arXiv subjects

Dong Chen

Publications and source records attributed to Dong Chen.

At least 19 recordsLinked to original sources

arXiv2026

Behind Video Generation: Observations and Insights on Multimodal Understanding from Kling

Video generation has progressed from isolated clips to coherent short-form audiovisual sequences, and the bottleneck is shifting from rendering to semantic control. This report summarizes the observations and insights on multimodal understanding gained from successive generations of Kling. We argue that generation quality is the projection of multimodal understanding through the entire data--conditioning--alignment--evaluation chain, and we examine this chain module by module. Kling-MLLM, a shared multimodal foundation model that unifies image, video, audio, synchronized audio--video, and interleaved inputs, is specialized for each downstream module. Because a DiT is trained on far fewer training samples than a language model, we treat captioning as a semantic contract that maximizes description density, and extend it to reference-grounded captioning that makes reference bindings explicit. Prompt enhancement translates user intent into this caption distribution: basic PE is derived from captions of compelling videos and refined with text-level and generator-in-the-loop reinforcement learning, while agentic PE refines the story and retrieves compelling examples. We show that a reliable text encoder comes from perceptual training rather than from stronger language ability, and that candidates can be screened from text alone before end-to-end training. Reward modeling pairs a general reward model for explainable, localized critiques with a decision reward model for fast multidimensional scoring, treating perceptual bandwidth as a first-class design parameter. Finally, we review task-specific automatic evaluation, focusing on protocols that expose scoring shortcuts and help trace failures to their upstream source. We hope these lessons offer practical guidance for building the understanding stack behind controllable video generation.

cs.CV↗

arXiv2026

PlantVLM-Edge: Quantized Vision-Language Models for On-Device Crop Health Diagnosis

Timely and accurate crop health diagnosis is essential for sustainable agricultural production, yet accurate diagnosis often requires expert knowledge that is not readily available. Recent vision-language models (VLMs) have demonstrated remarkable capabilities in visual understanding, reasoning, and natural-language interaction, creating new opportunities for intelligent crop health assessment. However, deploying VLMs on resource-constrained devices remains challenging due to their substantial computational and memory requirements. This paper presents PlantVLM-Edge, a quantized vision-language framework for conversational crop health diagnosis that can run entirely on resource-constrained edge devices. PlantVLM-Edge combines agricultural domain adaptation, post-training quantization, and on-device serving with an offline chat interface, allowing growers to ask open-ended questions and receive diagnoses, symptom explanations, and management guidance without cloud connectivity. The framework is backbone-agnostic: we validate it with two different VLM types with varying scale and architecture (Qwen3-VL-2B and LLaVA-1.5-7B), four quantization schemes, and two diagnostic domains (plant diseases and insect pests), and we deploy all variants on an NVIDIA Jetson AGX Orin. Eight-bit quantization affects answer quality minimally relative to the full-precision model, and 4-bit activation-aware weight quantization (AWQ) retains 98.6% of full-precision answer quality on both domains while reducing the weight memory of the 2B backbone by 49%. In its most compact form, PlantVLM-Edge requires 2.01 GiB of weight memory, returns its first token in about 0.12 s, and completes a 64-token diagnostic response in 0.78 s. These results demonstrate that timely and accurate crop health diagnosis is practical on edge devices, providing a foundation for next-generation crop monitoring and decision support systems.

cs.CV↗

arXiv2026

Token-as-a-Utility: A Model-Agnostic Architecture for Metering and Provisioning AI Inference Services

Token-based subscriptions are emerging as a commercial model for large language model inference, but extending them into interoperable utility services requires comparable consumption accounting. Raw token counts do not consistently measure inference resource use because demand varies with model architecture, context length, tokenisation, and concurrent serving conditions. This paper presents Token-as-a-Utility, a model-agnostic architecture integrating inference metering, service-level-aware provisioning, and inter-operator settlement. We prove that, under batched serving, no per-request meter can be additive, independent of co-resident traffic, and exactly cost-reflective at once. This motivates separating provisioned capacity, metered consumption, and contracted quality into distinct service products. Consumption is expressed in Reference Inference Units (RIUs) derived from prefill and decode demand, while occupancy-indexed operator ratings recover shared serving costs without altering the common unit. Versioned inference records support auditing and re-rating across operators; software-enforced capacity leases and quality-constrained routing enable provisioning on shared accelerators. The resource model is measured on three NVIDIA RTX 5000 Ada GPUs, and multi-tenant behaviour is evaluated with a trace-driven model calibrated to them. For the reference model, occupancy-indexed rating limits statement energy error to 9% under measured traffic, compared with up to 70.5% under fixed-occupancy rating. In calibrated multi-tenant experiments, tenant mispricing decreases from 7.36 times under flat token counting to 2.39 times with RIUs and 1.06 times after occupancy-based reconciliation. These findings demonstrate how portable inference accounting can be combined with load-dependent cost recovery and contract-based provisioning to support heterogeneous inference services across operators.

