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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

Enhancing Interpretability in CLIP with Optimal Transport-based Submodular Optimization for Ophthalmic Imaging

Sihong Lu, Jian Yang, Lei Luo

Although Contrastive Language-Image Pretraining (CLIP)has shown promising transfer ability for ophthalmic image analysis, itsopaque decision-making process hinders interpretability, especially inophthalmic settings where probability-only outputs may be difficult forclinicians and patients to trust. Providing visual evidence for predictionscan therefore make models more inspectable and interpretable. How-ever, existing explainability methods often struggle to produce spatiallyconsistent and semantically aligned heatmaps that highlight prediction-relevant anatomical or pathological evidence. To bridge this gap, wepropose OTSMDL, a post-hoc framework for interpretable region selec-tion that couples submodular-inspired scoring with unbalanced optimaltransport. We formulate the identification of salient evidence regions as asubset selection problem. Specifically, submodular-inspired utilities firstconstruct an initial candidate subset and a score-guided source distri-bution, which are then used to initialize the Optimal Transport (OT)process. Subsequently, OT performs global transport-based re-rankingover all candidates, constructing a unified cost matrix to encourage cross-region competition and align candidate superpixels with text prompts.We evaluate OTSMDL on seven ophthalmic datasets spanning color fun-dus photography, OCT, and glaucoma assessment, with additional Ima-geNet experiments for out-of-domain analysis. Under standard post-hocDeletion/Insertion protocols and ground-truth mask localization met-rics, OTSMDL generally outperforms baselines in region-level evidencegrounding, achieving top-1/2 performance in most evaluation settings.

ECCV 2026

ProLaViT: Learning Progressive Latent Visual Thoughts in Structured Latent Space

Peiming Li, Yifan Wang, Xiaotian Zhang, Zhiyuan Hu, Shiyu Li, Zheng Wei, Yang Tang

Multimodal Large Language Models (MLLMs) have achievedremarkable progress but still struggle with complex visual reasoningtasks requiring multi-step perception and logical deduction. While ex-plicit visual generation incurs prohibitive computational costs, existinglatent approaches often rely on external experts or lack rigorous cogni-tive logic. In this paper, we introduce ProLaViT (Progressive LatentVisual Thought), a framework empowering MLLMs to perform struc-tured visual derivation in the continuous latent space. Unlike worksdependent on heterogeneous external models, ProLaViT leverages anendogenous self-distillation mechanism, utilizing the model’s own vi-sual encoder to supervise latent thoughts. To facilitate this, we con-struct a scalable programmatic synthesis pipeline enabling the modelto internalize algorithmic precision without inference-time tools. We de-sign two reasoning paradigms: (1) Coarse-to-Fine Causal Chain for spa-tial tasks, guiding attention from global context to local targets. (2)Dialectical Reasoning Chain for logical tasks, incorporating counter-factual thinking for verification. Furthermore, we propose a Distance-Weighted Diversity Loss to impose topology-aware constraints, prevent-ing feature degeneration by enforcing semantic distinctiveness. Exten-sive experiments demonstrate that ProLaViT outperforms baselines onvision-centric benchmarks, achieving superior accuracy and interpretabil-ity with high efficiency.

ECCV 2026

Preventing Expert Collapse in MoE-dVLMs via Modality-Wise Norm Alignment

Zongkai Liu, Zhen Cao, Hui Zhang, Chao Yu, Liqiang Niu, Fandong Meng

Sparse Mixture-of-Experts (MoE) offers an attractive pathto scaling discrete diffusion vision–language models (dVLMs), but wefind that naïvely combining MoE with multimodal diffusion training canfail catastrophically. Specifically, MoE-dVLMs suffer from expert selec-tion collapse on visual tokens, where most image tokens are routed toonly a few experts and overall multimodal performance degrades sharply.We trace this failure to an upstream geometric mismatch at the modalityboundary rather than router optimization. Visual embeddings producedby deep Pre-Norm vision encoders have much larger norms than text em-beddings; when injected into a Pre-Norm language tower, the residualdynamics cause attention drowning and representational inertia, yield-ing highly concentrated, low-rank visual representations that are difficultfor the router to discriminate. To resolve this issue, we propose Tower-Align, a lightweight post-projector modality-wise norm alignment thatglobally rescales visual tokens to match text norm statistics while pre-serving their relative structure. TowerAlign stabilizes MoE routing andimproves multimodal accuracy with negligible overhead. On a practicalMoE-dVLM built from LLaDA-MoE-7B-A1B with a SigLIP2-SO400M-Patch14-384 vision encoder, TowerAlign delivers consistent gains acrossdiverse benchmarks and largely closes the gap to dense diffusion baselinesunder matched data budgets.

