Abhi10699/hangman-arena

Hangman-arena is a nascent project (0 stars, 0 days old) that targets a very specific failure mode of LLMs: character-level reasoning and the limitations imposed by tokenization. While testing LLMs on Hangman is a valid and scientifically interesting way to probe their internal world models and linguistic 'glitch tokens,' the project currently lacks any defensibility. The logic for a Hangman engine is a standard coding exercise, and the value of a benchmark lies in its adoption, leaderboard authority, and data volume—none of which are present here yet. It faces stiff competition from established evaluation frameworks like OpenAI's 'evals', the UK AI Safety Institute's 'Inspect AI', and the widely used 'LMSYS Chatbot Arena.' Frontier labs are unlikely to build a dedicated Hangman tool, but they regularly incorporate similar character-level tasks into their massive internal evaluation suites (like BIG-bench). The 'Arena' branding is currently aspirational; without a community or a dataset of results, it remains a personal experiment.