NeurIPS 2026 论文列表

Despite recent advances in camera-based online HD map construction, these systems remain susceptible to sensor failures due to their reliance on onboard cameras. In contrast, satellite images remain available independently of real-time sensing conditions as they are cached offline. Existing methods utilizing satellite images, however, primarily leverage them under nominal conditions, without explicitly addressing as redundancy when sensor degrades. To address this gap, we propose ReSMap, a redundancy-aware camera-satellite fusion framework for robust online HD map construction. Our framework explicitly leverages satellite images as a complementary source to improve robustness against camera failures. A Predict-then-Fuse (PTF) module fuses camera and satellite BEV features by using each branch's own prediction confidence as a per-pixel reliability weight, with no learned router. An Object-centric cooperative Multi-modal Query (OCMQ) decoder then propagates this isolation to the query level: each modality cross-attends only to its own BEV, and per-modality updates are merged through a learned per-token gate. Our model achieves state-of-the-art performance on nuScenes across all splits and range settings. More importantly, it also sets the new state-of-the-art under camera-failure scenarios, demonstrating that satellite images serve not only as a strong prior but as practical redundancy for robust online HD mapping.

Large reasoning models (LRMs) improve complex problem-solving by generating long intermediate reasoning traces, but this substantially increases inference costs. NVFP4 inference offers a promising approach to reduce both computational and memory costs through hardware-supported low-precision execution. However, directly applying NVFP4 to LRMs introduces two practical limitations: reasoning accuracy degrades under quantization, and existing NVFP4 kernels do not fully realize latency benefits in small-batch autoregressive decoding. In this work, we analyze the effect of NVFP4 quantization on token-level uncertainty during reasoning. We show that quantization increases incorrect sampling at low-entropy symbolic tokens, while causing over-concentration on a small set of tokens in high-uncertainty reasoning steps. Based on this observation, we propose \textbf{ReSET}, a reasoning-step entropy-based temperature-scaling method that estimates step-level uncertainty online and adapts the decoding temperature using both token-level and step-level entropy signals. To address the latency gap, we further design a CUDA-core small-$M$ NVFP4 kernel for latency-critical autoregressive decoding. Across reasoning benchmarks and model scales, ReSET improves NVFP4 reasoning accuracy by up to $\sim$2 points over the NVFP4 baseline. Our CUDA-core small-$M$ kernel further improves latency-critical decoding, delivering up to $2.5\times$ kernel-level speedup over NVFP4 vLLM and approximately $2\times$ end-to-end decoding speedup over BF16. Code will be released.

Simultaneous Sign Language Translation (SLT) is critical for real-time communication, yet existing methods remain largely confined to sentence-level, offline settings that assume pre-segmented inputs. These assumptions hinder deployment in realistic scenarios involving continuous, unsegmented video streams. We present ReSCUE, a unified framework for simultaneous SLT on unsegmented long-form sign language videos that aligns training and inference with realistic streaming conditions. ReSCUE combines inference-aware training to handle partial inputs, non-signing pauses, and multi-sentence contexts, stabilized re-translation to enable low-latency yet revisable predictions with reduced output flicker, and a sentence commitment mechanism for online segmentation and memory management. Experiments on standard sentence-level benchmarks show that ReSCUE achieves lower latency and the best translation quality under low-latency settings. On long-form unsegmented datasets, ReSCUE approaches the translation quality of oracle offline systems that use ground-truth sentence boundaries, while operating at substantially lower latency, demonstrating its practicality for real-world streaming scenarios.

Recent pixel-space generative models avoid the reconstruction bottleneck of latent diffusion, but direct denoising over high-dimensional pixel tokens remains computationally demanding. This paper studies a recursive alternative for efficient pixel-space diffusion. We propose RePiD, a hierarchical patch-level diffusion framework that recursively partitions an image and performs the core denoising computation on lower-dimensional pixel groups instead of repeatedly operating on full-resolution images. To preserve spatial coherence across patch-wise processing, RePiD introduces a neighborhood embedding module that conditions each patch on its surrounding regions. Experiments on class-to-image generation and unpaired image-to-image translation show that RePiD provides a favorable quality--efficiency trade-off for direct pixel-space generation. These results suggest that recursive patch-level denoising is a practical design direction for efficient pixel-space generative modeling. Our code is publicly available at \textcolor[HTML]{387FB9}{https://anonymous.4open.science/r/Recursive-Pixel-based-Diffusion-Model-1735}.

