# Long-Video Memory

The work introduces Grounded Entity Biographies (GEB), a long-video memory framework that groups visually grounded observations of the same physical instance across clips into retrievable biographies and retrieves a biography alongside episodic evidence at question time; across four benchmarks including day-long and week-long recordings it improves both multiple-choice and open-ended question answering over prior memory frameworks, reaching 72.0% accuracy on EgoLifeQA, 4.4 percentage points above the best published result, with ablations showing that grounded identity association and biography reading each contribute and that additional descriptions alone do not fully recover the gains.

The work introduces Grounded Entity Biographies (GEB), a long-video memory framework that groups visually grounded observations of the same physical instance across clips into retrievable biographies and retrieves a biography alongside episodic evidence at question time; across four benchmarks including day-long and week-long recordings it improves both multiple-choice and open-ended question answering over prior memory frameworks, reaching 72.0% accuracy on EgoLifeQA, 4.4 percentage points above the best published result, with ablations showing that grounded identity association and biography reading each contribute and that additional descriptions alone do not fully recover the gains.

The work presents Realtime-Venus, a proactive full-duplex interaction system with two separately trained 9B models, Realtime-Venus-Omni for audio-visual interaction and Realtime-Venus-Audio for spoken interaction, which align continuous perception, conversational control, native speech generation, and private delegation requests on a shared causal timeline while Realtime-Venus-Harness executes background capabilities asynchronously in a dual-loop runtime, achieving the highest scores among compared online models on six of eight video benchmarks and leading or matching on several audio understanding, spoken question answering, and full-duplex overlap metrics.

The work presents Realtime-Venus, a proactive full-duplex interaction system with two separately trained 9B models, Realtime-Venus-Omni for audio-visual interaction and Realtime-Venus-Audio for spoken interaction, which align continuous perception, conversational control, native speech generation, and private delegation requests on a shared causal timeline while Realtime-Venus-Harness executes background capabilities asynchronously in a dual-loop runtime, achieving the highest scores among compared online models on six of eight video benchmarks and leading or matching on several audio understanding, spoken question answering, and full-duplex overlap metrics.