IdleSpec improves LLM agent accuracy by generating and aggregating speculative plans during idle time between tool calls and observations using complementary drafting strategies.
Chain-of-thought prompting elicits reasoning in large language models
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PTRM adds stochastic Gaussian noise to Tiny Recursive Model recursion for parallel trajectory exploration and Q-head selection, raising Sudoku-Extreme accuracy from 87.4% to 98.75% and Pencil Puzzle Bench from 62.6% to 91.2% without retraining.
citing papers explorer
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IdleSpec: Exploiting Idle Time via Speculative Planning for LLM Agents
IdleSpec improves LLM agent accuracy by generating and aggregating speculative plans during idle time between tool calls and observations using complementary drafting strategies.
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Probabilistic Tiny Recursive Model
PTRM adds stochastic Gaussian noise to Tiny Recursive Model recursion for parallel trajectory exploration and Q-head selection, raising Sudoku-Extreme accuracy from 87.4% to 98.75% and Pencil Puzzle Bench from 62.6% to 91.2% without retraining.