NeurIPS 2026 论文列表

Multimodal evidence can improve topic discovery, but dense auxiliary signals can make topics harder to interpret when they replace readable descriptors. This creates an evidence-role problem: symbolic signals should define what topics mean to a reader, while dense aligned signals should help infer which topics a document expresses. We introduce M3-HNTM, a hyperspherical multimodal neural topic model that separates these roles through a shared document-level latent direction. M3-HNTM utilizes a shared von Mises-Fisher (vMF) posterior for document-level topic inference and vMF-mixture symbolic decoders that represent topics as concentration-aware semantic regions. Text Bag-of-Words and speech Bag-of-Acoustic-Words provide reconstructable symbolic evidence, while aligned image-text embeddings enter posterior inference as contextual evidence and help preserve semantic structure. The model applies to image-text, speech-text, and image-speech-text corpora while keeping topics readable through symbolic descriptors. Experiments on five datasets show that M3-HNTM improves the coherence-diversity quality score over strong text-only, hyperspherical, and multimodal baselines. On SpokenCOCO-Tri, the full tri-modal model outperforms both image-text and speech-text variants, indicating that visual and acoustic signals provide complementary evidence for interpretable topic discovery.

Encoding input coordinates with sinusoidal functions into multi-layer perceptrons (MLPs) has proven effective for implicit neural representations (INRs) of surfaces defined as zero-level sets. However, existing methods often struggle to balance training efficiency, rendering speed, and noise robustness: single-MLP approaches are expensive at inference, grid-based representations are fast but can limit surface smoothness and overfit input noise, and previous multiscale approaches frequently capture noise and produce artifacts due to hard spectral truncation. To address these limitations, we propose M-plicits, a multiscale framework that models surfaces as a residual sum of MLPs trained via a sequence of nested neighborhoods. Unlike existing residual approaches that rely on standard domain-wide sampling and require costly mesh extraction for visualization, our method strictly localizes supervision to narrow bands around the previous zero-level sets. This nested design naturally provides robustness against noisy input data: the coarse network acts as a low-pass filter that establishes a clean geometric prior, while subsequent residuals progressively refine the geometry without fitting to high-frequency artifacts. We further introduce a multiscale sphere-tracing algorithm and a GEMM-based analytical normal computation that bypasses auto-differentiation entirely, yielding high-fidelity real-time rendering. On Stanford and Thingi32, M-plicits achieves the best mean Chamfer distance in the coarse configuration and the best median Chamfer distance and IoU in the fine configuration, with substantially better noise robustness than iNGP, BACON, and IDF, while using an order of magnitude fewer parameters than grid-based baselines. Code and models will be released.

We are entering a new era of *composite model architectures* that integrate diverse components such as vision encoders, language backbones, diffusion and flow heads, audio codecs, action generators, and world-model predictors. Such architectures underpin a broad class of multimodal models, including unified multimodal models, omni models, speech-language models, vision-language-action policies, and world models. However, existing model serving frameworks were built on narrow assumptions about model structure, making them ill-suited to accommodate this new architectural diversity. Here we present M*, a universal serving system for efficient serving of composite AI models. M* represents models as dataflow graphs, processing requests spanning diverse modalities and tasks as traversals over these graphs. The core insight is a modular abstraction that supports arbitrary composition of model components, flexible placement onto a physical cluster, and model-agnostic optimizations within a distributed runtime. We call this abstraction the *Walk Graph* and show how it can concisely capture composite models from a broad range of families. We instantiate M* on representative models and find that it achieves, on average, 30\% lower end-to-end latency than vLLM-Omni for text-to-image workloads on BAGEL, while delivering a lower real-time factor and higher throughput - by up to 15\% - for text-to-speech workloads on Qwen3-Omni. M* also outperforms the V-JEPA 2-AC rollout baseline for robotic planning by up to $12.5\times$. Thus, our work paves the road towards more efficient serving of complex models with minimal developer effort.

