Music Flamingo: Scaling Music Understanding in Audio Language Models
Authors: Sreyan Ghosh
Arushi Goel
Lasha Koroshinadze
Sang-gil Lee
Zhifeng Kong
Joao Felipe Santos
Ramani Duraiswami
Dinesh Manocha
Wei Ping
Mohammad Shoeybi
Bryan Catanzaro
Venue: ICLR 2026 Poster (2026)
Keywords: music
audio
multi-modal
language model
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A foundational large audio-language model focused on music understanding and reasoning

Abstract: We introduce Music Flamingo, a novel large audio–language model, designed to advance music (including song) understanding in foundational audio models. While audio–language research has progressed rapidly, music remains challenging due to its dynamic, layered, and information-dense nature. Progress has been further limited by the difficulty of scaling open audio understanding models, primarily because of the scarcity of high-quality music data and annotations. As a result, prior models are restricted to producing short, high-level captions, answering only surface-level questions, and showing limited generalization across diverse musical cultures. To address these challenges, we curate MF-Skills, a large-scale dataset labeled through a multi-stage pipeline that yields rich captions and question–answer pairs covering harmony, structure, timbre, lyrics, and cultural context. We fine-tune an enhanced Audio Flamingo 3 backbone on MF-Skills and further strengthen multiple skills relevant to music understanding. To improve the model's reasoning abilities, we introduce a post-training recipe: we first cold-start with MF-Think, a novel chain-of-thought dataset grounded in music theory, followed by GRPO-based reinforcement learning with custom rewards. Music Flamingo achieves state-of-the-art results across 10+ benchmarks for music understanding and reasoning, establishing itself as a generalist and musically intelligent audio–language model. Beyond strong empirical results, Music Flamingo sets a new standard for advanced music understanding by demonstrating how models can move from surface-level recognition towards layered, human-like perception of songs. We believe this work provides both a benchmark and a foundation for the community to build the next generation of models that engage with music as meaningfully as humans do. Demo: https://musicflamingo.github.io

Published as a conference paper at ICLR 2026
MUSIC FLAMINGO: SCALING MUSIC UNDERSTANDING IN AUDIO LANGUAGE MODELS
Sreyan Ghosh 12- Arushi Goel',, Lasha Koroshinadze* Sang-gil Lee', Zhifeng Kong', Joao Felipe Santos', Ramani Duraiswami", Dinesh Manocha', Wei Ping'. Mohammad Shoeybi', Bryan Catanzaro'
NVIDIA, CA, USA', University of Maryland, College Park, USA?
Correspondence: sreyang @ayy.n@uud.edu,, mushig@nvidia.com
Project: http::/eeaaarcc...oa.aacaccc///a
ABSTRACT
We introduce Music Flamingo, novel large audio-language model, designed advance music (including song) understanding in foundational audio models. While audio audio- language research has progressed rapidly, music remains challenging due to its dynamic, layered, and coormation-densse nature. Progress has been further limited by the difficulty of scaling open audio understanding models, primarily because of the scarcity of high-quality music data and annotations. As result, prior models are restricted to producing short, high-level captions, answering only surface-level questions, and showing limited generalization across diverse musical cultures. To address these challenges, we curate MF-Skills, a large-scale dataset labeled through a multi-stage pipeline that yields rich captions and question answer pairs covering harmony, structure, timbre, lyrics, and cultural context. We fine-tune enhanced Audio Flamingo 3 backbone MF-Skills and further strengthen multiple skills relevant to music understanding. To improve the model's 's reasoning abilities, we introduce a post-training recipe: first cold-start with MF-Think. a novel -h-innof-thought dataset grounded in music theory, followed by GRPO-based reinforcement learning with custom rewards. Music Flamingo achieves state-of- the-art results across 10+ benchmarks for music understanding and reasoning, establishing itself as a generalist and musically intelligent audio-language model. Beyond strong empirical results, Music Flamingo sets new standard for advanced music understanding by demonstrating how models can move from surface-level recognition towards layered, human-lik like perception of songs. We believe this work provides both a benchmark and a foundation for the community to build the next generation of models that engage with music as meaningfully humans do.
"Without music, life would be a mistake." Friedrich Nietzsche
INTRODUCTION
Audio - including speech, environmental sounds, and music - is central to human perception and interaction. It enables us converse, perceive our surroundings, express emotions, interpret multime- dia, and engage with cultural artifacts. Among these, music is particularly significant: the creation, sharing, discovery, and understanding of music are daily activities for billions worldwide. Recent progress in Audio-Languee enabling impressive advances in speech and sound understanding. Yet, music remains fundamentally distinct from other forms of audio. Core musical attributes such as key, tempo, harmony, instru- mentation, and vocal styles are not present in non-musical audio and require specialized reasoning. Moreover, tasks adapted from speech and sound (e.g., captioning, transcription, retrieval) demand unique treatment when applied to music. To date, no model has achieved music understanding on par with the multi-modal breakthroughs seen in vision speech. Improved music understanding
Models (ALMs) has extended language models into the auditory domain,
Equally contributed and led the project. Names randomly ordered." "* Significant technical contrihution.

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Figure 1: Comparison of captions for two diverse, full-length, in-the-wild songs by Music Flamingo and other frontier models. Prior models, such as AF3, tend output short, surface-level descriptions (e-g.. broad genre, tempo, or instrumentation), while Qwen3- Omni offers isolated observations without forming coherent musical narrative. In contrast, Music Flamingo produces detailed, multi- luyti-layered captions that integrate theory-aware analysis with performance context. It links surface attributes (tempo, key, etc.) to mid-level structures (chord progressions, vocal phrasing, etc) and higher-level dimensions (lyrical meaning, emotional trajectory, etc.). This ability to connect one aspect of music another results in richer, more holistic captions that resemble how trained musicians describe songs. Detailed expert analysis in Appendix E. and F.
would unlock richer applications in creation, recommendation, cross-cultural analysis, education, and interactive systems, enabling models to engage with music as deeply as humans do.
Despite advances in scaling LALMs (Goel et al., 2025; Chu et al., 2024; KimiTTemm et al., 2025; Tang et al., 2024; Bertin-Mahieux et al., 2011), effective music understanding remains an open challenge (hereafier, we use "music" to refer broadly to both instrumental pieces and songs). Current frontier LALMs, when captioning even widely recognizable tracks, often produce short and generic descriptions, misidentify surface-level attributes such as tempo or key, and sometimes rely on text-derived knowledge rather than genuine auditory analysis (Gemini team, 2025). We argue this stems largely from data: most available music-caption caption pairs originate from early datasets like MusicCaps (Agostinelli et al., 2023b), and subsequent datasets inherit its stylistic limitations of short, surface-level summaries, limitations of short, surface-level summaries that omit bar/time localization, harmonic and formal structure, vocal/tyric grounding, and cultural context, and often with a narrow focus on instrumental-ont snippets. This prevents models from learning the layered nature of music, spanning surface attributes (tempo. key, timbre), mid-level structures (chord progressions, rhythm, phrasing), and higher-level dimensions (lyrics, emotional ares, cultural context). Architecturally, training practices for most music L.LMs or captioners still constrain holistic learning - for example, the use of encoders (like CLAP (Elizalde et al., 2022)) that do not capture spoken content or low-level features like pitch in their representations (see our study in Appendix G). thereby constraining learning of vocal timbre, lyrical alignment, and expressive nuances in songs. We contend that even a task as basic as music captioning, when re-imagined beyond surface-level summaries, is inherently explorative and compositional: a musically informed description requires reasoning through multiple layers of structure and meaning, and admits not one single answer but a spectrum of valid interpretations shaped by theory, perception, and artistry.
Main Contributions. In this paper, we introduce Music Flamingo, new and open-source large audio-language model specifically designed to advance music understanding. Unlike speech or environmental sounds, music is inherently layered, expressive, and structured, combining surface- level acoustic attributes (tempo, key, timbre) with mid-level organization (harmony, form, rhythm) and higher-level dimensions (lyrics, style. affect, cultural context). Capturing this multi-faceter nature of music requires models that can move beyond surface-level recognition toward reasoning and interpretation more akin to trained musician.
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To build Music Flamingo, re-imagine the scope of music understanding and recast conventional tasks, such as music captioning and question answering, into comprehensive formulations that demand deliberate, step-by-step reasoning (Fig. 3). To support this reframing, we introduce new strategies for both data curation and model training. First, we present MF-Skills, a dataset with 4M+ high- quality samples for training nusie-understanding models. Unlike prior corpora dominated by short, instrumental snippets, MF-Skills scales to long, multicultural full-length songs with vocals drawn from diverse sources. We propose a multi-step labeling pipeline that yields detailed, multi-aspect, layered captions capturing harmony, structure, timbre, lyries, and cultural context - designed to elicit musician- level reasoning. Beyond captions, MF-Skills includes carefully curated question-ane answer pairs that move past simple instrument identification toward tasks requiring temporal understanding, harmonic analysis, lyrical grounding, and other skills. On the modeling side, we first identify core limitations in Audio Flamingo 3 and preetiun-r--triining it to build stronger backbone by fine-tuning it on multilingual, multi-speaker ASR and extended audio reasoning datasets before specializing it for music. Next, we propose a post-training stage specifically designed to enhance reasoning. For this stage, we further introduce MF-Think, a dataset of 300K chain-of-thought examples grounded in music theory, which we use for cold-start reasoning training. Finally, we apply GRPO-based reinforcement learning with custom rewards, enabling explicit step-by-step musical reasoning. In summary, our contributions are:
We propose Music Flamingo, a new LALM for advancing music understanding. We re-imagine conventional music tasks (e.g., captioning, QA) as reasoning-centric formulations and introduce novel training strategies tailored to these tasks.
To support training, release MF-Skills and MF-Think, two large-scale datasets containing music-caption and mu pairs designed to promote deliberate reasoning. Unlike prior datasets limited short instrumental clips, ours include full-length, multi-cultural songs with detailed, multi-aspect annotations.
Music Flamingo achieves ofate-of-the-art results on 12 music understanding and reasoning bench- marks. Beyond academic benchmarks, expert evaluations show its outputs are more accurate and preferred by trained musicians than existing models.
We have released code, training recipes, and our new datasets under an appropriate research-only nvidia. www/caos/ Labs//dl//////
license at httts::
2 RELATED WORK
Multimodal lango-la modeling. The rapid progress of LLMs has accelerated the devel- opment of multimodal LLMs (MLLMs) capable of understanding and reasoning across diverse modalities, including audio. Within this space, ALMs focus specifically on reasoning over auditory inputs such as speech, sounds, and music. Architecturally. ALMs generally follow two paradigms: (i Encoder-only ALMs, which learn joint embedding space for audio and text, enabling tasks like cross-modal retrieval. Representative models include CLAP (Elizalde et al., 2022), Wav2CLIP (Wu al., 2021), and AudioCLIP (Guzhov al., 2021). (ii) Encoder-decoded ALMs (often called Large Audio-Language Models, LALMs), which augment decoder-only LLMs with audio encoders. No- table examples include LTU (Gong al., 2023b), LTU-AS (Gong et al., 2023a), SALMONN (Tang et al., 2024), Pengi (Deshmukh et al., 2023), Audio Flamingo (Kong et al., 2024), Audio Flamingo 2 (Ghosh et al., 2025), Audio Flamingo 3 (Goel et al., 2025), AudioGPT (Huang et al., 2023), GAMA (Ghosh et al., 2024), Qwen-Audio (Chu et al., 2023), and Qwen2-Audio (Chu et al., 2024). There has also been a surge of LALMS that specifically focus on music, including Mu-LLaMA (Liu et al., 2024a), MusiLingo (Deng et al., 2024), M2UGen (Liu et al., 2024b), SALMONN (Tang et al., 2024). and LLARK (Gardner et al., 2024). These LALMS have substantially advanced core audio understanding tasks such as automatic speech recognition (ASR) (Radford et al., 2022), audio captioning (Kim et al., 2019), and acoustic scene classification (Chen et al., 2022). More importantly, they have enabled open-ended audio question answering, which requires both complex auditory reasoning and external world knowledge. While music has often been included as a modality within these models, it has rarely been central focus.
Scaling music understanding within ALMs has proven particularly difficult. For instance, while the Audio Flamingo series has expanded its training data substantially from version 1 to 3, the music component of training data has increased by only zs =00, compared to much larger growth in speech and environmental sounds. Similarly, models such Kimi (KimiTeam al., 2025) and
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1. Annotation
Detailed music euptions of segmeents
MF-Skills
Music Source
Selection
Music Caption
LALM
LLM
Reasuning choins
Willesl
Keys
Music Clip
Chords
P QA Pairs
Beats
Tyrics
MF-Think
Music Metadata
Training Pipeline
Cold Start Ja
Reasoning
Ressoning d Scaling
Full line-luning
i
MF-Skills and COnnext extension
WT MF-Think
w/ custom rewards
Audio Flamingo
Music Flamingo
Music Flamingo w/
GRPO
Figure 2: I. Annotation pipeline for constructing our proposed datascts from diverse music clips. II. Training pipeline of Music Flamingo: begin by improving Audio Flamingo then perform full fine-tuning music datasets to build the Music Flamingo foundation model. Finally, the model undergoes reasoning cold-start training followed by GRPO fine-tuning to enable step-by-step reasoning.
Step Audio (Huang et al., 2025) (where training data disclosures exist) show comparable imbalances. Finally, models like LLARK (Gardner et al., 2024) and MU-LLaMA (Liu et al., 2024a) curate music captions and question-answer pairs from existing open-source datasets, which lack diversity and skills. This is due to several challenges: the difficulty of collecting high-quality and culturally diverse music audio (Kumar et al., 2025), curating reliable annotations (Christodoulou et al., 2024), and the reliance of most works on private, proprietary datasets (Agnew et al., 2024). Large labs often construct in-house collections of lyries and metadata by scraping online lyric repositories, translations, and song databases (Ahmed et al., 2025). Models such as Jukebox and Neural Melody Reconstruction exemplify this paradigm. Finally, noted earlier, most publicly available datasets emphasize short instrumental clips, with very limited coverage of full-length songs containing vocals, hindering a comprehensive understanding of music (Kumar al., 2025).
Music information retrieval and captioning. Beyond LALMs, music understanding has a long history in Music Information Retrieval (MIR), encompassing retrieval, classification, and captioning. Foundational tasks such as key detection (Chai & Vercoe, 2005), chord recognition (Sheh & Ellis, 2003), and tempo estimation (Scheirer, 1998) have been extensively studied, largely in instrumental music. Lyrics transcription has also been explored, posing more difficult challenge than ASR due to overlapping vocals, diverse singing styles, and background instrumentation (Mesaros Virtanen, 2010). As discussed earlier, music captioning has been studied in analogy to audio event captioning, but typically produces short, high-level semantic descriptions rather than layered, theory-aware accounts. Importantly, improved captioning not only benefits downstream music understanding but also supports the training of generative music models by providing high-quality text supervision for in-the-we wild audio (Agostinelli et al., 2023b). This connection has recently been emphasized in both standalone music modeling efforts and in broader video generation systems (Chen et al., 2025).
METHODOLOGY
To build Music Flamingo, we first curate high-quality songs, followed by labeling them and finally fine-tuning the model the curated data. Music Flamingo is specialized music understanding model built by fine-tuning a version of Audio Flamingo 3, specifically with high-quality data to close the gap skills and tasks erucial for music understanding. Finally, the model is fine-tuned using reinforcement learning to enable step-by-step music reasoning.
