AI Video Papers2026
Generates temporally grounded captions for dense multi-event videos by restructuring autoregressive token dependencies to enable lossless parallel decoding; introduces a latent global planning module and event-factorized parallel decoding to improve grounding accuracy and achieve large decoding speedups.
AI Agent Papers2026
Proposes SkillOpt-Lite, a minimal pipeline for optimizing LLM agent skills by treating rollout traces as filesystem files and applying trajectory exploration, consensus mining, and independent validation; integrates as a one-line VSCode Copilot command and reports cross-benchmark improvements that let smaller models sometimes outperform larger ones.
AI Video Papers2026
Provides a reflexive agentic framework for long-horizon video understanding that replaces costly iterative reasoning with dual contextual states: a consolidated global multimodal script and parametric latent states for fast retrieval and response, improving speed and memory efficiency.