal analysis. Extensive evaluations demonstrate that Wi-Fi and acoustic sensing exhibit complementary strengths, particularly under complex trajectories and challenging propagation conditions, thereby paving the way for novel research in the field of multi-modality wireless sensing.} The dataset and code are available at https://github.com/warrior-087/XGait.

cs.HC↗

arXiv2026

What Language Does and What the Evidence Supports: A Functional Role Taxonomy and Evidence Audit of Language Grounding in Embodied Agents

Foundation models place language throughout embodied agents, but its presence does not show what it contributes or how well that contribution is grounded. This survey separates these two questions. We define five non-exclusive functional roles for language: Specification, Embodied Representation, Action Orchestration, Grounding Regulation, and Execution Coupling. For each role, we trace the path from linguistic content to its embodied consumer and identify the observations or interventions that can test the claimed responsibility. Applying this framework to the reviewed literature reveals a recurring gap between functional use and evidential support. Interpretable or revised linguistic intermediates may be incorrect, go unused, or fail to affect later behavior. Even when actions are directly conditioned on language, system-level success does not by itself isolate language's contribution. We therefore evaluate grounding claim by claim, asking whether the reported evidence supports the specific responsibility assigned to language. Using role claims rather than architectures as the unit of comparison allows us to compare modular and end-to-end embodied agents without extending conclusions beyond the reported evidence.

cs.CL↗

arXiv2026

Ensuring Trustworthy Online A/B Testing: Addressing Five Key Questions on CUPED

A/B testing has become the gold standard for data-driven decision-making in large-scale online experimentation, providing critical guidance for feature launch, pricing optimization, and user experience enhancement. To maximize statistical sensitivity, many technology companies routinely employ Controlled-experiment Using Pre-Experiment Data (CUPED), a technique that achieves substantial variance reduction while preserving the unbiasedness of estimating the average treatment effect. Despite its widespread adoption, several critical methodological and practical nuances of CUPED remain underexplored. This paper systematically addresses five frequently encountered yet overlooked questions regarding the application of CUPED. First, we provide a comparative analysis of various post-CUPED estimators to identify the optimal adjustment specification. Second, we evaluate the validity of regression-based adjustments and delineate robust variance estimation methods tailored for such frameworks. Finally, we extend our investigation to complex but common scenarios, including multi-arm experiments and two-stage sampling designs. Our findings reveal that in these settings, naive reliance on standard variance estimators can lead to severely misleading inferences. By offering rigorous theoretical insights and extensive experimental validation, this work deepens the conceptual understanding of CUPED. Notably, the recommended methodologies have been successfully deployed and integrated into ByteDance's experimentation platform.

stat.AP↗

arXiv2026

Algorithmic Feature Highlighting for Human-AI Decision-Making

Human decision-makers often face choices about complex cases with many potentially relevant features, but limited bandwidth to inspect and integrate all available information. In such settings, we study algorithms that highlight a small subset of case-specific features for human consideration, rather than producing a single prediction or recommendation. We model highlighting as a constrained information policy that selects a small number of features to reveal. A central issue is how humans interpret the algorithm's choice of features: a sophisticated agent correctly conditions on the selection rule, while a naive agent updates only on revealed feature values and treats the selection event as exogenous. We show that optimizing highlighting for sophisticated agents can be computationally intractable, even in simple discrete and binary settings, whereas optimizing for naive agents is tractable as long as the maximal bandwidth is fixed. We also show that a highlighting policy that is optimal for sophisticated agents can perform arbitrarily poorly when deployed to naive agents, motivating robust, implementable alternatives. We illustrate our framework in a calibrated empirical exercise based on the American Housing Survey. Overall, our results establish the value of highlighting a context-specific set of features rather than a fixed one as a practically appealing and computationally feasible tool for achieving human-algorithm complementarity.

cs.GT↗

arXiv2026

FAS-aided Robust Anti-Jamming Communications: Continuous and Discrete Positioning Designs

