o1’s Thoughts on LNMs and LMMs

TL;DR We asked o1 to share its thoughts on our recent LNM/LMM post.

https://www.artificial-intelligence.show/the-ai-podcast/o1s-thoughts-on-lnms-and-lmms

What is your take on blog post “Why AI Needs Large Numerical Models (LNMs) for Mathematical Mastery“?

Thought about large numerical and mathematics models for a few seconds.
Confirming Additional Breakthroughs
OK, I’m confirming if LNMs/LMMs need more than Transformer models to match LLM performance, and noting the user’s comprehensive response.

Yes. While the Transformer architecture provided the foundation for LLMs to excel at natural language tasks, achieving similarly high-level performance for numerical and symbolic mathematics (LNMs and LMMs) will likely require additional innovations. Here’s why and what might be needed:

Limitations of Current Transformers

• Numerical Precision … transformers aren’t inherently optimized for high-precision arithmetic or iterative numerical methods, which LNMs would require.

• Symbolic Reasoning … proving theorems or manipulating symbolic expressions involves strict logical rules and structured data—tasks that standard Transformers do not handle naturally.

• Computational Overhead … transformers’ attention mechanisms can become inefficient for the very large or highly structured data common in mathematical domains.

Needed Research Breakthroughs

• Hybrid Architectures … combine deep learning with traditional numerical solvers or logic engines. For LNMs, this could mean integrating GPU/TPU-accelerated numerical libraries. For LMMs, it might involve pairing neural networks with theorem provers or symbolic algebra systems.

• Neuro-Symbolic Approaches … develop architectures that inherently blend neural inference (pattern recognition) with symbolic reasoning (rule-based logic). This could mean specialized modules or memory structures that allow models to store and manipulate symbolic representations more effectively than standard Transformers.

• Graph- and Tree-Based Models … since mathematical expressions and proofs naturally form hierarchical or graph-like structures, moving beyond sequence-focused attention to graph neural networks or other structured representation models could enable more logical and proof-oriented reasoning.

• Precision and Stability Tools … introduce new training objectives and loss functions that prioritize numerical stability and adherence to mathematical rules, ensuring that LNMs produce consistent, correct solutions rather than just approximations.

• Custom Hardware and Efficient Scaling … High-precision arithmetic or complex symbolic operations may benefit from custom hardware accelerators and more memory-efficient architectures, ensuring that LNMs/LMMs can scale in size and complexity without exploding computational costs.

• Curriculum and Reinforcement Learning … Using curriculum learning to gradually teach models from basic arithmetic to complex proofs, and employing reinforcement learning to optimize multi-step problem-solving strategies, could yield robust mathematical reasoning capabilities.

To achieve LNMs and LMMs on par with LLMs in their own domains, researchers will likely need to develop architectures and training methods that extend beyond the Transformer blueprint. By integrating numerical methods, symbolic logic, graph-based representations, and potentially new hardware support, future AI models could reach the same level of transformative impact on mathematics as LLMs have on language.

Do you have any thoughts on how AI models could benefit from potentially being architected more similarly to the human brain, for example having 3D neural networks versus the “2D” approach current neural networks have (including thoughts on hardware as well as software)? Also, the brain is way more energy efficient than our current AI systems, how could that be achieved? And would such changes benefit mathematical and numerical-focused AI?

Thought about AI brain-inspired architecture for a second.

Shifting Toward Brain-Inspired Architectures

Current deep learning systems, including large language models, are largely based on two-dimensional grid-like structures (matrices or tensors) and operate on relatively straightforward dataflows. In contrast, the human brain is a three-dimensional network of neurons interconnected in extremely complex patterns. Rethinking AI architectures to more closely resemble the brain’s structural and functional properties could yield several advantages, both in terms of performance and efficiency.

1. Moving from 2D to 3D Neural Architectures

• 3D Structural Connectivity … human brain connectivity is inherently three-dimensional, with neurons densely packed and connected across multiple layers and cortical columns. By modeling artificial neural networks in a 3D space, we could explore new ways of arranging neurons and layers, potentially reducing the “distance” between units that need to communicate. This could decrease the amount of redundant computation and allow more natural hierarchical structures.

• Spatial Embeddings of Data … many real-world problems (e.g., in physics, fluid dynamics, or complex simulations) have three-dimensional data at their core. A 3D neural architecture might handle spatial data more naturally, treating depth, width, and height consistently rather than flattening or reshaping it. This could benefit LNMs or LMMs dealing with inherently spatial mathematical tasks, such as partial differential equations defined over 3D domains.