3.1 IMPROVED AUDIO FLAMINGO 3 BASELINE
Data. We first strengthen Audio Flamingo to serve as the backbone for Music Flamingo. Unlike instrumental-only music, songs contain vocals that contribute not only lyrics but also timbre, style,
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Figure Examples from MF-Skills Caption MF-Skills QA and MF-Think We emphasize that our
re-imagined captions are denser, more informative, and designed require deliberate reasoning generate. Additional examples are provided Appendix G.I.
and expressive variation. Capturing these elements requires stronger spoken language understanding than prior baselines. Thus, in addition to the data used for AF3 training, we add the following to the mix: 1) Across all fine-tuning stages (1-3), we incorporate large-scale multilingual ASR data (sources Emilia dataset (He et al., 2024), CoVoST (Wang et al., 2020), MUST (Qin et al., 2025), Amazon-Siie -IFT (Pandey et al., 2025); details in Appendix 5) to better capture global vocal diversity, 2) In stage 3, we add multi-talker ASR data, including CHIME (Watanabe et al., 2020; Cornell et al., 2023), Switchboard (Godfrey et al., 1992) and ALI meeting (Yu al., 2022). enabling the model to parse turn-taking and overlapping voices, which is critical for understanding duets and ensemble singing and, 3) We expand the data mix with speech-centric skills, including phoneme recognition and lyries transcription, improving alignment between vocal content and musical context.
Training Pipeline. We adopt the training paradigm introduced in Audio Flamingo 3 (Goel et al., 2025) to fine-tune the model the diverse of speech data described above. The resulting fine- tuned model then serves as the foundation for developing the music-focused foundational model.
3.2 BUILDING FOUNDATIONAL MUSIC UNDERSTANDING
MF-Skills. Prior captioning datasets mostly provide surface-level summaries, while existing QA datasets are dominated by simple classification tasks (e.g., instrument or tempo detection). Even large-scale skill-focused datasets such as Au- dioSkills focus primarily sounds and speech for their diverse skill-specific QAs, with music data reduced basic information extraction. To mitigate this gap, we design MF-Skills and cap- ture the layered nature of music to train models for deliberate reasoning. Figure 2 illustrates our data curation pipeline, and Figure 3 provides examples from our curated dataset.
We begin by collecting full-length songs from diverse cultures (~3M in total), as shown in Fig- ure 4, thereby moving beyond the short, Western instrumental clips that dominate prior datasets. As shown in Figure 2, our pipeline consists of four stages: 1) Inîtial caption synthesis: Generate short, surface-level captions for 30s segments using frontier music models to min- imize hallucinations, 2) Metadata extraction: We apply conventional MIR tools, including
Figure Genres (inner circle) & Cultures (outer circle) distribution of songs.
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madmom (Böck al., 2016) (beat), essent (Bogdanov et al., 2013) (key), Chordino (Mauch & Dixon, 2010) (chords). Parakeet (NVIDIA, 2025) (lyrics) to provide reliable low-level at- tributes and, 3) Caption & QA creation: Using metadata and initial captions, we prompt an LLM (with music-theory grounding) to produce detailed, multi-aspect captions covering several aspects including: a) low-level information (tempo, BPM, keys), b) instrumentation & production, e) lyries, and lyrical themes (including structural segmentations such as verses, choruses, and bridges), d) song structure & dynamics e) theoretical insight (e.g., chord transitions and harmonic movements) and f) overall mood & context. Our final captions have an average of 451.65 words. For QA, we analyze skill gaps AF3 using benchmarks such as MMAU (Sakshi et al., 2024), MMAU-Pro (Kumar et al., 2025), MuChoMusic (Weck et al., 2024), MusicCaps (Agostinelli et al., 2023b), MusicQA (Li al., 2022), and NSynth (Engel et al., 2017), then generate novel QA targeting five skills: a) Temporal understanding. b) Attribute identification, c) Harmonic & theoretical analysis, d) Lyric and vocal grounding. e) Comparative and structural reasoning. This approach also mitigates distribution gaps, e.g. instrument identification in complex, multi-layered songs rather than isolated clips, and cul- ture-skills gaps, e.g. identifying ragas in Indian music or polyrhythms in African drumming, which prior datasets neglect they were dominated by Western music. 4) Quality filtering: A frontier MLLM is used to verify and retain only high-quality captions and QAs. The final dataset contains 5..2 examples (~3.44 captions and 1.8M QAs).
Beyond curating new data, we also refine existing music datasets such as MSD (Bertin-Mahien al. 2011). Music4All (Geiger al.. 2025), and the music subset of AudioSkills-XL (Goel et al.. 2025). We (a) rewrite captions to add lyrical themes, vocal attributes, and correct mislabels of tempo, key, and timbre using metadata, and (b) reframe MCQ-style questions to reduce language priors and guessing, an issue highlighted in recent benchmarks (MMAU-Pro (Kumar et al., 2025), RUListening (Zang et al., 2025)). We cluster Q&As by trait and rephrase them with metadata to require genuine auditory perception. We provide examples below:
Existing caption (from AudioSkills-MSD): This is an upbeat 1980s pop-rock track with a danceable 4/4 beat around 140 BPM, featuring bright guitar riffs and melodic synthesizers. The song carries an energetic and catchy feel, blending indie rock elements with disco-inspired rhythms, typical of early 80s production.
Our modified caption: This upbeat 1980s pop-rock track in B minor rides a danceable 4/4 beat around 120 BPM, driven by bright electric guitars, shimmering synths, and lively drums. Its catchy, energetic melody blends indie and disco influences, ... The lyrics add a layer of intimate storytelling, weaving lines about moments of fleeting connection and dreamy recollection ("I close my eyes and count to ten we were strangers a moment ago").
Existing QA (from Audio What genre does this track? Choose one among the following options: (A) Jazz (B) Classical (C) Rock (D) Spoken Word ("Spoken Word" stands out as unique option among all other options, which are music genres)
Additional plausible distractors options added to modified Q4: (E) Audiobook narration
excerpt (F) Rap a cappella (no beat) (G) Podcast monologue intro (H) Documentary voice-over bed (I) Theatrical monologue with ambiance (J) Spokenword/poetryy
Training Methodology. We begin with the improved base model derived from re-training Audio Flamingo 3, as outlined in Section 3.1. This model is subsequently fine-tuned on the proposed MF-Skills dataset, the improved QA datasets described above, and other music datasets derived from the training mix of Audio Flamingo 3 (Goel et al., 2025). Inspired by our study in Appendix G, we also incorporate data for learning low-level music properties, such as chords, keys, and BPM. Dataset details in Appendix C
We also encounter two primary limitations in training the model. First, the Audio Flamingo backbone supports maximum context length of 8,192 tokens and 10 minutes of audio, whereas our curated datasets predominantly contain much longer captions and full-length songs up to 20 minutes. We extend the context length to ~44 tokens, and adopt fully sharded training to handle the increased memory requirements. Second, music understanding requires fine- mee-griined temporal perception, including chord progressions, tempo, key changes, and vocal dynamics. To capture these transitions, we incorporate time-aware representations into the audio encoder outputs before feeding tokens into the L.M. Specifically, we employ Rotary Time Embeddings (RoTE) (Goel et al., 2024),

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which define the rotation angle using absolute timestamps rather than token indices. Unlike standard RoPE, where the rotation angle depends on the token index i as t- -i 2T,, RoTE defines 0 using the token's 's absolute timestamp T;: 0 T4 2T. For audio tokens produced at fixed stride of 40ms (Radford et al., 2022; Goel et al., 2025), we interpolate discrete time positions 7; and feed them into the RoTE module to obtain lightweight, temporally grounded representations.
3.3 POST-TRAINING WITH REINFOR LEARNING
While prior music understanding tasks rarely required reasoning. our formulation explicitly demands For example, generating caption in Figure requires the model to progressively connect surface properties (tempo, key) with higher-level structures (harmony, form, production, lyries) and then articulate them as coherent musical narrative- process that is non-trivial even for trained musicians. To enable this, we introduce post-training stage beyond large-scale SFT that strengthens Music Flamingo's oss reasoning abilities. First, we construct MF-Think, a high-quality Chain-of-Thour (CoT) dataset used for cold-start reasoning. We then fine-tune with MF-Think before applying GRPO with custou- -ustindd rewards, encouraging explicit step-by-ster reasoning.
MF-Think. We begin with diverse, high-quality subset of MF-Skills. Since not all QAs demand deep reasoning. we sub-sample the most challenging examples by prompting gpt gpt-oss post-oss-lee 120b with both the audio and QA (prompt in Appendix H). CoT Generation. For each selected QA caption, we prompt ept-oss-1200 -20b with metadata from MF-Skills to generate long, reasoning chains. Prompts include constraints length, grounding music theory, and exemplar demonstrations. Quality Filtering. Each reasoning chain is segmented into smaller steps, which fact-checked using our post-SIT MF (Yes/No verification against the audio). We rewrite chains with minor errors and discard those with >30% incorrect steps. The final dataset contains = CoT examples, including 11k QA and =59k captioning samples, providing a rich foundation for reasoning- enhannced training.
Supervised Fine-Tuning with MF-Think. To equip the model with advanced reasoning capa- bilities, we first perform SFT of the music foundation model on our curated MF-Think dataset. During this stage, we append the prompt: Output the thinking process in <think> </think> and final answer in <answer> </answer> the original questions in the MF-Think dataset, to encourage the model to explicitly generate reasoning chains within the <think> </think> tags and the final answer within the <answer> </ </answer> tags. This process instills structured reasoning for both the question-answering and This SFT stage acts initial warm-up phase, effectively priming the model for subsequent reinforcement learning (RL) fine-tuning.
tasks.
GRPO for Music reasoning and understanding. Building the advancements in the GRPO algorithm, we adhere to the standard GRPO algorithm to train our model. GRPO obviates the need for additional value function and uses the average reward of multiple sampled outputs for the same question to estimate the advantage. For each given question the policy model generates a group of candidate responses {or, 02, ... ) from the old policy Te... along with their corresponding rewards {r), (T..72..... rG} which are computed using rule-based reward functions (e.g., format and accuracy). The model my subsequently optimized using the following objective function:
C
[(0)=Eg,(o.)
G
min To(oila) Thad (or Ad, clip Tolas g)
Te (o.lg) ( e,1 + e) 3DL II mer))
(I)
where E is the clipping range of the importance sampling ratio, ,3 is the regularization strength of the KL-penalty term that encourages the learned policy to stay close to the reference policy, and G is the group size, ie., the number of candidate responses (samples) the policy generates for each input question, which is set to 5 in experiments. To stabilize training, the sampled rewards normalized to compute the advantages A, as: mean (mrnn(((....... ......rr)) Next, we discuss the custom [r.( reward functions we designed for GRPO training, which play a pivotal role in optimization.
Format Reward. In order encourage the model to generate outputs that adhere to the prescribed response format, we use the standard eegex-based format reward (DeepSeek-AL, 2025). Specifically, the model is instructed to produce reasoning traces enclosed within <think> </thinka tags,
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followed by the final answer enclosed within <answer> </answer> tags. If the output strictly follows the required tag structure, the model gets a reward of clse O. This binary reward function ensures that the model learns to consistently produce well-structured responses.
Accuracy Reward. For question- answoerin (QA) tasks, we employ the accuracy reward to encourage the model to generate accurate final answers. Given question with the corresponding ground-truth answer, the model generates a candidate output o, where the final answer is extracted from within the <answer> </answer> tags. The accuracy reward directly matches the normalized predicted and ground truth answers, enforcing strict answer correctness.
Structured Thinking Reward. For caption generation tasks, the standard accuracy reward cannot be directly applied due to the long and open-ended nature of the generated captions. To address this, we design custom reward function that evaluates generated captions against structured ground-truth metadata. To achieve this, we first generate ground-truth structured metadata shown below using gpt-oss-120b (OpenAI, 2025) for the subset of the captions the MF-Skill dataset as follows:
("Genre": Americana, "BPM": 125, "Key": G minor, "Meter": 4/4, "Structure": Intro, Verse, Verse. Bridge, Solo, Chorus, Outro. "Instruments": fingerstyle acoustic guitar, banjo, mandolin, spoken-word voice, "Vocal Character": male spoken-words deep resonant timbre, clcar/deliberate, light reverb, "Lyric Themes": forgiveness, humility, spiritual prayer, desert frontier, betrayal, outlaw narrative, "Theory": G minor center; modal interchange with relative major; F#tuug - Eb6 - D7 resolution; Gmaj7/G7 brighten prayer sections, "Mix Notes": high- fidelity organic; wide natural stereo panning; minimal reverb; warm, clear, light compression; close-mic intimacy, "Dynamics": bridge increases harmonic rhythh/urgency.)
The structured thinking reward function computes string match for each answer in the category of the structured ground The total reward is obtained by normalizing the number of matching words by the total number of metadata categories.
-ruth metadata (e.g. Genre, Subgenre, BPM etc.) and the generated caption.
The overall reward function used GRPO training integrates the format reward with the accuracy reward for the data with question-answer subset, while for the caption subset of data, it combines the format reward with the structured reasoning reward.
4 EXPERIMENTS
Experimental Setup. We train Music Flamingo on 128 NVIDIA A100 GPUs (80GB). Details on batch size, learning rates, and optimizers for each stage of training are in Appendix D.
Baselines. We evaluate our model against recent SOTA LALMs, including GAMA (Ghosh et al., 2024), Audio Flamingo (Kong et al., 2024), Audio Flamingo 2 (Ghosh et al., 2025), Audio Flamingo 3 (Goel et al., 2025), Qwen-Audio (Chu al., 2023), Qwen2-Audio (Chu et al., 2024). Qwen2-Audin Instruct, Qwen2.5- Phi-4-mm (Abouclenin et al., 2025). Baichun Audio (Li et al., 2025b), Step-Audio-Cul al., 2025), LTU (Gong al., 2023b), LTU-AS (Gong et al., 2023a), SALMONN (Tang et al., 2024), AudioGPT (Huang et al., 2023), and Gemini (2.0 Flash, 1.5 Pro, 2.5 Flash and 2.5 Pro) (Team et al.,
(Xu et al., 2025), RI-AQA (Li et al., 2025a), Pengi (Deshmukh et al., 2023),
(Huang
2023), as well GPa-io- (Hurst et al., 2024). For Table 1, we only compare against open LALMs. All results reported in the tables correspond to the best-performing model.
Evaluation Datasets. We evaluate AF3 across broad set of benchmarks spanning music information retrieval (MIR), question answering, lyrics transcription, reasoning, and our proposed dataset Song- Caps. SongCaps consists of 1,000 culturally diverse songs curated assess captioning capabilities across multiple dimensions (see Section 3.2). Rather than relying on lexical overlap metrics, we evaluate captions using human-expert judgments and augeee assessments. For MIR, we use NSynth (Source and Instrument) (Engel et al., 2017), MusicCaps (Agostinelli et al., 2023a), Medley-Solos-DB (instrument classification) (Lostanlen et al., 2019), and GTZAN (genre classi- fication) (Tzanetakis & Cook. 2002). For QA and reasoning, we include MusicAVQA (Li et al., 2022), Music Instruct (Deng et al., 2024), MMAU (v05.15.25) (Sakshi et al., 2024). MMAU-Pro (Ku- mar et al., 2025), MuChoMusic (perceptual version) (Zang al., 2025; Week et al., 2024), and MMAR (Ma et al., 2025). For lyrics transcription, we evaluate on Opencpop (Wang et al., 2022) a dataset for chinese songs and MUSDB18 18 1 Lyrics (Rafi al., 2019) dataset for English songs. We
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Table Comparison Music Flamingo (w/ GRPO) with other LALMs on various benchmarks (WER (Word Error Rate), ACC 1 (Accuracy), Score (1-10) 1 and GPTS t (GPT evaluation)). We report scores for only the top-performing prior LALM. We highlight closed source, open weights, and open source models.
