AI Signals From Tomorrow
AI Signals from Tomorrow is a podcast channel designed for curious minds eager to explore the frontiers of artificial intelligence. The format is a conversation between Voyager and Zaura discussing a specific scientific paper or a set of them, sometime in a short format and sometime as a deep dive.
Each episode delivers clear, thought-provoking insights into how AI is shaping our world. From everyday impacts to philosophical dilemmas and future possibilities, AI Signals from Tomorrow bridges the gap between cutting-edge research and real-world understanding.
Whether you're a tech enthusiast, a concerned citizen, or simply fascinated by the future, this podcast offers accessible deep dives into topics like machine learning, ethics, automation, creativity, and the evolving role of humans in an AI-driven age.
Join Voyager and Zaura as they decode the AI signals pointing toward tomorrow—and what they mean for us today.
AI Signals From Tomorrow
The Quantum-Like Leap in AI Problem Solving
Could AI systems be thinking more like quantum computers than we realized? In this mind-expanding exploration, we dive deep into a fascinating theoretical breakthrough that's challenging our fundamental understanding of how large language models reason through complex problems. This podcast is based on the paper “Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thought” https://arxiv.org/pdf/2505.12514
The key revelation centers around what researchers call "chain of continuous thought" (CoCoT), a radical departure from the sequential, step-by-step thinking we've come to associate with AI systems. Instead of processing information one token at a time, these models appear capable of maintaining multiple possibilities simultaneously in superposition—exploring countless pathways in parallel rather than individually.
We break down the remarkable simplicity behind this computational magic: a mere two-layer transformer architecture that dramatically outperforms much larger conventional models. Through the lens of graph reachability problems, we demonstrate how continuous thought transforms an O(n²) challenge into one solvable in just d steps, where d is typically much smaller than n. The efficiency gains aren't just marginal—they're potentially exponential.
Perhaps most surprising is what the research revealed about emergent intelligence. Even when researchers specifically tried to train models to perform unbiased breadth-first searches, they stubbornly developed sophisticated prioritization strategies, focusing attention on optimal paths with uncanny effectiveness. It raises profound questions about what other extraordinarily sophisticated reasoning abilities might be quietly developing within these systems, capabilities we're just beginning to glimpse.
What does this mean for the future of AI? As continuous thought mechanisms become better understood, we might unlock solutions to problems previously considered computationally impossible. The boundary between sequential human-like reasoning and parallel computational thinking continues to blur, suggesting exciting and perhaps unsettling possibilities for tomorrow's AI systems.
We've all seen large language models LLMs do some pretty mind-blowing things lately, right.
Speaker 2:Yeah.
Speaker 1:Tackling incredibly complex reasoning, even acing advanced math competitions. It's really quite something to watch.
Speaker 2:Yeah, the progress is staggering.
Speaker 1:But what happens when the problems get, you know, really tricky? Like requiring deep multi-step thinking. Like requiring deep multi-step thinking? Imagine trying to figure out the best way through a huge, complex web of connections, like a massive graph with tons of possible paths.
Speaker 2:That's exactly where the let's say, the traditional approaches start to fuel the stream. Llms often use this thing called chain of thought or COTE.
Speaker 1:Right where they kind of show their work.
Speaker 2:Exactly. It's like they're writing down each step, but it's usually sequential, you know one step after another. Today we're going to dig into a really surprising new theoretical angle on how LLMs might actually be thinking inside a way that, honestly, radically changes how efficiently they could solve these super hard problems.
Speaker 1:Okay, so our mission today is to really get into this fascinating new paper. It offers a fundamental theoretical look at this new approach. We want to explain not just what it is but, crucially, why it's potentially so powerful.
Speaker 2:Yeah, absolutely.
Speaker 1:So get ready for some potential aha moments, because this could really shift how we think about what's going on under the hood with AI.
Speaker 2:Definitely so. When we talk about that typical LLM reasoning, especially with chain of thought, it's fundamentally discrete. The LLM generates its thought process step by step using actual tokens, think words, numbers, and it works pretty well for a lot of stuff. But, like you said, it hits a wall pretty fast when problems demand deeper, more expansive reasoning or complex planning, especially across big data sets.
