NeurIPS 2026 论文列表

Reconstructing patient-specific cyclic cardiac motion at arbitrary phases is central to dynamic cardiac assessment, underpinning measurements such as ejection fraction, stroke volume, and regional myocardial deformation. Existing methods---pair-wise registration, time-conditioned decoders, and coordinate-based implicit fields---share a common implicit assumption: the cardiac cycle is treated as a sequence of discrete phases, with temporal structure stitched together through learned mappings supervised at the observed frames. Yet the cycle is, by physical fact, a closed periodic process. We propose \textbf{CycleSpectra}, a spectral motion representation that takes the cyclic structure as a starting point of the representation rather than as a property to be learned. Given a pair of sparsely sampled phase observations, the model learns the spectral structure of the underlying cyclic cardiac motion in a low-dimensional latent space, and recovers the displacement at any phase through a fixed analytic synthesis layer---time never enters the network as a learnable signal, only as a coordinate the readout takes by construction. To support evaluation, we collect a thin-slice phase-resolved 4D cardiac CT cohort and benchmark under both canonical and non-canonical input protocols, reporting whole-cycle motion-fidelity metrics alongside standard image and anatomical metrics. Under arbitrary two-frame input, CycleSpectra achieves the lowest ejection-fraction error and highest ventricular volume--time curve correlation among recent baselines, while remaining competitive on image fidelity. Beyond two-frame inference, the proposed framework supports test-time multi-frame latent fusion for higher accuracy, validated on both the in-house cohort and a cross-modality 4D cardiac MR cohort. Code will be available.

Chain-of-thought (CoT) monitoring is increasingly relied upon to detect misbehavior in LLMs, under the implicit assumption that misbehaving actors must reason about their misbehavior, leaving detectable traces in the CoT. We show this assumption fails for a class of attacks we call planning-execution decoupled attacks, in which misbehavior is injected via a corrupted plan before the actor begins reasoning. Using the investigator agent framework of Li et al. [2025], we discover that exposing an actor to a corrupted reasoning plan upstream causes it to reproduce the flawed reasoning in its own CoT, embedding misbehavior into natural-looking reasoning with few suspicious traces. The attack scales to stronger reasoning models and harder tasks, steering them toward misbehavior while evading detection. Evaluating a suite of thinking and non-thinking monitors, we find that thinking monitors substantially outperform non-thinking ones, though even the best miss a meaningful fraction of attacks. Critically, the relationship between monitor thinking budget and detection is not monotonic: extra reasoning sometimes improves detection but can also hurt it when monitors talk themselves into accepting corrupted CoTs as benign. This refines Guan et al. [2025], who show thinking budget generally helps monitoring; we find detection also depends on whether the additional reasoning is directed toward critical evaluation rather than rationalization.

Next-token prediction leaves hidden-state trajectories largely unconstrained, motivating recent geometric regularization methods based on angular penalties. However, these approaches control only the direction of motion and provide no explicit bound on trajectory deviation. We introduce the Controlled Joint Embedding Predictive Architecture (ControlJEPA), a trajectory regularization framework grounded in discrete-time Input-to-State Stability theory. Our novel Lyapunov Tube Loss enforces a contraction condition on a normalized measure of transversal deviation, yielding a provable guarantee that confines hidden states within a tube of explicit width determined by two interpretable hyperparameters. ControlJEPA integrates seamlessly with standard training and requires no architectural changes. Across six benchmarks and seven model families, it consistently outperforms standard fine-tuning and prior trajectory regularizers, improving accuracy and data efficiency while preserving output diversity. Notably, the induced geometric structure persists at inference time, indicating that the method reshapes the representation space rather than memorizing training trajectories.

Robots operating in semantic environments often need to satisfy linear temporal logic (LTL) tasks before the semantic map is fully known. Many practical semantic-LTL planners maintain semantic beliefs but execute a single task policy induced by a point semantic interpretation, such as a maximum-a-posteriori (MAP) semantic map, possibly supported by risk checks or active perception. This premature commitment can be brittle when distant semantic observations are weak or correlated. We propose Coherence-Aware Transition-Intent Fusion, an online planner that preserves automaton-relevant semantic uncertainty using a finite top-$K$ intent abstraction. Given a conservative current automaton estimate, the planner propagates plausible semantic assignments through the automaton, converts feasible successor transitions into weighted reach-avoid intents, and fuses their risk-gated Dijkstra progress scores. When competing intents create incoherent motion, a KL-regularized calibration step shifts weight toward intents consistent with recent physical progress. Experiments on random-obstacle and room-like semantic maps show improved satisfaction over single-intent and raw-fusion variants, and over a TOAPP* MAP-plus-active-perception baseline under the same static-view sensor. Ablations attribute the gain primarily to intent-level calibration.

