Robotic foundation models currently rely on multi-step flow matching to generate actions, which creates a severe inference bottleneck for real-time control. This paper proposes Kinematic MeanFlow (K-MF), a one-step action policy that reformulates MeanFlow by decoupling the time derivative across two sub-intervals to accommodate the unique late-stage surge and widening spread of local acceleration in robotic velocity fields. By introducing an intermediate anchor point and a learnable partition strategy, K-MF cuts action-head latency by up to 74% while matching or exceeding the success rates of multi-step baselines across manipulation and navigation tasks.

This paper studies adversarial attacks under a shared global mean-$\ell_1$ budget with per-coordinate magnitude caps. The core idea is to add a centered, clean-gradient importance prior to the attack objective to softly guide perturbation mass toward model-sensitive regions without changing the feasible set. This matters because, under local capacity constraints, simply finding a useful gradient direction is insufficient—attackers must also decide where to spend a budget that cannot be arbitrarily concentrated. Empirically, the mechanism raises attack success by 2.52–17.70 percentage points over matched baselines across ten robust model–dataset configurations.

MorphoBranch tackles the problem of accurately quantifying fine, weakly labeled, and densely branched cellular structures—such as neuronal axons and microglial processes—from fluorescence microscopy images. The core idea is an integrated workbench that pairs a deterministic image-to-graph Morphometry Engine (multiscale ridge evidence, hysteresis segmentation, and evidence-constrained skeleton refinement) with an optional LLM-assisted Refinement Engine that translates natural-language instructions into editable, human-reviewable analysis plans. This separation of flexible interaction from deterministic computation aims to reduce operator-dependent bias while keeping morphometric workflows inspectable and reproducible.

World Action Models jointly predict future actions and video, but standard training independently corrupts actions and video with noise, creating a physical mismatch: perturbed actions imply a different visual future, yet the noised video stays tied to the original recording. CtrlWAM fixes this by executing perturbed actions in a simulator and pairing the rendered consequences with the perturbed actions for training, augmented with warped video–action noise schedules and a structured multi-agent conditioning interface. The approach targets more physically consistent and controllable world models for autonomous driving and robotic manipulation.

Lang3DSeg tackles annotation-free open-vocabulary 3D segmentation of sparse outdoor LiDAR using a point transformer backbone trained from scratch. Its core idea is to carefully curate 2D-to-3D pseudo-labels via class-priority rasterization and an instance-wise depth-gap test that truncates projected masks at the first occlusion gap, removing systematic depth-ambiguity errors before training. This matters because prior methods rely on voxel-based backbones and temporal voting to compensate for projection noise, whereas Lang3DSeg shows that a point transformer can match and exceed those results in real time when paired with cleaner single-sweep targets.

Open Set Recognition (OSR) requires models to classify known classes accurately while rejecting samples from unseen categories, which depends critically on learning compact, well-separated feature clusters. This paper proposes SmoothOperator (SmoothOP), a plug-in that adaptively sets the label smoothing coefficient for each training sample based on its prominence—an embedding-space signal measuring how clearly a sample's own class dominates its strongest competitor. By modulating smoothing sample-by-sample, SmoothOP yields tighter and better dispersed class representations, improving OSR performance across fine-grained benchmarks.

Vision-language models are increasingly used as backbones for all-in-one low-level image restoration, yet how they internally organize pixel-level perceptual representations remains unclear. This paper introduces GeoSim, a unified four-level framework that probes global similarity, local geometry, sparse feature decomposition, and topological structure across 24 tasks in autoregressive and diffusion transformer VLMs. By revealing that task representations are mechanism-centric and that cross-model agreement is primarily global rather than local, the study offers an actionable interpretability lens for designing transferable unified restoration systems.

Predicting how complex scenes evolve is essential for intelligent systems, yet dense video generation expends enormous compute on appearance rather than dynamics. This paper introduces Myriad, an autoregressive diffusion model that predicts future motion via sparse point trajectories, explicitly avoiding the 'visual tax' of pixel-level generation. By modeling step-wise uncertainty accumulation through flow matching and utilizing fused transformer blocks, the method achieves throughput of 2200 samples/min compared to less than 1 for video models, while matching or exceeding their predictive accuracy on motion-focused benchmarks.

This paper introduces Vision Transformer (ViT), which applies a standard Transformer encoder directly to sequences of image patches for image classification. The core insight is that convolutional inductive biases (locality and translation equivariance) are unnecessary when models are pre-trained at sufficient scale—specifically on datasets containing 14M to 300M images. When transferred to downstream benchmarks, ViT matches or exceeds state-of-the-art CNNs while requiring substantially less computational resources to pre-train.

Dynamic visual effects like explosions require complex temporal reasoning that is difficult to capture in text prompts. P-Flow introduces a training-free framework that treats prompts as optimization variables, using vision-language models to iteratively refine descriptions based on discrepancies between generated and reference videos. The method combines flow-matching noise inversion with lightweight historical context to achieve model-agnostic customization without fine-tuning.