Vision-Language-Action (VLA) models remain brittle in long-horizon, contact-rich manipulation because success-only imitation provides little supervision for execution drift, while failed rollouts are often discarded. We introduce RePO-VLA, a recovery-driven policy optimization framework that assigns distinct roles to success, recovery, and failure trajectories. RePO-VLA first applies Recovery-Aware Initialization (RAI), slicing recovery segments and resetting history so corrective actions depend on the current adverse state rather than the preceding failure. It then learns a Progress-Aware Semantic Value Function (PAS-VF), aligning spatiotemporal trajectory features with instructions and successful references. The resulting labels salvage useful failure prefixes via reliability decay, while low-value labels mark drift and terminal breakdowns, teaching differences among nominal, failed, and corrective actions. The data engine turns adverse states into planner-generated or human-collected corrective rollouts, teaching recovery to the success manifold. Value-Conditioned Refinement (VCR) trains the policy to prefer high-progress actions. At deployment, a fixed high value ($v=1.0$) biases actions toward the learned success manifold without online failure detectors or heuristic retries. We introduce FRBench, with standardized error injection and recovery-focused evaluation. Across simulated and real-world bimanual tasks, RePO-VLA improves robustness, raising adversarial success from 20\% to 75\% on average and up to 80\% in scaled real-world trials.

Large language models (LLMs) can translate natural language into optimization code, but *silent failures* pose a critical risk: code that executes and returns solver-feasible solutions may encode semantically incorrect formulations—a feasibility–correctness gap reaching 90 percentage points on compositional problems. We introduce **ReLoop**, which addresses this gap through two complementary mechanisms. *Structured generation* decomposes code production into a four-stage reasoning chain (understand, formalize, synthesize, verify), preventing formulation errors at their source. *Behavioral verification* detects errors that survive generation by testing whether the formulation responds correctly to solver-based parameter perturbation—an external semantic signal that bypasses LLM self-review and requires no ground truth. The two mechanisms are complementary by error structure: structured generation drives the largest gains on compositional problems (**+8.5pp** accuracy on RetailOpt-190 with Claude Opus 4.6), while behavioral verification dominates on localized defects (**+4.4pp** on MAMO-ComplexLP, its largest contribution across benchmarks). Combined with diagnostic execution recovery, ReLoop reaches **100% executable code** on Claude Opus 4.6 and consistently improves accuracy on chat-tuned foundation models across three benchmarks; we further identify a known limitation of narrowly-tuned SFT models, whose learned output formats are brittle to chain-of-thought prompts—an interaction we document and analyze. We release **RetailOpt-190**, 190 compositional retail optimization scenarios targeting the multi-constraint interactions where LLMs most frequently fail. Code and benchmark will be released upon acceptance.

In recent years there has been a flurry of activity around using pretrained diffusion models as informed data priors for solving inverse problems, and more generally around steering these models towards certain reward models. Training-free methods like gradient guidance have offered simple, flexible approaches for these tasks, but when the reward is not informative enough, e.g., in inverse problems with highly compressive measurements, these techniques can veer off the data manifold, failing to produce realistic data samples. To address this challenge, we devise a simple algorithm, *ReGuidance*, that leverages prior methods' solutions as strong initializations and substantially enhancing their realism. Given a candidate solution $x$ produced by a given method, we propose inverting the solution by running the unconditional probability flow ODE in reverse starting from $x$, and then using the resulting latent as an initialization for a deterministic steering process. Empirically, we evaluate our algorithm on difficult image restoration tasks including large box inpainting, heavily downscaled superresolution, and high noise deblurring with both linear and nonlinear blurring operations. We find that, using a wide range of baseline methods as initializations, applying our method results in much stronger samples with better realism and measurement consistency. We complement these results with rigorous proofs in commonly studied theoretical settings showing our technique boosts the reward and brings $x$ closer to the data manifold.

Video world models aim to simulate environmental dynamics under interaction. However, most existing approaches entangle transition reasoning with pixel-level generation within a single model, thereby limiting narrative continuity and long-horizon coherence. We reformulate video world modeling as an agentic process that synergizes reasoning and generation, and present ReGen, which couples a streaming vision-language planner with a causal diffusion generator. At each step, the planner infers a structured action-state specification from history, while the generator realizes it as the next video segment. The planner further verifies the outcome and decides to continue, end, or re-generate, forming a closed-loop sense–plan–act–verify pipeline. To support this formulation, we carefully construct a large-scale action-grounded video dataset with over 2M segments that instantiates the planner–generator interface, and introduce action-grounded reward alignment that turns the action specification into an enforced reward and mitigates cross-segment drift. Experiments show that ReGen improves long-horizon narrative coherence over prior work, supporting autonomous long-horizon rollouts without step-by-step user intervention.

