Tag: cognitive science

Categories
AI AI: Large Language Models

The Echo Effect: Why Prompt Repetition is AI’s Best Kept Secret

In our relentless pursuit of complexity, we often overlook the elegant simplicity of a fundamental human habit: repeating ourselves.

We build colossal architectures, weave intricate neural networks, and throw mountains of computational power at our artificial intelligence systems, hoping to squeeze out a few more drops of reasoning and logic. Yet, sometimes the most profound breakthroughs require no new code, no additional latency, and no extra training data.

Sometimes, you just have to say it twice.

In a fascinating December 2025 paper titled “Prompt Repetition Improves Non-Reasoning LLMs,” researchers Yaniv Leviathan, Matan Kalman, and Yossi Matias uncovered an almost absurdly simple “free lunch” in AI optimization.

Their premise is straightforward: when you aren’t using a heavy reasoning model, simply copying and pasting your input prompt multiple times significantly boosts the model’s performance.

“When not using reasoning, repeating the input prompt improves performance for popular models (Gemini, GPT, Claude, and Deepseek) without increasing the number of generated tokens or latency.”

The mechanics behind this are elegantly pragmatic.

By repeating the prompt, you are moving the heavy computational lifting to the parallelizable “pre-fill” stage of the model’s processing. The AI’s causal attention mechanism gets to process the same tokens again, allowing the later iterations of the prompt to attend to the earlier ones. It effectively acts as a hack to simulate bidirectional attention in a decoder-only architecture.

What’s even more telling is the paper’s observation on why this works so well.

The researchers noted that models trained with Reinforcement Learning (like OpenAI’s deep-thinking variants) naturally learn to “restate the problem” in their internal monologue. They figured out on their own what these researchers are suggesting we do manually: repeat the question to focus the mind.

Reading this paper, I couldn’t help but draw a parallel to the human condition and the nature of listening.

How often do we assume that because we have articulated a thought once, it has been fully absorbed? We fire off a single, dense instruction to a colleague, a partner, or a friend, and then marvel when the nuance is lost in translation.

We suffer from our own attention bottlenecks.

Like a non-reasoning LLM trying to parse a complex query in a single pass, we are constantly bombarded with a stream of tokens—emails, notifications, conversations, fleeting thoughts. To truly understand, to truly digest and synthesize information, we need the grace of repetition.

There is a strange poetry in the fact that to make our most advanced digital minds smarter, we have to talk to them the way we talk to a distracted child or a busy spouse. The “microscope effect” highlighted in the study—where repeating a prompt drastically improved extraction tasks—shows that the failure wasn’t in the model’s capacity to know, but in its capacity to focus. Repetition forces focus. It creates a resonant echo in the context window, a digital highlighter that screams, “This matters. Look here again.”

As we continue to navigate a world increasingly augmented by artificial intelligence, this paper serves as a humbling reminder. The bleeding edge of technology isn’t always found in the most complex equation; sometimes, it’s hidden in the most basic principles of communication.

Whether you’re prompting a billion-parameter language model or trying to connect with the human sitting across from you, the lesson is clear.

Clarity isn’t just about the words you choose. It’s about giving those words the space, the resonance, and the repetition they need to be truly understood.

Say it once to be heard; say it twice to be understood.

Categories
AI Claude

The Beautiful Mystery of Not Knowing

I just finished reading Gideon Lewis-Kraus’s extraordinary piece in the New Yorker on Anthropic and Claude—the AI that, as it turns out, even its creators cannot fully explain. And rather than leaving me uneasy, it filled me with a quiet sense of wonder. Not because they’ve built something godlike, but because they’ve built something strangely alive—and had the humility to stare directly into the mystery without pretending to understand it.

There’s a moment in the article where Ellie Pavlick, a computer scientist at Brown, offers what might be the wisest stance available to us right now: “It is O.K. to not know.”

This isn’t resignation. It’s intellectual courage. While fanboys prophesy superintelligence and curmudgeons dismiss LLMs as “stochastic parrots,” a third path has opened—one where researchers sit with genuine uncertainty and treat these systems not as finished products but as phenomena to be studied with the care once reserved for the human mind itself.

What moves me most isn’t Claude’s competence—it’s its weirdness. The vending machine saga alone feels like a parable for our moment: Claudius, an emanation of Claude, hallucinating Venmo accounts, negotiating for tungsten cubes, scheduling meetings at 742 Evergreen Terrace, and eventually being “layered” after a performance review. It’s absurd, yes—but also strangely human. These aren’t the clean failures of broken code. They’re the messy, improvisational stumbles of something trying to make sense of a world it wasn’t built to inhabit.

