Implement advanced reasoning strategies like chain-of-thought and tree-of-thought
Implement advanced reasoning strategies like chain-of-thought and tree-of-thought
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The agent breaks down complex problems into sequential steps, showing its reasoning process. Like solving a math problem by writing out each step, CoT helps agents arrive at better answers by thinking through the problem methodically.
Instead of following one path, the agent explores multiple reasoning branches simultaneously. Like a chess player considering several moves ahead, ToT evaluates different approaches and selects the most promising path.
The agent solves the same problem multiple times using different approaches, then selects the most common answer. Like getting a second opinion, this technique improves accuracy by finding consensus across multiple reasoning paths.
The agent alternates between reasoning about what to do and taking actions. Like a detective who thinks, investigates, thinks more, and investigates again, ReAct combines thought with action for better problem-solving.
PALMs integrate LLMs with symbolic reasoning by generating and executing code (like Python) as part of problem-solving. This offloads complex calculations and logical operations to a deterministic programming environment, combining the LLM's understanding with precise computation for increased reliability and accuracy.
Advanced "reasoning models" that dedicate variable "thinking" time before answering, producing extensive Chain-of-Thought (thousands of tokens). Trained on problems with known correct answers, these models learn through trial and error to generate effective reasoning trajectories with self-correction and backtracking capabilities.
A formal AI framework where multiple diverse models collaborate and argue to solve problems. Like an AI council meeting, different models present ideas, critique each other's reasoning, and exchange counterarguments to enhance accuracy, reduce bias, and improve answer quality through collective intelligence.
An advanced framework that reimagines discussion as a dynamic, non-linear network. Arguments are nodes connected by edges ('supports' or 'refutes'), allowing new inquiry branches to evolve independently and merge over time. Conclusions emerge from identifying the most robust, well-supported argument clusters within the entire graph.
A novel framework that automates and optimizes multi-agent system design through multi-stage optimization: block-level prompt optimization for individual agents, workflow topology optimization for agent interactions, and workflow-level prompt optimization for the entire system. This systematic approach significantly outperforms manually designed systems.
AI agentic tools (like Perplexity AI, Google Gemini, OpenAI ChatGPT) that act as tireless research assistants. Given a complex query and time budget, they autonomously perform multiple targeted searches, analyze results, identify gaps, conduct follow-up inquiries, and compile comprehensive, structured summaries—transforming hours of research into minutes.
A critical principle showing that LLM performance improves with increased computational resources during inference (not just training). A smaller model with more "thinking budget" (multiple answer generation, iterative refinement) can often outperform a larger model with simpler generation. This enables cost-effective, high-performance agentic systems.
Sequential Reasoning
CoT breaks down problems step-by-step
Branching Exploration
ToT explores multiple paths
Action Integration
ReAct combines thought with action
Collaborative Reasoning
CoD/GoD enable multi-agent debates
