[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-392a2147ca9e7207-why-bm25-is-the-secret-weapon-for-agentic-search-summary":3,"summaries-facets-categories":133,"summary-related-392a2147ca9e7207-why-bm25-is-the-secret-weapon-for-agentic-search-summary":7839},{"id":4,"title":5,"ai":6,"body":13,"categories":90,"created_at":92,"date_modified":92,"description":83,"extension":93,"faq":92,"featured":94,"kicker_label":92,"meta":95,"navigation":112,"path":113,"published_at":114,"question":92,"scraped_at":115,"seo":116,"sitemap":117,"source_id":118,"source_name":119,"source_type":120,"source_url":121,"stem":122,"tags":123,"thumbnail_url":128,"tldr":129,"tweet":130,"unknown_tags":131,"__hash__":132},"summaries\u002Fsummaries\u002F392a2147ca9e7207-why-bm25-is-the-secret-weapon-for-agentic-search-summary.md","Why BM25 is the Secret Weapon for Agentic Search",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",7090,717,4128,0.002848,{"type":14,"value":15,"toc":82},"minimark",[16,21,25,29,32,36,39,62,66],[17,18,20],"h2",{"id":19},"the-rise-of-the-super-user","The Rise of the 'Super-User'",[22,23,24],"p",{},"BM25 (Best Match 25) is a 30-year-old lexical scoring function that has regained relevance not because the algorithm changed, but because the user did. Historically, human search queries were short (2-3 words). Modern LLMs, however, possess vast internal knowledge of entities, dates, and identifiers. They act as 'super-users' capable of generating long, highly specific queries and executing them in rapid, iterative loops. Because LLMs are trained to excel at coding and shell primitives, they are uniquely adept at using lexical search tools to navigate information.",[17,26,28],{"id":27},"reasoning-vs-retrieval-bottlenecks","Reasoning vs. Retrieval Bottlenecks",[22,30,31],{},"Research using the BrowseComp+ benchmark (830 riddle-like questions over 100,000 documents) demonstrates that reasoning is rarely the bottleneck for modern models. When relevant evidence is manually placed in the context window, accuracy remains high. Accuracy only drops when the model is forced to use a retrieval tool, proving that the quality of the search harness and query formulation is the primary constraint. Since context windows—which the author compares to 1.4MB floppy disks—remain limited, the ability to selectively retrieve and filter information is essential.",[17,33,35],{"id":34},"the-case-for-lexical-primitives","The Case for Lexical Primitives",[22,37,38],{},"BM25 remains a powerful primitive for three reasons:",[40,41,42,50,56],"ol",{},[43,44,45,49],"li",{},[46,47,48],"strong",{},"Explainability:"," Because it relies on literal term matching, the model can easily inspect why a result was returned and adjust its next query accordingly.",[43,51,52,55],{},[46,53,54],{},"Precision:"," It excels at matching specific entities, SKUs, and codes that dense embedding models often struggle to represent accurately.",[43,57,58,61],{},[46,59,60],{},"Efficiency:"," It is computationally inexpensive and requires less infrastructure than high-parameter embedding models.",[17,63,65],{"id":64},"scaling-via-dynamic-workspaces","Scaling via Dynamic Workspaces",[22,67,68,69,73,74,77,78,81],{},"The author advocates for a 'file system' approach to retrieval. Instead of forcing the model to process a single ranked list, developers should dump retrieved documents into a workspace. This allows the model to use familiar tools like ",[70,71,72],"code",{},"grep",", ",[70,75,76],{},"sed",", and ",[70,79,80],{},"awk"," to perform progressive disclosure—reading titles and snippets first, then deciding which documents to fully ingest. This paradigm aligns with how frontier models are already being trained to interact with code and bash environments, ensuring that search infrastructure improves in tandem with model capabilities.",{"title":83,"searchDepth":84,"depth":84,"links":85},"",2,[86,87,88,89],{"id":19,"depth":84,"text":20},{"id":27,"depth":84,"text":28},{"id":34,"depth":84,"text":35},{"id":64,"depth":84,"text":65},[91],"AI & LLMs",null,"md",false,{"content_references":96,"triage":107},[97,102],{"type":98,"title":99,"author":100,"context":101},"paper","Scaling direct corpus interaction via dynamic workspace expansion","Jimmy Lin's group (University of Waterloo)","recommended",{"type":103,"title":104,"url":105,"context":106},"tool","Hornet.dev","https:\u002F\u002Fhornet.dev\u002F","mentioned",{"relevance":108,"novelty":109,"quality":108,"actionability":109,"composite":110,"reasoning":111},4,3,3.6,"Category: AI & LLMs. The article discusses the resurgence of BM25 in the context of LLMs and agentic workflows, addressing a specific audience pain point regarding effective search and retrieval methods. It provides insights into how LLMs can leverage BM25 for better query formulation, which is actionable but lacks detailed implementation steps.",true,"\u002Fsummaries\u002F392a2147ca9e7207-why-bm25-is-the-secret-weapon-for-agentic-search-summary","2026-09-16 13:30:32","2026-09-17 03:11:52",{"title":5,"description":83},{"loc":113},"392a2147ca9e7207","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fZH97QHHYjY","summaries\u002F392a2147ca9e7207-why-bm25-is-the-secret-weapon-for-agentic-search-summary",[124,125,126,127],"llm","agents","search","retrieval","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FfZH97QHHYjY\u002Fhqdefault.jpg","BM25 is seeing a resurgence in agentic workflows because LLMs act as 'super-users' who can write complex, multi-step queries that exploit the precision of lexical matching, which is often more effective and explainable than dense embeddings.","This talk explains why the 30-year-old [BM25](https:\u002F\u002Fhornet.dev\u002F) algorithm is seeing a resurgence in agentic workflows: because LLMs are better at writing long, specific queries than humans ever were. The speaker argues that reasoning isn't the bottleneck for agents—retrieval quality is—and suggests treating retrieved documents as a file system for the model to query rather than relying on complex embedding-based 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While these are effective for candidate discovery, they treat documents as static, bounded results. This prevents agents from performing complex operations—such as cross-document verification, filtering, or reorganization—that require a more flexible, interactive environment. While Direct Corpus Interaction (DCI) allows for shell-executable commands, it becomes unstable and slow as the corpus size increases, making it impractical for large-scale data.",[17,7858,7860],{"id":7859},"the-dr-dci-framework","The DR-DCI Framework",[22,7862,7863],{},"DR-DCI (Retriever-steered DCI) solves this by treating retrieval as an agent-callable action that populates a local, dynamic workspace. Instead of forcing the agent to operate on the entire corpus, the agent retrieves relevant documents and pulls them into a local environment where it can execute DCI operations. This architecture provides two distinct advantages:",[7865,7866,7867,7873],"ul",{},[43,7868,7869,7872],{},[46,7870,7871],{},"Scalability:"," By limiting the scope of DCI operations to a retrieved subset, the system remains performant even as the total corpus grows to 10M+ documents.",[43,7874,7875,7877],{},[46,7876,54],{}," The agent retains the ability to perform complex, terminal-style operations (filtering, comparison, verification) on the documents it has selected.",[17,7879,7881],{"id":7880},"performance-and-scaling-results","Performance and Scaling Results",[22,7883,7884],{},"Experiments demonstrate that DR-DCI consistently outperforms raw DCI and standard retrieval baselines. On the Browsecomp-Plus benchmark, DR-DCI achieved 71.2% accuracy, which increased to 73.3% when utilizing workspace-preserving context resets. Key findings include:",[7865,7886,7887,7892,7898],{},[43,7888,7889,7891],{},[46,7890,60],{}," The framework reduces tool usage, wall time, and overall computational cost compared to raw DCI.",[43,7893,7894,7897],{},[46,7895,7896],{},"Robustness:"," While raw DCI fails as corpus size increases, DR-DCI maintains effectiveness across scales ranging from 100K to 10M