This paper investigates the joint optimization of beamforming and antenna positions in fluid antenna system (FAS)-aided anti-jamming communications. We consider a multi-user multiple-input multiple-output downlink scenario where multiple malicious jammers exist and the jammer channel state information is imperfect. The goal is to maximize the worst-case sum-rate under quality-of-service and transmit power constraints. To achieve this, we develop two distinct optimization frameworks for continuous and discrete antenna position designs, respectively. For continuous design, we propose an alternating optimization (AO) framework that integrates successive convex approximation and majorization minimization (MM) to handle the highly non-convex problem. For discrete design, based on the minimum mean squared error criterion and MM, we reformulate the problem as a sparse recovery task and propose a low-complexity block coordinate descent and simultaneous orthogonal matching pursuit, which enables joint design rather than AO. Through systematic comparison, we uncover a practical phenomenon: the discrete joint design yields superior sum-rate performance compared to the AO-based continuous counterpart under identical conditions. This superiority stems from the sparse recovery formulation which effectively circumvents the severe local optima. Our findings challenge the conventional view that continuous optimization is inherently superior, and reveal that discretization combined with sparse recovery can offer a more effective paradigm for exploiting spatial degrees-of-freedom in FAS-aided anti-jamming communications.

eess.SP↗

arXiv2025

NLoS Localization with Single Base Station Based on Radio Map

Accurate outdoor localization in Non-Line-of-Sight (NLoS) environments remains a critical challenge for wireless communication and sensing systems. Existing methods, including positioning based on the Global Navigation Satellite System (GNSS) and triple Base Stations (BSs) techniques, cannot provide reliable performance under NLoS conditions, particularly in dense urban areas with strong multipath effects. To address this limitation, we propose a single BS localization framework that integrates sequential signal measurements with prior radio information embedded in the Radio Map (RM). Using temporal measurement features and matching them with radio maps, the proposed method effectively mitigates the adverse impact of multipath propagation and reduces the dependence on LoS paths. Simulation experiments further evaluate the impact of different radio map construction strategies and the varying lengths of the measurement sequence on localization accuracy. Results demonstrate that the proposed scheme achieves sub-meter positioning accuracy in typical NLoS environments, highlighting its potential as a practical and robust solution for single-base-station deployment.

eess.SY↗

arXiv2025

Guidewire-driven deployment of high density ECoG arrays for large area brain-computer interface

Electrocorticographic brain computer interfaces are powerful emergent technologies for advancing basic neuroscience research and targeted clinical interventions. However, existing devices require trade-offs between coverage area, electrode density, surgical invasiveness and complication risk: limitations that fail to meet the demands of next-generation BCI. Here, we report a guidewire-driven deployable ECoG BCI device that can be epidurally implanted using minimally invasive procedures. Our ultra-flexible but strong thin-film electrode array, which packs 256 electrodes into 4 cm2, can be folded, pulled through millimetre-sized skull holes, and unfurled seamlessly onto the brain dura mater. When deployed on the canine brain, it captures abundant high-quality auditory neural signals with distinct features of hearing that can be used to classify sound types with over 80% accuracy using various standard machine learning models. Our device is biocompatible for chronic monitoring, easy and fast to deploy and importantly, resolves the key trade-offs limiting current BCI technologies.

physics.med-ph↗

arXiv2025

Data-driven multifidelity and multiscale topology optimization based on phasor-based evolutionary de-homogenization

Multiscale topology optimization is crucial for designing porous infill structures with high stiffness-to-weight ratios and excellent energy absorption. Although gradient-based methods provide a rigorous framework, they are computationally expensive and struggle to capture cross-scale sensitivities in nonlinear settings. Moreover, the resulting hierarchical geometries are often overly complex and lack macroscopically meaningful features. To overcome these issues, we propose an evolutionary de-homogenization framework that couples MultiFidelity Topology Design (MFTD) with a phasor-based de-homogenization technique. The framework translates low-dimensional geometric descriptors into manufacturable high-resolution structures through a hybrid evolutionary algorithm integrating NSGA-II selection, VAE-enabled latent space crossover, and a novel image deformation-based mutation operator. This gradient-free approach achieves efficient optimization while ensuring geometric continuity. Numerical results confirm that the method effectively balances efficiency and design flexibility, offering a scalable pathway for fabrication-aware multiscale structural optimization.

