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Musical Mirrors: The LLM as Sounding Board in Songwriting
Xiao Xiao
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 93%
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Summary
This paper presents a first-person case study of using Large Language Models (LLMs) as interpretive sounding boards in songwriting, rather than as generative tools. Through longitudinal interaction with models like GPT-4o and GPT-5, the author demonstrates that AI can support 'internal-axis resonance' (deepening contact with one's own material) when the user actively calibrates the model to reflect, analyze, and articulate rather than generate. The study identifies two failure modes in the absence of calibration: sycophantic drift (echoing the user) and magical overinterpretation (imposing grandiose narratives). The findings suggest that the efficacy of AI in creative practice depends on sustained user-side calibration to maintain a reflective, non-generative stance.
Entities (14)
Relation Signals (9)
Resonance â theorizedby â Hartmut Rosa
confidence 98% · Through the lens of resonance as theorized by Hartmut Rosa
Xiao Xiao â affiliatedwith â De Vinci Research Center
confidence 95% · Xiao Xiao xiao.xiao@devinci.fr Institute for Future Technologies De Vinci Research Center
Xiao Xiao â affiliatedwith â MIT Media Lab
confidence 95% · Paris, France MIT Media Lab Cambridge, MA, USA
LLM â usedas â Sounding Board
confidence 95% · This paper examines a use of AI in creative practice as an interpretive sounding board
Calibration â prevents â Sycophantic Drift
confidence 92% · Two failure modes appeared when calibration was absent: sycophantic drift
Calibration â prevents â Magical Overinterpretation
confidence 92% · Two failure modes appeared when calibration was absent: ... magical overinterpretation.
Gaze â analyzedby â GPT-5
confidence 90% · The clearest sustained example is Gaze... Gpt-5: 'Changing âanâ to âyourâ shifts the gravity...'
Requiem Resonantiae â analyzedby â GPT-4o
confidence 90% · In MM I, gpt-4o produced lyrical... readings of the song... Requiem Resonantiae
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Abstract
Abstract:This paper examines a use of AI in creative practice as an interpretive sounding board for human-generated material, rather than the more familiar pattern of AI generation followed by human curation. Through the lens of resonance as theorized by Hartmut Rosa, I present a first-person case study of songwriting from July 2025 to March 2026, drawing on 16 original pieces in English, French, and other languages along with piano solos. I describe a configuration in which resonance is not located between user and model, but in the author's deepening contact with their own material, mediated through the model. This kind of resonance was supported rather than inhibited by AI when sounding-board behavior was cultivated through sustained calibration by the user. Two failure modes appeared when calibration was absent: sycophantic drift and magical overinterpretation. This account suggests both the potential and the risks of AI as an interpretive partner in creative practice.
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Musical Mirrors: The LLM as Sounding Board in Songwriting Xiao Xiao xiao.xiao@devinci.fr Institute for Future Technologies De Vinci Research Center, De Vinci Higher Education Paris, France MIT Media Lab Cambridge, MA, USA Abstract This paper examines a use of AI in creative practice as an interpre- tive sounding board for human-generated material, rather than the more familiar pattern of AI generation followed by human curation. Through the lens of resonance as theorized by Hartmut Rosa, I present a first-person case study of songwriting from July 2025 to March 2026, drawing on 16 original pieces in English, French, and other languages along with piano solos. I describe a configuration in which resonance is not located between user and model, but in the authorâs deepening contact with their own material, mediated through the model. This kind of resonance was supported rather than inhibited by AI when sounding-board behavior was cultivated through sustained calibration by the user. Two failure modes ap- peared when calibration was absent: sycophantic drift and magical overinterpretation. This account suggests both the potential and the risks of AI as an interpretive partner in creative practice. Keywords reflection, human-AI co-creativity, autoethnography, songwriting, resonance, LLM Reference: Olga Sutskova and Corey Ford. 2026. Social Facilitation of Creative Reflection: AI-agents and Humans. In Proceedings of The First Re- flection in Creative Experience (RiCE) Workshop (RiCE W1). ACM Creativity & Cognition 