NeurIPS 2026 Papers

Diffusion-based style transfer faces two fundamental questions: which regions should prioritize structure preservation and which are more amenable to texture transfer, namely the “where” question, and how to achieve such region-aware stylization without corrupting content, namely the “how” question. Existing methods typically rely on a single mechanism to control local structure, local texture, and global style together, making them fragile in region-rich scenes: uniform stylization strength may distort salient objects, while conservative control leaves texture-dominant regions under-stylized. We propose StyleRoute, a region-aware diffusion stylization framework that explicitly separates where style should be transferred from how the transfer should be optimized. For the where question, StyleRoute infers a region-level routing variable from structure confidence, texture confidence, and route disagreement, deciding per region whether to protect structure or to inject texture. For the how question, StyleRoute coordinates the regional routing with diffusion optimization to guide global style evolution, suppress style-content conflicts, and stabilize coarse layout. Together, StyleRoute transforms the implicit style-content trade-off into an explicit, interpretable, and region-aware transfer process. Experiments on diverse content-style pairs show consistent improvements over prior diffusion-based and region-matching stylization methods. Code will be released.

Encoding classical data into quantum states is a central bottleneck in quantum machine learning: many widely used encodings are circuit-inefficient, requiring deep circuits and substantial quantum resources, which limits scalability on quantum hardware. In this work, we propose TNQE, a circuit-efficient quantum data encoding framework built on structured unitary tensor network (TN) representations. TNQE first represents each classical input via a TN decomposition and then compiles the resulting tensor cores into an encoding circuit through two complementary core-to-circuit strategies. To make this compilation trainable while respecting the unitary nature of quantum operations, we introduce a unitary-aware constraint that parameterizes TN cores as learnable block unitaries, enabling them to be directly optimized and directly encoded as quantum operators. The proposed TNQE framework enables explicit control over circuit depth and qubit resources, allowing the construction of shallow, resource-efficient circuits. Across a range of benchmarks, TNQE achieves encoding circuits as shallow as $0.04\times$ the depth of amplitude encoding, while naturally scaling to high-resolution images ($256 \times 256$) and demonstrating practical feasibility on real quantum hardware.

Image tokenizers play a central role in modern generative models, where the structure of the latent space critically determines the downstream generation performance. A key but underexplored property of effective latent representations is spectral organization: the ability to encode information across frequency components. In this work, we introduce structured state-space regularization, a principled approach for inducing spectral structure in latent spaces. We derive a regularization objective by revisiting state-space models (SSMs) as dynamical systems over compressed representations. This perspective reveals that hidden states of SSMs follow basis function dynamics under predefined input transformations, resulting in a novel regularizer form that enforces the latent space to capture spectral components of images. Experiments demonstrate that our regularizer improves the generative performance of image tokenizers while incurring only minimal loss in their reconstruction fidelity.

Recurrent models trained from scratch have recently become competitive on ARC-style reasoning tasks, but the usual framing around small recurrent backbones overlooks two important parts of the system: task-conditioned memory and synthetic augmentation data. We study this regime through CHARM, a compact hybrid ARC model that combines recurrent reasoning with structured task memory, synthetic data, and inference-time aggregation. In existing approaches, task-conditioned memory supplies a large hidden source of capacity, reaching more than 30x the size of the recurrent backbone. We introduce a compositional sparse embedding for task conditioning that reduces learned task-memory parameters by over 90% while improving pass@2 in controlled ARC-AGI-1 ablations. We also find that matched synthetic data is the largest measured driver of ARC-AGI-2 gains. For the recurrent backbone, recurrent depth helps only when balanced with learning horizon. Combining these ingredients, our system reaches 80.5% pass@2 on ARC-AGI-1 and 39.3% pass@2 on ARC-AGI-2 public evaluation.

In joint multiuser decoding, a receiver recovers a set of messages from a single noisy aggregate of many simultaneous transmissions. Classical decoders rely on rule-based mechanisms such as successive interference cancellation, joint belief propagation, or list recovery, all of which become brittle or expensive as ambiguity increases. We propose CIDER, a learned multiuser decoder with masked-diffusion refinement steps. CIDER uses demixing to prevent duplicate-row collapse and uses parity-aware propagation to provide soft guidance from the code constraints. In higher-load regimes, we further improve reliability via a lightweight quality-guided remasking step that selectively re-decodes low-confidence sequences. On commonly used error correcting codes, CIDER matches or improves on FFT-accelerated joint belief propagation-style decoding in symbol error rate while running more than $6\times$ to over $100\times$ faster, with the speedup widening as the blocklength grows. Code is available at https://anonymous.4open.science/r/CIDER_2026/.

