Beyond predictions: the secrets of reasoning models

Reasoning is a fundamental aspect of human intelligence, enabling us to solve problems, make decisions, and understand the world around us. For artificial intelligence (AI), developing sophisticated reasoning capabilities has long been a holy grail. From image classifiers and language generators to robotics and autonomous systems, AI has made impressive leaps in perception and decision-making. Yet, despite their capabilities, many of today’s AI systems remain narrow in scope – good at specific tasks but limited in general understanding.

This pursuit has given rise to the field of reasoning models in AI, a fascinating and rapidly evolving area that promises to unlock new frontiers in machine intelligence. The growing prominence of reasoning is evident in recent significant releases, including models like DeepSeek-R1, Google’s Gemini or OpenAI’s o-series, all of which are increasingly integrating more sophisticated reasoning mechanisms.

What Are Reasoning Models?

At their core, reasoning models are AI systems designed to simulate human-like thought processes: deduction, induction, planning, and problem-solving. Unlike conventional models that often operate as "black boxes" (e.g., deep neural networks that primarily map input to output through pattern recognition), reasoning models aim to understand why something happens, not just what will happen. They strive for a deeper, more explicit understanding of cause and effect, relationships, and logical implications.

They tackle complex tasks that require more than just pattern matching, such as:

• Solving logic puzzles: Moving beyond simple true/false statements to multi-variable constraints and deductions.
• Answering multi-hop questions: For example, "If Alice is taller than Bob, and Bob is taller than Carol, who is the shortest?" This requires chaining multiple pieces of information.
• Performing step-by-step mathematical reasoning: Not just calculating an answer, but showing the intermediate steps, formulas applied, and underlying principles.
• Planning sequences of actions in dynamic environments: This is crucial for robotics, autonomous vehicles, and strategic game playing, where the AI must anticipate consequences and adapt its plans.
• Diagnosing complex systems: Identifying root causes of issues in software, hardware, or biological systems.

These capabilities aren’t entirely new – classical AI, often referred to as "Good Old-Fashioned AI" (GOFAI), was built on logical reasoning systems. It’s expert systems and knowledge representation languages that leverage symbolic logic to mimic human experts in specific domains.

Classical AI focuses on the manual creation of formalized world models by developers and domain experts. The key components of this approach include:

• Logical models
• Ontologies
• Knowledge graphs
• Expert systems

These structures support deductive reasoning based on predefined rules and encoded knowledge. However, GOFAI has limited ability to learn from data, which makes it less adaptable in dynamic or unpredictable environments.

According to Gary Marcus in “Rebooting AI”, deep learning is good at learning but terrible at building cognitive models. Classical AI is good at building cognitive models but very mediocre at learning.

As part of our own GOFAI research, the QuData team developed a universal language called DemonScript, built on many-valued logic. This allows models not only to work with true or false statements, but also to generate probabilistic logical inferences, accounting for uncertainty and ambiguity in the real world.

Using DemonScript, we could describe basic spatial relationships between objects in a simplified model of the world – allowing for the modeling of situations where precise data is unavailable or ambiguous. This approach combines the strength of classical AI in building models with modern requirements for adaptability and uncertainty handling.

However, today’s reasoning models are fundamentally different: they combine the rigor of symbolic logic with the power of modern machine learning, bringing the best of both worlds through neural-symbolic integration and advanced prompting techniques.

Why Reasoning Matters

While today’s large language models (LLMs) like GPT by OpenAI or Claude by Antropic can mimic reasoning through massive-scale training, they often fall short when pushed to reason consistently, especially in novel or complex scenarios. You’ve likely seen examples of AI hallucinations, wrong calculations, or broken chains of logic. That’s because conventional LLMs are pattern matchers and next-token predictors, not true reasoners that explicitly understand causality or logical necessity.

