NeurIPS 2026 论文列表

Optical Music Recognition (OMR), the task of transcribing sheet music into a structured textual representation, is currently bottlenecked by a lack of large-scale, annotated datasets of real scans. This forces models to rely on either few-shot transfer or synthetic training pipelines that remain overly simplistic. A secondary challenge is encoding non-uniqueness: in the popular Humdrum **kern format for transcribing music, multiple different text encodings can render into the same visual sheet music. This one-to-many mapping creates a harder learning task and introduces high uncertainty during decoding. We propose Transcoda, an OMR system built on (i) an advanced synthetic data generation pipeline, (ii) a normalization of the **kern encoding to enforce a unique normal form and (iii) grammar-based decoding to ensure the syntactic correctness of the output. This approach allows us to train a compact 59M-parameter model in just 6 hours on a single GPU that outperforms billion-parameter baselines. Transcoda achieves the best score among state of the art baselines on a newly curated benchmark of synthetically rendered scores at 18.46% OMR-NED (compared to 43.91% for the next-best system, Legato) and reduces the error rate on historical Polish scans to 63.97% OMR-NED (down from 80.16% for SMT++). We will make our model, data and generation pipeline publicly available upon publication of the paper.

Foundation models for zero-shot time series forecasting face challenges in efficient long-horizon prediction and reproducibility, with existing synthetic-only approaches underperforming on challenging benchmarks. This paper presents TempoPFN, a univariate time series foundation model based on linear Recurrent Neural Networks (RNNs) pre-trained exclusively on synthetic data. The model uses a GatedDeltaProduct architecture with state-weaving for fully parallelizable training across sequence lengths, eliminating the need for windowing or summarization techniques while maintaining robust temporal state-tracking. Our comprehensive synthetic data pipeline unifies diverse generators, including stochastic differential equations, Gaussian processes, and audio synthesis, with novel augmentations. In zero-shot evaluations on the Gift-Eval, fev-bench and Chronos-ZS benchmarks, TempoPFN achieves top-tier competitive performance, surpassing the majority of models trained on real-world data, while being more efficient than existing baselines by leveraging fully parallelizable training and inference. We open-source our complete data generation pipeline and training code, providing a reproducible foundation for future research.

The *synthetic control method* (SCM) estimates causal effects in panel data with a single-treated unit by constructing a counterfactual outcome as a weighted combination of untreated control units that matches the pre-treatment trajectory. In this paper, we introduce the **targeted synthetic control (TSC)** method, a new two-stage estimator that directly estimates the counterfactual outcome. Specifically, our TSC method (1) yields a targeted debiasing estimator, in the sense that the targeted updating refines the initial weights to produce more stable weights; and (2) ensures that the final counterfactual estimation is a convex combination of observed control outcomes to enable direct interpretation of the synthetic control weights. TSC is flexible and can be instantiated with arbitrary machine learning models. Methodologically, TSC starts from an initial set of synthetic-control weights via a one-dimensional targeted update through the weight-tilting submodel, which calibrates the weights to reduce bias of weights estimation arising from pre-treatment fit. Furthermore, TSC avoids key shortcomings of existing methods (e.g., the augmented SCM), which can produce unbounded counterfactual estimates. Across extensive synthetic and real-world experiments, TSC consistently improves estimation accuracy over state-of-the-art SCM baselines.

Generative models are increasingly used to simulate risk-sensitive systems, from financial markets and energy demand to healthcare, where downstream decisions depend on rare but consequential events. In this setting, matching average behavior is not enough: rare tail events drive stress testing, capital allocation, and policy analysis. Yet tail objectives such as Value-at-Risk and Expected Shortfall are difficult to optimize directly because empirical estimators rely on sorting, thresholding, and subset selection. We introduce TailDiff, a training-free inference-time guidance method for pretrained diffusion models that uses jointly elicitable Fissler-Ziegel scoring rules to construct differentiable tail-aware objectives. We analyze the induced guidance field locally in the low-noise regime, characterizing tail-shaping and boundary-coupling behavior together with controlled perturbations under small realized effective drift. On synthetic data, TailDiff approaches the oracle finite-sample floor, on a challenging financial tail-risk simulation benchmark, it outperforms the state-of-the-art on tail metrics while preserving bulk structure.

