Reasoning LLM-based AI Model

A Reasoning LLM-based AI Model is an LLM that can perform explicit step-by-step logical analysis.

• AKA: Chain-of-Thought LLM, Step-by-Step Reasoning LLM, Logical Reasoning AI, Thinking LLM.
• Context:
  • It can typically generate Explicit Reasoning Chains through prompting techniques like chain-of-thought.
  • It can typically perform Multi-Step Deduction through logical inference rules and sequential thoughts.
  • It can typically solve Complex Reasoning Problems through explicit reasoning steps rather than implicit processing.
  • It can typically provide Reasoning Transparency through verbalized thought processes that humans can audit.
  • It can typically reduce Reasoning Errors through explicit verification steps and self-correction mechanisms.
  • It can typically break down complex problems into smaller manageable steps for systematic resolution.
  • It can typically draw logical conclusions based on given information through structured reasoning processes.
  • ...
  • It can often implement Mathematical Reasoning through step-by-step calculations and formula applications.
  • It can often perform Scientific Reasoning through hypothesis testing, evidence evaluation, and conclusion formation.
  • It can often conduct Ethical Reasoning through moral principle application and consequence analysis.
  • It can often employ Common Sense Reasoning through everyday knowledge application and plausibility checks.
  • It can often utilize Counterfactual Reasoning through alternative scenario comparison and causality analysis.
  • It can often execute Natural Language Program through code generation and logical flow implementation.
  • It can often conduct Probabilistic Reasoning through statistical inference and uncertainty modeling.
  • It can often perform Meta Reasoning through reasoning method selection and approach adaptation.
  • It can often apply knowledge to novel situations through knowledge transfer mechanisms.
  • It can often engage in causal reasoning through cause-effect relationship analysis.
  • ...
  • It can range from being a Basic Reasoning LLM to being an Advanced Reasoning LLM, depending on its reasoning depth and reasoning accuracy.
  • It can range from being a Narrow Reasoning LLM to being a Broad Reasoning LLM, depending on its reasoning domain coverage.
  • It can range from being a Supervised Reasoning LLM to being a Self-Supervised Reasoning LLM, depending on its reasoning training approach.
  • It can range from being a Reasoning-Assisted LLM to being a Reasoning-Native LLM, depending on its reasoning architecture integration.
  • It can range from being a Simple Logical Reasoner to being a Complex Problem Solver, depending on its reasoning capability complexity.
  • It can range from being a Domain Specific Reasoner to being a General Purpose Reasoner, depending on its application scope.
  • ...
  • It can have Architectural Enhancements for supporting reasoning capabilitys such as special tokens or feedback loops.
  • It can have Thinking Tokens for creating dedicated computational pathways for reasoning steps.
  • It can have Processing Mode Toggles for activating different reasoning modes during inference.
  • It can have Hidden State Feedback Loops for implementing breadth-first-search-like reasoning.
  • It can have Reasoning Memory for storing reasoning history and key deductions.
  • It can have Reasoning Techniques including chain of thought prompting, tree of thoughts, rationale engineering, and self-taught reasoning.
  • It can leverage External Tool through integration interfaces to augment reasoning capacity.
  • It can handle Causal Inference through logical analysis of cause-effect relationships.
  • ...
  • It can be Reasoning Limited during knowledge gaps or when facing domain complexity beyond its capabilities.
  • It can be Reasoning Uncertain when dealing with ambiguous premises or incomplete information.
  • It can be Reasoning Biased due to training data skew or reasoning pattern preference.
  • It can be Reasoning Inconsistent across different types of reasoning tasks due to capability variation.
  • It can be Reasoning Plausible but Incorrect when generating convincing but false explanations.
  • ...
• Task Input: Complex Problem Statements, Reasoning Querys, Decision Scenarios
  • • Optional Input: Domain Knowledge Context, Reasoning Constraints, Verification Requirements
• Task Output: Explicit Reasoning Chains, Justified Conclusions, Transparent Decisions
  • • Optional Output: Reasoning Confidence Scores, Alternative Reasoning Paths, Reasoning Limitation Notes
• Task Performance Measure: Reasoning Quality Metrics such as logical consistency, reasoning depth, and conclusion validity
  • Reasoning Consistency across different problem types and domains
  • Reasoning Accuracy compared to ground truth
  • Reasoning Transparency for human interpretability
  • Reasoning Efficiency in terms of step count and computational resources
  • ...
• Examples:
  • Reasoning LLM Architectural Approaches, such as:
    • Standard Transformer Reasoning LLMs, such as:
      • Chain-of-Thought LLM for explicit reasoning step generation without architectural changes.
      • Reinforcement Learning from Human Feedback Reasoning LLM for reasoning alignment with human expectations.
    • Modified Architecture Reasoning LLMs, such as:
      • Thinking Token Enhanced LLM using special tokens for dedicated reasoning pathways.
      • Chain of Continuous Thought LLM implementing hidden state feedback loops for iterative reasoning.
      • Processing Mode Toggle LLM featuring architectural thinking toggles for different reasoning modes.
  • Reasoning LLM Techniques, such as:
    • Chain of Thought Approaches, such as:
      • Zero-Shot Chain of Thought for reasoning capability without explicit examples.
      • Few-Shot Chain of Thought for reasoning improvement through demonstration learning.
      • Self-Taught Reasoner for reasoning capability improvement through iterative self-refinement.
    • Tree of Thoughts Approaches, such as:
      • Breadth-First Tree of Thoughts for comprehensive reasoning path exploration.
      • Depth-First Tree of Thoughts for deep reasoning path investigation.
      • Guided Tree of Thoughts for efficient reasoning path selection.
    • Rationale Engineering Approaches, such as:
      • Verification-Based Rationale Engineering for reasoning correctness checks.
      • Exploration-Based Rationale Engineering for alternative reasoning approach discovery.
      • Refinement-Based Rationale Engineering for reasoning quality improvement.
  • Reasoning LLM Applications, such as:
    • Scientific Research Reasoning LLMs, such as:
      • Hypothesis Generation Reasoning LLM for scientific discovery support.
      • Experimental Analysis Reasoning LLM for scientific data interpretation.
    • Educational Reasoning LLMs, such as:
      • Problem-Solving Tutorial Reasoning LLM for step-by-step explanation generation.
      • Concept Explanation Reasoning LLM for educational content creation.
    • Decision Support Reasoning LLMs, such as:
      • Complex Decision Analysis Reasoning LLM for multi-factor decision support.
      • Risk Assessment Reasoning LLM for outcome probability evaluation.
  • Reasoning LLM Implementations, such as:
    • Commercial Reasoning LLMs, such as:
      • Claude 3.7 Sonnet (2025) for enterprise reasoning tasks with architectural thinking toggles.
      • OpenAI o1 (2024) for specialized reasoning capabilitys through optimized reasoning architecture.
      • OpenAI o3 (2024) for adaptive thinking and reasoning flexibility.
      • DeepSeek R1 (2024) for streamlined reasoning processes.
      • GPT-4 (2023) for advanced reasoning applications across diverse reasoning domains.
    • Research Reasoning LLMs, such as:
      • Coconut (2023) for chain of continuous thought using hidden state feedback loops.
      • Reasoning-PaLM (2022) for experimental reasoning capability exploration.
  • ...
• Counter-Examples:
  • Pre-trained Base LLMs, which maintain standard transformer architecture without reasoning-specific enhancements or training.
  • Instruction-tuned LLMs, which focus on instruction following rather than autonomous reasoning, though they share similar architecture.
  • Conversation-tuned LLMs, which prioritize dialogue coherence over complex reasoning, while maintaining base transformer architecture.
  • Basic Text Generation Models, which lack structured reasoning capability and focus on fluent text production.
  • Simple Chatbots, which emphasize pattern matching and response retrieval over logical inference.
  • Pure Retrieval Models, which prioritize information lookup over reasoning processes.
  • Pattern Recognition Models, which rely on statistical correlation rather than causal understanding.
• See: Chain of Thought, Tree of Thoughts, Logical Reasoning, Rationale Engineering, LLM Capability, Step-by-Step Problem Solving, Transformer Architecture, LLM Evolution, Causal Inference, Meta Reasoning, Symbolic Reasoning, Commonsense Reasoning, Arithmetic Reasoning, Reasoning Benchmark, Self Taught Reasoner, Hybrid Reasoning Approach.

