Reasoning on English AI Terms Dictionary

Tree of Thoughts

Sat, 18 Jul 2026 10:18:52 +0000

Definition

Tree of Thoughts (ToT) extends traditional chain-of-thought prompting by allowing the model to explore multiple distinct reasoning paths at each step, forming a tree structure. The model evaluates these ’thoughts’ to decide which branches to pursue further, enabling it to look ahead, backtrack from dead ends, and make global planning decisions. This approach significantly improves performance on tasks requiring strategic planning, creative generation, or complex problem-solving.

Summary

Tree of Thoughts is a reasoning framework that explores multiple possible reasoning paths simultaneously, evaluating them to select the most promising next step.

Reflection

Sat, 18 Jul 2026 10:13:50 +0000

Definition

In AI, reflection is a paradigm where a model pauses to evaluate its own generation process or output before finalizing it. This can involve checking for logical consistency, factual accuracy, or adherence to safety guidelines. By reflecting on its own actions, the system can correct errors, refine arguments, or adjust its tone. This technique is often implemented via chain-of-thought prompting or separate critique models, significantly enhancing the reliability and quality of complex reasoning tasks.

Neuro-symbolic AI

Sat, 18 Jul 2026 10:08:54 +0000

Definition

Neuro-symbolic AI integrates sub-symbolic neural learning methods with symbolic logic-based reasoning systems. This hybrid approach aims to overcome the limitations of pure deep learning, such as lack of interpretability and poor generalization from few examples, by incorporating explicit knowledge structures. It enables systems to learn from data while maintaining logical consistency and providing explainable decisions through rule-based inference.

Summary

An AI approach combining neural networks’ learning capabilities with symbolic reasoning’s logic and transparency.

Kimi K2

Sat, 18 Jul 2026 10:03:41 +0000

Definition

Kimi K2 represents a significant iteration in Moonshot AI’s series of large language models. It is characterized by its enhanced capabilities in complex logical reasoning, mathematical problem-solving, and natural language processing. The model supports extremely long context windows, allowing it to process and analyze vast amounts of information simultaneously. It is optimized for both Chinese and English tasks, aiming to provide high-quality assistance in professional and creative domains through improved alignment and efficiency.

DeepSeek V3

Sat, 18 Jul 2026 09:55:14 +0000

Definition

DeepSeek V3 is an advanced iteration in the DeepSeek model family, characterized by its dense activation of only a small subset of parameters during inference via Mixture-of-Experts routing. This architecture allows it to scale up parameter count dramatically while keeping computational costs manageable. It demonstrates exceptional proficiency in mathematics, coding, and logical reasoning, often outperforming larger dense models. The model was trained using a novel hybrid optimization strategy and extensive high-quality data, making it a leading choice for developers seeking high-performance open-source LLMs for complex task execution.

Dataset:Jackrong/Qwen3.5 Reasoning 700X

Sat, 18 Jul 2026 09:53:44 +0000

Definition

This entry refers to a specific dataset repository identified by the identifier ‘Jackrong/Qwen3.5 Reasoning 700X’. It is typically used in the context of supervised fine-tuning (SFT) or reinforcement learning from human feedback (RLHF) to improve the logical deduction and problem-solving skills of base models. The dataset likely contains high-quality reasoning traces, chain-of-thought examples, or mathematical/logical puzzles designed to push the boundaries of a model’s analytical performance, specifically targeting the Qwen architecture family.

Commonsense knowledge

Sat, 18 Jul 2026 09:50:01 +0000

Definition

Commonsense knowledge refers to the vast amount of implicit information about everyday life, physics, social norms, and cause-and-effect relationships that humans acquire naturally. In AI, acquiring this type of knowledge is a significant challenge because it is rarely explicitly stated in training data yet crucial for reasoning. Systems lacking commonsense may fail at simple tasks like understanding that a glass will break if dropped. Projects like ConceptNet and ATOMIC aim to encode these facts to help AI systems interpret context, infer intentions, and make logical deductions similar to human intuition.

Case-based reasoning

Sat, 18 Jul 2026 09:48:49 +0000

Definition

CBR operates on the principle that similar problems have similar solutions. The process involves retrieving the most similar historical case from a knowledge base, adapting its solution to fit the current context, and retaining the new experience for future use. This paradigm is particularly useful in domains where explicit rules are difficult to define, such as legal reasoning, medical diagnosis, and customer service automation, leveraging experiential knowledge rather than purely symbolic logic.

Agentive logic

Sat, 18 Jul 2026 09:45:08 +0000

Definition

It extends traditional logic to account for agency, allowing systems to represent beliefs, desires, and intentions (BDI models). This logic enables agents to plan actions dynamically based on changing environments and internal states. By formalizing how agents perceive their world and choose actions to achieve goals, agentive logic supports the development of sophisticated autonomous systems capable of complex, goal-directed behavior in uncertain environments.

Summary

Agentive logic refers to the formal reasoning frameworks used to model the intentions, goals, and decision-making processes of autonomous agents.

ReAct

Sat, 18 Jul 2026 09:42:48 +0000

Definition

The ReAct framework enables LLMs to generate both reasoning traces and task-specific actions in an interleaved manner. By simulating human-like thought processes, it allows models to interact with external environments, such as search engines or calculators, to verify facts and solve problems step-by-step. This synergy reduces hallucinations and enhances the accuracy of responses in question-answering and planning scenarios.

Summary

ReAct is a prompting paradigm that combines reasoning and acting to improve the performance of large language models on complex tasks.

Chain-of-Thought Prompting

Sat, 18 Jul 2026 09:40:12 +0000

Definition

Chain-of-Thought (CoT) prompting improves the performance of large language models on complex reasoning tasks by explicitly asking the model to articulate its step-by-step logic. Instead of jumping directly to a conclusion, the model generates intermediate sentences that represent its thought process. This approach mimics human problem-solving strategies, significantly enhancing accuracy in mathematics, logic puzzles, and multi-step deductions. It can be implemented via few-shot examples or simple instructions like ‘Let’s think step by step.’

multi-step

Sat, 18 Jul 2026 09:39:01 +0000

Definition

Multi-step methods involve breaking down a complex query or task into smaller, executable steps. This approach is critical in reasoning tasks, such as mathematical problem solving or code generation, where intermediate conclusions are necessary to derive the final answer. It often relies on chain-of-thought prompting or explicit algorithmic sequencing to ensure accuracy and traceability.

Summary

A problem-solving strategy that requires performing a sequence of logical operations or calculations to reach a final solution.

Chain-of-Thought

Sat, 18 Jul 2026 07:38:30 +0000

Definition

Chain-of-Thought (CoT) prompting is a strategy where large language models are guided to produce step-by-step reasoning explanations before arriving at a final answer. By breaking down complex problems into intermediate logical steps, CoT enhances the model’s ability to handle arithmetic, commonsense, and symbolic reasoning tasks. This technique leverages the model’s latent reasoning capabilities, significantly improving accuracy and interpretability in solving multi-step problems.

Summary

A prompting technique that encourages LLMs to generate intermediate reasoning steps before answering.