To consider synthesizability in structure-based drug design (SBDD), a recent approach has proposed to co-design synthetic pathways with molecular conformers of ligands. However, the intermediate conformers are predicted based solely on the current intermediate molecule, without information about the synthons that will be added later along the synthetic pathway. As a result, the intermediate conformers do not adequately reflect the upcoming substructures, creating the geometric mismatch that leads to cumulative errors along the synthetic trajectory. To this end, we propose SYnthon Contrastive LEarning (SYCLE), a synthesizable SBDD framework that injects future synthon information into intermediate conformer prediction via contrastive learning. SYCLE further utilizes this information in the pathway generation policy to guide synthon selection. SYCLE achieves state-of-the-art binding affinity across all 15 LIT-PCBA targets. On CrossDocked2020, SYCLE attains -9.80 kcal/mol and an AiZynthFinder success rate of 68.8\%, nearly double that of the best baseline.

Existing peptide models saturate natural-sequence proteomics benchmarks, but whether they capture the underlying physicochemical dynamics or merely pattern-match on sequence statistics remains unclear. Public peptide datasets offer no axis along which to test this: non-canonical residues and stereochemical variation are essentially absent. We introduce StereoPep, a benchmark of 48,788 chemically synthesized peptides spanning 11 libraries, produced in the laboratory via high-throughput flow synthesis and characterized experimentally by reverse-phase liquid chromatography retention time. Unlike model-generated synthetic data, every peptide in StereoPep is a real molecule with a real chromatographic measurement. The dataset is constructed around matched diastereomeric pairs (sequences identical in primary structure but differing at a single stereocenter), providing a direct test of whether models capture three-dimensional stereochemical information. StereoPep enables progress across multiple fields: it serves as a benchmark for stereochemistry-aware molecular representations and geometric deep learning, supports adapting protein language models to non-canonical residues, and underpins emerging proteomics multiplexing strategies that encode samples through retention-time shifts. Across specialized retention-time models, graph-based architectures, and protein language models, all approaches predict overall retention reasonably well (Pearson $r \approx 0.80$) but collapse on diastereomer pair discrimination (AUC $\approx 0.55$), revealing that current representations encode atomic composition far better than 3D structure.

Producing output that conforms to a specified JSON schema underlies tool use, structured extraction, and knowledge base construction in modern large language models. Despite this centrality, public datasets for the task remain small, synthetic, or text-only, and rarely pair real page content with the prompts and schemas used in practice. We introduce ScrapeGraphAI-100k, 93{,}695 schema-constrained extraction events collected via opt-in ScrapeGraphAI telemetry in Q2--Q3 2025, deduplicated and balanced by schema from 9M raw events. The corpus spans 18{,}000+ unique schemas across 15 named languages plus a long-tail Other category, with English and Traditional Chinese covering 88\% of detected content, each instance pairs Markdown-converted page content with a prompt, schema, LLM response, and per-example jsonschema-rs structural conformance labels (semantic correctness is out of scope, and raw HTML is deferred beyond v1.0). We characterize structural diversity across the corpus and identify sharp failure thresholds as schema complexity grows. As a case study, a 1.7B student fine-tuned on this data closely tracks the output distribution of its GPT-5-nano teacher, though it still trails a 30B-A3B reference (3.3B active parameters) on schema compliance. We offer this distillation result as preliminary evidence that grounding schema-constrained generation in real practitioner workloads at scale enables training and benchmarking that prior synthetic or text-only corpora could not support.

