arXiv AI
Knowledge Graphs as Files: An Agent Harness Where Tool Creation and Tool Use Co-Evolve
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Aug 18
Schema-Agnostic Graph Reasoning Agent for Hybrid Knowledge Graphs
arXiv:2608. 15834v1 Announce Type: new Abstract: Tool-calling LLM agents navigate unfamiliar codebases with a handful of generic primitives for listing, reading and searching files (ls, cat, grep).
arXiv Computation and Language
Oct 7
Wikidata Search Traces: A Dataset for Training Knowledge Graph Search Agents
arXiv:2610.06650v2 Announce Type: replace Abstract: Wikidata is one of the largest open knowledge bases, yet answering a complex question over it still requires a SPARQL query that names the right en...
arXiv AI
Jul 28
CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases
arXiv:2408. 03910v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) excel in stand-alone code tasks like HumanEval and MBPP, but struggle with handling entire code repositories.
arXiv AI
2d ago
Explore Separately, Adopt Selectively: Execution-Guided Tool Use over Large Tool Libraries
The paper introduces BRIA (BRanch-Inspect-Adopt), an agent that isolates tool‑candidate investigations into separate branches, executing each candidate independently and then inspecting the execution evidence to adopt only useful results. BRIA dynamically retrieves additional tools as new needs arise during task execution and is evaluated on PlanBench‑XL and MCP‑Atlas with a library of 1,897 tools, outperforming or matching the strongest retrieval baselines. On MCP‑Atlas, BRIA achieves an average claim score of 89.77 versus 75.40 for MCP‑Zero, and it lies on the Pareto frontier for task performance and input‑token cost.
arXiv Machine Learning
Jul 14
ToolAtlas: Learning Once, Reusing Everywhere with Tool-Side Memory
arXiv:2607. 11126v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external tools served by shared providers and accessed by heterogeneous downstream agents.
arXiv AI
Jun 12
Agents-K1: Towards Agent-native Knowledge Orchestration
arXiv:2606. 13669v1 Announce Type: new Abstract: Current LLM-based research agents have advanced through agent orchestration, yet largely overlook scientific knowledge orchestration.