arXiv subjects

Jiachen Xu

Publications and source records attributed to Jiachen Xu.

At least 19 recordsLinked to original sources

arXiv2026

Easy-3Dfy: Object-Level Geometric Composition for Single-Image 3D Scene Construction

Single-image 3D scene construction is a fundamental problem in 3D vision. Recent methods such as SAM-3Dfy enable end-to-end reconstruction of 3D assets and their scene layouts, but still struggle with fine-grained scene geometry and asset quality. Meanwhile, specialized 3D object generators provide detailed object geometry, while 3D foundation models offer strong and generalizable spatial understanding. We propose Easy-3Dfy, a compositional workflow that decouples object generation from scene layout estimation, allowing specialized models to fully exploit their respective strengths. An RPF-based aligner then bridges these capabilities by establishing geometric correspondence between object-local meshes and partial scene pointmaps, recovering object layout for scene composition. Without requiring complete scene-level supervision, the proposed workflow consistently outperforms SAM-3Dfy across multiple geometric metrics on Scene-Maker and 3D-FUTURE200. Our results demonstrate that explicit geometric composition can effectively combine high-quality object generation with strong spatial understanding, while enabling the workflow to benefit independently from advances in both 3D object generation and foundation models.

cs.CV↗

arXiv2026

FlexPart: Text-Controlled Semantics-Aware 3D Part Segmentation at Flexible Granularity

A 3D asset admits multiple valid part decompositions, while semantics-aware segmentation should reflect both the desired granularity and the parts a user wants to extract. Ideally, a practical segmentation model should strive for a simple principle: what you ask for is what you get. However, realizing this goal is hindered by two core obstacles: the lack of multi-granularity training data and the absence of a holistic control mechanism. We propose FLEXPART, a unified framework for text-controlled 3D mesh segmentation. To establish flexible control, we introduce a holistic control prompt comprising semantic part names with location and shape clauses. To support this mechanism, we utilize an automated Vision-Language Model (VLM) pipeline to construct a large-scale dataset of multi-granularity partitions and their corresponding prompts. Driven by this data, FLEXPART generates a view-independent part-identity field in native 3D space, directly assigning discrete part IDs to mesh faces. To compel the model to respect semantic conditions rather than rely on geometric priors, we train the model using randomized background dropping. Furthermore, an inference-time VLM planner can automatically complete descriptive clauses from 2D renderings, allowing users to simply name the desired parts in ordinary words. FLEXPART with planner-completed prompts outperforms the strongest evaluated baseline by 15% in mIoU, even when that baseline receives ground-truth guidance. On our held-out FLEXPARTBENCH, FLEXPART leads at each granularity and improves average mIoU by 22% over the strongest baseline. With only the requested part names supplied by the user, it also improves IoU by 35% over the strongest click-driven baseline on selective segmentation.

cs.CV↗

arXiv2026

Toward Optimal Time-Space Tradeoffs for Set Reconciliation

Set reconciliation, where two parties each holding a large set of elements aim to identify their set difference, is a fundamental task in many areas. Time (computation cost) and space (communication cost) are two important metrics in this problem, and much existing work optimizes one at the expense of the other, creating a clear trade-off. We study this trade-off through algorithmic upper bounds and matching space lower bounds. On one side, we propose XYZ-Sketch. For sufficiently large $d$, XYZ-Sketch achieves high-probability reconciliation for sets whose differing elements are uniformly random, using only $(1+\varepsilon)d$ elements for communication, while achieving $O(\log(1/\varepsilon))$ insertion time and expected $O(d\log V\log^{3/2}(1/\varepsilon))$ decoding time, where $d$ and $V$ denote the size of the difference between two sets and the universe size, respectively. On the other side, we establish a broad fixed-support canonical model and prove a matching space lower bound, establishing the asymptotic optimality of XYZ-Sketch. Our proof also establishes an extremality theorem resolving Walzer's conjecture. Experiments demonstrate the practical advantages of XYZ-Sketch, and the source code is available at https://github.com/djwj233/XYZ-Sketch.

