arXiv subjects

Hao Chen

Publications and source records attributed to Hao Chen.

At least 127 records · Page 7Linked to original sources

arXiv2026

Towards Terabit/$λ$/s Multidimensional Silicon Photonic Engine

Increasing artificial intelligence (AI) workloads drive co-packaged optics (CPO), which integrates optical engines with electronic components. Optical interconnects can extend transmission distances and reduce latency, allowing distributed clusters in AI factories to operate as a unified computational unit. However, escalating data throughput necessitates greater parallelization of light within ultracompact form factors while maintaining stringent energy efficiency and latency constraints. Here, we present a multidimensional silicon photonic engine that achieves a communication capacity exceeding 1.8 terabit/s/lambda/s. By monolithically integrating transceivers, spatial and polarization (de)multiplexers, and optical signal processors on a single chip, we eliminate bulky discrete (de)multiplexers and power-hungry digital signal processing (DSP). In experiments, the photonic engine can be self-configured to identify two, four, or six concurrent spatial and polarization channels per fiber while mitigating dynamic channel crosstalk. Compared with the state-of-art DSP, our approach achieves >5,000-fold reductions in both power consumption and processing latency at a MIMO processing order of six. Furthermore, we demonstrate full-duplex, modulation-format-transparent inter-chip communication over 300-meter fiber. These results represent a paradigm shift for optical engines in future high-performance computing and AI-driven data centers.

physics.optics↗

arXiv2026

Adaptive Source-Channel Coding for Bi-static Integrated Sensing and Semantic Communications

Semantic communication (SemCom) has emerged as a new paradigm to facilitate the performance of integrated sensing and communication systems in 6G, due to its potential to enhance transmission efficiency by transmitting task-relevant semantic features rather than raw bits. However, most of the existing works mainly focus on sensing data compression to reduce the subsequent communication overheads, without considering the integrated transmission framework for both the SemCom and sensing tasks. This paper proposes a sensing-aware adaptive source-channel coding (SA-ASCC) and beamforming design framework for bi-static integrated sensing and SemCom (ISSC) systems by jointly optimizing the coding rate for SemCom task and the transmit beamforming for both the SemCom and sensing tasks. Specifically, an end-to-end semantic distortion function is approximated by deriving an upper bound composing of source and channel coding induced components, and then a hybrid Cramér-Rao bound (HCRB) is derived for target position under imperfect time synchronization due to the transceiver deployed at different places in our considered bi-static ISSC system. To characterize the achievable region between SemCom and sensing performance, a distortion minimization problem is formulated by considering the HCRB threshold, channel uses, and power budget, which is non-convex due to the coupled design variables and the mixed-integer program. Subsequently, an alternating optimization (AO) algorithm is proposed to decompose this problem into the model selection and joint rate and beamforming optimization subproblems, which are solved by the exhaustive search method and the combination of successive convex approximation and fractional programming, respectively. Finally, simulation results demonstrate that the proposed scheme outperforms the DJSCC-WF-ZF and BPG-WF-ZF benchmarks.

eess.SP↗

arXiv2026

R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video

Long-horizon egocentric video is a rich substrate for wearable AI assistants, but object-centric questions such as where an item was moved, when it last changed state, or why it was relocated remain difficult because caption- and transcript-based memories rarely preserve persistent object identity or structured spatial change. Existing long-video QA methods mainly emphasize temporal grounding and clip retrieval, while prior 3D scene-graph methods typically assume stronger geometry than free-motion wearable RGB video provides, including point clouds, RGB-D input, posed views, sparse reconstruction, or reconstructed scenes. R4DSG introduces a relative 4D scene graph memory for long egocentric video. Instead of storing raw graph sequences, R4DSG converts video into compact queryable memory entries indexed by time, place, persistent objects, anchor-relative change, and local interaction context. The main idea is to separate stable anchors from dynamic objects, maintain persistent object identity across frames, and represent object state through anchor-relative transitions rather than a globally aligned world model. Built on recent RGB-only advances in promptable video segmentation, temporal propagation, and relative 3D lifting, the method produces a retrieval-ready memory directly usable for long-horizon question answering. Evaluation on a 255-question object-related subset from EgoLifeQA shows, under question-only retrieval, a 6.7-point overall gain over EgoRAG-Text and a 12.5-point gain on when questions, which highlights the value of temporally organized object memory. These results position relative 4D scene graphs as a practical memory substrate for wearable assistants, AR systems, and embodied multimedia agents. GitHub Page: https://dualtransparency.github.io/R4DSG/.

