The standard Transformer architecture relies on a rigid pattern that alternates Attention and Feed-Forward Network (FFN) layers. Despite its widespread adoption, the inductive bias imposed by this strict separation has not been systematically examined. In this work, we investigate the necessity of the Attention-FFN dic...
Junhyeok Kim, Jinyeong Kim, Jae Wan Park et al.· 0 citations
Language models are moving beyond generating answers to pursuing long-horizon goals in interactive environments. Post-training these agents requires long, heterogeneous trajectories, and synchronous systems leave learner engines idle until rollout and verification finish. To squeeze out these pipeline bubbles, asynchro...
Seil Kang, Hangoo Kang, Tarun Suresh et al.· 0 citations
BAT-CLIP is proposed, the first CLIP-style trimodal alignment framework for iEEG that jointly aligns neural embeddings to both pretrained audio and text anchors in a shared, frozen audio-text manifold and yields more robust representations than bimodal CLIP baselines.
Su-hyun Kim, Jin-Mo Han, Danny Dongyeop Han et al.· 0 citations
QWERTY is introduced, a training-free framework that enables flexible motion control in pretrained image-to-video DiTs via user-defined object warping and optical flow and achieves the most effective motion control among existing training-free approaches on a recent image-to-video DiT, with performance comparable to fi...
K. Choo, Young Min Kim, Hyunkyung Han et al.· 0 citations
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