mlsanityData to 14 Oct 2026

Can Competition Enhance the Proficiency of Agents Powered by Large Language Models in the Realm of News-driven Time Series Forecasting?

arXiv 2504.10210 ↗First posted 14 Apr 2025cs.AIcited by 2

On the map: LLM agents and tool use → Financial trading and forecasting agents

Abstract (arXiv)

Multi-agents-based news-driven time series forecasting is considered as a potential paradigm shift in the era of large language models (LLMs). The challenge of this task lies in measuring the influences of different news events towards the fluctuations of time series. This requires agents to possess stronger abilities of innovative thinking and the identifying misleading logic. However, the existing multi-agent discussion framework has limited enhancement on time series prediction in terms of optimizing these two capabilities. Inspired by the role of competition in fostering innovation, this study embeds a competition mechanism within the multi-agent discussion to enhance agents' capability of generating innovative thoughts. Furthermore, to bolster the model's proficiency in identifying misleading information, we incorporate a fine-tuned small-scale LLM model within the reflective stage, offering auxiliary decision-making support. Experimental results confirm that the competition can boost agents' capacity for innovative thinking, which can significantly improve the performances of time series prediction. Similar to the findings of social science, the intensity of competition within this framework can influence the performances of agents, providing a new perspective for studying LLMs-based multi-agent systems.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 24 of its 43 references that are in the map. First posted on arXiv 14 Apr 2025.

Builds on

24 of 43 references.

References · 24

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
  5. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  6. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
  7. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
  8. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  9. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  10. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 632
  11. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 500
  12. 2023Self-collaboration Code Generation via ChatGPTcited by 258
12 more
  1. 2023Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology Viewcited by 222
  2. 2023TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Seriescited by 169
  3. 2024AgentScope: A Flexible yet Robust Multi-Agent Platformcited by 121
  4. 2023Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debatecited by 97
  5. 2023CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agentscited by 65
  6. 2023LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplaycited by 63
  7. 2023TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usagecited by 55
  8. 2024Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilitiescited by 48
  9. 2024Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agentscited by 48
  10. 2023Exploring the Intersection of Large Language Models and Agent-Based Modeling via Prompt Engineeringcited by 17
  11. 2023It's Not Easy Being Wrong: Large Language Models Struggle with Process of Elimination Reasoningcited by 12
  12. 2020On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimalitycited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  2. 2026TimeClaw: A Time-Series AI Agent with Exploratory Execution Learningcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2024From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection
  2. 2026Information Specialization and Constrained Synthesis in Multi-Agent LLM Forecasting: A Prospective Live-Study of the 2026 FIFA World Cup
  3. 2026LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications
  4. 2026Nexus : An Agentic Framework for Time Series Forecasting
  5. 2024Approaching Human-Level Forecasting with Language Models
  6. 2026Agentic Time Machine as an Infrastructure for Future-Event Forecasting
  7. 2026KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning
  8. 2024MIRAI: Evaluating LLM Agents for Event Forecasting