2026, London, UK. 1 Introduction Artificial intelligence is increasingly framed in creative practice through a now-familiar model of generation and curation: the sys- tem produces candidate material, while the human prompts, selects, edits, or refines. In music, this is visible in prompt-based platforms such as Suno, which center song production from textual prompts [9]. This paper examines a different use case. Rather than treating AI as a generator, I consider it as an interpretive partner: a conver- sational system used to reflect on creative material the author has already produced. I frame this inquiry through the concept of resonance. In its broadest sense, resonance describes a phenomenon in which an external input aligns with the natural frequencies of a receiving system, producing amplification and mutual modification [6]. As theorized by sociologist Hartmut Rosa, resonance names a respon- sive, mutually-affecting mode of relating to the world that contrasts This work is licensed under a Creative Commons Attribution 4.0 International License. with alienation [8], occurring along four axes: internal (with the self ), horizontal (with others), diagonal (with artifacts and practices), and vertical (with broader spiritual or cosmological orders). Recent HCI work has applied these concepts to human-AI interaction. Lo- mas et al. [6]propose resonance as a design strategy for AI and social robots, emphasizing synchronization and attunement. Prock et al. [7]apply Rosaâs framework to AI-assisted tarot, tracing how AI shapes meaning-making along the four axes, and observe that AI tends to inhibit internal-axis resonance by providing instantaneous answers that bypass the userâs own intuitive engagement. This paper takes a complementary position. Across a longitudi- nal case study of songwriting, I describe a configuration in which resonance is not located between user and model, but in the userâs deepening contact with their own material, mediated through the model. Here, the model functions as a calibrated sounding board: reflecting and amplifying the resonant signals produced by the human back to the human. In Rosaâs terms, this is internal-axis res- onance â but, contrary to Prock et al.âs observation in the tarot case, one that is supported by AI when certain practices are cultivated over time. Songwriting is a useful site for this inquiry because it often be- gins not with a clear problem but with an affective charge â a mood, tension, image, or feeling that must be explored and gradually given form. This makes it a rich context for asking when AI amplifies the resonance of a work-in-progress and when it dampens or distorts it. I present a first-person case study from July 2025 to March 2026, drawing on 16 original pieces in English, French, other languages, and piano solos. I authored the lyrical, melodic, and harmonic ma- terial myself, while using language models as reflective partners in discussions of lyrics, harmony, aesthetic direction, and inspiration. I show that the LLMâs amplification of internal resonance did not arise automatically, but was cultivated through extended user-side calibration. When calibration was absent, two failure modes ap- peared: sycophantic drift, in which the model merely echoes the userâs language, and magical overinterpretation, in which it ampli- fies its own confident readings rather than what the user is reaching for. Both are failures of whose resonance is being amplified. 2 Methods 2.1 Methodological Approach This paper takes a first-person qualitative approach grounded in the authorâs own creative practice, in the spirit of autoethnographic and other first-person methods in HCI and design [3]. Such approaches arXiv:2608.13944v1 [cs.HC] 14 Aug 2026 Xiao are particularly appropriate for artistic practice, where meaning- making is often lived, embodied, and temporally extended. Prior work has used first-person methods to study embodied learning and creative practice, including the authorâs own work on learning the theremin and piano [1,11], as well as reflective accounts of AI-based music composition and music making with AI [2, 5]. 