As IT systems grow in complexity, automated agent-driven incident response becomes increasingly critical. A key capability for these agents is summarizing large log streams into human-readable reports. Further progress requires meaningfully evaluating this function, yet existing benchmarks leave end-to-end summarization from large log streams underexplored due to the prohibitive cost and complexity of collecting real-world data. We introduce SLS-Bench, a benchmark for incident log summarization featuring 90 tasks grounded in real-world incident reports, each with a synthetic log stream and a reference summary. To construct SLS-Bench, we developed a data generation pipeline that converts incident reports into structured incident models, which guide the generation of synthetic logs and reference summaries. This pipeline combines narrative grounding, explicit incident modeling, generator-verifier loops, and statistical comparisons with real-world log streams to produce realistic and controllable observability data. Our evaluation of 14 LLMs on SLS-Bench shows that while frontier models can produce useful summaries, they do so at substantial cost and latency, and open-weight models lag significantly. SLS-Bench provides a framework for systematic evaluation of log analysis agents and a scalable blueprint for future synthetic observability benchmarks.

Synthetic data generation is becoming increasingly prevalent as advances in generative modeling accelerate. However, theoretical understanding of when and how synthetic data can improve downstream estimation and prediction remains underdeveloped. This question is especially relevant when synthetic samples are generated from proposal mechanisms informed by trained generators or domain knowledge, whose distributions may differ from the target population. To address this question, we propose REAL-Syn: Risk-guided Estimation-aware Acceptance for Learning with Synthetic Data, a framework for statistical learning with synthetic data selection. REAL-Syn uses selected subsets of candidate synthetic data to reduce estimator variability and often improve downstream prediction under target-proposal mismatch. The proposed procedure uses a covariance-based risk surrogate to select from the set of candidate synthetic samples; the accepted samples are then reweighted and pooled with the real observations for inference. We present theoretical results on the surrogate-optimal acceptance rule and the proposed selection procedure. We empirically show that REAL-Syn achieves competitive performance in simulations and an application to solar flare intensity forecasting.

Deep research agents extend the role of search engines from retrieving keyword-matched pages to synthesizing knowledge, fundamentally changing how humans interact with information. However, frontier systems remain proprietary, while existing open agents often generalize poorly across different task types, leaving unclear how to train a broadly capable deep research agent. We release Quest, a family of open models (ranging from 2B to 35B) that serve as general-purpose deep research agents designed to handle a wide range of search tasks, with strong capabilities in fact seeking, citation grounding, and report synthesis. To build Quest, we propose an effective training recipe combining mid-training, supervised fine-tuning, and reinforcement learning. Central to this recipe is a curated data synthesis pipeline based on unified rubric trees, which applies to different task types and enables synthesizing training data with verifiable rewards without human annotation. In addition, Quest incorporates a built-in context management mechanism that enables effective long-horizon reasoning and knowledge synthesis. Using only 8K synthesized tasks, Quest approaches or even surpasses frontier closed-source agents across eight deep research benchmarks spanning diverse task types, and achieves the best overall performance among recent open-weight agents. Notably, Quest-35B achieves 30.7\% on Mind2Web 2, outperforming OpenAI DeepResearch (28\%) and Gemini DeepResearch (18\%). We plan to release all the resources for further research.

Vision-Language Models (VLMs) for radiology report generation are typically trained on retrospective clinical reports, which suffer from omission noise: clinically present findings are left unreported due to the omission of subtle findings. For example, prior studies show that cardiomegaly may be omitted from ICU chest X-ray reports when the imaging request is focused on monitoring support device placement. As a result, models trained with standard approaches inherit these omissions, learning to under-report findings themselves. We propose PU-DPO, a preference optimization framework that constructs contrastive pairs via editing model responses, producing variants that explicitly mention or omit a specific finding. To prevent omission noise from corrupting the preference signal, we reformulate the objective under a positive-unlabeled (PU) learning framework, treating absent mentions as unlabeled rather than truly negative. Across semi-synthetic experiments and preliminary analyses on real-world chest radiograph benchmarks where adjudicated labels are available, PU-DPO yields consistent gains in detection rates across multiple pathologies without compromising specificity or overall report quality, and is more robust to omission noise than prior approaches.

