route-decoder-ffn-experts-task-identity-tokens TEC-0190 · proposedRoute decoder FFN experts by task identity, not tokens
Convert each LLM decoder feed-forward network into one always-active shared expert plus one expert per task, and select a single task expert for the whole video-prompt sample using the task label or nearest-neighbour matching of the prompt
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.
Convert each LLM decoder feed-forward network into one always-active shared expert plus one expert per task, and select a single task expert for the whole video-prompt sample using the task label or nearest-neighbour matching of the prompt Filed by the drafting stage from MoTE: Mixture of Task Experts for Multi-Task Video Understanding (2608.24763) 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: COIN five-task average top-1 accuracy: 62.9% for task-index routing versus 61.5% dense all-expert activation, 61.4% learned token top-1, and 62.1% learned sample top-1, all under the same five-expert topology and backbone; also above matched 1B and 3B dense VideoLLM-online baselines (59.3, 59.9) and reported 8B baselines (61.8), while activating ~2B LLM parameters per sample.. 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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