---
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id: "99dfd4ca72314790fe96e119f18d18c461926d165257b4f1eaaac883349f2c57"
canonical_url: "https://aidr.today/99dfd4ca?lang=en"
title: "Stanford Prefix Sliding Speeds AI Reasoning 3x to Scale Beyond 100K Tokens"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
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published_at: "2026-08-30T18:37:32.000Z"
category: "Research"
topics: ["reasoning","inference","llm","stanford"]
source_urls: ["https://huggingnews.com/ai/stanford-prefix-sliding-speeds-ai-reasoning-3x-to-scale-beyond-100k-toke-f3299bb2","https://x.com/shipfrontierai/status/2094076006025798127","https://x.com/omarsar0/status/2094109398604099888","https://x.com/askalphaxiv/status/2093794601156870264"]
summary: "Researchers at Stanford University developed a method to reduce the computational cost of long-chain-of-thought inference by discarding unimportant intermediate tokens from memory. The technique, called Prefix Sliding, maintains the original prompt and a sliding window of recent reasoning, allowing existing models to run up to 3x faster while matching full-attention performance without requiring retraining. The approach addresses the tendency for inference costs and memory usage to grow as reasoning chains lengthen. When combined with reinforcement learning fine-tuning, Prefix Sliding allows AI agents to scale reasoning beyond 100,000 tokens by capping total memory regardless of the chain length."
---

# Stanford Prefix Sliding Speeds AI Reasoning 3x to Scale Beyond 100K Tokens

> [Open the canonical story](<https://aidr.today/99dfd4ca?lang=en>)

**Published:** 2026-08-30T18:37:32.000Z
**Category:** Research
**Topics:** reasoning, inference, llm, stanford

## Summary

Researchers at Stanford University developed a method to reduce the computational cost of long\-chain\-of\-thought inference by discarding unimportant intermediate tokens from memory\. The technique, called Prefix Sliding, maintains the original prompt and a sliding window of recent reasoning, allowing existing models to run up to 3x faster while matching full\-attention performance without requiring retraining\. The approach addresses the tendency for inference costs and memory usage to grow as reasoning chains lengthen\. When combined with reinforcement learning fine\-tuning, Prefix Sliding allows AI agents to scale reasoning beyond 100,000 tokens by capping total memory regardless of the chain length\.

## Sources

- [Story source](<https://huggingnews.com/ai/stanford-prefix-sliding-speeds-ai-reasoning-3x-to-scale-beyond-100k-toke-f3299bb2>)
- [Story source](<https://x.com/shipfrontierai/status/2094076006025798127>)
- [Supporting source](<https://x.com/omarsar0/status/2094109398604099888>)
- [Supporting source](<https://x.com/askalphaxiv/status/2093794601156870264>)