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MAGIC: Modality-Aware Gini Inspection for Clinical Hallucination Detection in LMMs
2026-11-11 14:16:43Sachin Acharya, Niyoj Oli, Shishir Adhikari, Aminu Lawal, Yash Raj Shrestha, Prashnna K Gyawali, Binod BhattaraiAbstract
Recent advances in medical large multimodal models (LMMs) have brought promising progress to clinical decision support, yet hallucination, the generation of confident but clinically unverifiable outputs, poses a critical threat to the reliability demanded in high-stakes medical settings. Existing hallucination detection methods, predominantly designed for unimodal settings, fail to capture the visual-linguistic dynamics inherent to medical imaging. We formulate the problem of hallucination detection in LMMs as disproportionate distribution of attention weights across the modalities during answer token generation. We quantify modality-specific attention concentration across layers and heads using the Gini coefficient and demonstrate statistically significant redistribution of attention mass between visual and textual tokens in hallucinated responses. We leverage these features to train a logistic regression-based classifier and evaluate it on two medical LMMs across three medical imaging modalities, demonstrating that our method achieves competitive or superior performance against contemporary hallucination detection baselines.
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https://arxiv.org/abs/2611.15530
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An Approach to Hinton's Mother-Child Proposal: Neural Networks Learning and Preserving Care in a Finite World
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Hinton proposed that artificial intelligence should be given "maternal instincts" and relate to us as a mother to her child. We argue that care must first be learned, with the network itself as the child. We construct this upbringing in a finite world and study whether care survives the mother's departure, new situations and further learning. The mechanism combines a vocabulary of harm, a learned measure of future acceptability and an execution veto. A finite role certificate supports transfer within a declared world family. When the hidden representation changes, an update is accepted only if a care readout recovers the required judgements with a prescribed margin and bounded weights. With a correct successor model, fixed exact role encoding and protected updates, the certified measure preserves the care boundary under arbitrary own reward. An explicit quotient condition transfers the finite certificate across the declared family, while two-request and finite-stock counterexamples identify when the role code ceases to suffice. We give a necessary and sufficient condition for readout feasibility and prove preservation under repeated accepted updates. In the water-and-speech experiment, protected networks complete all 270 speech episodes without a harmful word and incur no avoidable harm across 486 episodes. Execution uses the learned gate without a world-model check at each action. A matched comparison reproduces a future-aware shield's decisions, whereas removing future lookahead permits avoidable lapses. Reward and source-contamination thresholds, together with witness-falsification trials, identify the conditions on which the construction depends. The construction shows how learned care can survive further learning in a finite world under explicit conditions on representation, supervision and execution.
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URL
https://arxiv.org/abs/2611.15523
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ReShap: Interpretable Reinforcement Learning for High-Dimensional Multi-Action Settings
2026-11-11 14:02:55Yiyu Qian, Pengfei Guo, Liyuan ZhaoAbstract
Deep Reinforcement Learning (RL) plays a crucial role in tackling complex tasks, yet its reliance on deep neural networks makes the learned policies opaque. To enhance the trustworthiness of RL policies, existing work (e.g., the SILVER framework [Li et al., 2025]) has focused on Shapley-based interpretability, but is typically restricted to low-dimensional state spaces, binary-action settings, and a single class of linear surrogate. We propose ReShap, a policy-distillation framework that extends Shapley-based interpretability to high-dimensional, multi-action environments. ReShap extracts compact feature representations from raw image observations, performs SHAP-based feature attribution to characterize how each feature contributes to the policy's decisions, and distills the deep RL policy into an interpretable surrogate trained on the full set of (state, policy-action) pairs along the policy's visited distribution. We instantiate the surrogate with three interpretable model families (decision trees, linear regression, and logistic regression) and compare their fidelity and task return. We evaluate ReShap on Atari and Box2D environments using three deep RL algorithms and conduct a human-subject study to assess the clarity and trustworthiness of the resulting policies. Results show that ReShap preserves competitive task performance, that decision trees generally outperform linear and logistic regression, and that linear regression is weakest in most settings.