Embodied Spatial Reasoning (ESR) is central to deploying Vision-Language Models (VLMs) in real-world embodied tasks, yet remains inadequately evaluated. Existing benchmarks primarily assess spatial reasoning in static images or synthetic indoor environments, where semantic shortcuts often suffice, making it difficult to faithfully evaluate spatial reasoning capabilities in the wild. In light of this, we present Lost on Campus, a benchmark for evaluating ESR in large-scale real-world outdoor 3D environments reconstructed by 3D Gaussian Splatting. Our benchmark introduces a unified reasoning-action evaluation framework that seamlessly integrates diagnostic QA for isolated reasoning with closed-loop interactive navigation for active reasoning under multimodal instructions. To enable fine-grained diagnosis, we systematically decompose ESR into six fundamental capabilities: action grounding, spatial foresight, metric awareness, goal-directed planning, global localization, and spatio-temporal consistency. Extensive experiments reveal that: (i) Compared to indoor settings, real-world outdoor environments impose substantially higher demands on spatial reasoning, where existing models exhibit poor performance; (ii) VLMs still struggle with fine-grained visual alignment, with failures in spatial foresight and self-aware localization emerging as primary bottlenecks; (iii) Improving multimodal interaction capabilities and long-term spatial reasoning is crucial for advancing embodied intelligence. These findings highlight that faithful evaluation of ESR demands benchmarks tightly coupling perception, reasoning and action within realistic and continuous environments.

Structured pruning offers hardware-agnostic acceleration for Large Language Models (LLMs) but typically degrades performance by strictly adhering to the original static sequential execution graph. In this paper, we challenge this rigidity with LoopPrune, an evolutionary framework that transforms model compression into an architectural re-discovery process. By treating pre-trained layers as a library of reusable primitives, LoopPrune optimizes a dynamic execution path where modules can be skipped, executed once, or iterated via controlled loops. This approach effectively decouples effective model depth from parameter count, allowing compressed models to maintain high reasoning capacity. To navigate the vast combinatorial search space, we introduce a hybrid evolutionary strategy enhanced by a Multi-Tier Population Initialization mechanism, which stratifies the search start-point using cross-model elite transfer, structural priors, and Wanda score importance estimation. Extensive experiments on diverse LLM families demonstrate that LoopPrune sets a new state-of-the-art across all sparsity levels. Notably, LoopPrune identifies efficient iterative patterns that allow compressed models to match or even surpass the zero-shot reasoning accuracy of their dense counterparts on specific benchmarks, while maintaining near-lossless perplexity at high retention rates.

3D object understanding and generation methods in indoor scenes produce impressive results, yet they often overlook a pervasive source of information in real-world scenes: repeated objects. We introduce the task of lookalike object detection in indoor scenes, which leverages repeated and complementary cues from identical and near-identical object pairs. Given an input scene, the task is to classify pairs of objects as identical, similar or different using multiview images as input. To address this, we present Lookalike3D, a multiview image transformer that effectively distinguishes such object pairs through similarity learning by harnessing strong semantic priors from large image foundation models. To support this task, we collected the 3DTwins dataset, containing 76k manually annotated identical, similar and different pairs of objects based on ScanNet++, and show an improvement of 14\% IoU over baselines on the task of lookalike object detection. We also demonstrate how our method improves the downstream tasks of joint 3D object reconstruction and part co-segmentation, turning repeated and lookalike objects into a powerful cue for consistent, high-quality 3D scene understanding. Our code, dataset and models will be made publicly available.

Transformers dominate video recognition. They split videos into tokens, and processing them has expensive superlinear computational cost. Yet videos are filled with redundancy, so we can question the need for this expense. We introduce LookWhen, a selector–extractor framework that factorizes video recognition into learning when, where, and what to compute. Our shallow selector gets a scaled-down video and quickly scores all tokens across space-time, while our deep extractor gets the top-K selected tokens to approximate full-video representations without actually processing all the tokens. A key challenge is defining effective supervision for selection and extraction. For selection pre-training, we introduce a score on representations that ranks tokens by uniqueness using a simple nearest-neighbor distance. For extraction pre-training, we distill both a video teacher and an image teacher, for which we normalize its frame-wise representations to learn what changes within videos. Through these strategies, our selector-extractor learns general and efficient representations for feature extraction or fine-tuning to a task. Through experiments on Kinetics-400, SSv2, Epic-Kitchens, Diving48, Jester, and Charades, we show that LookWhen achieves a better accuracy-computation trade-off than efficient models and upgraded baselines of similar size. LookWhen Pareto-dominates in accuracy-FLOPs on 9 of 12 cases (6 tasks X 2 settings) and roughly matches on 3. In accuracy-throughput, measuring time in practice, LookWhen is more efficient still at 6.7$\times$ faster than InternVideo2-B at equal accuracy.

