Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform
The article discusses the limitations of large language models (LLMs) in tasks requiring causal reasoning and long-horizon planning. It introduces the concept of Latent Dynamics Inference (LDI) and presents a new environment called Flux for empirical investigation. The findings suggest that models with access to latent state spaces perform significantly better in dynamic reasoning tasks compared to LLMs.
- ▪Large language models excel in language generation but struggle with causal reasoning and persistent state tracking.
- ▪The authors propose Latent Dynamics Inference (LDI) to address the limitations of LLMs.
- ▪In a case study using the Flux environment, agents with access to latent state spaces achieved a win rate of approximately 79%, compared to 11% for LLMs.
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Computer Science > Artificial Intelligence arXiv:2605.23972 (cs) [Submitted on 13 May 2026] Title:Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform Authors:Feisal Alaswad, Batoul Aljaddouh, Maher Alrahhal, Poovammal E, Talal Bonny View a PDF of the paper titled Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform, by Feisal Alaswad and 3 other authors View PDF HTML (experimental) Abstract:Large language models achieve strong performance in language generation and knowledge-intensive tasks, yet remain limited in settings requiring causal reasoning, persistent state tracking, and long-horizon planning.
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