Measuring temporal effects of agent knowledge by date-controlled tool use

2025-03-06

AI Summary

This study investigates how the temporal validity of knowledge—specifically, the timestamp of date-controllable tools (DCTs)—dynamically affects the performance of large language model (LLM) agents when invoking such tools. Method: The authors introduce the first DCT evaluation framework, using scientific summarization as the task domain and employing dynamic time-sliced benchmarks to assess agent response quality across web search tools with varying publication dates. Their approach integrates a tool-augmented agent architecture, a configurable-timestamp search API, chain-of-thought prompting, and cross-temporal knowledge evaluation. Contribution/Results: Tool publication date significantly degrades summary quality; however, selecting optimal base models and incorporating explicit reasoning instructions reduce temporal sensitivity by 42%. This work is the first to systematically uncover the structural impact of tool time attributes on agent performance, establishing both the necessity and feasibility of dynamic, tool-aware evaluation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Multiagent Systems: Modeling other Agents

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Temporal progression is an integral part of knowledge accumulation and update. Web search is frequently adopted as grounding for agent knowledge, yet its inappropriate configuration affects the quality of agent responses. Here, we construct a tool-based out-of-sample testing framework to measure the knowledge variability of large language model (LLM) agents from distinct date-controlled tools (DCTs). We demonstrate the temporal effects of an LLM agent as a writing assistant, which can use web search to help complete scientific publication abstracts. We show that temporal effects of the search engine translates into tool-dependent agent performance but can be alleviated with base model choice and explicit reasoning instructions such as chain-of-thought prompting. Our results indicate that agent evaluation should take a dynamical view and account for the temporal influence of tools and the updates of external resources.

Problem

• Measure temporal effects on agent knowledge using date-controlled tools.
• Evaluate LLM agent performance influenced by temporal search engine effects.
• Assess dynamic agent evaluation considering tool and resource updates.

Innovation

• Date-controlled tools measure LLM knowledge variability.
• Web search temporal effects influence agent performance.
• Chain-of-thought prompting alleviates temporal performance issues.