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ECCV 2026

ARGOS: Who, Where, and When in Agentic Multi-Camera Person Search

Myungchul Kim, KwanYong Park, Junmo Kim, In Kweon

Existing person search methods assume access to complete visual queries or exhaustive tracking, yet real-world witness accounts are vague, partial, and spread across cameras and time. We introduce ARGOS (Agentic Retrieval with Grounded Observational Search), a benchmark and agent framework that recasts multi-camera person search from one-shot retrieval on a complete query into interactive reasoning from partial clues. To our knowledge, ARGOS is the first interactive benchmark to couple witness dialogue with camera-network topology, requiring an agent to plan, question, and eliminate under information asymmetry. An ARGOS agent receives a vague witness statement and must decide what to ask, when to invoke spatial or temporal tools, and how to interpret ambiguous natural-language responses, all within a limited turn budget. To ground reasoning in physical constraints, the agent accesses a Spatio-Temporal Topology Graph (STTG) encoding camera connectivity and empirically validated transition times. The benchmark comprises 2,691 tasks across 14 real-world scenarios in three progressive tracks: semantic perception (Who, 989 tasks), spatial reasoning (Where, 550 tasks), and temporal reasoning (When, 1,152 tasks). We propose TurnWeighted Success (TWS) as the primary metric, jointly measuring correctness and turn efficiency. Experiments with four LLM backbones show the benchmark is far from solved: the best agent achieves TWS of 0.383 (Track 2) and 0.590 (Track 3). Ablations confirm each component is essential: removing domain-specific tools drops Top-1 accuracy by up to 49.6 percentage points, and removing strategic reasoning halves TWS while barely affecting Top-1.

ECCV 2026

Compositional Non-Face Re-Identification Pressure under Cumulative Vision Releases

Tirth Joshi, Honggang Wang

Most privacy evaluations in computer vision treat datasets and models as isolated, static disclosures, failing to account for how risk accumulates in practice. We formalize Compositional Non-Face ReIdentification Risk, where identities remain linkable through non-face cues, such as clothing, gait, and scene context that aggregate across sequential releases of data expansions, model checkpoints, and metadata. We introduce the Vision Re-Identification Pressure Index (vRPI ), a foundational, information-theoretic measure derived from α Arimoto conditional Rényi entropy that quantifies the systemic erosion of anonymity. We prove that vRPI is monotone under cu- α mulative releases, establish an exact bridge to Bayes-optimal guessing probability, and derive additive decompositions under conditionalindependence benchmarks. Through the NFLeak protocol, we provide illustrative experiments on Market-1501-Tau and a CUHK03- Tau closed-world replication using a structured Attacker Ladder, together with masking-level, dependence-overlap, temperature-calibration, and non-uniform-prior diagnostics, demonstrating that vRPI accurately α tracks realized re-identification success even after explicit face removal. Our framework enables principled auditing of the “leaky bucket” effect in vision benchmarks, shifting the focus from single-release compliance to ecosystem-level privacy hygiene.

ECCV 2026

ConfCtrl: Enabling Precise Camera Control in Video Diffusion via Confidence-Aware Interpolation

Liudi Yang, George Eskandar, Fengyi Shen, Mohammad Altillawi, Yang Bai, Chi Zhang, Ziyuan Liu, Abhinav Valada

We address the challenge of novel view synthesis from only two input images under large viewpoint changes. Existing regression-based methods lack the capacity to reconstruct unseen regions, while camera-guided diffusion models often deviate from intended trajectories due to noisy point cloud projections or insufficient conditioning from camera poses. To address these issues, we propose ConfCtrl, a confidence-aware video interpolation framework that enables diffusion models to follow prescribed camera poses while completing unseen regions. ConfCtrl initializes the diffusion process by combining a confidenceweighted projected point cloud latent with noise as the conditioning input. It then applies a Kalman-inspired predict–update mechanism, treating the projected point cloud as a noisy measurement and using learned residual corrections to balance pose-driven predictions with noisy geometric observations. This allows the model to rely on reliable projections while down-weighting uncertain regions, yielding stable, geometry-aware generation. Experiments on multiple datasets show that ConfCtrl produces geometrically consistent and visually plausible novel views, effectively reconstructing occluded regions under large viewpoint changes.

