arXiv subjects

Jirong Liu

Publications and source records attributed to Jirong Liu.

5 recordsLinked to original sources

arXiv2026

Text2GraphQuery-Bench: A Text to Graph Query Benchmark

Graph models are fundamental to data analysis in domains rich with complex relationships. Unlike SQL, which benefits from a rel- atively unified standard and widespread familiarity, graph query languages are diverse (e.g., Cypher, GQL, SQL/PGQ) and far less fa- miliar to most users, making them significantly harder to learn and use. Text-to-Graph-Query systems address this barrier by trans- lating natural language into executable graph queries, enabling LLMs to serve as interfaces for Graph Database Management Systems (GDBMS). Existing benchmarks are limited in language coverage, rely on rigid synthesis, and lack comprehensive evaluation. We present Text2GraphQuery-Bench, the first benchmark covering all mainstream declarative property graph query languages (Cypher, GQL, and SQL/PGQ). It contains 267,276 (Question, Graph Query) pairs across 34 databases and 13 domains. Its construction supports adaptation from heterogeneous resources and domain-aware synthesis, while its Graph-IR-based design enables rapid extension to new languages. The evaluation protocol reports Grammar, GLEU, Similarity, and EX under graph-native difficulty, question abstraction, and schema aliasing. Experiments on 8 LLMs reveal: (i) a significant language gap exists - zero-shot GQL and SQL/PGQ Grammar is far below Cypher, yet few-shot prompting largely recovers it; (ii) fine-tuning an 8B model reaches or exceeds zero-shot large models, indicating unfamiliarity - rather than model capacity - is the primary barrier; (iii) as supervision increases, syntax errors recede, shifting bottlenecks to aggregation logic in GQL and schema linking in SQL/PGQ; (iv) higher question abstraction degrades EX due to intent-to-schema grounding issues, while schema aliasing has minimal impact; (v) EX consistently degrades from Easy to Extra Hard, with Extra Hard remaining a persistent bottleneck. *(Due to arXiv constraints, this abstract is shortened. See PDF for the full version.)*

cs.AI↗

arXiv2025

Emu3.5: Native Multimodal Models are World Learners

We introduce Emu3.5, a large-scale multimodal world model that natively predicts the next state across vision and language. Emu3.5 is pre-trained end-to-end with a unified next-token prediction objective on a corpus of vision-language interleaved data containing over 10 trillion tokens, primarily derived from sequential frames and transcripts of internet videos. The model naturally accepts interleaved vision-language inputs and generates interleaved vision-language outputs. Emu3.5 is further post-trained with large-scale reinforcement learning to enhance multimodal reasoning and generation. To improve inference efficiency, we propose Discrete Diffusion Adaptation (DiDA), which converts token-by-token decoding into bidirectional parallel prediction, accelerating per-image inference by about 20x without sacrificing performance. Emu3.5 exhibits strong native multimodal capabilities, including long-horizon vision-language generation, any-to-image (X2I) generation, and complex text-rich image generation. It also exhibits generalizable world-modeling abilities, enabling spatiotemporally consistent world exploration and open-world embodied manipulation across diverse scenarios and tasks. For comparison, Emu3.5 achieves performance comparable to Gemini 2.5 Flash Image (Nano Banana) on image generation and editing tasks and demonstrates superior results on a suite of interleaved generation tasks. We open-source Emu3.5 at https://github.com/baaivision/Emu3.5 to support community research.

cs.CV↗

arXiv2024

What Matters in Building Vision-Language-Action Models for Generalist Robots

To utilize Foundation Vision Language Models (VLMs) for robotic tasks and motion planning, the community has proposed different methods for injecting action components into VLMs and building the Vision-Language-Action models (VLAs). In this work, we disclose the key factors that significantly influence the performance of VLA on robot manipulation problems and focus on answering three essential design choices: which backbone to select, how to formulate the VLA architectures, and when to add cross-embodiment data. The obtained results convince us firmly to explain why we prefer VLA and develop a new family of VLAs, RoboVLMs, which require very few manual designs and achieve a new state-of-the-art performance in three simulation tasks and real-world experiments. Through our extensive experiments, which include over 8 VLM backbones, 4 policy architectures, and over 600 distinct designed experiments, we provide a detailed guidebook for the future design of VLAs. In addition to the study, the highly flexible RoboVLMs framework, which supports easy integrations of new VLMs and free combinations of various design choices, is made public to facilitate future research. We open-source all details, including codes, models, datasets, and toolkits, along with detailed training and evaluation recipes at: robovlms.github.io.

cs.RO↗

arXiv2023

RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot

A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots. Recent research in one-shot imitation learning has shown promise in transferring trained policies to new tasks based on demonstrations. This feature is attractive for enabling robots to acquire new skills and improving task and motion planning. However, due to limitations in the training dataset, the current focus of the community has mainly been on simple cases, such as push or pick-place tasks, relying solely on visual guidance. In reality, there are many complex skills, some of which may even require both visual and tactile perception to solve. This paper aims to unlock the potential for an agent to generalize to hundreds of real-world skills with multi-modal perception. To achieve this, we have collected a dataset comprising over 110,000 contact-rich robot manipulation sequences across diverse skills, contexts, robots, and camera viewpoints, all collected in the real world. Each sequence in the dataset includes visual, force, audio, and action information. Moreover, we also provide a corresponding human demonstration video and a language description for each robot sequence. We have invested significant efforts in calibrating all the sensors and ensuring a high-quality dataset. The dataset is made publicly available at rh20t.github.io

cs.RO↗

arXiv2023

AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains

As the basis for prehensile manipulation, it is vital to enable robots to grasp as robustly as humans. Our innate grasping system is prompt, accurate, flexible, and continuous across spatial and temporal domains. Few existing methods cover all these properties for robot grasping. In this paper, we propose AnyGrasp for grasp perception to enable robots these abilities using a parallel gripper. Specifically, we develop a dense supervision strategy with real perception and analytic labels in the spatial-temporal domain. Additional awareness of objects' center-of-mass is incorporated into the learning process to help improve grasping stability. Utilization of grasp correspondence across observations enables dynamic grasp tracking. Our model can efficiently generate accurate, 7-DoF, dense, and temporally-smooth grasp poses and works robustly against large depth-sensing noise. Using AnyGrasp, we achieve a 93.3% success rate when clearing bins with over 300 unseen objects, which is on par with human subjects under controlled conditions. Over 900 mean-picks-per-hour is reported on a single-arm system. For dynamic grasping, we demonstrate catching swimming robot fish in the water. Our project page is at https://graspnet.net/anygrasp.html

cs.RO↗