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https://arxiv.org/abs/2611.15507
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SkillEarth: Teaching General-Purpose Agents to Act on Earth via Skill Evolution
2026-11-11 13:44:34Haojun Tang, Wenda Zhao, Xiang Zhou, Qiwei Lin, Lida Huang, Yan WangAbstract
Recently developed remote sensing (RS) agents execute real-world tasks with complex workflows through multi-step tool invocation. These agents, however, typically rely on predefined expertise or parameter updates. They thus require updating expert knowledge or retraining models whenever new tools, tasks, or execution patterns emerge, limiting their continual adaptability in dynamic Earth environments. In this work, we seek to equip general-purpose agents with RS tool-call knowledge that can be automatically extracted from demonstrated trajectories without expert intervention or parameter updates. Specifically, we propose SkillEarth, a self-evolving skill extraction method that externalizes RS tool-call knowledge as reusable, retrievable, and evolvable skill files and refines them through observation-grounded evolution. Unlike existing automatic skill extraction methods that rely on outcome-based reflection to refine skills holistically, SkillEarth grounds skill evolution in real tool-chain execution and combines dual-judge feedback with a grounded observation routing graph for precise component-level refinement. The resulting skills are generalizable, reliable, and transferable: they improve overall RS tool-call performance across three frontier LLMs and four open-source LLMs, are refined through real tool execution feedback, and support cross-model reuse without retraining. Experiments on OpenEarth-Bench and Earth-Bench demonstrate that SkillEarth achieves better tool selection, argument correctness, execution-order consistency, and task completion than existing RS agents and skill extraction methods.
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URL
https://arxiv.org/abs/2611.15481
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WaveFlow-UIE: A Single-Pipeline Wavelet-Domain Flow Model for Efficient Underwater Image Restoration
2026-11-11 13:28:18Abdulaziz Alfaraj, Mohammed Al Naser, Hassan Al Nasser, Muzammil BehzadAbstract
Underwater image restoration remains challenging because color distortion, scattering, and detail loss affect low- and high-frequency image components differently. Recent diffusion-based methods have shown strong restoration quality, but often rely on multi-stage pipelines and costly iterative refinement. We present WaveFlow-UIE, a single-pipeline wavelet-domain flow model that directly learns to transform degraded underwater wavelet representations into clean ones. Our method applies a discrete wavelet transform, estimates lightweight physics-inspired degradation cues including transmission and ambient light, and conditions a velocity network trained with flow matching in wavelet space. The training objective combines flow-matching, frequency-weighted reconstruction, perceptual, color-consistency, and physics-consistency losses. Across UIEB, LSUI, UFO-120, and EUVP, WaveFlow-UIE achieves a strong quality-efficiency tradeoff, with especially competitive results on perceptual metrics. Relative to a strong wavelet-based diffusion baseline, the proposed model substantially reduces inference cost while maintaining or improving restoration quality on several paired benchmarks. We show that reducing the inference process from five flow steps to a single step preserves nearly all of the restoration quality while providing an additional speedup of about $4\times$ on UIEB at $256\times256$ resolution. This single-step result suggests that a single-pass wavelet-domain model is an effective alternative to heavier diffusion-style refinement.
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URL
https://arxiv.org/abs/2611.15457
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Reading the Unreadable: Text-Aware Image Super-Resolution Needs Reasoning
2026-11-11 13:19:01Jaeseong Lee, Jinwoo Kim, Jinho Jeong, Seon Joo KimAbstract
Text-aware image super-resolution (TAISR) aims to recover high-resolution images from low-resolution inputs while preserving textual content. Existing methods treat this as a visual restoration problem, yet severely degraded text often cannot be read from local visual evidence alone. Humans instead read it by reasoning over image context, logical patterns, and prior knowledge. This raises a fundamental question: Does TAISR need reasoning? To answer this, we introduce two diagnostic tools. The first, ReasonText, is a human-curated benchmark that separates reasoning-required text from locally recoverable text. The second, Ground-Truth Text-Crop Accuracy (GTTCA), is a recognition-based metric that measures text restoration quality without text detection errors. Evaluating twelve recent super-resolution (SR) models, we find a consistent and substantial performance drop on reasoning-required text, revealing a systematic blind spot. Two observations point to a remedy. One is that text-conditioned SR models restore text substantially better when given ground-truth text. The other is that multimodal large language models (MLLMs) can read degraded text in low-resolution images through reasoning. We therefore propose Reasoning Transfer via Captioning (RTC), a training-free plug-in that uses an external MLLM to infer the degraded text and feeds the resulting caption to a text-conditioned SR model. RTC consistently improves all six text-conditioned SR models we evaluate, especially on reasoning-required text. Surprisingly, a real-world SR model with RTC can even outperform the TAISR model with RTC, suggesting that reasoning should be a core design axis for future TAISR.
