NeurIPS 2026 论文列表

Large pretrained models have achieved remarkable success across diverse domains, yet adapting them to multiple downstream tasks remains challenging: full fine-tuning is costly, while maintaining separate task-specific models is storage-inefficient and fails to produce a unified multi-task model. Low-Rank Adaptation (LoRA) offers an efficient alternative for Model Merging by representing each task adaptation as a low-rank update over frozen pretrained weights. However, existing LoRA merging methods often treat task-specific updates as directly comparable objects and merge them in their original low-rank parameter spaces. This overlooks two key factors: which update directions provide reliable shared support across tasks, and how these directions should be re-parameterized with respect to the pretrained weights before merging. To address this limitation, we propose Pretrained-Guided Shared Basis (PGSB), a two-stage framework for LoRA model merging. PGSB first identifies shared directions among task-specific LoRA updates, and then re-parameterizes these directions using their interaction with the pretrained weights, producing a more suitable basis for merging. Extensive experiments demonstrate that PGSB consistently outperforms state-of-the-art baselines across multiple benchmarks.

3D Gaussian Splatting (3DGS) has become an established representation for real-time novel view synthesis. However, preserving fine geometric structures and appearance details typically requires millions of Gaussian primitives, resulting in substantial storage and transmission overhead. 3DGS compression has been extensively studied through attribute quantization, entropy coding, and lightweight reparameterization. However, existing methods often operate on a fixed backbone and therefore do not explicitly model the joint redundancy between geometry and appearance in the original dense Gaussian set. To address this limitation, we propose \textbf{Pyramidal Gaussian Mixture Splatting} (PGMS), a plug-and-play 3DGS compression framework that reformulates dense Gaussian primitives into a rate-controllable pyramidal representation with scale-dependent attributes. Specifically, PGMS first constructs a mixture pyramid by progressively partitioning dense Gaussians into hierarchical mixture centers, where different levels perform attribute-specific clustering to form shared prototypes. It then employs a rate-adaptive capacity allocator to determine the tiered budget under a target compression ratio. To further improve rendering quality, we introduce a compositing-aware parameter update rule that incorporates information from dense Gaussians into pyramid primitives according to their image-space contributions. Experiments on standard benchmarks show that our method delivers strong compression with high rendering fidelity and consistently outperforms state-of-the-art 3DGS compression methods.

Camera localization methods are mostly multi-stage pipelines relying on dedicated and complex maps that are costly to construct, store, and maintain over time. Alternatively, well-structured maps that organize scenes with compact yet expressive primitives are often readily available, providing rich structural and semantic cues that humans can interpret. However, their potential for direct camera localization remains largely unexplored due to the significant modality gap with visual observations. This motivates a new concept for localization, which estimates camera poses by *grounding query observations to map primitives*. In this paper, we instantiate the concept in the context of planar primitives with **PGGT**, the **P**rimitive-**G**rounded **G**eometry **T**ransformer network trained to predict multiple localization targets from a single query image and a set of planar map primitives, enabling feed-forward 6-DoF camera localization that generalizes to unseen environments. Based on this flexible and scalable architecture, our method pushes the state-of-the-art localization performance on both detailed planar surface maps and minimal architectural layouts. The code and models will be made publicly available.

Extending feed-forward 3D Gaussian Splatting (3DGS) to large-scale scenes remains challenging. Existing global reconstruction methods incur prohibitive computational overhead on long sequences; while streaming-based online methods are more efficient, their inherent forgetting behavior and unidirectional dependence still limit reconstruction quality. To address this, we propose PF-SGS, a pose-free streaming feed-forward 3DGS for large-scale scene reconstruction. PF-SGS leverages a persistent hidden state as scene memory to integrate streaming inputs frame-by-frame, progressively recovering scene geometry and camera poses in a self-supervised manner. At its core lie two targeted designs: Adaptive State Gating, which suppresses long-term memory degradation by dynamically regulating update magnitudes to ensure stable state evolution; and Delayed Gaussian Modeling, which introduces bounded look-ahead observations to compensate for insufficient local geometric constraints under streaming inputs, thereby improving the geometric consistency. Experiments demonstrate that PF-SGS processes sequences of varying lengths with linear time complexity, achieving quality comparable to pose-prior-based large-scale reconstruction baselines.

