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3 results for “attention heads”
Asymmetric Attention Heads: Structured Head-Wise Context Allocation for Transformer Attention
A new research paper introduces Asymmetric Attention Heads (AAH), a method that allocates different context lengths to different attention heads in Transformer models based on their functional roles, improving validation loss in controlled experiments.
Aug 21, 2026
chessformer_lens demo: ablating 1 of a chess transformer's 128 attention heads makes the model stop finding Morphy's queen sacrifice [P]
A Reddit user shared a GIF and GitHub notebooks demonstrating that ablating a single attention head in a chess-specific transformer model causes it to fail on a historically famous chess tactic — Morphy’s queen sacrifice — suggesting fine-grained interpretability insights.
Aug 13, 2026
MultAttnAttrib: Training-Free Multimodal Attribution in Long Document Question Answering
Researchers introduced MultAttnAttrib, a training-free method for attributing AI-generated answers to multimodal evidence in long documents, alongside MultAttrEval — the first benchmark dataset for fine-grained multimodal attribution — to address trust and safety gaps in grounded QA systems.
Published Jul 3, 2026 · Analyzed Jul 6, 2026