Detailed image captioning is usually treated as a global coverage task, but captions are generated as ordered sequences where later tokens can degrade in different ways. This paper introduces a position-aware rubric that decomposes captions into quartile-wise supported, hallucinated, and generic content, exposing two distinct failure modes: mid-caption unsupported specificity and tail generic escape. Motivated by this diagnosis, the authors propose PAGER, a GRPO reward with separate signals for middle evidence, tail density, and length control, achieving strong pairwise wins while preserving competitive global quality metrics.

HiSTEVE tackles the challenge of building Minecraft agents that can perform long-horizon tasks using only native keyboard-and-mouse control, without relying on environment APIs or predefined skills. It proposes a hierarchical VLA agent that pairs a capability-aware harness—equipped with structured memory, a VLM planner, and a policy-coupled completion detector—with a single low-level VLA policy for visuomotor execution. The core idea is to close the loop between high-level planning and raw pixel-to-action control in a partially observable open world, which matters for developing generally capable game-playing agents.

Fine-tuning diffusion models has yielded over 100K community adapters, but merging them interactively creates a high-dimensional, expensive black-box optimization problem: prior work requires live image generation in every round and is limited to a few dozen preselected adapters. HimmBO splits the search into a screening stage that recovers promising adapters via level-set estimation over a precomputed gallery, followed by a refinement stage that optimizes merge coefficients only within that low-dimensional active set. A style-aware Gaussian-process kernel transfers preference information between visually similar adapters, allowing the system to scale to libraries of over one thousand adapters while using under one tenth the image generations of prior work.

Multimodal sentiment analysis fuses linguistic, acoustic, and visual signals, yet standard fusion methods entangle modality-specific and cross-modal contributions in opaque joint representations. This paper proposes I2-KAN, a multi-pathway framework that explicitly maintains intra-modal and pairwise inter-modal information in separate pathways, refines each with a Task-Decoupled Linear-Gated Denoising Bottleneck, and fuses them via a structured Kolmogorov–Arnold Network. The core motivation is that a modality's standalone predictive utility can differ sharply from its interaction-conditioned utility, and the work aims to make this reasoning traceable at pathway, feature, and functional-edge levels.

Cast shadows are a clean, observable signature of a hidden physical variable: the direction of light. The authors introduce ShadowBench, a benchmark built from real photographs that asks VLMs to read, infer, and validate cast-shadow direction under carefully matched composites. They find a sharp dissociation—VLMs read visible shadows well yet infer and validate them at chance—and show that routing a VLM through an image generation model supplies the missing prior, motivating reconstruction-based objectives for illumination-aware VLMs.

This paper studies how non-expert malicious actors can use web-sourced knowledge and LLMs to evade AI-generated image detectors with simple CPU-based image processing. The authors introduce WildFakeToolBench, a benchmark of 156 validated executable tools derived from 220 public sources across 142 domains, synthesized without detector feedback. On 105 images from five generators, they evaluate six state-of-the-art detectors and demonstrate substantial, detector-specific vulnerabilities, with single-tool attack success rates reaching 25%–100% and worst-case multi-tool coverage exceeding 80% for every detector. The work matters because it quantifies a realistic, low-resource threat model grounded in public forums and tutorials, rather than in white-box optimization.

Lightweight Vision-Language-Action models such as SmolVLA rely on a single dense action expert for all manipulation tasks, which limits multi-task capacity under tight compute budgets. SmolVLA-MoE replaces the dense FFN layers in the action expert with sparse Mixture-of-Experts blocks—parallel expert FFNs, a token-level router, and a tunable shared expert—so each token adaptively selects a specialized expert without increasing inference cost. By keeping total activated parameters at ~500M (matching the dense baseline) while storing ~800M total parameters, the method raises average success on the LIBERO benchmark from 82.75% to 84.25% and outperforms the much larger OpenVLA-7B, offering a practical efficiency win for resource-constrained robot platforms.

ResGVC attacks the two core weaknesses of current generative video compression (GVC): decoder-side diffusion without explicit source guidance risks drifting away from the original video, and latent-space denoising suffers irreversible detail loss through the autoencoder bottleneck. The paper proposes to decompose the distortion between a learned codec reconstruction and the original pictures into a sequence of pixel-space residual segments, one per flow-matching denoising step, and to compress those residuals with a diffusion-transformer entropy model. By transmitting only the most fidelity-critical segments and generating the rest, ResGVC claims to advance both perceptual realism and signal fidelity, reporting >60% LPIPS and >80% PSNR BD-rate reductions over prior art.

This paper tackles batik image captioning when expert text annotations are scarce by injecting predicted class labels from a LoRA-adapted DINOv2 classifier into a 4-bit NF4 Qwen2.5-VL captioner. The core idea is that compact class supervision can guide vision-language generation in cultural-heritage settings where only sparse metadata exists. The authors evaluate whether the cascade retains correct cultural identities or propagates upstream classification errors across six captioning conditions. Their findings matter for digitizing heritage collections, though they are hedged by overlapping partitions and tiny local test sets.

Video foundation models now handle both understanding and generation of dynamic visual content, but the field lacks a principled way to relate these tasks to an underlying physical world. This survey proposes a world-modeling lens that partitions the literature into four operations—reading, writing, sharing, and interacting—each connecting observed video traces to a latent temporal world. By distinguishing mere temporal coherence in generated sequences from true consistency of the underlying world state, the work reframes evaluation criteria and identifies open challenges in building unified video world models.