Simulating human behavior with Large Language Models (LLMs) offers a scalable laboratory for social science and stress-testing AI products. However, limitations remain in matching LLM outputs against the full breadth of human diversity. Current techniques for creating synthetic humans typically optimize for density matching, which often enforces behavioral uniformity and overlooks the rare, consequential outliers. We argue that robust simulation requires shifting the focus toward support coverage: ensuring synthetic populations span the entire landscape of possible human traits, opinions, and preferences. This paper introduces Persona Generators, functions that can produce diverse synthetic populations tailored to arbitrary contexts. We apply an iterative improvement loop based on AlphaEvolve, using LLMs as mutation operators to evolve our Persona Generator code rather than the personas themselves. The optimization process produces lightweight functions that can automatically expand small descriptions into populations of diverse synthetic personas that are maximizing coverage along relevant diversity axes. We demonstrate that evolved generators substantially outperform existing baselines across six diversity metrics on held-out contexts. Furthermore, by successfully mitigating standard LLM mode collapse, our generated populations actually capture real human trait distributions better than baselines specifically designed to match human statistics.

We study seamless world generation, where a model must update a persistent visual world according to online user operations rather than full next-scene descriptions. To make this task well-defined, we introduce an operation-centric taxonomy that represents each scene with a global-local schema and each boundary with a typed operator program specifying change and preservation. This taxonomy enables SeamlessWorld, a synthetic data and benchmark pipeline with 8 operation families, 23 atomic operators, dual prompt surfaces, and schema-derived evaluation probes. We then develop OopsWorld, a causal video generator that learns from SeamlessWorld using operation-conditioned decoupled distillation: the student is driven by operation prompts and visual history, while scene-conditioned teachers provide target-state supervision. We further apply dynamics curriculum learning to progressively compose simple edits into high-dynamic world transitions. OopsWorld substantially improves dynamic responsiveness and long-video quality, achieving 91.67 VBench Dynamics and 80.42 Long-Quality with 100K samples. A systematic explicit--implicit study further reveals a persistent operation-prompting gap in current systems, especially for preserving visual anchors, calling for operation-driven world models beyond next-scene prompt switching.

Synthetic data has become a promising way to scale model training beyond limited human-generated data but it may also induce strong model collapse (Dohmatob et al., 2024), where any fixed fraction of synthetic data prevents model performance from improving under data scaling, leaving a non-vanishing excess risk floor. In this paper, we studied how synthetic data affects the generalization of one-pass SGD in high-dimensional linear regression with model shift. We show that mixed training induces strong model collapse while two-stage training avoids this by using synthetic data only in the first stage, followed by real-data training in the second stage, showing that strong model collapse is not inevitable through a simple data curriculum. We further establish scaling-law upper bounds for both protocols under a random sketch model, showing that larger models amplify synthetic-induced degradation in mixed training and giving an explicit characterization of how high-quality synthetic training may reduce bias in two-stage training. Overall, our results highlight that synthetic data is neither inherently harmful nor beneficial; its effect depends critically on both its quality and the training protocol used to incorporate it.

Pixel diffusion models have recently regained attention for visual generation. However, training advanced pixel-space models from scratch demands prohibitive computational and data resources. To address this, we propose the \textbf{Latent-to-Pixel (L2P)} transfer paradigm, an efficient framework that directly harnesses the rich knowledge of pre-trained LDMs to build powerful pixel-space models. Specifically, L2P discards the VAE in favor of large-patch tokenization and freezes the source LDM's intermediate layers, exclusively training shallow layers to learn the latent-to-pixel transformation. By utilizing LDM-generated synthetic images as the sole training corpus, L2P fits an already smooth data manifold, enabling rapid convergence with zero real-data collection. This strategy allows L2P to seamlessly migrate massive latent priors to the pixel space using only 8 GPUs. Furthermore, eliminating the VAE memory bottleneck unlocks native 4K ultra-high resolution generation. Extensive experiments across mainstream LDM architectures show that L2P incurs negligible training overhead, yet performs on par with the source LDM on DPG-Bench and reaches 93\% performance on GenEval.