cs.CV↗

arXiv2026

"Will This Data Break My Task?" - Interactive Synthesis of Task-Aware Data Unit Tests

Data is a central resource for modern enterprises and institutions, and data errors propagating through data pipelines lead to serious impact in production. Therefore, data validation is essential for ensuring the reliability of downstream applications. This led to the development of data unit tests, executable programs that test data before moving it around through large data pipelines. However, existing frameworks derive data unit tests from observed data alone, ignoring the semantics of the code that consumes the data downstream. To this end, we present PrismaDV, a compound AI system that synthesizes task-aware data unit tests for tabular data by jointly analyzing data and downstream task code. PrismaDV decomposes the test generation into multiple LLM-powered steps: data profiling, detection of column accesses, data flow analysis in the task code, and the inference of implicit data assumptions. It subsequently synthesizes code for the data unit test, and maintains an internal ``data-code assumption graph'' that links generated data constraints back to the task's source code. We demonstrate PrismaDV through an interactive web-based interface where attendees run the system on five real-world datasets with 60 downstream tasks, synthesize, inspect and refine both natural language assumptions about the data and executable data constraints. The interface allows attendees to navigate the data-code assumption graph, compare task-aware data unit tests against task-agnostic baselines on erroneous data batches, and interactively edit assumptions and data constraints. Furthermore, attendees can observe how a custom prompt optimizer adapts the system to specific datasets over time.

cs.DB↗

arXiv2026

Deep Koopman risk-preview supervised LTV-MPC for direct yaw moment control of distributed drive electric vehicles

Always-on direct yaw moment control (DYC) improves vehicle stability during critical maneuvers but can introduce unnecessary interventions under low-risk conditions. This paper proposes a Koopman risk-gated linear time-varying model predictive control (KRG-LTV-MPC) framework for low-intervention yaw stability assistance. Instead of replacing the physics-based execution model with a fully data-driven control predictor, this framework separates Koopman-based phase-risk preview from safety-critical execution. A Deep Koopman model predicts the nominal evolution of the sideslip-yaw rate phase risk to determine whether the constrained quadratic programming (QP) problem should be solved or skipped at each sampling time. When the gate is active, the LTV-MPC layer calculates the additional yaw moment; otherwise, the QP is skipped and the previously commanded moment is tapered to zero under a bounded-rate rule. Event-level shadow-mode evaluation shows that the Koopman predictor provides positive warning lead times of 0.19-0.28 s under low-friction and friction-transition conditions, whereas the LTV predictor gives delayed warnings. Under closed-loop low-friction conditions, KRG-LTV-MPC reduces the cumulative yaw-moment intervention by 43.9% relative to LTV-MPC and solves the QP for only 41.0% of the samples while maintaining vehicle stability within the phase plane. These results support the use of Koopman phase-risk information as an intelligent supervisory layer for low-intervention DYC.

eess.SY↗

arXiv2026

Quasi-Sinusoidal Single Diamond Structure in Royal Jewel Butterfly: An Angle-Independent Photonic Structure

Structural colouration with narrow spectral photonic bandwidth and high reflectivity is of critical importance for modern optical applications, including displays, laser systems, and optical sensing, etc. Achieving such angle independent colouration typically relies on polycrystalline or inherent structural disorder. However, balancing angular uniformity with high brightness and strong colour contrast remains challenging. Herein, we uncover the structural origin of the spectacular bright, angle-independent blue colouration of Hypochrysops polycletus, a sapphire-like Royal Jewel butterfly. Three-dimensional (3D) electron microscopy reveals that the dorsal wing scale has a single diamond structure, a 3D photonic crystal previously documented only in beetles and weevils. The crystal domains form an extraordinary quasi sinusoidal surface geometry with a distinct template morphology-guided arrangement. Unlike typically thicker biophotonic structures that support multiple high symmetry stopbands, this design contains only 3-4 unit cells in the propagation direction. Its optical response is dominated by the fundamental stopband, with two dominant scattering mechanisms: specular reflection at the {111} inclined sidewalls of the hierarchical structure, and funnelling into localised quasi-normal modes enabled by a strongly anisotropic Bloch transport. By mimicking these features with two-photon polymerisation, we artificially reproduced the optical response in the infrared region. The study opens a pathway towards bioinspired brilliant diffuse colouration and angle-robust photonic devices.

