NeurIPS 2026 论文列表

Variable-order Markov models generate by backing off to the longest usable suffix of the generated history, while regular constraints describe finite horizon controls such as fixed endings, metrical patterns, and anti-copy rules. Existing belief-propagation methods give exact regular-constrained sampling for first-order Markov chains, but a first-order state merges histories that a variable-order generator deliberately keeps distinct. We give the corresponding variable-order construction: replace the Markov state by the sparse observed context state and compose this context graph with the regular constraint automaton. For a fixed context graph and automaton, inference is linear in the horizon and in the number of reachable product edges, without expanding to all $|\mathcal{V}|^K$ histories; the same source-row interface handles reversible augmentation by inverse count lookup, without materializing transformed corpora. Tiny enumerated examples verify exact partition functions and conditional probabilities; Bach Prelude experiments show sparse regular-constrained sampling at $K=6$ and 12-key virtual transposition semantics from $592$ stored events.

Automating optimization modeling from natural language faces two key challenges: training corpora lack structural diversity, and data generation pipelines remain static and decoupled from model learning. To address these challenges, we propose EvoOptiGraph, a novel framework where data and model co-evolve, driven by model weaknesses. EvoOptiGraph represents each mixed-integer linear program (MILP) as an attributed bipartite graph and applies validity-preserving evolutionary operators to generate structurally diverse instances. The evolved graphs are converted into solver code and natural language via deterministic compilation and verified back translation. Training proceeds in two stages: supervised fine-tuning (SFT) on an initial dataset, followed by reinforcement learning with verifiable rewards (RLVR), where graph-derived weakness signals dynamically evolve new instances that target the model's failures, forming a closed loop that continuously updates the training distribution. Empirical results on six public datasets show that EvoOptiGraph significantly outperforms larger generalist models, agentic methods, and specialized baselines in accuracy, executability, and generalization. These results demonstrate that targeted data–model coevolution is an effective strategy for improving LLMs on optimization modeling tasks.

Access to real-world information is often noisy and fragmented. Constructing a coherent narrative from such fragments requires models to reconstruct missing spans within a broader storyline, commonly referred to as text infilling, while preserving consistency with both the local context and the global storyline. Despite using text infilling as a pre-training objective in many Large Language Models (LLMs), their actual performance on real-world narrative infilling remains underexplored. In this paper, we address this gap by introducing a multi-domain benchmark of ∼9.2K instances for narrative infilling, constructed by masking one to three sentences across four narrative types: encyclopedic text, commonsense stories, news articles, and visual narratives. Using this benchmark, we evaluate 20 instruction-tuned open-source LLMs ranging from 1.5B to 70B parameters across varying levels of instruction specificity and reasoning guidance. Outputs are assessed using standard automatic metrics and a qualitative framework covering five narrative dimensions. Results show that model scale does not reliably predict infilling quality: Gemma-2-2B achieves the highest qualitative score (4.02/5), outperforming models over ten times larger, including DeepSeek-Qwen-32B (3.77/5, ↓6.6%) and LLaMA-3.3-70B (3.71/5, ↓8.3%). We further find that explicit reasoning offers limited benefits as chain-of-thought reasoning yields only a marginal improvement (+0.6%). Additionally, short narratives and domain characteristics emerge as stronger predictors of task difficulty than infill position alone for narrative infilling in current LLMs.

Modern systems are often expected to transfer across tasks that were not specified during training, leading to the question: what data facilitates generalization in new, unanticipated settings? One hypothesis is that data with more structural information could contain shared circuits and subprograms that could be recycled in a wider array of downstream settings. Epiplexity, a recently proposed measure of the structural information a compute-bounded learner can extract from data, provides a mechanism to reason about this relationship. In this paper, we show how to operationalize epiplexity as an online training signal for data selection and generation. For selection, we fit scaling laws to the training loss curves of natural data domains to predict the expected epiplexity gain as a function of training tokens, and use this signal to adaptively determine the sampling weights over domains during training. For synthetic data generation, we define a generator's reward as the change in learner epiplexity over a buffer of previously generated data and use REINFORCE policy gradients to guide the generator toward an epiplexity-maximizing distribution. In both cases, higher epiplexity predicts improved downstream performance on zero-shot and fine-tuning based tasks, supporting the hypothesis that data rich in structural information yields representations that transfer across domains.

