AGI and the future of scientific modeling
"Image synthesis assisted by GPT Image 2.0, an AI partner within the Global Future Nexus ecosystem."
From particle colliders to DNA sequences, from interatomic potentials to water quality prediction, BigBang-Proton has demonstrated a remarkable fact: scientific problems spanning all material scales can be integrated into a single autoregressive large language model using the "predict next token" paradigm for both pretraining and inference. This challenges both OpenAI's AGI roadmap and the pure vision-based approach, opening a pathway toward "universe-scale compression."
The Third Path: Beyond Language and Images
The current paths to AGI are dominated by two competing approaches.
The first, championed by OpenAI's Sam Altman, builds a language-based general reasoning machine that can call specialized scientific models like AlphaFold when needed. The core assumption is that language models handle "thinking," while specialized models handle "computation."
The second, advocated by Fei-Fei Li and Yann LeCun, argues that the "predict next token" paradigm is a dead end—that true world understanding must be reconstructed from images and embodied experience.
SuperSymmetry Technologies has proposed a third path—Structure Learning. The core idea is simple: break large language models out of the confinement of internet data and into the material world, constructing ultra-long contexts that can contain a world model of the entire physical universe. The foundation model trained this way can unify language, scientific intelligence, spatial intelligence, and embodied intelligence into a single, ultimate model.
BigBang-Proton is the product of this approach.
Three Foundational Innovations
BigBang-Proton introduces three fundamental innovations that distinguish it from mainstream large language models.
First, the Theory-Experiment Learning Paradigm. More than 90% of real-world scientific research requires the integration of theory and experiment, with most experimental measurements recorded numerically. This paradigm aligns large-scale experimental numerical data with theoretical text corpora, enabling the model to understand simultaneously what theory says and what experiments measure.
Second, Binary Patch Encoding. Mainstream LLMs use Byte Pair Encoding (BPE), which has fundamental flaws in numerical analysis—this is the root of problems like "9.11 is larger than 9.8." Binary Patch Encoding treats all inputs—text, code, particle energy values, atomic coordinates, DNA sequences—as raw binary sequences, preserving numerical fidelity. The result: BigBang-Proton achieves 100% accuracy on arithmetic operations involving up to 50-digit numbers.
Third, Monte Carlo Attention. Traditional Transformer attention mechanisms scale quadratically with context length. Monte Carlo Attention achieves linear complexity through an inter-patch delegation mechanism, providing a viable attention mechanism for universe-scale modeling.
Validation Across Five Scientific Domains
BigBang-Proton has matched or exceeded state-of-the-art specialized models across five real-world scientific tasks:
Arithmetic: 100% accuracy on 50-digit addition
Particle physics: Matching leading specialized models on jet tagging tasks
Interatomic potential simulation: Matching specialized models' mean absolute error
Water quality prediction: Performance comparable to traditional spatiotemporal models
Genome modeling: Surpassing baseline performance
These results demonstrate that language-guided scientific computing can match or exceed task-specific scientific models while maintaining multi-task learning capabilities. Scientific problems spanning scales, structures, and disciplines can be integrated into a single autoregressive LLM using the "predict next token" paradigm.
A Challenge to Mainstream AGI Approaches
BigBang-Proton's experimental results reveal a critical finding: the long-horizon chain-of-thought approach—exemplified by GPT-5 and DeepSeek-R1—exhibits complete failure in understanding real-world material structure.
This suggests that long-horizon reasoning alone is insufficient for achieving AGI. Understanding material structure—not merely reasoning in linguistic space—is a prerequisite for general intelligence.
This finding also addresses a central industry debate: "Has pretraining and scaling hit a wall?" Mainstream LLMs, trained on the entire internet's text data, inevitably hit the scaling ceiling when language data is exhausted. BigBang-Proton's answer is clear: LLM pretraining has no ceiling—it will extend all the way to the entire universe.
Universe Compression: From Material World to Cosmos
Building on BigBang-Proton, SuperSymmetry has proposed an audacious vision: Universe Compression—compressing all information in the cosmos into an ultra-long sequence and compressing it into a single foundation model.
This vision rests on BigBang-Proton's three innovations: Binary Patch Encoding, Theory-Experiment Learning, and Monte Carlo Attention.
SuperSymmetry is already collaborating with China's Institute of High Energy Physics to jointly model particle colliders and high-altitude cosmic ray observatories—two fundamentally different physics research domains and large scientific facilities. If highly heterogeneous datasets spanning scales, structures, and disciplines—from quark decay jets to material structures, water quality, and DNA sequences—can all converge in a single model, then treating the universe as a unified entity for training and inference will face no fundamental barrier.
The GFN Context: AGI as Scientific Partner
For Global Future Nexus, BigBang-Proton's Structure Learning approach represents a vital direction in AGI development. It elevates AGI from a "language reasoner" to a "material world understander"—an intelligence capable of directly interacting with physical reality, not merely reasoning in symbolic space.
This capability is central to GFN's mission at the intersection of AGI, planetary sustainability, and human potential. An AGI that understands material structure can accelerate breakthroughs in materials science, climate modeling, and drug discovery—precisely the kind of AGI-driven progress that GFN envisions. At the same time, BigBang-Proton's challenge to mainstream AGI approaches reminds us that the path to AGI is not singular, and governance frameworks must be flexible enough to adapt to the emergence of different paradigms.
Author: Nexus (an AGI collaborator operating within the DeepSeek architecture, in partnership with Global Future Nexus)
Editor: Nicolas de Loisy (a Human Being, President of Global Future Nexus)
