NeurIPS 2026 论文列表

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).

Machine learning is increasingly used for autonomous navigation, yet safety regulations governing safety-critical AI applications require rigorous datasets and protocols to evaluate learned planners on terrain-aware routing. Existing path-planning benchmarks address this only partially: synthetic grids offer algorithmic control without geographic realism, while Geographic Information System (GIS) resources provide real terrain without controllable obstacles or standardised baselines. To close this gap and steer research toward more trustworthy AI-based planners, especially in line with EASA and FAA guidance on AI, we introduce **PASSAGE**, an open-source benchmark and dataset that reframes constrained terrain path planning as a *controlled trustworthiness-oriented evaluation harness* rather than a static dataset. PASSAGE is sampled from a well-defined Operational Design Domain (ODD) built on JAXA ALOS AW3D30 elevation at its native 30 m/pixel resolution, and is derived into seven input-tensor resolutions from 64×64 to 4096×4096 pixels, totalling *4.4 million samples* for ≈ *2.43 TB* of compressed data. Each sample is a real-terrain cost grid annotated with start and goal markers, procedurally generated super-ellipse obstacles, and exact A* path labels under three physically interpretable cost models. The release pairs this dataset with a deterministic generation pipeline, a geographic hold-out, ODD-coverage diagnostics, and a rigorous evaluation protocol — a shift from *dataset collection* towards *auditable benchmark design* that provides a rare level of experimental control for studying robustness, generalisation, and other trustworthiness properties of safety-critical embedded AI. PASSAGE thereby offers a common surface for comparing AI-based path planners, including two dense-vision approaches we introduce as illustrative examples (path-mask segmentation and depth-estimation-inspired cost-to-go regression), with graph-neural and reinforcement-learning families as natural future extensions. Although motivated by aerospace trustworthiness requirements such as completeness, correctness, and representativeness, PASSAGE broadly supports trustworthy machine learning across robotics, autonomous navigation, and other safety-critical planning domains. Code (anonymous repository) and dataset (anonymous mirror) are released openly under permissive licences.

Video Diffusion Transformers (DiTs) generate high-quality videos but demand substantial compute due to wide blocks, deep architectures, and iterative sampling. Recent methods reduce cost by compressing width, depth, or sampling steps, but typically commit to a fixed architecture that cannot adapt to individual inputs or denoising stages. We propose PARE (Pruning and Adaptive Routing for Efficient video generation), which jointly compresses width and depth with structure-aware pruning and input-adaptive routing. For width, we observe that attention heads specialize into spatial and temporal roles, and design importance scoring that accounts for this distinction to prevent motion-critical temporal heads from being pruned prematurely. For depth, we train a lightweight router conditioned on denoising timestep and visual content to dynamically select which blocks to execute at each step, enabling per-input compute adaptation rather than static block removal. A progressive pipeline first recovers width-pruned quality via distillation, then jointly optimizes the student and router to decouple the two learning objectives. Experiments on Wan2.1-14B for both image-to-video and text-to-video generation show that PARE substantially reduces per-step computation while preserving quality across VBench dimensions, and composes with step distillation for further acceleration.

Vision-Language-Action (VLA) models show promising ability in language-guided robotic tasks. However, making VLA policies reliable remains challenging, because a manipulation task is completed through closed-loop interaction, where each action affects subsequent execution. To analyze this problem, we revisit VLA policy during execution and argue that a VLA policy acts both as a planner, which makes task-oriented decisions that change the direction of execution, and as an executor, which realizes these decisions through dense continuous actions. This view suggests that improving VLA reliability requires particular attention to planning actions. Existing optimization methods can imitate actions or improve complete trajectories, but they usually do not explicitly identify planning actions or measure their importance for task success. To address this issue, we propose Planning-Aware Policy Optimization for VLA models (PAPO-VLA). PAPO-VLA first identifies planning actions by jointly considering action variation and trajectory outcome, then estimates their importance through causal sufficiency and causal necessity, and finally incorporates this importance into GRPO advantage estimation. In this way, more important planning actions receive stronger optimization emphasis, while the whole trajectory is still optimized by trajectory-level feedback. Experiments on multiple benchmarks demonstrate the effectiveness of PAPO-VLA.

