NeurIPS 2026 论文列表

Generative AI models now produce images indistinguishable from real data, making publicly verifiable provenance essential; however, existing watermarking methods become vulnerable to forgeries and adversarial optimization attacks once their detectors are made public. We propose PP-Mark, a provenance framework that anchors lightweight statistical detection to a zero-knowledge proof of the embedding process, ensuring that forged content cannot pass full verification by construction. We establish formal unforgeability under standard cryptographic assumptions and validate PP-Mark against representative baselines across two architectures, demonstrating resistance to black-box imprint forgery, white-box optimization, and regeneration attacks while remaining practical for decentralized verification.

Multimodal large language models (MLLMs) leverage object-agnostic tokenization by default, which represents images as flat grids of patch tokens and uniformly discretizes semantically rich foreground regions and information-sparse background areas. This introduces substantial redundancy and constitutes a major source of inference cost. Existing object-level tokenizers could either discard intra-region saliency with predefined region grouping and coarse pooling or limit to a single semantic scale by employing slot attention to the final encoder feature map only. In this paper, we propose a two-stage framework named POST that decouples unsupervised object-centric grouping from language-grounded multimodal reasoning to achieve progressively semantic-aligned object-level visual tokenizers. Specifically, we develop Progressive Slot Attention (PSA) in Stage I to introduce slot attention across multiple layers of a frozen ViT encoder and propagates slot states through a cross-layer refinement chain for learning hierarchical slot--patch correspondences without segmentation supervision. Subsequently, we transfer the learned PSA to the LLaVA pipeline in Stage II for object-level visual tokenization. We design slot-weighted merging to aggregate ViT patch tokens into object-grounded visual tokens and remove low-support slots with adaptive slot pruning. Remarkably, POST eliminates the need for external segmentation models, enables progressive object-centric refinement, and adapts visual token budget to image complexity in visual tokenization. Experimental results show that PSA achieves strong unsupervised object-centric segmentation on PASCAL VOC and COCO. Furthermore, POST is comparable or superior to LLaVA-1.5 on most multimodal benchmarks using only 8.3% visual token budget and improves referring expression comprehension on RefCOCO/+/g. It also outperforms recent patch-level and object-level token compression baselines at comparable budgets.

We introduce Post-Optimization Model Edit (POME), a new algorithm that enhances the performance of fine-tuned large language models using only their pretrained and fine-tuned checkpoints, without requiring extra data or further optimization. The core idea is to apply a muon-style projection to $\Delta W$, the difference between the fine-tuned and pretrained weights. This projection uses truncated singular value decomposition (SVD) to equalize the influence of dominant update directions and prune small singular values, which often represent noise. As a simple post-processing step, POME is completely decoupled from the training pipeline. It requires zero modifications and imposes no overhead, making it universally compatible with any optimizer or distributed framework. POME delivers consistent gains, boosting average performance by +2.5\% on GSM8K and +1.0\% on code generation. Its broad applicability—from 7B foundation models to 72B RLHF-instructed models—establishes it as a practical, zero-cost enhancement for any fine-tuning pipeline.

Prompt tuning (PT) provides a parameter-efficient way to adapt frozen large language models, but its behavior becomes less clear when task contains multiple related yet distinguishable patterns. We formalize this setting as a latent mixture target distribution, where the target behavior is modeled as a finite mixture of latent task components. We first show that static prompting can retain a non-vanishing approximation gap when one shared prompt is insufficient to capture the target distribution. Then we formalize instance-specific prompting as an input-dependent prompt mapping and show that, under ideal prompt assignment, its achievable regret can approach zero. Guided by this analysis, we propose POISE (Prompt Optimization via Instance-Specific Experts), a structured and parameter-efficient approximation to instance-specific prompting. POISE represents the prompt space with learnable expert prompts, combines them through input-dependent routing, and introduces a shared low-rank residual correction to capture common structure. Extensive experiments across multiple benchmarks show that POISE achieves strong performance while maintaining high parameter efficiency.