Task
Dataset
Model
Metries
Results
MMAU (Music)
Audio Flamingo 3 Music Flamingo
73.95
74.47
full-test text-mini
ACC 1
76.83
76.35
Gemini-2.5 Flash
64.90
MMAu-Pro-Music
Music Flamingo
ACC 1
65.60
Qwen3-O Music Flamingo
52.10
Music QA
MuChoMusic
ACC 1
74.58
and
Reasoning
MMAR (Music)
Qwen2.5-O
46.12
Music Flamingo
ACC 1
48.66
Audio Flamingo 3
92.7
Music Instruct
GPT5
Music Flamingo
97.1
Audio Flamingo 3
76.7
Music AVQA
ACC 1
Music Flamingo
73.6
SongCaps (Ours)
Audio Flamingo
6.5
6.7 6.2
Score
Human Coverace SP73-Correciness
Music Flamingo
8.3
8.8 8.0
NSynth
Audio Flamingo 3
65.5 78.9
ACC 1
Source Instrument
Music Flamingo
75.89 80.76
Music
GTZAN
Pengi Music Flamingo
80.00
ACC 1
Information
Genre
84.45
Retrieval
Medley-Solos-DB
Audio Flamingo 2
85,80
ACC 1
Instrument
Music Flamingo
90.86
Qwen3-O
7.2
MusicCaps
GPT5
Music Flamingo
8.8
Openepop
GPT-4o Qwen2.5-O
53.7 55.7
Lyrics
Chinese
Music Flamingo
WER
12.9
Transcription
MUSDB18
GPT-4o Qwen2.5-0
32.7
68.7
English
Music Flamingo
WER
19.6
acknowledge the existence of numerous other MIR baselines and benchmarks, as MIR encompasses broad range of tusks. For the scope of this paper, however, restrici our comparisons to LALMs and the benchmarks most commonly used the LALM literature. We encourage the community to further expand evaluations to wider set MIR baselines in future work.
Music Understanding and Reasoning Evaluation. Table shows that Music Flamingo consistently sets the bar across music QA, reasoning, MIR, and lyrics transcription benchmarks. On MMAU- Music, reaches a competitive 76.83 accuracy, surpassing both closed and open-source models. and MuChoMusic benchmarks, where Music Flamingo scores 65.6 and 74.58, respectively, a clear evidence of its robustness on complex datasets. Without reinforcement learning fine-tuning with thinking traces, performance drops 63.9 and 69,5 respectively, highlighting the value of step-by-step reasoning and exploration. In MIR tasks, Music Flamingo continues dominate: on NSynth, achieves 80.76% accuracy in instrument recognition, and on Medley Solos DB, it reaches 90.86% for fine-grained instrument classification. It also delivers a significantly lower WER on Chinese and English lyrics transcription than both open and dlosed-ouuree LALMs. These results establish Music Flamingo as the most capable model to date for detailed music understanding and reasoning.
The gap widens on the tougher MMAU-Pro-Minn
On our proposed SongCaps benchmark, designed to evaluate music captioning, human raters scored the model outputs on scale of 1-10. Music Flamingo achieves high rating 8.3 outperforming Audio Flamingo 3. Furthermore, correctness and coverage of the caption. Music Flamingo achieves 8.0 for correctness and 8.8 for coverage, outperforming Audio Flamingo 3. These results highlight that Music Flamingo not only excels on structured QA and recognition tasks but also produces richer, more faithful natural language descriptions of music
evaluate the captions using LLM-as-a-judde measuring both
Qualitative Evaluation. We perform a thorough qualitative evaluation of Music Flamingo's outputs, assessed by trained music experts, in comparison with state-of-the-ar LALMSs in this domain. Due to

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space constraints, refer the readers Appendix E for analysis on songs of varying genres and popularity, and additionally analysis of songs from different cultures in Appendix
5 CONCLUSION, LIMITATIONS AND FUTURE WORK
We introduced Music Flamingo, large ludio-language model designed to advance music understand- ing. By curating MF-Skills and MF-Think we scale beyond short, instrumental clips to full-length, multi-cultural songs with layered annotations, and incorporate chain-of-thoupe reasoning for richer music analysis. Through improved pretraining. fine-tuning, and post-training with reinforcement learning, Music Flamingo achieves SOTA results across diverse music understanding and reasoning benchmarks. Beyond empirical gains, it demonstrates how models can move from surface-level recognition toward layered, human-like perception of songs.
Music Flamingo still has a few limitations, including:
(i) limited understanding of underrepresented skewed cultural traditions, highlighting the need expand training data across more diverse global music; (ii) gaps in specialized tasks, such as fine-graines piano technique recognition and other instrument-specinn skills; and (iii) the need to broaden coverage across additional musical skills to achieve more comprehensive understanding.
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APPENDIX
A
ETHICS STATEMENT
This work studies audio, music, and singing-voice understanding across culturally diverse material. Our experiments rely on publicly available datasets and/or content licensed for research use. We do not release copyrighted audio, stems, or lyrics; any examples used for qualitative illustration are either (i) already distributed by the originating dataset under a research-permissee replaced by non-copyrightable descriptors (e.g., metadata, short transcriptions for analysis) when licenses are restrictive. No personally identifying information is collected, and no human-subjects experiments were conducted; institutional review board (IRB) approval was therefore not required.
license, or (ii)
Cultural representation and bias. Music corpora are uneven across regions, languages, and genres. Such imbalance can yield biased estimates degrade performance on underrepresented traditions. We mitigate this by (a) documenting dataset composition and selection criteria, (b) emphasizing vocal and multicultural material in evaluation, and (c) reporting known limitations. We encourage downstream users to avoid normative claims about "quality" across cultures and to treat our benchmarks as descriptive rather than prescriptive.
Copyright and content ownership. Models trained on musical recordings risk reproducing protected content. We do not deploy or evaluate generative audio synthesis; our outputs are textual (QA, cap- tions, reasoning traces). We avoid releasing any asset that could enable reconstruction of substantial portions of copyrighted works and provide guidance for filtering long verbatim lyric reproduction in evaluation outputs.
Privacy and safety. Singing voices may implicitly encode sensitive traits. We use only public research datasets and focus on musical attributes (rhythm, harmony, timbre, structure) rather than identifying individuals. Potential misuse includes intrusive listener profiling or surveillance via audio analysis; to discourage these, we release only research artifacts (documentation, evaluation protocols, non-identifying metadata) and clearly scope permitted use licenses when possible.
B REPRODUCIBILITY STATEMENT
We provide all details needed to reproduce our results within the paper and appendix: dataset sources and splits; audio preprocessing (sampling rates, normalization, chunking/segment lengths); model architectures and parameter counts; training schedules (optimizers, learning-rate policies, batch sizes, gradient clipping), HRPOOppst--rainine (temperature, decoding constraints); and exact evaluation protocols and metrics for every benchmark. We report hardware used where applicable, and mean=std for repeated trials. We will release code, checkpoints and data upon acceptance.
settings and reward definitions; inference settings
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C MUSIC FLAMINGO TRAINING DATASETS
Table 2 summarizes all datasets used to train Music Flamingo, including total hours, number of audio-QA pairs, and the number of epochs (passes over the dataset) used at each training stage. Similar to (Ghosh et al., 2025; Goel et al., 2025), we convert all foundational datasets (captioning, classification, etc.) into QA formats, using the same set of prompts for each task mentioned in (Ghosh et al., 2025; Goel et al., 2025).
Table 2: List of fine pre-training and fine-tuning datasets together with their training composition.
Dataset
Hours
Num. Pairs
AF3-St. 3
MF-SFT
MF-Warmup
MF-GRPO
AF3 training mix (Goel al., 2025)
30M
1.0
(Ours)
MF-Think (Ours)
176k
1.0
1.0
MusicBench (Melcchovskky.oo et 2023)
115.5 hrs
686k
1.0
1.0
Mu-LLAMA
2024g)
62.9 hrs
70k
MusicAVQA (La atal. 2022)
77.1 hrs
5.7K
1.0
1.0
MusicQA (Ouyang et al, 2025)
62.9
LP JPPMusicCapoMss (Dob et ad. 2023)
5805.7 hrs
1331,8K
1.0
1.0
L. Ca-MurcCaPesATT (DDn
20235
126.4 hrs
46.9K
1.0
1.0
L-MMusiCCapsue
2025]
7.4
7.9K
MusicCaps (Agostinelli mi. 2023b)
7.4 hrs
2.6K
1.0
1.0
NSynth (Enge) al, 2017)
321.3 hrs
BULDB-HQ 2017)
29.1 hrs
10.2K
1.0
1.0
FMA (Defferrard at, 2016)
860.7 hrs
104.2K
1.0
1.0
Music4All Captoos tours)
910.5
109K
Music4All QA (ours)
1505.7 has
180k
1.0
1.0
MSD Captions (ours)
15449.9 hrs
1.4M
MSD QA (ours)
20906 2 hrs
935k
1.0
1.0
CHIME (Foster et
2015)
342 hrs
30k
1.0
Meeling Me (X.
118.75
387k
EMILIA (He eral,
5000 hours
1.7M
1.0
(Qin al. 2025)
CoVoST at. at, 2020)
2880 his
SM
1.0
Multi talti- Swilchhordd (Coii et 1992)
109.9 hrs
76.6K
1.0
D MUSIC FLAMINGO TRAINING DETAILS
In this section, we present the training settings of our model across all stages, each with specific configurations. Details in Table 3.
Settings
A F33-FTT
MF-SFT
MF-WarrUp
MF-GRPO
global batch size learning rate
128
128
128
1.5e-5
1.5e-5
le-5
1e-6
learning schedule
Cosine decay
warm up ratio
0.03
weight decay
0.0
epoch
1
bfl6
V
grad accumulate
8
FSDP - full shard
V
V
GPUs
1000
Table 3: Training settings across stages.
E USER STUDY ON MUSIC FLAMINGO
We undergo user study with trained music experts comparing Music Flamingo to an open-source LALM-Qwent Omni, and two closed-source LALMs- and Gemini 2.5 Pro) qualitatively. To achieve this, we selected a subset of songs: 4 songs in English and 4 songs Brazilian Portuguese. From these songs, half of them are by extremely popular artists in Western music, and other half from
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Aspect
Music Flamingo
Qwen3-Omni
GPT4o-Audio
Gemini-2.5 Pro
General technicul char- acteristics (tempo, key, time signature)
Consistently outputs tempo (bpm) and key: sometimes time signatures (3 songs cor-
Rarely detailed; often
Sometimes outputs tempo (bpm usually
outputs tempo (closest bpm NO DON'T). Some key mistakes. Time signatures generally omitted.
Sometimes
omits tempolkeytime signature.
ballpark). Less
consistent with key.
recty 4/4). Mistakes usually in relative major/minor.
Genre classification
Reasonably good. misclassified ninc felcctr--unkk dance-pop). Stuuggled with Brazilian music genres (occusional mismatches).
Superficial: wrong with Brazilian genres.
often Some correct, but not consistent.
Best at identifying gen- res overall, but hallu- cinated (ABBA ska cover: Wild Things dream pop with drums).
Emotional content & Good understanding: lyries
Similar to Music
More superficial under- standing than others.
Good understanding: one small lyric mistake. Also lacks cultural/his- torical muance.
captures emotional context. Lacks cultur- al/historical nuance.
Plamingo: misses deeper cultural con-
text.
Complex technical characteristics (chord progressions, structure, production)
Attempts deeper de- tail, but sometimes inaccurate with chord- stvoicings/ttuucure.. Sometimes halluci- genre-related elements.
Least detailed: only superficial compo-
Gives more detail
More detailed for fa- mous songs; halluci- nations (e-g., nonexis- tent drums, overstated synths). Genre mis- classifications cascade into wrong technical details.
when recognizing songs; otherwise shallow.
sibion'arrangement notes.
General observations
Strong consistent technical reporting. but accuracy varies on deeper features.
Shallowest outputs.
Relies on recognition famous songs; may pull from text knowl- edge insteud audio.
Similar GPT4o Audio; detailed when recognizing famous songs. Most prone to hallucinations tied genre assumptions.
Table 4: Comparison of Music Flamingo, Qwen3-Omni, GPT4o-Audio, and Gemini 2.5 Pro across different evaluation aspects.
less known artists. The following songs were used: 1) ABBA Money Money Money, 2) Michael Sembello Maniac (From Flashdance). 3) Chandler Leighton NO DON T, 4) Lø Spirit Wild Things, 5) Antônio Carlos Jobim Águas De Março, 6) Michel Teló Ai Se Eu Pego, 7) Paulinho da Viola Apoteose Ao Samba and 8) Ave Sangria Seu Waldir.
Table 4 shows a summary of the detailed analysis comparing different musical aspects and features across models. Among the four models, Music Flamingo performs the best overall while some limitations in accurately identifying deeper context remain.
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F COMPARATIVE ANALYSIS ACROSS SONGS FROM DIFFERENT CULTURES
Furthermore, we compare the strengths of Music Flamingo on five commercially released songs spanning cultures, languages and styles: Niuver Enamorados (Spanish, Latin ballad) (Figure 5), Annika Wells Jim & Pam (English, indie/acoustic (Figure 6). Louane La fille (French, piano-fed (op)(Figure 7). Michel Telo Ai Se Eu Te Pego (Portuguese, Brazilian serganejo)(Figure 8), and Zemlyane Trava u doma (Russian, Soviet rock) 9). Below we present detailed summary of this comparison.
General technical characteristics (tempo, time signature, key). MF consistently produced numeric tempos and keys that matched canonical analyses or widely observed half/double-timg readings, and it explicitly handled relative-minor vs. metadata-major ambiguities (e.g., Enamorados: metadata in C major while harmony centers on A minor). GPT-4o and Gemini often described tempo qualitatively or gave numeric ranges but omitted keys; when numeric BPMs were provided, both models occasionally drifted toward club-tempo values that better reflect remixes than the canonical singles (c.g., Ai Se Eu Te Pego: 128-140 140-100BB BPM claimed vs. 96 BPM on the hit version). Qwen3 frequently omitted numerics altogether. Time signature was rarely stated by any model; where implied, 4/4 matched all five tracks.
Illustrative cases. (i) Jim & Pam: MF reported the double-time 155 BPM and the correct key (D), aligning with a felt pulse 79 BPM: GPT-4o gave the correct qualitative tempo band but no key; Gemini mis-estimated to 120-125 BPM and mis-keyed E. (ii) La fille: MF aligned with ~ 128 BPM in C: Gemini and GPT-4o underestimated (90-100 BPM) and/or mis-keyed. (ii) Trava u doma: MF matched A minor and 130 BPM: GPT-4o underestimated to ~ ~1000100 10 BPM....PM.
Genre. All models could identify the broad style family. Gemini held a narrow edge on matching canonical catalog labels and regional taxonomy (e.g., Ai Se Eu Te Pego: sertanejo universitário with dance-pop pop trappings). MF was directionally correct across the set and, in two cases, selected closely related tags when salient timbres or arrangement scale were misleading (Ai Se Eu Te Pego: forró inferred from accordion timbre; Trava u doma: prog-leaning language for synth/space- colored Soviet pop/rock record). Importantly, these adjacent picks did not derail MF's downstream harmony/structure reasoning and are straightforward to normalize to catalog labels. GPT-4o typically described the stylistic feel accurately (c.g., dance-pop with Brazilian flair; piano-led ballad) but often stopped short of naming the canonical label. Qwen3 alternated between sensible tags (French indie/pop ballad) and broad era styles ("80s arena/soft rock"), and twice misframed vocal songs as instrumental (see below), which contaminated the subsequent genre claim.
