Many-shot CoT-ICL functions as test-time learning when demonstrations are ordered for smooth conceptual progression rather than similarity, enabling a new selection method that improves reasoning performance.
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ReasoningBank distills generalizable reasoning strategies from agent successes and failures to enable self-evolution, with memory-aware test-time scaling amplifying gains over raw-trajectory or success-only memory on web and software benchmarks.
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Many-Shot CoT-ICL: Making In-Context Learning Truly Learn
Many-shot CoT-ICL functions as test-time learning when demonstrations are ordered for smooth conceptual progression rather than similarity, enabling a new selection method that improves reasoning performance.
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ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory
ReasoningBank distills generalizable reasoning strategies from agent successes and failures to enable self-evolution, with memory-aware test-time scaling amplifying gains over raw-trajectory or success-only memory on web and software benchmarks.