Papers with token-level logit update restricted

1 papers
Temporal Contrastive Decoding: A Training-Free Method for Large Audio-Language Models (2026.findings-acl)

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Challenge: Large audio-language models (LALMs) can exhibit a temporal smoothing bias . unified decoders can produce less specific audio-grounded outputs .
Approach: They propose a temporally blurred slow-path view that is re-encoded by a token-level logit update.
Outcome: Experiments on MMAU and AIR-Bench show consistent improvements on strong unified LALMs.

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