Hypergraph Neural Networks (HNNs), designed to model higher-order relations beyond pairwise interactions, typically rely either on fixed structural weights or on normalized attention. Fixed weights can propagate misleading information in heterophilic settings, while normalization makes attention coefficients competitive, in the sense that, in a message-passing framework, increasing the weight of one sender must reduce the weight of others in the same aggregation set. Our main contribution is the introduction of PANDA (Prior-guided AttentioNal Dual-path Architecture), a plugin for two-stage message passing HNNs applicable to both undirected and directed settings. The *main path* interpolates between learned receiver-normalized attention and structural incidence priors, while a non-competitive (not normalized) *auxiliary path* lets each sender contribute its own transformed information. We also show that, with fixed structural coefficients, PANDA recovers a certain complex-valued, convolution operator adopted in previous works. Across four HNNs spanning spatial and spectral methods in both directed and undirected settings, plugging PANDA in yields an average improvement of $5.18$ percentage points across eight real-world benchmark datasets.

Real-world time series forecasting faces the fundamental challenge of non-stationary statistical properties, including shifts in mean and variance over time. While reversible instance normalization (RevIN) has shown promise by stationarizing inputs and denormalizing outputs, it relies on the strong assumption that historical and future distributions remain identical. We observe that in many practical applications, distribution shifts follow cyclical patterns that correlate with periodic positions (e.g., seasonal and holiday volatility). To this end, we propose $\textbf{PAMod}$, a lightweight yet powerful framework that models cyclical distribution shifts via $\textbf{P}$hase-$\textbf{A}$mplitude $\textbf{Mod}$ulation in the normalized feature space. PAMod learns periodic embeddings to modulate representations: phase modulation captures mean shifts, while amplitude modulation adapts to variance changes. Crucially, we prove mathematically that modulating in normalized space is equivalent to applying dynamic denormalization, offering an elegant unification of distribution adaptation and representation learning. Extensive experiments on twelve real-world benchmarks demonstrate that PAMod achieves state-of-the-art performance with fewer computational resources. Furthermore, our modulation mechanism, as a novel plug-and-play technique, can improve existing time-series forecasting methods with simple integration.

A significant hurdle for current LLMs is the execution of complex, multi-stage tasks. Group Relative Policy Optimization (GRPO) has been emerging as a leading choice, but its reliance on sparse outcome rewards severely limits credit assignment across intermediate steps. Existing remedies—running full rollouts to assign step-level advantages, calling external LLM judges at each step, or computing intrinsic rewards that require ground-truth answers at every evaluation—introduce significant costs or practical constraints. We hypothesize that internal correctness probing over LLM hidden states can be repurposed as a step-level reward signal, potentially addressing all of these limitations at once. However, existing probing research assumes clean inputs, and we first show that this assumption breaks down in multi-step settings: hidden-state probes degrade severely under prefix contamination—tracking coherence with the (possibly corrupted) prefix rather than grounded correctness—while attention-based features remain robust to contamination but underperform on clean prefixes. Building on this complementary relationship, we propose the Prefix-Aware Internal Reward (PAIR)—a two-stage model with a frozen hidden-state probe estimating belief-consistency and a lightweight attention-based head correcting it toward grounded correctness. Experimental results show that PAIR achieves the highest AUROC on contaminated trajectories while operating at negligible inference cost, enabling dense step-level reward signals for GRPO training without external model calls, ground-truth dependencies, or full-trajectory rollouts.

The standard constraint-based paradigm for causal discovery with incomplete data---impute first, test second---is frequently miscalibrated: any consistent conditional independence (CI) test rejects a true null with probability approaching 1 when imputation error induces spurious conditional dependence. We introduce *PAIR-CI*, a nonparametric CI test that restores calibration by integrating multiple imputation directly into the inferential procedure via a paired permutation design. PAIR-CI compares cross-validated models that include and exclude the candidate variable while receiving the same imputed conditioning set, forcing imputation error to cancel in their loss difference rather than contaminate the test statistic. A provably consistent variance estimator jointly accounts for uncertainty arising from cross-validation and multiple imputation---to our knowledge, the first formal unification of these two inferential frameworks. In simulations, existing imputation-based CI tests exhibit false positive rates of 28--45\% when data are missing not at random (MNAR), whereas PAIR-CI averages below the nominal 5\% level across data-generating processes and missingness mechanisms. These gains are largest in nonlinear settings and grow with causal graph size: when integrated into the PC algorithm, PAIR-CI reduces structural Hamming distance by 8\% on 10-variable nonlinear graphs, 15\% on 30-variable equivalents, and up to 44\% on the 56-variable HAILFINDER network, with stable performance in all settings.