cs.DS↗

arXiv2026

JointEdit3D: Feed-Forward 3D Scene Editing in a Unified Latent Space

Existing 3D scene editing methods typically rely on per-scene optimization over explicit 3D representations or cascaded edit-and-reconstruct pipelines, resulting in high test-time cost, limited 3D awareness, and structural inconsistencies. To couple appearance synthesis with geometry prediction, we adapt a pretrained unified RGB-geometry latent space to feed-forward scene editing. Given a source video and an edited reference image, JointEdit3D performs asymmetric latent inpainting: it observes only the edited RGB reference latent and jointly generates the remaining RGB latents and the entire geometry latent along the source trajectory. JointEdit3D introduces a dedicated SceneAnchor Branch to inject source-scene structure without forcing direct copying, and adopts edit/background-aware losses to balance edited-region fidelity with unedited-content preservation. To address the lack of paired resources for standardized 3D scene editing evaluation, we introduce SceneEdit3D-15K, a dataset with 15K paired editing samples and renderer-provided 3D annotations, together with SceneEdit3D-Bench, a curated 100-sample benchmark. Experiments show that JointEdit3D improves edited-region quality and 3D structural completeness over prior baselines while maintaining competitive background preservation.

cs.CV↗

arXiv2026

Exemplar2VQA: A Scalable Exemplar-Driven Visual Question Answering Generation Framework via Multi-Agent Coding

Advancing spatial intelligence in Multimodal Large Language Models (MLLMs) is bottlenecked by the scarcity of complex, scalable 3D question-answer (QA) data. While manual annotation is labor-intensive, directly utilizing LLMs to synthesize these QA pairs often fails due to their inherent deficiencies in spatial and geometric computation. We introduce Exemplar2VQA, a scalable exemplar-driven visual question answering generation framework that rapidly synthesizes large-scale spatial QA pairs in simulated environments via multi-agent coding. By equipping collaborative agents with a meticulously designed library of geometric utilities, Exemplar2VQA bypasses LLMs' spatial reasoning flaws through deterministic code execution. Crucially, the framework exhibits remarkable versatility: taking diverse static object-centric spatial query templates as exemplars, it seamlessly and autonomously scales them into massive, high-fidelity synthetic datasets. Fine-tuning Qwen2.5-VL (3B/7B) exclusively on Exemplar2VQA-generated synthetic indoor data yields significant performance improvements across various diverse benchmarks. Furthermore, its effectiveness is not limited to in-domain indoor datasets but also robustly extends to outdoor and mixed-scene benchmarks. These results establish Exemplar2VQA as a scalable and powerful paradigm for bridging the sim-to-real gap in Embodied AI. Our code is at https://github.com/yingjiayu12/Exemplar2VQA

cs.CV↗

arXiv2026

Robustness Analysis of Agentic AI to Inconsistent and Incomplete Tool Responses

Tool-using agents increasingly rely on external tools to complete multi-step tasks, but tool returns can fail in different ways and require different recovery actions. Existing robustness studies often use uncertainty-based measures to detect when an agent becomes unreliable. These measures can reveal that something has gone wrong, but they do not directly identify the type of tool failure or the appropriate response. We address this limitation by analyzing tool failures at the moment a return enters the agent context. Our approach combines two complementary signals. The first compares the likelihood of the returned content under the tool schema and under the full trajectory prefix. The second measures the agent's probability distribution over its legal next actions. We evaluate the approach by injecting incomplete and inconsistent returns into a retail customer-service benchmark. The results show that likelihood-based signals clearly capture incomplete returns and some direct inconsistencies, while action-based signals reveal how strongly a failure changes the next decision. Some failures that are weak under likelihood signals can still redirect the agent toward state-changing actions. These findings show that tool failures can be recognized at the return boundary, but reliable diagnosis requires combining multiple signals.

cs.AI↗

arXiv2026

A-CPES: A Reference Framework for Agentic AI in Cyber-Physical Energy Systems

Energy system operation contains a loop of work that automation has never taken over: posing the optimization problem the current cycle should solve, disposing of infeasibility, sequencing a solution into interlocked switching orders, assembling evidence no single model holds, negotiating adjustable capacity with many parties, and settling experience into practice. Licensed dispatchers carry all of it in person, and the rising share of variable renewable generation is making that loop turn faster than their number can grow. Agentic AI supplies the abilities it requires, but enters as the outer loop of control: it calls SCED and the other decision models rather than being called by them. We propose A-CPES, three nested rings, an authorization and accountability frame around an agentic control outer loop around a six-layer CPES core. We argue the loop is indivisible, tune where and how tightly it may close, state eight structural failure modes as falsifiable predictions, and specify six governance modules that rebuild the authorization frame until it covers the loop, before the loop starts turning.