documents.",[43,7899,7900,7903],{},[46,7901,7902],{},"Versatility:"," In a 20M-scale file-per-document setting (Wiki-18 QA), the model achieved an average score of 63.0 across six benchmarks, outperforming existing search-agent baselines.",{"title":83,"searchDepth":84,"depth":84,"links":7905},[7906,7907,7908],{"id":7852,"depth":84,"text":7853},{"id":7859,"depth":84,"text":7860},{"id":7880,"depth":84,"text":7881},[91],{"content_references":7911,"triage":7912},[],{"relevance":108,"novelty":108,"quality":108,"actionability":109,"composite":7913,"reasoning":7914},3.8,"Category: AI & LLMs. The article discusses a novel framework (DR-DCI) that enhances agentic search, addressing a specific pain point of scalability and precision in AI-powered search applications. It provides insights into performance improvements, but lacks detailed actionable steps for implementation.","\u002Fsummaries\u002Fba92a281793a1572-scaling-agentic-search-with-dynamic-workspace-expa-summary","2026-06-16 12:56:53",{"title":7842,"description":83},{"loc":7915},"ba92a281793a1572","arXiv cs.AI","article","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.14885","summaries\u002Fba92a281793a1572-scaling-agentic-search-with-dynamic-workspace-expa-summary",[125,7925,126,127],"ai-llms","DR-DCI improves agentic search by combining retriever-based scalability with local terminal-style operations, allowing agents to dynamically pull documents into a workspace for precise analysis.",[7925,126,127],"PS5Xy5sI4QMO8WtKVvYDm2vVOV-DQUOpY4-surrcoH4",{"id":7930,"title":7931,"ai":7932,"body":7937,"categories":8015,"created_at":92,"date_modified":92,"description":83,"extension":93,"faq":92,"featured":94,"kicker_label":92,"meta":8016,"navigation":112,"path":8032,"published_at":8033,"question":92,"scraped_at":8033,"seo":8034,"sitemap":8035,"source_id":8036,"source_name":8037,"source_type":7921,"source_url":8038,"stem":8039,"tags":8040,"thumbnail_url":92,"tldr":8042,"tweet":92,"unknown_tags":8043,"__hash__":8044},"summaries\u002Fsummaries\u002F73c67fb584b2873f-harness-1-offloading-bookkeeping-to-improve-search-summary.md","Harness-1: Offloading Bookkeeping to Improve Search Agent Performance",{"provider":7,"model":8,"input_tokens":7933,"output_tokens":7934,"processing_time_ms":7935,"cost_usd":7936},9437,801,5156,0.00356075,{"type":14,"value":7938,"toc":8010},[7939,7943,7946,7950,7953,7973,7986,7990,7993,8007],[17,7940,7942],{"id":7941},"stateful-cognitive-offloading","Stateful Cognitive Offloading",[22,7944,7945],{},"Most search agents struggle because they attempt to manage both high-level search strategy and low-level bookkeeping (tracking evidence, deduplication, and state maintenance) within a single, growing transcript. Harness-1, a 20B model built on gpt-oss-20b, addresses this by implementing \"stateful cognitive offloading.\" The model acts as a policy that makes semantic decisions, while an external state-machine harness manages the \"working memory\" and routine operations.",[17,7947,7949],{"id":7948},"the-harness-architecture","The Harness Architecture",[22,7951,7952],{},"The harness maintains a recoverable state that includes:",[7865,7954,7955,7961,7967],{},[43,7956,7957,7960],{},[46,7958,7959],{},"Candidate Pool:"," A deduplicated set of retrieved documents.",[43,7962,7963,7966],{},[46,7964,7965],{},"Curated Set:"," A final output set (capped at 30 documents) tagged by importance (very_high, high, fair, low).",[43,7968,7969,7972],{},[46,7970,7971],{},"Evidence Graph:"," A structure that uses regex extraction to identify frequent entities and bridge documents, helping the agent identify follow-up leads.",[22,7974,7975,7976,73,7979,77,7982,7985],{},"By offloading this state, the agent avoids the performance degradation associated with managing complex bookkeeping inside the prompt. The harness provides eight tools to the model, including ",[70,7977,7978],{},"fan_out_search",[70,7980,7981],{},"curate",[70,7983,7984],{},"verify",". A key design choice is the \"warm-start\" mechanism, where the first successful search auto-seeds the curated set, shifting the agent's task from building from