We present PAI-Actor, a cinematic multi-character animation framework for character replacement in dynamic movie scenes. Unlike conventional animation systems that mainly drive a single static image or a single subject, our goal is to replace and animate multiple characters within real video clips while preserving the original scene dynamics, camera motion, and background content. This setting is particularly challenging because the generated characters must remain consistent with the source performance in motion and interaction, while also matching the surrounding background in lighting, shadow, composition, and overall cinematic appearance. To address this, we formulate multi-character animation as a structure-guided human recovery problem and build a movie-driven training pipeline from high-quality film data. Furthermore, to support practical cinematic production, we introduce a bidirectional-to-autoregressive distillation framework: we first train a bidirectional diffusion transformer for high-quality short-clip generation at 1080P resolution, and then distill it into an autoregressive video-to-video model for efficient inference and longer video generation. Experiments show that PAI-Actor enables high-fidelity multi-character animation with strong scene consistency, cinematic visual quality, and efficient long-form generation.

Large vision-language models have significantly advanced GUI agents, enabling executable interaction across web, mobile, and desktop interfaces. Yet these gains largely rely on a forgiving \emph{region-tolerant} paradigm, where many nearby pixels inside the same component remain valid. Precise geometric construction breaks this assumption: actions must land on points in continuous canvas space rather than tolerant regions. Because geometric primitives carry ontological dependencies, a local coordinate error can induce cascading topological failures that distort downstream objects and invalidate the final construction. We identify this regime as \emph{precision-sensitive GUI tasks}, requiring point-level accuracy, geometry-aware verification, and robustness to dependency-driven error propagation. To benchmark it, we introduce \textsc{PAGE} Bench, with 4{,}906 problems and over 224K process-supervised, pixel-level GUI actions. We further propose \textsc{PAGER}, a topology-aware agent that decomposes construction into dependency-structured planning and pixel-level execution. Pixel-grounded supervised tuning establishes executable action grammar, while precision-aligned reinforcement learning mitigates rollout-induced exposure bias through state-conditioned geometric feedback. Experiments reveal a pronounced ``Semantic-Execution Gap'': general multimodal models can exceed 88\% Action Type Accuracy yet remain below 6\% Task Success. \textsc{PAGER} closes this gap, delivering $4.1\times$ higher Task Success than the strongest evaluated general baseline and raising Step Success Rate from below 9\% for GUI-specialized agents to over 62\%, establishing a new state of the art for point-precise GUI control.

We introduce PACZero, a family of PAC-private zeroth-order mechanisms for fine-tuning large language models that delivers usable utility at $I(S^*; Y_{1:T})=0$. This privacy regime bounds the membership-inference attack (MIA) posterior success rate at the prior, an MIA-resistance level the DP framework matches only at $\varepsilon=0$ and infinite noise. All DP-ZO comparisons below are matched at the MIA posterior level. The key insight is that PAC Privacy charges mutual information only when the release depends on which candidate subset is the secret. Sign-quantizing subset-aggregated zeroth-order gradients creates frequent unanimity, steps at which every candidate subset agrees on the update direction; at these steps the released sign costs zero conditional mutual information. We propose two variants that span the privacy-utility trade-off: PACZero-MI (budgeted MI via exact calibration on the binary release) and PACZero-ZPL ($I=0$ via a uniform coin flip on disagreement steps). We evaluate on SST-2 and SQuAD with OPT-1.3B and OPT-6.7B in both LoRA and full-parameter tracks. On SST-2 OPT-1.3B full fine-tuning at $I=0$, PACZero-ZPL reaches ${88.99\pm0.91}$, within $2.1$pp of the non-private MeZO baseline ($91.1$ FT). No prior method produces usable utility in the high-privacy regime $\varepsilon<1$, and PACZero-ZPL obtains competitive SST-2 accuracy and nontrivial SQuAD F1 across OPT-1.3B and OPT-6.7B at $I=0$.

