Unverified50% confidenceFactExact time
Modern large reasoning models (LRMs) adopt an inductive paradigm, first inferring the program or sequence of instructions that produces the answer, then executing it to obtain the result, and predicting the natural-language program/reasoning chain that leads to the answer.
1
Sources
50%
Confidence
Long-term
Relevance
10/6/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLLM 工具将思维链输出到 stderr、将最终响应输出到 stdout,使得推理过程可实时显示而结构化结果流入下游工具76% similarUnverifiedThe core idea of this method is that after an LLM provides an answer and explanation, concepts not mentioned in the explanation are removed from the original input, and the model is re-queried with the simplified input.76% similarUnverifiedThe core idea of ReAct is to have LLMs perform explicit reasoning before generating actions, and to feed execution results back to the model as observations to form a closed loop.75% similarVerifiedModern LLMs have acquired capabilities for planning, reasoning, and tool use through instruction tuning and reinforcement learning from human feedback (RLHF).75% similarUnverifiedModern reasoning models generally use RLHF or its variant RLAIF for alignment training, where the model learns during training when to stop reasoning and when to output an answer.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/980195API
curl https://kongchang.com/api/v1/knowledge/claims/980195MCP
get_claim(id=980195)