Diffusion models provide strong priors for generating structured data, but many tasks require outputs beyond the scale on which these models are typically trained. Compositional generation addresses this by composing overlapping local plans from a pretrained short-horizon prior into a long-horizon output. However, standard composition primarily enforces agreement between neighboring local plans, yielding local consistency without directly specifying the global structure of the full composition. As a result, locally compatible plans may still form an implausible route, task sequence, or temporal evolution. Existing methods improve global coherence by repeatedly propagating local consistency signals or by adding inference-time optimization, but these procedures become expensive as the number or dimensionality of local plans increases. We propose Coarse-to-Fine Compositional Diffusion (CoFi), an inference-time sampler that separates global structure formation from local detail refinement. CoFi first aligns local denoised estimates around a shared coarse structure, producing a global scaffold that captures the long-range task-level arrangement. It then diffuses this scaffold to an intermediate noise level and denoises it with the same pretrained local prior, restoring local fine structure while preserving the scaffold-induced global coherence. Across long-horizon robotic planning, panoramic image generation, and long video generation, CoFi not only improves both global coherence and local sample quality over prior compositional baselines, but also requires $2$--$8\times$ fewer denoiser evaluations.

End-to-end autonomous driving aims to generate safe and plausible planning policies from raw sensor inputs, and constructing an effective scene representation is a critical challenge. Driving world models have shown great potential in learning rich representations by predicting the future evolution of a driving scene. However, existing driving world models primarily focus on visual scene representation, and motion representation is not explicitly designed to be planner-shared and inheritable, leaving a schism between the optimization of visual scene generation and the requirements of precise motion planning. We present WorldDrive, a holistic framework that couples scene generation and real-time planning via unifying vision and motion representation. We first introduce a Trajectory-aware Driving World Model, which conditions on a trajectory vocabulary to enforce consistency between visual dynamics and motion intentions, enabling the generation of diverse and plausible future scenes conditioned on a specific trajectory. We transfer the vision and motion encoders to a downstream Multi-modal Planner, ensuring the driving policy operates on mature representations pre-optimized by scene generation. A simple interaction between motion representation, visual representation, and ego status can generate high-quality, multi-modal trajectories. Furthermore, to exploit the world model’s foresight, we propose a Future-aware Rewarder, which distills future latent representation from the frozen world model to evaluate and select optimal trajectories in real-time. Extensive experiments demonstrate that WorldDrive achieves leading planning performance among vision-only methods while maintaining high-fidelity action-controlled video generation capabilities. Our code and model will be made publicly available.

Monte-Carlo Tree Search (MCTS) typically stores only scalar mean estimates, which makes it poorly suited for tail-risk objectives such as conditional value-at-risk (CVaR). We introduce **Categorical Thompson Sampling with Optimism (CATSO)** and **Particle Thompson Sampling with Optimism (PATSO)**, two distributional MCTS algorithms that maintain finite-support empirical backup laws at Q-edges. CATSO represents Q-edge laws with categorical atoms and Dirichlet Thompson sampling, while PATSO represents them with adaptive particles. Both algorithms select actions using the CVaR of a Thompson-sampled Q-edge law plus a polynomial optimism bonus, and propagate scalar continuation values through visit-weighted averages of child Q-edge CVaR scores. For the finite-depth nested CVaR planning target, we prove an $O(n^{-1/2})$ bound on the root value error after $n$ rollouts, which upper-bounds the risk-sensitive simple regret. CATSO adds a fixed-grid discretization bias, while capped PATSO adds a tunable Wasserstein compression bias. Experiments on stochastic planning benchmarks show that distributional Q-edges improve lower-tail return and reduce catastrophic outcomes when mean-optimal and risk-sensitive behavior differ.