arXiv Computer Vision
What Do Verifiable Rewards Teach Video-Language Models About Time? A Controlled Multi-Model Study
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arXiv Computer Vision
Oct 5
Behavior Pack Optimization for Video MLLM Post-Training
The paper introduces Behavior Pack Optimization (BPO), a post‑training method for video multimodal large language models that replaces single‑response rewards with a set of outputs across counterfactual views. BPO enforces stability when interventions are irrelevant, sensitivity when key evidence is removed, and abstention when no evidence remains, using an anchor‑relative advantage to keep the objective stable with small pack sizes. Experiments on datasets such as TempCompass, MVBench, and NExT‑QA show that BPO improves macro accuracy and abstention metrics for models like Qwen2.5‑VL‑7B‑Instruct, with gains that transfer to other benchmarks and models.
arXiv Computer Vision
Sep 23
Video-HopChain: Multi-Hop Questions and Confidence-Gated Exploration for Video Reasoning Models
Video-HopChain introduces a new dataset of 22,550 multi‑hop video questions over 13,378 videos, each question consisting of three to six yes/no sub‑questions whose integer answers sum to a verifiable reward. Training a Qwen3‑VL‑8B model with GRPO on this dataset improves performance across eight video‑understanding benchmarks from 55.4 to 57.9, and the addition of Confidence‑Gated Exploration (CGE) raises the mean to 59.3. The authors release the dataset, checkpoint, and training code for further research.
arXiv Computation and Language
Sep 1
Multi-Agent Self-Improving Reinforcement Learning for Video Reasoning
arXiv:2608.28675v1 Announce Type: cross Abstract: Video reasoning tasks such as grounded video question answering and temporal grounding require selecting temporal evidence that supports the query. I...
arXiv Machine Learning
Jun 2
Perception First: A Frontier Native-Video Model with Self-Consistency for Implicit Video Question Answering
arXiv:2606. 01485v1 Announce Type: cross Abstract: We describe our submission to the VRR Challenge @ CVPR 2026, built on the \emph{ImplicitQA} / \emph{VRR-QA} benchmark~\cite{implicitqa}: multiple-choice video question answering in which answers are deliberately \emph{not} observable in any single frame and must be inferred from spatial layout, motion, depth, viewpoint, causality, and social context across discontinuous frames of creative video.
arXiv AI
Jun 24
video-SALMONN-R$^3$: Learning to ReWatch, ReAsk, and ReAnswer for Efficient Video Understanding
arXiv:2606. 24477v1 Announce Type: cross Abstract: Video large language models (LLMs) are often constrained by computation and memory budgets, leading them to use reduced frame rates and spatial resolutions, which may cause them to miss critical information for question answering (QA).
arXiv Computer Vision
Oct 12
OWLET: On-Policy Weighted Learning via Evidence Transfer for Video Large Language Model Post-Training
The paper introduces OWLET, an on-policy weighted learning method for post-training Video Large Language Models. OWLET leverages a richer teacher view that combines global temporal context with question-conditioned local windows to provide visual evidence, and it employs evidence-aware credit redistribution and spatiotemporal state alignment to better utilize this evidence during reinforcement learning. The approach aims to improve video reasoning by aligning token-level supervision with spatiotemporal representations.