SPOT: Span-level Pause-of-Thought for Efficient and Interpretable Latent Reasoning in Large Language Models

TL;DR

SPOT introduces a span-level semantic alignment method that compresses chain-of-thought reasoning into interpretable latent tokens, improving reasoning accuracy and efficiency in large language models.

Contribution

It proposes a novel span-level semantic alignment technique with a frozen-head decoding constraint, enhancing interpretability and performance of latent reasoning in LLMs.

Findings

Achieves 2.3 point accuracy improvement on reasoning benchmarks.

Reduces token generation by 37.5%.

Provides faithful semantic interpretations of latent reasoning.

Abstract

Explicit Chain-of-Thought improves the reasoning performance of large language models but often incurs high inference cost due to verbose token-level traces. While recent approaches reduce this overhead via concise prompting or step pruning, they largely truncate what the model says rather than internalize what the model thinks. Latent reasoning offers a promising alternative by performing computation in the hidden space, yet prior methods face two critical challenges. Many existing approaches rely on rigid point-to-point alignment, forcing a latent token to approximate the final representation of a reasoning step, which can be insufficient to capture the dense, variable-length semantics of an entire reasoning segment. Furthermore, these methods often suffer from a lack of interpretability: latent states are commonly produced by unconstrained optimization or embedding mixing, yielding vectors that are difficult to decode or audit under the pretrained language head. We propose SPOT, a flexible framework that compresses explicit CoT into compact latent pause tokens without enforcing a fixed response template. At the core of SPOT is Span-level Semantic Alignment, a Sinkhorn optimal-transport objective that softly matches each pause token to the semantics of an entire reasoning segment, overcoming the rigidity of step-end alignment. To further improve interpretability, SPOT introduces a Frozen-Head Decoding Constraint that keeps latent states directly decodable as token distributions under the frozen pretrained LM head, enabling readable keyword interpretations of latent thoughts. Experiments on reasoning benchmarks demonstrate that SPOT improves accuracy by 2.3 points on average while reducing generated tokens by 37.5% and provides faithful semantic interpretations of the latent reasoning process.