AdaBoost sequentially fits so-called weak learners to minimize an exponential loss, which penalizes misclassified data points more severely than other loss functions like cross-entropy. Paradoxically, AdaBoost generalizes well in practice as the number of weak learners grows. In the present work, we introduce Penalized Exponential Loss (PENEX), a new formulation of the multi-class exponential loss that is theoretically grounded and, in contrast to the existing formulation, amenable to optimization via first-order methods, making it a practical objective for training neural networks. We demonstrate that PENEX effectively increases margins of data points, which can be translated into a generalization bound. Empirically, across computer vision and language tasks, PENEX improves neural network generalization in low-data regimes, matching and in some settings outperforming established regularizers at comparable computational cost. Our results highlight the potential of the exponential loss beyond its application in AdaBoost.

Diffusion image generators owe much of their practical success to accurate denoising networks and strong numerical samplers. Yet the two components are not fully matched. During sampling, the denoiser is asked to make predictions based on imperfect noisy states formed by model predictions that differ from the training noisy states formed by real data. This train-test gap, known otherwise as exposure bias in literature, caps image generation quality (i.e., blurry, lacking in details or unnaturally-shaped). To mitigate the problem, we propose PEEK, a training-free deterministic sampler plugin. At each eligible denoising step, PEEK first performs a provisional one-step update, evaluates the denoiser once at the look-ahead state, and either keeps the current clean prediction or replaces it with this one-step-later prediction to form the actual noisy state. By taking the additional step, we generally obtain a higher-signal, more-image-like prediction for the noisy state, mitigating the exposure bias. To optimize the replacement positions, PEEK searches for a binary replacement schedule with a greedy metric-driven search made efficient by the PEEK prefix and baseline noise-schedule-rebasing caches; for a 9-step search, the caches reduce the one-candidate worst-case vanilla denoiser-evaluation count by 69.3\%. Across pixel-space and latent-space diffusion models with unconditional, label-guided, and text-guided settings on CIFAR-10, CelebA-HQ, LSUN Bedroom, ImageNet, and MS-COCO datasets, PEEK improves over NFE-matched DPM-Solver++ and DEIS baselines, with five-run mean FID reductions up to 39.3\% under the same deployment cost. Code will be released.

Large language model (LLM) agents increasingly operate over long and recurring external contexts, like document corpora and code repositories. Across invocations, existing approaches preserve either the agent's trajectory, passive access to raw material, or task-level strategies. None of them preserves what we argue is most needed for repeated same-context workloads: reusable orientation knowledge (e.g., what the context contains, how it is organized, and which entities, constants, and schemas have historically been useful) about the recurring context itself. We introduce **PEEK**, a system that caches and maintains this orientation knowledge as a context map: a small, constant-sized artifact in the agent's prompt that gives it a persistent peek into the external context. The map is maintained by a programmable cache policy with three modules: a Distiller that extracts transferable knowledge from inference-time signals, a Cartographer that translates it into structured edits, and a priority-based Evictor that enforces a fixed token budget. On long-context reasoning and information aggregation, PEEK improves over strong baselines by 6.3–34.0\% while using 93–145 fewer iterations and incurring 1.7–5.8$\times$ lower cost than the state-of-the-art prompt-learning framework, ACE. On context learning, PEEK improves solving rate and rubric accuracy by 6.0–14.0\% and 7.8–12.1\%, respectively, at 1.4$\times$ lower cost than ACE. These gains generalize across LMs and agent architectures, including OpenAI Codex, a production-grade coding agent. Together, these results show that a context map helps long-context LLM agents interact with recurring external contexts more accurately and efficiently.

Shapley value-based explanations are widely used to attribute model predictions, but standard variants capture associative rather than causal relationships. Recent causally informed Shapley methods add a path-wise decomposition, but suffer from two key limitations: they assign zero attribution to chain-mediated paths, and their per-path values do not align with classical mediation estimands. We propose PE-SHAP, a Shapley-inspired path-wise decomposition built on the portion-eliminated effect. Per-mediator values sum to the total mediated effect at every sample, aggregate to the Natural Indirect Effect at the population level, and match the Path-Specific Effect under additive separability. These are properties prior Shapley-based path-wise methods do not provide. We validate PE-SHAP analytically and through Monte-Carlo experiments on synthetic SCMs with known path effects, and demonstrate it on real-world fairness case studies using the German Credit and COMPAS datasets.