math.OC↗

arXiv2025

Dual-end Fluid Antennas For Robust Anti-jamming in Low-altitude Air-ground Communications

This paper addresses the challenge of co-channel interference and intentional jamming in low-altitude air-ground communications. Since conventional fixed-position antenna (FPA) systems lack spatial adaptability to dynamically balance signal enhancement against interference suppression, we propose a transformative fluid antenna system (FAS)-assisted heterogeneous dual-layer transmission architecture. Specifically, a terrestrial base station with FPA serves ground users, while a low altitude-serving base station equipped with FAS communicates with the aerial user, also equipped with FAS, under the attack of a malicious jammer. We formulate a worst-case achievable rate maximization problem for aerial user subject to constraints including quality-of-service for terrestrial users, imperfect jamming directions, minimum antenna separation, etc. To address the non-convex problem, we propose a fractional programming-block coordinate descent algorithm that alternately optimizes the transmit precoders, receive combiner, and antenna positions at both transceiver sides. Convex hull-based approach and geometric boundary method are used to handle the jamming uncertainty and antenna placement constraints in confined spatial regions, respectively. Extensive simulations validate significant performance gains. The FAS achieves up to 56\% higher data rates than FPA under equivalent power constraints. Strategic antenna repositioning demonstrably enhances signal quality while suppressing interference, maintaining robustness across diverse jammer channel uncertainties.

eess.SP↗

arXiv2025

Consistency-Aware Padding for Incomplete Multi-Modal Alignment Clustering Based on Self-Repellent Greedy Anchor Search

Multimodal representation is faithful and highly effective in describing real-world data samples' characteristics by describing their complementary information. However, the collected data often exhibits incomplete and misaligned characteristics due to factors such as inconsistent sensor frequencies and device malfunctions. Existing research has not effectively addressed the issue of filling missing data in scenarios where multiview data are both imbalanced and misaligned. Instead, it relies on class-level alignment of the available data. Thus, it results in some data samples not being well-matched, thereby affecting the quality of data fusion. In this paper, we propose the Consistency-Aware Padding for Incomplete Multimodal Alignment Clustering Based on Self-Repellent Greedy Anchor Search(CAPIMAC) to tackle the problem of filling imbalanced and misaligned data in multimodal datasets. Specifically, we propose a self-repellent greedy anchor search module(SRGASM), which employs a self-repellent random walk combined with a greedy algorithm to identify anchor points for re-representing incomplete and misaligned multimodal data. Subsequently, based on noise-contrastive learning, we design a consistency-aware padding module (CAPM) to effectively interpolate and align imbalanced and misaligned data, thereby improving the quality of multimodal data fusion. Experimental results demonstrate the superiority of our method over benchmark datasets. The code will be publicly released at https://github.com/Autism-mm/CAPIMAC.git.

cs.LG↗

arXiv2025

Triton-distributed: Programming Overlapping Kernels on Distributed AI Systems with the Triton Compiler

In this report, we propose Triton-distributed, an extension of existing Triton compiler, to overcome the programming challenges in distributed AI systems. Triton-distributed is the first compiler that supports native overlapping optimizations for distributed AI workloads, providing a good coverage of existing optimizations from different frameworks. First, we integrate communication primitives compliant with the OpenSHMEM standard into the compiler. This enables programmers to utilize these primitives with a higher-level Python programming model. Second, we illustrate how to achieve complex joint optimization of computation, memory access, and communication with the assistance of the compiler. In particular, we show how to use overlapping techniques to hide latency and present our compiler-based programming methods in both single-node and multi-node scenarios. Finally, we showcase the performance of the code generated by our compiler. In a test environment with up to 64 devices, our compiler can fully utilize heterogeneous communication and computation resources to provide effective overlapping and high performance. In many cases, the performance of the generated code can even outperform hand-optimized code. Moreover, the development difficulty and the time cost for development using our compiler are far less than those of low-level programming such as CUDA/C++, which clearly demonstrates significant productivity advantages.