This paper addresses weakly-supervised video scene graph generation (WS-VSGG), where models must parse videos into structured relational triplets using only sparse unlocalized annotations without bounding boxes. The core insight is that off-the-shelf object detectors indiscriminately detect all visible objects, overwhelming relation models with noisy non-interactive pairs, while fully-supervised detectors implicitly filter relationally irrelevant objects. To bridge this gap, the authors propose a three-component framework: Relation-Aware Matching (RAM) refines pseudo-labels via vision-language grounding, Pair Affinity Learning and Scoring (PALS) learns to distinguish interactive from non-interactive pairs, and Pair Affinity Modulation (PAM) gates attention based on affinity scores. This substantially narrows the gap to full supervision while reducing annotation costs.

This paper tackles test-time adaptation (TTA) for large multimodal 3D vision-language models under distribution shifts. The core idea is BayesMM, which models both textual and geometric features as Gaussian distributions and fuses them via Bayesian model averaging. Unlike cache-based methods that store discrete samples, this approach claims to avoid progressive information loss and heuristic hyperparameter tuning while maintaining training-free operation.

GeoFusion-CAD tackles the scalability bottleneck in parametric CAD generation, where Transformer-based methods struggle with long command sequences due to quadratic attention costs. The authors propose an end-to-end diffusion framework that encodes CAD programs as hierarchical trees and processes them with G-Mamba blocks—geometry-conditioned state-space models that achieve linear complexity $\mathcal{O}(Ld)$ while capturing geometric and topological dependencies. This enables scaling to sequences of up to 240 commands while maintaining high geometric fidelity.

Multiple Instance Learning (MIL) for gigapixel pathology images relies on a single linear layer to transform general patch features into task-specific representations before aggregation. This paper identifies this linear layer as a critical yet overlooked bottleneck and proposes Mammoth, a parameter-efficient mixture-of-experts module that replaces it with multi-headed soft routing to specialized low-rank experts. By routing morphologically similar patches to distinct expert slots, Mammoth achieves superior performance without increasing model size, demonstrating that the feature transformation step matters more than the choice of aggregation function.

Single-image 3D reconstruction is fundamentally ill-posed: one view admits many valid 3D explanations, especially under occlusion and structural variation. This paper tackles the problem by learning an adaptive part-whole hierarchy rather than fixed-decomposition or monolithic representations. The core idea is a slot-based architecture where an image-conditioned gating mechanism predicts which latent structural slots to activate per instance, coupled with a class-agnostic prototype bank that aligns active slots to shared geometric priors via soft attention. This eliminates the need for user-specified part counts while encouraging cross-category reuse of recurring structural patterns like legs or handles.

Pre-trained vision encoders excel at 2D recognition but lack 3D spatial awareness. SpatialBoost addresses this by converting dense 3D spatial information from 2D images into linguistic expressions, then injecting them into frozen vision encoders via LLM-based training with a novel dual-channel attention mechanism. The framework improves performance on spatial tasks (depth estimation, robot control) while maintaining or enhancing general vision capabilities (ImageNet classification), suggesting language serves as an effective supervision signal for geometric understanding.

Hand-object interaction (HOI) video generation is currently split between pose-only synthesis, static appearance generation, and motion methods requiring ground-truth first frames. This paper introduces PAM, a three-stage Pose–Appearance–Motion engine that generates high-resolution HOI videos from only initial/target poses and object geometry, achieving true sim-to-real transfer. The system combines GraspXL for pose trajectory generation, Flux for appearance synthesis with multimodal ControlNet conditioning, and CogVideoX for motion generation, producing 480×720 videos while improving FVD from 38.83 to 29.13 on DexYCB compared to prior work.

Video-LLMs struggle with high computational costs from massive visual token volumes (e.g., 6,272 tokens for a 32-frame video). This paper challenges the standard two-stage spatiotemporal compression paradigm—which assumes spatial and temporal redundancy are separable—by reformulating compression as a global allocation problem. The authors propose a unified selection mechanism combining attention weights and semantic similarity to identify high-contribution, low-redundancy tokens, plus a text-aware merging module for secondary compression inside the LLM. The result is a training-free, plug-and-play method that retains ~90% performance with only 2% of tokens.

This paper benchmarks Vision Transformer backbones (ViT-B, ViT-L, ViT-H) within a Local pattern Self-Supervised Auxiliary Task (L-SSAT) framework. The core idea fuses Local Directional Pattern (LDP) texture descriptors with RGB inputs via Masked Autoencoder reconstruction as an auxiliary task to primary face classification. The study addresses whether a unified backbone exists across diverse face analysis tasks including deepfake detection (FaceForensics++), attribute prediction (CelebA), and emotion recognition (AffectNet).

Direct Preference Optimization (DPO) for Vision-Language Models suffers from Likelihood Displacement, where optimization collapses the probabilities of both chosen and rejected responses, causing models to abandon visual evidence for language priors. This paper proposes Asymmetric Constrained Preference Optimization (ACPO), which applies dynamic, length-aware scaling exclusively to the rejected reward term, preserving the chosen distribution as a stable anchor while selectively suppressing incorrect outputs.