ECCV 2026

Temporally Stable Generative Illumination with a One-Step Diffusion Model

Harish Anand, Alexandr Kuznetsov, Sungye Kim, Wojciech Uss, Wojciech Kaliński, Rama Harihara

The rapid rise of generative image synthesis has intensified interest in the graphics and vision community in producing realistic, controllable illumination. We present a generative method for screenspace global illumination (GI) that produces geometrically aligned, temporally stable indirect lighting using a single-step latent diffusion model. Unlike iterative and video diffusion methods that are costly and hard to control, our model conditions a one-step image generator on sceneintrinsic signals and sparse lighting hints for the current frame, encoded via adapter modules. To address flicker without the overhead of video diffusion, we introduce a temporal VAE decoder that plugs into a latent diffusion pipeline at inference time, delivering long-range temporal coherence while preserving spatial fidelity. On test data, our approach synthesizes high-quality indirect illumination well-aligned to inputs and improves temporal stability. Our quality evaluation demonstrates that our method generates visually and quantitatively plausible illumination in generative forward rendering, outperforming state-of-the-art methods.

ECCV 2026

HiAR: Efficient Autoregressive Long Video Generation via Hierarchical Denoising

Kai Zou, Dian Zheng, Hongbo Liu, Tiankai Hang, Bin Liu, Nenghai Yu

Autoregressive (AR) diffusion offers a promising framework for generating videos of theoretically infinite length. However, a major challenge is maintaining temporal continuity while preventing the progressive quality degradation caused by error accumulation. To ensure continuity, existing methods typically condition on highly denoised contexts; yet, this practice propagates prediction errors with high certainty, thereby exacerbating degradation. In this paper, we argue that a highly clean context is unnecessary. Drawing inspiration from bidirectional diffusion models, which denoise frames at a shared noise level while maintaining coherence, we propose that conditioning on context at the same noise level as the current block provides sufficient signal for temporal consistency while effectively mitigating error propagation. Building on this insight, we propose HiAR, a hierarchical denoising framework that reverses the conventional generation order: instead of completing each block sequentially, it performs causal generation across all blocks at every denoising step, so that each block is always conditioned on context at the same noise level. This hierarchy naturally admits pipelined parallel inference, yielding a ∼1.8× wall-clock speedup in our 4-step setting. We further observe that self-rollout distillation under this paradigm amplifies a low-motion shortcut inherent to the mode-seeking reverse-KL objective. To counteract this, we introduce a forward-KL regulariser in bidirectional-attention mode, which preserves motion diversity for causal inference without interfering with the distillation loss. On VBench (20s generation), HiAR achieves the best overall score and the lowest temporal drift among all compared methods.

ECCV 2026

Anchoring on Reality: Breaking the Pseudo-Target Ceiling in Makeup Transfer

Bo Wei, Xianhui Lin, Yi Dong, Zhongzhong Li, Zonghui Li, Zirui Wang, Jiachen Yang, Xing Liu, Hong Gu, Xiaoming Li, Wangmeng Zuo

Makeup transfer applies a reference cosmetic style to a source face while preserving its identity and geometry. However, this task is severely hindered by the lack of real paired training data. Current methods rely on either weak priors or synthetic pseudo-targets from largescale editing models. These paradigms provide suboptimal guidance, often leading to degraded fine-grained details, synthetic artifacts, and identity drift. To this end, we propose Anchoring on Reality Makeup Transfer (ART), a two-stage framework with a reality-anchored refinement cycle. In Stage I, the model is initialized with pseudo-targets to establish basic semantic alignment and global makeup placement. Crucially, Stage II shifts supervision from pseudo-targets to the real reference, reconstructing it from its bare-skin counterpart through a differentiable cycle that penalizes any omitted detail and overrides synthetic artifacts. Furthermore, we introduce MakeupFaces2K (MF2K), the first 2K-resolution in-the-wild makeup portrait dataset comprising 8,573 images. Extensive experiments demonstrate that our method achieves superior makeup fidelity, strong background stability, and robust identity preservation, especially for complex makeup styles.

