arXiv:2607.00341v1 Announce Type: new Abstract: Large language models achieve strong performance on many reasoning tasks when allowed to externalize intermediate steps as Chain-of-Thought (CoT). However, many questions require the model to internalize the multi-step reasoning within a single forward pass before generating the answer. We study this challenge through two-hop reasoning, a representative task where the model must compose multiple pieces of parametric knowledge within a single forward pass. Standard non-recurrent Transformers suffer from a depth-local storage problem: facts learned
The continuous drive to improve large language models' reasoning capabilities, particularly for complex, multi-step tasks, necessitates architectural innovations like DiscoLoop.
Improving multi-hop reasoning within a single forward pass reduces computational overhead and increases the efficiency and reliability of AI systems, expanding their applicability to more sophisticated problems.
This research suggests a potential architectural improvement that could make AI models more adept at internalized, complex reasoning, rather than relying solely on externalized Chain-of-Thought processes.
- · AI Research & Development
- · Large Language Model Developers
- · AI-powered SaaS providers
- · Inefficient multi-hop reasoning architectures
AI models become more efficient and capable of complex reasoning without explicit externalization.
This could lead to a new generation of AI applications that perform intricate tasks with less computational cost and latency.
More robust and internalized reasoning could accelerate the development of truly autonomous AI agents capable of higher-order problem-solving.
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Read at arXiv cs.CL