Tag
A paper presents a framework where LLM agents are represented as computational graphs with nodes as operations and edges as information flow, enabling automatic optimization of both node prompts and edge connectivity via RL, turning scattered agent swarms into a single optimizable graph.
This paper presents LP Mining with LP2Graph, a method that extracts and taxonomizes LP and MILP formulations from the literature into a reproducible dataset using typed variable-equation graphs, demonstrated on railway rescheduling.
The Agent Instruction Protocol (AIP) proposes modeling AI agent skills as directed execution graphs with schema-validated YAML specifications, replacing free-form prose instructions. Experiments show AIP compilation raised Claude Sonnet's task reward from 0.60 to 0.71 and pass rate from 53% to 67% across 27 real agent tasks.
This paper presents GRID, an end-to-end framework for constructing security knowledge graphs from cyber threat intelligence (CTI) articles using LLMs, introducing a task-bank reward training method to improve precision and recall without expensive LLM-as-judge rewards. The approach achieves strong results on a benchmark of 249 CTI articles from five sources.
This paper introduces Agent-BOM, a unified graph representation for security auditing in LLM-based agentic systems. It addresses the semantic gap in post-hoc auditing by modeling static capabilities and dynamic runtime states to detect complex attack chains like memory poisoning and tool misuse.