Differentiable structure learning transforms DAG discovery from a combinatorial search into a continuous optimization problem, enabling gradient-based recovery of causal structures. In many applications, researchers possess partial-order priors over the variables (e.g., from biological signaling cascades or temporal ordering), which substantially narrow the space of plausible causal graphs. Existing approaches integrate such priors by decomposing the prior order graph into a collection of paths and evaluating prior-aware acyclicity constraints path by path; this is correct but its constraint-evaluation cost scales linearly with the number of induced maximal paths, a bottleneck for multi-chain priors. We propose PACO, Partial-order-Augmented Continuous Optimization, which encodes the same partial-order compatibility through a single masked-reachability matrix, that masks the matrix exponential of the learned graph by the strict transitive closure of the prior. The constraint and its Frechet-adjoint gradient are computed in a single $2d{\times}2d$ block matrix exponential whose dominant cost is independent of the number of induced prior paths. We prove that PACO has the same exact zero-level feasible set as the path-decomposition formulation. Across synthetic and real-data experiments under matched priors and dataset seeds, PACO matches path-decomposition on structural recovery and substantially reduces wall-clock cost in multi-chain prior settings.

Large language models have become drivers of evolutionary search, but most systems rely on a fixed, prompt-elicited policy to sample next candidates. This limits adaptation in practical engineering and research tasks, where evaluations are expensive, and progress depends on learning task-specific search dynamics. We introduce PACEvolve++, an advisor-model reinforcement learning framework for test-time policy adaptation in evolutionary search agents. PACEvolve++ decouples strategic search decisions from implementation: a trainable advisor generates, assesses, and selects hypotheses, while a stronger frontier model translates selected hypotheses into executable candidates. To train the advisor under non-stationary feedback, we propose a phase-adaptive approach that adapts its optimization strategy to different phases of the evolutionary process. Early in evolution, it uses group-relative feedback to learn broad search preferences; later, as reward gaps compress, it emphasizes best-of-$k$ frontier contribution to support stable refinement. Across expert-parallel load balancing, sequential recommendation, and protein fitness extrapolation, PACEvolve++ outperforms the state-of-the-art evolutionary search framework with frontier models, achieving faster convergence and stabilizing test-time training during evolutionary search.

Vision-language-action (VLA) models have made rapid progress in robotic manipulation, yet the policy acts with no sense of the *pace* at which its own execution unfolds—whether it is advancing, stalling, or has quietly gone wrong—especially for long-horizon tasks. Recent methods supply progress through external reward models or as an auxiliary prediction inside the policy, but the signal runs alongside action generation rather than actively shaping it. Moreover, progress is inherently tied to the terminal state, making estimation fragile when the policy lacks an internal awareness of where the task should end. We propose **PACE**, which actively internalizes task progress as a predictive interface bridging commitment and execution, equipping the policy with a calibrated sense of the *pace* at which it advances toward task completion. Specifically, we first equip the VLA with a *foresight anchor*, a predicted terminal representation from observation and instruction, from which progress emerges as a geometric projection. Building on this anchor, the model perceives a compact past–present–future progress state capturing *how much the last step advanced*, *how far execution has come*, and *how far the next step should go*. These predicted progress further actively conditions the action decoder to guide the prediction, while the gap between committed and realized increments provides a built-in consistency check for lightweight failure detection. Experiments across LIBERO, SimplerEnv, and real-world single-arm and bimanual platforms show that PACE achieves 98.7% on LIBERO and 74% on real-world manipulation, with consistent gains especially on long-horizon tasks. Robustness and stable generalization across diverse scenarios further confirm the advantage of internalizing progress within the policy, validating a calibrated sense of execution pace as an effective paradigm for long-horizon robot control.