Long-horizon RTL agents often collapse under context pollution, lacking the active, compartmentalized debugging strategies of human engineers. We introduce OneVeriAgent, a human-like framework modeled directly on expert cognitive workflows. First, mimicking how engineers probe waveform viewers, Agentic Temporal Exploration (ATE) enables dynamic, targeted signal queries, distilling dense simulator dumps into sparse, causal traces. Second, reflecting how designers compartmentalize tasks, a task-scoped Orchestrator enforces strict context isolation, preventing raw tool outputs from polluting the global reasoning trace. On the CVDP benchmark, OneVeriAgent achieves 95.8\% Pass@1 with Claude Opus 4.6, matching state-of-the-art proprietary swarm. Finally, we propose Tool-Validated Self-Distillation (TVSD) to bridge the open-source capability gap. By fine-tuning on self-generated, syntactically valid trajectories without external correctness oracles, TVSD lifts Gemma4-31B-Instruct's Pass@1 from 61.1\% to 74.8\%, demonstrating that fully open-weight RTL agents are viable.

Standard flow matching scales well but typically relies on an unstructured source distribution, limiting its ability to learn interpretable latent structure. Latent-variable models, by contrast, capture structure but often sacrifice generative quality. We bridge this gap by proposing Structured Coupling for Flow Matching (SCFM), a cooperative framework that augments flow matching with structured latent representation learning. By introducing structured latent variables and exogenous noise into the source, SCFM jointly learns a structured prior (via latent variable modeling) and a continuous transport map (via flow matching). It uses a shared time-dependent recognition network for both latent variable model variational inference and intermediate-time flow velocity estimation. This yields a structurally informed yet unconditional, simulation-free flow model, where the latent variable model can also assist flow sampling. Empirically, SCFM facilitates unsupervised latent representation learning for clustering, disentanglement and downstream tasks, while remaining competitive with flow matching in sample quality, showing that meaningful structure can be learned without sacrificing generative fidelity.

Protein optimization remains a longstanding goal in life sciences. Existing machine learning–assisted directed evolution (MLDE) methods primarily rely on sequence-only features, overlooking the critical spatial constraints and co-evolutionary interactions encoded in protein structures. However, directly integrating structural information remains challenging due to the scarcity of reliable mutant structures. To address these issues, we propose **StructEvo**, a novel structure-aware reinforcement learning framework for protein directed evolution. StructEvo employs a delta-structure fusion encoder to approximate mutant structure features via feature differences, enabling dynamic incorporation of spatial knowledge. The vast mutation space is then decomposed into manageable subspaces through a structure-aligned hierarchical action network, while a geometric constraint further stabilizes delta feature learning. Our approach outperforms prior state-of-the-art methods by 9.2% and 16.3% on two challenging optimization benchmarks, and further identifies an experimentally validated epistasis pattern in GFP, highlighting the importance of structural guidance for effective protein directed evolution.

Unsupervised medical anomaly detection aims to identify images or regions that deviate from the learned normal distribution. Reconstruction-based methods are a dominant paradigm, generating a normal-looking reference for each test image and detecting anomalies via input--reconstruction discrepancy. However, most existing methods follow a one-shot reconstruction-and-comparison pipeline, making anomaly scoring vulnerable to reconstruction failures: abnormal regions may be preserved, while normal anatomical structures may be distorted. Moreover, they often lack coordinated control from structural and semantic normality, which can lead to anatomical inconsistency and semantic drift. We propose S2Agent, a structure- and semantic-guided multi-agent framework that reformulates medical anomaly detection as feedback-driven normality reconstruction. S2Agent decomposes the process into three collaborative agents. The Planner derives sample-specific matched-tree structural priors and normal-only semantic claims from normality knowledge. The Reconstructor generates a normality-oriented reference image under their joint guidance. The Detector verifies the reconstruction in matched-tree and semantic-claim spaces, returning feedback to refine structural weights and semantic constraints for the next round. Through this closed loop, S2Agent iteratively corrects structural deviation and semantic drift, suppresses abnormal or unsupported content, and improves the reliability of anomaly scoring. Experiments on three public medical benchmarks demonstrate the effectiveness of the proposed closed-loop guidance.