The paper addresses vision-based policy learning for laparoscopic robotic manipulation, where end-to-end RL suffers from sparse visual signals and stringent precision requirements, while imitation learning demands scarce expert demonstrations. The authors propose distilling a privileged state-based teacher into an RGB-based student by matching their Gaussian action distributions, with particular emphasis on theoretically analyzing first- and second-order properties of matching objectives including forward KL, reverse KL, Jeffreys divergence, and Wasserstein distance. Extensive experiments in the SurRoL simulator indicate that uncertainty-aware distribution matching—especially Jeffreys divergence—achieves markedly more stable and higher success rates on complex surgical tasks than general RL or imitation learning baselines.

Feed-forward 3D models such as VGGT-Omega deliver excellent local geometry but are limited to short windows, so long videos must be processed as overlapping chunks. HiP3R addresses the resulting frame-level inconsistencies and scale drift with a hierarchical pose graph: a coarse chunk-level $\mathrm{Sim}(3)$ graph first recovers global layout and scale, and then a fixed-layout frame-level $\mathrm{SE}(3)$ graph estimates one shared camera per frame to reconcile conflicting cross-window predictions. It is purely post-processing, requires no retraining or fine-tuning, and applies to both chunk-based and streaming backends.

Category-level 6D pose estimation recovers object rotation and translation without instance CAD models, but prevailing methods force an unappealing trade-off between brittle intermediate alignments and costly iterative diffusion sampling. F2G-Pose addresses this by proposing a direct feed-forward architecture that lifts frozen foundation-model features onto partial point clouds, fuses local geometry via DGCNN and a Mixture-of-Experts Transformer, and jointly regresses pose, metric size, and camera-frame completed shape in one pass. Its practical significance is pushing deterministic regression to state-of-the-art synthetic-benchmark accuracy while running at 28 FPS and showing real-world robotic deployment potential, though its gains rely heavily on a strong frozen backbone and it offers no explicit uncertainty model.

As AI-generated videos approach photorealism, detectors must do more than classify real versus fake; they need to surface the specific visual artifacts that ground the decision. Verus-Insight decomposes artifact detection into three explicit capabilities—deciding what to inspect, verifying whether those inspections reveal genuine generation artifacts, and aggregating the verified evidence into a final verdict—and trains an MLLM with human-anchored, full-process supervision to strengthen each stage. The result is a detection system that improves not only video-level accuracy but also the fidelity of fine-grained artifact recovery, supported by a new benchmark with dense human annotations of spatiotemporal artifacts.

PhyEvolve addresses a core failure mode in text-to-video generation: user prompts underspecify physical mechanisms and state dependencies, leading to incoherent dynamics. The paper proposes a training-free, three-agent framework—Director, Physical Evolver, and Composer—that plans explicit causal state trajectories, including during occlusions, and rewrites the prompt before feeding it to a frozen video generator. The work matters because it lifts physical consistency into an explicit planning stage rather than hoping the diffusion model hallucinates correct physics from vague text.

Historical document binarization struggles with faded ink, bleed-through, and stains that behave differently across visible and near-infrared wavelengths. This paper proposes a conditional GAN with adaptive cross-spectral feature fusion at encoder depth $L=3$, gated by edge maps derived from a targeted 600 nm visible + 1000 nm NIR pair. On 30 SMADI pages under strict five-fold document-level cross-validation, the method achieves statistically significant gains in $F_1$, pseudo-FM, and IoU over fixed averaging and generic attention, showing that physics-informed structural guidance can regulate multispectral restoration without discarding any spectral band.

Single-image 3D generators perform well on curated studio photos but fail on real-world images distorted by blur, uneven illumination, or occlusions. Gate3DGen proposes a selective pre-processing pipeline: a degradation-aware router that leaves clean images untouched and a one-step diffusion purifier that repairs corrupted inputs before they reach a frozen 3D generator. The core insight is that improving the observation, rather than retraining the generator, can close most of the fidelity gap while running in a fraction of the time required by generic image editors.

As populations age, robotics and ambient AI are increasingly proposed to help older adults live independently in private homes and residential aged care (RAC) facilities. This chapter frames both settings as extreme human-robot interaction (HRI) environments—cluttered, dynamic, and technically under-supported—and surveys the sensors, perception algorithms, interaction designs, privacy safeguards, and evaluation frameworks needed to make such deployments viable. Its central thesis is that success requires moving beyond lab accuracy toward real-world operational metrics, privacy-by-design, and sustained acceptability.

Most cell-tracking links are trivial nearest-neighbor associations that bury rare but biologically critical errors in cell divisions and state changes. This paper introduces CellTrack-Bench, a hard-mined benchmark that isolates these difficult local associations from 15 datasets across four imaging modalities and evaluates whether vision-language models can act as cognitive correctors—by linking cells directly, repairing tracker graphs, or inspecting them for human review. It matters because it provides a controlled diagnostic setting to test whether frontier VLMs genuinely reason about biological dynamics or simply exploit spatial proximity.

Post-hoc calibration usually adjusts confidence scores globally, which ignores that neural networks can be miscalibrated differently in different regions of feature space. This paper proposes Local Adaptive Probability Estimation (LAPE), a residual framework that keeps an existing global calibrator as a stable anchor and adds a non-parametric local correction drawn from neighboring calibration samples in the learned feature representation. It also extends this idea to multi-view inputs through Cascade Feature Augmentation Fusion (CFAF). By exploiting local geometry without retraining, the work targets high-stakes settings where uniform global corrections leave spatially varying errors untouched.