cs.AI↗

arXiv2026

Hunyuan3D-Buffalo 1.0: A Unified Multimodal Model for Scalable 3D Generation, Understanding, and Editing

Recent advances in image generation have demonstrated the potential of unified multimodal models that integrate understanding, generation, and editing. However, unified 3D modeling remains constrained by scarce multimodal data, particularly the lack of large-scale and geometrically consistent editing data. To address this limitation, we propose Hunyuan3D-Buffalo 1.0, a unified framework supporting 3D understanding, text-to-3D generation, instruction-guided 3D editing, and text-grounded part generation within a single architecture. To enable scalable training, we construct an 87M-scale 3D multimodal corpus, comprising 25M understanding samples, 50M text-to-3D pairs, and 12M editing pairs generated using Nano3D-v2. Architecturally, the framework combines Hunyuan3D-VLM for semantic, structural, and spatial understanding with Hunyuan3D DiT for high-fidelity 3D synthesis. The VLM provides multimodal semantic conditions for generation, while editing and part generation additionally condition the diffusion process on the source object representation to preserve its overall structure and unedited regions. Extensive experiments show that Hunyuan3D-Buffalo 1.0 achieves state-of-the-art or leading performance on text-to-3D generation and 3D editing benchmarks, while exhibiting strong understanding and part-generation capabilities. Our analysis further shows that both generation and understanding improve editing, demonstrating the effectiveness of unified 3D multimodal training. Project Page: https://tencent-hunyuan.github.io/Hunyuan3D-Buffalo1.0/

cs.CV↗

arXiv2026

InSituANN: Revisiting IVF for PCIe-Efficient Billion-Scale Vector Search

Approximate nearest neighbor search (ANNS) over billion-scale vector datasets has become a foundational operator for modern retrieval systems, powering large-scale recommendation, semantic search, and LLM/RAG workloads. Although GPUs offer massive parallelism and high-bandwidth memory for batched vector search, their limited VRAM capacity makes fully GPU-resident billion-scale indexes difficult to deploy. In CPU-GPU heterogeneous designs, keeping the base vectors in host memory avoids this capacity limit, but naively offloading fine search to the GPU introduces a new bottleneck: large volumes of base-vector data must be streamed over PCIe. We present InSituANN, an IVF-based ANNS engine that enables billion-scale vector search on a single commodity GPU. InSituANN keeps original base vectors in host memory, performs fine search in situ, and uses the GPU for compact routing and optional pruning. As a result, query processing avoids PCIe transfers of high-dimensional base vectors while retaining the simplicity of IVF. Beyond query performance, we further design an ultra-fast IVF construction path for InSituANN. On SIFT-1B, InSituANN builds the IVF index in 5.2 minutes, about 350x faster than the measured 30.4-hour HNSW build. At matched recall on billion-scale datasets, InSituANN improves end-to-end throughput by 104.9x-4298.2x over the PCIe-bound Rummy baseline and by 2.4x-4.6x over DiskANN on SIFT-1B and DEEP-1B. Together with strong recall-throughput trade-offs and lower index space than graph-based alternatives, these gains make billion-scale retrieval practical on cost-efficient hardware. We open-source InSituANN at https://github.com/mindtravel/InSituANN-OpenSource.

cs.DB↗

arXiv2026

A Comparative Evaluation of Large Vision-Language Models for 2D Object Detection under SOTIF Conditions

Reliable environmental perception remains one of the main obstacles for safe operation of automated vehicles. Safety of the Intended Functionality (SOTIF) concerns safety risks from perception insufficiencies, particularly under adverse conditions where conventional detectors often falter. While Large Vision-Language Models (LVLMs) demonstrate promising semantic reasoning, their quantitative effectiveness for safety-critical 2D object detection is underexplored. This paper presents a systematic evaluation of ten representative LVLMs using the PeSOTIF dataset, a benchmark specifically curated for long-tail traffic scenarios and environmental degradations. Performance is quantitatively compared against two specialized detectors: the anchor-based YOLOv5 and the transformer-based RT-DETRv4. Experimental results reveal a critical trade-off: top-performing LVLMs (e.g., Gemini 3) surpass the YOLOv5 in recall by over 25% and closely match RT-DETRv4 under natural visual degradation, while specialized detectors retain an advantage in geometric precision for handcrafted perturbations. These findings highlight the complementary strengths of semantic reasoning versus geometric regression, supporting the use of LVLMs as high-level safety validators in SOTIF-oriented automated driving systems.