While agentic frameworks have advanced repository-level code localization, their reliance on static snapshots often leads to a $\textit{traceability gap}$—the loss of implicit links between issue symptoms and code implementation. To bridge this information deficit, we introduce $\textbf{T}$raceability $\textbf{A}$ugmented $\textbf{Co}$de Localization (TACO), a framework that recovers retrievable and usable traceability from repository evolution to guide localization agents. TACO offline crystallizes historical pull requests into a dual-index knowledge base through a prompt auto-tuning technique, capturing high-value semantic hints and architectural rationales. In the online phase, TACO employs a synergistic dual-track retrieval workflow, cross-validated by an arbiter and aligned temporally to handle version drift. Extensive evaluations on SWE-bench Lite and SWE-bench Verified across five state-of-the-art agentic baselines demonstrate that TACO significantly improves exact-match localization accuracy (Acc@1) by an average of 11.29\%, 13.20\%, and 12.87\% at the file, module, and function levels, respectively. Moreover, TACO significantly streamlines the exploration process, achieving an average reduction of 29.2\% in token usage and 30.4\% in monetary costs, with minimal offline maintenance overhead.

When large language models (LLMs) are used for creative tasks where the evaluation is largely subjective, two crucial activities are ideation (in which a list of candidate ideas is produced) and pruning (in which the initial candidates are prioritized through a process of comparison). In this framework, the subjective results are influenced by inherent biases in the LLM's generation and evaluation, and it is therefore important to understand how these subjective biases operate. We define and explore a set of stylized tasks that allow us to highlight the effects of these subjective biases in LLM evaluation. In the process, we find evidence for several key underlying principles. First, there are multiple subjective LLM biases at play, and to understand the overall evaluation we must analyze the relative strengths of these biases and their interactions. Second, different prompts -- even when they seem superficially similar -- can have the effect of making different subjective LLM biases more or less salient. And third, there are complex interactions between the LLM's initial idea-generation phase and the subsequent decisions that go into prioritizing and pruning these ideas.

Symbolic regression (SR) aims to discover compact, interpretable mathematical expressions from data. This goal inherently requires iterative refinement rather than one-shot construction. Autoregressive methods lack the ability to perform targeted corrections, so whenever structural flaws appear, the remainder of the sequence must be discarded and regenerated. Meanwhile, search-based methods rely on costly and indirect replacement through enumeration. We propose EditFlowSR, which reframes SR as iterative editing of expression trees through insertions, deletions, and substitutions. To reconcile fitting accuracy with algebraic simplicity, we introduce dual trajectory supervision. One trajectory teaches the model to construct expressions from data, while the other teaches it to algebraically simplify them, and both are unified under the shared editing framework. At inference time, EditFlowSR starts from a randomly initialized expression and progressively refines it through iterative editing, simultaneously constructing valid structure and removing redundancy. Across the standard SRBench, EditFlowSR achieves first-rank Pareto dominance in predictive accuracy, expression tree size, and time complexity. These results establish edit-based generation as a principled and effective paradigm for end-to-end symbolic regression. Code is available at \url{https://anonymous.4open.science/r/EditFlowSR_NIPS2026}.

Unified 3D foundation models aspire to generate 3D assets and reason about them in language within a single backbone, but their text–3D interaction remains largely implicit: existing methods concatenate text and 3D tokens into a flat sequence and rely on self-attention, collapsing coarse structural cues and fine geometric details into one undifferentiated representation. We argue that the central design problem is not richer geometry alone, but **scale-coupled cross-modal collaboration**. We introduce **ELSA3D**, a unified 3D model that addresses this by structuring language and geometric reasoning jointly along matched abstraction scales. The two streams are coupled through **Anchor Tokens**: a small, dynamic set of cross-modal fusion units that bind selected text tokens to geometric evidence at a learned scale and write the fused signal back, keeping interaction sparse yet precise. A lightweight per-block router makes both computation and reasoning elastic, choosing which text tokens instantiate anchors at which geometric scale so that cross-modal capacity concentrates where alignment is hardest. ELSA3D establishes new state-of-the-art on image-to-3D, text-to-3D, and 3D captioning, outperforming the strongest unified baseline on every reported metric while roughly **halving** FLOPs and inference latency relative to a non-elastic version of the same model.