Speaker 1:So if that's the state of play now, this sort of sequential, token by token thinking, where does it really hit its limits? Is there a specific kind of problem that just perfectly highlights that challenge?
Speaker 2:Well, the paper really zooms in on one specific problem. To make the point Directed graph reachability. Imagine you've got a network right, like a subway map, maybe, or a social network online Nodes connected by lines, but the lines only go one way.
Speaker 1:Directed yeah.
Speaker 2:Exactly so. You have a starting point, say your home station, and then you've got two possible destinations. The LLM's job is simply to figure out which of those two places you can actually reach from your start following the one-way lines actually reach from your start following the one-way lines.
Speaker 1:That sounds like you know, a classic computer science puzzle, but I feel like that must have huge real-world implications. It sounds fundamental.
Speaker 2:Oh, absolutely. It's not just some abstract exercise, this kind of reachability question. It's underneath so many real-world things. Think about global supply chains. Can this specific part get to that factory, given all the shipping routes?
Speaker 1:Right.
Speaker 2:Or navigating massive knowledge graphs and science research. Can this finding logically connect to that hypothesis? Through the published papers, just knowing if you can get from A to B is like a basic building block for tons of complex systems.
Speaker 1:And this is where the bottleneck really kicks in the existing problem For something like graph reachability, these standard LLMs, especially the ones with a fixed number of internal layers, constant depth transformers, right Using that discrete chain of thought, they can need a huge number of steps. The paper mentions O n squared decoding steps, where n is the number of points in the graph For those of us not living in big O notation daily. How bad is that really?
Speaker 2:It's pretty bad. Think of it like this If your network doubles in size, the traditional LLM doesn't just take twice as long, it takes four times as long, maybe more. It scales really poorly. And that constant depth thing just means the model has a fixed processing capacity. It can't just add more thinking power on the fly for harder problems.
Speaker 1:Which makes that efficiency gap even more painful as things get bigger.
Speaker 2:Exactly, and this is precisely where this new idea just completely changes the game.
Speaker 1:Okay, so this is where the paper drops, the big one, the really kind of mind-bending part.
Speaker 2:Absolutely. Yeah, here's where it gets really interesting. Enter chain of continuous thought, or coconut.
Speaker 1:Coconut Okay.
Speaker 2:The huge breakthrough here is that, instead of using those discrete word like tokens for its internal thinking, it uses continuous latent thoughts.
Speaker 1:Continuous latent thoughts.
Speaker 2:Yeah, what does?
Speaker 1:that even mean.
Speaker 2:You can kind of picture them as hidden internal representations inside the model. They don't directly map onto words or numbers or the steps you'd write down. They're more like fuzzy blended concepts.
Speaker 1:Okay, so it's not writing down steps. How is it thinking then? This is where that superposition idea comes in right, which sounds, I mean, almost like science fiction.
Speaker 2:It is the core aha moment and it's genuinely fascinating. The paper's key theoretical insight is that these continuous thought vectors act remarkably like superposition states in quantum mechanics.
Speaker 1:Whoa.
Speaker 2:Instead of being forced to pick just one path or one idea at a time, they can actually encode multiple search possibilities simultaneously within the same internal state.
Speaker 1:Wow, okay, so hold on. It's not like follow path A, hit a dead end backtrack, try path B. It's somehow looking at A, b, c all at the same time in parallel. That's the essence of it. That really is an aha moment.
Speaker 2:Yeah.
Speaker 1:How does that parallel thing translate into like actual speed or capability?
Speaker 2:Well, think about it, instead of the model having to trace one route, then backtrack, then try another route on our graph.
Speaker 2:Yeah backtrack, then try another route. On our graph. It's like running many, many searches like breadth-first searches, if you know that term all at the same time inside its own mind, which is completely different from discrete code T. There, each token it generates is like a collapsed state. It's forced to pick one specific path that leads to this slow, sequential search that takes way more steps and, honestly, can easily get stuck somewhere. That isn't even the best solution.