Real-world robot task planning must operate under both stochastic action execution and partial observability, yet constructing Partially Observable Markov Decision Process (POMDP) models for real robotics domains remains difficult and labor-intensive. We introduce PO-PDDL, a symbolic formulation of POMDPs that preserves the relational structure and LLM-friendly syntax of the Planning Domain Definition Language (PDDL), while explicitly modeling partial observability, stochasticity, and beliefs. Building on this formulation, we propose a demonstration-driven pipeline for learning PO-PDDL models. The proposed method reconstructs latent symbolic state trajectories from real-robot execution videos, identifies partial observability via inconsistencies between inferred states and visual observations, and learns stochastic transition and observation models accordingly. The resulting PO-PDDL domains are reusable across tasks and enable online belief-space planning under both perception and execution uncertainty. Experiments on real-world long-horizon manipulation tasks show that our method consistently outperforms existing PDDL and POMDP model-learning approaches, achieving robust task planning under uncertainty with significantly lower planning cost.

Precision medicine relies on integrating diverse patient data, including genetic variants, clinical phenotypes, and medical imaging, to inform personalized prevention, diagnosis, and treatment. However, learning general patient-level representations from these heterogeneous modalities remains challenging due to their high dimensionality and pervasive missingness. We propose PM1, a multimodal foundation model that learns meaningful patient-level representations across genetic, clinical, and imaging data from 337,129 individuals in the UK Biobank, with a focus on retinal imaging and ophthalmic traits. To address modality dimensionality, PM1 adopts an intermediate fusion architecture with modality-specific encoders and a shared Transformer backbone, where a novel pathway-based genotype encoder enables the incorporation of over 600,000 genetic variants by reducing dimensionality while preserving trait-relevant variation. In addition, a contrastive loss invariant to modality missingness allows PM1 to learn robust patient-level representations despite only 6\% of UK Biobank participants having complete modality coverage. By partitioning modalities into interpretable tokens, PM1 enables interpretability methods to investigate known and potentially novel associations across genetic, clinical, and imaging features, and their interactions. Evaluated across downstream tasks, including phenotype prediction, conditioned image generation, and attribution analyses, PM1 demonstrates the value of multimodal patient representations, improving predictive performance over unimodal baselines and competing methods while enabling interpretable analyses in settings with heterogeneous and incomplete patient data.

In continual learning, most parameter-efficient fine-tuning methods rely on task-specific low-rank adapters to incrementally adapt pre-trained models to new tasks. However, they overlook two critical challenges: one is the accumulation of numerous adapters that leads to linear growth in parameters and memory, and the other is the lack of an explicit mechanism to preserve previously learned knowledge during continual adaptation. In this work, we propose a simple yet scalable framework, termed Progressive Merging of Low-Rank Adapters (PM-LoRA), to address both issues simultaneously. Specifically, PM-LoRA trains a small task-specific spectral orthogonal adapter for each new task and progressively integrates it into a single evolving LoRA adapter that encodes all prior knowledge. This merging process enables continuous knowledge integration while directly mitigating catastrophic forgetting at the parameter level. Benefiting from this design, PM-LoRA achieves true scalability by maintaining constant parameter overhead throughout continual training. Extensive experiments on four benchmark datasets demonstrate that PM-LoRA achieves state-of-the-art performance with superior memory and computation efficiency.

