NeurIPS 2026 论文列表

CLIP serves as a foundational vision-language model and the de facto vision encoder for downstream VLMs such as LLaVA. Post-training offers a resource-efficient route to refine CLIP, but recent work argues that the standard contrastive loss is unsuitable for post-training due to catastrophic forgetting under small batches, motivating designs that abandon the contrastive objective in favor of distillation. We revisit this premise and find that the reported forgetting is not caused by insufficient negatives, but by an inappropriate magnitude of the contrastive temperature $\tau$: with $\tau$ set sufficiently small, contrastive loss becomes the strongest single-loss post-training objective. Building on this finding, we propose \textbf{ComCLIP}, a complementary post-training framework that freezes CLIP's text encoder---preserving compatibility with downstream VLMs---and trains the vision encoder under three complementary losses operating in the shared contrastive space: a properly-tempered contrastive loss for image-text alignment, an MSE anchoring loss against the original CLIP for preserving the pretrained feature manifold, and a relational distillation loss from DINOv2 for fine-grained visual knowledge injection. With only one epoch on small data, ComCLIP consistently outperforms prior post-training methods on ViT-B/16 and ViT-L/14 across zero-shot classification, retrieval, linear probing, and MMVP, achieving up to $\mathbf{+7.41}$ points on MMVP. Furthermore, ComCLIP serves as a drop-in vision encoder for LLaVA-7B, improving average performance across $8$ standard VLM benchmarks without any re-alignment of the LLM or projector.

We give a computer-assisted counterexample to the open question posed by Rudin, Schapire, and Daubechies in COLT 2012, of whether exhaustive AdaBoost always converges to a finite cycle. The construction is based on a block-product gadget whose two factors share an exact period-2 orbit for their 5-step branch maps, but whose linearized return maps have dominant eigenvalues with an irrational logarithmic ratio. This irrationality forces the burst-winner sequence to have an irrational asymptotic frequency, precluding eventual periodicity. All assertions are certified by exact rational arithmetic in two independent computer algebra systems. We also document the collaborative workflow that led to the construction, in which one model produced the key technical arguments; another model summarized and critiqued different versions of that work, proposing new directions to pursue; and the authors orchestrated the process by selecting directions to prioritize, resolving ambiguities, heavily refining each argument, and verifying the final solution. We present this as one of the first in-depth case studies of LLM-assisted mathematical research in which a long-standing open problem from theoretical machine learning is resolved, and detail the advantages and challenges of such research.

The Panoptic Quality (PQ) metric is the standard for jointly evaluating instance and semantic segmentation. However, its original definition relies on a One-to-One matching between predicted and ground truth segments, which is only straightforward when the IoU threshold exceeds 0.5. Below 0.5, multiple matching strategies emerge in a poorly explored problem space. We systematically elucidate this space by recasting segment matching as a constrained bipartite assignment problem. Independently bounding the prediction- and ground-truth-side degrees yields four matching strategies: One-to-One, Many-to-One, One-to-Many, and Many-to-Many. We show that the first three are well-defined within the PQ framework, while Many-to-Many falls outside it. These strategies become relevant when instances are fragmented, adjacent objects are difficult to delineate, or annotations are noisy. Central to our framework is a vertex-based accounting of TP, FN, and FP, anchored to ground truth and predicted segments rather than to matching edges. We further show that the framework extends naturally to part-aware panoptic segmentation, and we explore part-aware evaluation on biomedical data. Across configurable case studies we report how different combinations of thresholds and matching strategies behave in practice. We release a unified open-source package built on Panoptica. It exposes Voronoi-based region-wise analysis, part-aware evaluation, and Area Under Threshold Curve computations as configurable options.

