Singapore

This paper traces how “control” is socially coded in contemporary music technology, and offers an account of the circuit of control to bridge the conceptual gap between the sociopolitical and the technical. The analysis redirects attention from the controllability of intelligent systems to the social formations through which control is allocated and recognized. To frame this shift, the paper theorizes the gear economy—a lens set alongside the much frequented concept gig economy in sociology—to explain how AI-music tools (e.g., Suno, Udio) are legitimated through existing music gear markets. The claim is coalition rather than addition: AI tools fold into pre-existing markets that price and credential controllability. Formally, the paper adopts a triptych that deliberately echoes Deleuze’s influential “Postscript” in title and cadence: Historical, Logic, Programming to progress its analysis. Rather than constructing a new vocabulary of control in music technologies, this paper demonstrates that the parameters and regimes of control—technical, economic, and political—are themselves social indices. The aim is a design-oriented ethical intervention that speaks to technologists, musicians, and the general public, rendering these arrangements visible and reconfigurable and redirecting control toward humans so that AI music tools augment rather than shrink creative agency.

AAAI 2026

Postscript on the Musics of Control

human-machine relations

ai music tools

gear economy

ai ethics

control

workshop paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

We present Artificial Dancing Intelligence (ADI), an interactive neural music visualizer that is accessed through a web app, but performs inference entirely on local devices. Our approach enables anyone to visually express music, while leveraging the expressive, and sometimes unpredictable dynamics of self-organized systems. ADI uses an audio stream’s average energy (known as RMS) to modulate a neural cellular automata (NCA) to produce visual patterns that move and ’dance’ along with the audio stream in real time. Through the web interface, users can adjust the relationship between the music’s energy and the NCA system to create unique visual performances out of any music audio file. ADI achieves smooth, real-time responsiveness on modern consumer devices.

Artificial Dancing Intelligence: Neural Cellular Automata for Visual Performance of Music

Artists and rights holders face growing concerns about unauthorized use of their copyrighted works in training generative models. We introduce TS-RaMIA, a practical auditing framework enabling creators to test whether their symbolic music has been used without authorization. Unlike existing likelihood-based approaches that are confounded by piece length and density, TS-RaMIA exploits \emph{structural tokens}—bar lines, positions, and tempo markers encoding musical phrasing—through sample-level analysis and rigorous debiasing. Our method combines (i) length matching and conditional calibration to remove spurious confounders, (ii) tail-of-top- aggregation on structural tokens to amplify sparse memorization signals, and (iii) a lightweight meta-attacker fusing statistical cues via composer-stratified cross-validation. Evaluated on a 67M-parameter REMI Transformer trained on MAESTRO pieces, TS-RaMIA achieves AUC 0.826 and TPR@1%FPR 14.6%, while a debiased baseline drops to AUC 0.563. Cross-representation validation on NotaGen (ABC notation) yields comparable performance (AUC 0.73, TPR@1%FPR 8.9%), demonstrating transferability. We release our code at https://anonymous.4open.science/r/TS-RaMIA-63F0.

TS-RaMIA: Membership Inference Attacks for Symbolic Music Generation Models

Multimodal Large Language Models (MLLMs) claim “musical understanding,” yet most evaluations conflate listening with score reading. We benchmark three SOTA LLMs (Gemini 2.5 Pro, Gemini 2.5 Flash, and Qwen2.5-Omni) across three core music skills: Syncopation Scoring (rhythm perception), Transposition Detection (melody perception), and Chord Quality Identification (harmony perception). Moreover, we separate three sources of variability: (i) perceptual limitations (by contrasting audio recordings vs. symbolic MIDI inputs), (ii) exposure to prior examples (zero- vs. few-shot manipulations), and (iii) reasoning strategies (Standalone, Chain of Thought, LogicLM). For the latter we adapt LogicLM, a framework combining LLMs with symbolic solvers to perform structured reasoning. In LogicLM, LLMs act as perceptual formulators, generating strict, machinecheckable schemas (onset grids, interval sequences) that deterministic solvers execute with self-refinement. Our results reveal a clear perceptual gap: models perform near ceiling on MIDI but show substantial accuracy drops on audio. Reasoning and few-shot prompting o!er minimal gains. This is expected for MIDI, where performance reaches saturation, but more surprising for audio, where LogicLM, despite near-perfect MIDI accuracy, remains notably brittle. Among models, Gemini Pro achieves the highest performance across most conditions. Transposition yields the highest accuracies across models, while Chord Identification scores slightly below Syncopation. Overall, current systems reason well over symbols (MIDI) but do not yet ”listen” reliably from audio, with reasoning strategies having little impact over accuracy. Our method and dataset make the perception–reasoning boundary explicit and o!er actionable guidance for building robust, audio music systems.

LLMs can read music, but struggle to hear it: An evaluation of core music perception tasks

Alternative Fairness and Accuracy Optimization in Criminal Justice

Constructive Alignment: Reframing AI Alignment as Value Co-Evolution

Human Centered AI Alone Will Not Ensure A Path To Human Empowerment

A Broad Framework for Ethical Oversight and Integration Inspired by Albania's Appointment of Diella

Maturity Expectation Bias in Multi-User LLMs Mediation

Charaka Vinayak Kumar, Ashok Urlana, Gopichand Kanumolu, Bala Mallikarjunarao Garlapati, Pruthwik Mishra

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Artificial Dancing Intelligence: Neural Cellular Automata for Visual Performance of Music

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