Singapore

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.

AAAI 2026

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

generative media

web-based applications

real- time systems

music visualizations

neural cellular automata

interactive visualizations

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**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

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**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

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CogSci 2021

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

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.

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

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Time for Music

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