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ECCV 2026

ReasonCLIP-58M: Visually Grounded Commonsense Reasoning Supervision for CLIP

Sicheng Zhang, Muhammad Muzammal Naseer, Binzhu Xie, Naufal Suryanto, Shi Qiu, Jamal Bentahar, Naveed Akhtar, Shah Mubarak

CLIP and its variants are widely adopted visual backbonesin multimodal systems, but their pretraining remains dominated by de-scriptive image-text alignment. As downstream applications increasinglydemand visually grounded commonsense inference and compositionalreasoning, it remains unclear whether CLIP-style encoders can supportsuch reasoning without architectural changes. To address this, we presentReasonCLIP-58M, a continual pretraining framework that integrates large-scale reasoning supervision into CLIP-style models through our two-stage strategy, which progressively integrates reasoning signals while pre-serving descriptive alignment, followed by category-structured reason-ing supervision. To support this framework, we construct two comple-mentary datasets and a benchmark: ReasonLite-42M, with open-form,visually verifiable reasoning captions; ReasonPro-16M, with category-specific reasoning supervision; and RCLIP-Bench for diagnostic evalua-tion of visually grounded reasoning. We train a family of ReasonCLIPthat improves visually grounded commonsense and compositional reason-ing while also enhancing zero-shot retrieval performance. As a drop-invisual encoder for multimodal large language models such as LLaVA-NeXT, ReasonCLIP delivers consistent gains without additional infer-ence cost, demonstrating that structured reasoning supervision enhancesthe expressive capacity of CLIP-style visual representations. All datasets,models, and training code are available at https://github.com/RISys-Lab/ReasonCLIP.

ECCV 2026

RAF: Reliability-Aware Fusion of Camera, LiDAR, and 4D RADAR for Robust 3D Object Detection in Adverse Weather

Heejun Park, Jaeseok Jeong, Kuk-Jin Yoon

Robust 3D object detection in adverse weather conditions is challenging due to sensor limitations. Although combining complementary modalities such as LiDAR and 4D RADAR has shown promise, the sparsity of these sensors becomes apparent in adverse weather with reduced reflections, leading to objects with few or no point cloud returns. To address this limitation, camera sensors provide visual cues even when LiDAR and RADAR signals are weakened. However, cameras themselves are also vulnerable to adverse weather, where some regions become unreliable due to snow or rain occluding the camera lens. While some camerafusion methods designed for adverse weather learn to weigh image regions via confidence maps, these maps receive no direct supervision and are learned solely through the detection loss. We introduce Reliability-Aware Fusion (RAF), which explicitly supervises per-pixel reliability estimation and provides a direct learning signal for identifying and suppressing unreliable visual cues. Our framework leverages pretrained LiDAR–RADAR networks, keeping their backbones frozen while only training the added camera branch, BEV fusion encoder, and detection head. Extensive experiments on the K-Radar and VoD datasets demonstrate that integrating RAF consistently improves detection accuracy over LiDAR–RADAR baselines, achieving up to +6.5 AP and +7.4 AP gains. Code is BEV 3D available at https://github.com/parkie0517/RAF.

ECCV 2026

NUN: Nested Unfolding Network for Real-World Concealed Object Segmentation

Chunming He, Rihan Zhang, Longxiang Tang, Dingming Zhang, Bojian Zhang, Fengyang Xiao, Jingjia Feng, Sina Farsiu

Real-world visual perception demands joint handling of low-level image degradation and high-level semantic understanding, yet thesetwo objectives inherently conflict: restoration seeks fine-grained texturerecovery while segmentation prioritizes semantic contrast. We proposethe nested unfolding network (NUN), a principled framework that re-solves this conflict by nesting one deep unfolding network (DUN) in-side another. NUN embeds a degradation-resistant unfolding network(DeRUN) within each stage of a segmentation-oriented unfolding network(SODUN), enabling both tasks to optimize in their own subspaces whileinteracting in a controlled manner. DeRUN handles unknown degrada-tion through proximal gradient unfolding with learnable operators thatimplicitly approximate it, while SODUN performs reversible foreground-background estimation. A bi-directional unfolding interaction mechanismuses IQA to select optimal DeRUN outputs, and a cross-stage consis-tency loss ensures robust predictions under varying restoration quality.Theoretically, under local assumptions, we show that NUN reduces di-rect parameter-level gradient conflict through disjoint parameter setsand achieves a degradation-sensitivity bound, where degradation affectssegmentation only through the inner-loop optimization and restorationapproximation errors. We instantiate NUN on concealed object segmen-tation and demonstrate consistent superiority over SOTA alternativesacross 12 benchmarks. The code is available at https://github.com/ChunmingHe/NUN.