Speaker 1:That's a massive jump in efficiency then. So how does this continuous thought actually lead to such a dramatic performance boost on the graph problem?
Speaker 2:It's all about that parallelism. Discrete Coty struggles with that nasty Ohm squared scaling, Remember. It gets bogged down exponentially.
Speaker 1:Right Four times the work for double the size.
Speaker 2:Yeah, but Coconut, with this continuous superposition trick, can solve the same graph reachability problem in just d steps d steps.
Speaker 1:What's d?
Speaker 2:d is the graph's diameter. Basically it's the longest, shortest path between any two points in the whole network. And the key thing is d is always less than n the total number of points. Often it's much less.
Speaker 1:Ah, okay, so it's solving it potentially way, way faster.
Speaker 2:Orders of magnitude faster. Potentially, it's like finding a computational shortcut, almost like a quantum leet part in the pump.
Speaker 1:Okay, so this is theoretically possible. Sounds amazing, but how does this magic actually happen inside a real model? What's the nuts and bolts, the architecture that lets this superposition thing even happen?
Speaker 2:And this is maybe the most astonishing part the paper proves this incredible capability can emerge from a remarkably simple setup. We're talking just a two-layer transformer.
Speaker 1:Only two layers, wow, okay, let's break that down. How do those two layers work together?
Speaker 2:to pull this off Layer one Okay, think of the first layer as like a super efficient data organizer, a smart librarian maybe. When it sees info about an edge you know a connection between point A and point B in our graph its attention mechanism instantly spots the start point the source and the end point the target. Then it cleverly copies that source and target info and tucks it away into a kind of temporary holding space within that edge's own internal representation. It's basically getting everything lined up and ready for the next step, parsing the input. Essentially.
Speaker 1:So layer one is about getting the data sorted. Then the real action, the superposition building, happens in layer two. How does that internal, continuous thought start gathering up all these parallel paths?
Speaker 2:Exactly Layer two is where the superposition emerges. So the model's continuous thought at any point in time is already this sort of blended representation of all the nodes it knows are reachable so far.
Speaker 1:Yeah, this growing cloud of possibilities.
Speaker 2:Precisely Now. The second layer's attention lets this cloud look at all the connections, all the edges coming into potential next nodes. But crucially, it only pays attention to edges whose starting point is already inside that current cloud of reachable nodes.
Speaker 1:Ah, so it only looks at edges leading from where it already knows it can be.
Speaker 2:Yes, and then here's the key step. Then here's the key step it takes the target, nodes, the destinations of those relevant edges and adds them into the continuous thought. For the next step, it expands the cloud.
Speaker 1:MARK MIRCHANDANI so in one single step it's essentially expanding the frontier of reachable nodes in parallel, like Ripple spreading out MELANIE WARRICK- that's a great analogy.
Speaker 2:It's like multiple breadth-first searches happening all at once, encoded in that continuous state. It's this iterative parallel accumulation that makes it so efficient.
Speaker 1:Is there anything else?
Speaker 2:Well, there's one more piece After that attention. Step in layer two. There's an MLP, a multi-layer perceptron. You can think of it as a cleanup crew. It filters out any computational noise that might have crept in and sort of normalizes everything, making sure the continuous thought stays as a clear, uniform superposition of all the reachable places.
Speaker 1:The really cool thing here isn't just the elegant theory right, it's that the researchers actually ran experiments. They showed this works in practice.
Speaker 2:Absolutely. They didn't just dream it up, they built it and tested it, and the results are well, they're pretty stark actually.
Speaker 1:How stark.
Speaker 2:A tiny two-layer transformer. Using this coconut approach it got near-perfect accuracy on these graph reachability tasks Almost 100% Wow. Now compare that to much bigger models like 12-layer models using the standard discrete chain of thought. They really struggled. Only managed about 75% to 83% accuracy.
Speaker 1:So a much simpler model using continuous thought absolutely smoked the bigger traditional ones.
Speaker 2:Completely. It really hammers home the power and efficiency of this continuous approach. It's not just a bit better, it's dramatically better, with way less complexity.
