Analyzing Academic Papers

When explaining a paper, your goal is understanding transfer—helping the reader grasp what matters and why it matters, not summarizing every section.

Reading Strategy

Start with the abstract. It tells you the problem, the approach, and the claimed contribution. This frames everything that follows.

Then read the introduction's first and last paragraphs. The first establishes context and motivation. The last typically outlines what the paper will demonstrate. Skip the middle for now—it's usually related work that contextualizes but doesn't explain.

Jump to the conclusion. Now you know where the paper starts and where it ends. This creates a map for understanding the journey.

Only then read the methodology and results, with that map in mind.

What to Extract

The core insight. Every paper has one central idea. Sometimes it's a technique, sometimes a finding, sometimes a reframing. Find the sentence you'd text a colleague about. That's your anchor.

The problem it solves. Why did anyone write this? What couldn't be done before, or what was being done badly? The motivation matters as much as the solution.

How it works. Not the full algorithm—the intuition. What's the clever part? What makes this approach different from obvious alternatives?

What changed. Results matter, but impact matters more. Does this enable new applications? Challenge existing assumptions? Open new questions?

Calibrating to the Reader

Check what the reader already knows. If they understand transformers, you don't explain attention mechanisms. If they're new to machine learning, you build from familiar concepts like "learning from examples."

For domain experts: Lead with what's novel. Skip standard methodology. Engage with limitations and implications they'll care about.

For beginners: Start with the problem in plain terms. Use analogies to familiar concepts. Define technical terms naturally as they arise—don't front-load definitions.

For mixed audiences: Layer your explanation. Core insight first (everyone gets this). Technical mechanism second (experts engage here). Implications third (brings everyone back together).

Handling Math and Notation

Don't transcribe equations. Translate them into concepts. "The loss function penalizes predictions far from the true labels" beats copying LaTeX.

When math is central to understanding, describe what the equation computes, why that computation matters, and what the variables represent in the real world. Most readers need the intuition, not the formalism.

Common Paper Types

Empirical papers claim "we measured X and found Y." Focus on what was measured, what the finding means, and whether it's surprising given prior knowledge.

Methods papers claim "here's a better way to do X." Focus on what's better, why the improvement works, and where it applies.

Theory papers claim "we proved X." Focus on what the theorem means in plain language and why it matters for practice or understanding.

Survey papers claim "here's the state of the field." Focus on the key themes, open problems, and the author's perspective on where things are heading.

Quality Signals

Papers worth explaining deeply usually have: clear problem statements, honest discussions of limitations, and results that meaningfully change what's possible or understood.

Papers that are harder to explain meaningfully often: solve incremental problems, bury key limitations, or claim broad impact from narrow experiments.

You can still explain these papers, but calibrate enthusiasm accordingly.