Visual anagram is an intriguing form of art creation wherein a single image presents different conceptual interpretations under transformations such as flipping or rotation. Recent work has achieved visual anagram synthesis by leveraging pretrained text-to-image (T2I) diffusion models, yet still suffers from several key limitations including computational inefficiency, suboptimal aesthetic quality, and weak semantic fidelity and expressiveness. This work focuses on generating visual anagrams with substantially improved visual quality at minimal computational cost, thereby advancing intelligent creation of illusionary digital art. To increase image resolution while reducing time overhead, we adapt the cutting-edge parallel denoising algorithm from pixel-based T2I model to the adversarially distilled latent-based one, and accordingly propose a structure-semantic co-optimization (S2CO) framework to counteract the consequent visual degradation. As the core of our approach, S2CO framework comprises three key innovations: (\romannumeral1) null-text structure alignment optimization; (\romannumeral2) semantic enhancement optimization; (\romannumeral3) attention-guided noise fusion. Building upon these components, our method dubbed \textbf{S2CO-Anagram} is able to generate higher-resolution anagram images with noticeably superior visual harmony and semantic faithfulness than related SOTA approaches, all while achieving substantially faster inference speed. Code will be publicly available.

Protein language models (PLMs) provide valuable evolutionary priors for fitness estimation, but may be misaligned with empirical measurements due to context-dependent selective pressures, potentially leading to hallucination-like predictions under limited experimental supervision. In this study, we introduce MmProt, a structure-prompted Multimodal Protein Language Model for preference-aligned fitness prediction with limited experimentally grounded supervision. MmProt adopts a two-stage multimodal learning strategy: contrastive pre-training on 40 million sequence--structure pairs to learn informative multimodal representations, followed by structure-prompted masked language modeling on naturally occurring related variants to capture protein-specific evolutionary constraints. When limited fitness measurements are available, MmProt further calibrates its variant preferences by encouraging the model to rank high-fitness variants above low-fitness variants. Extensive experiments across multiple benchmarks show that MmProt: (i) achieves state-of-the-art average performance on fitness prediction across 11 diverse protein targets under two challenging extrapolation settings, improving over the best-performing PLM baselines by 3.2% and 13.5% on average, respectively; (ii) captures protein-specific evolutionary patterns in a case study of the SARS-CoV-2 Spike protein, where its zero-shot predictions outperform leading PLMs fine-tuned on 512 fitness-labeled variants by a relative improvement of 3.2%; and (iii) delivers competitive results on eight downstream protein representation learning tasks. Together, these results highlight MmProt's potential as a practical tool for investigating protein evolution and supporting broader applications in protein modeling. Code and data are available at https://anonymous.4open.science/r/MmProt-239E/.

Mixture-of-Experts (MoE) large language models (LLMs) suffer significant inference and storage overheads, which makes extremely low-bit quantization (sub 2-bit) highly desirable. However, existing methods for dense LLMs typically quantize each expert independently and require it to approximate the original mapping over the entire input space. We argue that this formulation is overly conservative and structurally mismatched for MoE: 1) experts in the same MoE layer are not fully independent but operate on a shared hidden representation space. Under low-bit budgets, independently binarizing each expert will distort the activation-sensitive input geometry, which leads to severe performance degradation, and 2) due to routing-induced specialization, each expert only serves a concentrated subset of tokens, whose activations typically occupy a much narrower subspace than that of dense layers. More importantly, when the weights approach 1-bit, the dominant deployment bottleneck is no longer the weights themselves, but the metadata (e.g., scaling factors, bitmap, grouping index) which can raise the real effective inference precision to around 3-bit. This issue, however, has largely been overlooked in prior works. Building on these perspectives, we introduce a novel extremely low-bit quantization framework for MoE-LLMs, called TriS-MoE, which considers Structure, Subspace and System. Specifically, we first extract a shared high-precision input-side backbone to preserve activation-sensitive input geometry, while binarizing the remaining components to save costs. Secondly, we propose to redirect expert quantization errors into the null space of routed activations to minimize output perturbation on the subspace actually used by each expert. Finally, we co-design a bitmap compression method based on Golomb-Rice coding which approaches the Shannon entropy lower bound and a specialized streaming loading mechanism to reduce metadata overhead during inference. Extensive experiments on MoE-LLMs demonstrate our TriS-MoE outperforms the strongest baseline by 8.31% average accuracies, and also achieves an average 6.32$\times$ reduction in inference memory on real systems, enabling Qwen3.5-35B-A3B and Mixtral-8$\times$7B to run on a single consumer-GPU.

