Reasoning Models

Extracted Attributes

• Types

  Commonsense reasoning models, Analogical reasoning models, Deductive reasoning, Structured reasoning models

• Benefits

  Accelerate time to value, drive efficiency, mitigate risks, enhance user experiences, automate complex workflows, improve business processes

• Category

  Large Language Models (LLMs)

• Challenges

  AI hallucination, ethical concerns (inheriting biases from training data), practical deployment hurdles leading to 'Trough of Disillusionment'

• Performance

  Outperform traditional autoregressive LLMs on logical, mathematical, and programmatic tasks

• Applications

  Artificial intelligence (AI), cognitive science, decision-making systems, security threat detection, personalized customer interactions, logistics optimization, conversational AI, virtual assistants, case-based reasoning, creative problem-solving, innovation strategies

• Key Capabilities

  Logical inference, structured reasoning, contextual understanding, backtracking, employ test-time compute

• Primary Function

  Solve multi-step reasoning tasks

• Scaling Dimensions

  Training examples, parameter count, train-time compute, test-time compute

• Distinguishing Feature

  Simulate human-like problem-solving; apply logical inference and structured reasoning rather than solely pattern recognition; often include intermediate 'thought' processes or chains of thought (CoT).

Timeline

• OpenAI releases initial reasoning models like o1-preview, designed to spend more time thinking before responding and demonstrating high accuracy in verifiable tasks such as math and coding. (Source: Web Search)

  2024-05-01

• Enterprises encounter practical challenges such as AI hallucination, contributing to many being in the 'Trough of Disillusionment' within the Hype Cycle for AI technologies. (Source: Related Document)

  Ongoing

Reasoning language model

Reasoning language models (RLMs) are large language models that have been further trained to solve multi-step reasoning tasks. These models perform better on logical, mathematical or programmatic tasks than traditional autoregressive LLMs, have the ability to backtrack, and employ test-time compute as an additional scaling axis beyond training examples, parameter count, and train-time compute.

Web Search Results

• Reasoning Model: Definition, Types, and Applications | Denodo

  A reasoning model is a structured framework or computational method used to process information, draw inferences, and make decisions based on logic, data, or rules. These models are fundamental in artificial intelligence (AI), cognitive science, and decision-making systems, helping to simulate human reasoning or automate problem-solving tasks. ## Types of Reasoning Models: ## Applications of Reasoning Models: ## Key Benefits of Reasoning Models: [...] Reasoning models are essential in a wide variety of domains, from AI to business analytics, providing structured approaches to decision-making and problem-solving. As technology advances, reasoning models will continue to evolve, enhancing automation, intelligence, and efficiency across industries. Back to Glossary page ### Related Terms ## Related Content thumbnail Blog Post ### Leverage enterprise data with Denodo and Vertex AI for generative AI applications thumbnail e-Book

• 7 reasons why AI reasoning models accelerate time to value

  What are AI reasoning models? ----------------------------- Image 5 AI reasoning models are advanced systems designed to simulate human-like problem-solving and management capabilities. Unlike traditional machine learning models that rely on pattern recognition, these models apply logical inference, structured reasoning, and contextual understanding to interpret and generate responses. [...] Reasoning models ensure decisions align with real-time conditions by detecting security threats, personalizing customer interactions, and optimizing logistics. This responsiveness drives efficiency, mitigates risks, and enhances user experiences across various applications. [...] Commonsense reasoning models: Designed to understand everyday scenarios, these models simulate human intuition and general knowledge. They improve interactions in conversational AI and virtual assistants. Analogical reasoning models: These models draw comparisons between different situations to solve problems based on prior knowledge. They enhance case-based reasoning, creative problem-solving, and innovation strategies.

• Understanding Reasoning LLMs - Sebastian Raschka

  Additionally, most LLMs branded as reasoning models today include a “thought” or “thinking” process as part of their response. Whether and how an LLM actually “thinks” is a separate discussion. Intermediate steps in reasoning models can appear in two ways. First, they may be explicitly included in the response, as shown in the previous figure. Second, some reasoning LLMs, such as OpenAI’s o1, run multiple iterations with intermediate steps that are not shown to the user. Image 9: img [...] Image 8: img A regular LLM may only provide a short answer (as shown on the left), whereas reasoning models typically include intermediate steps that reveal part of the thought process. (Note that many LLMs who have not been specifically developed for reasoning tasks can also provide intermediate reasoning steps in their answers.) [...] Most modern LLMs are capable of basic reasoning and can answer questions like, “If a train is moving at 60 mph and travels for 3 hours, how far does it go?” So, today, when we refer to reasoning models, we typically mean LLMs that excel at more complex reasoning tasks, such as solving puzzles, riddles, and mathematical proofs.

• Demystifying Reasoning Models - by Cameron R. Wolfe, Ph.D.

  #### Initial Reasoning Models: o1 and o1-mini > _“We've developed a new series of AI models designed to spend more time thinking before they respond.”_ - from \[4\] The release of o1-preview \[4, 5\] by OpenAI made two things very clear: 1. Reasoning models can solve verifiable tasks—_such as math and coding tasks_—very accurately. 2. The approach taken by reasoning models to solve these problems is very different from that of a traditional LLM. [...] Additionally, reasoning models logically separate their CoT from the final output of the model. For example, OpenAI avoids exposing the long CoT directly to users and instead provides an LLM-generated summary of the long CoT to supplement the reasoning model’s final answer. Such a logical separation is fundamentally necessary due to the length of CoT. Most users will only read the final answer—_reading the entire reasoning trace would be incredibly time consuming_. Image 26 (from \[15\]) [...] Long CoT. The main difference between a reasoning model and a standard LLM is the ability to “think” before answering a question. The reasoning model’s thoughts are just long chains of thought—_or_ _long CoT for short, sometimes referred to as a reasoning trace or trajectory_—outputted by the LLM. This long CoT is generated no differently than any other sequence of text. However, these reasoning trajectories exhibit very interesting properties that are more akin to search algorithms than

• What is AI reasoning in 2025? - Lumenalta

  AI reasoning depends on multiple foundational components that allow artificial intelligence to interpret data, apply logic, and generate conclusions. These elements combine to support AI problem solving, automate complex workflows, and improve efficiency in business processes. Structured reasoning models allow AI to go beyond simple pattern recognition by applying structured logic, probabilistic assessment, and adaptive learning. Each component contributes to refining AI for reasoning, making [...] AI reasoning applies different methods to interpret data, draw conclusions, and refine decision processes. Each type of reasoning in artificial intelligence contributes to improving AI problem solving by allowing systems to analyze information logically and adapt to new conditions. Structured reasoning models combine rule-based logic, probability assessments, and adaptive learning techniques to enhance AI reasoning engines. ### Deductive reasoning [...] Ensuring ethicaland unbiased reasoning: AI reasoning models inherit biases in training data, leading to unintended ethical concerns. AI for reasoning must incorporate bias detection and mitigation strategies to prevent discrimination in automated decision processes. Regulatory frameworks and ethical guidelines help organizations build AI reasoning systems that align with fairness and accountability standards.