cs.DC↗

arXiv2025

Meta-Learning for Hybrid Precoding in Millimeter Wave MIMO System

The hybrid analog/digital architecture that connects a limited number of RF chains to multiple antennas through phase shifters could effectively address the energy consumption issues in massive multiple-input multiple-output (MIMO) systems. However, the main challenges in hybrid precoding lie in the coupling between analog and digital precoders and the constant modulus constraint. Generally, traditional optimization algorithms for this problem typically suffer from high computational complexity or suboptimal performance, while deep learning based solutions exhibit poor scalability and robustness. This paper proposes a plug and play, free of pre-training solution that leverages gradient guided meta learning (GGML) framework to maximize the spectral efficiency of MIMO systems through hybrid precoding. Specifically, GGML utilizes gradient information as network input to facilitate the sharing of gradient information flow. We retain the iterative process of traditional algorithms and leverage meta learning to alternately optimize the precoder. Simulation results show that this method outperforms existing methods, demonstrates robustness to variations in system parameters, and can even exceed the performance of fully digital weighted minimum mean square error (WMMSE) precoding with the same number of antennas.

cs.IT↗

arXiv2024

Robust Hybrid Precoding for Millimeter Wave MU-MISO System Via Meta-Learning

Thanks to the low cost and power consumption, hybrid analog-digital architectures are considered as a promising energy-efficient solution for massive multiple-input multiple-output (MIMO) systems. The key idea is to connect one RF chain to multiple antennas through low-cost phase shifters. However, due to the non-convex objective function and constraints, we propose a gradient-guided meta-learning (GGML) based alternating optimization framework to solve this challenging problem. The GGML based hybrid precoding framework is \textit{free-of-training} and \textit{plug-and-play}. Specifically, GGML feeds the raw gradient information into a neural network, leveraging gradient descent to alternately optimize sub-problems from a local perspective, while a lightweight neural network embedded within the meta-learning framework is updated from a global perspective. We also extend the proposed framework to include precoding with imperfect channel state information. Simulation results demonstrate that GGML can significantly enhance spectral efficiency, and speed up the convergence by 8 times faster compared to traditional approaches. Moreover, GGML could even outperform fully digital weighted minimum mean square error (WMMSE) precoding with the same number of antennas.

cs.IT↗

arXiv2024

Deconfounded Time Series Forecasting: A Causal Inference Approach

Time series forecasting is a critical task in various domains, where accurate predictions can drive informed decision-making. Traditional forecasting methods often rely on current observations of variables to predict future outcomes, typically overlooking the influence of latent confounders, unobserved variables that simultaneously affect both the predictors and the target outcomes. This oversight can introduce bias and degrade the performance of predictive models. In this study, we address this challenge by proposing an enhanced forecasting approach that incorporates representations of latent confounders derived from historical data. By integrating these confounders into the predictive process, our method aims to improve the accuracy and robustness of time series forecasts. The proposed approach is demonstrated through its application to climate science data, showing significant improvements over traditional methods that do not account for confounders.

cs.LG↗

arXiv2024

iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative Learning

This paper considers a Min-Max Multiple Traveling Salesman Problem (MTSP), where the goal is to find a set of tours, one for each agent, to collectively visit all the cities while minimizing the length of the longest tour. Though MTSP has been widely studied, obtaining near-optimal solutions for large-scale problems is still challenging due to its NP-hardness. Recent efforts in data-driven methods face challenges of the need for hard-to-obtain supervision and issues with high variance in gradient estimations, leading to slow convergence and highly suboptimal solutions. We address these issues by reformulating MTSP as a bilevel optimization problem, using the concept of imperative learning (IL). This involves introducing an allocation network that decomposes the MTSP into multiple single-agent traveling salesman problems (TSPs). The longest tour from these TSP solutions is then used to self-supervise the allocation network, resulting in a new self-supervised, bilevel, end-to-end learning framework, which we refer to as imperative MTSP (iMTSP). Additionally, to tackle the high-variance gradient issues during the optimization, we introduce a control variate-based gradient estimation algorithm. Our experiments showed that these innovative designs enable our gradient estimator to converge 20% faster than the advanced reinforcement learning baseline and find up to 80% shorter tour length compared with Google OR-Tools MTSP solver, especially in large-scale problems (e.g. 1000 cities and 15 agents).

cs.AI↗