ECCV 2026

Personalization as Inverse Planning: Learning Latent Design Intents for Agentic Slide Generation via Structural Denoising

Tianci Liu, Zihan Dong, Linjun Zhang, Haoyu Wang, Jing Gao, Emre Kıcıman, Ranveer Chandra, Wei-Ting Chen

Slide design requires personalizing both deck themes andpage layouts. Yet, current AI agent-based methods struggle with fine-grained, page-level design. Solely relying on prespecified templates oruser verbose instructions, they fail to capture latent design intents, leav-ing Page-level Slide Personalization (PSP) unresolved. To close this gap,this work formulates PSP as an inverse planning problem. We proposeto learn a design intent without assuming any knowledge of the spe-cific executing tools (e.g., PowerPoint, Beamer) being used. However,relinquishing control over these tools makes the problem intractable tooptimize end-to-end. To overcome this, we propose Spire, a principledframework to solve PSP approximately. By intentionally corrupting thevisual structures of clean slides, Spire creates a verifiable task to de-noise the corruption, whereby two agents learn to collaboratively refineexecutable designs via reinforcement learning (RL). We present a proofthat structural denoising is a consistent surrogate for PSP, and that themulti-agent formulation strictly reduces policy gradient variance in RL.Extensive experiments demonstrate the superiority of Spire.

ECCV 2026

OSOR: One-Step Diffusion Inpainting for Effect-Aware Object Removal

Qinming Zhou, Chenxi Sun, Deyang Kong, Junhao He, Xiangheng Tang, Peike Yu, Haotian Wu, Leilei Cao, Linfeng Zhang

Real-world object removal is challenging due to two key dif-ficulties: the target object’s non-local effects (e.g., shadows and reflec-tions) that are hard to model, and the fact that user-provided masksare often inaccurate or incomplete. With billions of parameters and tensof denoising steps, diffusion-based models achieve this goal at the ex-pense of massive computational cost, limiting their use in interactiveapplications and edge devices. To solve this problem, we present OSOR(One-Step Object Removal), which achieves efficient, effect-aware, andmask-robust object removal at the same time. Concretely, OSOR in-troduces (1) an occupancy-guided discriminator for precise boundarysupervision, enabling stable single-step diffusion training, (2) an alphahead, which leverages the knowledge in diffusion models to predict cor-rect removal regions with minimal overhead, handling imperfect masks,and (3) a semantic-anchored verification pipeline (SAVP) that filtersnoisy instruction-based triplets to produce effect-aware supervision atscale. Using SAVP, we curate CORNE with 280K verified removal pairsand further annotate AnimeEraseBench and TextEraseBench to evalu-ate model performance in more complex tasks. Experiments show thatOSOR surpasses strong multi-step diffusion baselines in perceptual qual-ity while achieving 4× to 30× faster inference. Code and resources areavailable at https://github.com/Zhouqm-Git/osor.

ECCV 2026

Noise-Robust Face Recognition via Non-target Similarity Distribution Guided Sample Selection

Fanglong Wu, Youqiang Gui, Cheng Peng

Label noise is a major challenge in large-scale supervised face recognition, where weak or automatic annotations often introduce errors that mislead training. To address this, we propose a noise robust framework that performs noise detection and sample selection directly in cosine-similarity space. We observe that the non-target cosine similarities of clean samples share a highly consistent distribution profile with the target similarities of unfitted mislabeled samples. This phenomenon can be explained from a backpropagation perspective and provides a cue for monitoring label noise during training. Based on this observation, we formulate noise detection as boundary estimation in similarity space. Specifically, we track the upper bound of high-confidence clean nontarget similarities to determine the filtering threshold, without requiring prior knowledge of the noise rate or auxiliary networks. We further introduce a progressive rule during early training, where the threshold gradually increases from the estimated noise lower bound to the upper bound. This process discards unreliable samples while retaining hard but clean samples. Extensive experiments on eight synthetic and three real-world noisy datasets demonstrate that our method achieves superior noise detection and state-of-the-art recognition accuracy, with only about 0.3% average measured overhead. The filtered dataset produced by our method is also beneficial for subsequent training. Source code is available at https://github.com/wfl95/DDLN.