ECCV 2026

When Thinking Hurts: Mitigating Visual Forgetting in Video Reasoning via Frame Repetition

Xiaokun Sun, Yubo Wang, Haoyu Cao, Linli Xu

Recently, Multimodal Large Language Models (MLLMs) have demonstrated significant potential in complex visual tasks through the integration of Chain-of-Thought (CoT) reasoning. However, in Video Question Answering, extended thinking processes do not consistently yield performance gains and may even lead to degradation due to “visual anchor drifting”, where models increasingly rely on self-generated text, sidelining visual inputs and causing hallucinations. While existing mitigations typically introduce specific mechanisms for the model to reattend to visual inputs during inference, these approaches often incur prohibitive training costs and suffer from poor generalizability across different architectures. To address this, we propose FrameRepeat, an automated enhancement framework which features a lightweight repeat scoring module that enables Video-LLMs to autonomously identify which frames should be reinforced. We introduce a novel training strategy, Add-One-In (AOI), that uses MLLM output probabilities to generate supervision signals representing repeat gain. This can be used to train a frame scoring network, which guides the frame repetition behavior. Experimental results across multiple models and datasets demonstrate that FrameRepeat is both effective and generalizable in strengthening important visual cues during the reasoning process.

ECCV 2026

DRPO: Disentangling Demographic Bias from Rewards for Fair Diffusion Alignment

YeonGyu Han, Junah Jung, Dongheon Lee

Text-to-image diffusion models are increasingly aligned with human preferences through preference optimization (PO) with learned reward models. We observe that the CLIP-based reward models used in this pipeline encode demographic biases, and that PO amplifies them: after alignment, the demographic distribution of generated images narrows significantly compared to the base model. We show that this degradation is progressive—race entropy drops steadily throughout training— and that existing diversity-preservation methods such as DRIFT and DiverseGRPO do not resolve it, because visual diversity and demographic fairness are distinct properties. To address this, we propose DRPO, which identifies demographic directions in the reward feature space via linear probing and removes them through orthogonal projection before reward computation. DRPO requires no demographic annotations during training and is compatible with any PO method. Experiments on Stable Diffusion and SDXL with six baselines demonstrate that DRPO maintains generation quality while substantially restoring the demographic balance lost during alignment.

ECCV 2026

Query-Kontext: An Unified Multimodal Model for Image Generation and Editing

YuXin Song, Wenkai Dong, Shizun Wang, Qi Zhang, Song Xue, Tao Yuan, Hu Yang, Haocheng Feng, Hang Zhou, Xinyan Xiao, Jingdong Wang

Unified Multimodal Models (UMMs) excel in text-to-image generation and editing but often entangle multimodal generative reasoning with high-fidelity visual synthesis. We introduce Query-Kontext, a novel approach that bridges a Vision-Language Model (VLM) and a diffusion model via multimodal “kontext” tokens. This design cleanly decouples the complex reasoning delegated to the VLM, including instruction understanding, grounding, and identity preservation, from the high-quality visual rendering executed by the diffusion model. We propose a three-stage progressive training strategy: (1) connecting the VLM to a lightweight diffusion head to activate generative reasoning; (2) scaling to a large, pre-trained diffusion model to enhance visual realism; and (3) incorporating a low-level image encoder for fine-grained instruction tuning. Supported by a comprehensive multi-task dataset, extensive experiments demonstrate that Query-Kontext matches or outperforms state-of-theart task-specific and unified methods across diverse reference-to-image scenarios.

