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ARXIV:2605.13511 · LLM REASONING · SUBMITTED 14 MAY · 20:10 UTC · FRESHNESS FRESH
ARXIV:2605.13511LLM REASONINGSUBMITTED 14 MAY · 20:10 UTCFRESHNESS FRESHTsz Ting Chung · Lemao Liu · Mo Yu · Dit-Yan Yeung · arXiv
A framework for many-shot chain-of-thought in-context learning that reframes LLM prompting as in-context test-time learning.
Opportunity summary
Pain A framework for many-shot chain-of-thought in-context learning that reframes LLM prompting as in-context test-time learning.
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Blocker Evidence unverified
A framework for many-shot chain-of-thought in-context learning that reframes LLM prompting as in-context test-time learning. With long-context models, many-shot ICL can use dozens to hundreds of examples and achieve performance comparable to fine-tuning, yet…
In-context learning (ICL) adapts large language models (LLMs) to new tasks by conditioning on demonstrations in the prompt without parameter updates. With long-context models, many-shot ICL can use dozens to hundreds of examples and…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. With long-context models, many-shot ICL can use dozens to hundreds of examples and achieve performance comparable to fine-tuning, yet current understanding of its scaling…
LLM Reasoning moved forward this cycle; last verified May 2026. Public score 6.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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A framework for many-shot chain-of-thought in-context learning that reframes LLM prompting as in-context test-time learning.
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10.48550/arXiv.2605.13511A framework for many-shot chain-of-thought in-context learning that reframes LLM prompting as in-context test-time learning.
Abstract
In-context learning (ICL) adapts large language models (LLMs) to new tasks by conditioning on demonstrations in the prompt without parameter updates. With long-context models, many-shot ICL can use dozens to hundreds of examples and achieve performance comparable to fine-tuning, yet current understanding of its scaling behavior is largely derived from non-reasoning tasks. We study many-shot chain-of-thought in-context learning (CoT-ICL) for reasoning and show that standard many-shot rules do not transfer. Across non-reasoning and reasoning-oriented LLMs and across non-reasoning and reasoning tasks, we find: (i) a setting-dependent scaling effect, where increasing the number of CoT demonstrations is unstable for non-reasoning LLMs and benefits mainly reasoning-oriented LLMs; (ii) similarity-based retrieval helps on non-reasoning tasks but fails on reasoning, since semantic similarity poorly predicts procedural (i.e., CoT) compatibility; and (iii) an order-scaling effect, where performance variance grows with more CoT demonstrations. We interpret these behaviors by viewing many-shot CoT-ICL as in-context test-time learning rather than scaled pattern matching, and suggests two principles: (i) demonstrations should be easy for the target model to understand, and (ii) they should be ordered to support a smooth conceptual progression. Guided by the principle, we propose Curvilinear Demonstration Selection (CDS), a simple ordering method that yields up to a 5.42 percentage-point gain on geometry with 64 demonstrations. Overall, our results reframe the long context window from a retrieval buffer into a structured curriculum for in-context test-time learning.
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PROBLEM
A framework for many-shot chain-of-thought in-context learning that reframes LLM prompting as in-context test-time learning. With long-context models, many-shot ICL can use dozens to hundreds of examples and achieve performance comparable to fine-tuning, yet current understandin...
METHOD
In-context learning (ICL) adapts large language models (LLMs) to new tasks by conditioning on demonstrations in the prompt without parameter updates. With long-context models, many-shot ICL can use dozens to hundreds of examples and achieve performance comparable to fine-tuning,...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. With long-context models, many-shot ICL can use dozens to hundreds of examples and achieve performance comparable to fine-tuning, yet current understanding of its scaling behavior is largely derived from...
WHY NOW
LLM Reasoning moved forward this cycle; last verified May 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework for many-shot chain-of-thought in-context learning that reframes LLM prompting as in-context test-time learning. With long-context models, many-shot ICL can use dozens to hundreds of examples and achieve performance comparable to fine-tuning, yet current understanding of its scaling behavior is largely derived from non-reasoning tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
In-context learning (ICL) adapts large language models (LLMs) to new tasks by conditioning on demonstrations in the prompt without parameter updates. With long-context models, many-shot ICL can use dozens to hundreds of examples and achieve performance comparable to fine-tuning, yet current understanding of its scaling behavior is largely derived from non-reasoning tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. With long-context models, many-shot ICL can use dozens to hundreds of examples and achieve performance comparable to fine-tuning, yet current understanding of its scaling behavior is largely derived from non-reasoning tasks. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Reasoning moved forward this cycle; last verified May 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework for many-shot chain-of-thought in-context learning that reframes LLM prompting as in-context test-time learning.
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LLM Reasoning
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