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cs.LG2025
PoGO: A Scalable Proof of Useful Work via Quantized Gradient Descent and Merkle Proofs
José I. Orlicki
We present a design called Proof of Gradient Optimization (PoGO) for blockchain consensus, where miners produce verifiable evidence of training large-scale machine-learning models.…
cs.CL2025
Beyond Words: A Latent Memory Approach to Internal Reasoning in LLMs
José I. Orlicki
Recent advances in large language models (LLMs) have popularized the chain-of-thought (CoT) paradigm, in which models produce explicit reasoning steps in natural language. Although…