cs.CV↗

arXiv2026

Multiplicative Oracle Inequalities for Transductive Learning via Level-Set Aggregation

We revisit transductive learning where predictions are made with the set of all covariates known in advance. In the leave-one-out (LOO) setting, the prediction is made with labels of the remaining sample points and evaluated by the average error. In particular, we study multiplicative oracle inequalities for agnostic transductive LOO prediction for a variety of tasks, including classification with 0-1 loss, squared loss regression, density estimation, and logistic regression. Specifically, we introduce \emph{Median of Level-Set Aggregation} (MLSA), an aggregation procedure built on near-ERM level sets (i.e., empirical-risk level sets around the ERM). We prove a general multiplicative oracle inequality for the LOO error of the form \[ LOO_S(MLSA) \;\le\; C \left( \frac{1}{n} \min_{h\in H} L_S(h) \;+\; \frac{\log |H|}{n}\right), \qquad C>1, \] where $H$ is the hypothesis/function class. This inequality holds for hypothesis classes under a local level-set growth condition together with losses satisfying a mild monotonicity assumption. For classification with VC classes under the $0$--$1$ loss, the $\log |H|$ factor can be improved to be $d\log n$, where $d$ is the VC dimension, recovering Long (1998) up to a $\log n$ factor. For logistic regression with bounded covariates and parameters, the $\log |H|$ factor can be improved to be $d\log n$ up to problem-dependent factors, where $d$ is the ambient dimension.

cs.LG↗

arXiv2026

NeuroLoRA: Context-Aware Neuromodulation for Parameter-Efficient Multi-Task Adaptation

Parameter-Efficient Fine-Tuning (PEFT) techniques, particularly Low-Rank Adaptation (LoRA), have become essential for adapting Large Language Models (LLMs) to downstream tasks. While the recent FlyLoRA framework successfully leverages bio-inspired sparse random projections to mitigate parameter interference, it relies on a static, magnitude-based routing mechanism that is agnostic to input context. In this paper, we propose NeuroLoRA, a novel Mixture-of-Experts (MoE) based LoRA framework inspired by biological neuromodulation -- the dynamic regulation of neuronal excitability based on context. NeuroLoRA retains the computational efficiency of frozen random projections while introducing a lightweight, learnable neuromodulation gate that contextually rescales the projection space prior to expert selection. We further propose a Contrastive Orthogonality Loss to explicitly enforce separation between expert subspaces, enhancing both task decoupling and continual learning capacity. Extensive experiments on MMLU, GSM8K, and ScienceQA demonstrate that NeuroLoRA consistently outperforms FlyLoRA and other strong baselines across single-task adaptation, multi-task model merging, and sequential continual learning scenarios, while maintaining comparable parameter efficiency.

cs.LG↗

arXiv2026

HY3D-Bench: Generation of 3D Assets

While recent advances in neural representations and generative models have revolutionized 3D content creation, the field remains constrained by significant data processing bottlenecks. To address this, we introduce HY3D-Bench, an open-source ecosystem designed to establish a unified, high-quality foundation for 3D generation. Our contributions are threefold: (1) We curate a library of 250k high-fidelity 3D objects distilled from large-scale repositories, employing a rigorous pipeline to deliver training-ready artifacts, including watertight meshes and multi-view renderings; (2) We introduce structured part-level decomposition, providing the granularity essential for fine-grained perception and controllable editing; and (3) We bridge real-world distribution gaps via a scalable AIGC synthesis pipeline, contributing 125k synthetic assets to enhance diversity in long-tail categories. Validated empirically through the training of Hunyuan3D-2.1-Small, HY3D-Bench democratizes access to robust data resources, aiming to catalyze innovation across 3D perception, robotics, and digital content creation.

cs.CV↗

arXiv2025

FinMR: A Knowledge-Intensive Multimodal Benchmark for Advanced Financial Reasoning