physics.optics↗

arXiv2026

Loom: Multi-Region Analysis of Spatial Transcriptomics with Local Neighborhoods and Global Trajectories

We present Loom, a spatial transcriptomics (ST) visual computing system to support the analysis of pseudo-temporal trajectories, comparative investigation across samples and regions of interest, and the examination of spatially structured processes within local microenvironments. ST is a molecular profiling technology that measures gene expression directly within a thin tissue section while preserving its spatial organization. For practical application-driven analyses, the ST local microenvironment data needs to be integrated with cell reference datasets and temporal simulations of cell behavior. This integration is challenging due to multi-modal registration issues and the complexity of the pseudo-temporal patterns, spatial enrichment data, and gene expression dynamics. Loom leverages a novel glyph coupled with a computational backbone to facilitate the detailed pseudo-temporal exploration of local microenvironments, cross-sample comparisons, and investigation of spatiotemporal biological mechanisms. We evaluate Loom through two case studies developed with experts in tissue pathology and oncologists and through an external usability study. The results demonstrate that Loom supports effectively the discovery of cellular transitions and spatiotemporal expression dynamics.

q-bio.QM↗

arXiv2026

AsymSpec: Efficient Cloud-Edge Speculative Decoding over Asymmetric Networks

Cloud-edge speculative decoding places a lightweight draft model at an edge gateway and a higher-quality target model in the cloud, but inserts communication into every speculative block. Under a constrained uplink, candidate messages may queue while the verifier is idle. Stop-and-wait scheduling leaves edge compute underutilized; optimistic same-request runahead can waste work when a rejection or an unexpected bonus token invalidates dependent drafts. We present AsymSpec, which addresses uplink-gated verification and invalid dependent work with two corresponding mechanisms. Its asymmetric verification protocol keeps the common-path acceptance upload compact and moves richer, rejection-only correction information to the downlink. A total-variation (TV) certificate for the residual distribution determines whether a small target top-K response suffices; if not, the protocol progressively escalates through proposal-based exact recovery before falling back to the full distribution. Its confirmed-prefix pipeline exposes only independent, valid requests to the edge scheduler and lets the cloud re-batch arrived blocks, hiding verification waits when another confirmed-prefix request is ready without using same-request runahead. Across three draft-target pairs, two workloads, and three asymmetric network profiles, our end-to-end evaluation shows that AsymSpec delivers 2.82-28.03$\times$ the output-token throughput of the strongest baseline.

cs.DC↗

arXiv2026

Two-Way Garment Transfer: Unified Diffusion Framework for Dressing and Undressing Synthesis

While recent advances in virtual try-on (VTON) have achieved realistic garment transfer to human subjects, its inverse task, virtual try-off (VTOFF), which aims to reconstruct canonical garment templates from dressed humans, remains critically underexplored and lacks systematic investigation. Existing works predominantly treat them as isolated tasks: VTON focuses on garment dressing while VTOFF addresses garment extraction, thereby neglecting their complementary symmetry. To bridge this fundamental gap, we propose the Two-Way Garment Transfer Model (TWGTM), to the best of our knowledge, the first unified framework for joint clothing-centric image synthesis that simultaneously resolves both mask-guided VTON and mask-free VTOFF through bidirectional feature disentanglement. Specifically, our framework employs dual-conditioned guidance from both latent and pixel spaces of reference images to seamlessly bridge the dual tasks. On the other hand, to resolve the inherent mask dependency asymmetry between mask-guided VTON and mask-free VTOFF, we devise a phased training paradigm that progressively bridges this modality gap. Extensive qualitative and quantitative experiments conducted across the DressCode and VITON-HD datasets validate the efficacy and competitive edge of our proposed approach.