ECCV 2026

Neural Gate: Mitigating Privacy Risks in LVLMs via Neuron-Level Gradient Gating

Xiangkui Cao, Jie Zhang, Meina Kan, Shiguang Shan, Xilin Chen

Large Vision-Language Models (LVLMs) have shown remark-able potential across a wide array of vision-language tasks, leading totheir adoption in critical domains such as finance and healthcare. How-ever, their growing deployment also introduces significant security andprivacy risks. Malicious actors could potentially exploit these models toextract sensitive information, highlighting a critical vulnerability. Recentstudies show that LVLMs often fail to consistently refuse instructionsdesigned to compromise user privacy. While existing work on privacyprotection has made meaningful progress in preventing the leakage ofsensitive data, they are constrained by limitations in both generalizationand non-destructiveness. They often struggle to robustly handle unseenprivacy-related queries and may inadvertently degrade a model’s per-formance on standard tasks. To address these challenges, we introduceNeural Gate, a novel method for mitigating privacy risks through neuron-level model editing. Our method improves a model’s privacy safeguardsby increasing its rate of refusal for privacy-related questions, cruciallyextending this protective behavior to novel sensitive queries not encoun-tered during the editing process. Neural Gate operates by learning a fea-ture vector to identify neurons associated with privacy-related conceptswithin the model’s representation of a subject. This localization then pre-cisely guides the update of model parameters. Through comprehensiveexperiments on MiniGPT and LLaVA, we demonstrate that our methodsignificantly boosts the model’s privacy protection while preserving itsoriginal utility. The code is available at https://github.com/Xiangkui-Cao/Neural-Gate.

ECCV 2026

NaVLM-PVC: Progressive Visual Compression for Efficient Native-Resolution Encoding in MLLMs

Shichu Sun, Yichen Zhang, Haolin Song, Zonghao Guo, Chi Chen, Yidan Zhang, Yuan Yao, Zhiyuan Liu, Maosong Sun

Visual encoding followed by token condensing has becomethe standard architectural paradigm in multi-modal large language mod-els (MLLMs). Many recent MLLMs increasingly favor global native-resolution visual encoding over slice-based methods. To investigate thistrend, we systematically compare their behavior on vision-language un-derstanding and attention patterns, revealing that global encoding en-hances overall capability but at the expense of greater computationaloverhead. To address this issue, we present NaVLM-PVC, an MLLM cen-tered upon our proposed Progressive Visual Compression (PVC) method,which can be seamlessly integrated into standard Vision Transformer(ViT) to enable efficient native-resolution encoding. The PVC approachconsists of two key modules: (i) refined patch embedding, which supportsflexible patch-size scaling for fine-grained visual modeling, (ii) windowedtoken compression, hierarchically deployed across ViT layers to progres-sively aggregate local token representations. Jointly modulated by thesetwo modules, a widely pretrained ViT can be reconfigured into an effi-cient architecture while largely preserving generality. Evaluated acrossextensive benchmarks, the transformed ViT, termed ViT-PVC, demon-strates competitive performance with MoonViT while reducing TTFT(time-to-first-token) by 2.4×, when developed within an identical MLLMarchitecture. Building upon ViT-PVC, NaVLM-PVC also achieves com-petitive performance to Qwen2-VL, while further reducing TTFT by1.9×. We will release all code and checkpoints to support future researchon efficient MLLMs.

