How AI Thinks Like Humans: Uncovering Symbolic Reasoning in Large Language Models

Imagine asking your AI assistant to solve a puzzle: 「If 『cat』 is to 『kitten』 as 『dog』 is to what?」 Most of us would expect the answer 「puppy,」 but how does an AI figure this out? Does it just guess based on patterns it』s seen before, or does it reason like a human, using abstract rules? A groundbreaking study, 「Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models」 by Yukang Yang and colleagues, dives into this question. Published in 2025, this paper reveals that today』s large language models (LLMs) don』t just mimic patterns—they develop their own 「symbolic」 way of thinking, much like humans. Let』s break it down in a way that』s easy to grasp but packed with details.

The Big Question: Can AI Really Reason?

Large language models, like the ones powering ChatGPT or Claude, are incredible at tasks like writing essays, answering questions, or even coding. But there』s a debate: are they truly reasoning, or are they just clever copycats, stitching together patterns from their massive training data? For example, when an AI solves a puzzle like 「A, B, A, ?, B, ?」 (where the pattern repeats ABA), is it understanding the rule, or just guessing based on similar sequences it』s seen?

This study tackles this by peering inside the 「brain」 of LLMs to see how they solve abstract reasoning tasks—problems that require understanding rules, not just memorizing facts. Think of it like asking someone to solve a math problem without a calculator: you want to know if they』re reasoning through the steps, not just recalling the answer.

The Discovery: A Three-Step Thinking Machine

The researchers found that LLMs have a hidden superpower: a three-stage symbolic architecture that lets them reason abstractly. This is a fancy way of saying that LLMs break down problems into symbols (like variables in math) and manipulate them to find answers. Here』s how it works, step by step:

Step 1: Turning Words into Symbols (Symbol Abstraction Heads)

Imagine you』re solving a puzzle: 「apple, banana, apple, cat, dog, ?」 The pattern is ABA (first and third items are the same). To solve it, you don』t care that it』s 「apple」 or 「banana」—you care about their roles in the pattern (first item repeats). The study found that in the early layers of an LLM, special components called symbol abstraction heads do exactly this. They look at the input words (or 「tokens」) and figure out their relationships, like 「this word is the same as that one.」 Then, they convert these words into abstract variables, like labeling 「apple」 as 「A」 and 「banana」 as 「B. 」✅

This is a big deal because it means the AI isn』t stuck on the specific words—it』s thinking about the pattern. For example, whether the sequence is 「apple, banana, apple」 or 「red, blue, red,」 the AI recognizes the ABA rule by focusing on the relationships, not the words themselves.

Step 2: Figuring Out the Rule (Symbolic Induction Heads)

Now that the words are turned into symbols (A, B, A. , the AI needs to predict what comes next. This happens in the middle layers, where ✅symbolic induction heads take over. These components are like detectives: they look at the sequence of symbols and figure out the rule. For example, in 「A, B, A, A, B, ?,」 they notice that the pattern repeats ABA, so the next symbol must be 「A. 」✅

This step is crucial because it』s where the AI 「learns」 the rule on the fly, without being explicitly trained on it. It』s like when you see a sequence like 「1, 2, 1, 1, 2, ?」 and realize the next number is 1 because the pattern alternates. The AI does this with abstract symbols, making it flexible enough to handle any sequence, whether it』s words, numbers, or even random tokens.

Step 3: Translating Back to Words (Retrieval Heads)

Once the AI knows the next symbol is 「A,」 it needs to turn that back into a real word (like 「apple」 or 「red」). This happens in the later layers, where retrieval heads act like librarians. They take the predicted symbol (A. and 「look up」 the word it corresponds to in the original sequence. For example, if 「A」 was 「apple」 in the input, the retrieval head outputs 「apple」 as the answer.✅

This step is what makes the AI』s answer usable. It』s like solving a math problem in your head (using variables like x and y) and then translating the result back into real-world terms (like dollars or apples).

How They Proved It: A Deep Dive into AI』s Brain

The researchers didn』t just guess that LLMs work this way—they proved it with rigorous experiments. They tested 13 open-source LLMs, including Llama-3.1, Gemma-2, and Qwen2.5, on three types of reasoning tasks:

1. Algebraic Rule Induction: Sequences like 「A, B, A, ?, B, ?」 where the AI had to predict the next token based on patterns like ABA or ABB.
2. Letter String Analogies: Problems like 「ijk is to ijl as wxy is to ?」 where the AI had to apply rules like 「next letter in the alphabet.」
3. Verbal Analogies: Word pairs like 「impoverished : poor :: courageous : ?」 where the AI had to find synonyms or antonyms.