Takeaway: Canonical label accuracy: Gemini 2 MF GPT-40 > Qwen3. Gemini's advantage is mostly in verbatim catalog taxonomy; MF's labels are correct at the family level and, when adjacent, remain musicologically consistent with its superior harmonic/structural analysis.
Emotional content and lyrics. MF, Gemini, and GPT-4o gave coherent, text-grounded readings of mood and themes across all songs (e.g., Enamorados: memory, time, and fading love; La fille: identity and self-doubt; Trava u doma: homesick cosmonaut narrative). Where lyrics were quoted or paraphrased, all three remained faithful to content and tone. Qwen3 produced reasonable affect reads when it acknowledged lyrics, but twice declared vocal track "instrumental" (Enamorados, Trava doma), leading to incorrect conclusions about narrative and emotion.
Observation. When models inferred emotion strictly from sonics without anchoring in lyric text, nuance decreased and culture- specific references were missed (e.g., Trava u doma as an iconic space-age anthem; Ai Se Eu Te Pego as a global sertanejo earworm driven by chant-like hooks).
Complex technical characteristics (chord progressions/voicnngs, song structure, production). MF generally provided the deepest narmonic/structurre content (naming cadential behavior, relative-minor centers, verse/chorus dynamics), and its structural reads were consistently plausible across all five songs. Its main failure mode was over-specification: occasionally asserting colorful altered/extended chords or percussion layers not supported by public charts or by the stems one would expect (Jim & Pam: introduced drum-machine and synth-bass in otherwise hand-clap/snaps, acoustic texture; La fille: added brushed kit and altered dominants to a piano-centric drum-light mix). GPT-4oos arrangements and sectioning were reliably correct (intm//eere/chonn//rridgg placement,
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dynamic swells), with conservative but accurate production notes; it rarely named specific harmonic content, which limited precision but avoided hallucination. Gemini's s arrangement commentary was serviceable and sometimes quite apt famous tracks (accordion/symth hook in Ai Se Eu Te Pego), but often remained generic and light on concrete harmony. Qwen3's technical layer was the sparsest and suffered when the top-level premise wrong ("instrumental"), cascading into inapplicable structure/production claims.
Failure-mode coupling. We repeatedly observed that genre misclassification leads to production hallucinations. For instance, mapping Ai Se Eu Te Pego to forró primed mentions of forró-typical percussion, and reading La fille as an indie/pop ballad with a "soft electronic beat" invited non-existent drum programming. Conversely, when models named the canonical genre, instrumentation and mix notes tended to be accurate (Gemini on Ai Se Fu Te Pego; MF and GPT-4o on the piano+voice core of La fille).
Model-specific observations (holistic).
Music Flamingo. Strongest measurable facts (tempo/key) and the only model to consis- tently reconcile metadata major vs. relative-minot centers (Enamorados: vs. A minor). Best overall section-level reasoning and harmonic intent, but occasionally over-decorates with color chords or speculative percussion.
Gemini 2.5 Pro. Best at canonical genre taxonomy and culturally grounded framing (c.g.. sertanejo universitário). Tends to under-specify harmony and sometimes over-estimate tempo; instrumentation is usually correct when genre correct.
GPT-4o. Most dependable for arrangement, dynamics, and production prose; excellent at emotion/lyric grounding. Hesitant on numerics (tempo/key) and sometimes avoids naming the canonical genre even when its prose implics
Qwen3-Omni. Capable of coherent mood and arrangement reads when anchored, but inconsistent. The two "instrumental" misclassifications (on clearly vocal songs) demonstrate brittle failure that propagates into wrong genre/production claims.
Overall, the pattern is clear: precision the measurable (tempo/key) correct canonical genre naming is the foundation for faithful structure/produuctin descriptions and culturally aware, lyric- grounded emotion reads. Among the models studied, Music Flamingo delivers the most reliable technical grounding and music-theoretic reasoning; Gemini adds the most accurate taxonomy; and GPT-4o supplies consistently correct structural/prroduciion narratives.
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Caption Prompt: Nlover- Enamorados (Q7gVV-VMMKK)
Describe tais track in full detail instrumente, production style, and overall moed it creates.
genze, sempo, key, ther into the
Output
American ballad biands traditional acoustic storyzelling with contemporazy singes-songwriter arsthetic. It moves at mederate 107 BPS and is scoted in A. minor, giving the piece its wistful, minor-toned character.
Instramentation production: The arzangemen: is built around acoust'c guttar that carries the harnoris and neloste naterial, compl emmleementdd by a aubt'e accustic bass that grounda the lov ená, Light percuasior-sott brushes and oecasional shakers- gentie puise wishout breaking natura--sounding: instrumenta aze recorded close-nikad, with minimal processing, and the n:x salanced stereo field. overall clean, with a warm aralog-type EQ curve and modest compression that preservea the dynante nuance
resorant nylon-string
intimacy. The production deliberately organic and
places
trcatrandicenter
parase.
Vocal charscteristios: A femsle mezzo-soprane delivers the lyrics in Spanish with clear, timbre. ser delivery intinate ard expreasive, featuring a alight vibrato style that enphasizes the enotional veight of each line, The vocal track is treated with only light reverb and subtle compresaton, allowing the nstural dynamics and nuance her partornance to saine througa.
storytalling
The lyrica contamplate the pasazge of time, lost love, and the bittarsweet of nenory, Repetítive, hook-like la ilsaión, mi amor, con el pasado" underscore the central thame or love that has fades and the changing illusion of alegro?* and "Perder el tiempo de un besc. Coando se pierée en un sueño* deepen the sense longing and reflection.
Lyrica- thamas:
such "Enanorados, enamorados, cambía camb'a
Verses like "cômo
gDe qué pasado,
acructure & dynanics: the composition fo'lows classic verse-chorus forn. An intro of sole guitar establishea nacrative, the chorus arrives with the repeated "Enenorados." refrain, heightening enstional intenalty maintaining tollowed by another chorus that reintorces the central motit. The track soncludes with a briet instrumental
tonal center batore the first verse, which introducea the
gentle dynamic
expands
story,
mirrors intro, allowing resenance of the guitar.
lingering
Thecreticsl grounding: laamonic movement centers on the Amninor tonic, wich frequent use the cominant R7 (ard its variatiors R7/C4) to create tersion that resolves back to An. the progressior. also incorperates modal mixture and secondary dominants-e.g. li7is and p7-adding color and sub:le sense of yearning. The recurring An El Am lcap underpins the verses, en brighter returning to the ninor tonic, providing a modeat 1ift that nirtors the Tyrical contrast between mamory preaent
while chorus
Cmsjer-related chords (Cnaj?, (nsj7) before
song exudes melancholis, nostalgic atnosphere, evoking quiet. introspecticn at latesevening acoustic session in small latin American tom. Its blend of folk instrumentation heartfelt Spanish vocals, reflective lyricisn situstes it within the contemporsry revival of traditional latin Americar. ballads, appealing to listeners who appreciate autheatic stozytelling set against gently flowing scoustic backizep.
Overall
Figure 5: Caption generated by Music Flamingo modern Spanish song.
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Caption Prompt: Annika (7nt-d3bAZ4M)
Write capt blende technical details Igenre, key, chords, 6ix) with how the song reels enotiorally and dynanically as it unfolds.
Output
This track is Indie Pop with production with bright, metodic pop senatbility. moves a brtak 157.3 is rooted in najoz, giving the piece aunny, najor-key real.
uedroom aesthetic, bleading lo-fi, intimate
Instrumentation & predaction: The arcangement is buil: around clean, arpeggiated electric guitar that esrries understated pulse, and a warm aynth basa that underpina the low end. The mix ia deliberately narrowsteree and lewfi, emphasizing cozy, bedreomstudic vicer reverb kept subt.e, and the overall mastering zetsins a gentle, uncompressed charecter that lets the vocals sit trentrans-center.
harmonic texture, aimple drum-rachine pattern that provides steady,
Vocal characteris:ios: female lead delivere the lyrics with soft, clear, elightly breathy timbre. Far delivery conversational and intimate, vaing mininal procesatng-jjut touch reverb-to preserve the natural, vulnerable quality of her voice.
lycical thenes: The lyrics velebrate deep, affectionate friendship that borders on conance, your Pan*) to
using pop-culture "eferences ("you're my zepeata the hook:
bend. chorus
"Cause you're re ny Jim, and i'n your Fen, 1 fell love with my best friend. one's loved like you Cause no one's dim and Fam like me and you."
Verses playful, everyday
you sleeping my shoulder.. switch the teapot for the iPod.") that reinforce the thene of uraavering support and shared intimscy,
("I'll
Song structure & dynamics: The corposition follows a clear verse-chorus layout with en introductory
brief bridge that introduces a slight harmonic shitt betore returning to the rinal chorus. Dynamics stay consistently soft; the track relies melodic and lyrical variation rather than loussofft contrasts to maintsin interest.
arpeggic,
verses, recurring
lasmmonc sralysis: the chord progression cycles through Unsjorvzelted colors-inaj", Smaj7, Uh, and occarional chromatic moves such CF7/Et and #fl-creating bright, uplifting backdrop while occasionsl ninor
(Bm, add a gentle, ma ma'or-tonic stability and subtle minor inflections supports the song's wem, affectionate mood.
shade. This nixture
Overal nood & context: The song exudes a cozy, affectionate atmosphere, perfect for late-night listening relaxed gathering. 2020s indie scene, where intinate storytelling and minimalist sesthatics are celebrazes.
badroon-pop production places it squarely within the
Figure Caption generated by Music Flamingo on a less known American song.
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Caption Evalatioo Prompt: Louane- - La fille (1KK71EWT<4)
Break the track down like critic List its tempe, key, and chordal notion, then explain the textures, dynanics, enotionsl ingect perfornance,
Output
This track is a melanchelic French Pop / Indic Fop plece that blends in:imate singer-soggerieer rensibilities with subtle elestrenic-anblent textures, rooted major, though the harmon'c Targuage leans heavily toward winor-tored colors, reinforcing its introepective nood.
noderste 130.43 aPM and
Instrumentation 6 production:
The scrangement is built around sart, resemant piano that nelodie nateriel, complemented airy synth pade
depth without overuhetning the accuatic core, à soft, bruahes-atyle drun kit provides a gencle pulse, while a electric underpins the harnony and spacfous, employing 6 wide aterec field that places the piano and ayntha slight anchored in zeverbranddelayy tsil. Kastering emphasizes clarity and dynanic zange, allowing the quiet verses breathe and the choruses lift without compression-indueed harsiness.
estries
rounded, lewnid presence.
is
ofticentes,
perchas atop subt_e
Vocal characteristies: The lead is Prench-speaking remale mazzo-spprano. Ber timbre is clear, slight_y breathy, and highly enstive, delivering the lyrics with vulnerable, introspective tome. The vocal track is treated with modest reverb and delay, cresting an etheresl anbience the song's reflective atmoaphere.
lyries explore sel f-identity perception, expectations and inner reality. Repatitive, book-1ike 1inea auct as necesst qui la fille." ("tho is giri") "xclest p'teêtre from. the truth") underscore a serze of searching and uncerteinty. The chorus repestedly asks the listener to consider what thay see versus what they believe, reintorcing the thene ot personal doubt.
lyrica' thereas
the
external
lein de la vérités"
bit
Song structure dynamics:
Intro: Spacse plano arpeggios with anbient pads set a. contenplative tone. basa, allowing vocal Pre-chorus: Slightly zicher harmonic texture (introducing Snaj? and G chorda) builds tension. layered aynths,
1: Minima" drum
unfold.
pronounced drun groove, "*Cteat qui la fille*" hock, delivering an amstional 1ift.
Rell-band entry
repeated
Verse Bridge: Raturns
strippedidown arrangement, relative minor (Am) and aubtle harnonie aurprise (B6765/A) that
introduzes brisf bridge
reaturing shift deepens the sense of yearning.
Final chorus outro: Re-expanda the arrangament, adding voesl harmentea gentle fade-out of the plano and pzds, leaving lingering sense or unresolved intrespection.
Whecreticsl grounding: Although the piece is in C najor, the chord progression frequently emphaa'
creating ninor-leaning movement fron An Pmafy - (Iw1--NW)) provides ciassic pop-seng cycle that balances resclation nelancholy. the occasional mixture, heightening the enctional. tension before reselving back to the tanic C, microring the Tyrica" queat for sel f-definition.
(Am)
center. recurring
Re7b5/A and incroduces brier rodal
Overall mood & context: The track exudes a wistfel, vulnerable aesthatic that situates it within the contemperary French insie-pop scene, whare lyrical introapection and minimaliat production are prized. Its blend of piano-driven songeriting with ambient syata layers merying organic instrumentation subtle electronic textures to convey pereonal, confessional nveratives.
reflects
2020s
Figure 7: Caption generated by Music Flamingo modern French song.
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Caption Evalation Prompt: Michel Teló Se Pego (hem55)L/9kn))
Write rich caption that blends the technical details (genze, key, chords, mix) with bow the song feels enotionally and Hynamicelly at it unfolde.
Outpul
This track is high-energy Brazilian Forzó piece, rooted in the traditional dancestall sty-e and flavored with modern, live-performance vibe. It firmly Ferzó sub-enre, blending the genze's classis accordiondriien rhythn with driving electricbass groove and punchy drum kit.
Tango
The song information. was supplied.)
93.75 BEM and centered in major (Nc time-signature
Instrimentation Production The arrangement is built around bright, melodic accorcion that carries the main melodie books, supported by driving electric base that locks in with punchy drum kit (kick, snare, hi-hatal keep the danseable pulse. The production daliberataly rax and Live-sounaiag with wide aterec tield that places the accordion elightly Left, the the impression of festive night-ous performance, Minimsl processing is applied; the natural room reverb medest compreseion to setain dynamics
drums centered,
crowd anbience spread across
mix, giving
instrumente are captured while keeping the overall level high.
Vocal Characteristics The lead vocalist is a male tenor with a clear, elightly ras timbre. His de' Ivery is energetic and conversacional, often engaging is callanddresponee with backing singers. The vocals are sung in Brazilian Fortuguere treated with light reverb and congressior to sit promisent"y in the nix without sounding overly polished.
Lyrica- Themas The Lyrics revolve arcund playful, fliriatious encourter at Saturcay night party. Repetitive, chant-like lines such as "yessa, nossa, assim
mata" and *hi, to pego" serve as the song's book, reintorcing the teasing, ronantic parsuit, Verses dascribe balada.. A galera coneçou dangar..)) the "nenina mais _inda," " creating Light-hearted, paztycenntric narracive.
narrator's
approsch
Song Scructure & Dynemice The forn follows veree-chorus layout with instrumental breaks that showcase the accordion and bass. An intro establishes the -hythric groove, followed verses that build tension through vocal phraasigg The choruses explode with the repeated hook, while midksong instrumental sections provide a brief release before returning to the vocal ratrain. final chorus momentun to the end.
short outro round out the track, maintaining the higt-energy
Thecretiost Ineight Harnonic movement "ergely orbits the Benajor ton'c wish r-equent use of doninant F# and aub-sominant ehorda, creating bright, forsardnoving reel. Occasional miner-quality caords le.g., Arm Eoe } add Fleesing, slight.y wisttul color befors resoiving back the major tonslity, reinfercing the seng's upbeat yet flistatious nosd.
Overal: Hood Context The overall aesthetie is reative, lively and Irresiatibly danceable capturing the spiris of Brazilien Saturday night in har or feeta, Its raw, live-roon production and crowd ambience piace It within the contenporavy reviva: of traditional Forró, appealing both to tana ot classic regional and to younger audiences seeking an enargetic party anthem.