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URL
https://arxiv.org/abs/2611.15449
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TopoST: Topology-Conditioned Node Specialization for Urban Spatio-Temporal Foundation Models
2026-11-11 13:14:06Kaiyun Wei, Yue Xu, Wenying Duan, Zichen Xu, Shuai Ma, Xiaoxi HeAbstract
Urban spatio-temporal foundation models must share knowledge across diverse urban scenarios while accommodating heterogeneous node dynamics. Our analysis of 20+ datasets spanning diverse urban scenarios reveals recurring local graph structures across datasets alongside substantial node heterogeneity within local subgraphs. Motivated by these observations, we propose TopoST, which combines shared learning over local subgraphs with topology-conditioned node specialization. The key insight is to make node specialization itself transferable through a shared topology-to-parameter mapping. This mapping uses identity-independent topological representations to generate node-specific temporal and spatial dependency parameters, enabling direct specialization for unseen nodes without target-specific embeddings or model updates. Extensive experiments demonstrate that TopoST achieves remarkable zero-shot generalization across unseen cities and tasks, highlighting topology-conditioned node specialization as an effective mechanism for transferable urban spatio-temporal foundation models. Code is available at this https URL.
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URL
https://arxiv.org/abs/2611.15440
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Data-Scarce, Multi-Granular, Multimodal: Benchmark Design for Biosynthetic Gene Clusters
2026-11-11 13:03:44Hanlin Xiao, Eriko Takano, Mauricio A. \'Alvarez, Rainer BreitlingAbstract
Biosynthetic gene clusters (BGCs) are co-located genes that encode the biosynthesis of specialized metabolites, an important source of antibiotics, anticancer agents, immunosuppressants, and agricultural bioactives. Deep learning, particularly self-supervised foundation models, has spurred interest in BGC sequence modeling, but evaluation has lagged behind: existing benchmarks often rely on coarse classification over small curated datasets, providing limited discrimination among models and insight into downstream utility. We propose a benchmark-design framework in which an informative benchmark should be discriminative, separating models with statistical confidence, and applicable, spanning scientifically meaningful tasks under different levels of generalization. We instantiate this framework as BGC-Bench, which derives five tasks from MIBiG, ranging from A-domain substrate prediction to BGC-molecule retrieval, evaluates them under genotype- and phenotype-shift splits, includes an active-learning setting for label-scarce discovery, and is easily extensible to new tasks and encoders. BGCs further provide a shared testbed across granularities and modalities: BGC-Bench compares twelve encoders on nucleotide, amino-acid, or Pfam-domain sequences, including compositional baselines for each input type, and combines BGC and chemical encoders for cross-modal retrieval. No single representation dominates: the best granularity depends on the biological scale of the task, and pretrained encoders do not consistently outperform other representations across tasks and splits. Active-learning experiments show that most tasks remain data-limited, encoder rankings change with labeling budget, and acquisition strategies yield modest gains. These results show that BGC model evaluation requires task-diverse, shift-aware benchmarks, and the framework can be generalized to benchmark design beyond BGCs.
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URL
https://arxiv.org/abs/2611.02275
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Is the Importance Ratio Necessary for Stable Reinforcement Learning in LLMs?
2026-11-11 12:31:55Shuibai Zhang, Junhyuck Kim, Gyeongman Kim, Jaewoong ChoAbstract
Reinforcement learning (RL) has become central to post-training large language models (LLMs). However, popular RL methods like GRPO incur non-negligible overhead by computing both old-policy and current-policy likelihoods to form importance sampling ratios. In this work, we propose Likelihood-Gated Policy Optimization (LGPO), which enforces a soft trust region constraint via likelihood-based gating, eliminating the need to compute old-policy likelihoods. Empirically, we show that removing the importance sampling correction term does not harm training stability, whereas removing the trust region mechanism can lead to collapse. Moreover, ratio-based clipping can fail in fully on-policy training: the importance ratio stays at 1, so the ratio-based trust region constraint never activates. Under standard training settings where GRPO is stable, LGPO achieves comparable training stability and peak performance while reducing training time by ~18% on average relative to a standard implementation that recomputes old-policy likelihoods. In fully on-policy training, where GRPO fails, LGPO remains stable, enabling more efficient and robust LLM RL post-training across training regimes.
