Evaluating robotic foundation models still relies on expensive closed-loop rollouts in simulation or reality. This paper proposes Sensorimotor Alignment ($S^2$), a training-free offline metric inspired by the Platonic Representation Hypothesis, which scores how well a model's sensory embeddings align with the geometry of recorded end-effector trajectories. The authors find positive correlations between $S^2$ and task success across vision-language-action models, raw vision-language models, world action models, and real-robot policies, and argue the metric can cheaply rank models and diagnose representation-level behavior.

PiHand tackles the synthesis of physically plausible dexterous hand-object interactions by learning a latent interaction prior from simulated mocap-tracking experts. Its three-stage pipeline—expert RL tracking, VAE distillation, and bounded residual latent exploration—enables zero-shot manipulation of unseen objects and trajectories, and even transfers to tasks like catching and bimanual handover that are rare or absent from the training data. The core thesis is that embedding interaction physics directly into the latent space outperforms both raw action-space RL and hand-only priors in sample efficiency and success rate.

AnyGameRenderer tackles the programmer-art problem of agent-built games by converting crude gameplay video into stylized, high-fidelity output via a rank-128 LoRA on the 33B MiniMax-H3 ref2va model. It accepts plain video inputs---gray blockouts, colored primitives, semantic maps, or wireframes---without engine G-buffers or per-object labels, and aims to preserve entity count, timing, and position across genres from tower defense to kart racing. The authors assemble roughly 10k paired clips from three sources and introduce an entity-level benchmark of 753 annotated entities to measure correspondence, improving temporal match over the zero-shot base.

Dataset-use auditing for text-to-image models is difficult because fine-tuned models absorb collection-level visual style without reproducing individual training images. SpectralCanary addresses this by blending a fixed canary image into the low-frequency Fourier content of every collection image before release, preserving captions and avoiding auxiliary generation or per-image optimization. A lightweight verifier then detects the transferred spectral signature in a suspect model's black-box outputs. The method achieves strong detection rates across multiple architectures and domains with minimal fidelity loss, establishing low-frequency traces as a practical auditing mechanism.

MixPT tackles the trade-off between computational cost and scene-wide interaction in point cloud transformers. It interleaves fixed-order window attention blocks with periodic global attention blocks, using spatial pooling to keep global attention feasible when the number of tokens is large. The authors show that this hybrid schedule lowers per-forward latency compared to both pure window and pure global baselines while maintaining competitive accuracy on indoor and outdoor 3D segmentation benchmarks.

This paper tackles cross-session indoor navigation, where an agent must reach a prior-session goal despite scene changes, sensor differences, and partial overlap. The key idea is a sparse topometric graph whose nodes compress local 3D geometry around traversable anchors and whose edges encode traversable distance, enabling both memory-efficient storage and learned graph matching for alignment. The approach matters because it promises to slash map memory by over two orders of magnitude while outperforming dense grids, appearance maps, and semantic graphs on standard benchmarks.

Local anomaly detection in images with multimodal inlier distributions—such as medical scans, faces, and X-ray baggage—remains challenging for industrial-defect methods that rely on heuristic reconstruction errors or feature distances. LACToS introduces a principled alternative by scoring superpixels via statistical typicality under the conditional inpainting posterior of an inlier diffusion model, made tractable by grouping pixels into superpixels and using a single denoising posterior as a cheap proxy. This shift from point estimates to distribution-aware testing matters because it directly addresses the false positives that arise when normal images contain diverse, context-dependent content.

This paper attacks the real-time deployment bottleneck for polyp segmentation by proposing a \sim77K-parameter Lightweight Frame Compensator (LFC) inserted before a frozen backbone. Under $K=3$ frame skipping, LFC fuses three consecutive frames via spatio-temporal attention into a single compensated image, claiming a $2.05\times$--$2.55\times$ throughput gain while recovering most accuracy loss. The core insight is that global-attention Transformers suffer the most from skip-frame jitter, making them the prime beneficiaries of lightweight temporal fusion. Unfortunately, the bulk of the evidence rests on pseudo-labels rather than expert-annotated video ground truth, which undermines the clinical certainty of the reported gains.

Existing vision-language models struggle with fine-grained visual perception, often hallucinating objects or missing fine details. WeVisOne-VL tackles this by unifying object-level and pixel-level tasks—detection, segmentation, matting, OCR, and more—inside a single autoregressive VLM built on Qwen3-VL. It introduces dedicated coordinate tokens and a lightweight 9.6M-parameter pixel decoder triggered by an <OBJ> token, enabling dense outputs without a separate segmentation backbone. Trained on roughly 100M curated images via a three-stage curriculum and mixed-task RL, it aims to move VLMs from broad understanding toward reliable, deployable perception.

Local feature matching underpins SfM and visual localization, but standard matchers process every image pair independently, which is inefficient and ignores multi-view cues available in star-graph neighbourhoods. MVLoMa addresses this by jointly matching a centre image against all neighbours in a single forward pass, reusing frozen geometry-aware tokens from a multi-view foundation model such as DA3-Giant and adding star-structured cross-attention so the centre aggregates evidence from all neighbours. It preserves the conventional sparse pairwise outputs required by RANSAC while improving strict-threshold pose AUC and cutting matcher compute by roughly $2.2\times$ at $25$ views.