Recovering 4D human-object interaction (HOI) from monocular video is a key step toward scalable 3D content creation, embodied AI, and simulation-based learning. Recent methods can reconstruct temporally coherent human and object trajectories, but these trajectories often remain visual artifacts while failing to preserve stable contact, functional manipulation, or physical plausibility when used as reference motions for humanoid-object simulation. This reveals a fundamental interaction gap: HOI reconstruction should not stop at tracking a human and an object, but should recover the relation that makes their motion a coherent interaction. We introduce **HA-HOI**, a framework for reconstructing physically plausible 4D HOI animation from in-the-wild monocular videos. Instead of treating the human and object as independent entities in an ambiguous monocular 3D space, we propose a \emph{human-first, object-follow} formulation. The human motion is recovered as the interaction anchor, and the object is reconstructed, aligned, and refined relative to the human action. The resulting kinematic trajectory is then projected into a physics-based humanoid-object simulation, where it acts as a teacher trajectory for stable physical rollout. Across benchmark and in-the-wild videos, **HA-HOI** improves human-object alignment, contact consistency, temporal stability, and simulation readiness over prior monocular HOI reconstruction methods. By moving beyond visually plausible trajectory recovery toward physically grounded interaction animation, our work takes a step toward turning general monocular HOI videos into scalable demonstrations for humanoid-object behavior.

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.

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.

Radar-based human pose estimation is a promising alternative to camera-based perception, but remains challenging due to sparse observations, noisy measurements, and severe pose ambiguity. Existing methods infer both global skeletal structure and flexible peripheral joints from sparse radar observations, often compromising structural plausibility and fine-grained local accuracy. Motivated by the coarse-to-fine nature of human visual perception, we propose RadarFlowPose, a framework that decomposes radar-based human pose estimation into coarse pose prior generation and flow-based refinement. The coarse model first produces a structurally plausible and temporally coherent pose prior from sparse radar observations, providing a global initialization for subsequent refinement. Instead of estimating the full pose from scratch, the refinement stage focuses on correcting the remaining local ambiguities and motion-induced errors. By incorporating localised Doppler motion cues into the flow-matching process, the model performs motion-aware residual refinement around the coarse prior, leading to more accurate and anatomically consistent pose estimates. Experiments on three benchmark radar pose datasets show consistent effectiveness, especially in motion-sensitive and fine-grained joint-level evaluation.

Vision Transformers (ViTs) achieve strong performance on image recognition and mobile vision applications, but their high-dimensional linear projections and attention computations still impose substantial storage and inference costs. Extremely low-bit quantization is a promising solution, yet ViTs often suffer severe accuracy degradation because conventional real-valued scalar codebooks are poorly matched to the directional geometry of Transformer projections. We present RPFQ-ViT, a Rotated Phase-Frame Quantization method that quantizes paired channels in two-dimensional phase planes, enabling low-bit codes to better preserve projection directions while recovering magnitude with lightweight scaling. RPFQ-ViT serves as a drop-in QAT replacement for \texttt{nn.Linear} and does not modify the standard real-valued attention, normalization, or activation computation graph. On ImageNet-1K, RPFQ-ViT-B/16 reaches 79.33\% Top-1 / 94.48\% Top-5 under W2/A4, Swin-T reaches 79.30\% Top-1 / 94.79\% Top-5 under W2/A8, and DeiT-S reaches 77.41\% Top-1 / 93.11\% Top-5 under W2/A8. Ablations, phase-geometry analysis, and direction-preservation metrics show that channel pairing, learnable rotation, phase-anchor learning, and residual phase refinement each improve quantization quality. We further deploy RPFQ-ViT image-classification models on native iOS and Android runtime stacks; with 2-bit packed weights, model size shrinks by roughly $5.4$--$7.1\times$ relative to FP32 and end-to-end on-device latency drops by $1.4$--$1.6\times$. All ImageNet results trained in our codebase use a matched 300-epoch recipe and are reported as mean accuracies over three independent runs. These results show that RPFQ-ViT provides a favorable trade-off among accuracy, compression, and practical mobile deployment for extremely low-bit ViTs.