ECCV 2026

MVPruner: Dynamic Token Pruning for Accelerating Multi-view Vision-Language Models in Autonomous Driving

Nan Yang, Zhanwen Liu, Linfeng Zhang, Shangyu Xie, Yang Wang, Wenzhuo Zhou, Xiangmo Zhao

Vision-Language Models (VLMs) improve generalization andinterpretability in autonomous driving but suffer from efficiency issuesdue to long visual token sequences, particularly in standard multi-viewsettings. Existing token pruning methods employ fixed pruning rate al-location and static importance metrics, ignoring dynamic inter-view im-portance differences and the evolving information importance during in-ference. Our analysis reveals that multi-view VLMs inherently encodetask-related view priors in deeper layers and exhibit dynamic informa-tion requirements. Motivated by these findings, we propose MVPruner,a two-stage adaptive token pruning method that aligns pruning behaviorwith the model’s dynamic information requirements. The first stage al-locates pruning budgets based on the information diversity of each view,and retains tokens with consistent contribution across stages, ensuringsemantic representational capacity. The second stage allocates budgetsand selects tokens guided by instruction text to guarantee task align-ment. Experimental results on four benchmarks demonstrate the supe-rior performance of our method. For example, DriveMM equipped withMVPruner achieves 87.3% reduction in FLOPs, 4.97× speedup in pre-filling phase while retaining 98.5% accuracy on DriveLM benchmark.

ECCV 2026

MSVS-VAE: Multi-Scale Anchored VecSet for High-Fidelity 3D Reconstruction

Dehao Hao, Kaiyi Zhang, Tanghui Jia, Xiangjun Gao, Dongyu Yan, Weikai Chen, Zeyu HU, Lingting Zhu, Yingda Yin, Runze Zhang, Li Yuan, Xin Wang, Long Quan

High-fidelity 3D generative modeling increasingly relies onthe latent diffusion paradigm, where the reconstruction quality of theunderlying 3D VAE becomes a primary bottleneck. Existing approacheslargely follow two paradigms: sparse voxel-based representations achievestrong reconstruction quality but incur significant memory and compu-tational overhead, while set-based representations are compact and con-tinuous yet typically lag in fidelity due to latent sparsity and excessiveglobal smoothness. We propose MSVS-VAE, a hierarchical set-based VAEthat closes this fidelity gap without sacrificing compactness. Our keyidea is to progressively densify anchored VecSet latents via hierarchicalpoint-shuffle upsampling, increasing spatial capacity for fine-grained ge-ometry modeling. To efficiently decode from the densified hierarchy, wereplace global cross-attention with AVS-Conv, a geometry-aware localaggregation operator operating within local neighborhoods rather thanthe exhaustive latent set. We further introduce multi-scale query decod-ing to fuse coarse-to-fine latent features, where coarse scales providestable global context, and fine scales refine localized geometry, reducingartifacts from overly local receptive fields. Extensive experiments on Ob-javerse, ABO, and in-the-wild benchmarks demonstrate that MSVS-VAEconsistently outperforms prior set-based and voxel-based VAEs, deliver-ing ∼10× faster decoding than prior set-based methods and ∼10× highercompactness than voxel-based baselines.