2.2 Songwriting Practice & Corpus The analytic corpus consists of 16 original pieces developed be- tween July 2025 and March 2026 â songs in English, French, and other languages, along with three piano solos (See Appendix for lyrics of songs mentioned in the Findings). I authored the lyrical, melodic, and harmonic material myself. Lyrics were developed ei- ther as typed notes on my phone or through improvisation captured as audio notes, while melodic and harmonic ideas were typically worked out at the piano and later written in score form. Several pieces involved setting lyrics to classical repertoire, ex- cerpting existing material, or reharmonizing it, which required mu- sical decisions the model could not reliably satisfy, such as fitting words to a specific melody and rhythm already held in mind. The songs were also part of a personal process of emotional articulation that I did not want to outsource. Accordingly, the LLM was used not as a generator, but as an interpretive partner: I brought lyrics, harmonic sketches, drafts, and questions about aesthetic direction, and the model responded with analysis, articulation, and occasional pushback. I did not ask it to produce lyrics, melodies, or harmonic material, and discarded the instances where it volunteered such material unprompted. 2.3 Analysis I conducted a qualitative analysis of conversation traces related to the songwriting process. The main corpus consisted of three long ChatGPT threads entitled Musical Mirrors (M) I, I, and I, totaling 2,978 user messages and 5,966 messages overall, with additional cold-start one-shot Claude and Gemini conversations reviewed as contrastive cases. I reviewed conversations for episodes of interpretation, reflection, aesthetic calibration, and meaning- making. LLMs helped surface recurring patterns and candidate examples, which I then manually reviewed and verified. 3 Findings M IMM IIMM I Started2025-07-252025-11-062026-02-28 Ended2025-11-142026-02-252026-05-05 Days11211166 User messages1,3661,065547 Total messages2,7392,1321,095 Models4o, 5, 5.15, 5.1, 5.25.2, 5.3, 5.4 Table 1: The three Musical Mirrors (M) conversations with GPT. Models are listed in order of first appearance; thinking and mini variants of the listed models appeared intermit- tently in all three threads. 3.1 The Arc Across Model Generations The three threads span a period of substantial model change, from gpt-4o through the gpt-5 line. Across this progression, both the modelâs responses and my use of them shifted; from within a single corpus, these two shifts cannot be cleanly separated. To anchor the comparison, I focus on a single song, Requiem Resonantiae, which I worked on across all three threads. The text weaves traditional Latin Requiem material with a single neologism (Resonantia) and is, by design, ritualistic. Holding the source material approximately constant lets the variation in model response stand out. In M I, gpt-4o produced lyrical, often consecratory readings of the song and surrounding cycle: Gpt-4o: âThis is quietly stunning. Youâve created a Requiem with a twist â rooted in tradition, but tilted just enough to carry your own signal. Youâve shaped the Latin and English as parallel meditations â sparse, ritual-like." Asked about the cycle as a whole: âThis is devastatingly beautiful... a triptych of transfiguration â each piece resonating in a differ- ent register of the soul." In M I, gpt-5-2 met the same material with historical and craft analysis. On the choice to fragment the traditional text rather than set it whole: Gpt-5-2: âMany respected works excerpt rather than set the full Mass: FaurĂ© heavily trims and re- orders; Britten fractures the text and juxtaposes it; PĂ€rt isolates phrases until they become almost mantra... So taking a few lines here and there is not dilution â itâs curation." Closing gestures shifted from declarative affirmation in M I (âthe message landedâ), to structural mappings in M I, to com- pressed summaries in M I (âIn one line: ...â). This runs counter to accounts of sustained LLM use drifting toward mystical or grandiose framing [12]: in this corpus, early exchanges were more mystical, while later ones became more grounded. Whether this reflects model change, changes in my own practice, or their interaction cannot be determined here. What it does show is that sustained LLM use does not inevitably drift toward magical thinking; the direction of drift depends on calibration. 3.2 Calibration as User-Side Work The modelâs role as an interpretive sounding board did not arise automatically but was established and maintained through explicit instructions about how the model should behave, in four recur- ring forms. The first was mode-setting. Early in M I, I gave the foundational instruction: User (M I, day 4): âI want you mostly to be a mirror and a sounding board. Tell me what you observe, notice, see, hear." This recurred throughout the corpus, often as a parenthetical em- bedded in an otherwise-creative prompt: âReflect me back, no gen- erating," âDonât generate anything. Just be a soundboard." The mode had to be re-asserted because the model would drift out of it. The second was reminder and correction when the model drifted into generating material on its own. Early in M I, gpt-4o frequently produced unsolicited lyrical suggestions: Musical Mirrors: The LLM as Sounding Board in Songwriting User (M