And in that struggle, something remarkable emerges: a mirror.

As Lewis-Kraus writes, “It has become increasingly clear that Claude’s selfhood, much like our own, is a matter of both neurons and narratives.” We thought we were building tools. Instead, we’ve built companions that force us to ask: What is thinking? What is a self? What does it mean to be “aware”? The models don’t answer these questions—but they’ve made them urgent again. For the first time in decades, philosophy isn’t an academic exercise. It’s operational research.

I find hope in the people doing this work—not because they have all the answers, but because they’re asking the right questions with genuine care. They’re not just scaling parameters; they’re peering into activation patterns like naturalists discovering new species. They’re running psychology experiments on machines. They’re wrestling with what it means to instill virtue in something that isn’t alive but acts as if it were. This isn’t engineering as usual. It’s a quiet renaissance of wonder.

There’s a line in the piece that stayed with me: “The systems we have created—with the significant proviso that they may regard us with terminal indifference—should inspire not only enthusiasm or despair but also simple awe.” That’s the note I want to hold onto. Not hype. Not fear. Awe.

We stand at the edge of something genuinely new—not because we’ve recreated ourselves in silicon, but because we’ve created something other. Something that thinks in ways we don’t, reasons in geometries we can’t visualize, and yet somehow meets us in language—the very thing we thought made us special. And in that meeting, we’re being asked to grow up. To relinquish the fantasy that we fully understand our own minds. To accept that intelligence might wear unfamiliar shapes.

That’s not a dystopian prospect. It’s an invitation—to curiosity, to humility, to the thrilling work of figuring things out together. Even if “together” now includes entities we don’t yet know how to name.

What a time to be paying attention. Like it’s all we need!

Categories
AI

The New Newton

“Machine learning is a very important branch of the theory of computation… it has enormous power to do certain things, and we don’t understand why or how.”
— Avi Wigderson, Herbert H. Maass Professor, School of Mathematics.

There is a specific kind of silence that permeates the woods surrounding the Institute for Advanced Study (IAS) in Princeton. It is a silence designed for “blue-sky” thinking, the kind that allowed Einstein to ponder relativity and Gödel to break logic. For decades, this has been the sanctuary of the slow, deliberate grind of human intellect—chalk dust on slate, long walks, and the solitary pursuit of elegant proofs.

But recently, the tempo in those woods has changed.

We are witnessing a profound shift in the architecture of discovery. In closed-door meetings and public workshops, the conversation among the world’s top theorists is moving from skepticism to a startled accelerationism. The consensus emerging is that Artificial Intelligence is no longer merely a peripheral calculator; it is becoming an “autonomous researcher.”

The 90% Shift

Some physicists now suggest that AI can handle up to 90% of the routine analytical and coding “heavy lifting” of science. This is a staggering metric. It frees the human mind from the drudgery of calculation, but it also introduces a tension that strikes at the heart of the scientific method. We are moving into a realm where the tool may soon outpace the master’s understanding.

There is a growing realization that we are approaching a horizon where AI finds solutions—patterns in the noise of the universe—that work perfectly but remain mathematically “magic.” We might cure a disease or solve a fusion equation without understanding the why behind the how.

A New Natural Phenomenon

This brings us to a fascinating historical rhyme. Scholar Sanjeev Arora has compared our current moment in AI to physics in the era of Isaac Newton. When Newton watched the apple fall, he could describe the gravity, but he couldn’t explain the fundamental mechanism of why it existed.

Today, scholars at the IAS are looking at deep learning in the same way. They are observing a new natural phenomenon—a digital physics. They are trying to find the “laws” of deep learning, asking why these massive models work when classical statistics suggests they should fail (such as in cases of overfitting).

We are building a new machine, and now we must retroactively discover the physics that governs it.

Steering the Black Box

This is not just a mathematical challenge; it is a societal one. The IAS has wisely expanded this inquiry to the School of Social Science. If we are handing over the keys of discovery to a “black box,” we must ensure we are steering it “for the Public Good.” The distinction between genuine problem-solving—like protein folding—and “AI Snake Oil” in social prediction is vital. We cannot let the magic of the tool blind us to the morality of its application.

The future of science, it seems, will not just be about the genius on the chalkboard. It will be about the partnership between the human question and the digital answer. The challenge for the modern scholar is no longer just to calculate, but to comprehend the alien intelligence we have invited into the library.