arXiv:2606. 28326v1 Announce Type: cross Abstract: This research aims to solve the challenge of video retrieval from massive datasets, caused by ambiguous user queries.
arXiv:2512.23044v3 Announce Type: replace Abstract: Autonomous agents are shifting information seeking from passive retrieval toward proactive, open-ended research, yet this progress has largely bypa...
Most keyframe selection studies focus on offline settings, assuming access to the full video and query in advance. In contrast, real-world streaming scenarios require online frame selection under unkn...
arXiv:2607. 00446v1 Announce Type: cross Abstract: As video corpora continue to expand in both scale and task complexity, there is increasing demand for approaches that retrieve relevant videos from large-scale corpora (inter-video reasoning) and subsequently perform fine-grained, query-conditioned tasks (intra-video reasoning) within the retrieved content, such as temporal grounding.
arXiv:2610.08954v1 Announce Type: new Abstract: Video large language models (Vid-LLMs) excel at diverse video-language tasks by reasoning over selected frames. However, frame selection for long video...
SVMemAgent introduces a streaming video memory (SVMem) that continuously updates a compact representation of observed frames for online keyframe selection without prior knowledge of video length, query, or future frames. The agent decides at each timestep whether to replace an existing memory frame with a new one or discard it, trained via Group Relative Policy Optimization using task-driven rewards from question-answer pairs. Experiments demonstrate that SVMemAgent outperforms existing online baselines and rivals offline methods, and its learned policy tends to favor frames containing textual information, potentially aiding downstream VideoQA tasks.
arXiv:2608.20805v1 Announce Type: new Abstract: Long-form video understanding remains challenging for video agents due to the mismatch between query demands and evidence acquisition strategies. Altho...
arXiv:2606. 05275v1 Announce Type: cross Abstract: We study the personal camera roll visual question answering setting.
arXiv:2608. 12627v1 Announce Type: cross Abstract: Long-horizon egocentric memory transforms continuous first-person video and audio into a searchable record of past experiences.
arXiv:2610.16927v1 Announce Type: new Abstract: Short-video search requires compact query rewriters that infer personalized intent from brief, ambiguous queries under strict latency constraints. The...
arXiv:2606. 19627v1 Announce Type: cross Abstract: The digital commerce landscape is shifting from static, search-driven catalogs to dynamic, immersive video feeds.
arXiv:2603. 26266v3 Announce Type: replace Abstract: Large vision-language models have endowed GUI agents with strong general capabilities for interface understanding and interaction.
QREF is a plug‑and‑play system that improves DeepSearch agents by refining search queries to match the current reasoning intent and filtering evidence to keep only what is relevant. The Query Refiner rewrites queries for complementary coverage across each search batch, while the Evidence Filter removes distracting retrieval results. Experiments show that QREF boosts DeepSearch performance on four benchmarks, achieving significant Pass@1 gains and dramatically reducing ineffective query batches without changing the agents’ architectures or retraining their reasoning policies.