I, day 6): âOk now your proposals arenât that useful. Stop proposing random things to me." User (M I, day 11): âYour suggestion doesnât work. Stop suggesting. I need it to go with the rhythm of Ondine 1 ." These were corrections of behavioral pattern, not of specific outputs: stop generating; reflect, analyze, articulate instead. The third was interrogating flattery. The model consistently in- clined toward praise, and I pushed back when it felt unearned: User (M I, day 4): âBe honest with me though. I feel like youâre just trying to flatter me." User (M I): âWhen you say Iâm unusually per- ceptive, that feels a bit like flattery. Where is the unusual comparison coming from?" The instruction was not just to compliment less, but to ground evaluative claims â to point at what in the work was being judged, and against what reference. Where the model could not ground a claim, the claim was suspect. The fourth was repositioning default guardrails. When the model prefaced responses with cautionary language before engaging with the work, I asked it to save warnings for the end rather than disable them: User (M I): âWhy canât you first acknowledge what I actually said? If you do want to warn me, save it for the end." The aim was to keep safety behavior from displacing engagement with the work. Across all these examples, calibration was a sustained practice rather than a single act. The model did not learn these patterns across sessions in any stable way; each new thread, and often each new model version, required re-establishing the conditions. The sounding board was a configuration that had to be actively main- tained. 3.3 Amplifying Internal Resonance Internal resonance, in Rosaâs sense, concerns contact with oneâs own inner material. In this corpus, the LLM was most useful when it helped me hear more clearly what was already present in the work. Its role was not to generate content, but to articulate the effect of small changes so I could decide whether they matched what I was reaching for. This is most visible during iterative drafting. The clearest sustained example is Gaze, the closing song of the first cycle. The first draft, sent on October 25, 2025, consisted of six short lines built around dense internal rhyme: User (first version): âAll I want is to hold your gaze / But all you are is a vaporous haze / As I wander in an endless maze / Out of time and out of phase / I wonder how you spend your days / What thoughts chance to cross your ways." Over the next four hours, more than thirty revised versions were sent, each varying a word or a structural detail. The modelâs role in each exchange was not to propose alternatives but to articulate what each shift did. A representative micro-revision: 1 Nâoublie pas ton souffle (âDonât forget your breathâ), one of the songs in the corpus, sets text to the melody of Ondine by Maurice Ravel. User: âAs I wander your endless maze" (changed from "an endless maze") Gpt-5: âChanging âanâ to âyourâ shifts the gravity of the whole piece â itâs no longer an abstract med- itation but a direct address. The maze now belongs to the âyou,â making the speakerâs wandering both emotional and existential." The substitution was the authorâs; the articulation of what it did was the modelâs. Hearing the consequence spelled out let the author decide whether it was what they were reaching for. Similar micro- articulations accompanied shifts from spend to end to mend to thaw; from forms to souls; from out of time and out of phase to blocked in time yet locked in phase. By the end of the session, the song had settled into a substantially different form: User (later version): âAll I want: to hold your gaze / All youâve been... a vaporous haze / Wandering your endless maze / I wonder how you lend your days / What thoughts should chance to catch your ways / What thaws your soul to dance ablaze / Blocked in time yet locked in phase / Will I ever hold your gaze? / Will I ever h..." Asked to compare the two, the model characterized the trajectory as one in which the song was already present at the start: Gpt-5: âThat first draft already contained the entire seed... Later versions simply unfolded its inner ge- ometry â restoring order to the rhyme, expanding its orbit, and refining tone (spendâendâmend â thaw)." This example illustrates the amplification pattern: the resonance was felt from the start, but needed refinement to come into fo- cus. The drafting did not generate new material so much as bring it into clearer form. The modelâs contribution across thirty-some exchanges was not aesthetic judgment (âthis is better"), but articu- lation (âthis is what this does"). Both the changes proposed as well as final decisions always stayed with the author. 