ECCV 2026

Look Less, Think Faster: Joint Token-Compute Adaptation for Multimodal LLMs

Pengcheng Wang, Zhiquan Wang, Jayoung Lee, Zhuoyan Xu, Ran Xu, Saurabh Bagchi, Yin Li, Somali Chaterji

Multimodal Large Language Models (MLLMs) have recentlydemonstrated strong performance across vision-language tasks. However,their high inference cost, arising from both the large number of inputvisual tokens and the heavy computation of the large language model(LLM), remains a key barrier to practical deployment. Recent work at-tempts to reduce the cost by adaptively optimizing individual dimen-sions, e.g., pruning redundant visual tokens or skipping LLM layers andheads. Nonetheless, prior approaches typically treat these dimensions in-dependently and overlook a fundamental coupling: the available computeresources must be dynamically allocated across all dimensions based onthe input content. To bridge the gap, we propose SmartVL, a unifiedadaptive inference framework that jointly controls vision token numberand model compute capability in response to varying input contents andcompute budgets. SmartVL introduces a vision-side token controllerthat dynamically selects informative visual tokens and an LLM-side com-pute controller that adaptively adjusts LLM computation. Importantly,these controllers are trained to coordinate with each other so that theoverall inference cost satisfies a target budget. To allow this joint schedul-ing, we connect the controllers using a shared budget encoding and lever-age a differentiable latency estimator for end-to-end training. This de-sign enables SmartVL to learn cross-stage allocation strategies thatadapt to both input complexity and runtime compute constraints. Exper-iments across multiple MLLM benchmarks demonstrate that, with jointscheduling, SmartVL consistently outperforms prior adaptive methodsand achieves superior accuracy–efficiency Pareto frontiers. Project page:https://www.schaterji.io/publications/2026/jointtokencompute.

ECCV 2026

Learning from Failure: Inference-Time Self-Improvement for Computer-Use Agents

Xueqiao Sun, Yuhui Zhang, Xiaohan Wang, Ludwig Schmidt, Serena Yeung-Levy

Computer-use agents, which leverage multimodal large lan-guage models (MLLMs) to operate computers and complete tasks, haveattracted significant attention for their utility and versatility. A majorchallenge in developing these agents is collecting large-scale, high-qualitytrajectories. The standard approach generates synthetic data through aself-improving loop: an agent is placed in a verifiable environment anditeratively fine-tuned on its successful trajectories. Despite its effective-ness, this paradigm exploits only successful trajectories and discards thefailed ones, even though failures carry rich information about a model’sweaknesses. In this work, we explore a complementary failure-driven self-improvement loop, a data-centric paradigm that turns failed trajecto-ries into agent improvements. Specifically, we employ an LLM to diag-nose failure modes, propose inference-time solutions, and generate codepatches—lightly verified by humans—that upgrade the agent. We vali-date this approach with the state-of-the-art OpenCUA-72B model on theOSWorld benchmark, improving the success rate from 42.3% to 48.9%,a gain of 6.6 percentage points, without any additional training costand with only modest inference overhead. Our results demonstrate thatfailure-driven self-improvement is a viable complement to success-basedpipelines, enabling more efficient agent improvement. Code is availableat https://github.com/snow10072740/Learning_from_Failure.

ECCV 2026

Learn to See the Unseen in Low-light Spike Streams

Liwen Hu, Yang Li, Mianzhi Liu, Guo Yijia, Shenghao Xie, Wenqiang Zu, gang ding, Tiejun Huang, Lei Ma

Spike camera, a type of neuromorphic sensor with high-temporal resolution, shows great promise for high-speed visual tasks.Unlike traditional cameras, spike camera continuously accumulates pho-tons and fires asynchronous spike streams. Due to unique data modality,spike streams require reconstruction methods to become perceptible to thehuman eye. However, under low-light high-speed conditions, spike streamsbecome highly sparse and noisy, making faithful pixel-wise recovery in-trinsically difficult. In this work, we propose Diff-SPK, a diffusion-basedframework for perceptual reconstruction from low-light spike streams.Diff-SPK leverages generative priors to produce visually plausible re-constructions while remaining constrained by spike-derived structuralconditions. Specifically, it first employs an Enhanced Texture f rom Inter-spike Interval (ETFI) to aggregate sparse structural information fromlow-light spike streams. Then, the encoded ETFI by a suitable encoderserves as the input of ControlNet for high-speed scenes generation. Toimprove the quality of results, we introduce an ETFI-based feature fusionmodule during the generation process.