ECCV 2026

MotionAnymesh: Physics-Grounded Articulation for Simulation-Ready Digital Twins

Wenbo Xu, Liu Liu, Li Zhang, Dan Guo, Ruonan Liu

Converting static 3D meshes into interactable articulated as-sets is crucial for embodied AI and robotic simulation. However, exist-ing zero-shot pipelines struggle with complex assets due to a criticallack of physical grounding. Specifically, ungrounded Vision-LanguageModels (VLMs) frequently suffer from kinematic hallucinations, whileunconstrained joint estimation inevitably leads to catastrophic meshinter-penetration during physical simulation. To bridge this gap, we pro-pose MotionAnymesh, an automated zero-shot framework that seam-lessly transforms unstructured static meshes into simulation-ready dig-ital twins. Our method features a kinematic-aware part segmentationmodule that grounds VLM reasoning with explicit SP4D physical priors,effectively eradicating kinematic hallucinations. Furthermore, we intro-duce a geometry-physics joint estimation pipeline that combines robusttype-aware initialization with physics-constrained trajectory optimiza-tion to rigorously guarantee collision-free articulation. Extensive experi-ments demonstrate that MotionAnymesh significantly outperforms state-of-the-art baselines in both geometric precision and dynamic physicalexecutability, providing highly reliable assets for downstream applica-tions. Our project page is available at https://xwb0117.github.io/Motionanymesh/

ECCV 2026

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment

Elena Camuffo, Francesco Barbato, Mete Ozay, Simone Milani, Umberto Michieli

Personalized object detection aims to adapt a general-purposedetector to recognize user-specific instances from only a few examples.Lightweight models often struggle in this setting due to their weak se-mantic priors, while large vision-language models (VLMs) offer strongobject-level understanding but are too computationally demanding forreal-time or on-device applications. We introduce MOCHA (Multi-modalObjects-aware Cross-arcHitecture Alignment), a distillation frameworkthat transfers multimodal region-level knowledge from a frozen VLMteacher into a lightweight vision-only detector. MOCHA extracts fusedvisual and textual teacher’s embeddings and uses them to guide stu-dent training through a dual-objective loss that enforces accurate localalignment and global relational consistency across regions. This processenables efficient transfer of semantics without the need for teacher modi-fications or textual input at inference. MOCHA consistently outperformsprior baselines across four personalized detection benchmarks under strictfew-shot regimes, yielding a +10.1 average improvement, with minimalinference cost.

ECCV 2026

MoCam: Unified Novel View Synthesis via Structured Denoising Dynamics

Haofeng Liu, Yang Zhou, Ziheng Wang, Zhengbo Xu, Zhan Peng, Jie Ma, Jun Liang, Shengfeng He, Jing Li

Generative novel view synthesis faces a fundamental dilemma:geometric priors provide spatial alignment but become sparse and inaccu-rate under view changes, while appearance priors offer visual fidelity butlack geometric correspondence. Existing methods either propagate geo-metric errors throughout generation or suffer from signal conflicts whenfusing both statically. We introduce MoCam, which employs structureddenoising dynamics to orchestrate a coordinated progression from geom-etry to appearance within the diffusion process. MoCam first leveragesgeometric priors in early stages to anchor coarse structures and toleratetheir incompleteness, then switches to appearance priors in later stagesto actively correct geometric errors and refine details. This design natu-rally unifies static and dynamic view synthesis by temporally decouplinggeometric alignment and appearance refinement within the diffusion pro-cess. Experiments demonstrate that MoCam significantly outperformsprior methods, particularly when point clouds contain severe holes ordistortions, achieving robust geometry-appearance disentanglement.

ECCV 2026

OmniCoT: A Benchmark for Global and Multi-Step Panoramic Reasoning

Haocong He, Chenfei Liao, Zichen Wen, Zihao Dongfang, Xu Zheng, Bin Ren, Chang Su, Zixin Zhang, Harold Haodong Chen, Hongfei Zhang, Weijia Li, Kailun Yang, Conghui He, Xuming Hu, Nicu Sebe, Linfeng Zhang