scratch to iterative refinement.",[17,7987,7989],{"id":7988},"training-and-performance","Training and Performance",[22,7991,7992],{},"Harness-1 uses a two-stage training process:",[40,7994,7995,8001],{},[43,7996,7997,8000],{},[46,7998,7999],{},"Supervised Fine-Tuning (SFT):"," Teaches the model how to operate the harness interface using 899 trajectories generated by a GPT-5.4 teacher.",[43,8002,8003,8006],{},[46,8004,8005],{},"Reinforcement Learning (RL):"," Uses on-policy CISPO with a terminal-only reward to optimize search decisions. A critical addition is a \"tool-diversity bonus,\" which prevents the agent from collapsing into repetitive search patterns; without this, curated recall plateaus significantly lower.",[22,8008,8009],{},"In benchmarks across web, finance, and patent data, Harness-1 achieved an average curated recall of 0.730, outperforming other open models and trailing only frontier-scale models like Opus-4.6. Notably, the model showed strong generalization, with a 2.2x larger gain on held-out benchmarks compared to tasks similar to its training data.",{"title":83,"searchDepth":84,"depth":84,"links":8011},[8012,8013,8014],{"id":7941,"depth":84,"text":7942},{"id":7948,"depth":84,"text":7949},{"id":7988,"depth":84,"text":7989},[91],{"content_references":8017,"triage":8028},[8018,8022,8025],{"type":98,"title":8019,"url":8020,"context":8021},"Harness-1: A 20B Retrieval Subagent Trained With Reinforcement Learning Inside a Stateful Search Harness on gpt-oss-20b","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2606.02373","cited",{"type":103,"title":8023,"url":8024,"context":101},"Harness-1 Model Weights","https:\u002F\u002Fhuggingface.co\u002Fpat-jj\u002Fharness-1",{"type":103,"title":8026,"url":8027,"context":101},"Harness-1 GitHub Repository","https:\u002F\u002Fgithub.com\u002Fpat-jj\u002Fharness-1",{"relevance":8029,"novelty":108,"quality":108,"actionability":109,"composite":8030,"reasoning":8031},5,4.15,"Category: AI & LLMs. The article provides a detailed exploration of the Harness-1 model, specifically addressing how it improves search agent performance through stateful cognitive offloading, which is a relevant topic for AI product builders. It presents new insights into the architecture and training processes, though it lacks specific actionable steps for implementation.","\u002Fsummaries\u002F73c67fb584b2873f-harness-1-offloading-bookkeeping-to-improve-search-summary","2026-06-07 12:56:24",{"title":7931,"description":83},{"loc":8032},"73c67fb584b2873f","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F06\u002F06\u002Fmeet-harness-1-a-20b-retrieval-subagent-trained-with-reinforcement-learning-inside-a-stateful-search-harness-on-gpt-oss-20b\u002F","summaries\u002F73c67fb584b2873f-harness-1-offloading-bookkeeping-to-improve-search-summary",[124,125,8041,127],"reinforcement-learning","Harness-1 improves retrieval performance by separating search policy from state management, using a stateful harness to handle bookkeeping and memory, allowing the 20B model to focus on semantic decisions.",[8041,127],"TkpWka_tCJDfhhmS91QVXnL-qiEepGUZu7rknsRyPaU",{"id":8046,"title":8047,"ai":8048,"body":8054,"categories":8082,"created_at":92,"date_modified":92,"description":83,"extension":93,"faq":92,"featured":94,"kicker_label":92,"meta":8083,"navigation":112,"path":8087,"published_at":8088,"question":92,"scraped_at":8088,"seo":8089,"sitemap":8090,"source_id":8091,"source_name":8092,"source_type":7921,"source_url":8093,"stem":8094,"tags":8095,"thumbnail_url":92,"tldr":8096,"tweet":92,"unknown_tags":8097,"__hash__":8098},"summaries\u002Fsummaries\u002F0ef58dbb67f9059c-uber-s-openai-powered-multi-agent-ai-optimizes-ear-summary.md","Uber's OpenAI-Powered Multi-Agent AI Optimizes Earnings and Booking",{"provider":7,"model":8049,"input_tokens":8050,"output_tokens":8051,"processing_time_ms":8052,"cost_usd":8053},"x-ai\u002Fgrok-4.1-fast",7179,1572,27560,0.0021993,{"type":14,"value":8055,"toc":8077},[8056,8060,8063,8067,8070,8074],[17,8057,8059],{"id":8058},"multi-agent-architecture-turns-marketplace-data-into-actionable-driver-insights","Multi-Agent