arXiv:2606. 12217v1 Announce Type: cross Abstract: World Action Models (WAMs) offer a promising route for robot manipulation by using video generation models to model future scene evolution before producing control actions.
World Action Models (WAMs) offer a promising route for robot manipulation by using video generation models to model future scene evolution before producing control actions. However, our empirical observations reveal a phenomenon: generating plausible visual futures does not always guarantee the extraction of accurate actions.
VideoTIR introduces a reinforcement‑learning approach to improve long‑video understanding by encouraging multimodal large language models to use comprehensive multi‑level toolkits efficiently. It combines Zero‑RL and SFT cold‑starting strategies to help models retrieve and focus on meaningful video segments, images, and regions, thereby reducing hallucinations. The method includes Toolkit Action Grouped Policy Optimization (TAGPO) to streamline tool‑calling and a sandbox‑based trajectory synthesis framework for high‑quality data, achieving strong results on three long‑video QA benchmarks.
arXiv:2602.05718v2 Announce Type: replace Abstract: Point-supervised Temporal Action Localization (PTAL) adopts a lightly frame-annotated paradigm (\textit{i.e.}, labeling only a single frame per act...
arXiv:2606. 00616v1 Announce Type: cross Abstract: Recent Vision-Language Models (VLMs) struggle with grounded reasoning, temporal consistency, and context aware planning in videos.
EgoActionBench is a new benchmark for dense action captioning in egocentric videos, featuring 1,407 episodes and 41,132 fine‑grained action annotations that specify which hand (left, right, or both) performs each temporally localized atomic action. The benchmark introduces an evaluation protocol that assesses semantic fidelity, temporal overlap, action precision, and recall, thereby measuring what happens, when it happens, and which hand is involved. Twenty baseline models were evaluated, and two backbones (Qwen3‑VL‑4B and LLaVA‑OneVision‑2‑8B) were fine‑tuned and reinforced, achieving significant score improvements and outperforming all open‑source baselines.
arXiv:2602.01257v2 Announce Type: replace Abstract: Recently, point-supervised temporal action localization has gained significant attention for its effective balance between labeling costs and local...
arXiv:2505.01583v2 Announce Type: replace Abstract: Understanding causal event relationships and achieving fine-grained temporal grounding in videos remain challenging for vision-language models (VLM...
arXiv:2606. 00054v1 Announce Type: cross Abstract: Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models.
arXiv:2606. 04806v1 Announce Type: cross Abstract: LLMs and agentic systems are increasingly deployed in social environments, making normative competence critical for safe and appropriate behavior.
WorldReward introduces a vision‑language model–based reward system for camera‑conditioned world models, combining action consistency and visual quality evaluation. It processes paired videos by splitting them into action‑aligned chunks, structuring visual evidence, and aggregating decisions through voting. The model is trained on a large, reasoning‑augmented preference dataset and outperforms GPT‑5.5 on a human‑annotated benchmark, improving both action execution and visual quality when applied to RL post‑training.
Generalized CycleGRPO is a unified reinforcement learning framework that jointly trains region understanding and localization across spatial, temporal, and spatio‑temporal domains. It uses a multimodal large language model to generate candidate descriptions for a given region, then localizes each description to reconstruct the original region, providing verifiable rewards for both captioning and localization. The approach extends beyond image masks and boxes to video intervals and timestamped spatial evidence, and introduces the DOVE benchmark to evaluate temporal distinctiveness, achieving significant performance gains on several grounding and temporal tasks.