Diffusion models have achieved impressive performance in video generation, but their iterative denoising process remains computationally expensive due to the large number of tokens processed at each timestep. Recently, progressive resolution sampling has emerged as a promising acceleration approach by reducing latent resolution in early stages. However, scaling this idea to video generation remains challenging, as the additional temporal dimension introduces diverse spatio-temporal demands across different videos, and compressing only a single dimension often leads to limited acceleration or degraded quality. Therefore, we propose \textbf{DVG}, a \textbf{D}ynamic \textbf{V}ideo \textbf{G}eneration framework that jointly allocates computation across time and space, automatically selecting content-aware acceleration strategies without manual tuning or retraining. \textbf{DVG} achieves near-lossless acceleration across models and tasks, reaching up to \textbf{7$\times$} speedup on HunyuanVideo and HunyuanVideo-1.5, and \textbf{18$\times$} when combined with distillation, demonstrating its potential as a key component in today’s large-scale efficient video generation systems. \emph{Our code is in supplementary material and will be released on Github.}

Consistency-based generative models like Shortcut and MeanFlow achieve impressive results via a target-aware design for solving the Probability Flow ODE (PF-ODE). Typically, such methods introduce a target time $r$ alongside the current time $t$ to modulate outputs between a local multi-step derivative ($r = t$) and a global few-step integral ($r = 0$). However, the conventional "one input, one output" paradigm enforces a partition of the training budget, often allocating a significant portion (e.g., 75% in MeanFlow) solely to the multi-step objective for stability. This separation forces a trade-off: allocating sufficient samples to the multi-step objective leaves the few-step generation undertrained, which harms convergence and limits scalability.To this end, we propose Duality Models (DuMo) via a "one input, dual output" paradigm. Using a shared backbone with dual heads, DuMo simultaneously predicts velocity $\mathbf{v}_t$ and flow-map $\mathbf{u}_t$ from a single input $\mathbf{x}_t$. This applies geometric constraints from the multi-step objective to every sample, bounding the few-step estimation without separating training objectives, thereby significantly improving stability and efficiency. On ImageNet $256 \times 256$, a 679M Diffusion Transformer with SD-VAE achieves a state-of-the-art (SOTA) FID of 1.79 in just 2 steps.Code will be publicly available.

Generating neural network weights in a single forward pass promises to amortize model training for fast sampling, transfer, and model-set construction. Yet accuracy alone is insufficient: existing generators can map diverse latent inputs to weights that remain functionally close to their training checkpoints. We study this functional mode collapse in the one-step regime and introduce DriftWeight, a MeanFlow-based generator with temperature-scaled repulsive drift in a checkpoint-augmented PCA proxy space. The method uses structured negatives from the current batch and optimization history to push generated weights away from observed clusters while preserving task performance. Our key finding is that repulsion is not automatically compatible with one-step inference. In a controlled ablation, applying the drift loss at the same boundary evaluation used for sampling collapses one-step accuracy even though multi-step integration remains accurate. DriftWeight mitigates this boundary-localized failure with a consistency objective that anchors the inference point to the training-weight manifold. Across MNIST, Fashion-MNIST, and CIFAR-10, DriftWeight matches or exceeds reported 100-step DeepWeightFlow accuracy in a single forward pass. On MNIST, where we directly evaluate error-set functional overlap, it reduces MaxIoU from the DeepWeightFlow regime of approximately $0.82$ to $0.66$. On CIFAR-100 with a ViT-Base target ($\sim$86M parameters), one-step samples remain within $1.7$ percentage points of the training-population mean.

Top-1 accuracy on ImageNet-1k remains the most universally reported metric in visual recognition, despite its well-documented label quality issues. In the paper, we describe the effort to obtain accurate and complete ImageNet-1k validation set annotations. The result -- ReImageNet -- is a from-scratch reannotation, which includes multilabel correction, object localisation, revised class definitions, and semantic attributes---text-recognition, rendition, reflection, crowd, and dominant. The reannotation reveals that $\approx13$\% of the original ImageNet-1k labels are incorrect, $32.7$\% of images are multilabel and $4.7$\% contain no object from an ImageNet-1k class. With the new labels, top-1 accuracy increases by $0.7$--$1.4$\% for supervised models and by $6$--$8$\% for MLLMs. We show that annotation errors in ImageNet-1k propagate into its derivative test sets, indicating that the problem is structural rather than specific to any single benchmark. We observed that LLMs outperform unaided crowd annotators, and that human and LLM collaboration with appropriate tooling represents the current quality ceiling for annotation at this scale. All [annotations](https://huggingface.co/datasets/c1rcuslegend/ReImageNet), [class definitions](https://gonikisgo.github.io/imagenet-annotation-preview/), [guidelines](https://gonikisgo.github.io/imagenet-annotation-preview/), and [analysis code](https://github.com/klarajanouskova/ImageNet) have been publicly released.