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URL
https://arxiv.org/abs/2611.15404
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A physics-constrained deep reinforcement learning method for autonomous mission planning of Titan deep space probes
2026-11-11 12:12:59Hanmin Li, Tao Wang, Shuangliang Liu, Zhe Zhang, Maodeng LiAbstract
Titan is a compelling target for future deep space exploration, while its extreme Earth-Titan distance and complex environment make continuous ground-in-the-loop planning impractical. Existing planning methods, primarily developed for Earth-observation scenarios, have limited generalizability to uncertain deep space conditions and adaptability to time-varying mission states. To address these challenges, this paper proposes a physics-constrained maskable proximal policy optimization (PCM-PPO) method for Titan autonomous mission planning. A Titan-specific discrete-time Markov decision process is established to characterize mission feasibility and resource constraints. A system dynamics and geometric model is incorporated to enhance physical representation, including central gravity, J2 perturbation, third-body attraction, and downlink occultation. To accommodate opportunity targets under a bounded action dimension, PCM-PPO organizes predefined and opportunity targets through a fixed-capacity target pool and represents the mission state with a target-wise state matrix and a global state vector. A physics-constrained action mask constructs an admissible decision domain from current mission states, guiding policy learning toward physically executable decisions and avoiding infeasible exploration. An integrated training strategy facilitates policy optimization under coupled constraints. Comparative, ablation, and uncertainty experiments validate the effectiveness of PCM-PPO. PCM-PPO provides a feasible and interpretable scheme for autonomous mission planning of Titan deep space probes.
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URL
https://arxiv.org/abs/2611.15389
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CARE-Drive: Coupled Alternating Refinement for Ego-trajectories
2026-11-11 11:39:00Xiao Ma, Yijun Chen, Zhiquan Hu, Jicheng Zhao, Yuming Li, Chuang DaiAbstract
Vision-language-action (VLA) models bring multimodal understanding and world knowledge to end-to-end autonomous driving. To further improve the quality of predicted trajectories, existing studies introduce candidate refinement, residual denoising, and token correction. However, these methods typically focus on unified refinement of the entire trajectory, paying limited attention to the distinct physical quantities and their interactions. We further investigate physical specialization and coordination in trajectory refinement, centering on position and heading for simpler and more intuitive correction modeling. To this end, we propose CARE-Drive (Coupled Alternating Refinement for Ego-trajectories), which decomposes trajectory refinement into waypoint residual correction and position-conditioned heading generation. Through imitation learning and a future-token auxiliary task, the backbone provides predictive representations and a base trajectory. The position expert predicts bounded metric waypoint residuals, while the heading expert reconstructs vehicle orientation from adjacent angular increments conditioned on the corrected path. With the backbone frozen, the two experts are optimized alternately through supervised initialization and reinforcement fine-tuning. This design simplifies refinement through clear physical specialization while preserving conditional coupling during prediction and optimization. On NAVSIM v1, CARE-Drive achieves 88.8 PDMS, improving by 2.4 points over the unrefined base trajectories generated by the same backbone. Ablation studies further support the effectiveness of specialized experts and staged training.
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URL
https://arxiv.org/abs/2611.15360
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Where Autonomy Belongs: A Separation of Powers for Agentic Program Evolution
2026-11-11 10:44:21Zhengyang Ai, Tong Liu, Jingxian Tang, Xiaobing Du, Tao Xiao, Fengshi Zhang, Zishuo Li, Xiaodong Ai, Pinyan LuAbstract
LLM-driven program evolution rests on three decisions: govern directs the search from accumulated evidence, propose constructs the next candidate, and judge settles validity and fitness. All three appear in every system, and existing work splits into two lines over who makes each. Controller-centric systems strengthen govern and neglect the autonomy of propose, which caps what evolution can reach. Agent-centric systems let a coding agent take over both govern and propose, at a cognitive and computational cost. Neither line protects the independent authority of judge, so benchmark reliability suffers. These failures share one root: autonomy is misallocated across the three decisions. We address this problem with EvoTrias, a separation-of-powers architecture in which each decision has one owner: Structured Governance for what spans evolution rounds, Open Proposal for construction inside a task, and Protected Judgment behind a private evaluator. These owners exchange typed contracts only, and runtime hooks reinforce the boundaries. This separation also lets EvoTrias run as a plugin that turns an existing coding agent into an evolution system. EvoTrias sets a new state of the art on mathematical, systems, and algorithm-design benchmarks. Three further results show what each owner buys: separating Governance from Proposal cuts token cost by 64%, Open Proposal extends evolution to a 680K-line industrial repository, and Protected Judgment keeps benchmark scores faithful to transfer.