Reflections reveal scene content beyond the camera's direct field of view, but faithfully recovering this hidden information is challenging as reflective observations are heterogeneous, incomplete, and physically modulated. Existing methods either rely on explicit reflection modeling with privileged geometric priors or defer to generative synthesis that may hallucinate plausible but unfaithful content. To address these challenges, we propose \textbf{ReFlex}, an observation-constrained framework that casts faithful panorama reconstruction as a controlled inverse problem. We first develop an \emph{Observation-Consistent Estimator} with hierarchical token prediction and signed routing to integrate heterogeneous reflection cues while stabilizing global structure. Subsequently, we propose a \emph{Reliability-Guided Refiner}, which estimates spatial reliability from token predictions and guides a frozen diffusion model to refine uncertain regions without overwriting observation-supported structures. To validate the effectiveness of ReFlex, we further build ReFlex-Bench, a paired benchmark with 110,760 reflective RGB inputs and aligned target panoramas across diverse scenes, reflective geometries, and observation regimes. Experiments show that ReFlex delivers superior reconstruction fidelity without normals, field-of-view annotations, masks, or other privileged information, and obtains the best performance on eight of nine downstream full-view perception protocols.

We present Reflow-regularized Flow Matching Policy Gradients (ReFPO), a simple online RL method that adds explicit Reflow regularization to FPO for efficient flow-based control. We uncover a key structural property: the gradient updates in Flow Matching Policy Gradients (FPO) can be interpreted as an implicit advantage-weighted Reflow process, providing a new geometric perspective on flow-based policy gradients. Building on this insight, ReFPO introduces an explicit geometric regularizer that can be implemented with a single line of code change without incurring additional computational overhead or auxiliary distillation stages. By synergizing advantage-guided updates with path rectification, our method reduces CFM proxy-ratio spikes, stabilizes PPO-style training, and enables high-fidelity one-step inference that often matches or exceeds multi-step performance. We experimentally demonstrate that ReFPO improves average performance and discretization robustness across GridWorld, MuJoCo Playground, and high-dimensional Humanoid Control tasks, providing a scalable and stable approach for generative policies in complex physical simulations.

In retrieval-augmented generation (RAG), knowledge conflicts arise when retrieved contexts disagree with each other or with the model's parametric memory. Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a standard approach to address such conflicts. However, existing RLVR-based methods are typically formulated under a single-conflict assumption. As a result, the type with stronger advantage signals dominates policy updates, leaving the other under-optimized and thus yielding imbalanced performance across conflict types. To address this challenge, we propose ReCon, a type-aware RLVR framework for balanced learning across Inter-Context and Context-Memory conflicts. Specifically, ReCon computes a step-level imbalance signal from type-wise cumulative advantages and triggers resampling on the weaker conflict type. Instead of resampling from scratch, ReCon branches from informative failures. It selects an informative failed trajectory using uncertainty and closeness signals, then branches at an entropy-jump point, concentrating additional rollouts on critical reasoning decisions. Experiments show that ReCon yields stronger and more balanced performance across knowledge-conflict and multi-hop QA benchmarks, and also generalizing well under mixed-conflict settings.

Pre-training large language models on massive GPU clusters has made hardware faults routine rather than rare, driving the need for resilient training systems. Yet existing frameworks either focus on specific parallelism schemes or risk drifting away from a failure-free training trajectory. We propose ReCoVer, a resilient LLM pre-training system that upholds a single invariant: each iteration keeps the number of microbatches constant, ensuring per-iteration gradients remain stochastically equivalent to a failure-free run. The framework is organized as three decoupled protocol layers: (1) Fault-tolerant collectives that isolate faults from propagating across replicas; (2) in-step fine-grained recovery that preserves intra-iteration progress and prevents gradient corruption; (3) versatile-workload policy that dynamically redistributes microbatch quotas across the survivors. The design is parallelism-agnostic, integrating directly with both 3D parallelism and Hybrid Sharded Data Parallel (HSDP) as a drop-in substrate. We evaluate our implementation on end-to-end pre-training tasks for up to 512 GPUs, ReCoVer successfully preserves the training trajectory from a failure-free reference despite of 256 GPUs lost spread across the run. For comparison with checkpoint-and-restart baselines, ReCoVer demonstrates $2.23\times$ higher effective throughput after successive failures. This advantage results in ReCoVer processing 74.9% more tokens at 234 GPU-hours, with the gap widening as the training prolongs.