Difficulty perception is essential for adaptive reasoning in large language models (LLMs). Previous studies rely on training auxiliary models or using extra reasoning rollouts to estimate difficulty, which incurs high computational costs. In this paper, we identify an intrinsic property of LLMs: their internal representations, even before explicit reasoning, encode an informative and useful signal that correlates with problem difficulty. Inspired by this property, we propose Referential Latent Difficulty Perception (RLDP), a training-free and rollout-free method that estimates difficulty directly from hidden activations in a single forward pass, and requires only minimal reference problems. Additionally, we introduce RLDP-AdaSwitch, a lightweight controller that dynamically allocates reasoning effort based on the difficulty signals provided by RLDP, enabling efficient trade-offs between accuracy and compute. Our experimental results across multiple LLMs and diverse datasets, including math reasoning, code generation, and QA, demonstrate that RLDP provides stable and effective difficulty discrimination. This further powers RLDP-AdaSwitch to achieve 1.34×–2.00× efficiency compared to rollout-based methods, while matching the performance of training-based methods.

Personalized image generation aims to synthesize text-driven images conditioned on reference images, while mainly casting the generation as image customization for foreground and style transfer for background. Previous arts of diffusion models suffers from the text misalignment with background for image customization and foreground for style transfer during the denoising process. Such facts, as we observed, rooted from the entanglement among hybrid frequency bands during the denoising process. In particular, the low-frequency band primarily captures color information, mid-frequency band encodes global structure, while high-frequency band corresponds to local texture. Besides, the early (high-noise) denoising stage mainly focuses on the low-and-mid frequency bands, while the late (low-noise) denoising stage progressively exhibits the high-frequency band, resulting in the dominance of low-and-mid over high frequency bands, especially when entangled within a single image reference, tends to reinforce each other at the early stage, hence the pitfall of overwhelming suppression of text prompt guidance. To address such salient limitation, in this paper, we study personalized generation based on dual references - customization and color and style reference - and propose a paradigm to disentangle these **Dual** image references within **F**requency-aware **D**iffusion **M**odels, dubbed **Dual-FDM**, to simultaneously tackle two crucial personalized image generation tasks: customization style transfer and color style transfer, by disentangling different frequency bands via mask strategy within frequency domain. For customization style transfer, we replace the mid-frequency band of the background in the style reference with that from the foreground of the customized reference. For color style transfer, we substitute the low-frequency band of the background in the style reference with that from both the foreground and background of the color reference. Both the substituted frequency bands are used as the key and value to reconstruct the query foreground and background of the denoised personalized image. We further compute the ratio of information entropy of the substituted frequency bands to adaptively modulate the denoising timesteps between the early and late stages. Extensive experiments validate the superiority of **Dual-FDM** over the state-of-the-art diffusion models for personalized image generation. Our code can be accessed from the supplementary material package.

Continuous diffusion is a natural framework for non-autoregressive generation but has consistently underperformed masked discrete diffusion (MDM) on discrete sequence generation. We argue this is not a limitation of continuity itself, but of how previous models parameterize the denoiser as a family of timestep-indexed regimes. We introduce \emph{Discrete Stochastic Localization} (DSL), a continuous-state framework with unit-sphere token embeddings whose Bayes-optimal denoiser is invariant to the nominal signal-to-noise ratio (SNR). One trained network then supports an entire family of per-token SNR paths, with masked diffusion as one special case. Fine-tuning a pretrained MDLM checkpoint with DSL substantially improves distributional faithfulness (MAUVE) on OpenWebText across all step budgets from $T{=}128$ to $T{=}1024$, and the same checkpoint supports random-order autoregressive sampling and a hybrid continuous-then-discrete sampler at as few as $T{=}48$ total steps---without distillation or retraining. On Text8, DSL gives the first continuous-state NLL estimate approaching the MDM range. Code is anonymously available at \url{https://anonymous.4open.science/r/DSL_anonymous-94B5/}.

