[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-5b466bfd41a8ba4c-schemarouter-field-aware-tool-routing-for-agentic-summary":3,"summaries-facets-categories":86,"summary-related-5b466bfd41a8ba4c-schemarouter-field-aware-tool-routing-for-agentic-summary":7792},{"id":4,"title":5,"ai":6,"body":13,"categories":46,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":51,"navigation":69,"path":70,"published_at":71,"question":48,"scraped_at":71,"seo":72,"sitemap":73,"source_id":74,"source_name":75,"source_type":76,"source_url":57,"stem":77,"tags":78,"thumbnail_url":48,"tldr":83,"tweet":48,"unknown_tags":84,"__hash__":85},"summaries\u002Fsummaries\u002F5b466bfd41a8ba4c-schemarouter-field-aware-tool-routing-for-agentic--summary.md","SchemaRouter: Field-Aware Tool Routing for Agentic RAG",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",4062,550,2881,0.0018405,{"type":14,"value":15,"toc":39},"minimark",[16,21,25,29,32,36],[17,18,20],"h2",{"id":19},"the-problem-with-semantic-routing","The Problem with Semantic Routing",[22,23,24],"p",{},"Traditional agentic RAG systems often rely on semantic similarity to route queries to tools. This approach frequently fails in heterogeneous environments where multiple tools share similar semantic descriptions but require different input schemas. When a router cannot distinguish between the specific fields required by a tool, it often selects the wrong tool or triggers unnecessary LLM calls, leading to increased latency and reduced accuracy.",[17,26,28],{"id":27},"field-aware-routing-logic","Field-Aware Routing Logic",[22,30,31],{},"SchemaRouter addresses this by shifting the routing focus from broad semantic intent to specific schema requirements. Instead of treating tools as black boxes, the system analyzes the input parameters (fields) required by each tool. By aligning the user's query directly with the expected schema fields, the router ensures that the selected tool is not just semantically relevant, but technically capable of processing the specific request. This field-aware approach significantly reduces \"hallucinated\" tool calls and improves the precision of multi-tool agentic workflows.",[17,33,35],{"id":34},"performance-and-implementation","Performance and Implementation",[22,37,38],{},"The research demonstrates that SchemaRouter reduces the overhead associated with tool selection in complex RAG pipelines. By minimizing the number of irrelevant tools considered during the routing phase, the system achieves faster response times and higher success rates in multi-step reasoning tasks. The authors provide a benchmark and implementation fixtures to help developers integrate this routing logic into existing agentic frameworks, making it a practical solution for scaling RAG applications that utilize diverse, heterogeneous data sources.",{"title":40,"searchDepth":41,"depth":41,"links":42},"",2,[43,44,45],{"id":19,"depth":41,"text":20},{"id":27,"depth":41,"text":28},{"id":34,"depth":41,"text":35},[47],"AI & LLMs",null,"md",false,{"content_references":52,"triage":64},[53,59],{"type":54,"title":55,"author":56,"url":57,"context":58},"other","SchemaRouter: Field-Aware Tool Routing for Efficient Heterogeneous Agentic RAG","JDeun","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.21375","reviewed",{"type":60,"title":61,"url":62,"context":63},"tool","SchemaRouter Research Repository","https:\u002F\u002Fgithub.com\u002FJDeun\u002FSchemaRouter_research","recommended",{"relevance":65,"novelty":66,"quality":66,"actionability":66,"composite":67,"reasoning":68},5,4,4.35,"Category: AI & LLMs. The article presents a novel approach to improving agentic RAG systems through field-aware routing, addressing a specific pain point of traditional semantic routing. It offers practical implementation details and benchmarks that developers can use to integrate this solution into their workflows.",true,"\u002Fsummaries\u002F5b466bfd41a8ba4c-schemarouter-field-aware-tool-routing-for-agentic-summary","2026-08-26 03:10:17",{"title":5,"description":40},{"loc":70},"5b466bfd41a8ba4c","arXiv cs.AI","article","summaries\u002F5b466bfd41a8ba4c-schemarouter-field-aware-tool-routing-for-agentic--summary",[79,80,81,82],"llm","agents","ai-tools","rag","SchemaRouter improves agentic RAG efficiency by using field-aware routing, which maps user queries to specific tool schemas rather than relying on generic semantic 