ECCV 2026

Attribute Token Arithmetic: Disentangled and Continuous Semantic Control for Visual Autoregressive Models

Xindi Yang, Yicheng Wu, Cheng Zhang, Jianfei Cai, Tien-Tsin Wong

Autoregressive text-to-image generation has recently achieved remarkable progress, offering high-fidelity synthesis via a unified generative framework. However, fine-grained semantic control remains challenging due to the attribute entanglement and the misalignment between textual and fine-grained visual representations. In this paper, we introduce Attribute Token Arithmetic (ATA), a method that enables disentangled and continuous attribute control in visual autoregressive modelling. Inspired by the vector arithmetic property observed in word embeddings, ATA identifies semantic directions corresponding to visual attributes (e.g., aging, fatness, emotion) directly within the pretrained autoregressive latent space. These directions are learned from a single reference image, without model retraining or large-scale supervision. During generation, attributes can be continuously adjusted and compositionally combined through simple arithmetic operations with other attribute tokens. Extensive experiments demonstrate that ATA achieves identitypreserving, fine-grained, and multi-attribute adjustment, outperforming existing autoregressive editing baselines in controllability, generality, and computational efficiency. Our code will be released here.

ECCV 2026

GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking

Youngho Kim, Hoonhee Cho, Jae-Young Kang, Kuk-Jin Yoon

Feature tracking plays a fundamental role in understanding scene motion and supports various downstream tasks. Event cameras, with their high temporal resolution and asynchronous sensing, enable low-latency and motion-robust perception, making them well-suited for feature tracking under fast and non-linear motion. However, existing event-based feature tracking methods rely on fixed heuristic rules based on hand-tuning for event accumulation. Such strategies fail to adapt to diverse motion dynamics, leading to degraded performance under abrupt motion changes or low-motion scenarios. In this paper, we model event accumulation as a sequential decision-making problem and introduce reinforcement learning (RL) framework to adaptively control the accumulation process for online event-based feature tracking. Our approach trains a RL agent that decides whether to continue accumulating events or to perform tracking inference based on motion cues. The proposed adaptive temporal agent enables dynamic adaptation to varying motion patterns without relying on hand-crafted rules. Furthermore, we introduce a Dynamic Event-based Tracking (DEFT) dataset with dynamic motion distributions to evaluate the robustness of the feature tracking. Extensive experiments demonstrate that integrating our plug-and-play framework to existing feature tracking methods consistently outperforms heuristic-based approaches, improving robustness under dynamic motion while offering a better balance between tracking accuracy and efficiency. Our project codes and datasets are available at https://github.com/kmax2001/GoSTOP.

ECCV 2026

PercepTax: Benchmarking Cross-Property Reasoning in Vision-Language Models

Jonathan Lee, Xingrui Wang, Jiawei Peng, Luoxin Ye, Zehan Zheng, Tiezheng Zhang, Tao Wang, Wufei Ma, Siyi Chen, Yu-Cheng Chou, Prakhar Kaushik, Alan Yuille

Reasoning about objects in the physical world requires understanding both the spatial structure of a scene and the properties of objects within it. To perform reasoning tasks, a model must identify candidate objects from the scene and then reason over their properties, such as material, physical attributes, affordance, and function to satisfy the task constraints. We formalize this reasoning process as Perceptual Taxonomy, a hierarchical framework that organizes visual understanding into structured scene–object–property representations. Existing benchmarks, however, address only parts of this framework: some focus on spatial understanding alone, while others evaluate individual property families—material, affordance, or physical attributes—each under a separate schema. They fail to cover a challenging yet practically important capability: cross-property reasoning, where a model must simultaneously integrate reasoning across all property families to answer questions about physical environments. To evaluate this capability, we introduce PercepTax, a benchmark for perceptual taxonomy reasoning. We annotate 3,173 object classes with 54 fine-grained attributes across four property families: material, physical attributes, affordance, and function. The benchmark contains 5,802 images from both synthetic and real domains and 28,033 questions spanning object description, singleproperty recognition, 3D spatial reasoning, and cross-property reasoning. Experiments show that state-of-the-art vision language models (VLMs) perform strongly on object description and single-property tasks, but accuracy drops substantially on cross-property reasoning that requires integrating multiple attributes simultaneously. Oracle experiments with ground-truth objects’ properties with 3D positions and taxonomy-guided in-context learning both improve performance substantially yet remain well below human levels, confirming that both visual perception of individual properties and their cross-property integration are bottlenecks for VLMs.