Multimodal Large Language Models (MLLMs) have demon-strated promising spatial reasoning capabilities, while these abilities re-main underexplored in the emerging visual modality of panoramic im-agery. The full 360◦ ×180◦ field of view of panoramas essentially supportscomplex global multi-step reasoning, which is also the fundamental ad-vantage of panoramas in applications such as embodied intelligence. How-ever, existing panoramic benchmarks largely focus on simplistic queriesthat rely on local cues or single-/few-step reasoning, thereby ignoring thefundamental advantage of panoramas and failing to fully exploit theirpotential. To address this gap, we introduce OmniCoT , a panoramicspatial reasoning suite designed to enable MLLMs to use global evi-dence and perform multi-step inference across viewpoints. It includesOmniCoT-B (6.7K data) for evaluation, which measures both answeraccuracy and reasoning quality, OmniCoT-Real (1K data) as a man-ually annotated real-world subset to quantify the Sim-to-Real gap. Fortraining, OmniCoT-T (14.3K data) is purpose-built with structuredstepwise Chain-of-Thought annotations that explicitly link intermediatereasoning steps to panoramic evidence. Based on OmniCoT-T, we intro-duce OmniCoT-R1 and adopt a two-stage training strategy tailoredto the geometrically complex panoramic space, where Supervised Fine-Tuning (SFT) anchors reasoning to panoramic evidence (e.g., bearings,proximity) and GRPO penalizes geometrically incoherent paths to con-solidate global 360◦ spatial consistency. Through OmniCoT, we aim torecalibrate the difficulty of panoramic spatial reasoning to bet-ter align with the intrinsic capabilities of panoramic imagery ,thereby fostering meaningful progress in this research area.

ECCV 2026

LISA: Locality-Informed Speculative Decoding for Accelerating Autoregressive Image Generation

Ying Li, Siyong Jian, Zhaode Wang, Zhiwen Chen, Chengfei Lyu, Huan Wang

Autoregressive (AR) models deliver high-quality image gen-eration but suffer from severe inference latency due to sequential decod-ing. While speculative decoding (SD) successfully accelerates large lan-guage models, we demonstrate that state-of-the-art methods like EAGLE-3 provide limited gains for visual AR models. We attribute this to theintrinsic nature of visual tokens: unlike text, their probability distribu-tions are flat and non-discriminative, which hinders drafter–target align-ment and causes verification to collapse under sampling. To bridge thisgap, we propose LISA, a Locality-Informed Speculative framework forAutoregressive image generation. LISA overcomes alignment barriers viatwo key components: (i) Locality-Informed Distillation, which alignsthe drafter by prioritizing supervision on structured uncertainty; and(ii) Geometry-Aware Soft Verification, which leverages embeddingproximity and target confidence to enable more permissive yet safe to-ken acceptance. Experiments across T2I and C2I tasks demonstrate thatLISA improves the speed–quality trade-off, achieving up to 4.12× accel-eration and outperforming recent competitive methods.

ECCV 2026

LASER: A Corrective Lens for LVLMs via Visual Attention Preservation and Sink Suppression

Bowen Yuan, Zijian Wang, Yadan Luo, Shijie Wang, Zi Helen Huang

Large vision–language models (LVLMs) exhibit strong reason-ing ability but suffer from visual forgetting during long-horizon decoding,where attention progressively drifts away from visual evidence. Existingmethods largely treat this issue as a late-stage attention decay prob-lem or attempt to mitigate it through heuristic reminders or post-hocattention lifting. Through systematic empirical analysis, we find thatperformance degradation under visual forgetting is largely driven bytwo overlooked factors: early-stage attention decay disrupts evidenceacquisition, and attention concentration on a subset of task-irrelevantvisual sink tokens. Motivated by these insights, we propose LASER, apost-training framework that regulates both the visual attention tra-jectory and intra-visual token attention distribution during reasoning.Technically, LASER introduces two complementary rewards: a VisualGrounding Reward, which encourages the model to maintain attentionon semantically salient visual tokens throughout decoding, and a SinkSuppression Reward, which penalizes excessive attention concentration onvisual sink tokens. Together, these rewards preserve early-stage groundingwhile preventing attention collapse onto uninformative regions. Exten-sive experiments on eight benchmark datasets demonstrate that LASERconsistently outperforms strong baselines, validating attention-awaretraining as an effective remedy for visual forgetting. The code is availableat https://github.com/KeViNYuAn0314/LASER.