Vision-language models (VLMs) have advanced rapidly in multimodal reasoning, yet they remain limited on tasks requiring fine-grained understanding of small or easily overlooked image regions. Recent *thinking with images* approaches mitigate this limitation by allowing models to inspect images through operations such as zooming, cropping, or code execution. While effective, their multi-turn interactions with VLMs introduce redundant token computation and slow inference, and separate region inspection from the reasoning step that depends on it. To address these issues, we propose **Lo**ok Before You **Re**ason (LoRe), an implicit visual thinking framework for efficient visual reasoning within a single-turn VLM interaction. Given an image and a question, LoRe predicts the most relevant image region, aligns it with the corresponding image patch features, and strengthens the associated key-value (KV) cache entries, enabling subsequent decoding to directly exploit the selected visual cues without additional visual inputs or explicit tool calls. We further employ reinforcement learning to guide the VLM toward identifying informative image regions and leveraging them during reasoning. To support fine-grained evaluation of multimodal reasoning, we introduce COCO-ObjQA, a large-scale benchmark for object-centric multimodal reasoning. Experiments on three challenging multimodal benchmarks demonstrate that LoRe consistently improves VLM reasoning performance with low additional inference overhead.

Despite the remarkable progress of large Vision-Language Models (VLMs) in Radiology Report Generation (RRG), existing black-box autoregressive generation paradigms lack intermediate clinical logical reasoning. This deficiency renders such models prone to generating linguistically fluent reports with erroneous diagnostic findings, which manifest as plausible hallucinations and thereby undermine clinical reliability. To this end, we propose a novel RRG paradigm termed "Look-Before-You-Leap", aimed at enhancing the clinical logical reasoning capabilities of the model to improve diagnostic accuracy. Specifically, we first construct a self-evolving Radiological Skill (RadSkill) repository to guide the teacher model in distilling high-confidence explicit clinical reasoning, and further internalize this reasoning capability into the implicit diagnostic intuition of the RRG baseline. Building upon this foundation, a novel preference optimization method termed Psychometric Preference Optimization (PSY-PO) is proposed. Grounded in Item Response Theory (IRT) from psychometrics, this method precisely samples clinically misleading dispreferred reports for preference optimization, thereby effectively mitigating plausible hallucinations. Extensive experimental results demonstrate that our proposed method significantly enhances both the generation quality and trustworthiness of radiology reports.

Long video out-painting is an important yet underexplored problem, while existing video out-painting methods are mainly developed for relatively short clips. However, extending video out-painting to long videos is highly challenging. A natural solution is iterative out-painting, \ie, generating videos chunk by chunk, but this introduces two major issues: unstable even crashed long-horizon generation caused by inter-chunk inconsistency and accumulated errors, and semantic drift over long temporal horizons. In this paper, we propose LongSCOP, short for Semantically Consistent long Video Out-Painting. First, we provide a fundamental training recipe with three components for smooth and stable long-horizon generation: self-forcing training, latent calibration, and future references. Built upon this stable generation foundation, a more advanced requirement is semantic consistency, for which we further develop a VLM-based agent that automatically selects the most informative reference frames from the whole video, enabling coherent subjects, scenes, and backgrounds throughout long-range generation. To support training and evaluation, we construct a large-scale, high-quality training dataset and introduce LVO-Bench, a dedicated benchmark curated by human experts for assessing long-horizon stability and semantic consistency. Extensive experiments demonstrate clear advantages of our method over existing approaches in both generation stability and semantic coherence, significantly improving the quality and reliability of long video out-painting.