cs.CV↗

arXiv2026

A Deployment-Friendly Foundational Framework for Efficient Computational Pathology

Pathology foundation models (PFMs) generalize well across computational pathology tasks but remain costly for gigapixel whole-slide image analysis. Here, we present LitePath, a deployment-friendly framework that addresses model over-parameterization and patch-level redundancy. LitePath combines LiteFM, a compact model distilled from Virchow2, H-Optimus-1 and UNI2 using 190 million patches, with the Adaptive Patch Selector for task-specific patch selection. Compared with Virchow2, LitePath uses 28x fewer parameters and 403.5x fewer FLOPs. On an NVIDIA Jetson Orin Nano Super, it processes 208 slides per hour, 104.5x faster than Virchow2, and consumes 0.36 kWh per 3,000 slides, 171x less energy than Virchow2 on an RTX 3090 GPU. We evaluated LitePath on 45 multicenter cohorts across four organs and 33 tasks, comprising 37 classification and 8 survival cohorts, including 33 internal, 10 external and 2 prospective cohorts, with 17,837 slides from 9,977 patients disjoint from the pretraining data. Among 22 PFMs, LitePath achieved the best average rank (6.56 vs. 6.58 for Virchow2), retained 99.71% of Virchow2's Macro-AUC across classification cohorts, and improved mean C-index by 2.14 percentage points across survival cohorts (71.91% vs. 69.77%). We further introduce the Deployability Score (D-Score), a weighted geometric mean of normalized task performance and FLOPs. LitePath achieved the highest D-Score (0.8455), outperforming H0-mini (0.7723) and Virchow2 (0.7297). In a randomized paired crossover study of four pathologists and 120 cases, LitePath increased diagnostic accuracy by 4.1-15.8 percentage points and reduced diagnostic time by 10.9-14.2%. These results demonstrate rapid, cost-effective and energy-efficient pathology image analysis on accessible hardware while maintaining competitive performance.

cs.CV↗

arXiv2026

VulnGym: Benchmarking Coding Agents for Repository-Level Vulnerability Detection

Recent advances in LLM-based vulnerability detection have shown promising results, while coding agents further extend this capability from isolated code snippets to complete repositories. This shift requires agents to autonomously explore repositories and locate vulnerability-relevant code, instead of performing detection on preselected functions. However, existing benchmarks primarily focus on vulnerability classification over preselected code snippets, limiting their ability to evaluate coding agents in repository-level vulnerability detection. Moreover, without fine-grained vulnerability trace annotations, the capability limitations underlying the detection process remain difficult to explore. To address these limitations, we present \textbf{VulnGym}, a real-world repository-level benchmark for evaluating vulnerability detection by coding agents. VulnGym aligns reviewed GitHub advisories with their corresponding vulnerable version repositories. It contains 184 advisories and 408 vulnerability entries across 23 repositories, with each entry annotated with line-level entry points, critical operations, and vulnerability traces. Using this fine-grained ground truth, VulnGym defines an end-to-end detection task and three oracle-based subtasks to jointly evaluate vulnerability detection and diagnose limitations in code localization and evidence construction. Our evaluation indicates that current coding agents remain limited in both end-to-end repository-level vulnerability detection and the construction of accurate supporting traces.

cs.SE↗

arXiv2026

The Primitive and Diamond surfaces locally minimize the variance of Gauss curvature

We prove that the Schwarz' Primitive (P) and Diamond (D) surfaces are local minimizers of the variance of Gaussian curvature among local deformations within the moduli space of triply periodic minimal surfaces of genus 3 (TPMSg3s). Our approach interprets the branch values of the Gauss map as a configuration of eight points on the sphere and expresses the variance of Gaussian curvature as the product of two integrals involving exponentials of Green's functions. We then show that the Hessian of this functional is positive definite at the cubic configuration when restricted to antipodal deformations, thereby establishing the local minimality of the P and D surfaces. Along the other deformation directions, the Hessian is positive definite except for a two-dimensional eigenspace with slightly negative eigenvalues, leaving promising hope that the Gyroid is also a local minimizer.