ECCV 2026

GradingBench: Evaluating End-to-End Compositional Reasoning of MLLMs for Automated Exam Grading

Yuting Wang, Zixian Guo, Weihao You, Zhilong Ji, Jinfeng Bai, Wangmeng Zuo

While Multi-modal Large Language Models (MLLMs) havedemonstrated superior performance on isolated tasks such as visual ques-tion answering, their reliability remains limited in real-world scenariosthat require highly compositional visual-language reasoning. A primeexample is automated exam grading, a real-world practical task thatdemands the integration of three core capabilities: detection and local-ization, text recognition, and reasoning for correctness judgment. Cur-rent benchmarks largely focus on isolated tasks and cannot fully eval-uate MLLMs’ end-to-end ability in such complex settings. To bridgethis gap, we present GradingBench, a comprehensive benchmark basedon automated exam grading in Chinese K–12 education, which system-atically evaluates MLLMs across the entire grading pipeline. Grading-Bench comprises full-page exam papers from real educational settings,containing 3,284 sub-questions annotated with reference answers. Weevaluate MLLMs across three levels: single-question, specified-question,and full-page grading. Our experiments show that localization failureis the main bottleneck and reveal limitations in integrated multi-taskexecution. Although multi-round interaction and supervised fine-tuningbring slight improvements, the limited gains reflect fundamental weak-nesses in compositional ability. GradingBench serves as a challenging,application-driven benchmark for the community, underscoring the needfor seamless perception-cognition fusion in MLLMs to unlock their po-tential in practical domains. All code and data are available at https://github.com/ERRORSEMI/GradingBench.

ECCV 2026

FastSTAR: Spatiotemporal Token Pruning for Efficient Autoregressive Video Synthesis

Sungwoong Yune, Suheon Jeong, Joo-Young Kim

Visual Autoregressive modeling (VAR) has emerged as ahighly efficient alternative to diffusion-based frameworks, achieving com-parable synthesis quality. However, as this paradigm extends to Space-time Autoregressive modeling (STAR) for video generation, scaling res-olution and frame counts leads to a "token explosion" that creates amassive computational bottleneck in the final refinement stages. To ad-dress this, we propose FastSTAR, a training-free acceleration frame-work designed for high-quality video generation. Our core method, Spa-tiotemporal Token Pruning, identifies essential tokens by integratingtwo specialized terms: (1) Spatial similarity, which evaluates structuralconvergence across hierarchical scales to skip computations in regionswhere further refinement becomes redundant, and (2) Temporal similar-ity, which identifies active motion trajectories by assessing feature-levelvariations relative to the preceding clip. Combined with a Partial Up-date mechanism, FastSTAR ensures that only non-converged regions arerefined, maintaining fluid motion while bypassing redundant computa-tions. Experimental results on InfinityStar demonstrate that FastSTARachieves up to a 2.01× speedup with a PSNR of 28.29 and less than 1%performance degradation, proving a superior efficiency-quality trade-offfor STAR-based video synthesis.

ECCV 2026

FairSteer: Cross-Attention Steering Towards a Fairer Text-Guided Image Generation

Tatiana Gaintseva, Akshit Achara, Greg Slabaugh, Jiankang Deng, Ismail Elezi

Text-to-image diffusion models power everyday creative tasks,but they still reproduce the demographic biases in their training data. Oncommon prompts such as “a photo of a nurse,” “a photo of a CEO”, theyskew their outputs toward one gender, driven by the statistics of trainingdata rather than anything in the text. Existing debiasing methods showpromise in narrow settings but require retraining, batch-level control, orprompt-specific tuning, limiting their scalability. We propose EquiSteer,a training-free method that works per sample by steering cross-attention(CA) activations at inference time. For each target attribute, EquiSteerprecomputes steering vectors from contrastive prompts. Then at gener-ation time, a prompt-aware gate leaves attribute-specific prompts un-touched, while for neutral ones it clears existing attribute signals fromthe CA activations and injects a target attribute. Across SD-1.5, SD-2.1,SDXL, and SANA, EquiSteer reduces the average parity gap by up to87%, with minimal effect on image quality and text-image alignment.Code is available at https://github.com/Atmyre/EquiSteer.

ECCV 2026

Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs

Haochen Han, Jue Wang, Alex Jinpeng Wang, Fangming Liu

Multimodal Large Language Models (MLLMs) have demon-strated remarkable success in various Remote Sensing (RS) tasks. How-ever, their ability to comprehend negation remains underexplored, limit-ing deployment in real-world applications where models must explicitlyidentify what is false or absent, e.g., emergency responders need to locatenon-flooded routes for evacuation. To comprehensively study this limi-tation, we introduce RS-Neg, the first benchmark to evaluate negationunderstanding across region-level to scene-level tasks. Specifically, we de-sign an automated data generation pipeline for RS imagery, using LLMsto synthesize diverse negation queries, and introduce a dynamic visualfocus module for verification. Our evaluation reveals that advanced RSMLLMs struggle with negation, exhibiting hallucinations and substantialperformance degradation. To close this gap, we propose NeFo, a noveltest-time adaptation method that explicitly incorporates the logical roleof negation into the model optimization. Remarkably, using about 5%unlabeled test samples, NeFo significantly improves the negation under-standing of models and shows strong generalization to unseen tasks.