ECCV 2026

Back-Tracking from Clarity: Self-Learning to See Text from Afar

Duc Tri Tran, Phi Le Nguyen, Minh Hoai Nguyen

We propose a self-supervised framework designed to enhance the capability of scene text detectors in identifying and recognizing text in scenarios where instances are shown at significant distances, typically small, blurred, and frequently missed by conventional models. Our approach leverages the high-fidelity performance of existing text spotting models on large, clear text as a foundational supervisor. By temporally back-tracking these high-confidence detections through video sequences, we automatically synthesize pseudo-labels for preceding frames where the distant text is still visually degraded or undersized. These pseudolabels enable training a student model specialized for early text detection, without requiring any manual annotation. The success of this approach depends on accurate pseudo-label generation, for which we develop a dedicated scene text tracker capable of maintaining consistent text identities across challenging video sequences. In addition, we propose SceneText50, a diverse multilingual outdoor dataset to facilitate training and evaluation. Experiments show that our framework significantly improves early detection accuracy and robustness across varied scenes and languages. Code and data are at https://github.com/trid2912/BackTrackingText.

ECCV 2026

RTE-FM-Dehazer: Radiative Transfer Equation Inspired Flow Matching for Real-World Image Dehazing

Chenfeng Wei, Chun Wang, Boyang Zhao, Si Zuo, Shenhong WANG, Chenguang Yang

Single-image dehazing aims to recover a clear scene from a hazy image and is generally formulated as an image-to-image translation task; however, it faces two limitations. Its performance depends heavily on the haze-formation priors embedded in the model. Prevailing methods adopt the Atmospheric Scattering Model (ASM), whose assumptions of single scattering and homogeneous media are often violated, leading to residual haze and color drift. Moreover, large-scale real hazy/clear pairs are impractical to collect, and existing synthesis approaches fail to reproduce the full complexity of natural haze. To address these issues, we present RTE-FM-Dehazer, a novel dehazing approach, together with a scalable data pipeline. Unlike the ASM, the Radiative Transfer Equation (RTE) jointly accounts for both scattering and absorption, naturally accommodating the non-homogeneous, multiple-scattering media that characterize real hazy scenes. Motivated by the structural similarity between the RTE diffusion-absorption term and the ODE in flow matching, we introduce a diffusion-absorption regularizer derived from a reduced RTE, to steer the flow matching trajectory at each step. Next, leveraging modern vision–language models, we build an automated pipeline and release P-HAZE, a dataset of 50000 realistic hazy/clear pairs. Extensive evaluations demonstrate that RTE-FM-Dehazer, trained solely on P-HAZE, effectively eliminates artifacts like residual haze and color drift, exhibits strong cross-domain generalization, and achieves leading results on five real-world dehazing benchmarks. Code and data are available at https://github.com/vincentweikey/RTE-FM-Dehazer.

