Rigorous content moderation is crucial for online advertising but leads to millions of daily rejections. This scale renders manual rectification infeasible, particularly for video advertisements.
arXiv:2607. 07318v1 Announce Type: cross Abstract: Rigorous content moderation is crucial for online advertising but leads to millions of daily rejections.
arXiv:2609.22094v1 Announce Type: cross Abstract: Content moderation systems traditionally entangle multimodal understanding with policy-specific classification, requiring full pipeline retraining fo...
WeVisCut is a long‑form script‑to‑video system that unifies narration, editing, and tool execution into a single agent. It uses a four‑stage data pipeline that reconstructs editing intent from finished videos, generating over 130K training examples to train a 9B policy capable of coordinating production and recovering from failures. The system introduces a 100‑task benchmark with five metrics, outperforming its base model, matching Claude Opus 5 in performance while using far fewer tokens, and surpassing task‑matched native editors on the proposed metrics.
arXiv:2606. 05748v1 Announce Type: cross Abstract: Global-scale video moderation faces a dual challenge: the need for fine-grained multi-modal reasoning and the demand for interpretable outputs to support downstream enforcement.
arXiv:2606. 05950v1 Announce Type: new Abstract: Text-guided image editing has advanced rapidly with diffusion models and unified multimodal foundation models.
arXiv:2608.20749v1 Announce Type: new Abstract: Identity-preserving video generation aims to synthesize videos that follow natural-language instructions while maintaining the visual identity of a giv...
TextualRL is a gradient‑free reinforcement learning framework that updates the textual context guiding large language model agents without altering their parameters. It critiques groups of rollouts from the same task, contrasts successful and failed runs, and uses cross‑group evidence to propose and validate context edits. Across six benchmarks, TextualRL boosts test accuracy by 24.4 and 15.3 percentage points for GPT‑5.5 and Qwen3.8‑27B, respectively.
The paper introduces PolicyLift, a three‑stage framework that improves decision policies using observational data for complex, structured actions such as text or images. Stage 1 learns discrete action codes that capture high‑level strategies, Stage 2 trains a context‑dependent policy over these codes, and Stage 3 generates full actions conditioned on the learned codes. The authors provide theoretical guarantees that improving the code‑level policy leads to improvement in the original action space and demonstrate consistent policy gains in synthetic and real‑world text generation experiments.
The paper introduces AdsCVR, a benchmark for e‑commerce cross‑video reasoning with 2,483 videos and 6,110 QA pairs across six reasoning dimensions. It proposes AdSeek, an agentic framework that actively selects visual and audio tools during multi‑turn exploration and uses an offline trajectory rectification mechanism to improve reinforcement learning. AdSeek achieves 74.30% accuracy on AdsCVR, outperforming its backbone by 27.90 percentage points and generalizes to the open‑domain CrossVid benchmark.
The paper introduces PEBench, a unified benchmark for evaluating prompt enhancers (PEs) in video generation across text-to-video, image-to-video, and reference-to-video tasks. It contains 1,100 expert-verified cases, 1,005 visual assets, and 35 fine-grained tasks, and employs a fact-extraction and rubric-based assessment framework covering 24 criteria. Evaluation of various PE methods shows a trend toward intent-preserving cinematic planning, with certain methods excelling in coverage or coherence, and human validation confirms that PEBench scores align with expert judgments and downstream video quality.
arXiv:2609.26015v2 Announce Type: replace Abstract: Instruction-guided video editing requires executing a requested change while preserving the source video's appearance and temporal structure. We pr...
The paper introduces a new protocol, Mistake Detection Video Question Answering (MD‑VQA), to evaluate whether models can determine if a step in a video follows its description, covering both seen and unseen actions. It proposes a post‑training approach for video‑language models that uses a reward function to highlight discrepancies between instructions and video content. Experiments show this method surpasses zero‑shot, fine‑tuned, and other post‑training baselines, especially on unseen procedures, improving performance by up to 11.6% on EP‑VQA.