Open agent systems (OASYS) are increasingly prevalent in real-world domains where the sets of agents and tasks change unpredictably over time. Such openness, including agent openness (AO) and task openness (TO), poses a fundamental challenge to multi-agent reinforcement learning (MARL), which typically assumes fixed state and action spaces. Existing methods address openness only partially: padding and masking approaches introduce artificial bounds, while recent graph-based or hypergraph methods handle one dimension of openness but still depend on restrictive assumptions. In this paper, we introduce Pointer Learner for Agent and Task Openness (PLATO), a pointer-network-based actor combined with a centralized graph neural network (GNN) critic, trained with multi-agent proximal policy optimization under a centralized training and decentralized execution paradigm. Our pointer-based actor outputs distributions directly over the current task set. This directly supports changing action spaces without masking or retraining. Our GNN critic encodes agent–task interactions as a graph that changes shape with task and agent composition. Together, these components consider AO and TO without the boundedness of existing approaches. We formalize PLATO in a Task-and-Agent-Open Markov Game (TaAgO-MG), extending prior task-open formulations, and prove it is well-defined over the resulting unbounded state and action spaces. We evaluate PLATO with the Methods for Open Agent Systems Evaluation Initiative (MOASEI) wildfire suppression domain, an environment designed for open multi-agent system evaluation, and we demonstrate strong performance and more consistent zero-shot generalization than state-of-the-art baselines in OASYS.

Streaming reconstruction from monocular image sequences remains challenging, as existing methods typically favor either high-quality rendering or accurate geometry, but rarely both. We present PLANING, an efficient on-the-fly reconstruction framework built on a hybrid representation that loosely couples explicit geometric primitives with neural Gaussians, enabling geometry and appearance to be modeled in a decoupled manner. This decoupling supports an online initialization and optimization strategy that separates geometry and appearance updates, yielding stable streaming reconstruction with substantially reduced structural redundancy. Despite operating online, PLANING improves dense mesh Chamfer-L2 by 18.52% over the offline baseline PGSR, surpasses the streaming baseline ARTDECO by 1.31 dB in PSNR, and reconstructs ScanNetV2 scenes in under 100 seconds, over 5x faster than offline 2D Gaussian Splatting while matching per-scene optimization quality. Beyond reconstruction quality, the structural clarity and computational efficiency of PLANING make it well suited for a broad range of downstream applications, such as enabling large-scale scene modeling and simulation-ready environments for embodied AI.

While While Vision Foundation Models (VFMs) are increasingly used in several computer vision tasks, their internal representations remain substantially opaque. This mostly stems from polysemanticity, i.e., individual neurons encode mixtures of unrelated visual patterns. In stark contrast, human reasoning is inherently monosemantic, i.e., it naturally relies on distinct, isolated features to process information. Therefore, monosemanticity needs to be implemented within the model's activation space to obtain representations that are human-understandable concepts. Existing methods mostly operate on convolutional networks, so when applied to VFMs their "crop-and-resize" approach distorts the model's representations by disrupting global self-attention mechanisms and discarding spatial geometry. Furthermore, prior work requires downstream classification labels and is based on KL-divergence, which requires to propagate the gradients back from the classifier head. Ultimately incurring in excessive concept extraction time, and making it hardly applicable to extract task-agnostic concepts. To overcome these issues, we propose PLACE, a fully unsupervised concept extraction framework that operates directly on the native patch-token activations of a VFM. By introducing a geometric Gram-matrix alignment loss, PLACE mechanistically controls downstream KL divergence, thus enforcing faithfulness -- i.e., the notion that extracted concepts, although obtained in an unsupervised fashion, are relevant to downstream classification tasks. Evaluations on image classification tasks (ImageNet and COCO) on DINOv2, ViT-MAE and ViT-B/16 show that PLACE (i) using Gram-alignment it extracts concepts about $20\times$ faster compared to KL-supervised extraction and $11\times$ faster than Sparse Autoencoders (SAEs). Moreover, PLACE yields highly sparse concepts that (ii) achieve up to 40 percentage points sparser compared to the state of the art (i.e., max 0.933 Gini sparsity score), and (iii) are up to 92.3% similar to those obtained under classifier supervision (i.e., 0.923 cosine similarity).

