Context Compaction: Query-Based and Objective Modes | Morph
Compaction drops 50-70% of an agent’s context while keeping every surviving line verbatim. Two modes: objective compaction strips filler with no guida...
AI Research
Summary
Factory Research evaluated context compression for AI agents using probe-based tests. They found structured summarization preserves more details than OpenAI or Anthropic methods during long sessions.
Highlights
- Traditional metrics like ROUGE fail to measure functional context preservation.
- Probe-based tests verify if agents recall specific details after compression.
- Structured summarization outperformed OpenAI and Anthropic in debugging tasks.
- Optimizing for tokens per task improves agent productivity over tokens per request.
- Testing covered debugging, code review, and ML research scenarios.
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Context
Audience
AI Engineers and Developers
DomainArtificial Intelligence
Formatlong-form article
Accessfree online
Topics
Related
Context CompressionAI AgentsStructured SummarizationOpenAIAnthropicEvaluation Frameworks
