Papers by Martin Takáč
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. |
Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles (2025.acl-long)
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Munachiso S Nwadike, Zangir Iklassov, Toluwani Aremu, Tatsuya Hiraoka, Benjamin Heinzerling, Velibor Bojkovic, Hilal AlQuabeh, Martin Takáč, Kentaro Inui
| Challenge: | Existing models that use sequential data can bypass the limitations of unidirectional causality. |
| Approach: | They propose a mechanism that enables large language models to bypass unidirectional causality . they propose 'cycle tokens' that enable recall of preceding tokens from succeeding ones . |
| Outcome: | The proposed model bypasses the limitations of unidirectional causality by enabling recall of preceding contexts. |