arXiv AI
EDGE: Engine for Deterministic Graph Evaluation through Conversation Simulation from Graph Structured DSL Configuration
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arXiv AI
Jul 22
Graph-Based Agentic AI with LangGraph: Workflow Pathways for Long-Running Stateful Business Processes
arXiv:2607. 19297v1 Announce Type: new Abstract: This paper is a practitioner guide to graph-based workflow pathways for long-running, stateful, multi-step generative AI systems in business processes.
arXiv AI
Jun 24
From Task-Guided Conversational Graphs to Goal-Oriented Dialogue Runtimes
arXiv:2606. 23797v1 Announce Type: cross Abstract: Graph and multi-agent orchestration frameworks make production large language model (LLM) workflows practical, but they do not by themselves solve conversational continuity when users maintain several interdependent objectives.
arXiv AI
Aug 18
From Sequence to Structure: Relational Uncertainty Propagation for LLM Agents
The paper introduces RUPA, a trajectory‑level uncertainty quantification framework for large language model agents. RUPA models an agent’s execution as a directed graph of reasoning states, tool interactions, and environment feedback, then propagates uncertainty across this graph to capture long‑range dependencies. Experiments on benchmarks such as τ‑2, Terminal‑Bench‑2, and GAIA show that RUPA outperforms existing methods, enabling earlier failure detection and more reliable agent execution.
arXiv AI
Aug 19
Graphectory Viewer: A Tool for Process-Centric Analysis of Agentic Software Trajectories
Graphectory Viewer is a web-based tool that enables interactive, process‑centric analysis of software‑agent trajectories. It converts heterogeneous raw trajectories into phase‑aware graphs, linking low‑level execution details with higher‑level behavioral structures. The tool supports multiple agent frameworks, offers node‑level inspection, search and filtering over large collections, and Sankey‑style summaries of problem‑solving phase transitions, allowing researchers to inspect individual runs, identify patterns, compare successes and failures, and analyze large corpora beyond final outcomes.
arXiv AI
Aug 7
Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents
arXiv:2608. 06329v1 Announce Type: cross Abstract: Task-oriented conversational agents are evaluated using curated or automatically generated benchmarks, yet benchmark quality is rarely assessed.
arXiv AI
Aug 5
HyperAgent: Planning and Acting over Tool-Schema Hypergraphs for Tool-Use LLM Agents
arXiv:2608. 02650v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external tools to complete complex real-world tasks.