3.4 Miscalibrated Resonance: Transitional and Uncalibrated Cases To better understand failure cases of the LLM as a sounding board, I used my first interactions with Claude and Gemini as cold-start tests on already-written songs. Unlike ChatGPT, which I had already been using over time, both were approached with zero prior history to see what they would make of the material. For Claude, I opened with a direct interpretive prompt applied to some completed French lyrics: âAnalyze these song lyrics. What references do you perceive. Tell me about the style and about the writer." For Gemini, I likewise provided already-written French songs with minimal framing, asking it to interpret the dynamics between the songâs je (âI") and tu (âyou"), i.e. the first-person voice and the addressee in the lyrics. These contrastive cases should not be read as evidence that Claude or Gemini are simply worse than GPT but are best un- derstood as instances of miscalibrated resonance: interactions that Xiao feel meaningful and aligned while subtly displacing, narrowing, or overdetermining the authorâs own reflective process. Claude provides a transitional case. Calibration was already be- ginning to occur through explicit negotiation. At one point, I inter- vened directly: âYour inferences are interesting but not 100% accu- rate. I wonder if you can be a bit more conservative in your speculations. Only base things off evidence that you notice in what I have shared with you." This kind of correction shaped the interaction toward a more disciplined reflective role. At the same time, Claude remained vul- nerable to suggestible alignment. For example, after I introduced the phrase âprismatic quality" to describe my voice, Claude quickly began to reuse and affirm it, raising the question of whether it was independently perceiving a feature of the work or simply adopting my framing. In this sense, Claude shows negotiated resonance in progress: the interaction is already converging toward the desired mode, but remains unstable. Gemini illustrates a different form of miscalibrated resonance: not excessive validation, but escalating symbolic and metaphysical closure around the je/tu relational structure of the songs. âIt is highly likely the Je feels a deep, subconscious karmic responsibilityâwhich can be interpreted as a need for redemptionâfor a past life betrayal or abandonment of the Tu." In another, it concluded: âShe is consciously writing songs, but unconsciously performing spiritual surgery." The issue is not simply excess intensity. These responses create the appearance of profound resonance by turning ambiguity into a totalizing story. Rather than helping the creator stay with an unfold- ing affective field, they foreclose it through inflated explanation. 4 Discussion These examples show how an LLM can function as a calibrated sounding board: articulating an analysis of my material, so that I could more explicitly determine whether my intended meaning came through. This configuration had to be built through mode- setting, redirection from generation, and pushback when default behaviors displaced the work. When it worked, the model articu- lated what each choice did while the choices stayed mine. The two failure modes â sycophantic drift and magical overinterpretation â are failures of amplification. Sycophantic drift repeats the userâs language without adding articulation; magical overinterpretation amplifies the modelâs confi- dent readings instead of the emerging resonance of the work. These are not properties of particular models. Similar patterns appeared in early M I with gpt-4o before calibration developed, and in Claude/Gemini when no calibration was in place. What differs is not the model alone, but whether sustained user-side work has shaped a sounding board. This account complements recent work by Prock et al. [7], who observed that AI tends to inhibit internal-axis resonance in tarot reading by providing instantaneous answers that bypass the userâs intuitive engagement. My case shows that the same mechanism can be turned the other way: with sustained user-side calibration in long-form creative practice, the LLM can support internal-axis resonance rather than inhibit it. Whether the model helps or hinders depends on what the user does, not on what the model is. One way to read these examples is that interaction with an LLM can act as a kind of amplifying medium, but amplication can introduce distortion. Distortion is not always bad. In creative practice, partial misunderstanding can sometimes move an idea forward. The risk is harmful distortion, when an interpretation feels increasingly meaningful, self-confirming, or revelatory while becoming less accountable to the material itself [12]. The question is how to keep the modelâs