DeXplicit addresses action-conditioned world modeling for dexterous manipulation by splitting prediction into an explicit metric 3D dynamics stage and a subsequent video rendering stage. Instead of entangling physics, viewpoint, and appearance in a monolithic video generator, it first forecasts structured object motion and deformation, then uses those predictions as geometric scaffolds for efficient causal video synthesis. The core claim is that making physical state explicit improves both geometric accuracy and generation speed.

Stream4D tackles online 4D reconstruction in dynamic scenes, where moving objects break static-scene assumptions and cause pose drift and ghosting artifacts in causal streaming pipelines. The core idea is a training-free plug-in that mines temporal motion cues from the KV cache of causal Transformers, fuses them with depth priors to generate dynamic masks, and suppresses dynamic keys via soft masking to prevent interference during historical aggregation. By operating directly on cached keys without architectural changes or retraining, it aims to make existing streaming backbones like Stream3R and StreamVGGT robust to dynamic content while retaining linear-time scalability.

Future lunar rovers will navigate tens of kilometers using cameras, but the feature detectors and matchers at the heart of visual odometry are benchmarked almost exclusively on urban and driving scenes. This paper evaluates eleven classical and learned detector–matcher combinations on nine synthetic, lunar-testbed, and terrestrial-analogue datasets, measuring how pairwise pose accuracy degrades with viewpoint change and how the resulting correspondences drive open-loop VO error. The central finding is that learned SuperPoint pairings outperform classical SIFT on real low-texture lunar terrain and remain robust across lighting, yet synthetic datasets alone are misleading predictors of real performance and the learned advantage narrows when short-baseline translation direction is scored jointly. The work directly addresses a critical gap for Artemis-era rover autonomy by showing precisely where classical front ends fail and under what conditions learned ones are worth their computational cost.

GLOW-FM attacks the high inference cost of diffusion-based low-light image enhancement (LLIE) by replacing iterative sampling with a single-step latent flow. The core idea is to run Terminal Velocity Matching (TVM) in the compact latent space of a frozen Tiny-VAE, directly predicting the displacement from a degraded low-light latent to its well-exposed counterpart in one function evaluation. If its quality claims hold up, the result would be a generative LLIE method practical enough for edge-device deployment.

FlexPart addresses a key limitation in controllable 3D asset creation: existing image-to-3D part generation systems are typically specialized for a single prompt type, forcing users into rigid workflows. The paper introduces a unified Diffusion Transformer framework that simultaneously accepts sparse point prompts, 2D bounding boxes, and dense masks via Gated Adaptive Layer Normalization, while an Asymmetric Geometric Guidance mechanism uses differentiable optimal transport to let dense spatial priors supervise sparse representations during training. By jointly training across prompt granularities, the method aims to improve part-level geometric fidelity and give users flexible control over how they specify structure.

Motion segmentation is conventionally treated as an object-level property, causing entire instances to be marked moving even when only a small part displaces or when the camera itself moves. This paper reframes motion as a relation between an ordered source–target frame pair and proposes MoRSe, a feed-forward model that predicts moving-region masks without optical flow, point tracks, or a frozen segmentation foundation model at inference. To support this formulation the authors introduce MoRSe-5K, a benchmark with Static, Dynamic-Static, and Dynamic splits that directly measures whether a model can suppress false positives under pure camera motion.

Vision-language models lag at visual in-context learning because they exploit textual shortcuts rather than learning from demonstration images. This work argues the root cause is insufficient training data that forces pure visual induction, and proposes MIME—a synthetic, task-agnostic dataset where the rule is never stated in text—and DTAD, a label-dropout mechanism that shifts attention to visual tokens. The result is an 8.1–18.0 point jump in vICL accuracy across multiple architectures, with an 8B model matching frontier proprietary systems, suggesting vICL-specific data should become a standard ingredient in VLM training.

H-2DGS addresses the need for sharp, photorealistic neurosurgical surface reconstruction that can fuse with MRI and tractography without obscuring internal brain structures. The authors propose a three-stage pipeline that bootstraps appearance with conventional 3D Gaussian Splatting, contracts volumetric density along camera rays using differentiable quantiles, and converts the fused TSDF surface into curved height-field Gaussian patches. By explicitly representing a thin, deformable cortical sheet rather than a diffuse volume, the method targets geometric precision and compactness at the cost of some rendering fidelity.

Multimodal large language models can answer clinical questions about retinal OCT scans, but without spatial grounding clinicians cannot verify whether a model attended to real pathology or relied on spurious shortcuts. This paper introduces RetinaGroundBench, the first benchmark for biomarker visual grounding in retinal OCT, together with $AggIoU$—a metric tailored to MLLMs that fragment lesions into multiple boxes—and RetGrounder, an 8B domain-adapted model that outperforms general-purpose models up to 38B parameters. The core message is that grounding remains largely unsolved: even the best open-source model achieves only 23.7% macro $F_1$, and VQA ability does not imply localization skill. The work matters because trustworthy clinical deployment of MLLMs requires precisely this kind of verifiable spatial evidence.

This paper audits SkinCon, a widely used dermatology concept dataset, for skin-tone-dependent annotation bias.
Within the same disease, erythema is labeled far less often on dark skin (odds ratio 0.17) while pigmentary concepts are labeled more often.
An evidential concept model trained on these annotations reproduces the bias, and three label-error detectors fall below a disease-only baseline at catching potential erythema omissions.
The work warns that concept bottleneck explanations for skin disease diagnosis cannot be trusted until annotations are audited within each subgroup.