Can Computation from Earlier Problems Help LLMs Solve New Ones?
Quick summary
arXiv:2609.39394v1 Announce Type: new Abstract: Large language models often solve independent problems in the same conversation. Can computation from earlier problems help them solve new ones? To answer this question, we first conduct preliminary experiments showing that retained history can raise or lower later-turn accuracy, even within the same domain. To understand these effects, we use controlled replay to isolate internal state changes specific to each problem-history pairing. Across different histories, these changes preserve similar relationships among current problems. To improve reas
Key takeaways
- arXiv:2609.39394v1 Announce Type: new Abstract: Large language models often solve independent problems in the same conversation.
- Can computation from earlier problems help them solve new ones?
- To answer this question, we first conduct preliminary experiments showing that retained history can raise or lower later-turn accuracy, even within the same domain.
Why it matters
“Can Computation from Earlier Problems Help LLMs Solve New Ones?” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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