Streaming long-video understanding is a realistic setting for always-on video assistants, where visual input arrives continuously and users may ask questions at arbitrary times. We study streaming long-video question understanding (QA) under a unified temporal formulation in which multiple questions are revealed over time and may concern the past, present, or future relative to their query times. The model receives no task-type labels and must operate under strict sequential access and bounded shared memory, deciding whether each active question is already answerable or should remain open for future evidence. This differs from prior streaming settings that often focus on short streams, prefix-answerable queries, separated temporal categories, or response timing without long-horizon shared memory. To evaluate this formulation, we propose Ref2Stream (Reference-to-Stream), a general benchmark-conversion protocol that turns temporally grounded offline video QA benchmarks into streaming QA benchmarks. Instantiated on LVBench, Ref2Stream yields LVBench-Ref2Stream, which mixes past, present, and future questions under strict sequential access and evaluates both answer accuracy and answer latency. Existing streaming video methods typically improve efficiency or response timing through predefined compression, retrieval, or response policies, while offline video agents perform adaptive evidence gathering but assume access to the full video and target question. We introduce PIVOT (Planning over Incremental Video Observations for Timely Answering), a unified agentic framework that incrementally updates bounded memory from the observed stream and uses a planner-controlled action loop to retrieve relevant memory, analyze current evidence, reflect on candidate answers, and decide whether to answer or defer. Our work suggests that agentic reasoning is a promising direction for building streaming long-video systems that can adaptively gather evidence and answer only when sufficient information has been observed.

Multi-modal cloud removal exploits complementary optical and synthetic aperture radar (SAR) images to restore cloud-degraded remote sensing images. Most existing methods rely on black-box, data-driven architectures with limited interpretability of cross-modal interactions. Although some attempts introduce physical priors into networks, their oversimplified formulations fail to faithfully characterize the underlying physical imaging process and the networks remain loosely coupled with optimization variables. To address these issues, we propose a physics-informed deep unfolding network for cloud removal via optical-SAR image fusion, termed as PIU-CR. We first formulate cloud removal as a physics-informed optimization problem. Specifically, we fundamentally ground the optimization in the physical imaging mechanism of cloud degradation by embedding the atmospheric scattering model into data fidelity term. Then, the regularization terms establish an explicit cross-modal interaction mechanism, overcoming the single-modal information scarcity and the obscure, uninterpretable interactions in black-box models. They are dedicated to SAR-guided structure and spectral regularization, jointly introducing cross-modal structural feature alignment and coordinated spectral consistency constraints. The resulting optimization problem is then unfolded into a multi-stage neural network with dedicated structure and spectral branches. Each stage corresponds to explict optimization iteration step, enabling interpretable and effective reconstruction process. Experiments demonstrate that the proposed method outperforms state-of-the-art methods.

Diffusion-based solvers have become a prominent approach to neural combinatorial optimization. At inference time, they assign each decision variable a prediction score. A greedy decoder ranks candidates by these scores and checks hard constraints to ensure feasibility. Top-ranked candidates should be both promising and jointly feasible, making prediction--constraint alignment critical. When this alignment is weak, greedy decoding skips constraint-violating candidates and falls back to lower-ranked alternatives, degrading solution quality. Existing test-time methods improve search, sampling, or decoding, but do not directly strengthen this alignment before decoding. We observe that trained denoisers already carry constraint-aligned structural signals. Building on this observation, we propose Perturbed Instance-Structure Guidance (PISG), a training-free method that amplifies these signals at test time. At each denoising step, PISG constructs a structure-degraded prediction and extrapolates the unperturbed prediction away from it. The resulting guided prediction improves candidate rankings before constrained decoding. Signal analysis shows that this guidance concentrates on constraint-critical variables rather than uniformly rescaling scores. Under greedy decoding and without downstream refinement, PISG improves all 16 TSP solver-scale pairs across DIFUSCO, T2T, FastT2T, and StruDiCO, and all four MIS-ER solvers, with gains up to +8.4\%. These results identify prediction-constraint alignment as an effective test-time enhancement direction for diffusion-based combinatorial optimization.