math.DG↗

arXiv2026

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer

Pathology foundation models (PFMs) provide strong tissue representations and have become central to digital pathology. However, deployment in disease-specific settings is limited by 1) the high computational cost of billion-parameter PFMs and 2) distribution mismatch and non-biological bias inherited from pan-cancer, multi-centre pre-training, including site-specific signatures and imbalanced disease prevalence. These factors can encourage shortcut learning and under-emphasise subtle morphology required for reliable modelling of a specific cancer type. We present SmartStu (a Smart Student), a framework to customise compact, breast-cancer-specific PFMs via distillation whilst mitigating confounding. SmartStu distils representations from multiple teacher PFMs into a lightweight student backbone. Crucially, we introduce adversarial distillation that leverages a dedicated noise model trained to predict nuisance, edge-dominated cues on the distillation set. Using this noise model as a counterexample, the adversarial objective encourages the student to recognise, yet suppress, features predictive of nuisance targets. We further incorporate multi-teacher ensemble distillation and an auxiliary self-supervised objective with artefact injection. We validate SmartStu on three external cohorts (Yale HER2, SLN-Breast, and BRACS) with multiple tiny backbones. SmartStu yields breast-cancer-specific PFMs that are over $30\times$ smaller than general PFMs whilst largely preserving, and sometimes improving, downstream performance measured by balanced accuracy (bAcc) and AUC. Code is available at https://github.com/zwchen03/advDistall.

cs.CV↗

arXiv2026

StateScribe: Towards Accessible Change Awareness Across Real-World Revisits

Real-world environments evolve continuously, yet blind and low-vision (BLV) individuals often have limited access to understanding how they change over time. Unexpected or relocated objects, layout modifications, and content updates (e.g., price changes) can introduce safety risks and cognitive burden. While existing visual assistive technologies can describe immediate surroundings, they operate as one-off interactions and lack mechanisms to surface meaningful changes across revisits. Informed by a survey of 33 BLV individuals, we develop StateScribe, a system that supports accessible awareness of real-world changes across revisits. StateScribe employs a dual-layer memory architecture that integrates episodic scene memory and object-centric temporal memory to enable scalable and structured change tracking. It provides both live descriptions of the current scene, and descriptions of what has changed, when and where it occurred across revisits, such as "The shop on your right has a "CLOSED" sign; it was open at this time last week.'' Our evaluation shows that StateScribe maintains high accuracy (F1-score=83.1%) across 11 revisits, while remaining low-latency (mean<1.54s) and memory-efficient (<54MB) across 110 revisits. A user study with nine BLV participants demonstrates that StateScribe improves change awareness across revisits in three real-world locations. Finally, we discuss implications for long-term AI-assisted companions that support broader change observation using multimodal sensing, extend beyond changes to other memory capabilities, and adapt to individual users, intents, and contexts.

cs.HC↗

arXiv2026

FootprintNet: State-Transition-Guided Dynamic Footprint Learning for Multi-temporal Remote Sensing Change Detection

Despite substantial progress in remote sensing multi-temporal change detection (MTCD), most existing MTCD methods still represent the dynamic process at each spatial location over the entire observation period using a single change category associated with the final observation. This implicit single-change assumption limits their ability to characterize regions of recurrent change closely related to human activities. To address this limitation, we introduce Urban Building Dynamics Detection (UBDD), which identifies building-change dynamic footprints, i.e., the temporal intervals in which changes occur, from multi-temporal imagery and produces pixel-wise classification masks. For regions undergoing two or more changes, UBDD introduces an independent multi-change class for unified representation, thereby enabling unified modeling of single- and multi-change processes. Furthermore, we propose FootprintNet, which abstracts building-change processes as interactions between latent states and actions, and imposes state-action transition constraints to guide the learning of causally coherent change trajectories. It further exploits temporal change-boundary cues to enhance feature contrast across boundary sides, thereby improving the discrimination among different dynamic footprints and enabling accurate detection of dynamic footprints. Moreover, we introduce the Building Change Dynamics Score (BCDS) to address the inability of conventional metrics to reflect the temporal proximity between predicted footprints and labels. It evaluates predictions according to their preservation of change semantics and temporal offsets from the corresponding labels. Extensive experiments on TSCD, MUDS, and WUSU demonstrate that FootprintNet outperforms current state-of-the-art methods. The code is available at https://github.com/zmoka-zht/FootprintNet.