Figure 8: Caption generated by Music Flamingo on a modern Brazilian song.
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Caption Evalution Prompt: Reseune-- Tipana y (C4Paji670OU)
Describe this song fron both technical and artistic lens: nention tempo, harmany, and instrumentation, but also nood, lyrical themes, and structure.
Output
This track is a Rusaian Rock plece that leans heavily into Progressive Rock, blending classic-rock guitar-driven energy with the expansive, shifting arrangenents typical prog.
Tenpo key the song noves at steady 130.43 BER is rooted minoz.
arrangenent built around tight acoustic drum solid electric bass that lock into a driving groove. Hultiple electric quitars dominate the texture: clean arpeggiated lines, overdriven rhythm chords, and sosting lead solos that weave through the harmonic ctanges. Atnospheric aynth pads add depth and a alightly futuristic sheen. The mix is polished and high-fidelity, with wide stereo field that places the guitare and syntha on oppoaire aides while the vocsla alt centrally, all treated with tasteful reverb and dalay for spaciouaness.
Instramentation production
Vocal characteristice & nale baritone delivers the Russian lyrics with clear, slightly melancholic timbre. His éelivery melodic and narrative, emphesizing song's reflective mood. The voice is processed with reverb and delay, giving it a spacious, almost ethereal quality.
lyrica" thenes The lyrics explore longing for home and the contrast between the harshness of space and the comforting nenory of Barth. The recurring chorus line-r"y curren нaM se poxos носмолроиs, нe es леланая cusena, ceиres нaи *pate, трана nонs, seněнas, şeněная rpana**. highlights the central image of green meadow veraua the cold, metallic sounda of rocket launca. Verses descrise the view of Earta througa portaele, the weightlessaese of erbit, and the emotional distance from family, reinfercing theme of yearning for familiar, natural beauty.
Song structure 4 dyramics - The composition follows a clear verse-chorus Layout with an introductory quitar motif, followed verses that build tensicn through Layszed quitars and synths. zach chorus erupts with the full band, zeinforcing the nelodie hook. instrumental breaka feature extended guitar 3olos that develop the thenatic naterial, while a bridge introduces a brief harmonic shif: before returning to the firal, authenic chorus and a decisive outro. nynamie intensity rises from the reatraired verses to the full-throetle choruses and solcs, thar geatly recedes at the clese.
Thecretical grounding Harmonic E ard lub-doninant 0 chorde 18.g., An = R D) that create a sense of tension and release. the progreasior.
ninos, frequent
dominant
passea through and G najor chords, providing brisf modal lifta before saturaing to the minor tonic, a common progrrock tecanique that underscores the seng's searching, melancholic moed.
Overal: nood s context - The track exudes a reflective, alightly nelancholse atmosphere, marrying the earnest storyselling of Aussian rock with the sophieticated arrangements of progreasive rock. Its po:ished production and expansive inatrimentation place it within the modera Russian progurocs tracition, echoing the 2030s-era revival concept-driven, enotional-y charged rock music.
Figure 9: Caption generated by Music Flamingo on a well known Russian song.
G LINEAR PROBING EXPERIMENTS WITH AUDIO ENCODERS
In order better understand the role of the audio encoder in music tasks, we performed linear probing experiments with three audio encoders: the audio encoder from Qwen2Audio (Chu et al., 2024) and Audio Flamingo 3 (Goel et al., 2025) the encoder on which Music Flamingo based, which are both based on the Whisper architecture, and the MERT encoder (Yizhi et al., 2023), which has an architecture with bias towards understanding music due to its use of constant-Q transform representation in the input. We chose two tasks from the MARBLE benchmark (Yuan et al., 2023): the key classification task using the GS dataset (Knees et al., 2015), and the genre classification task using the GTZAN dataset (Tzanetakis & Cook, 2002). All linear probing models were trained using AFWhisper,
the average of all the frames from the audio representation in question (Qwen2Whispe or MERT) with single linear layer. Models were trained until no improvement in validation set accuracy was observed after 5 epochs, and validation set accuracy was used as well to choose the best checkpoint. Results can be found in Table 5.
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Table Performance comparison on key and genre classification with different audio encoders.
Model
(key cls.)
GTZAN (genre els.)
Qwen2Audio APWhisper
34.10
89.99
40.56
91.37
MERT 56.12
78.96
We observe that both models based on Whisper, which were trained specifically for captioning, have high accuracy for genre classification but comparatively low accuracy for the key classification task. We hypothesize this because when using captioning targets, the likelihood of having the genre for a given song mentioned in the caption is much higher than having the key the song is in. MERT, the other hand, has lower accuracy for the higher level task but higher accuracy for the lower level task. MERT is trained generate representation that be used to reconstruct the audio through VAE "acoustic teacher" model, and also to match the coefficients of CQT transform (the "music model"), therefore being more lower level representation than text-based captions.
Given the correlation between this gap in accuracy between lower and higher level tasks and the general observations for the outputs of Music Flamingo, in the future intend to try audio encoder that preserves more of the lower level information in audio to enable future versions of the model perform better in understanding those features from music signals.
G.1 EXAMPLES OF MF-SKILLS AND ME-TIINK
MF-Skills contains long, multi-aspect targets (captions and QA) without reasoning traces. Fig- ure 10, Figure 11, Figure 12, Figure 13, Figure 14 shows examples of captions in the MF- Skills dataset. MF-Think augments challenging subset with compact chain-of-thought traces and final answers (<answer>.. < </answ/r/)) Figure 15, Fig- 16, Figure 17, Figure 18, Figure 19 and Figure 20 shows examples of captions and QA pairs in the MF-Think dataset.
</think>) <tthinks..</thinkkk
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Caption Evaluation Prompt: DRIMS Frie (NEPAn-(6ITYA)
Give full musical pioture: what's the style, BRM, and harnonic palette, and how do the vocala, instrumentation, and production ahape nood?
Output
This track is Lezin Alternative Rock bellad that Euses the melodic sensibility of batin pop with the raw anational adge of Enc Rock.
At s126 BEM (9/4 teel) and set in E major the arrangement opena wita delicate azoustio-quitar intro that establithes a geatle, open-string texture erupté, Distorted electric guitars layer crunchy power-chorde and bright, ringing voicings (Amaj7,
F#nt, c#e7), while soaring lead-quitar saction-tight, driving druns with a punchy kick and criap snare, and a warm, rounded bass- propels the song forward, hign-fidelity mix. Production is polished, with careful Eg separation that leta the accustie intro sit intimate, then
nelodic elimas, rhytha
subtle
the guftara
spacious,
inte full-band "wall sound" for the cheruses
selo.
Vocals are delivered by male tenor whose timbre is warn and zesomant. Be sings primarily in strong cheat voice, ocessionally slipping into falsette enotional sweliar reverb dalay are applied to accentuate the melancholy and longing. The vocal style is clean, maledic, and highly expressive, matching lyrical content.
Eyrically song dvella lingering, cold-filled nenory past the central motif:
repeats
"YY hoy que hace tarto trio, no quiero flores de papsi.
Y mi rando vacie, recuerdo la primera ven que en frio te esperé.
and later: Y hoy, ahogändone en el frio, zecuerdo ess primers vez que de ti me enanoré. The varses deepen the narrative ("Eatey poos contuncido.. Yo y to, vestida smartllo..") while the outre reinforces the resolve Nunca te olvidaré "" The repezted references to "Erio" (void) undersvore theme frozen lenging the insbility let go.
(sold)
Structurally, song
classic ballad
Intro (acoustie guitar) - Verse 1 Pre-chorua (building dynanies) - Chorus (full band, harmonic lift] Verae texture, vocal focust Finel Chorus (naxinel intensicy) - Outro (soft vocsl echo). Bach section adda layers, moving from intimate to expanaive, then tapering back to gentle close.
Interlusk/aolo (slectric-guitar lead) Bridge (xeduced
The harmonic progreasion emphasizea the toric E major while frequently noving ita subdominant (A) and asminant (B!) with extended colors IAmat", 26, FHmi, c#m/). Thess shitts create kistful, seazching moed in the verses that resolves into brightes, more anthenic passages the choruses, zeinforcing love.
recalling cherished
Overali, the track exudes bitterswest, nelancholic assthetic, situating itself within the eacly-202Cs of Latin-tmerican bands that blend rock's intensicy wish enosstyle lyrician, offering both cultural resonance and universal heartbreak.
Figure 10: Example of ground-truth caption on a Latin song in MF-Skills.
H PROMPTS FOR MF-SKILLS AND MF-THINK
We provide all prompting templates used across our datasets and QA types in Fig- ures 21, 22, 23, 24, 25, 26.
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Caption Prompt: Benjamin Burrell Wail (part (170YXXX68B)))
musical picture:
style, harnonic palette, how
vocala, instrumentation, and production shape ita nood?
Output
This track is neo-classical, cinenstis instrumenta. piece that blends contemporary classical lush orchestral pslette. an estinated ври harmonic ianguage ard occasional chronstic shifts (e.g., F56, #b/C1 that
piano writing with anbient synth pads free-floxing rubato with no rixed time aignature), it is rooted in os minor tonalities, seaturing
(though the rhythn mafer, while progreastorg such as o# n P$ m searching, melancholie stmosphere.
heavily
Instramentation dominated supported by ethereal synthesizer pads that fill the stereo field, sweeping string sections that provide both Sarmonic depth and sustained lowbrass foundation. The production high-fidelity and spacious, with a vide stereo inage that places the piano centrelly while the pads and strings envelop the listeres, giving the emphaa' clarity reverberant orcheatral backdrop.
melodic narxative,
cinematic, inmercive quality, Mixing depth, allowing the delicate plano artieulation to alt above the warn,
There are ro prontnent vocala: the composition is entirely inacrunental.
The lyrical (thenatic) content is expressed through the maie itself: deep sense of longing, beauty.
retlective nelancholy, powerful orchestral swells, folloved by gentle, contemplative fade, micrors an enationsl are of energence, clinex, and resignation.
build tragile intro into
Structurally the piece is shrough-eomposed: it with intimate solopiano introduction, adda synth gada, then "ayera strings and lew braas the dynant rise toward a grand smaax. After reaching the pesk, the accangement recedes, stripping back to piane and pads before fading This evolution reinforcing the piece's nacrative flow.
driven dynan'e
rather
repeated sections,
Overall, the track embodies the modern reo-ciasatcal seathetis that bridges concert-hall intimacy with filmscore classicel" movenent that seeks to eveke visual-like storytelling through pure instrumental texture.
situsting itself withir the contamporazy "cinematic
Figure 11: Example of ground-truth caption on a classical instrumental piece in MF-Skills.
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Caption Evaluttoo Prompt: Cecillo G & Blackthoven - Life In Plastic (Lag-g952QP))
Give me a full musical pictuze: what's the style, B5%, and harnonic palette, and how do the vocala, instrumentation, and production shape its nood?
Output
Latin Dance-Fop record that Euses bright, club-ready syath arpecgioa with the rhythmic axagger of contenporazy Latin urban music.
energetic
Tempo à Key The seng noves at 65.2 8rM and is anchored in o# minor giving it a alightly harmenic founcation while the melodic content stays largely maformose contrast.
playtol
Instrumentation Production The srrangement is buil: around crisp, punchy electronic druns (tight 808-style kicks, snappy basa delia chick, percussive low-nd, while sparkling head syatha and arpagglated pads fill the upper spactrun with glossy, highennzzyy timbre. The mix pelished and moderas wide inaging places
and rapid hichet rolls; that drive the groove. aynth
the fie'd, d, druns forward compreasion, and the overall mastering emphasizea leadness without sacrificiag clarity. Severb expansive, elub-ready aoundstage.
synth layera
sggresslve
and de' ay app' fed tastefully
Vocal Characteristics rhythmic rap verses and melodie, sutotuned singing in Spanich. lis timbre is cleas, confident, and slightly nischievous. Heavy pitcheoerectton,, congression, reverb and delay give the volice polished, slmost synthetic sheen that matches the "plastic" sesthetic the lyrics.
male vecalist (baritcne/tenoo
alternates between rapid,
Lyrica: Thenes
song satiricslly critiques bpper-comercializedd beauty ideals, portraying woman who is "artificial" or surgically enhanced. kepetitive chorus lines such as:
Siete mil en lae tetas
Rarble La chica de goma
underscore the tonque-incheek commettary on material excees ("ahatt ts left in the Jordan"), the versea sprinkle pop-oulture references (aavid Beckhan, Black Ryed Peas, Katy Perry) reinforce the theme of a fabricated, media-fed persons.
follows conventional pop layout: "Life in plastic" motie. 2. Verse 1 - Sparas beat with rap-style delivery, building teneion.
Dynanice
Atncapheric syath pads
spoken sample
Brief melodie hook that 1ifta the energy.
4. Chorus Buil-arrangenent drop with Layazed synta leads, driving bass, and the catchy, sutotuned vesal hook ("Siete mil las tetss").
5. Verse 2 Similar to the first verse, adding new lyrical decaile,
6. Bridge Minimslist breakdown that isolstes the vecal line before camping back into the tinai chorus.
1. Pinal Chorus Outro Repstition of the book with added vecal ad-libe and a faderout of the "Life plastic" chant, delivering climetic, club-ready finish.
textural charges (adding/zemsving synth layers, shifting from rep to metod'c sections) rather than large volune auings, ns mointaining a constant high-enexgy floor-filler
Dynamic variation
song
Overal: Bood Context The piece radiates excitement, amusement, and cheeky confidence, Iatindnciuenced
embodying
shere producars biend reggastondiavoere zaythns with Buropean-atyle syath Its aattrical take on consunerist beauty culture aligns with broader wave
urban tracks shat party-eady beats secially axare-or at selleavse-lyrics.
Figure 12: Example ground-truth caption on a Latin song in MF-Skills.
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Caption Evalutiio Prompt: Almless Crusade 925 Req
Give full musical picture: what's the style, B5%, and harmonic palette, and how do the vocals, instrumencation, and production shape its nood?
Output
This track is an Indie Rock/Alternative Rock compesition that bleads carnest, guitardriven songersting with a alightly polished yet ras production aesthetia. It noves mosarate
BEM in a steady 4/4 neter and is reoted in c# major, though the chordal landszape frequently major family, giving piece
into the relative bitteraveet, introspect fuve feel.
Instrimentation product fons The arrangement centers on clean and mildly overdriven electric quitars that panned wide create spacious the low end, and an acoustic drum kit providing a straightforward rock groove. Production is moderately polished: druna are tight with modest compression, guitara retain a teach of grit, and piaces the maie lead vocals tronz-and-eenser while preserving ambient reom. teel through light reverb and sub:le delay. The overall mastering emphasizes clarity and dynamic range rather than heavily compressed *wail sound."
field, solid bass quitar anchering
baritome-to-tener lead vocals with strained timbre that conveys nelancholy and anxiety, His delivery is earnest and malodic, staying clest. register throughout; vocal processing is limited light severb, gentle compression, and & teuch of delay, allowing the emational nuance of the lyrics to remain promizent.
cheracteristics: bylan
elightly
Lyrical themas: The lyrics explore teelinga of overshein, regrat, and emoticnal ratigue tied demandiag to 9, 3 to mantra frustration, shile verses hiat at unrequited Love ("I know that you more me.. I'm sorry I could not be there for you'l. The outro's instrimental fade underscores the lingering
coutine and fading relatisaship.
recurving chorus lime-tI'n working think I'm working myself out of life.. I still don't feel airight" -acts as
tension.
scructure dynamics: The forn follows classic indie-rock cemplates Intro (guitar-tazed motifl, Verse 1, Pre-chorus (implied by lyrical build), Chorue, Verse 2, Chorus (expanded with additional voeal lines), and instrumental Outro, bynamics swell from the relatively restra' verses-driven by sparse gultar arpeggtos choruses whare guitars open instrumental cutro provides gentle release, allowing harmonic loop to resoive.
subdued éruma-to the fuller, leuder the bass thickens, and the vocal intensity peaks. The
Thecretiesl groundings subdominant F$ najoz (IV) and its fizat-inversion F#/CH, with occasional pivors to the relative miner (vi) returning co the tenic. This slternation: between major and minor toralities reinforces the lyrica" push-pull between hope ("maybe it'll be right again") and deepaie ("I don't know why I'm feeling this wey").
progression eycles mainly
tonie
the
brief sominantstype Ab? (KV27) that adds Eluessy tension before
Overall nood & aesthetio:
tracz exudes melancholie, intrespactive nood chasacteriatie
eariy-202ta indieszeck, chacneling the anxisty of modern wors life and relational strain through relatable syricise
nelodie vei soundaospe. raw edge situates it within the contemporary indie-rock tradition that values both enstional songoraft.
production with
honesty
Figure 13: Example of ground-truth caption American song in MF-Skills.