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While efficient for simple queries, this approach destroys the inherent structure of complex documents. When a document is flattened, headings are separated from their content, tables are detached from their explanatory text, and the logical relationship between sections is lost. This forces the LLM to guess how disconnected fragments relate, often leading to hallucinations or incomplete answers when a query requires information spanning multiple sections.",[17,7811,7813],{"id":7812},"structural-navigation-as-an-alternative","Structural Navigation as an Alternative",[22,7815,7816],{},"Instead of flattening documents, you can treat them as trees that mirror their original authoring structure (titles, headers, sections, and subsections). 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When reading a paragraph, the model knows exactly which section and subsection it belongs to, as the path (including headings) is preserved.",[7824,7832,7833,7836],{},[7827,7834,7835],{},"Holistic Reasoning:"," The agent can hold its place, navigate to different branches of the document, and synthesize information across disparate sections, which is nearly impossible with flat vector similarity.",[17,7838,7840],{"id":7839},"implementation-and-trade-offs","Implementation and Trade-offs",[22,7842,7843,7844,7847],{},"To implement structural retrieval, you must first reconstruct the document's hierarchy from raw formats like PDFs, which typically lack semantic structure. Tools like ",[7827,7845,7846],{},"Docling"," are designed to parse these files into structured trees where reading order is preserved and tables remain intact.",[22,7849,7850],{},"However, this approach introduces specific trade-offs:",[7821,7852,7853,7859],{},[7824,7854,7855,7858],{},[7827,7856,7857],{},"Engineering Complexity:"," Parsing real-world documents into clean, usable trees is a significant technical challenge.",[7824,7860,7861,7864],{},[7827,7862,7863],{},"Latency and Cost:"," Unlike a single vector lookup, structural navigation requires multiple passes and back-and-forth reasoning with the model, increasing both latency and API usage.",[22,7866,7867],{},"Ultimately, structural retrieval is not a universal replacement for chunk-based RAG. Similarity search remains the superior tool for fuzzy, large-scale retrieval across millions of documents. The structural approach is most effective for long, highly organized documents where precision is critical and the relationships between parts of the document are the core of the user's query. In production systems, the most effective strategy is often a hybrid: using similarity search to locate the correct document, and structural navigation to extract the answer from within it.",{"title":40,"searchDepth":41,"depth":41,"links":7869},[7870,7871,7872],{"id":7805,"depth":41,"text":7806},{"id":7812,"depth":41,"text":7813},{"id":7839,"depth":41,"text":7840},[47],{"content_references":7875,"triage":7878},[7876],{"type":60,"title":7846,"url":7877,"context":63},"https:\u002F\u002Fgithub.com\u002FDS4SD\u002Fdocling",{"relevance":65,"novelty":66,"quality":66,"actionability":66,"composite":67,"reasoning":7879},"Category: AI & LLMs. The article discusses a novel approach to Retrieval Augmented Generation (RAG) that addresses a specific pain point in AI product development: the limitations of traditional chunking methods for complex documents. It provides actionable insights on implementing structural retrieval, including the use of tools like Docling to maintain document hierarchy.","\u002Fsummaries\u002F84068549d61515cf-moving-beyond-chunking-structural-retrieval-for-co-summary","2026-08-09 11:00:02","2026-08-10 03:20:51",{"title":7795,"description":40},{"loc":7880},"84068549d61515cf","IBM Technology","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=vRZNJWw78BQ","summaries\u002F84068549d61515cf-moving-beyond-chunking-structural-retrieval-for-co-summary",[79,80,81,82],"Standard RAG often fails on structured documents by destroying context through chunking. A better approach is to preserve the document's original tree structure and use an agent to navigate it, ensuring higher precision and better context retention.","This video explains the limitations of standard chunk-based RAG for complex documents and proposes an alternative: using hierarchical document structure to let an AI agent navigate a file like a human