arXiv AI
DSABench: Benchmarking Source Discovery, Coordination, and Execution for Multi-Database Agents
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arXiv AI
Aug 25
SchemaRouter: Field-Aware Tool Routing for Efficient Heterogeneous Agentic RAG
arXiv:2608.21375v1 Announce Type: new Abstract: Heterogeneous agentic retrieval-augmented generation (RAG) systems increasingly orchestrate external APIs, internal databases, vector stores, and graph...
arXiv AI
Sep 28
Cartograph: Federated Tool Discovery with Operator-Attested Retrieval for AI Agents
Cartograph is a federated Model Context Protocol (MCP) proxy that reduces AI agent tool discovery from linear catalog traversal to progressive disclosure, exposing only a few proxy tools instead of all definitions. It uses operator-attested capability cards, a three-layer confusable-cluster analysis called Rift, and a two-stage retrieval process to rank servers before tools. In a 22-server, 374-tool deployment, Cartograph achieves higher recall (R@5 = 0.816 vs. 0.592) and drastically fewer tokens (475 vs. 42,450) for discovery exchanges, with minimal latency overhead.
arXiv Machine Learning
Aug 4
Learning Compositional Meta-Routing for Agentic Workflows: An Executable Benchmark
arXiv:2608. 00106v1 Announce Type: new Abstract: Agentic systems must decide not only what answer to produce, but which reasoning and execution operations should precede it.
arXiv AI
Jun 30
Experience Graphs: The Data Foundation for Self-Improving Agents
arXiv:2606. 29823v1 Announce Type: cross Abstract: The database community has repeatedly advanced the state of the art by recognizing that new workloads demand new system architectures.
arXiv AI
Aug 28
BekchiAI: Measuring, Observing, and Controlling LLM Agents in One Click
BekchiAI introduces a benchmark and platform for evaluating large language model agents. The benchmark comprises 13 tool‑using ReAct agents across seven task categories, totaling 2,057 deterministic test tasks with verifier‑checkable gold answers. The platform offers web‑based observability, token and latency telemetry, and remote run termination for live agents.
arXiv AI
Sep 7
Iris: Climbing to the Search Frontier
The paper introduces Iris-mini and Iris-pro, two search agents trained at 35B and 397B parameter scales. They use a novel data pipeline that constructs reverse‑engineered multi‑hop queries from web hyperlinks, filters trajectories, and alternates supervised fine‑tuning with reinforcement learning in a process called SFT‑RL climbing. Evaluations on several benchmarks show that, with inference‑time context management, the agents achieve the best open‑source results in their parameter ranges.