arXiv subjects

Qiong Wu

Publications and source records attributed to Qiong Wu.

At least 19 recordsLinked to original sources

arXiv2026

Design Anything, Design at Scale

As multimodal resources are transformed into media outputs, agentic AI is expected to stay aligned with design intent over long horizons. Designers follow briefs specifying goals, constraints, and deliverables, with later work building on earlier decisions. Recent work reuses design experience across tasks, yet alignment among artifacts within one project remains challenging. Agents therefore need to preserve and apply this intent across domains and as projects grow. We introduce Design Anything, a graph-based harness that preserves design intent outside the model and binds deliverables to the workflow steps that produce them through an intent-execution graph. A task contract retains constraints and decisions, selected designs become explicit inputs to later generations, and a deterministic controller uses bindings and execution records to check output types and counts, identify outstanding deliverables, and guide subsequent turns. We also introduce DesignAnything-Bench, with 228 tasks across 19 design domains, including 38 project families at scales of 6, 14, and 24 artifacts. With the LLM held fixed, Design Anything satisfies 85.0% and 92.5% of the brief's factual constraints with DeepSeek V4.1 Flash High and GPT-5.6 Sol High, respectively, exceeding the strongest same-model baselines by 15.4% and 14.4%. These gains reach 25.1% and 23.0% on 24-artifact tasks. Under each model, Design Anything leads or ties in constraint satisfaction in 18 of 19 domains. Professional designers also prefer its complete deliverable sets over those of same-model baselines in a blind evaluation.

cs.CV↗

arXiv2026

SpecSyn: LLM-based Synthesis and Refinement of Formal Specifications for Real-world Program Verification

Program verification is a formal technique to rigorously ensure the correctness and fault-freeness of software systems. However, constructing comprehensive interprocedural specifications for full verification obligations is time-consuming and labor-intensive, giving rise to automated specification generation approaches. Despite the significant advancements in these approaches brought by Large Language Models (LLMs), existing LLM-empowered approaches still suffer from significant limitations: they lack effective strategies for handling sizable input programs, and are typically equipped with no mechanisms to evaluate and guarantee the strength of the generated specifications. The limitations impair their ability to extract precise specifications from real-world complicated programs to support the verification of target properties, thereby hindering the applicability of existing approaches in verification tasks on real-world programs. To remedy this gap, we propose SpecSyn, a novel LLM-based specification generation method. SpecSyn first decomposes the input program into individual segments, which are handled respectively by the subsequent iterative specification generation process. Innovatively, we incorporate into the process a specification refinement mechanism based on semantic-non-equivalent program mutations and variant discrimination, assessing and enhancing the semantic strength of the generated specifications. Extensive experiments show that SpecSyn maintains high precision over 90% and outstanding recall over 75%, significantly outperforming existing LLM-based approaches. In further evaluations, SpecSyn successfully handles 1022 out of 1421 target properties for open-source programs, proving its applicability on real-world program verification tasks.

cs.SE↗

arXiv2026

Kink boundary-induced polarity revealed by nonreciprocal charge transport in FeTe0.7Se0.3 superconductor

Nonreciprocal charge transport, characterized by direction-dependent electrical response, provides a powerful probe of electronic structure and a platform for rectification. However, its realization is typically governed by intrinsic crystal symmetry, restricting the range of suitable materials. Here, we demonstrate nonreciprocal charge transport in an otherwise centrosymmetric superconductor, FeTe0.7Se0.3 (FTS), through kink boundary-induced strain engineering. The stress relaxation during growth generates a cusp-like kink-band boundary with asymmetric curvature, producing a spatially nonuniform strain field and an effective in-plane polarity that breaks inversion symmetry. Pronounced nonreciprocal transport is observed under out-of-plane magnetic fields, with strong dependence on current and temperature across the superconducting transition, in contrast to the negligible response in flat nanosheets. Our results identify kink boundary-induced symmetry breaking as a general route to engineer nonreciprocal charge transport in centrosymmetric systems.

cond-mat.supr-con↗

arXiv2026

Unveiling High-Energy Ultrafast Responses Driven by Fermi-Level Gap Opening in Charge-Density Waves

Ultrafast pump-probe spectroscopy is indispensable for investigating interactions, collective excitations, and nonequilibrium dynamics in superconductors and correlated electron systems. However, how a small gap opening near Fermi surface affects the spectral changes in the high-energy interband transition region remains elusive. To address this issue, we perform systematic studies of carrier relaxation dynamics in the prototype charge-density-wave (CDW) material EuAl4. We find that the CDW gap shrinks substantially under optical pumping. This band renormalization strongly modulates interband transitions associated with the gap edge, inducing a pronounced transient reflectivity change at the corresponding probe energies. These results establish a direct link between low-energy order-parameter dynamics and high-energy spectral responses, providing a general framework for interpreting transient signals in gapped quantum materials.

cond-mat.str-el↗

arXiv2026

How to Reduce Localization Ambiguity? Geometry-Semantic Constrained BEV Representation Learning for Satellite-Ground Localization

Satellite-ground localization estimates the planar position and yaw orientation of a ground camera within a geo-referenced satellite image. Most recent methods map ground and satellite features into a shared bird's-eye-view (BEV) space and establish spatial correspondences. However, insufficient depth constraints can assign one ground feature to different distances along a viewing direction, creating geometric ambiguity in BEV feature placement. Similar appearances at different locations can also create descriptor matching ambiguity, while existing descriptor learning lacks explicit semantic supervision to distinguish them. We propose GeoSem-BEV, a geometry-semantic constrained BEV representation learning method. Radial depth supervision constrains distance assignment, and vertical height supervision constrains height aggregation. Shared explicit semantic supervision promotes consistent semantic predictions across views and helps distinguish locations with similar semantics. These constraints improve feature placement and descriptor discriminability, enhancing state-of-the-art BEV localization models. On VIGOR with unknown orientation, GeoSem-BEV reduces mean orientation error by 37.2% and 38.1% in the cross-area and same-area settings, respectively. The corresponding errors are reduced by 10.8% and 15.6% on DReSS-D. On KITTI-CVL, it reduces same-area mean orientation error by 26.8% under 10 degree orientation noise.

cs.CV↗

arXiv2026

Counterfactual Attention Policy Distillation for Temporal Video Grounding

Temporal video grounding is a key capability of advanced Multimodal Large Language Models (MLLMs) for the thorough understanding of video events, which is however often limited by repeated actions and visually similar contexts in long videos. In this paper, we study this issue from the perspective of On-policy distillation (OPD) and propose a new training regime for MLLMs termed Counterfactual Attention Policy Distillation (CAPD). In particular, OPD is a viable solution for MLLMs via providing dense teacher supervision on student-generated trajectories. But its next-token based teacher-student distillation is hard to identify the specific video segments supporting each predicted timestamp, which is critical for temporal grounding. In this case, CAPD measures how masking each temporal group changes the teacher's output distribution. The resulting counterfactual influence calibrates the teacher's attention and weights token-level distillation, allowing the student to learn the temporal evidence that affects boundary prediction. To validate CAPD, we trained it on Qwen3-VL-8B-Instruct using only 2,500 samples for one epoch, and evaluated it on the TimeLens and multiple general video benchmarks. Experimental results show that CAPD improves average recall by 12.0% relative to GRPO on TimeLens while preserving general video understanding, achieving comparable accuracy to the base model.

cs.CV↗

arXiv2026

AP Association for RHS-Enabled Cell-Free Uplink MIMO in Industrial Indoor UAV Networks

Indoor industrial UAV uplink networks face serious blockage and shadowing from shelves, metal equipment, and production facilities. UAVs are also often clustered and fly along similar straight inspection routes at fixed heights. These features make traditional small-cell deployment less suitable, especially when high reliability, continuous coverage, and good service for weak UAVs are required. Cell-free networks can improve robustness through distributed access points (APs) and UAV?centric communications. Reconfigurable holographic surface (RHS)-enabled APs provide programmable analog receive beams and generate scalar post-RHS observations, which are jointly processed at the CPU for distributed uplink MIMO detection at relatively low hardware cost. Conventional AP association relies on distance, large-scale fading, or post-combining SINR obtained with user-specific digital combiners. Here, however, all UAVs served by a single-feed RHS AP share one amplitude?constrained receive pattern and one scalar AP output. We therefore derive an SINR-like score from this physical output and, under weak inter-AP disturbance correlation, approximate the CPU-side log-det objective by an additive per-AP surrogate, yielding a low-complexity ranking rule. The results show that the nearest AP is not always the best choice, the AP-UAV height difference may have an optimal value, and larger serving clusters bring diminishing returns. Simulations show that the proposed method improves the minimum UAV data rate, average spectral efficiency, fairness, and energy efficiency compared with benchmark schemes.

cs.NI↗

arXiv2026

Active Movable-Element RIS Assisted Vehicular Semantic Communications: Modeling and Optimization

Severe signal blockage and fast-varying channels in vehicular environments pose critical challenges to reliable semantic communication. To address these, this paper proposes a novel Row-Movable Active Reconfigurable Intelligent Surface (RM-A-RIS) assisted vehicular semantic communication system. This architecture uniquely combines active signal amplification with element mobility to compensate for multiplicative fading and reconstruct channel geometry, thereby enhancing spatial diversity. We formulate a joint optimization problem to maximize Semantic Spectral Efficiency (SSE) by coordinating RIS element positions, active reflection coefficients, and semantic symbol length. An efficient Alternating Optimization (AO) algorithm is developed to tackle the coupled non-convexity. Simulation results demonstrate that the proposed scheme substantially outperforms existing benchmarks, achieving up to 132.9%, 9.2%, and 35.2% improvements in Sum-Semantic Spectral Efficiency (Sum-SSE) compared to the passive RIS, fixed-position active RIS, and QPSO baselines, respectively.

cs.NI↗

arXiv2026

Mobile Tracking via Target-Mounted IRS-Assisted ISAC System

This paper proposes a target-mounted intelligent reflecting surface (IRS)-assisted integrated sensing and communication framework for real-time unmanned aerial vehicle (UAV) tracking, addressing challenges such as link blockage and weak radar cross section in the low-altitude economy. By integrating the IRS onto the UAV, the system creates a mobile cooperative target that provides controllable line-of-sight echoes for self-tracking while acting as a mobile relay for ground communication enhancement. We establish a comprehensive three dimensions state evolution model for the maneuvering UAV. Based on this model, an extended Kalman filter is immediately implemented to achieve real time tracking of the moving UAV. To characterize the fundamental theoretical limits of this recursive estimation process, we derive the analytical posterior Cramer Rao bound and a closed form expression for the elliptical tradeoff performance bound to quantify the relationship between sensing precision and communication throughput. To ensure millisecond level responsiveness, we develop a low complexity joint beamforming design. By utilizing the analytical mapping between tracking and communication requirements, the proposed scheme yields closed form solutions for beamforming vectors, effectively bypassing the time consuming numerical iterations of conventional methods. Numerical simulations demonstrate that the proposed framework significantly outperforms traditional fixed-deployment benchmarks across complex maneuvering trajectories, achieving centimeter-level accuracy while substantially reducing transmit power and processing latency.

eess.SP↗

arXiv2026

Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learning

Federated learning (FL) over wireless networks suffers from significant training latency and degraded convergence due to unreliable wireless transmission, especially under blocked propagation environments. Although reconfigurable intelligent surfaces (RISs) can improve communication reliability, existing wireless FL studies rarely characterize the trade-off between learning convergence and communication delay under modulation-dependent transmission errors. In this paper, we consider a wireless FL system operating under RIS-assisted blocked-link propagation scenarios, and focus on adaptive modulation and sub-channel allocation for convergence-latency aware communication design. By characterizing the effect of symbol errors on uploaded local gradients, we derive a convergence-related upper bound that reveals the impact of symbol error rate (SER) on FL loss decay. Based on this result, we formulate a joint convergence-latency optimization problem, which is cast as a mixed-integer nonlinear programming (MINLP) problem, and solve it using a low-complexity hybrid alternating optimization framework. Extensive experiments on MNIST, CIFAR-10, and Speech Commands show that the proposed scheme consistently achieves faster convergence and higher test accuracy than existing adaptive communication schemes, especially in complex tasks and challenging wireless scenarios.

cs.LG↗

arXiv2026

Transmissive RIS Transceiver-Empowered ISAC Systems: Energy Efficiency Optimization for Perfect and Imperfect CSI

In this paper, a novel transmissive reconfigurable intelligent surface (TRIS) transceiver is employed to enable an integrated sensing and communication (ISAC) system supporting both communication and sensing. Under both perfect and imperfect channel state information (CSI), we study the transmit beamforming design for the TRIS transceiver to maximize the system energy efficiency (EE), subject to per-user minimum-rate guarantees, a minimum beampattern gain toward the sensing target, and per-antenna power constraints. The corresponding EE maximization problems are challenging to solve due to the fractional objective and non-convex constraints. In particular, under imperfect CSI, the resulting semi-infinite constraints further complicate the problem. For the perfect CSI case, we first apply the fractional programming (FP) methodology to obtain more tractable reformulations of the rate functions, and then propose an iterative algorithm based on the majorization-minimization (MM) framework. For the imperfect CSI case, we utilize the S-Procedure to transform the semi-infinite inequality constraints into linear matrix inequalities (LMIs), and further develop an efficient MM-based algorithm with the aid of slack variables. Numerical results demonstrate the convergence and effectiveness of the proposed algorithms and validate the EE gains of the TRIS transceiver-enabled ISAC system.

eess.SP↗

arXiv2026

Transmit Beamforming Design for Integrated Sensing and Communication Using Transmissive RIS Transceiver

Integrated sensing and communication (ISAC) is a key technology for future wireless networks, calling for hardware-efficient architectures to jointly support communication and sensing. In this paper, a transmissive reconfigurable intelligent surface (TRIS) transceiver is leveraged to enable an ISAC system. Under the considered system model, we investigate transmit beamforming design for the TRIS transceiver to maximize the sum-rate/beampattern gain, subject to the predefined sensing beampattern gain/communication rate thresholds and the per-unit power constraints of the TRIS transceiver. Since the objective functions and constraints are non-convex, the above two optimization problems are highly challenging. To resolve the difficult optimization problems, we combine the fractional programming (FP) method and the majorization-minimization (MM) framework to develop second-order cone programming (SOCP)-based solutions. Since the per-element power constraints introduce a large number of constraints, this increases the complexity of solving the optimization problems. By splitting the coupling constraints and applying the alternating direction method of multipliers (ADMM) framework, we propose two analytic-based algorithms for efficiently updating the beamformer configurations in the sum-rate and beampattern gain maximization problems, respectively. Simulation results demonstrate the convergence and effectiveness of the proposed algorithms, and show that the low-complexity algorithms achieve performance close to the SOCP-based benchmarks with substantially reduced computational complexity.

eess.SP↗

arXiv2026

Ultrafast giant enhancement of second harmonic generation in a strongly correlated cobaltite

