music production teams

Abstract: Code-based agents can operate creative software, but mixing requests often describe an intended sound without specifying the edits needed to achieve it. We present TOMORI, a code-based mixing-agent harness for native digital audio workstation (DAW) control. Custom plugins provide REAPER project inspection, optional audio measurement, and execution of model-generated Lua code. All observations return as text; delivery includes an editable project and rendered audio. TOMORI-BENCH pairs 110 human-reviewed reference edits with three instruction forms and evaluates native DAW execution end to end through operation structure, parameter precision, and rendered-audio outcomes. Across 990 cases from three primary models, explicit instructions yield high reference agreement, while abstract requests reduce project agreement and audio improvement. Paired ablations show that the full workflow improves explicit execution over a single submission. Prompted audio measurement succeeds in all 28 selected abstract cases but yields no clear overall improvement on the reference-based metrics and increases tool calls. These results expose the gap between executable DAW control and precise realization of perceptual mixing goals.

Abstract: Latent audio generative models are typically trained in two stages: an audio codec is learned first, followed by a latent generative model. This decomposition leads to a decoder train-generation mismatch: the codec decoder is trained on encoder-induced latents but deployed on generator-produced latents at inference time. Across diverse datasets and latent generative models, we observe clear reconstruction-generation gaps under both FD and FAD, showing that strong reconstruction quality does not necessarily translate into strong end-to-end generation quality. A natural remedy is to adapt the decoder on generation-produced latents, but generated latents lack correspondence with source audio and therefore cannot directly provide the paired supervision used for decoder fine-tuning. We introduce \textbf{AudioGAR}, which constructs intermediate latents by perturbing encoder latents and denoising them through the frozen latent diffusion model. These latents form a trajectory from reconstruction toward generation, with lower-noise latents retaining source correspondence and supporting paired decoder fine-tuning. We fine-tune only the codec decoder on these latents, while keeping the codec encoder and latent generative model frozen. When applied to AudioX, AudioGAR substantially improves generative performance. It requires only 1.5\% of the original training audio hours and 0.26\% of the original training cost.

Abstract: Prompt-conditioned piano MIDI-to-Music rendering aims to faithfully render target notes while reproducing the timbre of a reference recording. Existing approaches primarily follow two paradigms: autoregressive (AR) modeling and flow matching (or diffusion). Discrete-codec AR models provide causal temporal modeling, but quantization can discard acoustic detail. Flow matching better preserves acoustic structure in the cost of full-sequence attention costs and worse semantic structure. Continuous autoregressive models operate directly on continuous representations. It not only combines the condition-following ability of AR models and distribution-modeling capacity of flow matching but also bypasses the quantization bottleneck with lower computational costs. Building on this principle, we present Composer--Performer--Refiner (CPR) framework. Composer autoregressively predicts continuous hidden states, Performer generates 24kHz acoustic latents through local flow matching and Refiner then upsamples the waveform to 48 kHz. We further introduce Bottlenecked Representation Alignment (BREPA) and Modality--Time RoPE (MT-RoPE) to strengthen musical semantic structure in Composer hidden states and temporal alignments across modalities. Codes are available at https://github.com/FEAfeatherTHER/CPR_official