📚 A framework that fixes redundant flat-retrieval RAG by navigating a knowledge graph hierarchically. Title: LeanRAG URL: 📦 Overview LeanRAG is a graph-based RAG framework that combines knowledge graphs with hierarchical retrieval. It was accepted to AAAI 2026. 🔍 The problem it solves Naive retrieval just collects related chunks flatly, leading to overlapping, redundant content and ignoring structural links between documents. LeanRAG traverses semantically aggregated upper layers to produce well-grounded answers with less redundancy. 🛠 The method (5 stages) ・Semantic aggregation: cluster entities into summary nodes with adjacency relations ・Knowledge graph construction: connect entities and summaries in a multi-layer graph ・Hierarchical retrieval: anchor the query at fine-grained entities, then traverse upward to gather evidence ・Redundancy-aware synthesis: streamline overlapping paths ・Generation: an LLM answers from the gathered evidence 📊 Results ・About 46% lower retrieval redundancy versus flat retrieval ・On the Mix benchmark, LeanRAG 8.59 beats HiRAG 8.08, GraphRAG 7.87, and LightRAG 7.61 ・Win rates of 97.3% vs NaiveRAG, 78.1% vs GraphRAG, 81.2% vs LightRAG, and 100% vs FastGraphRAG A compelling option for anyone building a low-redundancy, knowledge-graph-grounded RAG. #RAG# #KnowledgeGraph#

Grok 4.1 Fast excels at real-time info retrieval and deep research. Paired with native X integration, code execution, and advanced web browsing, Grok 4.1 Fast + Agent Tools API tops agentic search benchmarks.

🔎 A provocative paper: "agentic search needs no embeddings or vector DB—just grep the raw corpus directly." Title: Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction URL: ❓ What is Direct Corpus Interaction (DCI)? 💡 A retrieval paradigm where the agent explores the raw corpus directly via grep, find, and shell commands—no embedding model, vector index, or retrieval API. It needs no offline indexing and adapts naturally to evolving local corpora. ❓ Why aren't conventional retrievers enough? 💡 Sparse or dense, they compress the corpus through a fixed similarity interface into top-k before reasoning begins. That makes exact lexical matching, combining weak clues, and checking local context hard—and evidence filtered out early can't be recovered no matter how strong the downstream reasoning. That's fatal for multi-step agent search. ❓ Can it really win without a retriever? 💡 Yes. On BrowseComp-Plus, swapping the retriever for DCI on the same Sonnet 4.6 lifts accuracy 69.0 → 80.0% (+11.0) while cutting cost 29.4%. Multi-hop QA averages 83.0 (+30.7 over the strongest baseline); IR ranking hits 68.5 NDCG@10 (+21.5). Even the lightweight GPT-5.4 nano version beats many baselines. ❓ What's actually driving it? 💡 The authors call it "retrieval interface resolution." Trajectory analysis shows the edge comes less from surfacing more gold documents and more from high-resolution local search and verification after reaching them. #AIAgents# #RAG#

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🔎 A paper that pinpoints the hidden reason RL training for search agents stalls partway. Title: Harness-G: A Graph-Structured Harness for Search Agents URL: ❓ Why does training collapse? 💡 It's "retrieval-equivalence collapse." The policy keeps generating differently-worded queries that fetch the same evidence, so same evidence → same answer → same reward, within-group advantages vanish, and the training signal dries up. ❓ How does Harness-G fix it? 💡 It stops free-form query generation and turns it into menu selection over a paragraph-sentence-entity graph built from the corpus. The policy picks action IDs, not strings. Being finite, verifiable, and previewable, it preserves diversity in what actually gets retrieved. ❓ What is the credit assignment (SNC)? 💡 A frozen answerer previews how much an action raises the gold-answer probability, scored against alternatives (frontier-relative). Non-myopic payoffs like "find the bridge entity first" propagate back through provenance edges (enablement). No extra rollouts needed. ❓ Does it work? 💡 Across six QA benchmarks it beats Graph-R1 by +10.74 at 1.5B and +3.98 at 3B, best at both scales, especially on multi-hop, with $0 API cost to build the graph. #SearchAgents# #ReinforcementLearning#