arXiv:2607. 18242v1 Announce Type: new Abstract: The coming era of autonomous AI agents demands a discovery mechanism capable of navigating millions of tools, yet existing solutions buckle under O(N) complexity and centralized governance.
arXiv:2605. 28787v2 Announce Type: replace-cross Abstract: In the era of autonomous agents, machine-actionable data is critical for data-driven workflows.
arXiv:2511.18354v2 Announce Type: replace-cross Abstract: AI agents, not humans, are becoming the web's primary consumers, yet the web still ships content structured for human browsing, with no nativ...
arXiv:2608. 16502v1 Announce Type: new Abstract: Large-scale agents increasingly rely on retrieval to access external capabilities.
arXiv:2604. 10311v2 Announce Type: replace Abstract: Artificial Intelligence (AI) models, encompassing both traditional machine learning (ML) and more advanced approaches such as deep learning and large language models (LLMs), play a central role in modern applications.
RENSA is a federated SPARQL query generation framework that extends SPARQL Builder Metadata to include class and authority information, enabling precise source selection and semantic constraint inference without runtime ASK queries. The generated metadata profiles occupy less than 1% of the original dataset triples, providing storage‑efficient insights. Evaluation on the LargeRDFBench benchmark shows that RENSA matches state‑of‑the‑art source selection performance while eliminating runtime communication overhead.
The paper discusses how the shift from centralized AI APIs to local edge inference enables Client‑Side Autonomous Agents (CSAAs) that collaborate in Agentic Peer‑to‑Peer (P2P) networks. Unlike traditional P2P systems that exchange static content, these networks share dynamic, heterogeneous capabilities that may be unsafe, necessitating semantic discovery, drift management, and risk‑aware execution. The authors propose a plane‑based reference architecture, signed soft‑state capability descriptors for intent‑aware routing, and a tiered verification spectrum (reputation, canary probes, cryptographic evidence) to align trust costs with task risks, showing improved workflow success in simulations.
arXiv:2607. 26352v1 Announce Type: cross Abstract: Networking research advances by turning hypotheses into empirical evidence, so accelerating it means reducing the lag between ideation (synthesizing a hypothesis) and generating the data that tests it.
arXiv:2608.21375v1 Announce Type: new Abstract: Heterogeneous agentic retrieval-augmented generation (RAG) systems increasingly orchestrate external APIs, internal databases, vector stores, and graph...
Hybrid Semantic Tool Discovery for Enterprise MCP Gateway presents SCOUT, a system that addresses two major challenges in large language model (LLM) agent tool usage: a context‑engineering bottleneck and a tool discoverability barrier. SCOUT reframes tool exposure as a context‑selection problem, injecting only relevant tools into the model’s context window and providing two MCP meta‑tools—tool_search and execute_tool—to perform hybrid retrieval via BM25 and dense vector search. In production at PayPal, SCOUT cuts MCP tool‑token consumption by 99%, dramatically reducing per‑query inference cost while remaining model‑agnostic and requiring no client‑side changes.
arXiv:2606. 07538v1 Announce Type: cross Abstract: Large language model (LLM)-based agents provide a novel paradigm for the automated processing of remote sensing(RS) data.
arXiv:2605. 10555v2 Announce Type: replace Abstract: As AI agents transition from research prototypes to enterprise production systems, the tool interfaces they consume remain rooted in human-oriented CRUD paradigms.
TRIAGE introduces a three-level routing framework for Large Language Model agents that reduces token consumption by reusing historical execution trajectories. The system classifies queries into direct reuse, skill substitution, and full ReAct levels, achieving significant token savings in large-scale security monitoring and cross-domain benchmarks. An automatic skill extraction mechanism further refines reusable patterns, creating a positive feedback loop that improves efficiency over time.