Full-dataset NeRF evaluation can require training and evaluating thousands of scenes, making exhaustive comparison expensive even when the benchmark is fixed. We introduce RASS, a risk-audited protocol for constructing compact NeRF benchmark subsets with explicit fidelity criteria. Given full-dataset per-scene logs, RASS repeatedly samples candidate subsets, checks whether their global metric means and score distributions match the full benchmark within user-defined tolerances, and recommends the smallest evaluated subset size whose Wilson lower confidence bound exceeds a target pass probability. We instantiate RASS on 3,522 Nutrition5k-derived food scenes using Zip-NeRF logs for the formal audit and PSNR, SSIM, LPIPS, and Kolmogorov-Smirnov (KS) distributional tests as fidelity criteria. The released 48-scene balanced operating point, RASS-48, is the lowest-cost evaluated screening subset that passes our declared global-fidelity audit and is intended for low-cost early ablations and rapid screening. A stricter target pass probability, $p_{\min}=0.20$, selects RASS-96, which we recommend as the compact reporting subset: it also passes joint fidelity audits over three and four view-synthesis methods. We also evaluate alternative subset generators, including uniform random sampling, farthest-first selection, and facility-location diversity sampling, and find that the RASS audit can validate different generators under the same fidelity contract. On DL3DV, a re-declared contract certifies 64 of 140 scenes for four methods under proportional allocation. We release the RASS-48 and RASS-96 scenes, configurations, descriptors, regime labels, seeds, audit scripts, and permitted logs to enable reproducible, compact NeRF evaluation. Project page: https://amughrabi.github.io/RASS/.

Large vision-language models (LVLMs) rely on increasingly long visual contexts, making the KV cache a major inference-time bottleneck. Existing KV cache compression methods typically use pre-decode signals such as prefill attention, visual redundancy, objectness, or layer-wise allocation, but these criteria do not directly indicate which visual tokens in the KV cache will be reused by future answer tokens. We introduce Q-ViK, a question-conditioned visual utility framework for LVLM KV cache eviction. During offline training, we run full-cache decoding and aggregate answer-to-visual attention over cached visual tokens, yielding a privileged *future-utility signal* that reflects the visual KV entries actually used during generation. We use this signal to train a lightweight question-conditioned scorer that predicts visual cache utility from prefill representations alone. At inference time, Q-ViK requires neither full-cache lookahead nor generated traces: it preserves textual KV entries and evicts low-utility visual KV entries with a single post-prefill scoring step. Experiments on seen and held-out multimodal benchmarks show that Q-ViK preserves task-relevant visual evidence in the KV cache more effectively than pre-decode saliency, especially under aggressive KV cache compression. Code will be available at [https://anonymous.4open.science/r/Q-ViK-1A75/README.md](https://anonymous.4open.science/r/Q-ViK-1A75/README.md).

Multi-modal large language models (MLLMs) with impressive perception and understanding capabilities will enable a range of downstream applications. However, their substantial computational and memory requirements hinder the real-world implementation, particularly for resources-constrained edge devices. Quantization has emerged as an effective solution for deploying large models. While post-training quantization (PTQ) successfully retrains the performance at moderate bit-width (\textit{e.g.}, W8A8), it suffers from severe performance degradation under low-bit quantization (\textit{e.g.}, W4A8/W8A8), especially for small-scale models that are more suitable for edge deployment. In this paper, we propose \texttt{ProQuant}, a novel weight-activation quantization-aware training (QAT) framework tailored for low-bit MLLMs. First, to align the limited finetuning dataset distribution with the pretrained dataset distribution, we propose a self-relabeling (SRL) scheme to resample the responses of the open-source multimodal dataset. Second, we are the \textit{first} to take a decoupled view of multi-modal (MM) understanding and language modeling abilities. We propose a progressive block activation (PBA) mechanism to prioritize the MM understanding recovery. We also introduce a vision-sensitive factor $\vec{\tau}$ that effectively identifies the MM-critical layers. Extensive experiments across various benchmarks, covering models with 2B$\sim$8B parameters, show that \texttt{ProQuant} significantly outperforms existing methods. For example, our W4A8 \texttt{Qwen3-VL-2B-Instruct} improves average accuracy by impressively 6.6\% and achieves performance comparable to full precision counterparts with only $\sim$1\% performance drop. Code will be released upon acceptance.

Recent multi-view Transformers (e.g., DUSt3R and VGGT) have advanced 3D reconstruction by scaling training data and model capacity from two views to many views and long sequences, subsuming the classical SfM/SLAM pipeline of feature extraction, matching, and pose estimation in a single feedforward pass. Yet this subsumption hides the interfaces between pipeline stages---correspondences, geometry, and pose are entangled in a single forward pass, **leaving supervised fine-tuning as the major path** to improvement and offering no mechanism to correct predictions from geometric evidence at test time. We propose PosePlaner to close this gap by reviving the classical coupling between correspondences and pose via flow matching, treating pose refinement as a conditional generation problem that takes coarse geometric matches and initial pose as input condition and fuses the resulting pose hypotheses into a single refined estimate, providing a continuous, data-driven analogue of RANSAC voting. Our method is trained efficiently on two-view data, yet serves as a periodic refiner for streaming n-view pose estimators with no retraining. Empirically, it corrects failures of feedforward predictors, substantially improves correspondence-based solvers, and reduces accumulated drift in long streaming sequences.

