Stand-up comedy depends as much on timing and embodied presence as on verbal content, yet computational humor has largely focused on text alone. This paper introduces TIC-TALK, a multimodal corpus of 90 professionally filmed Netflix specials (2015–2024) with temporally aligned annotations for language, gesture, and audience response. The processing pipeline combines BERTopic for thematic segmentation, Whisper-AT for laughter detection, and YOLOv8 for shot classification and pose keypoint extraction, all aligned hierarchically without resampling. The authors validate the resource through corpus-level findings including a negative correlation between kinetic energy and laughter rate ($r = -0.75$), consistent with a stillness-before-punchline pattern, and through a short-horizon laughter prediction benchmark.

This paper tackles Unsupervised Continuous Anomaly Detection (UCAD), where models must sequentially learn new product categories without forgetting previous ones or storing all raw data. The core idea is to augment visual-only approaches with learnable text prompts from CLIP, storing both modalities in a Continuous Multimodal Prompt Memory Bank (CMPMB) and fusing them via a Defect-Semantic-Guided Adaptive Fusion Mechanism (DSG-AFM). Benchmarked on MVTec AD and VisA, the authors claim state-of-the-art detection accuracy (+4.4% AUROC) and segmentation (+14.8% AUPR) over the prior UCAD baseline.

ThinkJEPA addresses the limitation of JEPA-style latent world models that rely on short, densely sampled windows, which bias predictions toward local dynamics while missing long-horizon semantics. The paper proposes a dual-temporal architecture combining a dense-frame V-JEPA branch for fine-grained motion with a sparsely sampled VLM "thinker" branch that provides semantic guidance via multi-layer feature pyramids. This matters because it attempts to marry the physical consistency of latent world models with the general knowledge of vision-language models for robust trajectory forecasting.

CVT-Bench evaluates whether multimodal LLMs can maintain stable spatial representations under counterfactual viewpoint transformations—such as inferring object relationships from a camera angle never shown in the image. Using 100 synthetic tabletop scenes and 6,000 relational queries across rotations from $0^{\circ}$ to $360^{\circ}$, the benchmark reveals that state-of-the-art models, despite high single-view accuracy, systematically fail at mental rotation tasks and degrade further under extended sequential context. These findings challenge the assumption that strong episodic spatial performance implies robust viewpoint-invariant representations, with critical implications for embodied AI and robotics applications requiring perspective-taking.

Autoregressive video diffusion models struggle with minute-scale generation due to error accumulation in long-horizon rollouts. This paper challenges the assumption that more memory is better, proposing instead to decompose KV-cache conditioning into three functional roles—Sink for global anchors, Tail for recent continuity, and dynamically selected History for mid-range structure. The result is a training-free inference method that improves motion dynamics by 66.8% while cutting attention overhead by roughly 2.6×.

This paper tackles the Close Small Object Unmixing (CSOU) problem for infrared imagery, where distant clustered targets appear as overlapping mixed spots due to optical diffraction limits. The authors propose DSCSNet, a deep-unfolded network that unrolls the ADMM algorithm with learnable parameters to recover target count, sub-pixel positions, and radiant intensities from mixed spots. The core idea is to replace the traditional ℓ2-norm smoothness terms with strict ℓ1-norm sparsity constraints and add a dynamic thresholding mechanism for scene-adaptive reconstruction.

FluidGaussian addresses a critical gap in 3D reconstruction: methods optimized solely for photometric losses often produce visually plausible but physically implausible geometries that fail in downstream simulations. The paper proposes coupling 3D Gaussian Splatting with incompressible fluid simulation (SPH/DFSPH) to define a simulation-based uncertainty metric—velocity divergence at the fluid-structure interface—and integrates it into active view selection. By reranking next-best-view candidates using this physical signal, the method improves both visual fidelity (PSNR) and physical plausibility (divergence) on synthetic and aerodynamic datasets.

Medical Vision-Language Models (Med-VLMs) for ultrasound analysis are vulnerable to subtle prompt variations that mimic real clinical communication patterns. This paper proposes a black-box attack framework using an LLM to generate minimal, clinically plausible text edits guided by Monte Carlo Tree Search (MCTS), requiring no access to the target model's weights or gradients. The study reveals that small adversarial rewrites can drastically degrade diagnostic QA accuracy—raising critical safety concerns for deploying such systems in point-of-care settings where prompt variability is inherent.

The paper addresses a fundamental limitation in 3D generation: image-conditioned models suffer from viewpoint bias and hallucinate unobserved regions, while text-conditioned models lack precise visual fidelity. The authors propose Text–Image Conditioned 3D Generation, a task requiring joint reasoning over visual exemplars and textual descriptions, and introduce TIGON—a minimalist dual-branch baseline that fuses separate image- and text-conditioned DiT backbones via zero-initialized cross-modal bridges and simple prediction averaging. This matters because it offers users more flexible control by combining pixel-aligned appearance cues with high-level semantic guidance.

This paper addresses video reasoning segmentation—segmenting objects in videos based on complex human instructions—by proposing TrajSeg, a unified framework built on Multimodal Large Language Models (MLLMs). The core innovation is bidirectional text-trajectory alignment, where the model learns both text-to-trajectory grounding and trajectory-to-text captioning, alongside a Frame-level Content Integration (FCI) module and a unified mask decoder that eliminates the need for separate key-frame and tracking models. The work matters because it simplifies training pipelines and aims to improve trajectory perception in dynamic video contexts.