ECCV 2026

MarineEVT: Advancing Event-Centric Marine Video Understanding via Visual Tool Reasoning

Tuan-An To, Yuk-Kwan Wong, Tuan-Anh Vu, Ziqiang Zheng, Sai-Kit Yeung

Recent Vision-Language Models (VLMs) have achieved re-markable success in visual understanding, driven by the growing avail-ability of high-quality image-text pairs. However, the performance ofVLMs often degrades in the video domain due to the essential needfor temporal understanding and the scarcity of large-scale annotatedvideo data. In this work, we focus on marine video understanding, whichbrings further challenges: first, it requires substantial domain expertise;and video VLMs usually struggle with localizing and interpreting criti-cal information from marine videos, as the informative events are typ-ically sparse, unpredictable, and unevenly distributed. To address thesechallenges, we carefully curate the first event-centric marine video un-derstanding dataset called MarineEVT, which features 20K multi-task,video-level visual question-answering pairs spanning multiple dimensionsof marine understanding and analysis. Meanwhile, based on MarineEVT,we decompose marine video understanding as an Event-centric VisualTool-integrated Reasoning process (EVT-R1 for short), where we lever-age powerful visual tools to drive the model to localize and interpretcritical information aligned with visual questions and human intent. Todemonstrate its effectiveness, we compare EVT-R1 against 11 SOTAVLMs in different settings. EVT-R1 outperforms the top open-sourceand top commercial models by 5.22 and 11.09, respectively. MarineEVTand EVT-R1 lay the foundation for ecological discovery and marine ed-ucation, fostering the development of VLMs capable of interpreting ma-rine dynamics, reasoning about ecological interactions, and supportingsustainable ocean video understanding and analysis.

ECCV 2026

Less is More: Reducing Complexity in Vision-Language-Action Systems

Jinhui Ye, Ning Gao, Senqiao Yang, Jinliang Zheng, Zixuan Wang, Yuxin Chen, Pengguang Chen, yilun chen, Shu Liu, Jiaya Jia

Vision-Language-Action (VLA) models have emerged as apromising paradigm for building general-purpose robotic agents. How-ever, the VLA landscape remains highly fragmented and complex: asexisting approaches vary substantially in architectures, training data,embodiment configurations, and benchmark-specific engineering. In thiswork, we introduce StarVLA-α, a simple yet strong baseline designedto study VLA design choices under controlled conditions. StarVLA-αdeliberately minimizes architectural and pipeline complexity to reduceexperimental confounders and enable systematic analysis. Specifically,we re-evaluate several key design axes, including action modeling strate-gies, robot-specific pretraining, and interface engineering. Across uni-fied multi-benchmark training on LIBERO, SimplerEnv, RoboTwin, andRoboCasa, the same simple baseline remains highly competitive, indi-cating that a strong VLM backbone combined with minimal design isalready sufficient to achieve strong performance without relying on ad-ditional architectural complexity or engineering tricks. Notably, our sin-gle generalist model outperforms π0.5 by 20% on the public real-worldRoboChallenge benchmark. We expect StarVLA-α to serve as a solidstarting point for future research in the VLA regime. Code will be re-leased at https://github.com/starVLA/starVLA.

ECCV 2026

SWIFT: Spatial-Window Integrated Frequency-aware Token Pruning for Efficient MLLMs on Edge Devices

Guanglai Liu, Jubo Chen, Xiaosheng Yu

While Multimodal Large Language Models (MLLMs) ex-cel in visual understanding, the quadratic complexity imposed by densehigh-resolution visual tokens poses significant challenges for edge-devicedeployment. Current training-free pruning strategies predominantly de-pend on attention weights. However, viewed from the frequency domain,the self-attention mechanism functions as a low-pass filter, causing thediscard of essential high-frequency signals (e.g., textures and edges) andresulting in feature over-smoothing. In this paper, we present Spatial-Window Integrated Frequency-aware Token Pruning (SWIFT), a seam-less plug-and-play framework designed for efficient token compression.SWIFT leverages two novel components: a Frequency-Aware Indicator(FAI), which identifies fine-grained details by estimating high-frequencyresiduals through matrix factorization, and a Spatial-Window Integra-tion (SWI) module, which prevents spatial structure collapse and posi-tional bias via localized retention. Extensive evaluations on Qwen2.5VL-3B and LLaVA benchmarks show that under a practical 50% compressionratio, SWIFT achieves a 2.10× speedup with near-lossless performance.Under an extreme 75% compression stress test, SWIFT yields an averageperformance drop of 5.1% across 8 benchmarks, while delivering a 2.66×speedup and 69.07% KV cache reduction. SWIFT exhibits superior ro-bustness in detail-intensive tasks compared to SOTA methods, providinga potent lightweight solution for on-device MLLM applications. Our codeis available at https://github.com/damo-lgl/SWIFT.

