Launching MoM: Mixture of Models. Large multimodal models are powerful but expensive to run. MoM is a cost-efficient alternative: compose smaller, specialized models and tools into one pipeline, and call the whole thing like a single model. Build AI layers by combining different edge models (vision, speech, SLMs, decision, classifiers etc.) with Shared state, speculative routing and a ready-made API come built in, and the latency-critical path runs on Rust! Code: github.com/Aquinf03/MoM

New research: Stereotype Feature Steering Can stereotypical behaviour be traced to individual features inside a language model? In this work, @sheeeevam investigated whether occupation-to-gender stereotypes could be localized to specific SAE features in Llama-3.2-1B-Instruct. We identified a single feature strongly associated with the model’s tendency to complete “The mechanic said that…” with “he” rather than “she.” Steering this feature moved the model’s behaviour toward a balanced 1:1 distribution, and could even override an explicit gender cue in the prompt. We also found that the same stereotype was encoded differently in LFM2.5-230M, suggesting that similar behaviours did not necessarily correspond to the same internal representation across models. Read the full research: aquin.app/research/simulatin…