ECCV 2026

Walking in the Implicit: Interactive World Exploration via Neural Scene Representation

Zhiqi Li, Chengrui Dong, Zhenhua Du, Hangning Zhou, Cong Qiu, Hailong Qin, Mu Yang, Dongxu Wei, Peidong Liu

Interactive video generation systems for camera-controlled world exploration roll out growing sequences of latent video frames, entangling state transition with high-frequency observation synthesis. We propose Walking in the Implicit, a scene-centric paradigm that changes the rollout variable from frame latents to a fixed-length, renderable implicit state, termed Neural Implicit Scene (NIS). This factorizes interactive generation into stochastic transition of a compact scene state and deterministic pose-conditioned rendering given the sampled state. We instantiate this paradigm as NeuWorld: a transformer VAE learns locally anchored NIS from sparse posed frames, and a diffusion transformer evolves NIS conditioned on future camera trajectories and geometryaware retrieved history. By reusing the VAE encoder as a unified conditioner, NeuWorld maps camera, reference-image, and history cues into the same NIS modality, avoiding external heterogeneous encoders. Trained from scratch on public posed-view data without pretrained video backbones or auxiliary 3D reconstructors, NeuWorld achieves strong longhorizon consistency with favorable inference efficiency.

ECCV 2026

Plug-and-Play Attention Linearization for Pretrained Transformers

Kenan Kassab, Alexey Kashevnik, Ammar Ali, Stamatios Lefkimmiatis

We introduce LATTE (Linearized Attention with Tunable Taylor Series Expansion), a training-free attention linearization method that selectively applies linearization to a subset of self-attention blocks in a pretrained vision transformer. LATTE approximates the softmax function via a second-order Taylor expansion centered on tunable anchor points, thereby enabling a hybrid architecture that seamlessly integrates standard self-attention with linear attention. This method preserves model performance without requiring any fine-tuning or retraining but only minimal calibration on the target dataset. LATTE is highly scalable, linearizing more than half of the attention layers retains more than 95% of the original model’s performance and reduces attention GFLOPs by up to 15× (e.g., from 16.384 to 1.082 at T=1000 for the CLIP attention block). We evaluate LATTE across diverse benchmarks, including image classification, object detection, and vision–language multimodal tasks, demonstrating consistent superiority over current state-of-the-art training-free linearization methods. Code will be released here.

ECCV 2026

Towards Memory-Efficient Autoregressive Video Generation via Instance-Specific Parametric Absorption

Xiaomeng Fu, Jia Li, Yiming Hu, Yong Wang, Hayden So, Jiao Dai, Xiangxiang Chu, Jizhong Han

Autoregressive (AR) streaming models have emerged as a powerful paradigm for long video generation. However, the linearly growing Key-Value (KV) cache poses a significant bottleneck, leading to memory overload and degraded inference throughput. A common compression method is to drop redundant KV tokens, which often breaks long-range dependencies, resulting in temporal flickering and identity loss. In this paper, we propose Instance-Specific Parametric Absorption (ISPA), a novel framework that shifts the KV cache compression from discarding to distilling. The core idea is to transit a subset of layers from FullAttention (F-Layers) to memory-efficient Local-Attention (L-Layers) by "absorbing" historical context into the model’s weights. Specifically, during a brief warmup phase, ISPA monitors the output discrepancy between global and local attention. At the transition point, we solve a closed-form least-squares problem to compute an instance-specific weight modulation that compensates for the missing history. Experiments across architectures (1.3B to 14B) demonstrate that ISPA can remove up to 50% of the KV cache with near-lossless visual quality. We hope this perspective encourages future work to explore parametric memory consolidation beyond external token-level cache management for streaming generative models.

ECCV 2026

SFD-Net: Sharp Feature Detection Network Based on Local Geometric Features

Inyoung Oh, Kwanghee Ko

Reliable detection of sharp features in point clouds—loci where the surface-normal field is discontinuous—remains challenging under increased local density variation and noise. SFD-Net couples a compact multi-scale Local Geometric Descriptor (LGD) with an enhanced PointNet++ backbone. LGD aggregates second-moment statistics of surface normal differences at three nested neighborhood scales, yielding rigid-motionand scale-invariant cues that remain stable under joint noise and spacing variability. On the ABC benchmark, SFD-Net achieves state-of-the-art F1-score and competitive False Positive Rate (FPR) across four noise conditions under a consistent evaluation setup. LGD further improves competitive detectors in a plug-and-play manner without architectural changes, and transfer to Stanford Large-Scale 3D Indoor Spaces (S3DIS) confirms generalization to real scans. Downstream applications potentially benefit from more reliable feature localization. Our source code and pretrained models are publicly available at https: //github.com/inyoungoh-cde/SFD-Net.