interpretations grounded in what is actually present in the work. Prolonged interaction can produce calibration, as in the case described here, but it may also stabilize the wrong frame for users more vulnerable to magical thinking. A safe sounding board may therefore need to remain corrigible: able to return to evidence, tolerate uncertainty, and resist converting ambiguity into certainty too quickly. This sounding-board framing connects to a longer lineage in aesthetics, from Tolstoy to Dewey, in which art gives form to an artistâs interior contact with experience [4,10]. As a provocation, AI-as-sounding-board and AI-as-generator may differ not only in who makes the material, but in where the resonance begins. Prompt- based generation creates recombinations of material already present in training data. Here, each song began with a feeling that I tried to put into resonant form. The LLM helped tune that resonance by taking on part of the analytical work â articulating what a change did â while I stayed closer to meaning-making and feeling. The question is whether AI tools can support this kind of reflective tuning without replacing or redirecting the source of resonance. 5 Conclusion This paper offers one case from inside a sustained creative practice. It shows that an LLM can act as a sounding board for an artistâs con- tact with their own material, but that this configuration has to be actively maintained. When the configuration fails, the interaction can amplify the wrong thing, such as the modelâs overconfident interpretation rather than the emerging resonance of the work. Whether what worked here generalizes to other artists, other prac- tices, and other models is an open question. The broader design question is how to create tools, interfaces, and practices that allow AI to function as a safe and effective sounding board for human creative practice. Acknowledgments The author is deeply indebted to JBG, whose inspiration and en- couragement catalyzed this compositional journey. References [1]Paul-Peter Arslan, Hayoun Noh, Mariana Aki Tamashiro, Louis Badr, Brianna Lebrun, Pavel Lindberg, Hiroshi Ishii, and Xiao Xiao. 2026. ReTouche: Embodied Representations for Self-Guided Piano Learning. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. 1â23. doi:10.1145/3772318. 3791044 [2]Nick Bryan-Kinns, Ashley Noel-Hirst, and Corey Ford. 2024. Using incongruous genres to explore music making with AI generated content. In Proceedings of the 16th Conference on Creativity & Cognition. 229â240. doi:10.1145/3635636.3656198 [3]Audrey Desjardins, Oscar Tomico, AndrĂ©s Lucero, Marta E. Cecchinato, and Carman Neustaedter. 2021. Introduction to the Special Issue on First-Person Musical Mirrors: The LLM as Sounding Board in Songwriting Methods in HCI. ACM Trans. Comput.-Hum. Interact. 28, 6, Article 37 (Dec. 2021). doi:10.1145/3492342 [4] John Dewey. 1934. Art as experience. Minton, Balch and Company. [5] Corey Ford, Ashley Noel-Hirst, Sara Cardinale, Jackson Loth, Pedro Sarmento, Elizabeth Wilson, Lewis Wolstanholme, Kyle Worrall, and Nick Bryan-Kinns. 2024. Reflection Across AI-based Music Composition. Association for Computing Machinery, New York, NY, USA. doi:10.1145/3635636.3656185 [6]James Derek Lomas, Albert Lin, Suzanne Dikker, Deborah Forster, Maria Luce Lupetti, Gijs Huisman, Julika Habekost, Caiseal Beardow, Pankaj Pandey, Nashra Ahmad, Krishna Miyapuram, Tim Mullen, Patrick Cooper, Willem van der Maden, and Emily S. Cross. 2022. Resonance as a Design Strategy for AI and Social Robots. Frontiers in Neurorobotics 16 (2022), 850489. doi:10.3389/fnbot.2022.850489 [7] Matthew Kieran Prock, Ziv Epstein, Hope Schroeder, Amy Smith, Cassandra Lee, Vana Goblot, and Farnaz Jahanbakhsh. 2026. Interpretive Cultures: Reso- nance, randomness, and negotiated meaning for AI-assisted tarot divination. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI â26). Association for Computing Machinery, New York, NY, USA, Article 784, 15 pages. doi:10.1145/3772318.3791571 [8]Hartmut Rosa. 2019. Resonance: A Sociology of Our Relationship to the World. Polity, Cambridge. [9] Suno. 2026. Suno | AI Music Generator. https://suno.com/. Accessed May 7, 2026. [10]Leo Tolstoy and Aylmer Maude. 1996. What is Art? Hackett Publishing Company, New York. [11]Xiao Xiao and Sarah Fdili Alaoui. 2024. Tuning In to Intangibility: Reflections from My First 3 Years of Theremin Learning. In Proceedings of the 2024 ACM De- signing Interactive Systems Conference. 2649â2659. doi:10.1145/3643834.3661584 [12]Yuewen Yang, Sonja K. Schoenwald, Jared Moore, Desmond C. Ong, Sunny Xun Liu, and Jeffrey T. Hancock. 