mlsanityData to 14 Oct 2026

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

arXiv 2503.04188 ↗First posted 6 Mar 2025cs.CL cs.IRcited by 1

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

Temporal progression is an integral part of knowledge accumulation and update. Web search is frequently adopted as grounding for agent knowledge, yet an improper configuration affects the quality of the agent's responses. Here, we assess the agent behavior using distinct date-controlled tools (DCTs) as stress test to measure the knowledge variability of large language model (LLM) agents. We demonstrate the temporal effects of an LLM agent as a writing assistant, which uses web search to complete scientific publication abstracts. We show that the temporality of 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 design and evaluations should take a dynamical view and implement measures to account for the temporal influence of external resources to ensure reliability.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 25 of its 50 references that are in the map. First posted on arXiv 6 Mar 2025.

Builds on

25 of 50 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Beyond Browsing: API-Based Web Agents21 Oct
  2. 2023WebArena: A Realistic Web Environment for Building Autonomous Agents25 Jul

Other references · 23

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2021True Few-Shot Learning with Language Modelscited by 344
  4. 2024TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Taskscited by 326
  5. 2021Mind the Gap: Assessing Temporal Generalization in Neural Language Modelscited by 230
  6. 2023Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Futurecited by 184
  7. 2024Empowering Biomedical Discovery with AI Agentscited by 118
  8. 2020Tighter risk certificates for neural networkscited by 112
  9. 2024Large Language Models Can Learn Temporal Reasoningcited by 111
  10. 2024ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stagescited by 82
  11. 2019Evaluating time series forecasting models: An empirical study on performance estimation methodscited by 53
  12. 2024Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilitiescited by 48
11 more
  1. 2024MIRAI: Evaluating LLM Agents for Event Forecastingcited by 41
  2. 2023Are Large Language Models Temporally Grounded?cited by 27
  3. 2024Set the Clock: Temporal Alignment of Pretrained Language Modelscited by 25
  4. 2025Agentic Systems: A Guide to Transforming Industries with Vertical AI Agentscited by 20
  5. 2024Prompt-Based Length Controlled Generation with Multiple Control Typescited by 19
  6. 2024Automating the Enterprise with Foundation Modelscited by 14
  7. 2021Progress in Self-Certified Neural Networkscited by 10
  8. 2024ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domainscited by 8
  9. 2024Analyzing Probabilistic Methods for Evaluating Agent Capabilitiescited by 4
  10. 2024Decoding complexity: how machine learning is redefining scientific discoverycited by 3
  11. 2025Robustness tests for biomedical foundation models should tailor to specificationscited by 3

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directionscited by 50

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