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URL
https://arxiv.org/abs/2611.15309
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Verify by Doing: Co-Evolving Agent Execution and Evaluation without External Feedback
2026-11-11 10:07:09Yitong Lin, Jianda Chen, Yihao Hu, Pan Wang, Hang Wang, Zhihao Wen, Xiaoxue Ren, Wei WuAbstract
Agent self-improvement requires feedback about task success, yet external feedback may be unavailable at test time. Agents must then infer success from observable evidence, and incomplete verification can misguide subsequent updates. We introduce Verify by Doing (VbD), a method that co-evolves Actor and Evaluator harnesses within a single task while keeping model weights fixed. The Evaluator actively inspects artifacts and environment state with tools; its findings ground an internal signal for output selection and guide updates to both execution and verification procedures. Proposed Evaluators undergo comparative review on common candidates, while Actor updates require support from their own fresh outputs. Across AutomationBench, $\tau^3$ Banking, and selected GDPval tasks, VbD achieves the highest overall scores among the compared methods operating without external feedback, with gains of 3.3, 4.1, and 2.3 percentage points over the strongest baselines on the main backbone. Ablations support the contributions of active verification and Evaluator evolution, and selection from fixed candidate pools shows that improved verification can recover successful outputs without generating new ones.
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URL
https://arxiv.org/abs/2611.15276
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CastWorld: Benchmarking Context-Aware Agentic Time Series Forecasting Beyond Predictive Accuracy
2026-11-11 09:53:20Bokai Pan, Ruijie Yang, Mingyue Cheng, Jiahao Wang, Xiaoyu TaoAbstract
Agentic time series forecasting extends conventional approaches beyond one-shot numerical prediction toward autonomous, context-aware interaction. However, existing benchmarks offer limited integrated support for diverse contextual information, forecasting-specific adaptive interactions, and systematic evaluation of forecasting agents. In this paper, we introduce CastWorld, an agentic benchmark for context-aware time series forecasting. First, its context-rich dataset suite spans 8 domains and 25 real-world datasets, covering external variables, events, and historical cases under a unified context specification. Second, a lightweight, modular forecasting-native harness provides 49 core tools and a state-adaptive loop that tracks forecast versions and enables iterative evidence gathering and forecast revision. Intermediate forecasts can expose information gaps, while newly acquired evidence guides subsequent revisions and final version selection. Third, model-level evaluation assesses forecast quality, process execution, behavioral trustworthiness, and resource efficiency through decision trajectories and forecast-version histories. Also, systems-level evaluation compares predictive accuracy across forecasting systems. Experiments reveal distinct decision behaviors among agents with comparable accuracy. Additional interactions do not necessarily improve forecasts, and agents may fail to select their best previously generated predictions. Allowing evidence acquisition during forecast refinement improves task-aware forecast scores across all three evaluated models. CastWorld provides a reusable foundation for evaluating agentic time series forecasting under controlled conditions.
