When Do Attention Circuits Form? Developmental Trajectories of Capability and Attention-Sink Emergence Across Three 1B-ClassArchitectures
arXiv:2606.02378v1 Announce Type: new Abstract: We track the developmental trajectory of attention-head circuit formation across three 1B-class language models spanning two architecture families (dense transformer, mixture-of-experts) and two pretraining corpora (The Pile, DCLM): Pythia 1B, OLMo 1B-0724-hf, and OLMoE 1B-7B-0924. At each of 10 log-spaced revisions per model -- 30 mechanistic-interpretability runs in total -- we apply a participation-ratio (PR) spectral signal and an all-head capability-specific selectivity screen to track induction, previous-token, and BOS-attractor heads as th
This research provides timely insight into the developmental mechanisms of attention circuits within language models, as the field increasingly focuses on mechanistic interpretability for safer and more robust AI.
Understanding the formation of attention circuits is crucial for debugging, improving, and aligning large language models, impacting the future reliability and capabilities of AI.
Our understanding of how specific cognitive functions, like attention, emerge during LLM pretraining is enhanced, allowing for more targeted architectural and training interventions.
- · AI researchers
- · AI safety practitioners
- · LLM developers
- · Companies building on foundational models
Improved mechanistic understanding leads to more predictable and controllable LLM behavior.
Enhanced interpretability tools accelerate the development of next-generation AI architectures and training methodologies.
More profound insights into 'intelligence' emergence in artificial systems could inform neuroscience research and vice versa.
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Read at arXiv cs.LG