Backpropagation is the default learning rule for artificial neural networks and is often treated as the settled approach whenever differentiability is available. In this work, we revisit this convention from the perspective of sample efficiency. We introduce a unified vectorized feedback framework for loss-based and reward-based learning on computational graphs, in which synthetic gradients emerge as a natural alternative to backpropagation. We characterize the conditions under which synthetic gradients can achieve a lower gradient-estimation mean squared error than backpropagation. We construct examples illustrating that this sample efficiency advantage can be arbitrarily large. Experiments on contextual bandits and reinforcement learning tasks demonstrate the potential of our theoretical findings.

We introduce **InvestigationWorlds**, an agentic environment for legal investigation. We build on an underused artifact of U.S. civil litigation: the summary judgment motion. This motion relies upon a record composed of real evidence exhibits, and results in a court-adopted hypothesis that is treated as ground truth for the purposes of deciding the motion. Each environment is built from a real U.S. Federal Court case retrieved from Public Access to Court Electronic Records (PACER) and augmented by an attorney-validated generation pipeline that synthesizes role-tagged documents around the original record. The resulting corpus admits multiple coherent factual readings, only one of which matches the court-adopted hypothesis. Evaluating on 100 cases, we find agents often commit to incorrect hypotheses despite retrieving relevant evidence, struggling to distinguish the court-adopted hypothesis from alternative hypotheses.

Data augmentation is widely recognized for improving generalization in deep networks, yet its impact on the geometry of learned representations remains poorly understood. In this work, we characterize how different data augmentation strategies reshape internal representations in neural networks. Using tools from shape analysis, we embed network hidden representations into a metric space where distance is invariant to scaling, translation, rotation and reflection. We show that increasing augmentation strength leads to well-behaved trajectories in this space, and that different augmentation types steer representations in distinct directions. Moreover, we investigate how neural representation shapes are distorted along data augmentation trajectories, and show that insights from neural geometry can predict which representations provide the most improvement when ensembling models. Our results reveal shared geometric patterns across architectures and seeds, and suggest that analyzing shape-space trajectories offers a principled tool for understanding and comparing data augmentation methods.

Individual-level genotype data underpin genetic discovery, disease-risk modeling, and therapeutic development, yet many critical settings remain data-limited, including underrepresented ancestries, rare or imbalanced disease cohorts, and restricted-access biobanks. Conditional synthetic genome generation would support privacy-aware benchmarking, simulation, and data augmentation, but requires learning an ultra-high-dimensional discrete distribution over hundreds of thousands to tens of millions of correlated single-nucleotide polymorphisms (SNPs), or sites of DNA variation. We introduce $\mathbf{HELICS}$: $\mathbf{H}$igh-fidelity g$\mathbf{E}$neration via $\mathbf{L}$atent $\mathbf{I}$nterpolant $\mathbf{C}$onditional $\mathbf{S}$ynthesis, a phasing-free latent flow matching framework for genome-wide SNP-level generation from biobank-scale genotype data. HELICS tokenizes chromosomes with convolutional autoencoders, reducing $>$$600{\rm K}$ SNPs to $4{,}096$ continuous latent tokens, and trains a transformer parameterized conditional flow matching model over the concatenated multi-chromosome latent representation. Trained on roughly $428\rm K$ genomes in the UK Biobank, HELICS generates genome-wide synthetic cohorts without haplotype phasing and achieves stronger fidelity to held-out real genotypes than existing haplotype- and genotype-based simulators, including the highest validation allele-frequency agreement ($R^2=0.992$) and superior preservation of local and long-range SNP correlation structure. Conditioning on ancestry and genetic liability summaries enables targeted generation across genetic risk profiles; in perturbation analyses, by increasing the conditioned disease trait liability, HELICS induces SNP-level dosage changes in generated genomes that are highly correlated with published association weights. HELICS provides a scalable generative modeling framework for SNP-level genome data and a path toward conditional synthetic cohorts for data-limited genetic studies.