Many applications require joint prediction of interdependent behavioral choices, yet existing models often treat each choice independently (e.g., through parallel prediction heads), overlooking the influence of one on the other. In this work, we propose Progressive Dual-Head Transformer (PDHFormer), a novel framework that performs two-step prediction: the model first estimates one choice and then conditions the second on this upstream estimate through an explicit head-to-head pathway. A shared encoder captures the common structure of two prediction tasks, while the dual-head module explicitly reflects cross-choice dependence. A gated residual mechanism integrated into the embedding layer and the dual-head module further improves the training stability and the prediction performance. Extensive experiments on real-world urban mobility, manufacturing, and food delivery application domains demonstrate that PDHFormer consistently outperforms state-of-the-art machine learning models, deep tabular models, as well as parallel-head Transformer variants across multiple metrics. Moreover, our ablation study confirms that both the proposed progressive dual-head and gated residual mechanism are key contributors to the observed gains in different prediction tasks.

Pose and motion priors play a crucial role in humanoid robotics. Although such priors have been widely studied in human motion recovery (HMR) domain with a range of models, their adoption for humanoid robots remains limited, largely due to the scarcity of high-quality humanoid motion data. In this work, we introduce Pose Distance Fields for Humanoid Robots (PDF-HR), a lightweight prior that represents the robot pose distribution as a continuous and differentiable manifold. Given an arbitrary pose, PDF-HR predicts its distance to a large corpus of retargeted robot poses, yielding a smooth measure of pose plausibility that is well suited for optimization and control. PDF-HR can be integrated as a reward shaping term, a regularizer, or a standalone plausibility scorer across diverse pipelines. We evaluate PDF-HR on various humanoid tasks, including single-trajectory motion tracking, general motion tracking, style-based motion mimicry, and general motion retargeting. Experiments show that this plug-and-play prior consistently and substantially strengthens strong baselines.

The success of vision transformers—especially for generative modeling—is limited by the quadratic cost and weak spatial inductive bias of self-attention. We propose PDE-SSM, a spatial state-space block that replaces attention with a learnable convection–diffusion–reaction partial differential equation. This operator encodes a strong spatial prior by modeling information flow via physically grounded dynamics rather than all-to-all token interactions. Solving the PDE in the Fourier domain yields global coupling with near-linear complexity, delivering a principled and scalable alternative to attention. We integrate PDE-SSM into a flow-matching generative model to obtain the PDE-based Diffusion Transformer PDE-SSM-DiT. Empirically, PDE-SSM-DiT matches or exceeds the performance of state-of-the-art Diffusion Transformers while substantially reducing compute. Our results show that, analogous to 1D settings where SSMs supplant attention, multi-dimensional PDE operators provide an efficient, inductive-bias-rich foundation for next-generation vision models.

Motivated by the success of large language models (LLMs) with broad generalizability and robustness to noisy or unreliable pre-training data, we seek to bring similar capabilities to PDE solvers. In addition, inspired by the posterior predictive mean-based inference mechanism of the in-context learning in prior-data fitted networks (PFNs), we propose PDE-PFN, a prior-data fitted neural solver that approximates the posterior predictive mean of PDE solutions via in-context learning. PDE-PFN builds on a PFN architecture with self- and cross-attention mechanisms of Transformer and is pre-trained on noisy approximate solutions generated by physics-informed neural networks, serving as diverse but not necessarily exact priors. Through experiments on a range of two-dimensional PDEs, we demonstrate that PDE-PFN achieves empirical generalization across heterogeneous equations, robustness under noisy priors, and zero-gradient-update in-context inference capability. Our approach not only outperforms task-specific baselines but also provides a flexible and robust framework for advancing SciML.

