Three New Research Papers Explore Latent Planning and Reasoning in Large Language Models

Three New Research Papers Explore Latent Planning and Reasoning in Large Language Models

Three recent papers on arXiv address challenges in large language model reasoning and planning capabilities.

iCLP: Implicit Cognition Latent Planning

According to arXiv paper 2512.24014v1, researchers have developed iCLP (Implicit Cognition Latent Planning) to address challenges in LLM reasoning. The paper notes that while LLMs can perform “reliable step-by-step reasoning” when guided by explicit textual plans, “generating accurate and effective textual plans remains challenging due to LLM hallucinations,” according to the abstract.

Dynamic Large Concept Models

A second paper (arXiv:2512.24617v1) introduces Dynamic Large Concept Models, which tackle the issue of computational efficiency. According to the abstract, current “Large Language Models (LLMs) apply uniform computation to all tokens, despite language exhibiting highly non-uniform information density.” The researchers argue this “token-uniform regime wastes capacity on locally predictable spans while under-allocating computation to” semantically dense content.

AINav: Adaptive Interactive Navigation

The third paper (arXiv:2503.22942v2) presents AINav, an LLM-based navigation system for robotics. According to the abstract, “Robotic navigation in complex environments remains a critical research challenge,” with traditional methods “struggling in environments lacking viable paths” as they focus on trajectory generation within fixed workspaces.