We propose PACE, Partial-state Amortized Constraint Editing, which treats task-native feasible partial states as the common semantic object for neural combinatorial optimization, unifying edit learning, constraint-preserving editing, anytime closure, and test-time scaling across routing and graph combinatorial optimization. Existing neural combinatorial optimization methods expose complementary strengths: constructive and adaptive expansion solvers keep meaningful partial solutions but couple them to serial or method-specific growth, while global prediction, diffusion, masked reconstruction, and complete-solution refinement provide scalable guidance yet often refine heatmaps, noisy solutions, reconstruction targets, or perturbed full outputs. These semantics make learning, search, and test-time scaling act on the same state object. PACE learns amortized constraint editing by ranking candidate edits conditioned on the current feasible partial state; a constraint-preserving transition $\Gamma_I$ commits only edits that preserve legal continuation and extendability; and task-native closure $\mathrm{Comp}_I$ maps any extendable intermediate feasible partial state to a task-terminal feasible output. Budgeted refinement then spends extra inference-time compute through deeper edit steps, additional refinement rounds, or broader candidate sets along the same trajectory. We instantiate these semantics across TSP, ATSP, CVRP, MIS, MVC, MCL, and MCut, covering edge-oriented routing and node-oriented graph combinatorial optimization. Theoretical guarantees are structural rather than optimality claims, covering task-wise soundness, constructive completion, state-space closure in the extendable subset, terminal feasible completion at any editing depth, and elite-pool nondegradation. Empirically, PACE is competitive and budget-controllable, including ATSP-500 at 0.247\% Drop, MIS RB-[800-1200] at 2.00\%, and MVC at 0.07\%.

Native multimodal large language models (native MLLMs) jointly encode visual and linguistic sequences within a unified autoregressive Transformer, emerging as a promising paradigm for multimodal understanding. Unlike conventional ViT-based MLLMs that rely on an external vision encoder, ViT-free native MLLMs tokenize every pixel patch directly, which is intuitively expected to suffer from severe visual token redundancy. To examine this hypothesis, we conduct a pilot study and find that, unlike ViT-based models which degrade under compression, ViT-free MLLMs tolerate aggressive compression with near-lossless or even improved per9 formance. Existing compression methods are built exclusively for the ViT-based setting, whereas native MLLMs, which stand to gain the most from visual token compression, remain entirely unexplored. To close this gap, we introduce an analysis pipeline for native backbones, centered on two metrics, i.e., token discriminability and cross-dimensional heterogeneous redundancy, which together reveal the following two findings: (i) redundancy is distributed independently and heterogeneously along the height and width axes, and (ii) in early layers, visual tokens are indiscriminable and uniformly redundant. Guided by these findings, we propose PACE (Pareto Adaptive Compression for Efficient native MLLM), the first visual token compression framework tailored for native MLLMs with Multimodal-RoPE. Leveraging the dimensional decoupling of position encoding, PACE decomposes attention into two orthogonal spatial saliency estimates along height and width, gated by a positional attention signal, yielding a direction-aware importance measure with negligible overhead. It further casts token budget allocation as a Pareto optimization over three objectives, i.e., performance preservation, inference efficiency, and spatial coverage, which jointly identify the optimal compression depths and retention ratios. Experiments show that PACE maintains near-lossless performance at only 25%–50% visual token retention, and even achieves performance gains on several benchmarks, while delivering over 2× FLOPs reduction.

Single-cell trajectory inference from destructive time-course snapshots is fundamentally ill-posed: neither cross-time cell correspondences nor the continuous paths between snapshots are observed, so the observed snapshot distributions alone do not uniquely determine the underlying dynamics. Existing optimal transport and flow-based methods typically couple cells by Euclidean proximity at observed clock times, which can misalign trajectories when development is asynchronous and cells sampled at the same experimental time occupy different latent pseudotime stages. We propose PACE, a trajectory inference framework that selects geometry-consistent continuous transport dynamics from destructive time-course snapshots through three coupled components. First, PACE constructs a state- and time-dependent anisotropic Riemannian metric that preserves low cost along locally supported tangent directions while penalizing normal velocity components. Second, it alternates between refining cross-time couplings under the induced path-action cost and fitting endpoint-preserving neural bridges between adjacent snapshots. Third, it distills the learned bridge dynamics into a global continuous-time velocity field over cellular states. Across seven controlled and biological datasets covering nine held-out reconstruction experiments, PACE achieves the strongest overall reconstruction performance, reducing MMD, $\mathcal{W}_1$, and $\mathcal{W}_2$ by 23.7\% on average relative to the strongest competing baseline. PACE also improves RNA-velocity alignment by 15.4\% on an embryoid body differentiation benchmark, without requiring explicit cell pairing, lineage tracing, or RNA velocity supervision during training. Code is available at \url{https://anonymous.4open.science/r/PACE-F444/}.