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Resolves #666. RIDER reranks retrieved passages by how well the LLM can actually answer from them, bridging the gap between high retrieval recall (98.4% R@5) and low end-to-end accuracy (17%). agent/rider.py (256 lines): - RIDER class with rerank(passages, query) method - Batch LLM prediction from each passage individually - Confidence-based scoring: specificity, grounding, hedge detection, query relevance, refusal penalty - Async scoring with configurable batch size - Convenience functions: rerank_passages(), is_rider_available() tools/session_search_tool.py: - Wired RIDER into session search pipeline after FTS5 results - Reranks sessions by LLM answerability before summarization - Graceful fallback if RIDER unavailable tests/test_reader_guided_reranking.py (10 tests): - Empty passages, few passages, disabled mode - Confidence scoring: short answers, hedging, grounding, refusal - Convenience function, availability check Config via env vars: RIDER_ENABLED, RIDER_TOP_K, RIDER_TOP_N, RIDER_MAX_TOKENS, RIDER_BATCH_SIZE.