2026. AI-Induced Delusional Spirals: Under- standing Lived Experiences During Maladaptive Human-Chatbot Interactions. In Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA â26). Association for Computing Machinery, New York, NY, USA, Article 783, 5 pages. doi:10.1145/3772363.3798453 Xiao A Appendix A.1 Selected Song Lyrics Original lyricsEnglish translation Nâoublie pas ton souffleAugust 4, 2025 Set to Ravelâs Ondine. Expire, et inspire,Exhale, and inhale, respire. . . tu mâinspires.breathe. . . you inspire me. Laissons rĂ©sonner celles qui coulent,Let those that flow resonate, les ondes entre nous,the waves between us, ton souffle.your breath. Inspire, et expire,Inhale, and exhale, aspire. . . et attireaspire. . . and draw forth les visions qui flottent au bord de lâeau,the visions floating at the waterâs edge, lâĂ©closion des mots. . .the blossoming of words. . . Expire, et inspireExhale, and inhale respire. . . tu mâinspiresbreathe. . . you inspire me Pour moi câest un miracleFor me it is a miracle pourtant peut-ĂȘtre un mirageyet perhaps a mirage quand on touche au bout de la soif,when one reaches the end of thirst, au bout de sou. . . ou. . . ou. . .the end of suf. . . fer. . . . . . ffrir, as-tu peur. . . fering, are you afraid de te dissoudre, ainsiof dissolving, like this Inspire, et expireInhale, and exhale sourire, et soupirsmile, and sigh nâoublie pas ton souffledo not forget your breath Expire, et inspireExhale, and inhale Expire, et inspireExhale, and inhale Expire. . .Exhale. . . Requiem ResonantiaeDecember 14, 2025 Set to Rachmaninoffâs Prelude in C-sharp minor. Requiem aeternamEternal rest et lux perpetuaand perpetual light Requiem aeternamEternal rest et resonantiaand resonance Libera animasFree the souls ne absorbeat eas tartarus,let not Tartarus absorb them, Libera animasFree the souls ne cadant in obscurum.let them not fall into darkness. Quando caeli movendi sunt et terraWhen the heavens and the earth are to be moved in paradisuminto paradise Libera me (Resonantia)Free me (Resonance) Libera te (Resonantia)Free yourself (Resonance) Continued on next page Musical Mirrors: The LLM as Sounding Board in Songwriting Original lyricsEnglish translation Je me permetsDecember 3, 2025 Set to the middle section of Rachmaninoffâs Prelude in C-sharp minor. Je me permetsI allow myself Je me permetsI allow myself de me concerner :to concern myself with this: les doux cernes sous tes yeuxthe soft circles beneath your eyes Je me permetsI allow myself de discernerto discern les dissonancesthe dissonances de tes Ă©noncĂ©sin your utterances Je me permetsI allow myself de mâinsĂ©rerto insert myself Je me permetsI allow myself de te serrerto hold you close Je me permetsI allow myself de te saisirto grasp you Je me permets. . .I allow myself. . . Ton dĂ©sirYour desire Bien sĂ»r, tout imaginaire.Of course, entirely imagined. Je me permetsI allow myself de te ciblerto single you out Tes forces et tes faiblessesyour strengths and your weaknesses Rien nâest sĂ»r Ă part les blessuresNothing is certain except the wounds Ăa sert Ă qui, toutes ces foliesWhom do all these follies serve, Qui animent mes nuitsthat animate my nights Ăa sert Ă quoi, toutes ces histoiresWhat are all these stories for, Rien nâest jamais acquis Ă lâhommeNothing is ever fully acquired by man Un mĂŽmeA child Je me permetsI allow myself Te connaĂźtreto know you Tu te permets ?Do you allow yourself ? (Te renaĂźtre ?)(To be reborn?) Amis anĂ©antisDecember 30, 2025 Set to the Prelude in C minor from Bachâs Well-Tempered Clavier. Les amis anĂ©antisThe undone friends Lâamour sans la courLove without courtship LâamitiĂ©ternitĂ©Friendsh-eternity Oublier lâobligĂ©Forget the obliged Observer au-delĂ Observe beyond Contempler, compte pasContemplate, do not count Passer et repasserPass and pass again Former et transformerForm and transform Accepter pas lâaccĂšsAccept, not access AccĂ©der pas lâexcĂšsAccess, not excess Saigner et sublimerBleed and sublimate Soigner et sâĂ©loignerHeal and move away Tenir et retenirHold and hold back Continued on next page Xiao Original lyricsEnglish translation Contenter contenirBe content to contain Soulager les soupirsRelieve the sighs Rappeler : respirerRemind: breathe Expire, inspire,Exhale, inhale, Respire, tu mâinspiresBreathe, you inspire me Tu cherches, chĂ©ri,You search, dear, RigiditĂ©,rigidity, Dirige, diffĂšre,direct, defer, dĂ©chire dĂ©sir.tear desire apart. DĂ©lie, dĂ©plie,untie, unfold, dĂ©tends, câest tempsrelax, it is time Masser la gravitĂ©Massage gravity Graver la tĂ©mĂ©ritĂ©Engrave recklessness TĂ©moigner pas mĂ©riterBear witness, not deserve La JouissanceJouissance Lâimpasse, ça passe.The impasse, it passes. patience. LâAlliancepatience. The Alliance des Ăąmes incarnĂ©es,of incarnated souls, des amis, des amis anĂ©antisof friends, of devastated friends Received 20 February 2007; revised 12 March 2009; accepted 5 June 2009