💡 Ever wished your AI could think more like a human, balancing between deep thought and clear expression? Meet SwiReasoning, a game-changing decoding-time framework that lets Large Language Models (LLMs) decide when to ponder in the shadows and when to spell out its thoughts. Here’s the lowdown on this exciting new method:
How it works:
Imagine a controller sitting behind the scenes, monitoring the LLM’s next-token confidence (using entropy trends). When confidence is low (entropy’s rising), the model goes into ‘latent reasoning’ mode, keeping its thoughts to itself. But when confidence picks up (entropy’s falling), it switches to ‘explicit reasoning’, sharing its chain-of-thought (CoT) tokens. A switch count control keeps overthinking in check. It’s like having a smart AI intern who knows when to brainstorm in private and when to present its findings.
What it achieves:
SwiReasoning reports impressive results across math and STEM tasks:
– Accuracy boosts: Up to +2.8% in math and +2.0% in STEM tasks with unlimited tokens, with an average +2.17% improvement over baseline methods.
– Token efficiency: Average gains of up to +79% under limited budgets, beating CoT in 13/15 evaluations with an +84% average improvement.
– Faster convergence: On AIME 2024/2025, it reaches peak accuracy 50% earlier than standard CoT.
Why it works:
Explicit CoT is clear but locks in a single path too soon, while latent reasoning is info-dense but can diffuse probability mass. SwiReasoning adds a confidence-guided alternation, letting the model explore when uncertain and commit to a solution when confident. The switch count control prevents diffusion-induced accuracy loss and token waste from overthinking.
Versus the competition:
SwiReasoning beats CoT with sampling, CoT greedy, and Soft Thinking, shifting the Pareto frontier outward for better accuracy-efficiency trade-offs. On AIME’24/’25, it reaches the performance ceiling with fewer samples, showing improved convergence behavior.
Key Takeaways:
– Training-free controller that alternates between latent and explicit CoT using next-token entropy trends.
– +56–79% average token-efficiency gains under constrained budgets.
– +1.5–2.8% average Pass@1 accuracy improvements on math/STEM benchmarks.
– Faster convergence to maximum reasoning accuracy on AIME 2024/2025.
Get Involved:
Check out the [Paper](https://arxiv.org/pdf/2510.05069) and [Project Page](https://marktechpost.com/2023/10/24/swireasoning-entropy-driven-alternation-of-latent-and-explicit-chain-of-thought-for-reasoning-llms/). For tutorials, codes, and notebooks, head to the [GitHub Page](https://github.com/MarkTechPost/SwiReasoning). Follow us on [Twitter](https://twitter.com/MarkTechPost) and join our [100k+ ML SubReddit](https://www.reddit.com/r/MachineLearning/) and [Newsletter](https://marktechpost.com/newsletter/). And if you’re on Telegram, join us there too! 📢