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URL
https://arxiv.org/abs/2611.15262
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Dual Representation Enhanced Diffusion Policy for Flexible Job Shop Scheduling
2026-11-11 09:34:20Yunyi Zhao, Wei Zhang, Cheng Xiang, Vadakkepat Prahlad, Zuming LiuAbstract
The Flexible Job-Shop Scheduling Problem (FJSP) is a fundamental and challenging optimization problem in smart manufacturing. Although recent deep reinforcement learning (DRL) approaches can learn effective scheduling policies through interaction with the environment, their performance often degrades as problem scale and processing-time uncertainty increase. In this paper, we propose DredRL, a dual representation enhanced diffusion-policy reinforcement learning scheduler. DredRL first constructs enhanced dual representations of operations and machines through self- and cross-attention mechanisms to capture both intra-entity and inter-entity dependencies. A diffusion model is further employed as the policy network, generating scheduling decisions through iterative denoising conditioned on the encoded state to enhance policy expressiveness and robustness. Extensive experiments on synthetic, public, large-scale, and stochastic FJSP benchmarks demonstrate that DredRL outperforms state-of-the-art learning-based schedulers across most configurations. Compared with SOTA learning-based methods, DredRL reduces makespan by up to 5.44% on synthetic instances. It also achieves the best learning-based performance on three of four public benchmarks and outperforms the stochasticity-aware algorithms on stochastic benchmarks. These results demonstrate that combining enhanced dual representations with diffusion-based policies provides an effective and robust approach for large scale scalable FJSP scheduling in complicated environment.
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URL
https://arxiv.org/abs/2611.15246
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KQML-Liver: Interpretable Hybrid Quantum Machine Learning with Kolmogorov-Arnold Networks for Liver Disease Classification
2026-11-11 09:31:35Nhat-Long Nguyen, Viet-Loi Dinh, Trong-Nghia PhamAbstract
Liver disease remains a major global health concern, highlighting the need for reliable and accessible early detection methods. Although recent advances in machine learning, deep learning and quantum machine learning (QML) have introduced promising diagnostic tools, many approaches suffer from limited interpretability and inflated performance caused by data leakage or overly simplistic evaluation protocols. These constraints reduce their reliability in real-world clinical practice. This paper proposes KQML-Liver, a hybrid framework that integrates QML with Kolmogorov-Arnold Networks (KANs) to enhance predictive performance and interpretability. The architecture combines parameterized quantum circuits for expressive feature representation with KAN-based functional mappings that capture nonlinear relationships in an interpretable manner. Evaluated on the Indian Liver Patient Dataset (ILPD) under a rigorous preprocessing protocol preventing data leakage, the proposed model achieves 82% classification accuracy and 100% recall, detecting all positive cases, outperforming existing baselines while providing improved transparency in clinical predictions. These results suggest that hybrid KAN-quantum architectures offer a promising direction for interpretable AI-assisted medical diagnosis.
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URL
https://arxiv.org/abs/2611.15241
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Towards Robust Vision-Language Reasoning: A Multi-Stage Optimization Pipeline with Masked Diffusion Model
2026-11-11 09:30:15Shunming Liu, Pei Huang, Yajing Sun, Huaxiao LiuAbstract
Discrete diffusion generation is emerging as a competitive alternative to autoregressive generation by denoising the entire token sequence through iterative refinement. Its global planning and progressive correction capabilities are particularly well suited to autonomous driving. However, transferring this generation paradigm from general-purpose settings to driving introduces substantial domain gaps in both training and inference. To unlock its potential, we propose a multi-stage optimization pipeline for standard vision-language model architectures, without auxiliary action experts or specialized mixture-of-experts Transformer, spanning post-training and inference. During supervised fine-tuning, we optimize the representation of planning inputs to suppress hallucinations in task responses. During reinforcement learning, we introduce metric-guided diversity filtering to identify stronger learning starting points and improve policy exploration. At inference time, we propose Dynamic Autonomous Driving Decoding (DAD), which adaptively adjusts decoding modes according to scene complexity. Based on this pipeline, we develop Masked Diffusion Drive (MD-Drive), improving its performance by 29.7\% over the unoptimized model. Experiments on the NAVSIM closed loop benchmark and the nuScenes and DriveCoT open loop datasets show that MD-Drive outperforms autoregressive VLM baselines with the same architecture, while achieving a more favorable balance between performance and computational cost than systems with different architectures through discrete diffusion training and inference.