ECCV 2026

ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation

Jingjing Qian, Zeyuan He, Chen Shi, Lei Xiao, Li Jiang

Coordinating navigation and manipulation with robust per-formance is essential for embodied AI in complex indoor environments.However, as tasks extend over long horizons, existing methods oftenstruggle due to catastrophic forgetting, spatial inconsistency, and rigidexecution. To address these issues, we propose ESCAPE (EpisodicSpatial memory Coupled with an Adaptive Policy for Execution), op-erating through a tightly coupled perception-grounding-execution work-flow. For robust perception, ESCAPE features a Spatio-Temporal FusionMapping module to autoregressively construct a depth-estimation-free,persistent 3D spatial memory, and a Memory-Driven Target Groundingmodule for precise interaction mask generation. To achieve flexible ac-tion, our Adaptive Execution Policy dynamically orchestrates proactiveglobal navigation and reactive local manipulation to seize opportunis-tic targets. ESCAPE achieves state-of-the-art performance on the AL-FRED benchmark, reaching 65.09% and 60.79% success rates in test seenand unseen environments with step-by-step instructions. By reducing re-dundant exploration, our ESCAPE substantially improves path-length-weighted metrics and remains robust (61.24%/56.04%) even without de-tailed guidance for long-horizon tasks.

ECCV 2026

EmoteGPT: 3D Human Facial Expression from Natural Language Descriptions

Haoran Wang, Mohit Mendiratta, Christian Theobalt, Adam Kortylewski

Precise control of 3D facial expressions from text is crucialfor virtual avatars, animation, and human–computer interaction, yet ex-isting text-to-3D methods jointly generate identity, expression, and tex-ture, making fine-grained expression control difficult. We instead for-mulate text-driven expression synthesis as a regression problem in thedisentangled parameter space of a 3D Morphable Model (3DMM). Thissetting, however, requires paired data linking detailed language to pre-cise expression parameters, which is missing from existing resources. Tofill this gap, we introduce Txt2Emote, a benchmark of diverse 3D facialexpressions with fine-grained textual annotations obtained from GPT-4o and a high-fidelity face tracker, providing both explicit descriptionsdetailing facial features and implicit descriptions referencing the situa-tional context behind the expression. Leveraging this dataset, we presentEmoteGPT, a text-to-3D expression framework based on a Multi-ModalLarge Language Model (MLLM) with a dedicated <Expr> token to se-mantically ground expression representations, which are then decodedinto 3DMM parameters. We further improves EmoteGPT by augmentingtraining with large-scale image-to-3DMM data, surpasses state-of-the-arttext-to-3D face synthesis methods on emotion recognition metrics andin perceived expressiveness. Integrated into avatar pipelines, our methodenables photorealistic and stylized 3D avatars, as well as expressive 3D-consistent 2D face synthesis from textual input.

ECCV 2026

DuoFlow: JVP-Free Finite-Difference Mean Flows for One-Step Image Generation

Zeming Li, Xiangyu Zhang, Ping Tan, Heung-Yeung Shum

MeanFlow training commonly enforces the meanflow identitywith Jacobian-vector products (JVP). This is computationally expensiveand hard to scale, since the JVP path often weakens fused-kernel andcompiler-level optimization benefits. We revisit JVP-free MeanFlow froman error-driven perspective. Rather than a drop-in JVP replacement, finitedifferencing exposes controllable error structure that directly informs al-gorithm design. We identify two dominant error channels: step-truncationerror and trajectory-velocity error.Guided by this decomposition, we introduce a JVP-free differential Mean-Flow framework for from-scratch MeanFlow-style one-step image gen-eration. For step-truncation error, we use stochastic signed one-sideddifferencing, which needs only one additional forward pass and recoverssecond-order truncation behavior in expectation. For trajectory-velocityerror, we propose DuoFlow, which predicts mean and instantaneous veloc-ities on the same sampled state to improve trajectory consistency in dif-ferential updates. We further introduce progressive self-bootstrapping todrive trajectory velocity toward a fixed-point-like self-consistent tendencyduring training. As a fast derivative-evaluation scheme, our one-forwarddifferential estimator is between 7.17× and 34.83× faster than JVP ondirectional-derivative micro-benchmarks for batch sizes from 1 to 64, andreduces peak memory by a factor between 1.71× and 2.68×. On ImageNet256×256 from-scratch training, it improves FID by 30% over a matchedJVP-based MeanFlow baseline. Code will be available at here.

