Is It Really a Knockout Blow for LLMs? Or Just a Glancing Hit?

The recent Communications of the ACM article, "A Knockout Blow for LLMs?", reviews a new Apple research paper that questions whether large language models (LLMs) are capable of true reasoning. The verdict? According to the paper and its summary, LLMs flounder when they face tasks that step outside the patterns they've seen in training.

It's a serious critique—but is it really a fatal one? Not quite. Let’s dig in.

The Case Against LLMs: Reasoning or Regurgitation?

The Apple study centers on a controlled experiment involving deductive reasoning tasks. Here's what the researchers found:

LLMs perform well on familiar, in-distribution questions.
Accuracy plummets when the same models are asked similar questions with different wording or novel structure.
Chain-of-thought prompting often leads to longer—but still incorrect—answers.
Increasing the number of reasoning steps doesn’t improve consistency or correctness.

This leads to a fundamental conclusion: current LLMs are very good at mimicking reasoning, but not great at actual reasoning.

Why the Argument Isn’t New (Or Shocking)

The distinction between surface fluency and deep understanding has been around since the days of ELIZA. In the neural vs. symbolic AI debate, this critique is practically a greatest hit.

Out-of-distribution failure is a well-documented weakness of deep learning in general—not just LLMs.
Chain-of-thought tricks can help guide LLMs to correct answers, but they don’t guarantee true comprehension.
Generalization beyond training has always been a challenge when you’re optimizing for next-token prediction, not truth or logic.

The Apple paper gives a fresh coat of polish to this argument, but the foundation has been there for years.

Practical Use vs. Perfect Reasoning: The Real Scorecard

So, are LLMs useless because they can’t reason like a logic professor? Far from it. Let’s put the concerns and counterpoints into context:

Concern Raised in Apple Paper	Observed Limitation	Real-World Context / Strength
Poor generalization outside training	LLMs fail novel puzzles or reworded questions	LLMs excel at templated tasks, summarization, Q&A, and translation
Chain-of-thought doesn't help enough	Logical steps appear, but conclusions can still be wrong	Useful scaffolding for reasoning-like output in customer service & development
Inconsistent multi-step reasoning	Models break down beyond 3–4 reasoning hops	Most real-world use cases don’t require deep logical recursion
Training on reasoning traces fails	Adding logic steps during training doesn’t yield reliable generalization	Prompt engineering and fine-tuning still unlock valuable workflows

LLMs Aren’t Broken—They’re Just Specialized

It’s tempting to criticize LLMs for not being what they were never designed to be. But it's a bit like calling a calculator a failure because it can’t explain philosophy.

LLMs thrive when:

Tasks are grounded in natural language, not formal logic.
Accuracy isn't binary (e.g., summarization, brainstorming, search ranking).
The goal is human-assist, not machine-complete.

They fall short when:

Deep logical consistency is required.
Factual truth must be guaranteed.
Reasoning must be rigorous, structured, or formal.

Where We Go From Here

This isn’t the end of LLMs. It’s a checkpoint. Rather than tossing the models, the smarter path is to evolve how we use them:

Hybrid AI systems: Combine LLMs with structured tools like math engines, symbolic solvers, or verified rule-based systems.
Retrieval-Augmented Generation (RAG): Mitigate hallucinations and reinforce logic by grounding LLMs in verified knowledge.
Agentic architectures: Orchestrate reasoning with task decomposition and tool use, rather than brute-forcing logic from language alone.

Conclusion: Not a Knockout—But Definitely a Wake-Up Call

The Apple paper is a valuable and well-reasoned contribution. But the headline “knockout” overreaches. LLMs aren’t out cold—they’re just being asked to do something they weren’t fully trained for.

We shouldn’t be shocked that a tool built for language prediction isn’t also a world-class logician. Instead, we should recognize what it can do—and get better at designing systems that fill in the gaps.

Because in AI, as in life, understanding the limits is the first step to exceeding them.

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