Singapore

AAAI 2026

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

algorithmic auditing

financial ai

ai governance

jailbreaking

red-teaming

ai safety

large language models

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.

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

Emergent Persuasion: Will LLMs Persuade Without Being Prompted?

Frédéric Berdoz, Luca A Lanzendörfer, Kaan Bayraktar, Roger Wattenhofer

Data Attribution in Large Language Models via Bidirectional Gradient Optimization

AI safety and alignment evaluations (benchmarks) have become de facto governance tools in the rapidly evolving landscape of artificial intelligence. However, these critical instruments themselves lack systematic governance oversight, raising the central question: "Who evaluates the evaluators?" Through a systematic mapping of 298 papers and in-depth thematic analysis of 50 core studies, this paper identifies fundamental governance challenges in current AI evaluation practices. The bibliometric analysis reveals explosive growth in this field since 2023, with persistent gaps in transparency, accountability, and institutional independence. This position paper synthesizes current knowledge on evaluation governance and proposes a future agenda grounded in three core principles: participatory design with adversarial scrutiny, radical transparency through "Evaluation Cards," and institutional pluralism to ensure independent oversight. The paper argues for a paradigm shift from "evaluating AI" to "governing evaluations," offering actionable recommendations for researchers, policymakers, and practitioners to build a trustworthy evaluation ecosystem.

Who Evaluates the Evaluators? Governance Challenges in AI Safety and Alignment Evaluations: A Review and Future Agenda

Offensive Yet Efficient: Semantic Summarization via Obscene Lexicon

Stop Harm Before It Starts: iCRAFT for Agentic LLM Governance

Gaurav Chawla, Caleb DeLeeuw, Vanessa Dietze, Aniket Sharma

The Secret Agenda: LLMs Strategically Lie and Our Current Safety Tools Are Blind

Panatchakorn Anantaprayoon, Nataliia Babina, Jad Tarifi, Nima Asgharbeygi

Dynamic Alignment for Collective Agency: Toward a Scalable Self-Improving Framework for Open-Ended LLM Alignment

Navigating Healthcare AI Governance: Regulatory Divergence and the Harmonization Challenge Across Medical Domains

We propose Poincaré Gradient Descent (PGD), a first-order optimization method on the Poincaré ball. PGD replaces the exponential map with a projection-based retraction that is first-order equivalent while preserving geodesic structure, thereby reducing per-iteration computational cost. We establish convergence guarantees for smooth geodesically convex and strongly convex objectives, showing that PGD matches the iteration complexity of Riemannian Gradient Descent (RGD). A Möbius isometric initialization and step-size equivalence analysis further ensure stable and efficient updates. Numerical experiments confirm that PGD achieves the same theoretical convergence rate as RGD but runs significantly faster in practice, offering a simple and effective framework for scalable optimization in hyperbolic space.

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

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PRESENTATIONS

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Next from AAAI 2026

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

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads