arXiv subjects

Linhao Wang

Publications and source records attributed to Linhao Wang.

10 recordsLinked to original sources

arXiv2026

TokenFlow: Latent Token Rectified Flow Matching for 3D Point Cloud Generation

3D point clouds generation requires capturing both part-level layout and surface detail while preserving permutation equivariance. Existing latent generative models often encode an entire shape into a single vector, which limits their ability to retain part-level structure. We propose TokenFlow, a hierarchical flow matching framework that represents each shape as a set of latent tokens and couples a rectified flow over these tokens with a conditional flow over points. A bottleneck of $M \ll N$ learnable tokens aggregates global context through cross-attention at $O(NM)$ cost. On ShapeNet, TokenFlow achieves the best MMD and coverage across airplane, car, and chair, while obtaining 1-NNA comparable to the strongest baselines. On ModelNet, its learned latent set reaches the best reported linear-evaluation accuracy on ModelNet10. These results show that representing a shape as a set of latent tokens, aggregated at $O(NM)$ cost, provides an effective representation for 3D point cloud generation.

cs.CV↗

arXiv2026

EviScene: Beyond "Is It AI?" for Agentic Image Factuality Assessment

Determining whether an image is AI-generated does not establish whether the situation it depicts is factually supported. We formulate Image Factuality Assessment: given an image and a generic verification instruction, a system returns a supported or refuted verdict with an evidence-grounded explanation. Without a supplied textual claim, the system must progressively refine factual questions during investigation and align external evidence with the specific situation depicted. We introduce EviLens, an expert-reviewed benchmark of 10,174 supported and refuted images from controlled construction and web collection, covering twelve factual error types and evaluating both verdict accuracy and evidence sufficiency. We develop EviScene, a multimodal ReAct agent that interleaves visual inspection, external retrieval, and evidence analysis, using new findings to refine its questions and revisit relevant image details. We train a compact policy with supervised fine-tuning on reviewed teacher trajectories, followed by privileged on-policy self-distillation applied to verified decisions from the policy's own investigations. On the 1,527-image test set, the trained Qwen3.5-9B agent achieves 79.05% balanced accuracy and a Strict Evidence Sufficiency Rate (SESR) of 52.26%, improving over the same-backbone base agent by 6.71 and 9.37 percentage points, respectively. Its SESR is the highest among the evaluated agents.Code, models, and data will be released at https://github.com/wangzian828/EVISCENE and https://huggingface.co/ZiAnwang/EviScene.

cs.CV↗

arXiv2026

Not All Errors Matter: Decision-Relevant Prediction Error Predicts Planning Quality

World models are typically trained and evaluated by prediction error, assuming that more accurate predictions lead to better decisions. We show that this assumption can fail because models with similar total error can differ substantially in planning performance when their errors occur on different state dimensions. We introduce Decision-Relevant Prediction Error (DRPE), which measures prediction error on the state dimensions that affect decisions. We also develop an iso-error evaluation protocol that varies error allocation while keeping total error fixed. In a factored gridworld with known state relevance and a standardized planner, we evaluate 55 controlled and learned models across different error levels and allocations. Total prediction error is weakly related to planning success (Spearman $ρ=-0.25$), whereas DRPE is strongly predictive ($ρ=-0.84$; $-0.98$ within the controlled family). Models with only a 1\% difference in total error can differ by 60 percentage points in planning success (97\% vs 37\%). The relevant error also depends on the task, with model rankings reversing across tasks at the same total error. Deeper imagination further amplifies decision-relevant errors, while learned models exhibit systematic bias on rare but decision-critical events. We formalize sufficient conditions under which DRPE correctly ranks models and total prediction error cannot.

cs.LG↗

arXiv2026

BlueprintAgent: Constraint-Triggered Targeted Revisits for Simulation-Ready Generation from Scanned Structural Blueprints