• Locality and Modularization … in the brain, neurons form local circuits that handle specific functions. Transferring this concept to AI, clusters of artificial neurons could specialize in particular sub-tasks or operations, enabling modularity and reducing the overhead of having a single monolithic network. Such modularity might allow for specialized “numerical circuits” or “symbolic reasoning circuits” within a larger architecture.

2. Hardware Innovations

• 3D Neuromorphic Chips … traditional GPU/TPU hardware relies on 2D transistor layouts. Emerging neuromorphic chips—like those developed by Intel (Loihi), IBM (TrueNorth), or other labs—attempt to mimic spiking neurons and synaptic connections in a more brain-like manner. Going further, 3D-stacked neuromorphic chips could place memory and compute elements in three dimensions, reducing data movement costs and latency.

• On-Chip Learning and Memory Integration … a key energy challenge in current AI systems is the constant shuttling of data between memory and processors. The brain is efficient partly because memory (synapses) and computation (neurons) are co-located. Future hardware could integrate memory at the transistor or device level, reducing the data movement overhead. 3D integration and advanced materials (e.g., memristors) could enable synapse-like memory elements that directly store weights.

• Spiking Neural Networks (SNNs) … the human brain uses spike-based communication. Spiking Neural Networks replace continuous activation functions with event-driven spikes. Such models can be run on neuromorphic hardware that only consumes energy when neurons fire, drastically reducing power consumption. For mathematical tasks, SNN-based LNMs/LMMs might handle certain iterative, event-driven computations more efficiently.

3. Energy Efficiency Through Brain-Like Methods

• Sparse and Event-Driven Computation … the brain is highly sparse—most neurons are quiet most of the time. Adopting sparsity in artificial networks can reduce unnecessary computations. Techniques such as conditional computation, where parts of the network are only activated when needed, can mimic this behavior. For LNMs solving complex problems, a sparse system might only “activate” the modules relevant to the current task, cutting down on energy usage.

• Low-Precision and Analog Computation … the brain doesn’t operate with the same digital precision as modern GPUs. Instead, it leverages analog signals and approximate computations. Adopting analog computation or lower-precision arithmetic in specialized hardware can vastly reduce power consumption. LNMs focusing on optimization or iterative numerical methods might tolerate (or even thrive on) slightly approximate computations that drastically cut power costs.

• Recurrent and Feedback Loops … the brain relies extensively on feedback loops—information flows not just forward but also backward and laterally. This allows constant refinement of activity. Incorporating efficient recurrent structures in hardware could produce a model that learns more quickly from fewer data points and self-corrects on the fly. For mathematics-focused AI, feedback loops might enable dynamic refinement of proofs, iterative solution methods, and better error correction.

4. Benefits of Mathematical and Numerical AI

• Handling Complexity Natively … complex mathematical problems often involve intricate structures (e.g., nonlinear PDEs, high-dimensional integrals). A brain-like architecture might navigate such complexity more naturally, using spatially arranged modules that specialize in certain types of computations and adaptively routing information.

• Energy-Efficient Computation for Large Problems … mathematical modeling and numerical simulation tasks can be extremely computationally heavy. Brain-inspired, energy-efficient architectures would allow LNMs/LMMs to scale up to larger, more complex problems without prohibitive energy costs.

• Robust and Adaptive Learning … the brain’s learning is continuous, robust to noise, and capable of transfer across tasks. Similarly, a more brain-inspired AI system could continuously refine its mathematical understanding, apply learned techniques to new domains, and adaptively improve its performance—crucial for research-level mathematics and open-ended problem sets.

5. Challenges and Considerations

• Engineering Complexity … implementing fully 3D neural architectures or neuromorphic chips at scale is a significant engineering challenge. Fabrication, cooling, and interconnect design in 3D chips are non-trivial obstacles.

• Algorithmic Paradigms … moving away from the Transformer and other established architectures would require new algorithms, training paradigms, and debugging tools. Translating brain-like concepts into reproducible and trainable software frameworks is non-trivial.

• Interdisciplinary Efforts … achieving brain-inspired efficiency will demand close collaboration between neuroscientists, materials scientists, hardware engineers, and AI researchers. Insights from cognitive science and neurology can guide the design of more biologically plausible and efficient systems.

Architecting AI systems to be more like the human brain—exploring 3D interconnectivity, neuromorphic hardware, event-driven computation, and local memory-compute integration—holds promise for overcoming some of the limitations of current 2D, Transformer-based models. Such systems could be more energy-efficient and better suited for complex, structured tasks like those required in advanced mathematics and numerical modeling. While significant technological and research hurdles remain, these bio-inspired approaches represent a promising frontier for future AI innovations.