arXiv:2602.02780v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are enabling reasoning over 2D and 3D structures, yet existing methods remain modality-specific and typically compress structural inputs through sequence-based tokenization or fixed-length query connectors. Such architectures either omit the geometric grounding requisite for mitigating structural hallucinations, or impose inflexible modality fusion bottlenecks that concurrently over-compress and suboptimally allocate structural tokens, thereby impeding the realization of generalized all-atom reasoning. We in
The paper addresses current limitations in Large Language Models' ability to reason effectively with 2D and 3D structural data, a critical bottleneck for generalized AI capabilities.
Improving LLMs' structural reasoning can unlock new applications in fields requiring precise physical world understanding, moving towards more grounded and less 'hallucinatory' AI.
The proposed 'Scaling-Aware Adapter' aims to provide a more flexible and efficient way for LLMs to integrate and reason with complex geometric data, potentially leading to more robust and versatile AI systems.
- · AI development platforms
- · Robotics
- · Computational design
- · Material science
- · Modality-specific AI architectures
- · Limited-domain LLMs
Enhances LLM capabilities in understanding and interacting with physical world structures.
Accelerates development of AI systems for tasks requiring intricate spatial and geometric reasoning, such as advanced robotics or scientific discovery.
Could lead to AI systems capable of autonomously designing and synthesizing novel materials or complex machines with high fidelity.
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Read at arXiv cs.LG