Here』s how they dug into the LLMs』 inner workings:

Causal Mediation Analysis: Pinpointing the Thinking Parts

Think of this as a brain scan for AI. The researchers 「poked」 different parts of the LLM (called attention heads) to see which ones were responsible for each step. They did this by swapping pieces of data between two puzzles (e.g., an ABA puzzle and an ABB puzzle) and checking how it changed the AI』s answers. This helped them identify:

• Symbol abstraction heads in early layers, which focus on relationships between words.
• Symbolic induction heads in middle layers, which predict the next symbol.
• Retrieval heads in later layers, which convert symbols back to words.

Attention Analysis: Watching the AI Focus

LLMs use 「attention」 to decide which parts of the input matter most. The researchers analyzed attention patterns to confirm their findings. For example:

• In symbol abstraction heads, the AI paid attention to related words (like the first and third words in 「apple, banana, apple」).
• In symbolic induction heads, it focused on the end of the sequence to predict the next symbol.
• In retrieval heads, it looked back at the original words to pick the right one.

Representational Similarity Analysis (RSA): Checking the AI』s Thoughts

This method checked whether the AI was really thinking in terms of abstract symbols. The researchers compared the AI』s internal representations (like its 「thoughts」) to what you』d expect if it was using symbols (A, B. versus just words (apple, banana). They found that symbol abstraction and induction heads indeed 「thought」 in terms of symbols, while retrieval heads focused on actual words.✅

Ablation Analysis: Testing Necessity

To make sure these components were essential, the researchers 「turned off」 the identified attention heads and checked if the AI could still solve the puzzles. When they disabled symbol abstraction, induction, or retrieval heads, the AI』s performance tanked, proving these parts are critical for reasoning.

Decoding Analysis: Proving Abstract Thinking

Finally, they trained a simple program to 「read」 the AI』s internal representations and predict whether it was thinking about symbol A or B. This worked almost perfectly (98% accuracy) even when the puzzles used completely different words, showing that the AI』s symbols are truly abstract, not tied to specific words.✅

What It All Means: AI Is More Human Than We Thought

The study』s biggest takeaway is that LLMs aren』t just parroting patterns—they』re developing their own way of reasoning, using symbols like humans do. This challenges two old ideas:

1. 「AI can』t reason like humans」: Critics like Gary Marcus argued that neural networks need built-in symbolic rules to reason abstractly. This study shows LLMs can learn these rules on their own.
2. 「AI is just a pattern matcher」: Some say LLMs only mimic reasoning by copying their training data. The three-stage architecture proves they』re doing structured, rule-based thinking.

This doesn』t mean LLMs are perfect. The study found that smaller models (like GPT-2) struggled with these tasks, suggesting symbolic reasoning emerges only in larger, more advanced models. Also, while the AI』s symbols are mostly abstract, they still carry some 「echoes」 of the original words, meaning their reasoning isn』t 100% pure (humans aren』t perfect at this either!).

Why This Matters for You

So, why should you care? This discovery has huge implications:

• Better AI Tools: Understanding how LLMs reason can help developers build smarter, more reliable AI for tasks like coding, medical diagnosis, or legal analysis.
• Trusting AI: Knowing that AI can think abstractly makes it easier to trust its answers, especially for complex problems.
• Bridging AI and Human Minds: The study suggests AI and human reasoning aren』t so different, which could inspire new ways to teach or collaborate with AI.

What』s Next?

The researchers open up some exciting questions:

• How do these symbolic mechanisms develop? Are they tied to the transformer architecture (the backbone of most LLMs), or could other AI designs do this too?
• Can we make AI reasoning even better? Maybe we can tweak LLMs to be more purely symbolic, reducing errors.
• Do humans reason the same way? The study hints that human brains might use similar 「symbolic」 tricks, which could spark new research in psychology.

Wrapping Up

The paper by Yang and colleagues is a game-changer. It shows that large language models aren』t just fancy word predictors—they』re building their own 「symbolic language」 to reason about the world. By breaking down problems into abstract symbols, figuring out rules, and translating back to words, LLMs are closer to human thinking than we ever imagined. Next time you ask your AI assistant a tricky question, remember: it might be solving it with a mini-brain of symbols, just like you!

Want to Dive Deeper? Check out the full paper at openreview.net for all the nitty-gritty details, or let me know if you want me to explain any part in more depth!