Diffusion language models (DLMs) provide a bidirectional generation framework naturally suited for infilling, yet their performance is constrained by the pre-specified infilling length. In this paper, we reveal that DLMs possess an inherent ability to discover the correct infilling length. We identify two key statistical phenomena in the first-step denoising confidence: a local Oracle Peak that emerges near the ground-truth length and a systematic Length Bias that often obscures this signal. By leveraging this signal and calibrating the bias, our training-free method CAL (Calibrated Adaptive Length) enables DLMs to approximate the optimal length through an efficient search before formal decoding. Empirical evaluations demonstrate that CAL improves Pass@1 by up to 47.7% over fixed-length baselines and 40.5% over chat-based adaptive methods in code infilling, while boosting BLEU-2 and ROUGE-L by up to 8.5% and 9.9% in text infilling. These results demonstrate that CAL paves the way for robust DLM infilling without requiring any specialized training.

Predicting transcriptional responses to genetic perturbations is fundamental to functional genomics and therapeutic discovery. Recent deep learning models have shown promise in single-cell perturbation response prediction, but they typically generate each response in isolation, without explicitly leveraging experimentally characterised responses to related perturbations. We introduce PT-RAG (Perturbation-aware Two-stage Retrieval-Augmented Generation), a plug-in retrieval-and-conditioning module for generative cellular perturbation response. PT-RAG augments an existing perturbation-response backbone with learned access to related perturbation contexts. The key challenge is that relevance is not fixed in this setting: functionally related genes may elicit different effects across cell types. PT-RAG addresses this with a two-stage retrieval mechanism: GenePT-based semantic retrieval first identifies $K$ candidate perturbations, after which a differentiable Gumbel-Softmax selector adaptively selects retrieved contexts conditioned on the control cell state, the query perturbation, and each candidate perturbation. Through cross-cell-type and cross-perturbation generalization tasks, PT-RAG consistently improves distributional similarity and often overall predictive quality; for example, on scGPT cross-cell-type results, Wasserstein distance drops by 10.7%. The code to reproduce our experiments is available at https://anonymous.4open.science/r/PT-RAG_NIPS-13D8/.

More than 80% of the 1.6B English speakers do not use Standard American English (SAE), yet LLMs often fail to correctly identify non-SAE dialects and generate stereotyped responses for their speakers. We introduce **DialectLLM**, the first large-scale framework for generating high-quality multi-dialectal conversational data encompassing the three pillars of written dialect---lexical (vocabulary), orthographic (spelling), and morphosyntactic (grammar) features. DialectLLM produces a dialect-parallel dialog dataset spanning nine English dialects. Partnering with native linguists, we design and validate SAE-to-dialect transformation rules, ensuring authenticity. Our approach challenges the prevailing practice of applying a single morphosyntactic feature set to both user utterances and model responses, showing that models should not reproduce up to 90% of the grammatical features of a dialect. Human evaluation confirms data quality, with annotators preferring DialectLLM over prior methods in 98.8% of pairwise comparisons for dialect naturalness. We then construct **DialectLLM-Bench**, a dialect-parallel benchmark with 50k+ dialogs, resulting in 97k+ QA pairs, and evaluate 17 LLMs on dialect identification and response generation tasks. Even frontier models achieve under 70% accuracy, fail to reach 50% for prominent dialects like Canadian English, and systematically misclassify non-SAE dialects as American or British. Beyond benchmarking, we show that DialectLLM data also serve as a scalable LLM post-training resource, suggesting a practical path toward dialect-aware conversational AI.

In reinforcement learning (RL), reward specification plays the fundamental role in shaping policy optimization. Inverse RL (IRL) offers a data-driven approach to inferring rewards from demonstrations, but prevalent neural-network-based methods often produce rewards lacking interpretability and flexibility for manual inspection or revision. In contrast, human-written or large language model (LLM)-generated reward code provides explicit semantic structure, yet its precise numerical parameters are often difficult to calibrate and prone to specification errors. To bridge this gap, we propose DiReCL (\textbf{Di}fferentiable \textbf{Re}ward \textbf{C}ode \textbf{L}earning), a novel framework that unifies these two paradigms by formulating reward learning in a differentiable reward code space. DiReCL first prompts an LLM to synthesize the reward code templates, then converts the numeric elements in these templates into differentiable parameters and align them with expert demonstrations through IRL. We further introduce a reflection mechanism that uses IRL diagnostics to provide evidence of inadequacies in the reward structure and guide template revision. Extensive experiments on MuJoCo locomotion and autonomous driving benchmarks demonstrate that DiReCL achieves over $1.25 \times$ the performance of both standard IRL and LLM-assisted reward design baselines, while preserving the crucial benefits of explicit, interpretable reward semantics.