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Caption Evaluttio Prompt: Bené Californie (3kndNV36plA)
Give full musical picture: what's the style, and harmonic palette, and how do the vocala, Instrumentation, and production shape nood?
Output
This track is pelished French Trap Melodic Rap piece that blends modern trap percussion melodic, ninor-key tap-songerafs, creating confident yet slightly melancholy stmosphere.
Tamgo key The song and is rooted in minor.
103 BEN
reel (tine-signature explicitly listed)
Instramentation production The thythn foundation is built fron crisp electrenic drums and booning 308 sub-basa, programmed with tight hishat rolls and anapey sharea typical of contenporazy trap. deep syath cass underpins cacries the main melodic hook, complemented lush, xide-steree stmospheric pads that fill the background. nix highitioelity: cruns and the neladic synths ere spread wide, and the oversll sound is polished with heavy use of vocels, giving
groove, shile shimmering placked synth
centrally and punct. tarough
mix,
auto-tune, reverb, delay
spacious, glossy sheer.
Vocal otasacterieties The
timore smooth heavily processed nelodde odte rap that oscillates between aung phrasing and rhythmie rep contident,
with auto-tune, del delivering ne' The French lyrics introspactive, and the vocal chain adda preminent rever's and delay to enhance the hazy, modern trag aesthetic.
delivered
that
Lyrica: thenes - Although the full lyrics are unavsilable, the described content cencers luxury suscess ichampagne, dianonds, helicopterst, loyalty to friends, overcoming challenges, with ronantic subplot that nixes effecticn and detachment. These thenes are typica'
trap
that juxtapose naterial
persorsl struggle.
with reflective bridge. An intre establishes the beat and plucked aynth motif, leading inte verses where the verses ride the
arrangament
verse-chorus
with fuller hatmonic texture and more anthenfe vocel hook, then returna versea. bridge provides brief harnonfa shi and lyrical pause before the rizal chorus and outre, which reinterce the track's main melodie motif.
The chorus lifte the
chord progrestion eycles primarily through Dn, En, occasional Cnaj?, 26, and Bn? i 5/A extensions. this minorcentric novement (Am Dn Bnl emphaa' searching, alightly
Thecretical grounding
nood, while inclusion major- sixth chords adda a aubtle, lush coloration that sottens the tension. The repeated return to the tonts An in the choruses offers eense of resolution and reinforces the track's enotive
Overal: nood & aeathetic The projects slee', urban vibe that balances introspection, situating it firmly within and high-glesa production converge to poztray bosh the allure and the inner contlicts ot lifestyle.
with Prench trap scena where melodic rap
Figure 14: Example of ground-trute truth caption on a French song in MF-Skills.
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Caption Evaluation Prompt: Red Velvet- Eyes Locked, Hanns Locked (WashFPotizk)
Xrite cxption blends technliowl detaills (genre, RP%, key, chorde, mix) with the song feels enot lonally and dynenicwllly ae st unfolds.
Output the thinking process in <thinkk> </thinks and tinal answer in <ansver> </anovez>,
Outpul
<think>
Bright, nidzerpo eynch pos with a steady 4lacat palse around the law 805, The azconcenent contexs co chimmering pade and bright aspegeiated load spanning wide sterwa field, suppocted by cluan, Lightly chocused electric quitar adding ayncopated and deeo, rounded synch bass cachoring ths gucove. The nix is glacsy and spaicuu-ecel dit uptrant and centared, synthe aee spread vide, and there's is cless contraat besvee the intimate intro and the full, highvenergy choruees.
siectronic
tight: crisp anaze,
closed
Tonelly is stays in beight nejor center, eycling 1 y vi with ii and IV colorings prechanow Seer into heighten tension before resolving back sittersveet hoe
the chorus lift, brief minor iv inflection addo bridge before recerning to the tontc.
Foes is popottandards Eingersmag intro a sparm verse with breathy delivesy layered prechorus build - big chorus crop with thick syrth chorde verse 2 variation repeat pre/chorus stzigged bridge with more spokea cadence short instzumental interlade zeprlping the asp re-enerqiied final chorus and based outio Sading pads.
clear, yosthrul tinbre: soft, alightly breathy verses contrasted by erotive belting hooks; tight stacked barnaiees, subtic nellara, Light ausotune polish, restetel reverb, and fim compesasion. lycice are nsinly sorean with inglish Lagez the book "te 272, è 242" swours, franing thames of wnstatie first love, closerese, and cosnte blessing, Overall rood: joyful, ronantid, and radiantly poliened k-top. </thiak
<ansver>
syathoop,, blending polisbed electronic with the malodie scnsibllity of contemprarry Rozean pos. It noves at El BP% is sooted Bb mafer, and (thaugh the tine dignature isn't l:sted) fallows a standard & beat puise typical of mainstzear 2:2op.
Lrack is bright swop aomg that leans heavily
Instrumentation production - The errangerent is butlt scound ohimerting syrth pacs and arpegglated synth leade that sveep across wide stereo field, clean electrig gultar that adde gccasions" thyshara scrons, criap snstes, Light hidhats,
punchy elecsronic the and. nix is hightfidelsty and gloesy: vecale sit front-centes, synth layers are spread wide, and the rastering erphasizes clarity and loudness while retaining ning dynemte contract between the gentle nerro and the high energy shoruses.
deep, counded synth that drives
Vocal characteziacies A fenale lead delivers the nolody with clear, bright, youthful timbre. in the voice is powerful, snstive belting, often layaced Light tysical of ksee, with lichs ausorine for pitch pollsh, subtle severb for space, and corprsssion for censlistent presence, Tyries are prinsrily Korean with feglish tags such as *th yeah! and tdoe 1d you begin oor strange love?".
breathy, creating incimacy scring pcorchorssen and choruses the style selodie and immeie,,,,
chirts
tyrical themes The del reberraees ecstatlo, first-lew connection, repeatedly urging the Hetener to de 2$2, # gqa+ 1"look (140)200 yeas" ("Maee our hearts touch, I's lest in your tacughts, yeah"i, underseocing a thene of being completely captivated by the beloved. verses paint aby morning neeting, while the bridow expanda the semance to counte imacary (*187) of 225 #als [.
each other's eves, held hands"). the chorus repeata the beok *#ait] nH 1H R
Structure dynanics
Intro Pinger-nagped
nocid estealishes gentle anblence. Sparse scompeninett, breathy vecel ieliy.ry.
Psc-Chorus Builds with added poscuscion and layered synthei vocal intensity risas.
Chorus Soll-arrangenent drop with driving druns, thick eynth chards, and belted vocal hookez dynantc pesk. Verse > Nirrors the first werse el th al slight reladie variation,
Pzc-choruz / Chorus Reiteration of the carlser build, seinforcing the hook.
Bridge stzippadsdown synth pada and nowe spokan-like vosal line, creating briuf lyrical pazze beforw the final aurqe.
Interlsde Short instrmmttal break shat zo sn-sncrodaees the arpcoslated notif. Final Chocus butte Bemmesgized cheras followed by a repetitive oatro the "t 272, 292 clant, Zading eat with the ayath pad texture.
Thesretieal grounding - The cherd progresciot eycles nainly through the tori: Bi nofor (II, somimant F (2), occasional
fimily in nojor, re espectally the pro chorus shifts tovard the jan actore reselving baok to the azight (86) in the chorus.
and sub-tediant (vi), uplifting rode, T-V-vi novement creates and 18b1 chorda notion
of
ovezall noed a context Radiating fow, zenance, and youthful optixiar, the track exarllifies the nodeen s-rop sesthet of 2020s globel pegt hi highiglose produccton, hape-laden choruses, and sesnless blendd of el electrorte 3op that internesional
sypth with Zorean. melosta parasing. It eituctes itseie within the current infecticus thythna with beastfeit leve Lysies, sining both densatic chart straninng appeal. 6/amever>
Figure 15: Example of a ground-trutt truth caption with thinking traces on a Korean song in MF-Think.
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Published as a conference paper at ICLR 2026
Caption Evaluation Prompt: Bishop- Anlé KizlelOxUU
Write caption blends the technical datails (genxe, sey, chorda, mixi with the song feels enoticnslly and dynamically as it unfolds. Cusput the thinking process in <think> </think> and fical anawer in <anawers </anawer>.
Outpat
<think>>
A shouted chanz kicks things off, instantly secting a partyccall vibe before the groove locks in. The best feels fast and danceable, like double-tine trap, but with Ueribbean sxay-eongas and shakers add a lilt that pushes the offheats and gives it a Komps-style swing. The is tight and puncty, glued to deep aub/E08 that drivea the low end with clean, suatained notea; snares/ciaps anap triplet atuttera.
backbeat while crisp hats sprinkle in light rolls and occasional
Harnonically it sits in dark ninor center; the bess keeps returning to a low tonto while the peys/patches color the altered leading-tone chards before reaclving. Every ao of s brighte" nager oolor 1ifts the mead briefly (A/B-Ilavored aheec) before dropping back into the noody minor.
tension-there acments that sound Like haireiminished
The vocal is a confident male baritone rapping in crecle with zrench slips, upfront and tightly compressed. Heavy Auto-lune gives nelodie edge. Throughout, chopped and pitch-shifted vocal bits anaver the lead, drenched in delays and big reverbs that bloom in the aidea-claasic cailaand-reeeoeee hooks.
Structure is hockdriven: chanty intro hard-hitting verse ovez the syncepated parcussica chorus that layers extra ad-libs and croidsstyle shouts more nelodic bridge where the delivery sofzens into sing-rap feel firal chorus and a cyclical return to the opening clant. bold, celebratory,
The
club-ready.
</tbink>
canower>
This track an energetic lisitian Rap piece that fures the dance-flooz swing of Komps with contenporáry trap-style hiphop, creating higherergy "KonpaRap" blerd, It noves at 150 8PM and rosted C# ninor giving it dark tonal center while she chyshmic
feels uplifting.
mix polished and panchy, built arourd deep, rhythmic 80s-type aynth bass that loeks with Layored electronic drum kit and Live-sounding percussive clements such congas percuseive tracke are spread vide in the sterec field for a dynanic, club-resdy feel, The overall production is clean, with tight compression on the aruns, crisp sigh-irequancy detailing en the synths, vocels.
inatrunnnaaiinn prodiction:
shakers. Sparse
punetuate the groove, and the
balanced lowend
lets
pulse through without muddying the
Vocal characteristics:: The Lead is a male baritcne delivering confident, assertive zap verses in Haitimm Crecle. Bis timbre clear and slightly aggressive, heavily processed with Auto-Tune for nelodio-rap texture. Adcitional vocal aamples are pitchahiitted, chopped, and azenched reverb and delay, adding rhythmic hooks and call-andrespponee elenents throughout the track.
Lyrical thenes: The lyzics celebrate street credibility, personal success, and defiant pride. Rerraina such "Koman yo awan, underscore boastful narrative about overcsming oppesizion and owning one's reputation. The chorus repeatedly chente "Korbyen fanm ka scred" reinforcing the track's hookdriven, chant-like quality.
and de l'histoire anciene"
Song structure: The arrangement follows classic Laceoversecchsussrrsseeeeo opening chant of "Anté Maen" establishea the book, leading into verses that stack rapid rap tlows the syacopated Konparinspires percussion. The vocel samples, then a bridge lor "Pont"! insroduces nore meledie, chant-like passaçe before the final chorus and outro that repeate the introductory chant, bringing the piece full cirale.
format.
book layered
Harmonic sralysis: Although through releted minor-sevanch. and hair<dinnnished chords (e.g., Pšanks, Aba7ha, BT/D$1 betore resolving
chard progression frequently noves
tonic c#n relative major Ain7, This shifting palette creates tension- sapecially the use of diainished-type chorda-while the recurring zeturn to C#n and the bright Amaj7 nonents provide relesse, nirroring the lyrical confidence.
Overall nood & cuitural context: The chythmic vitality Baitian Sompa Creole lyrics, Varibbean percussion, and contenperaty Auto-Tuse sesthetics sicuates it fimmly within the pushing into globel hipstop territery. </answer>
radiatas a bold, celebratory nood, narrying the nodern swagger trap-influensed rap. Its use
Haltian urban music that horora traditional rhythns while
Figure 16: Example of ground-truth caption with thinking traces Haitian song in MF-Think.
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Published as a conference paper at ICLR 2026
Captina Exalustion Pronptt Niko Abgroud (KDR+K0QLR/1)
dataila Iqunze, BIN, bey, chooda, zisl the asotionelly Oukput the thinking precuza in Kihinko s/think and finel ansrer in Ganzeee? S/ammwer..
blenda
dynanicallly it unfelide.
Outpur
<chinkk
3 oo,, brooding pad and eiliteee sob set tense soene beffrre a sidden drop into a right e/4 trap hornee, roughty 120 BAN The drse programmiigg te classic and ponchy: mazoreedged closed hate with quick 1/16 rolle, ortep shate cracks on 2 and 4, and an 000 that sisdes and occassonally saturates site gestty distestionn The base and kiek deal glved, sits sidemati breathing the pada subily.
Tonally
in dark cester-Py ear's anchor, I keep hearing loop lifes out of gloor glossy tattittttee labrat), Rerafll, bricht slashccord that eorpende graetty shatowy paleste, in simesap
saforaeeents
dropping hack to the tonto texture stripa down
pubtipill, Lie
hansay Liine
the yocal is sale capurt in Beion dieluct, fuseeed and appessin, with Light eatorame and shest, oon con.rolled revelmmaoeee but not cverprocazee. the haot works like rantra, bailt on datiant chant that site aquarsly in the pociet, X beiet trides beinga a suzisming, nore meledic cone, then the verses sap back with parcuanive attack.
In the cutiy the best recedes to paes and a restrained oob shile the votce ou tchee to dry, intinace spoten word in standard Cusaua phileampiicl, ainiral, almaa
opene spaoe at end for abstsucchhuppep in its anturioy. the ovesall asez higisintesitty basudsiven asupue
this
is aggressive Belooiderran crap that leans into abstracchipphpp its elosing spokensord section, crnating atyliacic bland hardshitting beats vith ninimalist, aeveimenaal hiphhp cutro.