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References

2024

• Perplexity.ai
  • A reasoning LLM (Large Language Model) is an advanced AI model designed to perform logical thinking, problem-solving, and inference tasks beyond simple text generation. Here are the key aspects of a reasoning LLM:
  • Capabilities
    • Break down complex problems into smaller, manageable steps
    • Draw logical conclusions based on given information
    • Provide step-by-step explanations for solutions
    • Apply knowledge to novel situations
    • Engage in causal reasoning and counterfactual thinking
  • Techniques
    • Chain-of-Thought Prompting: Encourages the model to show its work by generating intermediate steps[1].
    • Tree-of-Thoughts: Allows the model to explore multiple reasoning paths before reaching a conclusion[2].
    • Rationale Engineering: Improves the elicitation and use of reasoning through refinement, exploration, and verification of rationales[1].
  • Limitations
    • Lack of genuine understanding of concepts
    • Difficulty with novel situations outside training data
    • Inconsistent performance across different types of reasoning tasks
    • Potential for generating plausible but incorrect explanations
  • Applications
    • Scientific research and scientific discovery
    • Complex decision-making processes
    • Educational tools for problem-solving
    • Enhancing AI assistants for more sophisticated tasks
  • Ongoing Research
    • Meta-reasoning capabilities to dynamically select appropriate reasoning methods.[5]
    • Integration of external knowledge bases and external tools
    • Improvement of reasoning consistency and reasoing reliability
  • Citations:

[1] https://blog.paperspace.com/understanding-reasoning-in-llms/
[2] https://www.promptingguide.ai/research/llm-reasoning
[3] https://www.shaped.ai/blog/do-large-language-models-llms-reason
[4] https://www.finn-group.com/post/the-great-debate-do-language-models-reason
[5] https://arxiv.org/html/2406.11698v1
[6] https://www.reddit.com/r/MachineLearning/comments/1330rbb/d_knowledge_vs_reasoning_in_llms/
[7] https://huggingface.co/blog/KnutJaegersberg/active-reasoning
[8] https://www.kaggle.com/code/flaussy/large-language-models-reasoning-ability

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