would. It highlights [Docling](https:\u002F\u002Fgithub.com\u002FDS4SD\u002Fdocling) as the tool for reconstructing that structure from PDFs to enable \"chunkless\" retrieval.",[82],"fyIwb2hbJXS__ytiVCn1WDP8XI_sDZRUNhOfTPGOVGU",{"id":7896,"title":7897,"ai":7898,"body":7903,"categories":7923,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":7924,"navigation":69,"path":7935,"published_at":7936,"question":48,"scraped_at":7936,"seo":7937,"sitemap":7938,"source_id":7939,"source_name":75,"source_type":76,"source_url":7929,"stem":7940,"tags":7941,"thumbnail_url":48,"tldr":7942,"tweet":48,"unknown_tags":7943,"__hash__":7944},"summaries\u002Fsummaries\u002Fcedf57bb6ac50ed2-agentao-a-governed-local-first-runtime-for-ai-agen-summary.md","Agentao: A Governed Local-First Runtime for AI Agents",{"provider":7,"model":8,"input_tokens":7899,"output_tokens":7900,"processing_time_ms":7901,"cost_usd":7902},4039,405,2227,0.00161725,{"type":14,"value":7904,"toc":7919},[7905,7909,7912,7916],[17,7906,7908],{"id":7907},"the-shift-toward-local-first-agent-governance","The Shift Toward Local-First Agent Governance",[22,7910,7911],{},"Agentao addresses the critical security and control challenges inherent in deploying tool-using LLM agents. Current agentic workflows often rely on cloud-based runtimes where the execution environment is opaque, making it difficult to audit agent behavior, restrict tool access, or ensure data privacy. Agentao proposes a local-first runtime architecture that places the agent's execution within a governed, local environment. This approach allows developers to define strict boundaries for what tools an agent can access and how it interacts with local system resources, effectively mitigating the risks of unauthorized tool execution or data exfiltration.",[17,7913,7915],{"id":7914},"architecture-for-secure-tool-interaction","Architecture for Secure Tool Interaction",[22,7917,7918],{},"The core value of Agentao lies in its governance model. By moving the runtime to a local-first paradigm, the framework enables developers to implement fine-grained access control policies that are enforced at the execution layer rather than the prompt layer. This ensures that even if an LLM is prompted to perform an unauthorized action, the runtime environment acts as a hard constraint, preventing the execution of restricted tools or unauthorized file system access. This design is particularly relevant for multi-agent systems where individual agents may have varying levels of trust and permission scopes, providing a standardized way to manage these interactions without sacrificing the flexibility of LLM-based decision-making.",{"title":40,"searchDepth":41,"depth":41,"links":7920},[7921,7922],{"id":7907,"depth":41,"text":7908},{"id":7914,"depth":41,"text":7915},[47],{"content_references":7925,"triage":7931},[7926],{"type":54,"title":7927,"author":7928,"url":7929,"context":7930},"Agentao: A Governed Local-First Runtime for Tool-Using LLM Agents","Unknown","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.13574","mentioned",{"relevance":65,"novelty":66,"quality":66,"actionability":7932,"composite":7933,"reasoning":7934},3,4.15,"Category: AI & LLMs. The article discusses a local-first runtime for AI agents, addressing security and governance, which is highly relevant for developers integrating AI into products. It presents a novel approach to managing agent interactions and governance, though it lacks specific implementation details that would enhance actionability.","\u002Fsummaries\u002Fcedf57bb6ac50ed2-agentao-a-governed-local-first-runtime-for-ai-agen-summary","2026-08-18 03:10:24",{"title":7897,"description":40},{"loc":7935},"cedf57bb6ac50ed2","summaries\u002Fcedf57bb6ac50ed2-agentao-a-governed-local-first-runtime-for-ai-agen-summary",[80,79,81],"Agentao is a proposed local-first runtime designed to provide governance and security for tool-using LLM agents, shifting control from cloud-based black boxes to local execution environments.",[],"WWKqUt7tRqK8cLeNO9hxy6EPoNBwZ8lC4CIFsNSnBDc",{"id":7946,"title":7947,"ai":7948,"body":7953,"categories":8002,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":8003,"navigation":69,"path":8011,"published_at":8012,"question":48,"scraped_at":8012,"seo":8013,"sitemap":8014,"source_id":8015,"source_name":75,"source_type":76,"source_url":8008,"stem":8016,"tags":8017,"thumbnail_url":48,"tldr":8018,"tweet":48,"unknown_tags":8019,"__hash__":8020},"summaries\u002Fsummaries\u002F21fc8eb6f77f5baa-shape-your-feed-agentic-conversational-recommendat-summary.md","Shape