Photonic crystal fibres (PCFs) underpin critical applications in telecommunications, sensing, and high-power laser delivery, yet their design remains a manual, expert-intensive process that couples geometric intuition with deep waveguide physics. Vision-language models (VLMs) offer a promising path toward automating this pipeline, but can they truly reason about photonic physics from microscopy images, or do they merely recognise visual patterns? We introduce PCFBench, the first comprehensive benchmark for evaluating VLMs on the complete photonic inverse-design workflow. PCFBench spans 26 tasks organised into six categories of increasing cognitive demand (Geometry Perception, Physics Understanding, Text Generation, Multi-modal Reasoning, Inverse Design, and Code Generation), built from over 220K FDTD simulation samples across 18 structurally diverse fibre families. We evaluate both individual VLMs on isolated tasks and multi-agent systems (MAS) on the full sequential pipeline from perception to design. Our results reveal a striking perception-design gap: current models can identify fibre geometry with moderate success, yet consistently fail to translate that visual understanding into physics-aware design actions. Moreover, cascading agents through the pipeline exposes significant error propagation, where upstream perception mistakes compound into downstream design failures. These findings suggest that neither model scaling nor naive agent chaining suffices; closing the loop demands explicit physical grounding. PCFBench provides the community with a rigorous, multi-dimensional testbed to measure progress toward AI-assisted photonic engineering.

Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, including software development, education, and technical assistance. However, a fundamental question remains unresolved: can LLMs reason about physical constraints—the rules governing how abstract computations must map onto real-world hardware? This capability is distinct from, and arguably more demanding than, software generation alone, as it requires grounding language understanding in spatial, electrical, and topological constraints. To address this gap, we introduce PCEval (Physical Computing Evaluation), a benchmark and evaluation protocol for physical computing education that enables fully automatic, execution-based assessment of LLMs across both logical and physical implementation artifacts, without human grading. Our evaluation framework assesses LLMs in generating circuits and producing compatible code across varying levels of project complexity. Through comprehensive testing of 14 leading models, PCEval provides the first reproducible and automatically validated empirical assessment of LLMs' ability to reason about fundamental hardware implementation constraints within a simulation environment. Our findings reveal that while LLMs perform well in code generation and logical circuit design, they struggle significantly with physical breadboard layout creation, particularly in managing proper pin connections and avoiding circuit errors. PCEval advances our understanding of AI assistance in hardware-dependent computing environments and establishes a foundation for developing more effective tools to support physical computing education.

Infrared-visible image fusion (IVIF) aims to combine visible and thermal infrared observations for human perception and downstream vision tasks. Visible and thermal infrared observations are physically complementary but unreliable in different ways. Existing deep IVIF methods mainly improve feature interaction and reconstruction, but often lack an explicit local rule for deciding when visible appearance should dominate and when thermal responses should contribute. To address this issue, we reformulate IVIF as local Bayesian posterior rendering, where fusion is controlled by reliability posteriors rather than direct modality mixing. In this paper, we propose Proposal-Context-Detail Fusion (PCDFusion), a visible-anchored framework that implements this formulation through three rendering stages. PCDFusion preserves potentially useful thermal responses, updates exposure-degraded visible regions, and renders reliable band-limited infrared structures into the final luminance. Extensive experiments on public IVIF benchmarks show that PCDFusion improves fusion quality, target recovery, and downstream detection and segmentation performance under challenging illumination. To support reproducibility, the code will be publicly released upon acceptance.

LLM-based agents have made rapid progress on software engineering benchmarks, and executable evaluation is extending to chip-level hardware tasks. Board-level PCB design and upgrade has yet to receive comparable evaluation, largely because open-source PCB projects rarely provide the structured issue/PR/test histories that existing benchmarks rely on. We introduce PCBInnoBench, the first benchmark that evaluates LLM agents on executable PCB design-and-upgrade workflows. PCBInnoBench contains 372 expert-designed KiCad tasks grounded in real PCB projects and end-to-end engineering workflows. Each task pairs an agent-visible package (KiCad project and multimodal engineering references) with a hidden evaluation package (ground-truth design and expert-reviewed validators) used only for scoring. Agents must modify existing ECAD projects and pass structural, task-specific, and cross-artifact engineering-constraint checks. Each task is evaluated under two prompts of different guidance levels: one supplies an expert execution trajectory of ECAD edits together with the consulted references, the other states only the task objective, requiring agents to navigate the full workflow from requirement localization through cross-artifact editing. The best agent achieves 22.0% Pass@1 with guidance but only 7.3% without, indicating that both workflow discovery and ECAD execution remain substantial challenges for current agents. Beyond PCB, the workflow-grounded, expert-constructed evaluation approach may serve as a reference for other engineering domains where historical traces are sparse and correctness spans heterogeneous artifacts. Data and code are available at https://anonymous.4open.science/r/pcbinnobench-C841 .