cs.DC↗

arXiv2026

From Scientific Crop Imagery to Agronomic Reasoning: A Multimodal LLM Benchmark for Plant Phenotyping

High-throughput, effective and comprehensive phenotyping is a critical prerequisite for crop improvement. While phenotypic analysis was traditionally performed manually, recent advances in multimodal foundation models, especially in vision-language models, have offered new opportunities for automated plant phenotyping. However, plant science remains particularly challenging for general-purpose VLMs because it requires domain-specific knowledge, fine-grained visual interpretation, and complex agronomic reasoning. We introduce PlantXpert, an evidence-grounded multimodal reasoning benchmark for plant phenotyping across four major crops: soybean, cotton, rice, and corn. Our benchmark provides a structured and reproducible framework for agronomic adaptation of VLMs, and enables controlled comparison between base models and their domain-adapted counterparts. We constructed a dataset comprising 545 digital images and 4,351 benchmark samples spanning key agronomic domains including disease, pest control, weed management and yield. The benchmark can assess diverse capabilities including visual expertise, quantitative reasoning, and multi-step agronomic reasoning. We trained and evaluated a total of 11 state-of-the-art VLMs. Fine-tuning on our dataset improves overall accuracy for all models, with many achieving around 70% accuracy, and improvements on 9 of 11 remaining statistically significant after Holm correction. We also identify diminishing returns from model scaling, uneven performance across crop subsets, and persistent weaknesses in quantitative reasoning and specialized image analysis. These limitations show that general multimodal competence alone is insufficient for reliable agronomic reasoning and demonstrate the value of domain-specific adaptation. We hope PlantXpert can serve as a foundation for advancing evidence-grounded multimodal reasoning in plant science.

cs.CV↗

arXiv2026

RoAR-VAE: Improving Latent Diffusability through Robust Prediction

An effective visual tokenizer must preserve image fidelity while producing latents that downstream diffusion models can learn efficiently. Prior work improves latent diffusability through semantic alignment or joint tokenizer--diffusion training. Revisiting REPA-E, a representative approach, we find that removing its diffusion objective and dedicated generative blocks while retaining semantic prediction from corrupted latents yields comparable downstream generation performance under a matched setting. This observation motivates a general principle of robust prediction: generation-relevant information should remain recoverable from corrupted latents. We instantiate this principle with two complementary objectives. RobustAlign predicts pretrained semantic features from corrupted latents to preserve high-level structure, while RobustRecon reconstructs the clean image to retain fine-grained spatial and appearance information. Together, they form Robust Alignment and Reconstruction VAE (RoAR-VAE). Our analysis suggests that robust prediction improves diffusability by making useful information more accessible in high-variance latent directions, which diffusion models tend to learn earlier. Empirically, RoAR-VAE improves average generation benchmark performance by 2.19 points over the strongest baseline tokenizer under the same training budget, while improving small-text reconstruction accuracy by 16.93 percentage points over FLUX.1-VAE. These results demonstrate that RoAR-VAE achieves a favorable balance between reconstruction fidelity and downstream generative learnability.