3D-Layout-R1 tackles language-guided 3D spatial editing by training LLMs/VLMs to perform structured reasoning over explicit scene graphs. Instead of free-form chains-of-thought, the model outputs JSON graph edits that iteratively transform object poses and relations, combined with GRPO-based RL using dense 3D IoU and collision-aware rewards. This approach yields measurable gains in layout accuracy while maintaining interpretability across sorting, spatial alignment, and room-editing tasks.

Existing safety-critical scenario generation methods force collisions through brute-force perturbations, destroying trajectory realism. CounterScene reframes this as a counterfactual inference problem within diffusion-based BEV world models: given a safe scene, identify the single agent whose behavioral change would maximally increase collision risk, then minimally intervene on that agent alone via structured diffusion guidance. This targets the realism-adversarial trade-off by allowing danger to emerge through natural interaction propagation rather than global trajectory distortion.

Few-shot medical image segmentation (FSMIS) aims to segment anatomical structures with minimal annotations, but Segment Anything Model (SAM) based approaches suffer from over-segmentation due to ambiguous medical boundaries. This paper reformulates SAM-based FSMIS as a background-centric prompt localization task, proposing FoB (Focus on Background) to generate precise background prompts that constrain SAM’s predictions. By modeling contextual dependencies and ring-like structural priors, the method achieves state-of-the-art performance across CT, MRI, and dermatoscopic imaging while maintaining strong cross-domain generalization.

Multimodal skin cancer diagnosis with vision-language models faces a trilemma of computational cost, data scarcity, and black-box opacity. SkinCLIP-VL tackles this via a "frozen perception, adaptive reasoning" architecture that keeps CLIP frozen, adapts a quantized Qwen2.5-VL via LoRA, and introduces the Consistency-aware Focal Alignment (CFA) Loss to jointly handle class imbalance, cross-modal alignment, and calibration. The paper matters because it couples strong empirical performance with a clinician validation study, aiming to bridge the gap between AI accuracy and clinical trust.

GIDE addresses a key challenge in image editing: applying training-free editing techniques to Diffusion Large Language Models (DLLMs). Unlike continuous diffusion models where DDIM inversion is well-established, DLLMs use discrete tokenization that prevents direct application of standard noise inversion. GIDE introduces a three-stage framework (grounding, inversion, refinement) that enables precise localized editing via points, boxes, or text prompts while preserving background content. The significance lies in bridging discrete token spaces with high-fidelity inversion without additional training.

DGRNet addresses two critical gaps in brain tumor segmentation: reliable uncertainty quantification and under-utilization of radiology reports. The core idea transforms prediction disagreement among multiple lightweight view-specific adapters into an active signal that guides targeted refinement in ambiguous regions, integrated with clinical text conditioning. This approach achieves state-of-the-art accuracy on the TextBraTS benchmark while providing clinically meaningful uncertainty estimates calibrated to actual errors.

The paper presents a training-free pipeline for reconstructing instance-aware 3D scenes from 10-20 unposed RGB images and rendering novel views using diffusion. It combines MV-DUSt3R for geometry, SAM for 2D segmentation with warping-based cross-view unification, and the See3D diffusion model for inpainting holes in point-cloud projections. The system enables object-level editing by manipulating the point cloud directly, avoiding per-scene optimization.

OrbitStream addresses adaptive 360° video streaming for teleoperation by proposing a training-free framework that combines semantic scene understanding with robust control theory. It formulates viewport prediction as a Gravitational Viewport Prediction (GVP) problem where semantic objects (pedestrians, vehicles) generate potential fields that "attract" user gaze with task-relevant mass, while a Saturation-Based Proportional-Derivative (PD) Controller handles bitrate adaptation. This offers an interpretable, zero-shot alternative to black-box Deep Reinforcement Learning methods for safety-critical systems where deployment constraints prohibit lengthy training.

Medical vision-language models (VLMs) are increasingly evaluated for consistency—the invariance of predictions under paraphrased prompts—as a proxy for clinical reliability. This paper demonstrates that consistency alone is a fundamentally flawed safety metric because models can achieve perfect consistency by learning text shortcuts while completely ignoring the input image. The authors introduce a four-quadrant per-sample taxonomy that jointly evaluates consistency and image reliance, revealing that models optimized for low flip rates often shift samples into a 'Dangerous' quadrant where predictions are stable, accurate, and confident yet unchanged when the image is removed. Their findings expose a critical deployment trap: standard evaluation pipelines risk preferentially selecting models that appear reliable while being decision-invariant to visual evidence.

This paper investigates how vision-language models (VLMs) perform spatial reasoning—the binding of objects to spatial relations. It reveals that VLMs rely on two concurrent mechanisms: a dominant one where the vision encoder encodes object layout globally across visual tokens (extending into background regions), and a secondary one where the language model backbone forms ordering representations over object tokens. The finding that enhancing these vision-derived spatial representations improves performance without fine-tuning challenges the prevailing focus on LM backbones and highlights the critical role of vision encoders in multimodal reasoning.