This distinction becomes critically important in areas where stakes are high, problems are complex, or decisions demand accountability and transparency:

• Healthcare: Diagnosing based on symptoms, lab results, patient history, and genetic predispositions requires sophisticated inference, not just information retrieval. A reasoning AI could explain its diagnostic path.
• Law and Compliance: Legal reasoning involves applying abstract principles, precedents, and specific regulations to complex cases. AI could assist in legal research, contract analysis, and even predicting legal outcomes based on logical arguments.
• Science and Research: Forming and testing hypotheses, designing experiments, interpreting complex data, and deriving new theories requires multi-step causal reasoning and logical deduction. AI could become a true scientific collaborator.
• Autonomous Systems: A self-driving car must not only identify road signs and pedestrians but also plan safe routes, anticipate other drivers’ intentions, adapt to changing conditions (e.g., a sudden detour), and react to unexpected events – all of which require continuous, real-time reasoning.
• Financial Services: Detecting fraud, assessing credit risk, or making investment decisions requires reasoning about complex financial data, market dynamics, and potential causal relationships.

The ultimate goal is to build AI that can reason explicitly, verifiably, and generalizably – a leap from mere statistical mimicry to genuine computational thinking and problem-solving. This moves AI beyond being just a tool for prediction to a system that can understand, explain, and potentially even innovate.

Types of Reasoning in AI

Reasoning comes in many forms, and today’s AI models are beginning to tackle each in distinct ways, often combining them for more robust performance. While these are some of the most commonly discussed and actively researched types, the field of AI reasoning is vast, and many other specialized forms of reasoning exist and are under continuous development.

1. Deductive Reasoning

This is the process of drawing specific, logically certain conclusions from general rules or premises. If the premises are true, the conclusion must be true, making it a form of guaranteed inference.

Example:

• Rule: All birds have feathers.
• Fact: A sparrow is a bird.
• Conclusion: Therefore, a sparrow has feathers.

Historically, symbolic logic engines and rule-based systems excelled here, forming the backbone of early expert systems. Today, hybrid models are learning to map natural language into formal logical representations and deduce answers – e.g., using neural-symbolic systems like LogicNLP or Neural Theorem Provers that can verify mathematical proofs or answer questions based on logical axioms.

2. Inductive Reasoning

Induction involves generalizing from specific instances or observations to form broader rules or hypotheses. While the conclusion is probable, it is not logically guaranteed, as future observations could contradict it. This is the fundamental process of learning from experience.

Example:

• Observation: Every swan I have ever seen is white.
• Inference: All swans are white. (This conclusion was famously disproved by the discovery of black swans in Australia, highlighting the probabilistic and revisable nature of induction.)

Machine learning thrives on inductive reasoning, especially in pattern recognition, classification, and generalization from training data. However, advanced reasoning models go further, attempting to form structured hypotheses from observations, akin to scientific discovery or adaptive behavior in dynamic environments, rather than merely identifying statistical correlations.

3. Abductive Reasoning

Abductive reasoning aims to formulate the most likely explanation or hypothesis for a set of current observations. It’s often described as “inference to the best explanation,” as it involves selecting the most plausible cause from several possibilities.

Example:

• Observation: The grass outside is wet.
• Hypothesis 1: It probably rained (most likely explanation in many contexts).
• Hypothesis 2: The sprinklers were on (a possible alternative).
• Hypothesis 3: A water pipe burst (less likely, unless other supporting signs are present, like flooding).

Abduction is crucial in diagnosis (medical, mechanical, software debugging), crime solving (generating suspects based on evidence), and any situation where multiple explanations must be weighed to find the most plausible one given incomplete information.

4. Commonsense Reasoning

Often overlooked in formal systems, commonsense reasoning involves understanding how the world typically works based on our vast store of everyday experiences and intuitive knowledge. It’s the implicit, often unstated, knowledge that humans use constantly to navigate and understand their environment.

Examples:

• If I drop a fragile glass, it will likely break.
• If it’s cloudy and humid, it might rain soon.
• People typically open doors before walking through them, rather than walking into them.