cs.CV↗

arXiv2026

LensErase: A Foundation Model for Erase Anything

We introduce LensErase, a general-purpose mask-based Erase Anything foundation model designed to cover a broad spectrum of real-world erase scenarios. LensErase supports diverse objects, visual text, watermarks, associated visual effects, multiple-object removal, and a wide range of object scales, aspect ratios, and image resolutions. First, to enable such broad coverage, we construct LensErase-100M, a large-scale dataset containing 101.07 million paired source-mask-target examples spanning diverse erase scenarios. Second, we pretrain LensErase from the text-to-image model Lens, adapting its strong generative prior to mask-based erasing. To efficiently incorporate the source image and erase mask, we propose Mask-Aware RoPE, a 3D rotary positional encoding that directly injects mask information into the source-image tokens without introducing a separate mask token sequence. Finally, we refine the model through supervised fine-tuning and reinforcement learning with contract-guided, region-aware rewards, and apply TraceFusion at inference to preserve high-fidelity content outside the effective edit region. Across six public benchmarks and the proposed LensErase-Bench, LensErase achieves substantial improvements under both reference-based objective metrics and reference-free VLM evaluation. We hope LensErase can serve as a foundational model for erase-related applications and systems.

cs.CV↗

arXiv2026

APT: Feedback-Driven Agentic Post-Training for Text-to-Image Generation

Post-training is crucial for improving text-to-image models, yet existing pipelines typically rely on manually curated training data and fixed training distributions. We introduce APT (Agentic Post-Training), a fully automated framework that adaptively post-trains a base model according to its evolving capability profile. APT coordinates three agents to construct evaluation sets from user requirements, diagnose model-specific strengths and failure modes, and generate reinforcement learning data with an adaptive prompt distribution that emphasizes underperforming capabilities. By repeatedly evaluating, diagnosing, and updating the generator, APT lets the current model's behavior shape its next training tasks, forming a feedback loop inspired by recursive self-improvement. For general-purpose settings without user-specified evaluation scenarios, we further develop an evaluation harness that enables broad and diverse evaluation set generation. Experiments across multiple base models, benchmarks, and generation architectures demonstrate that APT is model-agnostic, benchmark-agnostic, architecture-agnostic, and fully automated.

cs.CV↗

arXiv2026

Simplifying and Improving On-Policy Distillation for Few-Step Diffusion

Few-step distillation reduces the sampling cost of diffusion and flow models, but existing approaches often require auxiliary score models or specialized architectures. We show that simple on-policy velocity regression already provides a strong foundation for 4-NFE generation. To represent teacher velocity variation within each coarse transition, we introduce Pivot, a training-only residual proxy that separates local corrections from the student's interval-mean prediction. We further repel the student from weaker teacher predictions, providing a lightweight mechanism for avoiding undesirable modes. The resulting framework, Simplified Matching Distillation (SMD), remains entirely regression-based, requires neither online score estimation nor adversarial training, and leaves the deployed generator unchanged. We evaluate SMD on image generation and editing, 3D generation, video synthesis, and joint audio--video generation, scaling to 33 billion parameters. At 4-NFE, SMD outperforms strong few-step baselines and surpasses multi-step teachers on several benchmarks. It also supports cross-model, multi-teacher, and causal-video distillation.

cs.CV↗

arXiv2026

AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards

Generating information-rich visual artifacts such as slides, posters, and dashboards as code is appealing because the outputs are structured, editable, and verifiable. Yet code models cannot see how their choices of layout and color come together on the canvas, often limiting spatial planning and aesthetic quality. Image generators excel at visual design but can misrender requested content, making their outputs unreliable targets for direct reproduction. We therefore introduce AesCode, which generates code grounded in the content prompt while using an image generated from the same prompt as an aesthetic guidance. To do so, AesCode represents the target as a design graph that describes the whole canvas and makes each property attributable. From this graph, we derive complementary cross-modal rewards: deterministic verifiers score properties parsable from code and rendering, while a VLM judge assesses the non-parsable ones with a sample-specific Visual Graph Rubric (VGR) tied to graph elements and relations. We optimize these decoupled rewards through reinforcement learning with per-channel normalization to jointly improve spatial planning, aesthetic quality, and verifiable correctness. To evaluate this setting, we build a benchmark that scores verifiable correctness and visual quality separately, with visual scores highly consistent with human judgments. We find that reference images substantially improve the visual quality of code generated by existing models, and ablations show that AesCode benefits from references both as model input and as a source of visual rewards. Relative to same-scale Qwen3-VL baselines given the same references, AesCode-8B and AesCode-32B improve visual quality by 31.1 and 22.4 percentage points, reaching scores comparable to those of closed-source frontier models such as GPT-5.5 and Claude Opus 4.8.