Most world models for future prediction prioritize aestetic quality of generated videos, but offer only weak physical grounding and limited geometric consistency over time, due to the lack of explicit 4D modeling. In this paper, we introduce plan4d, a possible next milestone towards building a generative plannable 4D world model that predicts future RGB videos together with dense 4D geometry, including depth, cameras, point clouds, and scene flow from an observed scene context and text-specified actions. Since training data with paired actions and dense 4D annotations is inherently scarce, plan4d leverages the complementary priors of foundational video generators and 3D reconstructors. Specifically, we propose a model-stitching architecture that couples Depth Anything 3 with LongCat-Video and augments it with an auxiliary DPT head for scene-flow prediction. The resulting model allows us to plan and generate complex, long-horizon, and fine-grained actions in a dynamic 3D scenes, while substantially improving both visual fidelity and geometric accuracy of the generated future videos. Extensive experiments on both synthetic and real-world datasets demonstrate that plan4d achieves state-of-the-art performance in generative 4D modeling of environmental reactions.

Vision-language verifiers judge whether an image faithfully matches a textual description, yet existing training pipelines typically rely on static supervision from stronger external judges or curated hard-negative data. Such supervision is costly, teacher-bounded, and difficult to adapt as model failures evolve. We introduce PROVE (Perturb-and-Repair Optimization for Vision-Language Evaluation), a self-play framework that trains verifiers through a closed perturb–verify–repairverify loop. Starting from a known-correct image-caption pair, a Mutator introduces a typed perturbation over objects, attributes, counts, or relations. The verifier then judges the perturbed caption and provides textual feedback, which a Repairer uses to recover a faithful caption for re-verification. These two verification rounds produce structured rewards for informative mismatches and visually grounded repairs. Since perturbation, verification, and repair are role-conditioned behaviors of the same evolving multimodal backbone, updating the shared parameters also improves the verifier itself. Experiments show that PROVE improves fine-grained compositional verification and visual-detail robustness while preserving general multimodal capability.

Large-scale visual generative models have achieved remarkable performance. However, their high computational and memory costs make deployment challenging in resource-constrained scenarios, such as interactive applications and personal single-GPU usage. Post-training quantization (PTQ) offers a practical solution by compressing pretrained models without expensive retraining. However, existing PTQ methods still suffer from severe quality degradation under extremely low-bit settings. In this paper, we identify channel ordering as an important but underexplored factor in per-group quantization. In this setting, each contiguous group shares one quantization scale. When channels with very different statistics are placed in the same group, the scale can be dominated by outliers and cause large quantization errors. Based on this observation, we propose PermuQuant, a simple and effective PTQ framework for low-bit diffusion models. PermuQuant sorts channels by a joint second-moment criterion before per-group quantization, placing channels with similar activation and weight statistics into the same group. It further uses a calibration-based acceptance rule to apply reordering only when the selected permutation reduces quantization error on calibration data. The selected permutations are absorbed into adjacent modules or applied to weights offline, avoiding explicit runtime permutation operations. Extensive experiments on multiple large diffusion models show that PermuQuant consistently reduces quantization error and outperforms existing PTQ baselines. On FLUX.1-dev with an NVIDIA RTX 5090, PermuQuant achieves up to a 1.8$\times$ single step speedup and reduces the DiT memory footprint by 3.5$\times$ under W4A4 NVFP4 quantization.