ECCV 2026

DLGStream: Dynamic Language-embedded Guassian Splatting for Open-vocabulary Enabled Free-viewpoint Video Streaming

Zhihui Ke, Yvyang Liu, Xiaobo Zhou, Tie Qiu

3D Gaussian Splatting (3DGS) has emerged as a promis-ing paradigm for reconstructing streamable free-viewpoint video (FVV)from multi-view videos. However, 3DGS-based FVVs typically lack userinteraction and editing capabilities, which diminishes the immersive ex-perience. Recent research has integrated language features from CLIPinto 3DGS via distillation, enabling open-vocabulary queries and sup-porting many downstream applications. Nevertheless, the stringent re-quirements of FVV, low frame size and high FPS, make current languageGaussian representations unsuitable for language-embedded FVV. In thispaper, we propose DLGStream, a novel language-embedded FVV repre-sentation that streams time-varying language features alongside Gaus-sian attributes to support 4D environment interaction, scene editing, andspatial intelligence. Specifically, we propose a dual-opacity dynamic lan-guage Gaussian representation, which maintains two opacity attributesfor color and language features to deal with performance degradationthat occurs when colors and features are jointly optimized. Further-more, we introduce an interpolation-based deformation field to reducetemporal redundancy. This deformation field can also be used for 4Dframe interpolation, boosting FVV sequences from low to high FPS. Ex-perimental results demonstrate that DLGStream achieves superior per-formance in both on open-vocabulary segmentation and reconstructionquality with an average frame size of merely 43 KB. The code is availableon https://github.com/kkkzh/DLGStream.

ECCV 2026

Discrete Diffusion Bridges for Spatiotemporally Aligned Image Translation and Generation

Xing Xie, Jiawei Liu, Shijun Zhou, Huijie Fan, Zhi Han, Yandong Tang, Liangqiong Qu

We propose Discrete Diffusion Bridges (DDB), a novel frame-work designed to resolve the fundamental spatiotemporal misalignmentof standard discrete diffusion in image translation and generation. Bycorrupting data into a pure mask state via a random schedule, the con-ventional forward process induces a twofold misalignment: spatially, thispure-mask destination entirely discards the rich structural priors of thesource image; temporally, the random masking order inherently contra-dicts the “easy-first, hard-last” decoding mechanism used during infer-ence. To address this, DDB constructs a direct and efficient trajectorybetween domains. Spatially, we introduce a hybrid absorption mechanismthat redefines the absorbing state to a stochastic mixture of mask andsource tokens, effectively injecting source prior as spatial anchors into thelatent space. Temporally, we design an information-guided noise schedulethat quantifies semantic variation to prioritize the corruption of high-information regions at earlier timesteps. This ensures the model learnsto resolve difficult semantic changes using robust context from invariantregions. Extensive experiments validate the versatility and robustnessof our framework across diverse generative paradigms. DDB effectivelybalances edit alignment with structural fidelity across both text-guidedsemantic manipulation and pure structural image translation, while in-herently complementing text-to-image generation and guaranteeing ro-bust high-quality decoding under extremely low sampling steps. Codeand models are available at https://github.com/HKU-HealthAI/DDB.