Multimodal Large Language Models (MLLMs) have made substantial progress in recent years. However, their rigorous evaluation within specialized domains like finance is hindered by the absence of datasets characterized by professional-level knowledge intensity, detailed annotations, and advanced reasoning complexity. To address this critical gap, we introduce FinMR, a high-quality, knowledge-intensive multimodal dataset explicitly designed to evaluate expert-level financial reasoning capabilities at a professional analyst's standard. FinMR comprises over 3,200 meticulously curated and expertly annotated question-answer pairs across 15 diverse financial topics, ensuring broad domain diversity and integrating sophisticated mathematical reasoning, advanced financial knowledge, and nuanced visual interpretation tasks across multiple image types. Through comprehensive benchmarking with leading closed-source and open-source MLLMs, we highlight significant performance disparities between these models and professional financial analysts, uncovering key areas for model advancement, such as precise image analysis, accurate application of complex financial formulas, and deeper contextual financial understanding. By providing richly varied visual content and thorough explanatory annotations, FinMR establishes itself as an essential benchmark tool for assessing and advancing multimodal financial reasoning toward professional analyst-level competence.

cs.AI↗

arXiv2025

MUSE: Multi-Treatment Experiment Design for Winner Selection and Effect Estimation

We study the design of experiments with multiple treatment levels, a setting common in clinical trials and online A/B/n testing. Unlike single-treatment studies, practical analyses of multi-treatment experiments typically first select a winning treatment, and then only estimate the effect therein. Motivated by this analysis paradigm, we propose a design for MUlti-treatment experiments that jointly maximizes the accuracy of winner Selection and effect Estimation (MUSE). Explicitly, we introduce a single objective that balances selection and estimation, and determine the unit allocation to treatments and control by optimizing this objective. Theoretically, we establish finite-sample guarantees and asymptotic equivalence between our proposal and the Neyman allocation for the true optimal treatment and control. Across simulations and a real data application, our method performs favorably in both selection and estimation compared to various standard alternatives.

stat.ME↗

arXiv2025

P3-SAM: Native 3D Part Segmentation

Segmenting 3D assets into their constituent parts is crucial for enhancing 3D understanding, facilitating model reuse, and supporting various applications such as part generation. However, current methods face limitations such as poor robustness when dealing with complex objects and cannot fully automate the process. In this paper, we propose a native 3D point-promptable part segmentation model termed P$^3$-SAM, designed to fully automate the segmentation of any 3D objects into components. Inspired by SAM, P$^3$-SAM consists of a feature extractor, multiple segmentation heads, and an IoU predictor, enabling interactive segmentation for users. We also propose an algorithm to automatically select and merge masks predicted by our model for part instance segmentation. Our model is trained on a newly built dataset containing nearly 3.7 million models with reasonable segmentation labels. Comparisons show that our method achieves precise segmentation results and strong robustness on any complex objects, attaining state-of-the-art performance. Our project page is available at https://murcherful.github.io/P3-SAM/.

cs.CV↗

arXiv2025

X-Part: high fidelity and structure coherent shape decomposition

Generating 3D shapes at part level is pivotal for downstream applications such as mesh retopology, UV mapping, and 3D printing. However, existing part-based generation methods often lack sufficient controllability and suffer from poor semantically meaningful decomposition. To this end, we introduce X-Part, a controllable generative model designed to decompose a holistic 3D object into semantically meaningful and structurally coherent parts with high geometric fidelity. X-Part exploits the bounding box as prompts for the part generation and injects point-wise semantic features for meaningful decomposition. Furthermore, we design an editable pipeline for interactive part generation. Extensive experimental results show that X-Part achieves state-of-the-art performance in part-level shape generation. This work establishes a new paradigm for creating production-ready, editable, and structurally sound 3D assets. Codes will be released for public research.

cs.GR↗

arXiv2025

Hunyuan3D Studio: End-to-End AI Pipeline for Game-Ready 3D Asset Generation

The creation of high-quality 3D assets, a cornerstone of modern game development, has long been characterized by labor-intensive and specialized workflows. This paper presents Hunyuan3D Studio, an end-to-end AI-powered content creation platform designed to revolutionize the game production pipeline by automating and streamlining the generation of game-ready 3D assets. At its core, Hunyuan3D Studio integrates a suite of advanced neural modules (such as Part-level 3D Generation, Polygon Generation, Semantic UV, etc.) into a cohesive and user-friendly system. This unified framework allows for the rapid transformation of a single concept image or textual description into a fully-realized, production-quality 3D model complete with optimized geometry and high-fidelity PBR textures. We demonstrate that assets generated by Hunyuan3D Studio are not only visually compelling but also adhere to the stringent technical requirements of contemporary game engines, significantly reducing iteration time and lowering the barrier to entry for 3D content creation. By providing a seamless bridge from creative intent to technical asset, Hunyuan3D Studio represents a significant leap forward for AI-assisted workflows in game development and interactive media.

cs.CV↗