Architecture Turns Marketplace Data into Actionable Driver Insights",[22,8061,8062],{},"Uber processes 40 million daily trips across 10 million drivers\u002Fcouriers in 15,000 cities over 70 countries, with 1.7 million concurrent rides influenced by traffic, weather, and events. Uber Assistant uses OpenAI frontier models to summarize complex signals like earnings trends and heatmaps into plain-language recommendations, answering queries like optimal positioning or switching rides to deliveries. This reduces cognitive load, helping new drivers ramp up faster than relying on hundreds of trips for platform learning. Experienced drivers return for follow-ups, boosting repeat engagement and on-platform time utilization. The system routes queries via multi-agent setup: nano\u002Fmini models for lightweight classification and speed, larger reasoning models for complex tasks. AI Guard screens prompts\u002Fresponses to enforce safety, privacy, policy compliance, cut hallucinations, and ensure low-latency mobile performance—critical since distrust causes quick user drop-off.",[17,8064,8066],{"id":8065},"voice-realtime-api-enables-natural-interactions-for-riders-and-drivers","Voice Realtime API Enables Natural Interactions for Riders and Drivers",[22,8068,8069],{},"OpenAI Realtime API powers voice features in the Uber app, letting users speak full intents like \"five pieces of luggage, five people, nice ride to airport\" for recommendations such as UberXL or saved locations like \"home.\" It syncs spoken and visual responses, removing multi-tap friction for complex requests. This broadens accessibility for older adults, visually impaired users, or hands-free drivers, orchestrating outcomes across the ecosystem without sequential tasks. Voice unlocks nuanced event sequences per trip\u002Fdrive, providing data to refine future features at Uber's scale.",[17,8071,8073],{"id":8072},"organizational-shifts-accelerate-ai-product-cycles","Organizational Shifts Accelerate AI Product Cycles",[22,8075,8076],{},"Uber embeds LLMs organization-wide, democratizing prompting, retrieval, evaluation, and orchestration beyond a central AI team—engaging engineering, product, legal, operations, and design for policy, testing, and UX. This fosters cross-team collaboration, faster experimentation, and iteration. Beta rollout reaches hundreds of thousands of U.S. drivers, prioritizing early-lifecycle support for better positioning\u002Ftrips, strong repeat use after value delivery, and optimized time via insights. Result: productive work, seamless transport, humanized logistics.",{"title":83,"searchDepth":84,"depth":84,"links":8078},[8079,8080,8081],{"id":8058,"depth":84,"text":8059},{"id":8065,"depth":84,"text":8066},{"id":8072,"depth":84,"text":8073},[91],{"content_references":8084,"triage":8085},[],{"relevance":8029,"novelty":108,"quality":108,"actionability":109,"composite":8030,"reasoning":8086},"Category: AI & LLMs. The article discusses Uber's implementation of OpenAI models in a multi-agent architecture, which directly addresses practical applications of AI in optimizing driver experiences and operational efficiency. It provides insights into how Uber leverages AI to enhance user interactions and streamline processes, making it relevant for product builders interested in AI integration.","\u002Fsummaries\u002F0ef58dbb67f9059c-uber-s-openai-powered-multi-agent-ai-optimizes-ear-summary","2026-05-11 15:04:26",{"title":8047,"description":83},{"loc":8087},"0ef58dbb67f9059c","OpenAI News","https:\u002F\u002Fopenai.com\u002Findex\u002Fuber","summaries\u002F0ef58dbb67f9059c-uber-s-openai-powered-multi-agent-ai-optimizes-ear-summary",[124,125],"Uber deploys OpenAI models via multi-agent architecture for Uber Assistant, delivering real-time driver guidance from marketplace data and voice-based ride booking, accelerating new driver ramp-up versus hundreds of trips via