In quantum materials, nonlinear optical responses are highly sensitive to electronic structure and many-body interactions. Probing and manipulating such nonlinear processes is a complex and subtle endeavor, yet it offers deep insights into emerging physics and functionalities. Here, we report an anomalous ultrafast enhancement of second harmonic generation (SHG) in a strongly correlated cobaltite YbBaCo$_4$O$_7$. Above-bandgap femtosecond pumping increases SHG intensity by up to 60 % within 200 fs, with the enhancement persisting for tens of picoseconds. The enhancement is strongly anisotropic, with substantial amplification of in-plane susceptibility tensors, whereas the out-of-plane counterpart shows negligible change. We attribute these anomalies to ultrafast photodoping-induced modulation of the on-site Coulomb repulsion, which dynamically renormalizes the band structure and selectively amplifies specific $χ^{(2)}$ components. These nonlinear dynamics encode rich information about the orbital symmetries and energies of the states involved, opening new avenues for all-optical probing of electronic structure in strongly correlated materials.

cond-mat.str-el↗

arXiv2026

Identifying and Resolving Pitfalls of Knowledge-Based VQA Benchmarks: Auditing, Repairing, and Augmenting

Knowledge-Based Visual Question Answering (KB-VQA) aims to evaluate whether Visual Language Models (VLMs) can retrieve, ground, and reason over external structured knowledge beyond visual evidence. In practice, answer accuracy is widely adopted as the primary evaluation metric, implicitly treating correctness as a proxy for knowledge-grounded reasoning. However, for existing KB-VQA benchmarks, this proxy relies on critical assumptions that are often overlooked and rendered unreliable by benchmark issues: annotated answer must be derivable from the associated knowledge base, question must be well-posed with sufficient constraints, and visual setting must meaningfully require grounded disambiguation. In this work, we show that these assumptions are systematically violated in existing KB-VQA benchmarks. Our audit reveals substantial instances with missing or contradicted answers and underspecified questions that render accuracy a misleading metric. Furthermore, we find that existing datasets rely on visually trivial, single-entity scenes that bypass the need for sophisticated visual-to-knowledge mapping. We demonstrate that even with controlled architectures, these flaws lead to distorted model rankings and overestimations of reasoning capabilities. To address this, we introduce (1) a principled audit-and-repair protocol that restores answer derivability and question clarity, and (2) a controlled multi-entity augmentation protocol that introduces visual ambiguity to challenge initial retrieval and grounded reasoning. Re-evaluation under corrected and augmented settings yields markedly different performance trends. Our findings call for rethinking evaluation protocols and designing more interaction-aware KB-VQA benchmarks that prioritize verifiable reasoning over simple matching.

cs.CL↗

arXiv2026

Towards a Dynamic and Fixed-budget Memory Bank for Efficient Streaming Video Understanding

Currently, streaming video understanding is still a daunting task for existing \emph{multimodal large language models} (MLLMs). Its difficulties not only lie in handling the ever-increasing video frames, but also in the unpredictability of future video content and input instructions. In this paper, we study this task from the perspective of constructing a dynamic but fixed-budget memory bank, and propose a novel and training-free approach termed \emph{\textbf{CausalMem}}. CausalMem is dedicated to constructing a dynamic visual memory update mechanism, thereby maximizing the amount of information in streaming video within a limited memory space, much like the human brain. In practice, CausalMem estimates the redundancy of visual tokens and updates the memory bank via an online semantic basis, which models the principal semantics of the observed video stream. To validate CausalMem, we apply it to two representative MLLMs, namely LLaVA-OneVision and Qwen2.5-VL respectively, and conduct extensive experiments on both streaming and offline video understanding benchmarks. The experimental results not only show the great advantages than existing methods under both streaming and offline settings, \emph{e.g.}, $+3.2\%$ and $+3.0\%$ average accuracy gains respectively, but also witness the superior semantic preservation for streaming videos, \emph{e.g.}, using 12$k$ token budgets to memorize hour-long streaming videos, which achieves more than \textbf{20$\times$} visual token compression ratio and only occupies about \textbf{82 MB} storage. \textbf{Our code} is given in \href{https://github.com/hktk07/CausalMem}{CausalMem}.

cs.CV↗

arXiv2026

Ground Then Rank: Revisiting Knowledge-Based VQA with Training-Free Entity Identification