Converting in-service reinforced-concrete (RC) building blueprints into simulation-ready models---structured frame representations that support deterministic FEM export and qualified-engineer review---underpins safety assessment and seismic retrofit, but the process remains manual. Direct prompting of a multimodal large language model (MLLM) over a scanned sheet is unreliable: outputs often violate engineering constraints on beam--column support, span count, or 3D continuity. We present BlueprintAgent (BPA), a constraint-triggered multimodal agent for simulation-ready frame extraction from scanned blueprints. BPA treats the MLLM as the primary reader and decision maker, with OCR and computer vision supplying localized evidence. Its central mechanism realizes engineering constraints as callable validators whose entity-level conflict reports trigger targeted MLLM revisits over the local region---an inference-time control distinct from fixed pipelines and free-form self-reflection. We evaluate BPA on 300 real scanned blueprint sheets from 20 anonymized RC frame projects, against five baselines and six ablations. BPA reaches a macro-averaged Beam F1 of 0.994, against 0.301 for single-MLLM zero-shot and 0.820 for a fixed pipeline; removing MLLM-led axis adjudication collapses Beam and Column F1 on complex multi-sheet projects. For dense technical drawings, engineering constraints are best deployed as triggers for entity-level targeted revisits rather than as post-hoc output filters.

cs.CL↗

arXiv2026

Hierarchical Flow Matching for 3D Point Cloud Generation

Generating high-quality 3D point clouds requires capturing both global shape topology and local geometric details. Existing flow-based methods rely on continuous normalizing flows (CNFs) that demand expensive ODE solving and trace estimation during training, while diffusion models require hundreds of iterative denoising steps. Moreover, most approaches adopt single-level generation directly in point space, disregarding the hierarchical structure natural to 3D shapes. We propose Hierarchical Flow Matching (HFM) that extends flow matching to bilevel structure for unconditional 3D point cloud generation. HFM decomposes the task into two levels via optimal-transport flow matching: a \textit{Latent Flow Matching} models the global shape manifold in a compact latent space, and a \textit{Conditional Point Flow Matching} reconstructs detailed point clouds conditioned on the latent code. Both flows are trained with simple MSE regression losses. The resulting straight OT paths enable efficient sampling with as few as 15 Euler steps per flow, while the structured latent space supports downstream tasks including classification. Extensive experiments on ShapeNet and ModelNet benchmarks demonstrate that HFM achieves competitive or even best performance compared with prior state-of-the-art methods.

cs.CV↗

arXiv2026

Boson peak and medium-range elastic heterogeneity in calcium silicate hydrate probed by terahertz spectroscopy and low-temperature calorimetry

The boson peak (BP), a universal vibrational anomaly of disordered solids, has been predicted but not systematically characterized in calcium silicate hydrate (C-S-H), the binding phase of hardened cement. Building on a preliminary terahertz survey, we characterize the BP across five Ca/Si ratios (0.5-1.7) using terahertz time-domain spectroscopy (THz-TDS) and low-temperature calorimetry, two probes of vibrational dynamics that complement the static picture of conventional structural methods. After Bruggeman correction for crystalline impurities, both probes locate the BP near 1 THz; they agree on frequency but diverge in intensity. The terahertz integrated spectral weight and the calorimetric Cp/T3 peak both fall monotonically with Ca/Si, whereas the apparent terahertz peak height is maximal at Ca/Si = 1.0, where damping is low and oscillator strength still substantial. This decoupling marks a structural crossover between silicate-chain depolymerization and interlayer calcium filling. From the BP we obtain a medium-range dynamical correlation length of order 1 nm (0.3-2 nm) and a coherent-potential elastic-heterogeneity parameter that decreases from gamma = 0.98 to 0.48 as Ca/Si rises; the Debye-normalized BP frequency (nu_BP/nu_D = 0.15-0.17) places C-S-H within the range reported for silicate glasses. Because gamma governs the distribution of energy barriers for local structural rearrangements, it provides a quantitative, composition-resolved descriptor relevant to the intrinsic creep and thermal transport of C-S-H, linking nanoscale vibrational dynamics to the macroscopic durability of concrete. The dual-probe boson-peak approach is transferable to other amorphous solids, including the supplementary cementitious materials of low-carbon cements.