trial-and-error.",[],"yGF9AUDRqSMC2I2dNWYkDz4g_Y6jWUMkTkDUbMB341M",{"id":8100,"title":8101,"ai":8102,"body":8107,"categories":8167,"created_at":92,"date_modified":92,"description":83,"extension":93,"faq":92,"featured":94,"kicker_label":92,"meta":8168,"navigation":112,"path":8176,"published_at":8177,"question":92,"scraped_at":8178,"seo":8179,"sitemap":8180,"source_id":8181,"source_name":8182,"source_type":7921,"source_url":8183,"stem":8184,"tags":8185,"thumbnail_url":92,"tldr":8186,"tweet":92,"unknown_tags":8187,"__hash__":8188},"summaries\u002Fsummaries\u002F6b8d02ecdd85e30f-reward-queries-to-fix-rag-agent-failures-summary.md","Reward Queries to Fix RAG Agent Failures",{"provider":7,"model":8049,"input_tokens":8103,"output_tokens":8104,"processing_time_ms":8105,"cost_usd":8106},3889,1366,12476,0.00143815,{"type":14,"value":8108,"toc":8163},[8109,8113,8116,8130,8133,8137,8140,8160],[17,8110,8112],{"id":8111},"why-initial-queries-derail-llm-search-agents","Why Initial Queries Derail LLM Search Agents",[22,8114,8115],{},"Most LLM-based search agents emphasize reasoning ('how to think') but neglect query quality ('how to ask'). A poor first query retrieves irrelevant results, cascading failures. In the ASearcher dataset example:",[7865,8117,8118,8121,8124,8127],{},[43,8119,8120],{},"User query: \"An Annapolis Story stars which American stage, film, and television actor born on February 15, 1914?\"",[43,8122,8123],{},"Agent query: \"birthdate of Kevin McCarthy\" (omits 'actor' constraint, causing entity ambiguity).",[43,8125,8126],{},"Retrieval: Politician Kevin McCarthy (born 1965).",[43,8128,8129],{},"Outcome: Agent concludes \"Not Found\".",[22,8131,8132],{},"This shows how early errors compound, as agents rarely self-correct without guidance. Fix by rewarding good intermediate searches and refining bad ones directly.",[17,8134,8136],{"id":8135},"smartsearch-process-rewards-for-query-refinement","SmartSearch: Process Rewards for Query Refinement",[22,8138,8139],{},"SmartSearch introduces reward-guided query refinement to upgrade agent policies. Core steps:",[40,8141,8142,8148,8154],{},[43,8143,8144,8147],{},[46,8145,8146],{},"Reward the Query",": Score intermediate queries by retrieval quality (e.g., relevance to user intent). High-reward queries reinforce successful patterns.",[43,8149,8150,8153],{},[46,8151,8152],{},"Fix the Retrieval",": For low-reward queries, automatically refine (e.g., add constraints like 'actor' or disambiguate entities) before re-retrieving.",[43,8155,8156,8159],{},[46,8157,8158],{},"Upgrade the Agent",": Bake refinements into the agent's policy via reinforcement learning or fine-tuning, making better querying habitual.",[22,8161,8162],{},"This shifts focus from end-to-end trajectories to process-level optimization, turning brittle agents into robust search systems. Early bad steps no longer doom the entire response.",{"title":83,"searchDepth":84,"depth":84,"links":8164},[8165,8166],{"id":8111,"depth":84,"text":8112},{"id":8135,"depth":84,"text":8136},[91],{"content_references":8169,"triage":8173},[8170],{"type":8171,"title":8172,"context":106},"dataset","ASearcher dataset",{"relevance":8029,"novelty":108,"quality":108,"actionability":108,"composite":8174,"reasoning":8175},4.35,"Category: AI & LLMs. The article provides a deep dive into improving LLM search agents by addressing query quality, which is a critical pain point for developers integrating AI. It offers a novel approach with the SmartSearch method, detailing actionable steps for refining queries through a reward system.","\u002Fsummaries\u002F6b8d02ecdd85e30f-reward-queries-to-fix-rag-agent-failures-summary","2026-05-01 20:30:10","2026-05-03 17:00:35",{"title":8101,"description":83},{"loc":8176},"6b8d02ecdd85e30f","Level Up Coding","https:\u002F\u002Flevelup.gitconnected.com\u002Fsmartsearch-reward-the-query-fix-the-retrieval-upgrade-the-agent-913c2f9eadcf?source=rss----5517fd7b58a6---4","summaries\u002F6b8d02ecdd85e30f-reward-queries-to-fix-rag-agent-failures-summary",[124,125],"LLM search agents fail from poor initial queries; SmartSearch uses process rewards to refine them, preventing bad retrievals like mistaking actor Kevin McCarthy (1914) for politician (1965).",[],"jh7-T6sGw1O6c9xX-Cj9l7QR0QDxOkm8zS6zycLTOuA"]