BigBang-Proton: Unified Scientific Model

• BigBang-Proton is a unified sequence-based architecture designed for scientific multitask learning across diverse domains, integrating theory with large-scale experimental data.
• It employs Binary Patch Encoding to preserve numerical precision and Monte Carlo Attention to efficiently handle ultra-long context sequences, achieving benchmark-level performance.
• The model’s next-token prediction pretraining enables seamless transfer learning across tasks, marking a shift from domain-specific architectures to a generalist scientific AI engine.

BigBang-Proton is a unified sequence-based architecture for auto-regressive language modeling tailored to scientific multitask learning across real-world domains. Unlike general-purpose LLMs, BigBang-Proton introduces a triad of innovations: Theory–Experiment Learning paradigm enabling systematic alignment of scientific theory with large-scale experimental data; Binary Patch Encoding supplanting BPE tokenization to preserve numerical structure and precision; and Monte Carlo Attention as a scalable alternative to quadratic-complexity transformer attention for ultra-long context integration. The model demonstrates benchmark-level generalization and accuracy on diverse scientific challenges, supporting the hypothesis that scaled pretraining and next-token prediction can provide foundational material-world representations.

1. Architecture: Unified Sequence Learning, Theory–Experiment Alignment, and Binary Patch Encoding

BigBang-Proton operationalizes a single auto-regressive architecture in which text, code, multi-digit numbers, and image sequences are encoded as binary patches. This approach is defined by three essential innovations:

• Theory–Experiment Learning Paradigm: Scientific tasks (e.g., jet tagging in particle physics) are represented as sequences blending experimental measurements (numerical features, e.g., momentum, energy, angles) directly with theoretical textual descriptors (e.g., particle species labels). This enables the network to simultaneously learn statistical regularities in scientific language and quantitative mappings across measurement and theory, enforcing cross-domain co-representation at the token level.
• Binary Patch Encoding: All information—text, numbers, symbols—is transformed into its raw binary representation (UTF-8 or similar) and segmented into contiguous 'patches' (e.g., 8–32 bytes). This encoding maintains positional and semantic integrity, allowing for operations such as multi-digit addition to be performed with full carry propagation accuracy. Binary Patch Encoding prevents fragmentation induced by conventional BPE tokenization, which can disrupt number structure (e.g., 12345 → ["12","34","5"]), thereby enabling learning of ALU-like operations.
• Monte Carlo Attention: To address prohibitive quadratic context scaling in transformers, BigBang-Proton introduces a patch-centric attention mechanism. The input sequence is divided into fixed-length patches (length $P$), and in each layer, a randomized 'delegate' selection exchanges select tokens between adjacent patches. Effective receptive field length $C(N)$ expands recursively as $C(N) = (P-1)P^N + C(N-1)$, so that with moderate values ($P=32$, $N=20$) the context approaches $10^{30}$ bytes. This recursive delegation strategy in principle permits context windows on the order of $10^{80}$—the baryon count of the universe—enabling universe-scale foundation model pretraining.

2. Pretraining Protocol and Empirical Performance Across Disciplines

BigBang-Proton is pretrained via next-token (or next-patch) prediction for continuation over multi-modal scientific corpora. All downstream scientific tasks are cast as sequence completions without domain-specific heads. Notable benchmark metrics include:

Task Domain	Accuracy/MAE	Comparative Model
Arithmetic (50-digit addition)	100%	ALU, LLMs
Particle Physics Jet Tagging (11-class)	~51.3%	ParticleNet, PT
Interatomic Potential Regression	MAE ~0.043 eV	Specialized GNNs
Water Quality (forecasting)	MAE ~0.58 μg/L, MAPE ~9.8%	CNN/RNN
Genome Modeling (next-nucleotide)	~56%	Fined-tuned LLM

• Arithmetic: Using Binary Patch Encoding, the model learns all 50-digit addition operations at 100% accuracy, capturing the full numeric structure and carry propagation.
• Jet Tagging: Sequence representations of high-energy collision events (both measured and theoretical descriptors) lead to classification performance competitive with domain-specialized deep architectures.
• Materials Simulation: BigBang-Proton predicts formation energies and atomic properties with errors matching top-performing neural models on material science data sets.
• Environmental and Genomic Modeling: The single architecture matches or surpasses domain-specific baselines in water quality and gene sequence prediction, demonstrating cross-domain sequence generalization.

3. Scientific Multitask Learner and Latent Representation

BigBang-Proton’s cross-discipline pretraining leads to a latent space that is shared across heterogeneous scientific tasks. Whether decoding sensor time series, simulating atomistic dynamics, or tagging particle events, every problem becomes a next-token prediction—allowing seamless transfer, rapid finetuning, and joint representation learning. Unlike specialist models, the architecture predicts both discrete and continuous quantities within a unified patch-encoded sequence.

The methodology of “learning by reading”—ingesting both experimental data and theoretical explanation—enables the model to transcend siloed methodologies, capturing correlations, analogies, and representations across disparate physical phenomena.

4. Universe-Scale Pretraining Hypothesis and Material World Foundational Model

A central hypothesis posited is that auto-regressive scaling is bounded only by the universe's informational complexity. Should model context and capacity be extended (e.g., $P=32$, $N=60$ yielding $10^{80}$ context window), pretraining on all available universe data—quantitative and textual—would allow BigBang-Proton (or its successors) to converge toward a Platonic material world foundational model. Two explicit conjectures:

1. Scaling law ultimate bound: “The scaling law of auto-regressive LLMs has not hit the wall. The limit…is the ultimate boundary of the universe.”
2. Physical structure reconstruction: “Simply by next-word-prediction, we can reestablish any physical structure existing in the universe from the quark scale upward.”

This paradigm implies that network representation will asymptotically encode the fundamental laws and structures of nature, creating a cross-disciplinary, language-guided, physics-grounded scientific engine.

5. Practical Impact, Challenges, and Future Directions

BigBang-Proton’s demonstrated task generalization (arithmetic to genomics) supports a shift away from domain-specific architectures toward foundation models able to unify scientific reasoning, numeric computation, and experimental data synthesis. This model infrastructure raises substantial technical questions:

• Hardware co-design: Universe-scale pretraining requires novel architectures (compute-in-memory, high-throughput context routing) to support exabyte-scale training.
• Safety and interpretability: Emergent capacities of models encoding physical law at cross-domain scales present risks and opportunities distinct from language-centric LLMs.
• Transfer learning: Sequence-aligned representations can serve as scaffolds for domain finetuning without retraining, accelerating research cycles across theoretical and experimental fields.

6. Summary

BigBang-Proton brings together binary-precision sequence encoding, physics-guided context propagation, and next-token multitask learning to produce a language-guided scientific computing model. By excelling in arithmetic, physics, chemistry, earth sciences, and genomics without specialized heads, it demonstrates the viability of a generalist scientific AI architecture. The hypothesis that context length and scaling laws can be extended to universe-scale foundational learning remains a provocative challenge, inviting further research at the intersection of artificial intelligence, scientific methodology, and physical law (Wu et al., 30 Sep 2025).