cs.AI↗

arXiv2026

VibeEdit: Image Editing with Canvas Instructions

In text-guided image editing, describing the desired change is often straightforward, but identifying the intended object or region can be cumbersome, especially when several objects look alike. We introduce a new image editing interface that lets users place spatial marks and optional short notes directly on the image. Together, these annotations form a canvas instruction that specifies where to edit and what to change. Our editor, VibeEdit, follows these instructions to perform object addition, removal, replacement, attribute modification, and movement without a separate text prompt. We construct 1.55 million source-target edit pairs with object masks and structured edit descriptions, from which we render canvas instructions during training. We adapt Qwen-Image-Edit with layer-decoupled conditioning that separately encodes source images and canvas instructions for image editing. We train the model with region-weighted supervised fine-tuning, followed by rubric-guided reinforcement learning to improve edit completion, local edit quality, and preservation of unedited regions. We evaluate VibeEdit on an independently constructed, human-curated benchmark of 419 cases emphasizing target selection among similar objects. VibeEdit achieves a VLM rubric score of 79.9 and an outside-region PSNR of 32.8 dB, compared with 67.4 and 24.0 dB for FireRed, the highest-scoring text-instructed baseline in our evaluation.

cs.CV↗

arXiv2026

From Prompting to Composing: A Spatial Canvas Interface for Poster Generation

Text prompting is an indirect interface for poster generation, requiring users to encode inherently two-dimensional composition intent into a one-dimensional sequence of words. We introduce a Spatial Canvas Interface that enables users to directly compose generation intent in space through four complementary binding types: semantic, identity, text, and pixel, together with Text Specifications for individual elements and global appearance. Based on this interface, we develop Compo, a poster generation model adapted from a pretrained image editing model to understand Spatial Canvas inputs and Text Specifications. Compo supports both direct inference, where users explicitly construct the canvas, and agentic mode, where a high-level request is automatically translated into a planned Spatial Canvas. To train Compo, we develop a scalable pipeline that automatically constructs supervision data for different binding types and their combinations, enabling efficient adaptation without training a specialized poster generator from scratch. We further introduce a benchmark that evaluates adherence to individual binding types and their joint composition. Experiments show that Compo achieves stronger compositional controllability than both general-purpose image generation models and dedicated poster generation systems while maintaining high visual quality. By decoupling intent specification from visual generation, our work shifts poster generation from prompting toward composing.

cs.CV↗

arXiv2026

VidAct: Learning Manipulation from In-the-Wild Videos with Object-Centric 3D Awareness

Video demonstrations offer a scalable alternative to costly robot data for learning manipulation, yet existing reconstruction-based approaches often rely on constrained camera viewpoints or human-to-robot retargeting, while the reconstructed trajectories are difficult to adapt to new objects configurations without distorting the trajectory shape. Another key limitation is that the resulting policies often lack precise object-level 3D geometry awareness, limiting object grounding and object shape awareness critical for precise manipulation. To bridge these gaps, we propose VidAct, an efficient video-to-robot framework that learns object-centric, 3D-aware manipulation policies from a single monocular video per task and enables zero-shot real-world deployment. VidAct consists of three key components. First, VidAct reconstructs object meshes and motion from arbitrary demo videos and canonicalizes the motion in the static object frame, avoiding embodiment-specific retargeting and accommodating diverse camera viewpoints. Second, VidAct employ residual trajectory transfer for adapting the reconstructed motion to novel object configurations while preserving its motion shape. Finally, as the key policy-learning component, VidAct predicts simulation-provided privileged complete-object point clouds at each frame as an auxiliary task while retaining RGB-only deployment, providing dense object-centric supervision over both object pose and 3D geometry. Experiments on human, robot, generated, and internet videos demonstrate broad video applicability and zero-shot deployment. Per-frame complete-object 3D supervision improves policy generalization and sim-to-real success, while residual trajectory transfer enables reliable trajectory adaptation with better shape preservation.

