IMPACT-CYCLE: Multi-Agent System for Long-Video Error Correction
Researchers have created a new system called IMPACT-CYCLE aimed at improving the understanding of long videos by enabling corrections at the claim level. This innovation addresses the high costs of fixing errors in existing multimodal systems, which often produce vague results, forcing annotators to sift through raw video to piece together the timeline. IMPACT-CYCLE approaches long-video comprehension as a process of maintaining a shared semantic memory that includes a detailed, versioned state with typed claims, a graph showing claim dependencies, and a log of provenance. It employs specialized agents that operate under specific contracts, focusing on verifying local object relationships, ensuring consistency over time, and maintaining overall semantic clarity. Corrections target only relevant structural areas to optimize human effort based on the errors identified. The study can be found on arXiv under the identifier 2604.20136.
Key facts
- IMPACT-CYCLE is a supervisory multi-agent system for long-video understanding.
- It enables claim-level maintenance of a shared semantic memory.
- The system includes typed claims, a claim dependency graph, and a provenance log.
- Role-specialized agents operate under explicit authority contracts.
- Verification is decomposed into local, cross-temporal, and global coherence checks.
- Corrections are confined to structurally relevant parts.
- The paper is available on arXiv with ID 2604.20136.
- It addresses the bottleneck of costly error correction in multimodal pipelines.
Entities
Institutions
- arXiv