IMPACT-CYCLE: A Contract-Based Multi-Agent System for Claim-Level Supervisory Correction of Long-Video Semantic Memory
📰 ArXiv cs.AI
Learn how IMPACT-CYCLE, a contract-based multi-agent system, enables efficient supervisory correction of long-video semantic memory errors, reducing annotation costs.
Action Steps
- Design a contract-based multi-agent system using IMPACT-CYCLE architecture
- Implement a supervisory interface for human annotators to inspect and correct intermediate states
- Apply the system to long-video semantic memory tasks to reduce annotation costs
- Configure the system to expose intermediate states for inspection and correction
- Test the system's performance on various video understanding tasks
- Compare the results with existing multimodal pipelines to evaluate efficiency gains
Who Needs to Know This
AI researchers and engineers working on multimodal pipelines and video understanding can benefit from this system, as it provides a supervisory interface for proportional human effort in error correction.
Key Insight
💡 A contract-based multi-agent system can provide a supervisory interface for proportional human effort in error correction, reducing annotation costs in long-video understanding.
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🚀 IMPACT-CYCLE: a contract-based multi-agent system for efficient supervisory correction of long-video semantic memory errors #AI #MultimodalPipelines
Key Takeaways
Learn how IMPACT-CYCLE, a contract-based multi-agent system, enables efficient supervisory correction of long-video semantic memory errors, reducing annotation costs.
Full Article
Title: IMPACT-CYCLE: A Contract-Based Multi-Agent System for Claim-Level Supervisory Correction of Long-Video Semantic Memory
Abstract:
arXiv:2604.20136v1 Announce Type: cross Abstract: Correcting errors in long-video understanding is disproportionately costly: existing multimodal pipelines produce opaque, end-to-end outputs that expose no intermediate state for inspection, forcing annotators to revisit raw video and reconstruct temporal logic from scratch. The core bottleneck is not generation quality alone, but the absence of a supervisory interface through which human effort can be proportional to the scope of each error. We
Abstract:
arXiv:2604.20136v1 Announce Type: cross Abstract: Correcting errors in long-video understanding is disproportionately costly: existing multimodal pipelines produce opaque, end-to-end outputs that expose no intermediate state for inspection, forcing annotators to revisit raw video and reconstruct temporal logic from scratch. The core bottleneck is not generation quality alone, but the absence of a supervisory interface through which human effort can be proportional to the scope of each error. We
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