Singapore

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.

AAAI 2026

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

copyright protection

membership inference attack

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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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.

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

Behind the Words: A Comprehensive Study of Bias Detection Methods in LLMs

Red-Teaming Financial AI Agents: Stress-Testing Governance Protections in LLMs Against Market Manipulation and Regulatory Evasion

A Fair-Baseline Protocol for Evaluating Multi-Agent Governance in Continuous AI Ethics Compliance

Vincent Chang, Thee Ho, Sunishchal Dev, Kevin Zhu, Shi Feng, Kellin Pelrine, Matthew Kowal

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