Embodied Visual Tracking (EVT) aims to autonomously control an agent to follow a target in 3D space, which is critical for robotic navigation and human-robot interaction. However, robust long-term EVT in real-world scenarios faces two key bottlenecks: frequent tracking failures caused by occlusions and distractors, and the lack of a long-term benchmark for model training and evaluation. To address these, we propose a systematic solution. First, we develop LT-VLA, a lightweight VLA model featuring a Target Memory module to maintain consistent target identification and an Adaptive Execution module to adjust tracking actions based on observation reliability. Second, we construct LTEVT, a unified long-term EVT benchmark comprising 11 high-fidelity indoor and outdoor environments, enriched with over 5,000 assets and 325 human targets to simulate occlusions and distractors. Along with the benchmark, we release the LTEVT-4500K dataset for large-scale training and a comprehensive evaluation toolkit. Extensive experiments demonstrate that our 0.6B model achieves state-of-the-art performance comparable to 7B-scale models while maintaining superior efficiency. Notably, LT-VLA achieves 62.4% average Success Rate on EVT-Bench and runs at 20 FPS on a Unitree G1 robot, delivering a practical and robust solution for real-world deployment.

Thinking with images empowers vision-language models (VLMs) to use an external zoom-in tool for fine-grained visual search. Recent works employ supervised fine-tuning (SFT) and reinforcement learning (RL) with tool-specific designs to enable such capabilities. However, achieving logical reasoning alongside accurate and efficient tool use remains a significant challenge. To address this, we shift the focus away from increasing RL complexity and instead identify SFT data quality as a key bottleneck. We then propose LogT (short for **Log**ically **T**hink with Images), a fully automated SFT data engine that explicitly enforces logic-chain-guided reasoning. LogT generates an SFT dataset featuring challenging, visually grounded questions and structured tool-use traces with human-like logic. We validate our approach by fine-tuning two base models, Qwen2.5-VL-7B and InternVL3.5-8B, on the dataset and evaluating them across seven benchmark test sets. We show that LogT-SFT based on Qwen2.5-VL-7B outperforms the state-of-the-art trained with both SFT and RL, despite using 3.1$\times$ less data and $>$120$\times$ fewer GPU hours. Furthermore, applying a simple RL recipe without tool-specific design on LogT-SFT yields a model that surpasses prior methods with complex RL setups by a substantial 9.59-point margin. In addition to strong performance gains, our model exhibits higher tool-use accuracy and efficiency, as well as more structured and logically consistent reasoning traces, directly validating the effectiveness of our approach.

We propose Log-Averaged Mirror Prox (LAMP), a linear-space primal-dual method for large-scale optimal transport. LAMP implements primal mirror prox updates by tracking an averaged dual sequence, reducing storage complexity from $\mathcal O(nm)$ to $\mathcal O(n+m)$ while preserving dense, GPU-friendly reductions. Consequently, LAMP preserves the last-iterate $\widetilde{\mathcal{O}}( nm\varepsilon^{-1})$ arithmetic complexity of conservatively parameterized primal-dual mirror prox. We further analyze LAMP as a direct optimal transport solver in a more performant parameter regime, providing a last-iterate sub-optimality certificate dependent on infeasibility and an explicit $\mathcal O(1/t)$ term. Moreover, we give a computable sufficient condition for best-iterate convergence to a saddle-point. Numerical experiments with an optimized CUDA implementation show that LAMP outperforms first-order baselines in several high-accuracy (entropic) optimal transport problems. LAMP is further shown to scale up to problems with $n=m=2^{18}$ marginal supports, which were previously beyond the reach of primal-dual first-order methods.