Building structured 3D scene layouts from a single image requires reconciling visual observations with physical and spatial constraints, a challenge that is difficult to address with direct prediction alone. In this work, we formulate monocular 3D layout estimation as a perceive-then-plan problem with vision-language models, where a Perceiver first grounds the 3D objects and then a Planner iteratively refines the scene hypothesis through actions that improve physical plausibility while preserving consistency with the input image. We propose Layout-as-Policy (LaP), which casts the planning stage as a policy learning problem: 3D layouts are represented as structured states, and refined via discrete actions such as translation, rotation, and rescaling. Starting from an observation-aligned initialization with the geometry-enhanced Perceiver, the LaP Planner is trained to produce action sequences that progressively resolve geometric inconsistencies and enforce realistic spatial relations. To enable effective learning, we combine supervised trajectory initialization with preference-based optimization, allowing the model to learn corrective behaviors without requiring explicit reward engineering. This formulation transforms layout estimation from a one-shot prediction task into an iterative refinement process, enabling better handling of global constraints and complex object interactions. Experiments demonstrate that our approach produces layouts that are more physically coherent and better aligned with visual observations, while naturally supporting downstream tasks such as scene editing and manipulation.

As Multimodal Large Language Models (MLLMs) continue to advance and find increasingly broad applications, a key challenge persists: the patch-based vision paradigms widely adopted by MLLMs inevitably introduce structural fragmentation when processing information-dense images such as tables and charts. Specifically, when a table row or chart axis spans multiple visual partitions, the lack of cross-partition communication causes the model to lose track of structural continuity, leading to performance fluctuations when layout changes --- a problem we term layout sensitivity. To address this fundamental limitation, we propose Patch-for-Patch (P4P), a lightweight module that can be plugged into existing patch-based vision encoders. P4P dynamically selects a sparse set of tokens as bridging anchors and uses them to establish cross-partition communication through bidirectional attention. Combined with a gated fusion strategy, P4P restores global structural coherence without the quadratic cost of global attention. Extensive experiments across Qwen2.5-VL, MiniCPM-V, and InternVL3.5 demonstrate that P4P consistently improves structured content visual understanding, notably boosting TableEval accuracy by up to 9.12\% and significantly reducing layout sensitivity. The source code is available at: https://anonymous.4open.science/r/P4P/.

Large Vision-Language Models (LVLMs) achieve strong performance across many multimodal tasks; however, they often exploit spurious object-background correlations, resulting in predictions driven by contextual shortcuts rather than object-relevant visual evidence. Despite growing interest in hallucination and robustness evaluation, existing benchmarks provide limited control over whether model predictions are grounded in the target object or induced by correlated background cues. In this work, we introduce PURGE (Partition-aware Unlearning for Removing spurious-correlation Generated Errors), a framework for constructing, benchmarking, and mitigating spurious-correlation-induced failures in LVLMs. The framework consists of two components: (1) Structured dataset construction, wherein we develop three complementary structured data construction strategies that partition examples by object-relevant evidence and spurious background cues, enabling controlled diagnosis of shortcut reliance; and (2) Partition-aware unlearning, which uses these partitions to selectively remove spurious object-background associations while preserving object-based reasoning. We evaluate the PURGE framework across multiple LVLMs, including LLaVA-1.6-7B, Qwen3-VL-8B-Instruct, and Qwen3.5-9B, together with CLIP as a vision-language encoder, on a diverse suite of benchmarks, including CHAIR, POPE, Causal-HalBench, MM-SpuBench, AMBER, MMHal, and Waterbirds. Our results show that PURGE consistently reduces hallucinations and spurious-correlation-driven errors while maintaining or improving overall performance, providing both a reusable evaluation protocol and an effective mitigation framework for more reliable LVLMs.

The evolution of colour vision is captivating, as it reveals the adaptive strategies of extinct species while simulta- neously inspiring innovations in modern imaging technol- ogy. In this study, we present a simplified model of vi- sual transduction in the retina, introducing a novel opsin layer. We quantify evolutionary pressures by measuring ma- chine vision recognition accuracy on colour images shaped by specific opsins. Building on this, we develop an evolu- tionary conservation optimisation algorithm to reconstruct the spectral sensitivity of opsins, enabling mutation-driven adaptations to to more effectively spot fruits or predators. This model condenses millions of years of evolution within seconds on GPU, providing an experimental framework to test long-standing hypotheses in evolutionary biology , such as vision of early mammals, primate trichromacy from gene duplication, retention of colour blindness, blue-shift of fish rod and multiple rod opsins with bioluminescence. More- over, the model enables speculative explorations of hypo- thetical species, such as organisms with eyes adapted to the conditions on Mars. Our findings suggest a minimalist yet effective approach to task-specific camera filter design, op- timising the spectral response function to meet application- driven demands. The code will be made publicly available upon acceptance.