cs.CV↗

arXiv2026

Beyond Action Imitation: Learning a Decision-Aware User Simulator for Online Advertising

Recent advances in LLM-based user simulation have shown promise for offline evaluation of recommendation and advertising systems. However, existing simulators typically infer user preferences from single-domain interaction histories and are primarily optimized to reproduce observable actions such as clicks. Consequently, they capture only a partial view of user preferences, while action-only prediction easily induces model shortcuts and limits both the fidelity and diagnostic value of simulation. To address these challenges, we propose DASH, a decision-aware user simulator that jointly generates thinking traces and predicts behavioral actions from heterogeneous cross-domain histories. DASH first introduces a Context Engineering stage that folds heterogeneous cross-domain histories into decision-relevant context, together with prompt optimization for effective reasoning over the folded context. To train a user simulator, DASH distills thinking trajectories from strong LLMs as SFT data, and further tailors a rubric-based reward model that evaluates thinking traces along form, content, and logic for RL training. Combined with the action reward, these signals jointly improve action prediction and thinking quality. Extensive experiments on real-world Tencent advertising data spanning five heterogeneous content domains demonstrate the effectiveness, efficiency, fidelity, and diagnostic value of DASH.

cs.IR↗

arXiv2026

ScalablePromptus: Scalable and High-Fidelity Prompt-Based Video Streaming

Prompt-based video streaming transmits compact semantic prompts instead of pixel-level content for generative reconstruction, enabling ultra-low-bitrate communication. However, the state-of-the-art Promptus framework is vulnerable to network fluctuation, where partially received prompts lead to catastrophic quality collapse. We propose ScalablePromptus, which enhances Promptus with semantic and color-aware prompt inversion, spherical linear interpolation for intermediate frames, and--most critically--a dropout training strategy that produces rank-ordered prompt representations. This allows the receiver to reconstruct meaningful video from arbitrarily truncated prompts without any adaptation. Under stable networks, ScalablePromptus achieves modest quality gains. Under lossy conditions, it reduces the performance degradation caused by truncation by 82%-95% compared to the baseline, making prompt-based streaming robust enough for real-world deployment.

eess.IV↗

arXiv2026

StanceFlip: A Comprehensive Multi-Dimensional Benchmark for Multimodal Conversational Stance Flipping Forecasting

Conversational stance detection has shifted from static text analysis to dynamic multimodal modeling. However, existing benchmarks exhibit three key limitations: failure to capture the dynamic evolution of beliefs, particularly during stance reversals; difficulty in disentangling affective states from logical reasoning; and neglect of the critical role of multimodal cues in resolving pragmatic ambiguities such as sarcasm. To address these limitations, we propose StanceFlip, a benchmark designed for multimodal conversational stance flipping forecasting over multi-turn dialogues across five modalities and multi-scenarios, which includes two novel subtasks: 1) Multimodal Stance Sextuple Extraction, extracting holder, target, emotion, sentiment, stance, and rationale as static state snapshots of dialogue to capture fine-grained cognitive structures. 2) Dynamic Stance Flip Attribution, tracking stance reversals across the conversation and identifying their underlying triggers. Alongside the dataset, we propose a dedicated framework, named ConStaFF, for Multimodal Conversational Stance Flipping Forecasting (MCSFF). Built upon a large language model, ConStaFF performs end-to-end stance reasoning, with a Thought-of-Stance (ToS) reasoning framework and a self-reflective verification mechanism integrated for structured stance modeling and faithful flip attribution. Specifically, ToS decomposes the reasoning process into specialized cognitive personas to formulate target propositions, resolve cross-modal conflicts, and infer historical stance trajectories. Extensive experiments show that our approach achieves state-of-the-art performance on both sextuple extraction and flip-trigger attribution, outperforming strong multimodal large language model baselines by substantial margins.

cs.CL↗