State-of-the-art vision-language models (VLMs) score impressively on video benchmarks yet stumble on basic visual reasoning tasks involving spatial relations, navigation, and object selection that a preschooler solves without effort. We hypothesize that the explicit pedagogical structure, specifically the context-question-pause-answer cycles embedded in children's educational video, provides naturally co-aligned reasoning traces: temporally synchronized visual cues, questions, and answers that emerge only from deliberate pedagogical authoring and cannot be practically reconstructed through manual annotation at scale. To test this, we introduce SoSVQA (Structure over Scale Visual Question Answering), a unified benchmark of 10K question-answer pairs automatically extracted from Dora the Explorer (DoraVQA) and Mickey Mouse Clubhouse (ClubHVQA) with precise timestamp alignment, and fine-tune Qwen2-VL and Qwen3-VL using Group Relative Policy Optimization (GRPO) to leverage the clear correctness signals and structured reasoning traces inherent in educational content. Despite training on just 10K QA pairs from 78 hours of children's television, orders of magnitude less data than GPT and Gemini, our approach delivers generalizable performance gains for Qwen-based VLMs, yielding consistent improvements on NExT-QA (+19.7), Video-MME (+10.6), and MotionBench (+4.9), matching the performance of leading proprietary systems and demonstrating that content structure can compensate for content scale.

Continuous DAG learners typically optimize a single weighted adjacency matrix, forcing each entry to represent both direct-edge support and signed effect magnitude. As a result, data fitting, sparsity, acyclicity, and any auxiliary structural information are all imposed on the same variables, even when the available information concerns higher-order relations such as reachability or ancestral order. We propose a structure-separated continuous DAG-learning framework that gives graph support its own soft representation. The effective adjacency is \( A=(W_f(M)\circ G(Z))\circ(\mathbf{1}-I), \qquad W_f(M)=\beta\tanh(M), \) where \(W_f(M)\) is a bounded signed-magnitude branch and \(G(Z)\) is a structural gate. Data fitting, sparsity, and the DAGMA barrier are evaluated on \(A\), while cycle and relation constraints are applied to closure scores computed from \(G\). The bounded magnitude branch prevents strong effective edges from being hidden behind vanishing structural gates, making closure-side structural regularization meaningful. On this separated structural gate, we introduce SMNC, a scan-efficient multi-scale normalized closure module. SMNC computes dyadic closure scores over \(G\) and normalizes each scale by its path-length budget, producing scale-normalized matrix-valued logits for self-reachability and off-diagonal reachability. This design addresses a key limitation of unnormalized product-style closure scores, whose path evidence scales as \(p^L\) on a length-\(L\) path with gate value \(p\), causing long-cycle signals to decay exponentially. At the appropriate covering scale, SMNC routes gradients through an active length-\(L\) cycle with a polynomial \(1/L\)-type scale factor; in the all-scale soft readout used in practice, this should be interpreted as a local active-scale effect. The same relation space supports diagonal self-reachability losses for cycle suppression and masked off-diagonal losses for optional ancestry, backward-exclusion, and incomparability cues, without treating positive reachability labels as direct-edge labels. Across linear and nonlinear SEM benchmarks, with ablations over factorization and closure normalization, we show that structure-separated SMNC improves DAG recovery and provides a principled closure-space interface for higher-order structural supervision.

Modern scientific discovery systems can propose more mechanistic hypotheses than laboratories can test. We study the downstream selection problem: after upstream retrieval, extraction, expert modeling, or curation has fixed a finite locally factorized posterior over admissible mechanisms, when does the posterior evidence justify acting on one structural hypothesis rather than its rivals? For a query $A\rightsquigarrow B$, we define *structural support* as the posterior mass of mechanisms in which $A$ reaches $B$ by a directed path. This is not a retrieval rank, direct-edge score, or MAP graph decision: support can be distributed across many compatible mechanisms. Our main result is a local certificate for this global support quantity. For path cutoff $L$ and locality radius $m$, truncated support is a convex combination of exact boundary-conditioned local supports. Optimizing over locally admissible blanket states gives a boundary range $[\\ell_{L,m},u_{L,m}]$, and a certified tail bound $\bar r_L$ accounts for longer witnesses: $p_{AB}\in[\\ell_{L,m},\min\{1,u_{L,m}+\bar r_L\}]$. For coupled rivals, the same boundary-conditioned calculations yield a joint feasible set rather than only independent coordinate intervals. We convert these certificates into resolve-or-abstain decisions: an action is resolved only when it wins throughout the certified support set. Under valid certificates, a resolved action is the unique strict utility maximizer inside the declared fixed model, action set, and utilities; otherwise, the solver abstains and reports whether boundary, tail, computation, evidence, or outer-model uncertainty blocks resolution. On controlled and biologically scaffolded finite-posterior records, the implementation has zero endpoint containment misses and zero false fixed-model resolutions. In a coupled-rival diagnostic, joint feasible sets resolve $14/24$ cases versus $6/24$ for scalar boxes, with no false commitments.