Comics and manga tell stories through characters whose appearances change from panel to panel, yet readers recognise them hundreds of pages later without ever consulting a cast list. Character re-identification models rarely face the same challenge: existing evaluations give them a fixed set of identities before the story begins. **Re:Cognize** instead asks models to recognise characters as the story unfolds and learn who is who along the way. Across three manga corpora, we evaluate five Re-ID backbones and **MECHA**, a memory-augmented baseline we introduce, and find that the central difficulty lies somewhere unexpected. Recognising familiar characters is nearly solved. Knowing which matches to trust is not. When models build a gallery and grow it from their own predictions, their performance deteriorates, even though correct identity labels would improve it by over twenty points. This raises a simple question: when should a model trust its own match? We derive a single comparison that determines when gallery updates improve recognition and use it to design **VOUCH**, which updates its memory only when the page provides supporting evidence. Without fitting additional parameters, VOUCH recovers between one-third and two-thirds of the improvement available from perfect labels. RE:COGNIZE measures whether a model can recognise characters as it reads; VOUCH shows how to remember them reliably. We focus our methodological claims on identity maintenance for previously encountered characters, while separately evaluating new-character emergence as a diagnostic under a fixed reference rule to distinguish recognition from discovery.

Masked diffusion language models (dLLMs) have recently emerged as a competitive alternative to autoregressive language models, with the promise of faster inference via parallel token generation. A notable limitation of the masked formulation, however, is that once a token has been unmasked it can no longer be revised, leaving dLLMs vulnerable to early sampling mistakes. To address this, a growing body of work has sought to extend masked dLLMs with self-correcting (remasking) capabilities. One appealing subset of these methods does so in a training-free, post-hoc manner based on token confidence, with encouraging early reported results. In this work, we revisit the empirical evaluation of a representative post-hoc remasking method, WINO, and find that under standard decoding settings (shorter block lengths) it brings little-to-no benefit over confidence-based unmasking alone. Extending the evaluation to non-greedy decoding, we find that while confidence-based remasking can mitigate errors introduced by increased stochasticity to some extent, it also exacerbates the diversity collapse previously reported for confidence-based unmasking. Overall, our results show that the benefits of post-hoc confidence-based remasking are highly setting-dependent, underscoring the need for a more comprehensive evaluation framework.

Recent 3D foundation models, such as DUSt3R, MASt3R, VGGT, $\pi^3$, and Depth Anything 3, provide strong feed-forward depth and pose estimates on pinhole imagery, but degrade sharply under fisheye camera geometry. We show that this failure is partly caused by a pinhole camera bias in the positional encodings of pretrained 3D foundation models, and propose RayTun3R, a lightweight camera adaptation approach. It keeps the pretrained network fixed and adapts only lightweight components tied to token position and camera geometry. RayTun3R learns parameter-efficient residual corrections to absolute and rotary positional encodings, together with parameter-free tokenization and corrections to prediction-grid coordinates that remove residual pinhole assumptions. The resulting adapter contains only 10,752 trainable parameters and can be learned from a short temporal segment using geometric losses. Once adapted, RayTun3R transfers effectively to the remaining frames of the sequence without incurring additional runtime costs. Across diverse fisheye datasets with fields of view from $110^\circ$ to $200^\circ$, our adapter reduces rotation error by $2-12\times$ relative to the unadapted model, outperforms LoRA while using $\sim14\times$ fewer trainable parameters, improves pose over adaptation-free baselines while avoiding their multi-view inference cost, and remains competitive on depth accuracy.