Existing code benchmarks measure whether an agent can produce any test that reproduces a known bug, or whether it can produce a patch that fixes a described issue. Neither isolates the distinct skill of property-based testing: deriving a semantic invariant from documentation, and then constructing an input-generation strategy precise enough to make a random search reveal the violation. We introduce PBT-Bench, a benchmark of 100 curated property-based testing problems across 40 real Python libraries. Each problem injects one or more semantic bugs (365 in total, mean 3.65 per problem) designed so that default-strategy random inputs almost never trigger them; the agent must read the library's documentation, identify the relevant invariant, and specify a Hypothesis @given strategy that concentrates mass in the trigger region. Bugs are stratified across three difficulty levels (L1–L3) spanning single-constraint boundary bugs to stateful, cross-function protocol violations. We evaluate eight contemporary LLMs under two prompting regimes (open-ended baseline vs. explicit Hypothesis scaffolding) for three independent runs per configuration. Bug recall under the PBT-guided prompt ranges from 42.1% to 83.4% across models; under the open-ended baseline, from 31.4% to 76.7%. Hypothesis scaffolding lifts mid-capability models by over 20 percentage points, but yields smaller gains for the strongest models, with two exceptions showing degradation, suggesting the structured prompt can interfere with certain model behaviours rather than complementing them. The hardest bugs prove model-specific: different architectures fail on different problems, leaving persistent gaps that no single model closes. We release the benchmark, harness, and full evaluation corpus to support downstream work on documentation-grounded semantic reasoning.

Large vision-language models (LVLMs) can caption images, answer visual questions, and reason over scientific diagrams, yet they still produce hallucinations unsupported by visual evidence. Existing approaches mitigate hallucinations either by reshaping the decoding distribution or by steering hidden activations with calibration-based directions. The former adjusts token probabilities but operates only at the output level, whereas hallucinations often arise when language priors dominate visual evidence in intermediate representations. The latter applies a fixed calibration intervention, which may fail to capture image-specific hallucination drift or inadvertently suppress grounded evidence. To address these limitations, we propose PAVE, a training-free method for input-specific hidden-state intervention. PAVE constructs offline subspaces for hallucination-drift suppression and visual-evidence amplification, and adapts the hidden-state update to each input using a per-image visual basis computed during prefill. By editing hidden states with this prefill-conditioned subspace, PAVE strengthens grounded evidence while suppressing hallucination drift, without relying solely on decoding-level corrections or fixed activation edits. Experiments on LLaVA-1.5-7B and Qwen-VL show that PAVE substantially reduces hallucinations on CHAIR and POPE while preserving grounded utility on MME. Code will be released.

This paper studies the problem of zero-shot text-attributed graph learning, which aims to generate high-quality node representations in unseen text-attributed graphs. Recent approaches usually utilize large language models (LLMs) instead of graph neural networks (GNNs) to extract semantics due to their strong generalization ability, which could neglect the intrinsic geometric structure. Towards this end, in this paper, we propose a novel approach named Prototypical Mutual Prompting Enhancement (PATH) for zero-shot text-attributed graph learning. The core of our PATH is to generate high-quality prompts using dual prototypical learning to combine the advantages of both language models and graph models. In particular, we first utilize dual graph pre-training from both instance and informativeness perspectives to generate a generalizable GNN. Then, we incorporate the frozen language and graph models into a mutual prompt learning framework. On the one hand, we extract node tokens with geometric relationships using the graph model, which will be sent to multiple prototypical projections to enhance the understanding of the language model. On the other hand, we extract graph information and task descriptions using the language model, which serves as instruction for the graph models. Extensive experiments on node classification and link prediction validate the effectiveness of the proposed PATH against strong baselines. Our code is available at [https://anonymous.4open.science/r/PATH](https://anonymous.4open.science/r/PATH).