Vision-Language Models (VLMs) are increasingly adopted as end-to-end driving planners, yet they suffer from cross-city domain shift. Existing methods treat this shift as a single quantity addressed by uniform alignment or single-site editing. We refute this view on two counts: ***(i) domain shift in VLM trajectory planners is inherently structured across modality and layer, and (ii) a locate-then-repair recipe matched to this structure outperforms every uniform-objective baseline***. We analyze domain shift via activation patching and uncover two regularities. **(F1) Modality axis.** Image and text tokens exhibit distinct shift patterns in each VLM. **(F2) Layer axis.** The layer-wise shift partition is architecture-determined and dataset-stable. These findings recast domain shift as a **two-dimensional tensor** indexed by *modality* (image vs. text tokens) and *layer*, motivating a simple principle: ***locate where shift occurs, and repair only there***. We instantiate this principle in **MoLaRx** (**Mo**dality–**La**yer **R**epair), a locate-then-repair framework. The Causal Importance Score (CIS) estimates the domain shift as a modality–layer tensor and partitions the layers into functional zones. CIS-Guided Selective Repair (CISR) then applies a matched operator (Maximum Mean Discrepancy (MMD) / low-rank adaptation (LoRA) / freezing) to repair each zone, with the composition adapting automatically to each architecture. We evaluate **MoLaRx** on three architecturally distinct VLMs (LFM2-VL-1.6B, Qwen2-VL-2B, and PaliGemma2-3B) across two cross-city benchmarks (nuScenes and Argoverse 2). MoLaRx attains the lowest cross-city trajectory-error gap on every (VLM, dataset) cell, while using $50$–$60\%$ fewer trainable parameters and $8.4$–$10.9$ ms lower inference latency. In contrast, the strongest scalar baseline (MMD-only) collapses on every cell—direct evidence that no scalar objective can repair structured shift. Code: [anonymous.4open.science/r/MoLaRx-E3B4](https://anonymous.4open.science/r/MoLaRx-E3B4/).

Vision-Language Models (VLMs) have achieved substantial progress across a wide range of understanding and reasoning tasks, driven by large-scale image-text training aimed at multimodal fusion. Ideally, replacing a textual question with its rendered-image counterpart should leave model performance essentially unaffected. In practice, however, such modality substitution induces dramatic performance degradation. We attribute this carrier sensitivity issue to an inherent bias in current training corpora. Across prevalent datasets such as image captioning, VQA, OCR, and web-sourced interleaved data, text and images are typically organized into distinct and asymmetric roles, with text serving as linguistic queries and images as visual references. Such data bias leads VLMs to exhibit distinct preferences for information acquisition across different modalities. Consequently, VLMs fail to align representations of semantically equivalent content across textual and visual carriers, making model reasoning fragile under modality substitution. To address this, we propose Local Modality Substitution (LoMo), a lightweight, architecture-agnostic data curation paradigm designed to provide supervision for cross-modal representational invariance between semantically equivalent text and image carriers. LoMo achieves this by reformulating single-modality prompts into seamlessly interleaved multimodal sequences. It dynamically selects target text spans and recasts them as rendered images, thereby preserving the same semantics across "text, visual, text" carriers. Extensive experiments across 13 diverse multimodal benchmarks demonstrate that LoMo significantly improves overall multimodal reasoning and yields deeper cross-modal fusion. Specifically, it delivers consistent gains across foundational models, improving over standard SFT by 2.67 points on LLaVA-OneVision-1.5-8B and 2.82 points on Qwen3.5-9B.

Remote sensing lithology interpretation is fundamental to geological surveys, mineral exploration, and regional geological mapping. Unlike general land-cover recognition, lithology interpretation is a knowledge-intensive task that requires experts to infer rock types from various features, e.g., subtle visual, making reliable automated interpretation highly challenging. Geological knowledge-guided large multimodal models offer new opportunities, yet their evaluation remains constrained by the lack of benchmarks that capture lithological annotations, multi-level geological semantics, and expert-informed assessment. Here, we propose LithoBench, a multi-level benchmark for evaluating geological semantic understanding in remote sensing lithology interpretation. LithoBench contains 10,000 expert-annotated interpretation instances across 12 representative lithological categories, including 4,000 multiple-choice and 6,000 open-ended tasks organized into five cognitive levels: Identification and Description, Comparative Analysis, Mechanism Explanation, Practical Application, and Comprehensive Reasoning. We further develop an expert-in-the-loop, knowledge-grounded semi-automated construction pipeline, coupling multi sub-processes, e.g., structured geological image descriptions, to enhance geological validity and evaluation reliability. Experiments with multiple large vision-language models reveal substantial limitations in geological semantic understanding, particularly on higher-order explanation, application, and reasoning tasks. Taken together, LithoBench establishes a dedicated testbed for advancing geological knowledge-guided multimodal models, supporting systematic model evaluation, domain-specific training and adaptation, and knowledge-enhanced reasoning toward expert-level lithology interpretation from remote sensing imagery.

