---
title: "Summary: 2026-04-24_16-45-51Z_ThinkingWithoutWords_EfficientLatentReasoningwithA.md"
date: 2026-04-24
tags: ['paper', 'research', 'ai']
---
# Summary: 2026-04-24_16-45-51Z_ThinkingWithoutWords_EfficientLatentReasoningwithA.md


**Source**: [Original Paper](http://arxiv.org/abs/2604.22709v1)
Saved: 2026-05-07 22:29
Source: 2026-04-24_16-45-51Z_ThinkingWithoutWords_EfficientLatentReasoningwithA.md
Model: None

---

## Summary
While long, explicit chains-of-thought (CoT) have proven effective on complex reasoning tasks, they are costly to generate during inference. Non-verbal reasoning methods have emerged with shorter generation lengths by leveraging continuous representations, yet their performance lags behind verbalized CoT. We propose $\textbf{Abstract Chain-of-Thought}$, a discrete latent reasoning post-training mechanism in which the language model produces a short sequence of tokens from a reserved vocabulary in lieu of a natural language CoT, before generating a response.

## Semantic links
- [[concepts/papers/2026-06-17_17-54-04Z_RethinkingRewardSupervision_Rubric_Conditio_summary.md|Summary: 2026-06-17_17-54-04Z_RethinkingRewardSupervision_Rubric_ConditionedSelf.md]] — 2 title terms overlap; shared tags: ai, paper, research; 6 summary/topic terms overlap

## Key Takeaways
- While long, explicit chains-of-thought (CoT) have proven effective on complex reasoning tasks, they are costly to generate during inference.
- Non-verbal reasoning methods have emerged with shorter generation lengths by leveraging continuous representations, yet their performance lags behind verbalized CoT.
- We propose $\textbf{Abstract Chain-of-Thought}$, a discrete latent reasoning post-training mechanism in which the language model produces a short sequence of tokens from a reserved vocabulary in lieu of a natural language CoT, before generating a response.

## Context
While long, explicit chains-of-thought (CoT) have proven effective on complex reasoning tasks, they are costly to generate during inference.

## Implications
Our findings highlight the potential for post-training latent reasoning mechanisms that enable efficient inference through a learned abstract reasoning language.

## Original Reference
- Title: Thinking Without Words: Efficient Latent Reasoning with Abstract Chain-of-Thought
- Authors: Keshav Ramji, Tahira Naseem, Ramón Fernandez Astudillo
- Published: 2026-04-24T16:45:51Z
- URL: http://arxiv.org/abs/2604.22709v1
- Source file: /home/rich/wiki/ai-research/raw/papers/2026-04-24_16-45-51Z_ThinkingWithoutWords_EfficientLatentReasoningwithA.md

[[raw/papers/2026-04-24_16-45-51Z_ThinkingWithoutWords_EfficientLatentReasoningwithA.md]]

## Related Concepts

- [[concepts/llm-models/llm-models-hub.md|LLM Models Hub]]
- [[concepts/reasoning/reasoning-hub.md|Reasoning Hub]]
- [[concepts/generative-models/generative-models-hub.md|Generative Models Hub]]
- [[concepts/search-retrieval/search-retrieval-hub.md|Search Retrieval Hub]]