[Fictional] Public Expert Break-Out Session: Evaluating the HART-SURYA Proposal
Location: The AI Conference 2025, Pier 48, San Francisco
Track: AI Frontiers
Time: 3:30 PM, Wednesday, September 17th
The room is packed, with standing room only. The screen behind the panelists displays the title: "Existential AI: Can a Smarter Model Save Our Electrical Grid from the Sun?"
Moderator: "Welcome, everyone. We have a special, unscripted session today to discuss a fascinating proposal that emerged from the open-source community, aimed at improving a critical NASA AI model called Surya. The goal of Surya is to understand our sun, but the stakes couldn't be higher. A Carrington-level solar event today could collapse our global power grid, sending us back to the dark ages. The question on the table is a proposal by a 'Martial Terran' called HART, or Heliocentric Adaptive-Rotation Tokenization. Is it a game-changer for predicting these events, or a complex distraction?
"Let's start with the big picture. DJ, as the former US Chief Data Scientist, frame this problem for us."
NK Palik: "Gladly. People need to understand this isn't just an academic exercise. We are, at this moment, flying blind. A massive Coronal Mass Ejection, or CME, could hit us with only hours of warning, if that. The result would be trillions in damages and a breakdown of society. It's not if, it's when. We have the data streaming from the sun, but we're not extracting the maximum intelligence from it. The current Surya model is a great step. But the core question this HART proposal raises is: can we make it fundamentally better? For a problem of this magnitude, we have a national security obligation to chase down every credible performance improvement."
Moderator: "Tris, you work on applying AI to grand scientific challenges at Deepmind. What's your take on the HART proposal's scientific merit?"
Tris Wtiarkenn: "From a first-principles perspective, it's incredibly elegant. What this 'Martial Terran' correctly identifies is that the current model is forced to waste a huge amount of its capacity learning a basic, predictable kinematic motion: the sun's differential rotation. It's like asking a genius to predict the stock market, but first forcing them to re-derive the laws of gravity every single time they look at the data. HART essentially says: let's handle the predictable physics in the data-processing step. Let's de-rotate the sun in the input data so the transformer can dedicate its entire intelligence to the much harder problem—the intrinsic evolution of solar features that actually lead to an eruption. It's a classic, beautiful example of physics-informed AI."
Ion Satoic: "Elegance is one thing, but petabytes of data are another." All eyes turn to the Berkeley professor and Databricks co-founder. "I read the proposal, and the engineer in me immediately got nervous. This 'Stage 2: Dynamic, Per-Band Image Warping' is computationally non-trivial. For every time-sequence of images, you are calculating a complex, non-linear flow field and resampling the image. You're shifting the computational burden from the model's inference stage to the data-ingestion pipeline. So, while you might get a more efficient model, your total pipeline cost and complexity could skyrocket. At NASA's scale, that's a massive engineering challenge. Is the trade-off worth it?"
Lin Qoia: "I'm with Ion on this. The proposal itself actually offers a much more practical first step. Why are we even debating the full, complex warping pipeline when 'Optimization 1: Masked Tokenization' is sitting right there?" she asks, leaning into her microphone. "The author points out that 21.5% of the input tokens are just black space. By simply masking out these tokens, we could get a 20% reduction in compute and memory usage right now with very low implementation risk. From a production AI standpoint, you always go for the low-hanging fruit first. Let's bank the 20% win, see how the model improves, and then use that as the baseline to evaluate whether the far more complex HART approach provides enough marginal benefit."
Jure Lekovsec: "I think we need to be careful about the potential downsides of the HART warping itself," the Stanford professor cautions. "This resampling operation, grid_sample, is an interpolation. Interpolation can introduce subtle artifacts or smooth over the very faint, high-frequency signals that might be the critical precursors to a solar flare. You could, in theory, 'de-rotate' the sun so well that you accidentally erase the very signal you're looking for. It's a clever feature engineering step, but it's not without risk. A more robust approach might be to use something like a graph neural network on a spherical projection of the sun, which is more native to the data's geometry and doesn't require resampling the source pixels."
Christopher Krihoffoch: "This technical debate is fantastic, but let's bring it back to the ground. Or, rather, to the grid," he says, cutting through the academic back-and-forth. "At the Pentagon's innovation unit, we had a mantra: 'Test it.' Right now, this is a proposal in a GitHub issue. We need a bake-off. It should be a three-way competition. Model 1 is the current Surya baseline. Model 2 is Martial's suggestion, which Lin endorses: Surya with the simple masked tokenization. Model 3 is Martial's full HART implementation. We then run historical data for the 100 biggest solar flares on record through all three models. The winner is the one that gives us the longest, most reliable warning time. Does one model give us 12 hours of warning when another gives us 4? That's the only metric that matters when civilization is on the line. This is a solvable, empirical question."
NK Palik: "Chris is exactly right. We need to operationalize this. We can't let the perfect be the enemy of the good. Lin's point is sharp: a 20% efficiency gain is not trivial. That could mean a faster, larger, or more frequently updated model today. But Tris's point about the elegance of the HART approach is the long-term goal. By encoding known physics, we could unlock a new level of predictive power. So, the path forward seems clear: implement the mask now. Benchmark the full HART proposal rigorously, paying close attention to Jure's concern about artifacts. And frame the entire effort around Christopher's metric: actionable warning time. We have a clear and present danger, and this proposal lays out a tangible path to improving our defenses."
Moderator: "So, the consensus is a pragmatic, two-track approach. An immediate, low-risk optimization and a higher-risk, higher-reward research track, all benchmarked against the single metric of saving the world. It seems even in the world of advanced AI, the simplest solution is often the best place to start. Thank you all for a truly spirited discussion."