We placed 100 memory-equipped large language model (LLM) agents in charge of a closed, money-conserving spatial economy on real Pokhara Lakeside geography (earning wages, running businesses, setting prices) and ran this multi-agent simulation for up to 26 simulated weeks, well past the 1-2 weeks typical of agent-societ...
S. Regmi, Siddhartha Pudasaini, Chetan Phakami Pun· 1 citation
Byte-level BPE tokenizers that use the HuggingFace ByteLevel pre-tokenizer inherit GPT-2's word regex, where a word is defined as \p{L}+, one or more Unicode letters, which splits each word at every vowel sign, and formalises this effect as a training-free lower bound on fertility.
S. Regmi, Siddhartha Pudasaini, Chetan Phakami Pun· 3 citations
We present Arkios, a 1.04B-parameter dense transformer pretrained from scratch on 150B tokens of bilingual English-Nepali text, using a custom single-file C/CUDA training stack and a Devanagari-aware byte-level BPE tokenizer built for this project. On ARC-Easy and ARC-Challenge, Arkios exceeds three comparably sized op...
S. Regmi, Siddhartha Pudasaini, Chetan Phakami Pun· 0 citations
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