cs.RO↗

arXiv2026

ReDeck: Step-Level Render-Grounded Refinement for Document-to-Slide Generation

Document-to-slide generation is challenging because slides are dense editable artifacts that require both faithful content selection and precise spatial layout. Recent slide agents adopt iterative reflection, but typically follow a monolithic "one version, one feedback" loop: a slide or deck is rewritten, rendered afterward, and critiqued only at the turn boundary. This delayed feedback makes local failures such as overflow, overlap, clipping, and off-canvas placement difficult to attribute and repair. We propose ReDeck, a step-level render-grounded refinement framework that decomposes slide revision into atomic edit actions and returns renderer-derived observations after each step, turning refinement into "one edit, one observation." To balance local repair with global quality, ReDeck uses multi-granular feedback: step-level render feedback for spatial errors, a turn-level adaptive critic for semantic and design guidance, and a submission-level gate for hard layout validation. We further introduce DeckQuiz, a benchmark that decouples content fidelity, spatial correctness, and design quality. Across GPT-5.4, Claude-4.6, and Gemini-3.1, ReDeck consistently outperforms existing slide-generation agents, and ablations confirm that feedback timing and granularity are critical for reliable slide refinement.

cs.AI↗

arXiv2026

Benchmarking Open-Source Speech Emotion Recognition in Naturalistic Mandarin Spine Clinic Consultations: A Pilot Validation Study

Speech emotion recognition (SER) may enable passive affect monitoring in clinical encounters, but most systems are validated on acted laboratory speech rather than naturalistic Mandarin outpatient consultations. We benchmarked three open-source SER models (emotion2vec+, SenseVoice, FunASR) against a researcher-consensus reference in naturalistic spine-clinic speech, assessing minority-state detection under class imbalance. In a retrospective analysis of prospectively collected single-center recordings, audio was loudness-normalized and only conversations among patients, family members, and clinicians were retained. Sixty-five utterances (5-50 s; one per participant; 31 patients, 34 family members) were labeled by six calibrated annotators into six categories (Happy, Sad, Fear, Anger, Neutral, Surprised). Consensus used majority vote with Fleiss kappa filtering and clinician adjudication for low-agreement segments. Metrics included unweighted accuracy (UA), macro-average per-class accuracy, class- and sample-level weighted accuracy (WA), and F1 with bootstrap 95% CIs. Labels were imbalanced (Neutral 58.5%); median Fleiss kappa was 0.230 (IQR 0.134-0.519). SenseVoice and FunASR achieved UA 61.5% (95% CI 49.2-73.8%), macro-average per-class accuracy 87.2%, class-level WA 92.5%, and sample-level WA 24.6%. emotion2vec+ yielded UA 55.4% (95% CI 42.5-67.7%) and macro-average per-class accuracy 85.1%, with class-level WA 90.6% and sample-level WA 24.2%. Despite high inter-model agreement (90.8%), all models had near-zero recall for Sad, Fear, Anger, and Surprised. In this pilot, majority-class accuracy was misleading: SER poorly detected minority emotions against a noisy naturalistic reference. Clinical deployment readiness cannot be inferred from acted-corpus benchmarks without domain adaptation, multimodal modeling, stronger reference standards, and outcome validation.

cs.SD↗

arXiv2026

Parametrization of Symmetry in Data

Symmetry plays a fundamental role in understanding natural phenomena and mathematical structures. This work develops a comprehensive theory for studying the persistent symmetries and degree of asymmetry of finite point configurations over parameterization in metric spaces. Leveraging category theory and span categories, we define persistent symmetry groups and introduce novel invariants called symmetry barcodes and polybarcodes that capture the birth, death, persistence, and reappearance of symmetries over parameter evolution. Metrics and stability theorems are established for these invariants. The concept of symmetry types is formalized via the action of isometry groups in configuration spaces. To quantitatively characterize symmetry and asymmetricity, measures such as degree of symmetry and symmetry defect are introduced, the latter revealing connections to approximate group theory in Euclidean settings. Moreover, a theory of persistence representations of persistence groups is developed, generalizing the classical decomposition theorem of persistence modules. Persistent Fourier analysis on persistence groups is further proposed to characterize dynamic phenomena including symmetry breaking and phase transitions. Algorithms for computing symmetry groups, barcodes, and symmetry defect in low-dimensional spaces are presented, complemented by discussions on extending symmetry analysis beyond geometric contexts. This work thus bridges geometric group theory, topological data analysis, representation theory, and machine learning, providing novel tools for the analysis of the parametrized symmetry of data.

math.AT↗

arXiv2026

UAV-Based Infrastructure Inspections: A Literature Review and Proposed Framework for AEC+FM