AI video generation is moving beyond general generation, which relies on exhaustive prompt-engineering and "cherry-picking", towards fine-grained, controllable generation and high-fidelity post-processing. In professional AI-assisted filmmaking, the core requirement is the ability to perform precise, targeted modifications. A key task is video instance insertion, which requires precise spatial-temporal placement, physically consistent scene interaction (e.g., shadows and reflections), and the faithful preservation of original dynamics - all achieved under minimal user effort. In this paper, we propose PISCO, a video diffusion model for precise video instance insertion with arbitrary sparse keyframe control. PISCO allows users to specify a single keyframe, start-and-end keyframes, or sparse keyframes at arbitrary timestamps, and automatically propagates object appearance, motion, and interaction. To stabilize generation under sparse conditioning, we introduce Variable-Information Guidance and Distribution-Preserving Temporal Masking, complemented by geometry-aware conditioning. We further construct PISCO-Bench, a benchmark with verified instance annotations and paired clean background videos, and evaluate performance using both reference-based and reference-free perceptual metrics. Experiments demonstrate that PISCO consistently outperforms existing baselines and scales effectively as additional control signals are provided.

Modern automatic-rigging predictors (UniRig, Make-It-Animatable, RigNet) place a skeleton on a 3D mesh in seconds, but the resulting skinning weights animate poorly. Linear-blend skinning (LBS) is non-linear in pose space, so noisy weights amplify into visible candy-wrapper artifacts---the surface twisting and contracting at joint bends---and volume collapse. ARAP-style remedies penalize these symptoms one pose at a time and cannot reach the cross-pose curvature that produces them. We propose \emph{Pose-Interpolation Smoothness} (PIS), a self-supervised, training-free, test-time refiner: minimize the per-vertex gap between the deformation at the SO(3) midpoint of two random poses and the linear average of the two endpoint deformations. The midpoint is obtained by orthogonally projecting the arithmetic mean of the two rotations back onto the rotation group, sidestepping the matrix-logarithm gradient that breaks down near identity and antipodal poses. Because PIS only re-tunes the weights LBS already consumes, it slots into any LBS-based pipeline---generative-3D outputs, automatic-rigging predictions, and the LBS pathways of game engines and AR/VR renderers---without touching shaders. On Objaverse-LVIS Balanced-102 (98 valid models, 51 LVIS categories), PIS reduces fragility (per-vertex deformation variance under random poses), ARAP energy, Jacobian anisotropy, and area distortion by $35.5$\%, $39.3$\%, $19.8$\%, and $43.2$\% on average; $93.9$\% of models improve on all four simultaneously, Wilcoxon $p<10^{-10}$. On the worst-case slice---the top decile of UniRig-initialized fragility, where animation visibly tears---PIS reduces unstable vertices by up to $10.2\times$. Effective bone count and weight entropy are unchanged, and the optimization runs in $\sim$30~s/model on a single H200 GPU.

The Physics-Informed Neural Network (PINN) literature has produced a rapidly expanding catalogue of methods. We divide a PINN training pipeline systematically into four components and observe that many existing benchmarks study these components in isolation. These components are domain-importance adaptation, loss balancing, the neural network architecture, and the optimization scheme. Moreover, the studies set the surrounding and not considered components of the pipeline to a minimal default that is no longer representative of current best practice approaches. Conclusions drawn under such conditions lead to suboptimal results. In this work, we propose a new evaluation protocol that (1) treats the four components as an integrated system; (2) requires as a key aspect a competitive implementation of the components not currently under investigation; (3) specifies the metrics and reporting format, and (4) is accompanied by structured documentation that ensures the traceability of evaluations across studies. Based on this comprehensive protocol, we release with PINNeval an innovative JAX package containing 18 different methods from the current literature as well as nine efficiently implemented benchmark problems. Using PINNeval, we illustrate our evaluation protocol in relation to established benchmark studies and report on results that challenge established assumptions.

