T4 — Temporal Reasoning Layer: COMPLETE ✅

**Completed:** 2026-02-19 **Status:** All deliverables shipped, 54/54 tests passing ## Problem Eval showed 80% on temporal reasoning — weakest dimension. The knowledge graph and RAG had zero temporal metadata. No way to answer "what were we working on last week?" or "when did we switch from X to Y?" ### 1. Temporal Retrieval Module **File:** `cognitive_twin/v3/tools/temporal_retrieval.py` (43KB, ~1100 lines) Core capabilities: - **Natural language date parsing** — "yesterday", "last week", "3 days ago", "February 2026", "Q1", "recently", ISO dates - **Query classification** — 6 query types: activity, timeline, recency, duration, sequence, search - **Recency scoring** — Exponential decay with configurable half-life (default 30 days) - **Temporal ordering** — Results ranked by combined relevance × recency score - **Timeline generation** — Chronological event listing for any topic - **Temporal edge traversal** — preceded_by, followed_by, concurrent_with, superseded_by Key classes: - `TemporalRetriever` — Main retrieval engine over graph + RAG - `TemporalRange` — Time range with start/end/granularity - `TemporalResult` / `TemporalQueryResult` — Structured result types - `parse_temporal_reference()` — NL date → TemporalRange - `classify_temporal_query()` — Query → type + params - `compute_recency_score()` — Exponential decay scoring