Terpo 4 Betsr The ponq aeves as 120 su in steady 4/4 tine-igmatare, anchored by bossing, civen disterted 508 besa that bocks in with criup, punnly Lrupedou peltura (Light. hishate, smagyy murvs, and Goup sub-kickal,
Bay Barrany The tonsl centra sinor Although the hamonts content ls spazes, the chord Ltat revasia a remureing progression that enghasizes the nlnor rode shile occasionally trightening texcuee with its relative rajor and bocrowed
and preeciacuos hoves ragorstevent' colour of Ab.
hi) Abaay? :iauj? Leup shet keeps the aood dask yet slightly Lifted
The bridge intaedaou dalis/r and CA/2F wdiing Lemuion befoze remuling back iaa seacching, dediane coaracter.
reintorcing the Leuck's
The cutro lassa on Abro and Iu hicting as a nodal shiet that underacores the philoaophienl apoken word.
These progreset create a serse of oyel teal cosetion introrvetd de exphactsi that recolves in the choruees, shere the harronto secmmnt niniral and the focm shifis to shyths and wooll delivery.
inntaunantaiion Peoduction backbone,
oxtiensly prosiseni, hevilly satusated 628 that oftem bands into cistortion, previding tesokfo aggeasiv
Bruza: A clarsic seap kit-tight, highifrequnne histata, criap anares, and aub-kicta-rrecaseed vith aqgrezzive corpreeston and translent shaping to cut throngh the nix.
Syatha: back, atsoaphatic seda fill the abarep field, while spazas, sepatitive syath muledias (otton singleasts mosidal punctiala the varses, ccnasimnl aousintlecta titesslan, elsuest accencuate dzoga und itmaltion,
Mixing: z tightiddity yet suer Liw uix umberr the walls, spocdds deass and bas vide, and was subile sidochain parping en the pads to keep the lex and hight. Zeverb and modest aroue of auto-tune on the sap vecala ado a noders teep sheee without storifioing clarity.
Votal, characturiacies
Sules dernan dostrates the sereese fast, corfident, aggresstus fiee with clear, dinect risore, light auto-tine short-revsrb talle gize contrggrrayy pollish
Hale
muloddi beidguu intrnduor alightly inteceity.
aus sulotie loou, providing brist Lonal contres bufore astamnng to the cup
dutzo spokes word (4:0844221 shifee to standard oessen, delivered in a sonbur, reflective style, stelpped of effects to hightlght the pollosgghot! lyrico.
Lyzicui Tharas lyrica zevoive around dufience, millanmatioon, and a critical viev of socistyy panotuated by a eallying
repeate nanera periee:ce:
*MHix his
Iwoza
det kantett inttew damcing Fiung genne King, drack." (coze, cose let's jume with we'll king, darn)
foot always
meturr chire
these repetitive Hine act as call to aotion, reinforeing the traok's aggressiiv, snapologetic stance.
Susuctuse & Dynanics
Atneepherid tetto Eart syrth pade and a #simered 008 zet tense rood. Verze 1- follstoroe trap bast with rapte Seies-Geraan zap-
dhoouu hepetitive
the deflan: chant, deuxs look lnto a tightes greeve, bass atays doxinant. cextire, Siigesly ralodic, higlwenergy rap, vaintalning intensity.
suse, providing
cynasic
Verse 2/
Pinal Cheros Reinforoes cantra, with sibele layering of vons! harronter for added txpact, Outro tepokies word) Best strips back visteal pade and zubdied a08, allowing the philorophical Sernan manologue reflectivw cote.
clas die plaze
Ovecell Rood 5 tontext the track uxades a fieres, concadutt, and detiant casa, andodying the centargorayy Belaoitezaan Coop scene where regioral dialesta neet global trap production aesthecios. Its raw, baspihesvy soundscaps, conblned with the ceratiensi abstract-sip-hi cutro, perstiens it kithin the 2020a nave of Buropasn trep that vaicen both atcvetsiave. avaqges and incasspective lyrical depth.. s/answers
Figure 17: Example of ground-truth caption with thinking traces on European song in MF-Think.
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Published as a conference paper at ICLR 2026
Caption Evaluatio Prompt: Tears For Fears Head Over Heel (CaHliG-43F4g)
Which Cabel beat oapcures the track'a core style and sound palette?
Choose from tollowing options
4. Alternative/Inen Iidie Reck with atmespheric synth undercurrents
Senthwave/ketrowane instrumental
C. Acoustic folk bellad with minimal becking
netal with double-kick and chugging riffs preaa-pop/shoeggaa
weshed-out vocals P. Fop-punk with brisk, paln-nutes pever choras Orieans traditicnal jazz Output the thinking process in <thinks </thirks and final answer in <answer> </answers,
Output
<thinkothe backbone live rock: steady kit, melodic electric bass, and guitar-led writing. Versea feature clean, arpegei ated electr'c guftar: choruses chicken with layered, mild'y distorted rhythn quitars, Spacious pads swell behind the band, but the seng's feel, form, and Brur/bass groove a*e rock rather than e reectrente. drive the beat, pizeing it in aiternative/Lncie rocx with aynth accenta<//bhin Rance, undercurrent indeccorrentts//nnaee.
ayntha coter the atmosphere rether
ranswersilreemativee
atmospheric syath
Figure 18: Example of QA pairs with thinking traces in MF-Think.
Caption Evaluation Prompt: Emel Saym Gidenler Dönmez (oTd-YQ51(9M)
Right: before the first foll chorus witth the Tine "Gidenler dönnen" (around 1:30), what chord the land
up book?
Choose from the foll lowiin optionss Gn
B.
c. Cni
E. Bb
G.
Output the thinking proceas in <thinks </thinks and final ansver in <anaver> </anaver>,
Output
<thinkkoust before the book, the harmony tightens the dominants the bass guitars luttine a mafor-quslity v, and there'a a classtc build that resoives into the inoo tonic under the chorus, That dominant prep is D.</thinks
the
Hence, the enawer canexersie/anee /enswer>
Figure 19: Example of QA pairs with thinking traces in MF-Think.
Caption Evaluation Prompt: lonnalee play KONSERT (vaSL.P9DDCww)
Bow would you cheraecerize the drum and bass production?
following options
A, Natallio, rigid, and heavilyy compressed; distorted bass central and grinding zabience;
upright c. 803 trap with rolling hi-hat triplets and sub-only bass with chopped anen breaks Acousefo pereusston
Ioose Live kit
dreakbest//umme
hand druma and shaker P. elitchshop swing with lopsided groover </thinks and final ansver kanawers </answer>,
Output the thinking process in
Output
<thinkothe snare is criap and induatrial, the kick punchy and tight; basa surges with esturation-sll locked nechine-like grid.</thinko Hence, the Kinal answer las Sonaverseetallio rigid, and heavily compressed; diacorted baas centra: and grindingc/ansswer>
Figure 20: Example of QA pairs with thinking traces in MF-Think.
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Published as a conference paper at ICLR 2026
MF-Shib Deteiee
Prospi
captiore netadata provided you, will yo0 with soveral types votadata about music, These veradata includer
You wosio
Synopaiæs a záchčy debailed globai. desceipsion covering various datadla about the inpus music, inciuding cunce, abb-qunew, -inestarget,
song qpestilly tleestaeped Ground Teuts Jenpus The everage tampe the anay 41 Ground Trues key. The graand trues bey for the zong. cong
Egpitess tyrtos
lstart, end, (yrteel). tyrics multiple acage.
Suration
Chord Foregraaiion
Generate caption with the follouing constraints:
In the ctption ahould have the tolloving detallst
at cleariy state the genee, sub-genre, and atyliatle bllend of the track, bi Include the taapo (288). sime aiqnsturs, and bay poovided is the astedata propisaze not details the synopeia only use Sumarise the vused chareciazirrion comtinng Linden, pundez, vocel, style Iclmm,, versaned, etosh, and sitecta.
placee
osparately previded to you's.
instromenttion prodrction cheloes
di cxpture the tyrinal therss with Elareratize exaeplar if lyrice are sratlable, Trelude repatieive lyrics, iite the chomse, etc. 2ighlight the zong rrricture (iner,, stores, Breakdom, boidge, fisale) and how it evoloss opsrically.. engiination enational relasze) If not possible, ignoce this point.
optionally.. by. in you hav esough knosedde moie Uherry uzzich
parosived siructures
thecratioul
how
read, raseleing
chocuses carsaced acound G, 6, and
conney the overall nood and asschetic, situating the track within its cotlore) or hlatorica) content if relesant, It some netadtta is not gna lbbe, you ail: be proviced wits Viic Aval ablett, If suet case, just ignore having that information captios, but not pat neeng inforration.
enargy deives forward with secentier cublle kick
228 in 414, primarily E asperplated aysithasccezs, Thes batmonic foundation is buils acound ricur cuckaree propessionn, octer. sevoiving besee rases, C nafer, and rayor chorde, with secasionss ciift coused nafor to brightes the neod, Thate cycles greate toote measten and selenin, ceintorcing
diatosted chord quitata, and piisa-ing
Lruckts racancloii yut antheric charanter. Insermmtttiion antude day syniheriedd cas, bright syntha,
coepressed clean sale
ralodic derala rspeano delivare pasetotata Franch lyeics with zerath and and haron sile sereano add ree ircennily in treakmons, lyrics exp theses pain, resillence, and secipe, with Lines like "Denain je para our Nays" (*losorccs leive for Mara':. dcructurally, the song evoives from conterplative syoth intro over descending mines proquamiion (Ro-D-E) into exoloaive chersass featuring staing cycles (da-G-D-t), beaskdoons anchoced on asataized pedal R, spotenserrd bridgn
provides aothentc harmodess,
drund Teads sharply throngh. overall, oephasi osebines elerttontc niphoria
stereo lenging, bright 20,
conpiraret
fevecity,
unbodying the 2010a Francs. Teatoworre s
estrppor only the genarated capeioo and nothing alzets, Bere to the topur informstion:
Figure 21: Prompt for generating detailed captions for the MF-Skills dataset.
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Published as a conference paper at ICLR 2026
MF-Skil QA Generation Prempt
"You will seceive N audio filea with par-Cile NEZADAZA blocks. For EACH Cila 1. (1 2..8),, produoc divarso sot OE expect-leved gak paiza grounced OnLY the sudio and the reradata.
Return THE walld 2508 object. Ita top-level baya must be *2","2", in the sime ozdez inouta, Each valus in objest with:
"path": string cunchanges, the tile's migqu path/idi,
"spat: I "quastion"; atzing, ttypet; topant "muitiichiiee" "couns" "sine-bases" "chood" "bachnique" "comaarian"" "aptiona":; [atzing. I inciude for suisircholoe: 2-5 plausinis optiona *ansoeent: st-ing, "sationala"; atzing, briee juttifiaatonn grounded audie/metadita "sofareneas"; II "Lixe seconds"s [float]?
exact//ones anewer
vhere the pecuze) "baza": (lnt)s I bar numbers referenced *chords"1 Istring128 if chords referenced HI
Linestapgs referenced
Cuistasinta quidance:
fast provided audio and actadatar aveid speculation,
Prefer
cheokible questions (tixings, instruxent onsets, entry voosls, chord naxes, of
chord libels,
aqainas audibler if conflies,
"Leaoeding
timestapps question
queation types ditrisuity inveis. 12-20 par
Saurples of accoptable eveaties
appropsiate:
varbasino:
"Moway through, Counting "Hox rany cotal volces singing?" Begnentationi the number songe the recording." Contrast /N00) "that changed nidway? key singer terpo beskground instrusenc"
singer begis?e
1-
using aopp tyrie text, grven interval.
sattonn duratien. Techstigee archestration (e.gi, spticaat,
Chord-tdertity
rubato) with bas/tize zeferences.
:stervel range (lovestshighest pitcht.
"1-4-1 dentificaation MCO:
Congarison between
segments
(f olear"y distinct).
Chorup stazt tixe: "When does the first shorus begin?" Drop tinings "At what tirestarp doea drop lendse
Iespo motoz (122)
aecer? 4/4, 3/4,
sixed") *Does the tampo change after 1:003 yeu,
voes?"
Say nodulation: "When in the first key chanas, and frem shich bey vhichs"
Groove shift: "then doea the gueove wuitch to corriii/ooddeeiiiin fiza: aspear?*
Lysio trigaez: "As what tine does the word Inderonent the outeo/latter par:2" fastrumett entrice/exita: "Miich Lead cole (late secticals
dircetly precedea chorse?"
instrument entara tisas atter the drop (timstarp)?"
bron/eeti i. countst "Fow rany drur *11ls socur after 19009* nnow vany repeace of the nath not Section dorations: "ioe long le the bridge its swcnds/bwwe) Preceding chord: "Which cherd is imediately before (11//9
Peograssion detail: "that is the titth maed in the propreanion?* Cadence typei "HE DC 300 J. 180
Sa mony under expreseton: "Matt is the harsony when robato cocure? (P413913 (N7611 / Ra765 /. 79991*
"1-V-1 rapping (nster IED1: tant1, 81359, 2edd13 Sfour options)
Helody specitics: "Where does the nain moris tepeat verbarin? (tinestapp)* "Identity one blue-rote comerances"
Styth festures: "Eive one tines tonp with clear syncopacion." J *is there a polychycha oz henicla? Wberot
Baselire functions *Is the base reatly rcot-motion, valking, or pedals Justify sish: cines." Inverston spot:ing: "dentidy an inveraion (e.e., 241 and its tine."
LrodiatLoa/nssing: *Mhich element is Sard-pemeed Soft/righh?" *2 aidecheia parpuse audible? Provide Linestapp."
Space 1 72: *that reverb character is on the vooa (pla:e/tom/aall) Plrst audible tail tine?* Dynanios: "Khere 15 pock loudness?" "Mark a clear orescendo span (start-endi.*
Retation details; tia which das doea the quicas first us palersute?" "Mhat are the left-hand piano nosea in bas 3
MEg: "Maat is H07 a cesson these two veralons soun rasiline? coinger/shythn/eee http:////////tt//////// orstentzation comparinon: "Meat inproweents vece zade in crchestration betsean segranta?"
"Taaae in so need to gesarate all types of queationa for every audio. Rather, generate only the eceations that are nost saitsble,*.
<riee the veradsts previ ded as reference, but sizc underatard the aodio on your oxn,
Do not include taings iibe tacoeddnn to the netadata", or snything sivilar in your question. Azzune the mode: vi.i train with this data will only get the song an input.
Octput CRL the Json obrect. Bo extea commntary. Now I will provide the diles and thair METADATA blocks in order:
Figure 22: Prompt for generating QA pairs for the Skillills dataset.
36

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Published as a conference paper at ICLR 2026
Caption Re-writing Prompt (with lyrics)
kill provide you with music caption. Together with caption, I will provide you with lyrics pbls tine anpeed and segnented). You need to rearite the caption with lyrics integration, An exemple is:
Original Captica: This upbeat blues-rock track festures lively quiter riffe and thythmic pians, supported by driving drans at brisk of around 92.75 arM, music blands rockabilly and country influenres with expressive, sporse voctls conveying a nestaigic, longing mood.
Beuritten Captions this upbest blues-rook track supported by driving druns at brisk teago around 93.75 BIM, The mastc blends rockabilly and country influences with expressive, lines resalling mana's wernings, farewells to lover, must be gona"]. The parfornance conveys a nostaigic, longing need while maintaining apizited energy.
live'y guitar riffs
rhythmic piano,
vocals that thenes of less and departure wich
pain noving on ("I'll gone,
In multiple cases, the transcript will be short or not make sense, integrazing with the criginal caption will lead to good final output caption. In such case, 60 nothing and just the original csption, Only return the Einal caption and nothing else.
Here the Input details:
Figure 23; Prompt for correcting existing captions with lyrics and metadata on Music4ALL and MSD datasets.
Carrectia Proupt
expezt. option.
