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Multi-hop reasoning refers to complex questions that require sequentially or jointly extracting information from multiple sources to answer; implementation methods include iterative retrieval, graph-structured retrieval, and agent-based approaches, which improve complex question answering but increase latency and the probability of errors.
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9/8/2026
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UnverifiedReasoning models perform better in information synthesis tasks requiring multi-step logical deduction, but have higher latency and should be selected appropriately based on task characteristics.77% similarUnverifiedTest-time compute scaling improves performance on complex reasoning tasks by investing more computation during the inference stage through methods such as extending the chain of thought, multi-path sampling with best-of-n selection, and self-verification reflection.77% similarUnverifiedReAct improves decision quality by alternating reasoning and action, Chain-of-Thought improves complex reasoning accuracy through explicit intermediate steps, and Tree-of-Thoughts handles highly uncertain problems through multi-path exploration.76% similarUnverifiedStreaming inference involves inference optimization techniques such as speculative decoding and continuous batching.74% similarVerifiedReasoning Effort is essentially a parameter that controls the depth of the model's chain of thought; the higher the level, the more internal reasoning steps the model is allowed, and latency and cost rise proportionally.73% similar
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