calibrate-value-vectors-synergy-attention-heads TEC-0101 · proposedCalibrate value vectors in synergy attention heads
Classify attention heads by causal noise intervention and counterfactual difference-in-differences, then at decode time rescale the visual and language value vectors of the mixed 'synergy' heads so their information ratio hits a target equi
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Classify attention heads by causal noise intervention and counterfactual difference-in-differences, then at decode time rescale the visual and language value vectors of the mixed 'synergy' heads so their information ratio hits a target equi Filed by the drafting stage from MLLMs Hallucinate when Information Distribution Drifts in Synergy Heads (2609.09206) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: POPE F1 87.7 and accuracy 88.3 and MME total 669.8 on LLaVA-1.5-7B versus 85.4/84.0/565.3 beam-search baseline and above ~20 published decoding and attention baselines; CHAIR_S 51.0 to 36.9 and CHAIR_I 15.2 to 10.7 with recall and output length maintained; gains repeated on five other backbones; head assignments 92-95% stable across four masking strategies.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
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Sources
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