Your Feed: Agentic Conversational Recommendation Systems",{"provider":7,"model":8,"input_tokens":7949,"output_tokens":7950,"processing_time_ms":7951,"cost_usd":7952},4037,586,3156,0.00188825,{"type":14,"value":7954,"toc":7997},[7955,7959,7962,7966,7969,7990,7994],[17,7956,7958],{"id":7957},"moving-beyond-static-recommendation-feeds","Moving Beyond Static Recommendation Feeds",[22,7960,7961],{},"Traditional recommendation systems often rely on static, batch-processed algorithms that struggle to incorporate immediate user feedback or nuanced preferences. The 'Shape Your Feed' framework introduces an agentic approach to this problem, leveraging Large Language Models (LLMs) to act as an intermediary between the user and the recommendation engine. By treating the feed as a conversational interface, the system allows users to refine their content streams through natural language, effectively turning the recommendation process into a dynamic, iterative dialogue.",[17,7963,7965],{"id":7964},"the-agentic-architecture","The Agentic Architecture",[22,7967,7968],{},"The core of the system is an agentic loop that manages the interaction between user intent and content retrieval. Instead of a single-shot prediction, the agent performs three critical functions:",[7970,7971,7972,7978,7984],"ol",{},[7824,7973,7974,7977],{},[7827,7975,7976],{},"Intent Interpretation:"," The LLM parses natural language input to extract specific constraints, preferences, or feedback on existing items.",[7824,7979,7980,7983],{},[7827,7981,7982],{},"Dynamic Retrieval Strategy:"," Rather than relying solely on pre-computed embeddings, the agent translates user intent into actionable queries that can adjust the retrieval parameters or re-rank the current feed.",[7824,7985,7986,7989],{},[7827,7987,7988],{},"Feedback Integration:"," The system maintains a stateful context of the conversation, allowing it to learn from negative feedback (e.g., 'I don't like this type of content') and positive reinforcement in real-time, significantly reducing the 'cold start' friction for new user preferences.",[17,7991,7993],{"id":7992},"practical-implications-for-industrial-systems","Practical Implications for Industrial Systems",[22,7995,7996],{},"Accepted for the RecSys 2026 Industrial Track, this approach addresses the scalability and latency challenges typically associated with LLM-integrated systems. By offloading complex reasoning to the agent while keeping the heavy-lifting retrieval tasks optimized, the system demonstrates that conversational recommendation can be performant enough for production environments. The primary trade-off is the increased complexity in managing state and context across long-running sessions, but the benefit is a significantly higher degree of user agency and satisfaction compared to traditional 'black-box' recommendation models.",{"title":40,"searchDepth":41,"depth":41,"links":7998},[7999,8000,8001],{"id":7957,"depth":41,"text":7958},{"id":7964,"depth":41,"text":7965},{"id":7992,"depth":41,"text":7993},[47],{"content_references":8004,"triage":8009},[8005],{"type":8006,"title":8007,"url":8008,"context":58},"paper","Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.06632",{"relevance":65,"novelty":66,"quality":66,"actionability":7932,"composite":7933,"reasoning":8010},"Category: AI & LLMs. The article presents a novel framework for transforming static recommendation systems into interactive, conversational experiences using LLMs, addressing a specific pain point of traditional systems. It provides insights into the architecture and functions of the agentic loop, though it lacks detailed actionable steps for implementation.","\u002Fsummaries\u002F21fc8eb6f77f5baa-shape-your-feed-agentic-conversational-recommendat-summary","2026-08-11 03:21:37",{"title":7947,"description":40},{"loc":8011},"21fc8eb6f77f5baa","summaries\u002F21fc8eb6f77f5baa-shape-your-feed-agentic-conversational-recommendat-summary",[79,80,81],"The 'Shape Your Feed' framework uses LLM-based agents to transform static recommendation feeds into interactive, conversational experiences that adapt to user feedback in