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An arXiv paper formulates payroll fraud detection as inductive link prediction on synthetic HR/ERP graphs, comparing analyst rules, path features, and GNNs. A gradient-boosted model on chained-join features outperformed a GraIL-style GNN on link AP, while the relational entity GNN led under failed identifier joins, showing multi-hop gains are not GNN-specific.
DiRAFT synthesizes financial transcripts by sampling concept-graph plans from aggregate discourse priors rather than individual source passages, and empirically evaluates residual confidentiality risks on two financial corpora, finding stochastic sampling best balances transition fidelity and privacy.
A thread presenting research that fits scaling laws to determine how much compute to invest in synthetic rewrites for improving data quality, and whether a model trained on rewrites can surpass the rewriting model itself.
Apple's new paper introduces SynthAPI, showing that for tool-use agents, diversity of apps and database states matters more than raw task count, deriving a data-mix scaling law that predicts held-out generalization far better than task-count-only laws.
The paper proposes FDSML, a formula-driven supervised metric learning approach that uses parameter-aware triplet loss on a refined fractal dataset (FineFractalDB) to improve synthetic pre-training for image retrieval, achieving notable Recall@1 gains over FDSL on Stanford Online Products and Stanford Cars.
该论文提出一个结合人工整理词汇表与少量人类范文的框架，利用启用推理的LLM为极低资源乃至未见语言生成流畅文本，为合成数据自举提供了一条实用路径。
ReCoPII is a relation-grounded framework for building contextual PII masking datasets without relying on author-based assumptions, producing the ReCoPII-Ko Korean corpus with MASK/KEEP annotations that significantly outperform SPY-style augmentation in human agreement (88.42% vs 26.27%).
提出联邦两步合成框架FITS，通过局部线性回归估计条件累积分布函数并结合continualization方法处理混合类型变量，在联邦设置下生成分布保真的表格合成数据。
The paper proposes a rewriting scaling law that predicts loss from model size, training tokens, rewrite fraction, and data epochs while accounting for rewrite generation compute, finding optimal rewrite fractions increase with compute and that models trained on rewrites can outperform their rewriters.
Google researchers propose a data-free on-policy self-distillation framework where LLMs synthesize their own curriculum of queries and privileged reasoning traces from coarse topic descriptions, achieving self-improvement across text and multimodal benchmarks without external supervision.
NVIDIA expanded its Cosmos platform with new training tools, simulation checkpoints and Cosmos Data Factory blueprints to help developers build, evaluate and deploy world models for physical AI. Partners including Porsche, Volkswagen Group, ANYbotics, Microsoft Azure, Nebius and CoreWeave are already using the blueprints to accelerate autonomous vehicle and robotics development.
论文提出 ChronoSynth，一种记忆增强、约束感知的时序知识图谱问题生成（TKGQG）框架，通过构建时序意图状态、检索适配问题模式并验证修订，在 CRONQUESTIONS 和 MultiTQ 上达到最先进性能。
The paper introduces Ask-to-Distill (A2D), a framework that RL-trains a student-initialized synthesizer to generate synthetic questions that elicit dense teacher supervision for on-policy distillation, without needing reference answers. It improves performance and teacher-gap recovery over unoptimized prompts and real-question banks across math, code, and agentic tasks.
TraceForge 提出一种两阶段（监督+对抗）方法，从教师答案中生成匹配学生模型自身风格的推理轨迹，无需访问隐藏思维链，在 HumanEval+ 和 MBPP+ 等编码基准上显著超越使用教师原始思考内容的训练效果。
Ask-to-Distill 提出了一种优化合成问题的框架，通过教师-学生分布差异作为代理信号，在无需参考答案的情况下提升 on-policy 蒸馏效果，在数学推理、代码生成和智能体任务上优于未优化的问题集和真实问题库。
The paper introduces ToMStories, an evaluation dataset of ~6,000 synthetic narrative vignettes for testing theory-of-mind inference in 13 open-weight language models, finding that large models match humans on multiple-choice but not open-ended questions and tend to oversimplify implicit psychological states.
VerifierBench introduces a benchmark of 222 tasks and 4,993 hidden test cases to evaluate how well LLM agents generate executable verifiers, finding that verifiers tend to over-reject valid solutions due to hidden assumptions that survive self-testing.
The paper introduces Data World, a multi-agent engine that synthesizes data-analysis environments, tasks, and grading rubrics from explicit generative mechanisms, enabling verifiable open-ended findings. RL-zero training on 2,135 generated tasks improves Qwen3.6-35B-A3B across multiple benchmarks, while evaluation of ten frontier models reveals a consistent gap between computation accuracy and insight derivation.
TempoPFN 发表于 NeurIPS 2026，提出一个合成时间序列生成器与增强库，用于预训练线性 RNN 时间序列基础模型，实现零样本预测，并已开源代码与模型权重。
论文提出 HybridResearcher，一个 4B 小型深度研究智能体，通过合成跨源轨迹与强化学习在结构化与非结构化数据之间进行推理，在 HybridDeepResearch 基准上超越更大的 Qwen3.5-397B-A17B，同时不遗忘单源能力。