Latest publications
Apple ML Research proposed a method for generalizing ML models to new domains without labels
Apple ML Research researchers developed an approach to domain generalization that relies on unlabeled data from a new domain instead of costly annotation.
Apple Introduced Conformal Thinking — Risk Management for Reasoning Models Without Extra Tokens
Apple ML Research presented the Conformal Thinking framework: a method that automatically manages token budgets for reasoning models, guaranteeing specified error rates with minimal computation.
Apple ML Research: how diffusion models learn to select tokens without manual heuristics
Apple ML Research proposes replacing manual token-selection heuristics in diffusion language models with learned policies to eliminate instability and the need for manual parameter tuning.
Apple ML Research proposed MemoryLLM — an interpretable “memory” for transformers
Apple researchers described feed-forward blocks in LLMs as a neural retrieval memory and proposed a method to analyze them in isolation — without accounting for the self-attention mechanism.
Apple ML Research: multi-agent LLM teams hold back expert agents
Apple ML Research showed that self-organizing teams of language models do not produce synergy — they hold back expert agents rather than strengthen them.