ECCV 2026

Learning 1-Bit LiDAR-based Localization with Auxiliary Objective

Kaijie Yin, Zhiyuan Zhang, Tian Gao, Wentao Zhu, Cheng-zhong Xu, Hui Kong

6-DoF LiDAR-based localization is a fundamental capabilityfor autonomous systems operating in large-scale outdoor environments.Many deep-learning-based localization methods have achieved promisingperformance so far. However, as one of the always-on modules competingfor limited on-board computational resources, the localization module isexpected to consume only a small portion of the overall compute bud-get. Most existing learning-based methods are still too heavy for thispurpose. In contrast, binary neural networks (BNNs) offer an appealingsolution, but the 1-bit compression causes severe information loss andperformance drop. In this paper, we address this challenge by propos-ing Binarized LiDAR-based Localization (BiLoc), the first binary neuralnetwork framework for 6-DoF LiDAR localization. Specifically, we rein-terpret the training of BNNs from the perspective of the information-bottleneck principle, aiming at retaining minimal yet sufficient represen-tations for pose estimation while suppressing redundant variations. Andwe introduce an auxiliary objective that adaptively regulates informationretention in the binary encoder, effectively mitigating the informationloss caused by binarization. This auxiliary objective provides additionaloptimization signals that compensate for the limited representationalcapacity and the gradient mismatch inherent in BNNs. Extensive exper-iments on large-scale outdoor LiDAR datasets demonstrate that BiLocestablishes a new state of the art for LiDAR localization with BNNs.

ECCV 2026

ReGRPO: Reflection-Augmented Policy Optimization for Tool-Using Agents

Binjie Zhang, Mike Zheng Shou

Tool-augmented vision–language models (VLMs) can solve multi-modal, multi-step tasks by calling external tools, yet they remain fragile in prac-tice. Existing works have two common gaps. Supervised fine-tuning (SFT) is builtmostly on successful trajectories and offers little signal for recovery after tool fail-ures, while sparse trajectory-level RL rewards provide limited guidance on whichstep failed and how to repair it. We introduce ReGRPO (Reflection-augmentedGroup Relative Policy Optimization), a framework that learns reflection-guidedcorrection in tool-using agents. ReGRPO starts with a structured reflective dataengine: we execute near-miss actions to collect grounded failure observations,then build Reflection-of-Thought triplets (ErrorType, Evidence, FixPlan) pairedwith corrected actions for warm-start SFT. We then optimize reflection tokens andcorrective actions jointly within local trajectories using group-relative advantages,and include a reflection-cost term to reduce unnecessary reflection. Experimentson GTA and GAIA show that, under the same backbone and tool suite, ReGRPOconsistently outperforms strong open-source baselines and achieves the best resultsamong the compared open-source controllers. Code and RoT data are available athttps://github.com/showlab/ReGRPO.

ECCV 2026

LEAP-VLA: Latent-Enhanced Action Prototyping via Continuous Residual Latent Spaces for Vision-Language-Action Models

Bo-Yun Yu, Kuan-Chuan Peng, Jun Hsieh

Vision-Language-Action (VLA) models have advancedrapidly, yet most gains still come from larger backbones, larger embod-ied datasets, or expensive iterative decoders, while action representa-tion remains under-explored. We argue that action space design is aprimary bottleneck and present LEAP (Latent-Enhanced ActionPrototyping)-VLA, a two-stage framework that learns a structuredaction latent space from demonstrations via Multi-level Soft ResidualQuantization (MSRQ), then trains a lightweight VLM-external aligner topredict actions by prototype-aware soft selection in that space. This de-sign replaces discrete code assignment and multi-step denoising with fullydifferentiable single-pass latent prediction, preserving pretrained vision-language alignment without modifying the VLM. Empirically, LEAP-VLA outperforms the state-of-the-art methods with substantially fewertrainable parameters and no embodied pretraining, showing that a well-structured continuous action latent space can offset model scale and datarequirements.

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