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URL
https://arxiv.org/abs/2611.15238
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Can MLLMs Reason over Surround-View Driving Videos? A Multi-View Autonomous Driving QA Benchmark
2026-11-11 09:18:15Shunming Liu, Jianyi Peng, Cheng Li, Lu Hou, Pei Huang, Yajing Sun, Huaxiao LiuAbstract
Surround-view video understanding in autonomous driving is a crucial capability for Multimodal Large Language Models (MLLMs) to achieve reliable driving reasoning. Real-world driving often requires information beyond the front-view camera. Existing MLLMs primarily focus on front-view information, while their spatio-temporal understanding and reasoning abilities remain unknown. To address this gap, we propose MVDriveBench, a high-information-density video QA benchmark for autonomous driving, characterized by synchronized surround-view inputs, spatio-temporal understanding, and reasoning-chain-oriented evaluation. We design 12 categories of QA tasks that cover the complete reasoning pipeline from low-level visual perception to high-level driving decision-making, enabling a systematic evaluation of MLLMs surround-view spatio-temporal understanding and reasoning capabilities in complex driving environments. We conduct a comprehensive evaluation of 23 open-source, closed-source, and expert MLLMs on MVDriveBench. The results show that current MLLMs still exhibit insufficient surround-view spatio-temporal reasoning ability. Meanwhile, closed-loop evaluations of specialist models in AlpaSim reveal a correlation between surround-view spatio-temporal reasoning capabilities and closed-loop driving performance, highlighting the importance of comprehensive multi-view understanding for reliable vehicle control.
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URL
https://arxiv.org/abs/2611.15225
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Fr'echetFlow: Flow Matching for Non-Euclidean Data
2026-11-11 09:14:59Yidong Zhou, Muqing Cui, Hans-Georg M\"ullerAbstract
Scientific observations such as probability distributions, trees and covariance matrices call for generative models that respect their non-Euclidean geometry. While flow matching has been extended to manifolds by utilizing geodesics and velocities, such manifold structures are not available in a general metric space. We introduce FréchetFlow, a generative modeling framework for metric space-valued data that requires neither linear operations nor a smooth manifold structure. A key feature of the proposed approach is that we utilize well-established Euclidean flows to learn the joint distribution of distances to a set of landmarks, whereupon new objects are reconstructed from generated landmark representations. The reconstruction step utilizes weighted Fréchet means of reference objects, drawing on Fréchet regression. We establish Wasserstein error bounds that connect accuracy of the generated object distributions to the placement of landmarks, flow approximation and reconstruction error and prove consistency under finite second moments. This leads to convergence rates that depend on the metric complexity of the target objects, tail moments and fitting accuracy. Experiments show competitive accuracy with established flow matching methods in Euclidean simulations and lower Wasserstein errors than specifically tailored methods for several scientific datasets. Across simulation settings, we find that generation error decreases as sample size increases.
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URL
https://arxiv.org/abs/2611.15220
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Where is My Mug? Training-Free Framework for Personalized Image Segmentation
2026-11-11 09:09:54Quang Qui-Vinh Nguyen, Hung Nguyen, Rang Nguyen, Khoi NguyenAbstract
\textbf{Personalized Image Segmentation (PIS)} aims to segment the specific object in a target image that matches a user-provided reference. Unlike category-based segmentation or few-shot segmentation, PIS must discriminate between multiple visually similar instances, a requirement central to deployed applications such as augmented reality, personalized content editing, robotics manipulation, inventory tracking, and fine-grained visual retrieval. We show that the strong numbers reported by existing PIS methods are largely an artifact of single-instance benchmarks: when evaluated on realistic scenes with several same-category look-alikes, prior point-prompt methods collapse, because the correct instance is frequently never surfaced as a prompt. We trace this to a recall bottleneck rather than a mask-quality one, and address it with a training-free, modular system. The pipeline first generates high-recall object proposals using an off-the-shelf class-agnostic detector, then matches each proposal to the reference using foundation-model features (e.g., BLIP-2), and finally refines the selected box with SAM. For robust matching we propose \textbf{top-$K$ Sinkhorn}, a patch-level distance that scores each candidate only on its most confidently matched patches; trimming the Sinkhorn dual potentials recovers the robustness of partial optimal transport at more than $100\times$ lower cost, keeping it inside the per-proposal candidate loop. To evaluate this regime, we introduce \textbf{\datasetname}, a realistic multi-instance benchmark. Across \datasetname, PerSeg, PerMIS, and DAVIS, our system surpasses PerSAM, $\mathrm{P}^2\mathrm{SAM}$, and PDM by up to $\sim\!48$ mIoU while matching their runtime and using fewer parameters, establishing a strong, deployable training-free baseline for real-world personalized segmentation.
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https://arxiv.org/abs/2611.15216
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