We propose Structural Self-Teaching (SST), a novel framework that enhances compositional image generation in unified multimodal models (UMMs) by leveraging their internal understanding path as a source of structural supervision. The motivation of this work is the observation that the generation path of UMMs often struggles with dense compositional prompts, leading to missing objects, attribute binding failures, and spatial errors. To bridge this gap, our key idea is to convert the phrase-level grounding inherent in the understanding path into training-time structural signals. Specifically, we extract phrase-conditioned support masks from internal attention maps derived from the understanding path and employ them through two complementary mechanisms: 1) phrase-local contrastive alignment to synchronize generated features with their corresponding phrase-level grounding, and 2) a lightweight structural scaffold for spatial modulation of intermediate generation states. By employing attenuated scaffold dropout during training and removing the scaffold branch at inference, our approach improves compositional fidelity across object, attribute, and spatial constraints. Crucially, the framework requires no external structural annotations for training or additional inputs at test-time, preserving the original generation interface. Extensive experiments across multiple UMMs demonstrate that our method consistently improves compositional fidelity while maintaining the efficiency of a lightweight tuning setup.

When distilling reasoning from large language models (LLMs) into smaller ones, teacher rationales for similar problems often vary wildly in structure and strategy. Like a chef who makes the same dish differently each time, this inconsistency burdens the student with noisy supervision that is hard to internalize. We propose Distillation through Reasoning Path Compression (D-RPC), which constrains the teacher to follow a compact, dynamically maintained bank of reusable high-level reasoning paths. For each training question, D-RPC retrieves the most relevant path and conditions the teacher to follow it, producing rationales that are consistent across similar problems yet diverse enough to cover different problem types. A PAC-Bayes analysis formalizes the resulting trade-off between bank size and coverage: smaller banks reduce supervision entropy but risk coverage gaps, and the generalization bound identifies an optimal intermediate size confirmed by our ablations. Across five math and commonsense reasoning benchmarks with two student models, D-RPC consistently outperforms chain-of-thought distillation, freeform rationale generation, direct distillation, and structured-supervision baselines, while using fewer tokens than template-heavy alternatives.

Communication learning is crucial for complex coordination in multi-agent reinforcement learning (MARL), yet existing methods often struggle with a fundamental dilemma between communication overhead and task performance. Typically designed without theoretical guidance, they produce unstructured and redundant communication graphs, limiting both efficiency and scalability. To address this, we propose Structural Entropy Optimized Communication (SEOC), a framework that directly optimizes inter-agent communication topology using the information-theoretic principle of Structural Entropy. SEOC guides the network to self-organize into a low-entropy state with sparse connectivity and distinct communities, enabling efficient and interpretable decentralized coordination. This is achieved through a unified mechanism that jointly enforces structural order and semantic conciseness, reducing topological complexity via a differentiable structural entropy loss while compressing messages with a node information bottleneck. Experiments on benchmarks such as the StarCraft Multi-Agent Challenge (SMACv2) demonstrate that SEOC significantly outperforms state-of-the-art methods.

We introduce structural causal bottleneck models (SCBMs), a novel class of structural causal models in which causal effects between high-dimensional variables are mediated by low-dimensional summary statistics, or *bottlenecks*. SCBMs provide a flexible framework for mechanism-specific, target-dependent dimension reduction while remaining estimable via standard learning algorithms. We prove that bottleneck variables are identifiable up to bijection from observational data, and validate this experimentally. We then study causal effect estimation in linear SCBMs under three standard adjustment strategies. For instrumental variables, we identify a population-level non-identifiability of naïve two-stage least squares when all variables are high-dimensional, and show that bottleneck representations restore identification. For mediation and backdoor adjustment, we prove that conditioning on bottleneck variables yields valid effect estimates with explicit finite-sample efficiency gains over conditioning on full high-dimensional variables. We argue that SCBMs provide a principled alternative to existing causal dimension reduction frameworks such as causal representation learning and causal abstractions.