Developing AI with robust commonsense reasoning is a monumental challenge due to the sheer volume and ambiguity of this knowledge. Datasets like ConceptNet or ATOMIC (which generate diverse commonsense inferences from text) have been instrumental in training AI to incorporate some level of commonsense into their reasoning, but full mastery, especially for truly novel or nuanced situations, remains elusive.

5. Analogical Reasoning

This involves solving new problems or understanding new concepts by drawing comparisons to similar, previously understood situations. If something holds true for one scenario, it’s often inferred to be true for a structurally similar one. This is crucial for learning from just a few examples and for creative problem-solving by leveraging past experience.

Example:

• If an AI understands the structure of an atom with electrons orbiting the nucleus, it might use analogical reasoning to grasp the structure of a solar system with planets orbiting the sun, recognizing the similar relational principle.

In AI, it’s being explored through models trained to recognize metaphors, analogies, and patterns of similarity across disparate inputs – useful in tutoring systems, cognitive modeling, and even scientific discovery.Case-based reasoning (CBR) systems directly apply analogical reasoning by retrieving and adapting solutions from past problems to new ones.

6. Temporal Reasoning

This form of reasoning deals with understanding and inferring relationships related to time: sequences of events, durations, and precise temporal order (e.g., “before,” “after,” “during”). It’s about modeling how the world changes and evolves over time.

Example:

• Understanding a recipe involves temporal reasoning: “Chop vegetables before sautéing them, then add sauce after they are browned.”

This type of reasoning is essential for understanding narratives in natural language, managing complex workflows, monitoring dynamic systems, and planning ahead for sequential tasks. AI uses temporal logic, sequence modeling (like recurrent neural networks (RNN) and transformers), and even formal tools like Linear Temporal Logic (LTL) to model and reason over time-based information, crucial for things like predicting future sales or controlling robotic actions.

7. Spatial Reasoning

Spatial reasoning involves understanding the physical world in terms of location, orientation, shape, size, and the complex relationships between objects in space (e.g., “above,” “below,” “inside,” “adjacent,” “overlapping”).

Example:

• To provide instructions for assembling IKEA furniture, an AI needs spatial reasoning to understand how various parts fit together in three dimensions.

This kind of reasoning is fundamental for robotics (for navigation, grasping, and object manipulation), computer vision (for understanding scene layouts and object interactions), and autonomous systems (like self-driving cars needing to calculate safe distances and paths within a dynamic environment). It often integrates techniques from geometry, topology, and deep learning models like convolutional neural networks (CNN) that are adept at processing visual and volumetric data.

Techniques Behind Reasoning Models

So how do these models actually work to achieve these reasoning capabilities? Several innovative approaches are emerging, often leveraging the power of large language models in novel ways:

1. Chain-of-Thought (CoT) Prompting

Instead of generating direct answers, models are prompted to explain their thinking step-by-step. This mimics how humans solve complex problems and allows for intermediate reasoning steps that can be checked, debugged, and improved.

Example:

• Q: If John is in Paris and wants to get to Berlin by train, what should he do?
• A: First, he needs to find a train route from Paris to Berlin. Then, he should check the schedules for available trains. After that, he needs to book a ticket. Finally, he should go to the correct station in Paris and board the train to Berlin.

This simple prompt engineering technique has significantly boosted reasoning accuracy and explainability in LLMs, revealing latent reasoning capabilities.

2. Tree of Thoughts (ToT)

Rather than linear chains, some researchers are exploring branching paths of reasoning, where multiple hypotheses, plans, or lines of thought are considered in parallel. Think of it as exploring a decision tree where each node represents a thought step, and the model evaluates which path is most promising. This technique is particularly powerful for planning complex tasks, solving puzzles with multiple intermediate states, or exploring different strategies in games.

3. Retrieval-Augmented Reasoning

Reasoning is often limited by what a model “knows” or has internalized during training. Retrieval-augmented systems (RAG) can fetch relevant documents or facts from an external knowledge base (like Wikipedia, a corporate database, or scientific papers) before reasoning. This bridges knowledge and logic, allowing the model to ground its reasoning in up-to-date and specific information, reducing hallucinations and improving factual accuracy.