real-time.",[],"aKrsHmqYkbUpdCuUSqvmWdXdBuKzCpBoCJsDP8jQ7rk",{"id":8022,"title":8023,"ai":8024,"body":8029,"categories":8077,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":8078,"navigation":69,"path":8085,"published_at":8086,"question":48,"scraped_at":8086,"seo":8087,"sitemap":8088,"source_id":8089,"source_name":75,"source_type":76,"source_url":8082,"stem":8090,"tags":8091,"thumbnail_url":48,"tldr":8092,"tweet":48,"unknown_tags":8093,"__hash__":8094},"summaries\u002Fsummaries\u002F2752bb59bcea1406-hyperagent-planning-with-tool-schema-hypergraphs-summary.md","HyperAgent: Planning with Tool-Schema Hypergraphs",{"provider":7,"model":8,"input_tokens":8025,"output_tokens":8026,"processing_time_ms":8027,"cost_usd":8028},4054,545,3128,0.001831,{"type":14,"value":8030,"toc":8072},[8031,8035,8038,8042,8045,8065,8069],[17,8032,8034],{"id":8033},"the-challenge-of-tool-use-in-llms","The Challenge of Tool-Use in LLMs",[22,8036,8037],{},"Modern LLM agents often struggle with complex tool-use scenarios because they lack a structured way to understand the relationships between various tools, their parameters, and their dependencies. Standard approaches often rely on flat lists of tool definitions, which fail to capture the semantic and functional topology required for multi-step reasoning. This leads to hallucinations, incorrect parameter selection, and failure to complete long-horizon tasks.",[17,8039,8041],{"id":8040},"the-hypergraph-approach","The Hypergraph Approach",[22,8043,8044],{},"HyperAgent addresses this by modeling tool schemas as a hypergraph. In this structure, nodes represent tools and their parameters, while hyperedges represent the functional relationships and data dependencies between them. By mapping the 'tool-space' into a graph, the agent can perform:",[7821,8046,8047,8053,8059],{},[7824,8048,8049,8052],{},[7827,8050,8051],{},"Structured Planning:"," Instead of guessing the next step, the agent traverses the hypergraph to identify valid sequences of tool calls that satisfy the goal requirements.",[7824,8054,8055,8058],{},[7827,8056,8057],{},"Context-Aware Execution:"," The hypergraph provides a global view of available operations, allowing the agent to prune irrelevant tools and focus on paths that lead to the desired output.",[7824,8060,8061,8064],{},[7827,8062,8063],{},"Dependency Management:"," By explicitly defining how the output of one tool serves as the input for another, the agent minimizes errors in data passing and type mismatching.",[17,8066,8068],{"id":8067},"practical-implications-for-agentic-workflows","Practical Implications for Agentic Workflows",[22,8070,8071],{},"By moving from flat tool definitions to a hypergraph-based schema, developers can build agents that are more robust in production environments. This approach allows for better scalability when adding new tools to an agent's library, as the hypergraph naturally integrates new nodes and edges without requiring a complete rewrite of the agent's prompt or logic. This structured representation acts as a 'map' for the agent, significantly reducing the search space during the planning phase and improving the reliability of autonomous task execution.",{"title":40,"searchDepth":41,"depth":41,"links":8073},[8074,8075,8076],{"id":8033,"depth":41,"text":8034},{"id":8040,"depth":41,"text":8041},{"id":8067,"depth":41,"text":8068},[47],{"content_references":8079,"triage":8083},[8080],{"type":8006,"title":8081,"url":8082,"context":58},"HyperAgent: Planning and Acting over Tool-Schema Hypergraphs for Tool-Use LLM Agents","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.02650",{"relevance":65,"novelty":66,"quality":66,"actionability":66,"composite":67,"reasoning":8084},"Category: AI & LLMs. The article presents a novel approach to improving LLM tool-use by introducing hypergraphs for structured planning, which directly addresses the pain points of developers working with AI agents. It provides actionable insights on how to implement this method in production environments, making it highly relevant for the target audience.","\u002Fsummaries\u002F2752bb59bcea1406-hyperagent-planning-with-tool-schema-hypergraphs-summary","2026-08-06 03:11:03",{"title":8023,"description":40},{"loc":8085},"2752bb59bcea1406","summaries\u002F2752bb59bcea1406-hyperagent-planning-with-tool-schema-hypergraphs-summary",[79,80,81],"HyperAgent improves LLM tool-use by representing tool schemas as hypergraphs, enabling more effective planning and execution in complex, multi-step tasks.",[],"wy-iNsUKRB3d_pEmDYj7QpkXpufNnn-28s27UezGEPw"]