(sayba
options,
guestions used train
audio understarding
Your sain provide bettes learning
the data (cuaation optios conbitation) zoze dirriests, that training the 6l eignalis this can be dons by elther "ephracing the guestion or angeenting the opeions, Con wy is to "ook at the questi and and opiions und sasves and punutabe as additional List er opticea, shich, when. sugaented vita the estaving aptiona, zabuo the sption using langrage prioress, This can done by generaring extering.
grection harder and sepedoces the ghance of greeting the confornding options
opesonn
Isput queations Kilen soenacio would this plece be vost suitable tory
Zaput opsiona:
(X Rorvor rilx ruspense scene
gase background
seandy nantage
Coscect, optimms Seimne fictios vadeo backgsound
additional genazated op aptions to make it hardsrr i"Putariacio Lilx traclue cold opent, "Saports arma hype stinpest, "Jech procuct hightight cosl", ", "Hois desective game munu sereen", "cpper-nutery suries isteratiticl" weottrppculpppcc seriea teuses"
cleanly sbectaense/Suuucilt trepes:
additional optiens
Finelly, it you foel the queation itault has u lunquaqu peice, cuphease the queasion iniitt you use gunarabinn nav oprione, gerenate anyuhene between and addtrions? optiens, as you dees fit, to the poire share you feel the quartton slauudy distinult. speione you inglicit factors..*
qastinu, optiens, stamess may soud they bave priers. to dia oan, trwe to ougrant. seplicit infermation provided, ressoning
any
not fust
Generate new question, new optiors, or boch, only ourpor a J936 with 3 keyss exoesstonns The original question or corrected one.. hopeionss) "ifet of stringe with opcions inolldiing the original optionsl, "Krswer") Just the original inever.)
trgur
Figure 24: Prompt for option augmentation of existing question-answer pairs on the Music4ALL and MSD datasets.
37

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Published as a conference paper at ICLR 2026
Generation Prompt
muiic caption provides detailed description of a song/music sample in an abstract and concise fastion. will use this caption for treining model for music understarding and captioning, where the model is expacted
music expert.
provide you
the ingut caption. Mddicionally, I provide you with extra ground-truch me netadata about the masie, like keys, chorda, lpp, lyrica, etc. You need to generate thinking would through for generating such caption.
generate
trace that human music
objective: The final geal is to train model to think before generating the provided caption, liatening so
only the input song, thinzing track directly, not using metadsta. Metadata is provided only to help you avoid mistakes and understand the song to better reflect how masician vould undarstand and think about 15, bet reasoring.
therefore writter
nentioned
would Feous on kay aspects: tanpo/tesl, shythm and groove, instrurentatien and production, tonalicy chords, harnenic colort, vocals (timbre, style, processingl, lyrics and thenes, structurs, nood, ganze context, uaing both the caption
Tnstruct fonsein- Generate natural, coherent thinking trace that reflects how human anslyze track arrive at captien.
the necadata.
Style should follow the sample provided (natural, descriptive, scep-by-step zoascaing) Aae Do not the captions only provide the reasoriag that could lead length: leas than 250 words.
output only the thinking trace, nothing clse.
Exeeple Horr style) the track opena with ghone-nsseage sample, setting gritty, streetuise nood before the beat drops. The tampo reels faat for trap, azound 130 BPW in 4/4, giving it driving, club-roady puise. The production is anchored by booning 908s, razor snare cracks, stuttering ai-hats, while polished mixing adds wide sterec pads and risera behind the drums, AnTonslly, the piece centers Amas?, Bbmaf push-pull tension that gives the track glossy sheen beyond typical trap minimelism, Vithe vocals mile baritone, rapped sgcressively subtle delay/severs keep waynsk? klo, alo"), repetitive and cstchy, cleerly designed settings. Aniyrical streat sxacger. The stricture cycles through intro hook verses pre-hooks - repeated book beastfi', polished German trap anther
ninor. hear fa7 the tonic, but also bright chords like Fraj? that briefly shift
upward before returning minat, creating
German. Frozessine is delivery confident
uptront. The book relies on chant (*Maynak, en earvorn for club centers braggatocie, luxury, speed, and doninance, zooted
outro, reinforeing the chant as the nain iotif.Andversll. with heavy low-end drive, ninor sonality cslored by glosey chords, chant-bared anergy club production. Taken together, these clenents define the track's overall charaetar and set the asage for concise description; with this analysia in nind, I can capture the essence of single capcion,"
high-
Here is the input informacion:lakn
Figure 25: Prompt for generating step-by-step reasoning for the detailed captions in the MF-Think dataset.
38

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Published as a conference paper at ICLR 2026
OrT Ganeaaka Proupt
song/nusic aseple, will soe this os tor ceaining scoel for undecssanctrgg and ressening, vhece model pata just the input song and is espected to generate the correct inswet. sasitionally will provide you with a caption and extre groudrteutt recedace bays, chorde, tom, lycics, etc, You reed pererate thinking trow that homin moiec expert would go theouge about the wisld, caption.
perenating
dejectiver The final goal. to trein model to think before generating the provided mover, given only the trpat sorg. the thtrktigg stotakes therefore the cessozing.
as lsstoning thack direct y, inderstand
only help you rotidata should
reffects how hore Votur on key supeates toep vhythe and grense, inotinanntati to and production clo,, tonality dees, chords, harmonie colory, weate trarbie, style, peocassings, syeica and thasse, atructuce, scod, and panre contese, caing beta the exption and the ratetate. Styce soozld collow che starples provided (natacal, cuscriptive step by stop ressoningt.
-enerstee satural, etherent thinking
would analyse trank sestee
koditionelly, venetiney the queation and the anvver would provide cecer tae metadata Gilb "The teaceta taepo oueeribes aliberently in teo places. What baago is states in the dsscrpptive tett, and stat Dm is iiated winentare is In the case, pleass reporase quaation, esevers and aprienally optiona
the
song" sraver siom the retedata "2 you mee Lock question. that pase, your rain taot
input, don't return anytning for the Ch Also, zoes quertiona have to make the data lipestion. t option comtination) rore di aittieui,, so tast training on the data can provide betber leaming aigrala, This reptrasing queation aupanting the
can be by
way
quistions optiona queation
and
chanos gasszing
option
priceats,
*xiinalyy aces questiona that ask for thinge lite bay and Bix may be soo slmple and not zequize thinking, in that case, don't pecczc. anything for the queation or return atort thinking trece, as you dean fib. Lanqth: 20za than 25D vorda.
exorp' e Triskings
"question"; *How vould you charectasioe the teack's prodictien style and crecell Fix qualscy?", *acavec"; "oo th, aupple basec productios thas ia nonezbeieza
mioad.", "thinking sracet; dras aound Like chapped sceata sighs tick, maapyy auae treated Desplte the 21 aeathatic, balanows
ylslding underground, qritey above the Loop, -ohc ure contaniled, and tounpient defonitcon ressins cleas do stta lo Et by design yes well mised." > taia is anal: thinking shiln, feel free go up to words, hit recemne rot put feselevant thinking pointe the tracee, intnourper JaCN with rephiaced),
mupple
neatly
(peesibly sugnootedl, "ceeansing truee 11ke tngiffs ("quastionts "anamee:: "cptions"s "thinking seaceft "gai")
Bere taw impat indocmation:
Figure 26: Prompt for generating step- step-by-step by-p reasoning for the question guestion-answer answer pairs in the MF-Think dataset.
39


Reviews:
[{"review_id": "Yg9VOPsnGi", "reviewer": "Reviewer_cqsC", "summary": "This work proposes Music Flamingo, a large-scale audio-language model established from a trained Audio Flamingo 3 model, plus 4 post-training steps (3 supervised fine-tuning, 1 RL policy update phase). At the same time, this work proposed MF-Skill and MF-Think, two large-scale synthesized datasets curated for the post-training steps of Music Flamingo. The proposed model achieved top performance on 13 benchmarks (Table 1).", "questions": "- Regarding quality filtering, what is the frontier model used to verify the captions? Also, what's the model used for re-writing the captions?\n\n- How does the re-writing of MF-Think work? Is that done by repetitive re-generation followed by the verification from the said frontier model?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "- Datasets and model proposed in this work are established on full-length songs rather than clips. This is important as some information can only be well determined by observing the full track (e.g. musical structure, mood, cultural context, etc.).\n\n- Making useful incorporation of speech-centric datasets, such as including multilingual ASR and multi-talker ASR to improve the model capability on bridging texts(e.g. lyrics) and phonemes (speech, vocal). \n\n- The annotations included in MF-Skills incorporated multiple important aspects with not only tags, but also captions and QA pairs. In particular, temporal information is comparatively scarce in public datasets. \n\n- Incorporating a reinforced learning step in the entire pipeline. It is insightful to see a successful adaption of GRPO for LALM.\n\n- Dataset and source code will be published for reproducibility.", "weaknesses": "- There's no ablation study on how each post training step improves the model, which is important to show the significance of introducing reinforcement learning. It's also a pity that there's no further discussion about the stability issue of GRPO.\n\n- Although it's a positive sign that proposed work is aware of the dataset issue on cultural bias, according to Figure 4(the same as Figure 27?), the composition of MF-Skills is still western-centric. It can be understood that collecting dataset that covering everything is difficult, however, I believe it is still not sufficient to claim the current composition as \"moving from western culture to diverse culture\". For example, African, Middle east, India, East asia and Southeast asia music are still under-represented despite they all have huge audience population.\n\n- The reference of GRPO seems to be missing: https://arxiv.org/pdf/2402.03300", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dLTcpyu8zY", "reviewer": "Reviewer_PZ26", "summary": "This paper introduces Music Flamingo (MF), an LLM-based model for music understading tasks. Authors created two large-scale datasets for music understanding tasks: MF-Skills, which contains 4M examples and MF-Think, which contains 300K CoT examples (derived from MF-Skills) for post-training MF to improve its reasoning abilities.  MF-Skills and MF-Think contain full-length music with detailed and multifaceted annotations, compared with previous music understanding datasets that often contain short and surface-level annotations.\n\nBased on the two datasets, authors developed MF by continue training the Audio Flamingo 3 (AF3) model. MF achieved SOTA performances on several public music understanding benchmarks. Human evaluation with music experts show that MF seems better compared with other models.", "questions": "- Appendix F: how generalizable is this user study? Have you tried with more music/songs, or music without vocals?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- I think MF-Skills and MF-Think are very good resources for music understanding, where annotations and datasets are rare. The two  datasets could potentially benefit follow-up work of developing better models in this field.\n\n- SOTA performance on several music understanding benchmarks.\n\n- Adding a post-training stage to improve reasoning of the music understanding model is well motivated. Current music understanding   models tend to output surface-level outputs about a music. It is great to see adding post-training RL is also effective for music  understanding.  Authors also show that without post-training, performance drops on MMAU-Pro-Music and MuchoMusic.", "weaknesses": "- MF stresses a lot on music with vocal (sec 3.1), and motivates to add several ASR datasets. While this motivation is valid, but non-vocal   music seem less covered in the discussions.\n\n- In the human evaluation from music experts (Tab4), it seems MF often attempts to provide deep details, but often inaccurate. This poses   some concerns on real-world use cases, beyond the good performance on benchmark numbers.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VMUSz5HdYG", "reviewer": "Reviewer_RXYf", "summary": "This paper presents Music Flamingo, a large multimodal model extending the Flamingo architecture to handle music, text, and symbolic representations jointly. The model is trained on large-scale music–text–symbol triplets and evaluated across multiple tasks, including music captioning, retrieval, tagging, and question answering. The paper aims to demonstrate the benefits of scaling and unified modeling for general-purpose “music understanding.”\n\nOverall, this is a solid and technically competent piece of work, with careful engineering, broad evaluation, and clear connections to the audio Flamingo. However, several conceptual and empirical gaps remain regarding the necessity of this architecture, its comparison to specialized systems, and its coverage of affective aspects of music understanding.", "questions": "Would you include case studies or analyses showing cross-task generalization or contextual reasoning that would be infeasible for traditional MIR pipelines.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "Comprehensive system design — The adaptation of the Flamingo multimodal framework to the music domain is well-motivated and technically executed. Training across multiple modalities (audio, symbolic, text) is a significant engineering contribution.\n\nUnified modeling framework — Unlike prior task-specific MIR models, the same model handles captioning, retrieval, tagging, and QA, demonstrating promising few-shot and zero-shot generalization.\n\nDataset and reproducibility — The paper provides a substantial and well-curated multimodal dataset and detailed training setup, which is a strength for community reuse.", "weaknesses": "1. Necessity over specialized MIR pipelines\n\nWhile the unified approach is appealing, it remains unclear why such a large, general model is necessary for tasks that existing MIR pipelines already solve effectively (e.g., onset detection, chord recognition, instrument tagging).\nThe paper could better justify what emergent capabilities arise from this unified model that cannot be achieved by composing established MIR tools.\n\n\n2. Missing comparisons to domain-specialized music–language models\n\nThe paper positions Music Flamingo as a general music–language model but provides limited comparison to existing specialized systems -- I see it cited MuLLaMa but it seems there is no comparison?\n\n3. Conceptual scope and framing\n\n\"Music understanding\" has cognitive or perceptual components implied by that phrase, not only including facts like pitch, timbre, but also a focus on the feeling and emotional response. This is definitely a hard task and the seems not the focus here. That said, framing the contribution as scaling multimodal representations for music would be more accurate and modest, aligning better with the presented evidence.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zIdUP4h2Nd", "reviewer": "Reviewer_qEKD", "summary": "This paper proposes Music Flamingo, a large audio–language model, designed to advance music understanding in foundational audio models. Music Flamingo is a fine-tuned model based on Audio Flamingo 3 backbone. This paper also introduces MF-Skills and MF-Think, two large-scale datasets containing music–caption and music–QA pairs designed to promote deliberate reasoning.", "questions": "- There seems no explanation of how and from where the 2M songs for MT-skills were collected. Additionally, is there a possibility that the data used for evaluation is included in the training data?\n- I could not find an explanation for why the number of examples increases from 2M songs to 3M examples in MT-Skills. Does this mean that captions are added to all 2M songs, and some songs also have QA, thus resulting in 3M examples including pairs of music-caption and/or music-QA?\n- Does AF3-St. 3 in Table 2 refer to the training data of the base model AudioFramingo3? Additionally, does 2.0 of MF-SFT mean adding MF-Skills twice? Are the other data also used in the training of AudioFramingo3 and then reused in fine-tuning for Music Framingo?\n- It would be beneficial to elaborate on how quality filtering is performed.\n- How were the categories in Figure 27 analyzed?\n- Will the created dataset and source code/checkpoints be made publicly available?\n- How were the inputs and outputs for ACC evaluation handled?\n- In Figure 3, the term \"MF-skills CoT\" is used, but the caption seems to explain it as \"MF-Think.\" It would be better to standardize the terminology.\n- It seems Figures 4 and 27 are identical, but only Figure 27 is mentioned in the text. It would be better to refer to Figure 4 and note in its caption that an enlarged version is included as Figure 27 in the Appendix.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "-\tMusic Flamingo achieves state-of-the-art results on 12 music understanding and reasoning benchmarks.\n-\tLow-level attributes are extracted using the MIR tool, and initial captions combined with these attributes are used to reduce hallucination, creating captions and QA data that include multifaceted information to construct MF-Skills data. Synthetic data is built with attention to hallucination by LLM, considering important characteristics in the music domain, and an effective approach has been proposed.", "weaknesses": "-\tThe ablation study is missing. Although improvements are made based on AudioFramingo 3, the model is trained in three stages: w/MF-skill, w/MF-think, and GRPO with custom rewards. Having evaluation results for each stage would help confirm each contribution.\n-\tSome details about the dataset creation are not described.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)