ECCV 2026

Cube-Splat: High-Fidelity 360° Gaussian Splatting SLAM via Cubemap Factorization and Adjoint-Consistent Optimization

Xiangfei Guo, Hao Shi, Yufan Zhang, Zhonghua Yi, maoyongqi maoyongqi, Xiaoting Yin, kaiwei wang

Recent progress in 3D Gaussian Splatting (3DGS) has en-abled dense visual SLAM with pinhole cameras, yet most pipelines arenot designed for panoramic imagery. We present Cube-Splat, the firstpanoramic GS-SLAM framework that factorizes each 360◦ frame into acubemap of four fixed-orientation virtual pinhole views sharing a singleoptical center. By designating the front face as the primary pose state, weaccumulate gradients from all faces via an adjoint mapping, thereby en-abling multi-face observations to coherently update a single state whilestrictly preserving cross-view geometric consistency. Concurrently, ourmapping module densifies and optimizes anisotropic Gaussians using ag-gregated cubemap rays for high-fidelity, dense reconstruction. Further-more, to rigorously evaluate panoramic SLAM under diverse and chal-lenging conditions, we introduce SynPano, a highly scalable, photoreal-istic synthetic dataset featuring parameterized complex trajectories andmulti-modal ground truth. Extensive evaluations on two public bench-marks (PALVIO and OmniBlender) and our SynPano dataset, collec-tively encompassing both indoor and outdoor scenes, demonstrate thatCube-Splat achieves state-of-the-art (SOTA) performance in tracking ac-curacy and reconstruction fidelity. Both the source code and the SynPanodataset are available at https://github.com/guoxf304/CubeSplat.

ECCV 2026

Code-in-the-Loop Forensics: Agentic Tool Use for Image Forgery Detection

Fanrui Zhang, Qiang Zhang, Sizhuo Zhou, Jianwen Sun, Chuanhao Li, Jiaxin Ai, Yukang Feng, Yujie Zhang, Wenjie Li, Zizhen Li, Yifan Chang, Jiawei Liu, Kaipeng Zhang

Existing image forgery detection (IFD) methods either ex-ploit low-level, semantics-agnostic artifacts or rely on multimodal largelanguage models (MLLMs) with high-level semantic knowledge. Althoughnaturally complementary, these two information streams are highly hetero-geneous in both paradigm and reasoning, making it difficult for existingmethods to unify them or effectively model their cross-level interactions.To address this gap, we propose ForenAgent, a multi-round interactiveIFD framework that enables MLLMs to autonomously invoke, execute,and iteratively refine Python-based low-level tools around the detectionobjective, thereby achieving more flexible and interpretable forgery analy-sis. ForenAgent adopts a two-stage training pipeline with Cold Start andReinforcement Fine-Tuning to progressively improve tool interaction andreasoning adaptability. We design a human-inspired dynamic reasoningloop with global perception, local focusing, iterative probing, and holisticadjudication, and implement it as both a data-sampling strategy anda task-aligned process reward. To support training and evaluation, webuilt FABench, a heterogeneous agent-forensics dataset with 100k imagesand about 200k agent-interaction question-answer pairs. Experimentsshow that ForenAgent exhibits emergent tool-use competence and re-flective reasoning on challenging IFD tasks when assisted by low-leveltools, charting a promising route toward general-purpose IFD. The codeis available at https://github.com/zfr00/ForenAgent.

ECCV 2026

Clue Matters: Empower Video Reasoning with Brain-Inspired Latent Clue Learning

Kaixin Zhang, Xiaohe Li, Jiahao Li, Haohua Wu, Xinyu Zhao, Zide Fan, Lei Wang

Multi-modal Large Language Models (MLLMs) have signifi-cantly advanced video reasoning, yet Video Question Answering (VideoQA)remains challenging due to its demand for temporal causal reasoningand evidence-grounded answer generation. Prevailing end-to-end MLLMframeworks lack explicit structured reasoning between visual percep-tion and answer derivation, causing severe hallucinations and poor in-terpretability. Existing methods also fail to address three core gaps:faithful visual clue extraction, utility-aware clue filtering, and end-to-end clue-answer alignment. Inspired by hierarchical human visual cogni-tion, we propose ClueNet, a clue-aware video reasoning framework with atwo-stage supervised fine-tuning paradigm without extensive base modelmodifications. Decoupled supervision aligns clue extraction and chain-based reasoning, while inference supervision with an adaptive clue filterrefines high-order reasoning, alongside lightweight modules for efficientinference. Experiments on NExT-QA, STAR, and MVBench show thatClueNet outperforms state-of-the-art methods by ≥ 1.1%, with strongbenchmark generalization, hallucination mitigation, and improved infer-ence efficiency. This work bridges the perception-to-generation gap inMLLM video understanding, providing an interpretable, faithful reason-ing paradigm for real-world VideoQA applications.

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