cond-mat.mtrl-sci↗

arXiv2026

VICR: Visual In-Context Restoration for Real-World Image Super-Resolution

Real-world image super-resolution (Real-ISR) requires balancing structural fidelity to degraded observations with realistic detail synthesis. However, existing generative Real-ISR methods often rely on entangled conditioning mechanisms, leading to structural drift or semantically inconsistent details. To address this issue, we propose Visual In-Context Restoration (VICR), a Diffusion Transformer (DiT)-based framework that formulates Real-ISR as image completion. Specifically, we introduce a decoupled visual prior injection mechanism that derives local and global cues from the low-quality (LQ) image: local cues help recover image structures and support high-frequency detail synthesis, while global cues guide overall generation and promote semantic consistency. For ambiguous regions under severe degradation, VICR employs an inference-time agent to refine semantic prompts using visual evidence from the LQ input while keeping model parameters fixed. Experiments show that VICR achieves state-of-the-art performance across multiple Real-ISR benchmarks with only 127M trainable parameters.

cs.CV↗

arXiv2026

Thinking in Structures: Evaluating Spatial Intelligence in Constraint-Governed Spaces

Spatial intelligence is crucial for vision--language models (VLMs), yet many scene-centric benchmarks evaluate unconstrained environments where a single image may admit multiple plausible 3D interpretations. We introduce SSI-Bench, a VQA benchmark for Structure-Centric Spatial Reasoning (SCSR) in constraint-governed spaces. Built from complex real-world 3D structures, it uses structural constraints from geometry, topology, and physical feasibility to make component relations more determinate from visual evidence. The benchmark contains 1,000 ranking questions spanning geometric and topological reasoning, where correct ordering requires resolving all candidate-wise 3D relations, imposing stronger demands on spatial understanding. It is created through a fully human-centered pipeline with over 400 researcher-hours of image curation, component annotation, and question design. Evaluating 31 VLMs reveals a large gap to humans: the best open-source model achieves 22.2% accuracy and the strongest closed-source model reaches 33.6%, while humans score 91.6%. Further results show that chain-of-thought reasoning brings only marginal gains, and error analysis reveals fundamental limitations in current models' spatial understanding within constraint-governed spaces. Project page: https://ssi-bench.github.io.

cs.CV↗

arXiv2026

Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning

Partial agent failure becomes inevitable when systems scale up, making it crucial to identify the subset of agents whose failure causes worst-case system performance degradations. We study this Vulnerable Agent Identification (VAI) problem in large-scale multi-agent reinforcement learning (MARL). We frame VAI as a Hierarchical Adversarial Decentralized Mean Field Control (HAD-MFC), where the upper level selects vulnerable agents as an NP-hard task and the lower level learns their worst-case adversarial policies via mean-field MARL. The two problems are coupled together, making HAD-MFC difficult to solve. To handle this, we first decouple the hierarchical process by Fenchel-Rockafellar transform, resulting a regularized mean-field Bellman operator for upper level that enables independent learning at each level, thus reducing computational complexity. We next reformulate the upper-level NP-hard problem as an MDP with dense rewards, allowing sequential identification of vulnerable agents via greedy and RL algorithms. This decomposition provably preserves the optimal solution. Experiments show our method effectively identifies more vulnerable agents in large-scale MARL and the rule-based system, fooling system into worse failures, and reveals the vulnerability of each agent in large systems. Code available at https://github.com/Waken-dream/VAI

cs.MA↗

arXiv2026

EAGLE: Contextual Point Cloud Generation via Adaptive Continuous Normalizing Flow with Self-Attention

As 3D point clouds become the prevailing shape representation in computer vision, generating high-quality point clouds remains a challenging problem. Flow-based models have shown strong potential due to exact likelihood estimation and invertible mappings. However, existing flow-based methods for point clouds typically rely on point-wise feature extractors, which limits their ability to model long-range dependencies and global structural relationships among points. Inspired by the wide adoption of Transformers, we explored the complementary roles of self-attention mechanisms, CNN, and flow-based model. To this end, we propose EAGLE, a probabilistic generative model that integrates self-attention mechanisms with adaptive continuous normalizing flows. The self-attention module explicitly models pairwise dependencies among points, enabling effective capture of global contextual information. In addition, we introduce an adaptive bias correction mechanism within flow-based models, which dynamically adjusts to different input contexts and alleviates bias-drift issues. Extensive experiments on ShapeNet and ModelNet datasets demonstrate the effectiveness of our proposed method.

eess.SP↗