Interactive person retrieval extends text-to-image person retrieval by allowing follow-up questions when the initial description is incomplete. The core challenge is to ask questions that uncover the most useful missing information for identifying the target person from visually similar candidates. Existing methods guide question selection with retriever-external signals, which may not reflect the information most useful to the current retriever. To address this, we propose SCOUT, an interactive person retrieval framework that derives questions from the retriever's own response. Specifically, we introduce a Perturbation-Sensitive Question Selection strategy that selects the next question based on Retrieval Perturbation Sensitivity (RPS), a test-time measure of perturbation-induced ranking change. Additionally, SCOUT requires no additional training and builds on off-the-shelf text-to-image person retrievers, avoiding the dialogue-data curation and retraining overhead of existing interactive methods. Extensive experiments on three standard benchmarks demonstrate that SCOUT yields consistent improvements over baseline methods, validating this RPS-based strategy for interactive person retrieval.

Embodied agents performing long-horizon tasks require a memory representation in which the state transitions of dynamic objects remain queryable in natural language across hours-to-days observation horizons. Existing systems either drop fine-grained motion (clip-level video-language embeddings), keep it only as raw coordinates (geometric SLAM), or organise it around immediate task context (agent working memories). None of them gives the agent a per-object timeline whose state transitions are themselves queryable in language. Our key contribution is \textbf{Linguistic Trajectory Encoding} (LTE), which compresses dynamic object motion histories via a hybrid representation combining natural language descriptions, sparse spatial anchors, and visual anchors. LTE adapts compression to motion complexity by anchoring periods without reliable observations to the last seen location, while representing motion with geometric waypoints and linguistic descriptions to preserve accuracy. To evaluate these capabilities across extended time horizons, we construct the \textbf{Spatial Memory Benchmark} (SMB) from EgoLife multi-day recordings, targeting capabilities absent in existing benchmarks: semantic trajectory retrieval and long-horizon object retrieval. On SMB, the LTE-based system achieves $45.3\%$ success in semantic trajectory retrieval and $48.7\%$ in long-horizon object retrieval, outperforming structured-memory and VLM baselines (best prior: $31.9\%$ and $34.4\%$). LTE achieves trajectory compression by factors of $8.7\times$ to $26.1\times$ with sub-second query latency on $24$\,h video. On Ego4D natural-language queries, the system reaches $28.75\%$ / $55.10\%$ R@1/R@5, $+15.80$ / $+31.30$ pts over EgoVLPv2.

With the rapid development of Video Large Language Models (VLLMs), online video understanding has emerged as a practical paradigm for real-world streaming applications. A key challenge is to construct a compact, query-agnostic visual memory from continuously growing video streams under strict token budgets. Existing token reduction methods typically rely on static and coarse-grained importance estimation, implicitly assuming that retained tokens contribute equally over time. This overlooks the dynamic nature of visual information, where the relevance of tokens evolves as the video progresses, leading to suboptimal prioritization and inefficient long-term memory usage. To address this limitation, we propose LifeStream, a training-free framework for lifecycle-aware visual token selection and hierarchical memory construction. LifeStream models the temporal evolution of visual tokens and reveals that tokens contribute unequally to downstream reasoning depending on their lifecycle states. Based on this insight, it organizes tokens into a hierarchical memory structure: informative non-stable tokens are preserved in an archive memory, while long-range history is progressively compressed into a global memory according to state-specific priorities. This design enables continuous retention of high-value information under limited token budgets, without requiring query-time retrieval or additional computation. Extensive experiments demonstrate that LifeStream discards over 80\% of visual tokens while improving performance. It improves LLaVA-OV-7B and Qwen3-VL-8B by 4.4\% and 3.5\% on StreamingBench, respectively, and achieves state-of-the-art results on one additional online benchmark and four long-video understanding benchmarks.