4. Neuro-Symbolic Models

These are architectures that explicitly combine neural networks with symbolic logic systems. The neural components handle perception, pattern recognition, and language understanding, while the symbolic parts handle logical inference, rule application, and knowledge representation.

Example:

• A neural network might extract entities and relationships from text (e.g., “Socrates is a human”). This structured information is then fed into a symbolic knowledge graph or logic engine, which can apply rules (e.g., “All humans are mortal”) to deduce new facts (“Socrates is mortal”). This hybrid approach aims for both the flexibility of neural networks and the explainability and logical rigor of symbolic AI.

5. Program Synthesis and Execution

Some advanced reasoning models don’t just generate text; they can generate executable code (e.g., Python programs, SQL queries) to solve problems. This allows the AI to “reason” by constructing a computational process, executing it, and then using the results. This approach ensures numerical accuracy for math problems and verifiable steps for data manipulation.

Challenges of Reasoning Models

Despite the exciting progress, true AI reasoning remains a profoundly hard problem with significant challenges:

• Data Scarcity for Structured Reasoning: While LLMs thrive on massive, unstructured text, logical reasoning often requires curated, structured data and well-defined problem sets, which are harder to acquire at scale.
• Robust Evaluation: It’s challenging to objectively test reasoning ability. A correct answer doesn’t always guarantee correct reasoning; the model might have arrived at it through a spurious correlation. Developing benchmarks that truly assess the process of reasoning, not just the output, is crucial.
• Scalability and Efficiency: Logical reasoning, especially when exploring vast search spaces (like in ToT or planning), can be computationally expensive and time-consuming, making it difficult to scale for real-time applications.
• Interpretability and Explainability: Even when neural-symbolic models reason correctly, understanding the intricate interplay between the neural and symbolic components can still be blurred. Achieving true transparency in complex reasoning processes remains a key challenge.
• Common Sense Acquisition: Instilling a deep, intuitive understanding of the world – common sense – remains one of the greatest hurdles. It's difficult to represent and teach the sheer volume of implicit knowledge that humans effortlessly apply in their reasoning.
• Handling Novelty and Abstraction: Current models can struggle with reasoning about truly novel situations or highly abstract concepts that deviate significantly from their training data.

Beyond Mimicry

Reasoning is a cornerstone of human intelligence – and an essential for any truly general AI. As models become more powerful and multimodal (integrating text, image, video, audio, and sensor data), integrating reasoning into their architectures will be critical. The synergy between perception, language, and reasoning will define the next generation of AI.

Exciting trends to watch in the coming years include:

• Agentic AI: Developing autonomous AI agents that can not only reason but also plan, act, and adapt across multiple tasks, often with the ability to self-correct and learn from their actions.
• Multimodal Reasoning: Combining information from various modalities (e.g., understanding a video by reasoning about the spoken dialogue, the visual context, and the sounds) to develop a holistic understanding of real-world situations.
• Interactive Reasoning: Models that don’t just output an answer but can engage in a dialogue with humans, asking clarifying questions, revising assumptions based on feedback, or collaborating on problem-solving.
• Ethical and Moral Reasoning: A critical frontier. Teaching AI to make value-based decisions, understand trade-offs, and reason about ethical dilemmas in complex social contexts is paramount for responsible AI deployment. This involves formalizing ethical principles and enabling AI to apply them logically.
• Embodied Reasoning: For robots and other physical AI systems, reasoning must be deeply integrated with their physical interaction with the world – how their actions affect the environment and how sensory input informs their understanding.

Reasoning models represent a fundamental shift from AI that merely mimics intelligence through pattern recognition to AI that strives to understand and apply logical thought processes. They push AI closer to being not just a powerful tool, but a genuine collaborator – a system that can analyze, explain, and think through problems in complex, meaningful ways. As research continues to accelerate, one thing is clear: the future of AI won’t just be about what it can generate or predict, but what it can truly understand and reason about.

Iryna Tkachenko, marketing manager