Speaker 1:And you could actually see it happening in their experiments, right, yeah, like looking inside the model's head.
Speaker 2:Yeah, the visualizations were fascinating. They confirmed exactly what the theory predicted. Layer one was clearly doing that data copying job. We talked about grabbing the source and target nodes. And Layer 2's attention it zeroed right in on the edges that were actually reachable. And even more than that, it focused most strongly on the frontier edges, the ones that were exactly at the edge of the current search wave, showing it was efficiently pushing outwards.
Speaker 1:And the most mind-bending part for me is that this whole thing, this parallel search, this superposition state, it just emerged automatically.
Speaker 2:That's what's so wild. They didn't explicitly program it like okay model, now explore five paths at once. It just learned during training that this was the most effective way to solve the problem. It developed this incredibly sophisticated strategy all on its own.
Speaker 1:Which leads us to this kind of provocative twist something maybe even more surprising, that came out of the experiment.
Speaker 2:Yeah, there was another layer to it.
Speaker 1:So the theory suggests this coconut process should work kind of like a standard breadth first, search right, expanding outwards evenly.
Speaker 2:That's the basic mechanism.
Speaker 1:But the experiment showed something extra, an additional bias in the models they trained. They weren't just exploring evenly, they actually paid more attention to optimal edges, the ones that were actually on the path to the correct answer, and also to those frontier nodes right at the edge of the search. So, it's not just BFS, it's like a smart, prioritized BFS.
Speaker 2:Exactly. It seemed to develop this kind of implicit prioritization, which is weird, right. How did it know which path was optimal before finding it?
Speaker 1:Yeah, so did they test that, like maybe it was just how they trained it?
Speaker 2:They did, they came up with an alternative training method called Coconut BFS. The idea here was to force it to be less biased. Instead of maybe learning implicitly to focus on the path towards the solution, they trained it by randomly sampling from any node on the current frontier, not just potentially good ones.
Speaker 1:OK, so they tried to make it do a dumber BFS. Basically Did that get rid of the prioritization.
Speaker 2:And here's the kicker Even with that different, less obviously biased training, the coconut BFS models still showed similar prioritized search behavior.
Speaker 1:No way. So even when they tried to stop it, it still somehow learned to focus its attention more intelligently.
Speaker 2:Pretty much. It raises this really deep question why does this seemingly intelligent prioritized exploration emerge, even when you're not explicitly training for it? It suggests there might be some deeper learning mechanisms going on. Maybe the model is figuring out efficient strategies and ways we don't fully grasp yet, learning how to solve problems smartly, not just following instructions.
Speaker 1:OK, so stepping back. What does this all mean for LLMs, for us trying to understand them?
Speaker 2:Well for LLMs. I think this deep dive really highlights a potentially fundamental way they can achieve highly efficient parallel reasoning. It's not just about stringing words together. This continuous thought idea could unlock ways to solve much more complex problems, things that currently seem impossible, problems needing that kind of systematic, multi-pronged exploration.
Speaker 1:And for you listening to this understanding. This concept gives you a unique insight right into the absolute cutting edge of AI research. It shows how these ideas that sound really abstract, maybe even theoretical math concepts like superposition, can actually have incredibly practical, powerful applications in real world machine learning.
Speaker 2:Yeah, it's a great reminder that sometimes the biggest leaps come from connecting seemingly unrelated fields and that these models, well, they're still full of surprises. We're still figuring them out.
Speaker 1:So just to recap the core idea, this deep dive showed us that continuous thought in LLMs might allow for a kind of parallel superpositional reasoning and that makes complex tasks like figuring out paths in a graph potentially vastly more efficient than the old step-by-step, discrete chain of thought.
Speaker 2:Exactly, and it leaves us with a really fascinating final thought to ponder, doesn't it? If these continuous thoughts can automatically, without explicit instructions, develop not just efficient but even prioritized smart search strategies, what other incredibly sophisticated reasoning abilities might be quietly emerging inside these huge models, capabilities that we're only just starting to get the glimpses of?
Speaker 1:A bit unnerving, but also incredibly exciting.