Time series foundation models (TSFMs) commonly adapt to new data by attaching a single trainable head to a frozen backbone, a one-size-fits-all setup that underfits heterogeneous regimes. Replacing the head with a mixture of experts is the standard upgrade, but on instance-normalized backbones (the dominant TSFM design class) it fails: routing entropy collapses to zero and one expert absorbs every input, a failure we call *normalization-induced routing collapse*. Standard MoE rescue mechanisms do not repair it, because the cause is in the router's input, not its optimization. Pre-encoder normalization strips the statistics a router would need to tell regimes apart. A mutual-information decomposition makes this precise and yields a signal-ratio that, computed before training, predicts dataset vulnerability (Spearman $\rho = -0.88$). Eight causal controls, including a vision-modality replication, isolate instance normalization as the cause. The prescription is a minimal causal intervention: *Raw-Routed Mixture of Adapters* (RR-MoA), which routes on the raw, pre-normalization input. Under a strictly frozen backbone, RR-MoA wins 54/54 comparisons against the strongest fixed adapter and significantly outperforms LoRA, TRACE, AdaMix, and full fine-tuning. The effect generalizes across six backbones and an imputation task. Frozen RR-MoA also beats full fine-tuning by 12–79% (the *Frozen Paradox*); two architecturally distinct variants confirm the principle generalizes beyond this specific router. Code is provided in the supplementary material.

Long-context recall in linear-time sequence models highlights a tradeoff in how they write to memory. State-based linear models, such as state-space models (SSMs) and linear Transformers, write densely, updating the entire state for each newly arrived token, which leads to interference and makes specific past tokens hard to recover. Sliding-window attention (SWA) exhibits the opposite behavior: it writes sparsely by storing explicit token representations, but only within a fixed window, so recall drops once the relevant token is evicted. Interpolating between these models, we introduce Raven, a linear-time sequence model that maintains a fixed set of memory slots and, at each step, decays and updates only a selected subset via learned, input-dependent routing. This lets Raven mitigate SWA's position-based overwriting and hard eviction while reducing interference from dense state updates in SSMs, thereby preserving long-range content much more effectively. Across recall-intensive benchmarks, Raven is competitive with or outperforms prior linear-time baselines, achieving strong long-context recall where both SWA and SSMs sharply degrade. It remains effective when extrapolating to context lengths as large as 16x its training length, with similar gains in hybrid architectures.

On-policy reinforcement learning has become a central paradigm for improving the reasoning abilities of large language models. However, its effectiveness is often limited by reward sparsity: when a model fails to discover correct trajectories for difficult problems, the optimization process receives little useful signal and may stagnate. Existing approaches mitigate this issue by incorporating off-policy demonstrations, expert traces, or model-generated solutions, but they typically require the auxiliary data to match the format of the reinforcement-learning task, often relying on rejection sampling from stronger models to obtain suitable training trajectories. We introduce Rationale-Guided Policy Optimization (RGPO), a framework that adaptively leverages ground-truth rationale information according to the model’s current capability while preserving its freedom to explore. Rather than treating reference solutions as fixed imitation targets, RGPO uses them as temporary scaffolds: rationales help the model generate improved responses, after which only higher-reward, model-generated solutions are transferred back to the original unguided setting. This design allows training to exploit available ground-truth information without requiring off-policy data to follow the same format as the RL task. Across both language-only and vision-language reasoning settings, RGPO consistently improves performance over RLVR baselines, and ablation studies show that adaptive rationale guidance is a key contributor to these gains. These results suggest that RGPO offers a practical and general approach for reducing reward sparsity, stabilizing reinforcement learning, and improving reasoning performance in both text-only and multimodal models.

Real-world television and streaming pipelines often deliver compressed HD video to increasingly capable displays, making video super-resolution (VSR) both practically important and fundamentally ambiguous. Existing generative VSR methods are largely receiver-only, requiring the model to infer missing high-frequency structures from degraded pixels alone. We propose MetaVSR, a sender--receiver collaborative framework that transmits compact structural edge metadata as bitrate-constrained side information for generative VSR. MetaVSR builds on a one-step video diffusion transformer and encodes both the low-quality video and transmitted metadata through the native 3D VAE, enabling token-level fusion within the original DiT attention blocks without additional control branches. We further introduce a rate-aware Canny edge metadata selection strategy that allocates metadata bits to structurally challenging regions, and evaluate reconstruction quality under matched total transmission budgets that include both compressed video and metadata. Across five public VSR benchmarks, MetaVSR shows that budgeted sender-side structural metadata can improve reconstruction over the receiver-only DOVE-5B baseline, increasing PSNR by 1.03 dB and SSIM by 0.055 on average while using a smaller 2B backbone. On MetaSPS, our rate--distortion evaluation shows approximately 20\% rate saving under light-noise degradation and more than 50\% under heavy-noise degradation compared with a no-metadata generative baseline. These results demonstrate that compact sender-side edge metadata can reduce reconstruction ambiguity and improve bitrate-efficient VSR when its transmission cost is explicitly accounted for.