Unmanned Aerial Vehicles (UAVs) are transforming infrastructure inspections in the Architecture, Engineering, Construction, and Facility Management (AEC+FM) domain. By synthesizing insights from over 150 studies, this review paper highlights UAV-based methodologies for data acquisition, photogrammetric modeling, defect detection, and decision-making support. Key innovations include path optimization, thermal integration, and advanced machine learning (ML) models such as YOLO and Faster R-CNN for anomaly detection. UAVs have demonstrated value in structural health monitoring (SHM), disaster response, urban infrastructure management, energy efficiency evaluations, and cultural heritage preservation. Despite these advancements, challenges in real-time processing, multimodal data fusion, and generalizability remain. A proposed workflow framework, informed by literature and a case study, integrates RGB imagery, LiDAR, and thermal sensing with transformer-based architectures to improve accuracy and reliability in detecting structural defects, thermal anomalies, and geometric inconsistencies. The proposed framework ensures precise and actionable insights by fusing multimodal data and dynamically adapting path planning for complex environments, presented as a comprehensive step-by-step guide to address these challenges effectively. This paper concludes with future research directions emphasizing lightweight AI models, adaptive flight planning, synthetic datasets, and richer modality fusion to streamline modern infrastructure inspections.

cs.CV↗

arXiv2026

FM-Bench: A Benchmark for Long-Horizon Management with Competing Agents

Language model agents now execute bounded tasks reliably. Whether they can sustain effective decision-making over long horizons, where actions have cumulative consequences and the environment responds to their choices, remains largely unmeasured. FM-Bench (Football Management Benchmark) measures this. An LLM agent runs a football club for 20 in-game years through 26 tools and roughly 340 to 400 decision stops. It drafts a squad on the same budget as every rival, trades players, negotiates contracts, invests in facilities and youth, sets lineups, and answers to a board that can fire it, while a deterministic engine accumulates every year into one final score with no LLM judge or human rater. The solo track plays each of 15 frontier models against a frozen scripted world, and the Arena places the same models plus a scripted anchor in one shared 20-year world; to our knowledge, the first head-to-head evaluation at this scale. We measure six behavioral capabilities behind the score. Across three seeds, all 15 models complete every horizon while the blind scripted baselines die out in most of theirs, and claude-fable-5 tops the solo board on mean score and the Arena, where the title nonetheless rotates among ten models. Neither scale, price, nor vendor predicts the order; the order settles only late in the horizon, and the best first-play human lands only at the bottom of the model board. What separates the models is managerial behavior rather than computation. Higher-scoring models reduce slow-payoff investment near the end, keep cash invested rather than idle, and open renewals well before the deadline, while token spend predicts nothing. No model learns the market's hidden prices from hundreds of rejected bids, and self-managed memory fails in two opposite modes: an archive that only grows or a plan rewritten every season. Code is available at https://github.com/Analogy-AI/fm-bench.

cs.AI↗

arXiv2026

Energy-Guided Flow Matching

Pixel-space generative models bypass lossy latent compression, yet necessitate joint learning of global structure and fine-grained details in a high-dimensional space. Standard flow matching interpolates noise toward a fixed clean-image endpoint, leaving the spectral evolution to be learned implicitly. In this paper, we introduce Energy-Guided Flow Matching(EG-FM) that explicitly models a coarse-to-fine generative trajectory by moving endpoint. Specifically, EG-FM replaces the fixed endpoint with a heat-kernel-filtered endpoint that evolves smoothly from low-frequency image to clean image. The fraction of high-frequency signal in moving endpoint is released by an image-specific energy-guided scheduling, leading to the re-targeting of velocity in flow matching. Our framework requires no adaptation of the backbone and training data, bringing negligible cost on the training and inference stages. In our experiment, EG-FM consistently achieves lower FID on the ImageNet class-conditional image generation task at $256 \times 256$ with fewer epochs, reaching an FID of 1.55 at 200 epochs and 1.45 at 600 epochs. We continue training the generation task on the setting of $512 \times 512$ resolution, yielding a FID of 1.58 after only 40 high-resolution adaptation epochs. Furthermore, we transfer EG-FM on text-to-image generation and achieve 0.85 on GenEval score and 83.9 on DPG-Bench. Code is available at https://github.com/ysng123/EG-FM.

cs.CV↗