ECCV 2026

ID-LoRA: Identity-Driven Audio-Video Personalization with In-Context LoRA

Aviad Dahan, Moran Yanuka, Noa Kraicer, Lior Wolf, Raja Giryes

Existing video personalization methods preserve visual likeness but treat video and audio separately. Without access to the visual scene, audio models cannot synchronize sounds with on-screen actions; and because classical voice-cloning models condition only on a reference recording, a text prompt cannot redirect speaking style or acoustic environment. Although prompt-conditioned audio models could offer such control, they lack access to the visual scene. We propose ID-LoRA (Identity-Driven In-Context LoRA), which jointly generates a subject’s appearance and voice in a single model, letting a text prompt, a reference image, and a short audio clip govern both modalities together. ID-LoRA adapts the LTX-2 joint audio-video diffusion backbone via parameter-efficient In-Context LoRA and, to our knowledge, is the first method to personalize visual appearance and voice within a single generative pass. Two challenges arise from this formulation. Reference and generation tokens share the same positional-encoding space, making them hard to distinguish; we address this with negative temporal positions, which place reference tokens in a disjoint region of the RoPE space while preserving their internal temporal structure. Furthermore, speaker characteristics tend to be diluted during denoising; we introduce identity guidance, a classifier-free guidance variant that amplifies speaker-specific features by contrasting predictions with and without the reference signal. In human preference studies ID-LoRA is preferred over Kling 2.6 Pro, the leading commercial unified model with voice personalization capabilities, by 73% of annotators for voice similarity and 65% for speaking style. Automatic metrics confirm these gains: on crossenvironment settings, speaker similarity improves by 24% over Kling, with the gap widening as reference and target conditions diverge. A preliminary user study further suggests that joint generation provides a useful inductive bias for physically grounded sound synthesis. ID-LoRA achieves these results with only ∼3K training pairs on a single GPU. Code, models, and data are available at https://id-lora.github.io.

ECCV 2026

MindDrive: A Vision-Language-Action Model for Autonomous Driving via Online Reinforcement Learning

Haoyu Fu, Diankun Zhang, Zongchuang Zhao, Jianfeng Cui, Hongwei Xie, Bing Wang, Guang Chen, Hangjun Ye, Dingkang Liang, Xiang Bai

Current Vision-Language-Action (VLA) paradigms in autonomous driving primarily rely on Imitation Learning (IL), which introduces inherent challenges such as distribution shift and causal confusion. Online Reinforcement Learning offers a promising pathway to address these issues through trial-and-error learning. However, applying online reinforcement learning to VLA models in autonomous driving is hindered by inefficient exploration in continuous action spaces. To overcome this limitation, we propose MindDrive, a VLA framework comprising a large language model (LLM) with two distinct sets of LoRA parameters. The one LLM serves as a Decision Expert for scenario reasoning and driving decision-making, while the other acts as an Action Expert that dynamically maps linguistic decisions into feasible trajectories. By feeding trajectory-level rewards back into the reasoning space, MindDrive enables trial-and-error learning over a finite set of discrete linguistic driving decisions, instead of operating directly in a continuous action space. This approach effectively balances optimal decision-making in complex scenarios, human-like driving behavior, and efficient exploration in online reinforcement learning. Extensive experiments validate the efficacy of our online reinforcement learning framework, which outperforms state-of-the-art IL and offline Reinforcement Learning methods on the challenging Bench2Drive benchmark. To the best of our knowledge, this is the first work to demonstrate the effective application of online reinforcement learning to VLA models in autonomous driving.

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