Unlocking the Working Memory of Large Language Models for Latent Reasoning

2026-05-28 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Reasoning in Memory (RiM) is a novel latent reasoning method designed to enhance large language models' capabilities by replacing autoregressive generation of intermediate reasoning steps with fixed memory blocks. Drawing inspiration from human working memory, RiM processes these special token sequences in a single forward pass, enabling compute-efficient latent reasoning without conflating internal computation with external communication. The method is operationalized through a two-stage curriculum: initially grounding memory blocks by predicting explicit reasoning steps, then iteratively refining the final answer. Experiments on reasoning benchmarks demonstrate that RiM matches or exceeds existing latent reasoning methods across various LLM families and sizes, effectively avoiding the computational overhead of autoregressive thought generation.

Key takeaway

For machine learning engineers focused on optimizing large language model inference, you should consider implementing Reasoning in Memory (RiM) to improve reasoning capabilities without the typical overhead of autoregressive thought generation. This method allows your models to perform efficient latent reasoning by processing fixed memory blocks in a single forward pass. Adopting RiM can potentially reduce test-time compute while matching or exceeding current latent reasoning performance, offering a practical path to more efficient and capable LLMs.

Key insights

Large language models can be trained to use working memory for efficient, latent reasoning, decoupling internal computation from external communication.

Principles

• Decouple internal computation from external communication.
• Fixed memory blocks enable compute-efficient latent reasoning.
• A two-stage curriculum effectively grounds latent reasoning.

Method

RiM replaces autoregressive reasoning steps with fixed memory blocks processed in a single forward pass. It uses a two-stage curriculum: predicting explicit steps, then iteratively refining the final answer.

In practice

• Implement fixed memory blocks for latent reasoning.
• Apply a two-stage curriculum for RiM training.
• Evaluate RiM against autoregressive methods for efficiency.

Topics

• Large Language Models
• Latent Reasoning
• Working Memory
• Compute Efficiency
• Test-Time Compute
• Autoregressive Generation
• RiM

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• Reasoning Core: A Scalable Procedural Data Generation Suite for Symbolic Pre-training and Post-Training — Reasoning Core generates verifiable symbolic data to enhance language model reasoning and can be used for pre-training and post-training.
• Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling — Adaptive Parallel Reasoning allows LLMs to dynamically manage parallel task decomposition, improving efficiency and accuracy.
• APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI — APEX-MEM enhances long-term conversational AI through structured memory, temporal grounding, and intelligent retrieval.
• Why isn’t LLM reasoning done in vector space instead of natural language?[D] — LLMs internally use vectors, but natural language reasoning offers crucial interpretability and debuggability.
• Titans: Learning to Memorize at Test Time — Training a dedicated neural memory module at inference time improves long-context sequence modeling on novel data.
• MemCoT: Test-Time Scaling through Memory-Driven Chain-of-Thought — MemCoT transforms LLM long-context reasoning into an iterative, stateful memory search process, significantly reducing hallucinations and forgetting.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.