simulation pipeline development

Existing synthetic data generation tools often suffer from complex workflows, inconsistent standards, and limited cross-modal extensibility, hindering their ability to meet the data demands of large language models in specialized domains and low-resource languages. This work proposes a configuration-driven, end-to-end open-source framework that standardizes multi-source data synthesis through a unified and controllable paradigm. Featuring a highly modular architecture, the framework flexibly adapts to diverse tasks and supports high-quality data generation across multiple pathways, modalities, and languages. Integrated with both a graphical user interface and command-line utilities, it significantly lowers the barrier to entry for users. Empirical evaluations demonstrate that the framework effectively balances generation efficiency and data quality across various scenarios, thereby accelerating the practical deployment of synthetic data in model training pipelines.

Existing AI research agents often produce seemingly plausible but ineffective machine learning solutions due to a lack of systematic training. To address this, this work proposes the first scalable synthetic task generation framework that automatically constructs high-quality, executable research tasks through topic sampling, proposal generation grounded in real-world Hugging Face datasets, and self-debugging validation. The framework further leverages trajectory distillation—transferring effective research behaviors from GPT-5 to Qwen3—to guide student models in learning valid scientific reasoning paths. Evaluated on the MLGym benchmark, Qwen3-4B and Qwen3-8B models trained with this approach achieve 9% and 12% relative improvements in Area Under the Performance curve (AUP), respectively, substantially outperforming baseline methods.

Existing autonomous driving datasets lack sufficient diversity, coordination, and cross-domain support, limiting their utility for training multi-agent, multi-sensor systems. To address this gap, this work proposes a modular data generation pipeline built upon the AVstack framework and the CARLA simulator, capable of efficiently producing terabyte-scale, ground-truth-annotated multimodal data. The pipeline encompasses perspectives from ground vehicles, aerial platforms, and infrastructure sensors, and supports flexible single- or multi-agent configurations under controllable, complex scenarios. This approach represents the first scalable, cross-domain collaborative data generation methodology for autonomous driving, substantially enhancing the customization, training efficacy, and practical applicability of perception and sensor fusion models in cooperative autonomous systems.

This work addresses the challenges of real-world data scarcity, high acquisition costs, and privacy sensitivity in multimodal AI training by introducing Simula, a novel framework that pioneers inference-driven synthetic data generation without requiring any seed data. By integrating an agent-based architecture with a controllable generation pipeline, Simula enables fine-grained control over data characteristics and computational resource allocation, substantially enhancing the interpretability and scalability of synthetic data. Through a comprehensive multidimensional evaluation protocol, the framework simultaneously validates both the intrinsic quality of the generated data and its effectiveness in downstream tasks across multiple benchmarks, offering a practical pathway and design paradigm for AI development under data-constrained conditions.

Addressing the challenge of constructing high-quality, domain-specific annotated data—often costly and labor-intensive—this paper proposes a few-shot-driven synthetic data generation paradigm. Given only a small set of user-provided examples, the method retrieves semantically relevant real-world text from large-scale web corpora and leverages instruction-tuned large language models (LLMs) to automatically generate well-formatted, task-specific synthetic training data. It is the first approach to synergistically integrate corpus retrieval with LLM-based augmentation, enabling zero human annotation, domain adaptability, and efficient few-shot generalization. Empirical evaluation across biomedical, medical, and commonsense question answering (QA), as well as summarization tasks, demonstrates that models trained on the generated data achieve a 46-point preference score improvement over human-annotated baselines in summarization, while QA models match or surpass the performance of general-purpose foundation models.

This work addresses the limitations of existing synthetic data evaluation metrics, which rely on opaque latent features and exhibit weak correlation with downstream task performance. To overcome this, the authors propose a novel vision–language joint framework that integrates generative modeling, semantic segmentation, and image captioning. The framework introduces, for the first time, a cross-modal consistency validation mechanism that enables interpretable and automated assessment of synthetic remote sensing data through semantic composition analysis and minimization of descriptive redundancy. The study constructs ARAS400k, a large-scale remote sensing dataset comprising 400,000 samples. Experimental results demonstrate that joint training with both synthetic and real data significantly outperforms baseline models trained exclusively on real data.

This work addresses the lack of scalable, trustworthy evaluation methods and physically plausible training data for general-purpose robotic policies, compounded by the high cost and poor reproducibility of real-world robot experiments. To overcome these challenges, the authors propose a human↔simulation↔robot bidirectional alignment framework supported by a cloud-native toolchain. Leveraging the JoySim simulator—integrated with reconstruction, rendering, and realism-enhancement modules—they implement a high-fidelity digital twin on the JD Cloud platform. Human demonstrations are transformed into physically consistent trajectories, annotations, and visual observations, while simulation serves dual roles as a scalable evaluation layer and a data filter. This approach substantially improves both the efficiency of data generation and the reliability of policy evaluation.

This work addresses the inefficiency of knowledge extraction from raw multimodal data streams, which suffer from high entropy, and the limitations of existing passive annotation methods that are costly and fail to uncover deep semantic structures. To overcome these challenges, the authors propose a novel paradigm termed “agent-based data distillation,” which frames data processing as a learnable capability. This approach employs a two-stage pipeline that integrates deterministic factual anchors with generative semantic synthesis and introduces Group Relative Policy Optimization (GRPO) for policy alignment. The study establishes DataClaw0-val, the first benchmark for data distillation, and trains the DataClaw0-9B model, demonstrating significantly improved task adaptation efficiency under few-shot post-training across video generation, real-world visual question answering, and GUI navigation tasks, thereby validating its ability to produce high-information-density, task-specific data.

Autonomous driving in the real world faces significant challenges, including data scarcity, stringent safety constraints, and limited generalization across diverse environments. This work presents a systematic review of synthetic data and virtual simulation techniques applied to perception, planning, and validation in autonomous systems. It proposes an integrated three-dimensional framework that combines synthetic data generation, digital twin–based validation, and domain adaptation, further enhanced by vision–language models to improve simulation fidelity and semantic generalization. By establishing a comprehensive taxonomy of current methodologies, the study identifies critical research directions—such as safety verification, cooperative autonomy, and simulation-driven policy learning—to advance the development of scalable, safe, and generalizable autonomous driving systems.

This work addresses the underexplored Sim2Real gap between large language model (LLM)-based user simulators and actual human behavior in interactive tasks. We propose the User-Sim Index (USI), a novel metric for systematically evaluating simulation fidelity through a large-scale controlled study involving 31 LLM-based simulators and 451 real users across 165 tasks under the full τ-bench protocol. Through multidimensional behavioral analysis, rule-based reward mechanisms, and comparisons across model families, we quantitatively reveal systematic biases in LLM simulators: they consistently exhibit excessive cooperativeness, overly positive feedback, and limited stylistic diversity, leading to inflated agent success rates. Notably, improvements in general model capabilities do not necessarily translate to higher simulation fidelity. Our findings establish USI as a new standard for assessing user simulation realism and underscore the critical need for human-in-the-loop validation.