How LLMs Really Think: The Guess → Refine Framework

• New research from UC Berkeley & Georgia Tech uncovers how LLMs use depth to build understanding.
• Models follow a Guess → Refine process:
  • Early layers make high-frequency token guesses.
  • Later layers refine them with context and meaning.
• Over 70% of early guesses are replaced before final output.
• Practical takeaway: Use adaptive-depth inference — go shallow for easy spans, deeper for hard reasoning.

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Understanding the Question: How Do LLMs Use Depth?

When we visualize a transformer, we often think of stacked computation blocks — identical layers repeating the same operation.

But in practice, each layer contributes differently to the model’s reasoning.

The paper How Do LLMs Use Their Depth? (Gupta et al., 2025) reveals that LLMs don’t predict tokens all at once.

Instead, they move through a two-phase reasoning process across depth:

“Early layers propose. Later layers reason.”

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Phase 1: The Guess Stage

These layers operate like a fast heuristic engine — surfacing likely tokens based purely on corpus-level frequency.

At this stage, there’s minimal contextual awareness; the model isn’t reasoning yet.

Using TunedLens, the researchers tracked when tokens climb to top rank during forward passes.

They observed that early layers often “guess wrong” — setting placeholders that deeper layers later revise.

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Phase 2: The Refine Stage

As we move deeper into the stack, the model shifts from statistics to context integration.

Representations evolve from surface-level probability to semantic coherence.

Here’s what happens:

• Context tokens begin interacting through attention consolidation.
• Token rankings fluctuate as the model weighs syntax, semantics, and global meaning.
• Function words stabilize early; content-heavy tokens finalize much later.

In fact, the research shows:

“For multi-token facts, the first token is often the hardest — and emerges latest.”

This explains why deep layers are essential for accurate reasoning and factual recall.

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🧠 Implications for Practitioners

Early Exit ≠ Efficiency

Some inference optimizations attempt to “early exit” — halting computation if the model seems confident mid-way.

But this research warns that such exits truncate the refinement phase, leading to higher semantic errors.

Adaptive Depth = Smart Compute

Rather than exiting early, design depth-aware routing:

• Use shallow passes for function words or short completions.
• Allocate deeper passes for reasoning-heavy or rare tokens.
• Cache stabilization states to reduce redundant recomputation.

Interpretability Gains

Tracking when a token stabilizes gives visibility into the model’s “thinking process.”

Developers can pinpoint:

• Which layers drive final decisions
• Where contextual understanding truly begins
• How hallucinations might emerge mid-stack

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Example: Depth-Aware Inference Design

def adaptive_forward(model, input_tokens, threshold=0.95):
    """
    Runs a forward pass with adaptive-depth routing.
    Stops once all token logits stabilize beyond a confidence threshold.
    """
    prev_logits = None
    for layer_idx, layer in enumerate(model.layers):
        output = layer(input_tokens)
        logits = model.head(output)

        if prev_logits is not None:
            stability = (logits.softmax(-1) * prev_logits.softmax(-1)).sum(-1).mean()
            if stability > threshold:
                print(f"Stopping early at layer {layer_idx}")
                return logits

        prev_logits = logits
    return logits***

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At Cognilum AI, we explore how large language models really think — from token-level dynamics to adaptive reasoning architectures.

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