Knowledge-Based Visual Question Answering (KB-VQA) requires grounding visual queries to external knowledge beyond directly observable content in images. While recent multi modal large language models (MLLMs) show strong perceptual abilities, they struggle on KB-VQA tasks requiring groundings from both fine-grained entity and evidence levels. Most existing multi-modal retrieval augmented generation (MM-RAG) methods tightly couple entity discrimination and section-level evidence ranking into a single re-ranking stage, leading to high cost and limited generalization. In this work, we revisit existing MM-RAG solutions from a workflow perspective and argue both entity-level and fact-level groundings are key bottlenecks. We observe that although MLLMs often fail under open-ended entity naming, they can better identify the correct entity when selecting from a small set of candidate names. Based on this insight, we propose a simple and training-free identify-before-answer IBA framework that decouples entity identification from section-level re-ranking. Our approach prompts an MLLM to select high-confidence entities using only candidate names, followed by an off-the-shelf textual re-ranker for evidence selection. Experiments on Encyclopedic-VQA and InfoSeek show that our method consistently outperforms fine-tuned multi-modal re-ranking baselines while reducing training and inference complexity. Additional analyses reveal that the improvements arise not only from better entity identification, but also from selecting more informative evidence once correct entity is fixed. Our implementation is made public to ease reproducibility.

cs.CL↗

arXiv2026

UME: A Unified Meta-Generalization Framework for Cross-Domain ETA

Accurate Estimated Time of Arrival (ETA) prediction on checkout page is crucial in instant logistics for enhancing user satisfaction, optimizing dispatching, and controlling operational costs. In international on-demand delivery platforms, where ETA data originates from diverse countries or regions with different patterns, multi-domain modeling is of great importance and has been widely adopted. However, existing methods still face three critical challenges in real-world deployment. First, current multi-domain models struggle to generalize to completely unseen domains, failing to achieve zero-shot prediction during the initial cold-start phase. Second, cross-domain feature spaces are often assumed to be consistent, whereas new domains commonly suffer from structural missingness of offline (statistical) features due to the lack of historical data. Third, such feature missingness often compels industrial systems to model mature and cold-start domains separately, hindering knowledge transfer and increasing maintenance overhead. To address these challenges, we propose \textbf{UME}, a \textbf{U}nified \textbf{M}eta-generalization framework for \textbf{E}TA. Specifically, UME integrates a unified dual-branch architecture with a novel meta-learning mechanism that employs a hypernetwork-based meta learner. By leveraging domain-level knowledge and instance-level context, the meta learner empowers three meta modules to dynamically modulate feature gating, expert attention, and final prediction, capturing cross-domain correlations and facilitating intra-domain adaptation. A knowledge distillation strategy is further introduce to enhance performance. UME has now been deployed in Meituan-keeta delivery platform (the largest international food delivery platform in China). Extensive offline experiments and online A/B tests demonstrate that UME significantly outperforms existing baselines.

cs.LG↗

arXiv2026

Unifying and Optimizing Data Values for Selection via Sequential Decision-Making

Data selection has emerged as a crucial downstream application of data valuation, yet the theoretical foundations for using data values in selection remain underexplored. We reformulate data selection as a sequential decision-making problem where the optimal selection sequence arises from dynamic programming, and data values can be understood as encodings of this optimal sequence. This framework unifies and reinterprets existing methods like Data Shapley through the lens of approximate dynamic programming, revealing them as myopic linear approximations to the sequential problem. We further analyze how selection optimality degrades with utility curvature under submodularity, explaining when and why these approximations fail. To bridge theory and practice, we propose an efficient bipartite graph-based surrogate that preserves submodular structure while enabling scalable greedy selection with provable guarantees. Experiments on classical ML benchmarks and large-scale LLM fine-tuning data selection demonstrate substantial improvements over existing methods. Code is publicly available at https://github.com/frankhlchi/SeqDataVal

cs.AI↗