We introduce PINNBench, a benchmark and evaluation study for training-policy selection in hybrid PINN-operator PDE solvers, contributed to the NeurIPS 2026 Evaluations and Datasets (ED) Track. PINNBench comprises 1,539 recorded runs and probes across 13 PDE configurations drawn from 8 equation families, 3–5 training policies per PDE, and 10 seeds per condition, with seven standardized metrics, Wilcoxon signed-rank tests, paired bootstrap 95% confidence intervals, Cohen's d, and Benjamini–Hochberg FDR correction. Baseline evaluation of 5 routing methods establishes three findings that bear directly on how PDE solvers should be evaluated. (i) No single policy dominates: hybrid_full wins 7/13, hybrid_no_gate 4/13, pinn_only 2/13. (ii) Accuracy and regret dissociate: under relative L2, a physics-loss heuristic and a learned Random Forest router reach identical 62% top-1 accuracy, but the heuristic suffers mean regret 0.0850 versus 0.0011 — a 77x gap. Evaluation by accuracy alone is misleading for PDE policy routing. (iii) Causal-loss effectiveness is stage-mediated: 7/9 PDEs improve with pretraining (+5.9% mean) while 5/9 degrade without it (-4.2%). A new leave-one-PDE-family-out evaluation shows that the strongest family-level generalizer uses no PDE-identity features, addressing the concern that PDE-aware routing memorizes PDE identity. All 1,539 result JSONs, PDE implementations, policy configurations, statistical pipeline, and routing infrastructure are released as open-source artifacts.

Protein--nucleic-acid recognition underlies aptamer discovery and nucleic-acid therapeutics, yet computational models often trade mechanistic interpretability for predictive flexibility. Hybrid interpretable--latent architectures risk fallback takeover, where the latent branch silently becomes the true predictor while the explicit pathway remains decorative. We introduce PIGRAM, a patch--motif grammar model that decomposes proteins into local residue patches and nucleic-acid partners into secondary-structure motifs, learning a sign-separated grammar over explicit physicochemical and geometric attributes to preserve rare favorable interactions. Heterogeneous rule families are selectively refined into a coarse-to-fine atlas, and a frozen-grammar residual Transformer provides sparse, bounded corrections without displacing the grammar as the primary predictor. On a strict sequence-pair-disjoint benchmark, PIGRAM achieves a Pearson correlation of 0.494, approaching the unconstrained Transformer (0.511) while exposing signed rules, fine subtypes, and route-gated corrections for each prediction. The learned rules recover bidirectional patch--motif mechanisms, and residual corrections concentrate selectively on grammar-hard regions. In fluorescence ELISA experiments, suppressive-rule relief improved GFP binding, whereas favorable-rule disruption weakened NELF binding, providing direct experimental support for grammar-guided design. Together, these results show that interpretable grammar can serve as both a predictive scaffold and an experimentally actionable design principle for aptamer optimization.

Full-space electron density generation is fundamental to modeling molecular quantum states, ground-state properties, and chemical interactions. DFT provides a principled route to electron density, but high-fidelity calculations require costly self-consistent iterations, limiting large-scale quantum-chemical modeling. Deep learning offers a promising surrogate, yet existing methods often rely on indirect density-related prior, including coordinate queries, predefined basis functions, orbital coefficients, or spherical atomic density grids. These prior introduce reconstruction overhead and can hinder continuous full-space modeling across sharp nuclear cores and sparse vacuum regions. Here, we introduce PI-EDG, a physics-informed voxel-space framework that maps molecular geometries directly to full-space electron-density fields by combining quantum-mechanical prior injection, hybrid FNO-CNN modeling of local and non-local interactions and magnitude-aware DS-LMoE routing for extreme density ranges. On EDBench, PI-EDG achieves an MAE of 3.405$\times$10$^{-3}$, a total electron-number MAE of 0.472, and an average inference time of 133.4 ms for one molecule. Compared with graph-based, basis-based, and voxel-based baselines, PI-EDG demonstrates better predictive accuracy, improved robustness across extreme density regimes, and better physical consistency while maintaining efficient full-space generation. These results suggest that PI-EDG offers a new, direct, and efficient route for accelerating DFT-based electron density calculations.