hermes-hierarchical-video-memory

name	hermes-hierarchical-video-memory
title	HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.14724
keywords	["video-understanding","kv-cache","hierarchical-memory","streaming","efficient-inference"]
description	Use KV cache as hierarchical memory for real-time video stream understanding with minimal GPU overhead, achieving 10x faster response times compared to standard methods. Use when processing continuous video streams where latency and memory efficiency are critical.

HERMES: Hierarchical Video Memory via KV Cache

This skill demonstrates how to leverage KV cache as hierarchical memory for efficient streaming video understanding, enabling real-time inference with significantly reduced computational and memory overhead.

When to Use

• Real-time video stream processing (surveillance, robotics, autonomous vehicles)
• Continuous video understanding with strict latency requirements
• Systems with limited GPU memory (mobile, edge devices)
• Applications requiring fast temporal reasoning over video
• Scenarios where 10x speedup in response time is valuable

When NOT to Use

• Offline batch video analysis (efficiency gains matter less)
• Short video clips (hierarchical memory overhead not justified)
• Single-frame analysis (inherently doesn't need temporal memory)
• Systems with unlimited compute resources and no latency constraints

Key Concept

Standard video transformers process entire video sequences, creating bottlenecks from expensive KV cache computations. HERMES restructures the KV cache as a hierarchical memory:

1. Frame-Level Cache: Store KV for individual frames
2. Temporal Compression: Compress older frames into summary representations
3. Hierarchical Queries: Efficient retrieval across time scales
4. Streaming Updates: Incrementally add new frames without recomputing entire history

This maintains temporal understanding while staying memory-efficient.

Implementation Pattern

Structure KV cache hierarchically for streaming video:

# Pseudocode for hierarchical KV cache management
class HierarchicalKVCache:
    def __init__(self, cache_levels=3, compression_ratio=4):
        self.cache_levels = cache_levels  # Multiple time scales
        self.compression_ratio = compression_ratio
        self.caches = [[] for _ in range(cache_levels)]

     ():
        
        frame_k, frame_v = .compute_kv(frame)

        
        .caches[].append((frame_k, frame_v))

        
         frame_idx % .compression_ratio == :
            .compress_to_next_level(from_level=, to_level=)

         frame_idx % (.compression_ratio ** ) == :
            .compress_to_next_level(from_level=, to_level=)

         frame_k, frame_v

     ():
        
        frames_to_compress = .caches[from_level][-.compression_ratio:]

        
        compressed_k = pool_keys(frames_to_compress)
        compressed_v = pool_values(frames_to_compress)

        .caches[to_level].append((compressed_k, compressed_v))

     ():
        
        context = []
        context.extend(.caches[])  
         (.caches[]) > :
            context.extend(.caches[])  
         (.caches[]) > :
            context.extend(.caches[])  

         context