Dolby (United States)

Observing that video and audio are perceived together yet most generative models treat them in isolation, this survey casts joint generation, cross-modal generation (one modality from the other) and joint editing as three problems defined on a single distribution over audio-visual pairs, organizes the field around one question—how the output is kept coherent across modalities in time and semantics—proposes a taxonomy along five design axes (generation strategy, audio representation, video representation, alignment enforcement, pretraining reuse), and, to the authors' knowledge, provides the first systematic taxonomy of joint audio-visual editing, mapped as nine edit categories spanning 28 edit types, together with methods, datasets and metrics for each setting and a closing list of open pr

Observing that video and audio are perceived together yet most generative models treat them in isolation, this survey casts joint generation, cross-modal generation (one modality from the other) and joint editing as three problems defined on a single distribution over audio-visual pairs, organizes the field around one question—how the output is kept coherent across modalities in time and semantics—proposes a taxonomy along five design axes (generation strategy, audio representation, video representation, alignment enforcement, pretraining reuse), and, to the authors' knowledge, provides the first systematic taxonomy of joint audio-visual editing, mapped as nine edit categories spanning 28 edit types, together with methods, datasets and metrics for each setting and a closing list of open pr

The work reframes LLM routing as coverage-oriented subset selection, uses a determinantal point process to capture both model competence and inter-model redundancy, and trains with a failure-set-marginalization objective that directly maximizes the probability that at least one selected model answers correctly, achieving higher average Success@10 (0.8632 and 0.9914) and more diverse subsets on the medium-pool (3811 candidate LLMs) and large-pool (5000 candidate LLMs) RouterEval settings, while an adaptive greedy stopping rule sets subset size per query.

The work reframes LLM routing as coverage-oriented subset selection, uses a determinantal point process to capture both model competence and inter-model redundancy, and trains with a failure-set-marginalization objective that directly maximizes the probability that at least one selected model answers correctly, achieving higher average Success@10 (0.8632 and 0.9914) and more diverse subsets on the medium-pool (3811 candidate LLMs) and large-pool (5000 candidate LLMs) RouterEval settings, while an adaptive greedy stopping rule sets subset size per query.

Tacit-TTS, distilled from IndexTTS2, replaces autoregressive text-to-semantic decoding with masked non-autoregressive generation, adds training-free acoustic length estimation, and accelerates the flow-matching renderer via ReFlow distillation, achieving competitive zero-shot cloning quality on two English and two Mandarin datasets while generating speech over 10x faster than IndexTTS2 for utterances longer than 5 seconds and supporting cross-lingual and non-lexical references without a reference transcript.

Tacit-TTS, distilled from IndexTTS2, replaces autoregressive text-to-semantic decoding with masked non-autoregressive generation, adds training-free acoustic length estimation, and accelerates the flow-matching renderer via ReFlow distillation, achieving competitive zero-shot cloning quality on two English and two Mandarin datasets while generating speech over 10x faster than IndexTTS2 for utterances longer than 5 seconds and supporting cross-lingual and non-lexical references without a reference transcript.