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Rogo Intelligence uses LLMs to create comprehensive entity profiles from multiple data sources, capturing insights beyond traditional CRMs for investment firms.
This paper introduces PRISM, a four-stage data synthesis framework for training multimodal LLMs to follow prioritized rubrics, and PRISM-Eval, a judge-free evaluation suite. With only 10K samples, PRISM lifts Qwen3-VL-4B from 9.5% to 30.1% Strict accuracy on PRISM-Eval while preserving general benchmark performance, and gains transfer to other open-source MLLMs.
Ego2Robot is a scalable pipeline that converts egocentric human manipulation videos into robot training data via action retargeting and visual synthesis, producing 18,561 hours of data across 15 robot morphologies. Experiments show that joint pretraining on this synthesized data improves out-of-distribution generalization for vision-language-action models, including on real-robot deployment.
HSS-Synth introduces the first data synthesis pipeline for humanities and social sciences, producing 237k high-quality instruction-tuning samples that outperform baselines and set new SOTA on Qwen3-8B.
BridgeAlign proposes a preference-alignment pipeline for humanities and social sciences, generating 210k synthetic preference samples and enabling Qwen3-8B to achieve strong results across 17 benchmarks.
Proposes a GAN-based framework (GT-GAN) for synthesizing high-fidelity data from incomplete LEO satellite Internet observations, showing robustness even with 40% missing data.
The Infinity-Parser2 technical report presents a large multimodal model for end-to-end document parsing, featuring a scalable data synthesis pipeline and multi-task reinforcement learning. It achieves state-of-the-art results on multiple benchmarks while releasing open-source model variants and a 5-million-sample bilingual corpus.
This paper introduces Goku, a million-scale dataset and benchmark for instruction-based video editing, supporting multi-task and structural manipulations. The accompanying model, Goku-Edit, achieves up to +8% improvement on instruction following over open-source models.
This paper introduces RODS, a reward-driven online data synthesis method that addresses the depletion of informative samples in static datasets for multi-turn tool-use agent training. It achieves comparable performance to larger offline pipelines with significantly fewer trajectories.
Edu-Theater is a data-efficient agent framework that uses LLM-powered generative agents to simulate learner behavior in educational settings. It employs a cohort-aware roll-call paradigm to infer learner states with fewer data and computational resources, achieving higher simulation accuracy.
Introduces Geometry-Aware Tabular Diffusion (GATD), which augments tabular diffusion denoisers with explicit pairwise geometric features. Achieves state-of-the-art performance on ten benchmarks while using significantly fewer parameters.
DOMINO is a novel framework that learns minimal sufficient domain representations from reference examples to synthesize domain-specific data for LLMs, improving code benchmark performance without requiring explicit domain descriptions.
This paper proposes a knowledge-aware Text-to-SQL framework that uses knowledge distillation to improve performance in low-resource settings by constructing task-specific knowledge bases and generating synthetic training data. Experiments on seven benchmarks show substantial improvements, especially for open-source models.
Terminal-World introduces a fully automated pipeline that uses agent skills to synthesize high-quality training data for terminal agents, enabling models to outperform baselines with only 1.2% of the training data. The method co-derives task instructions, environments, and teacher trajectories from skill primitives.
This paper proposes a pipeline for fine-tuning LLMs specifically for explainable misinformation detection and introduces LonsRex, a data synthesis method to generate necessary and sufficient rationales, addressing limitations of naive filtering based solely on label correctness.
Uni-Edit proposes using intelligent image editing as a single general task to simultaneously improve unified multimodal models' understanding, generation, and editing capabilities, with an automated data synthesis pipeline creating complex editing instructions.
FrontierSmith automatically generates diverse open-ended coding problems from closed-ended tasks, improving LLM coding performance on benchmarks through enhanced agent interactions and training data synthesis.
This paper introduces CUActSpot, a multimodal benchmark for evaluating computer-use agents, and a renderer-based data synthesis pipeline. The proposed Phi-Ground-Any-4B model outperforms open-source models under 32B parameters.
CoEvolve proposes an agent-data mutual evolution framework for training LLM agents through closed-loop, interaction-driven learning that adapts both the agent and its training data distribution. The method extracts feedback signals from rollout trajectories to guide LLM-based task synthesis, demonstrating significant improvements (15-19% absolute gains) across multiple Qwen models on AppWorld and BFCL benchmarks.
A dual-view data synthesis method using polarity reversal boosts instruction-following retrieval performance by 45% on the FollowIR benchmark.