{"repo":"NousResearch/hermes-agent","public_id":"16834","display_label":"Improve signal retention during context compression","summary":"These items explore enhancements to context compression pipelines, focusing on preserving high-quality or high-signal information across repeated compactions while filtering noise and deduplicating content. Approaches include pre-compression memory extraction, quality filtering, and LLM-based pre-filtering to reduce token cost.","subsystem":"agent","counts":{"members":4,"open_issues":2,"open_issues_distinct":2,"open_prs":1,"closed_issues":1,"merged_prs":0,"attempt_prs":1},"landed_fix":null,"resolution":{"state":"unresolved","by":null},"freshness":{"state":"never_verified","last_verified_at":null,"is_stale":false},"grouping":{"backbone":"STRUCTURAL_PARTIAL","link_density":0.2866666666666666},"fix_edges":[],"members":[{"kind":"issue","number":16834,"title":"feat(hindsight): LLM-based retain pre-filter to reduce noise and token cost","url":null,"state":"closed"},{"kind":"issue","number":37010,"title":"Pre-Compression Memory Extraction with Quality Filtering and Deduplication (Working Implementation)","url":null,"state":"open"},{"kind":"issue","number":57875,"title":"[Enhancement]: Preserve high-signal decisions during repeated context compression","url":null,"state":"open"},{"kind":"pr","number":62777,"title":"feat(compression): preserve fidelity across repeated compactions","url":null,"state":"open","close_reason":{"category":"uncategorized","implication":"needs_llm"},"merged":false}],"trust":{"default_tier":"verified","includes_suggested":false}}