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The goal of fair clustering is to find clusters such that the proportion of sensitive attributes (e.g., gender, race, etc) in each cluster is similar to the proportion of the entire data. Various fair clustering algorithms have been proposed, which modify standard $K$-means clustering to satisfy a given fairness constraint. A critical limitation of several existing fair clustering algorithms is that the number of parameters to be learned is proportional to the sample size because the cluster assignment of each datum should be optimized simultaneously with the cluster center, and thus scaling up the algorithms is difficult. In this paper, we propose a new fair clustering algorithm based on finite mixture model called Fair Model-based Clustering (FMC). A main advantage of FMC is that the number of learnable parameters is independent to the sample size and thus can be scaled up easily. In particular, a mini-batch learning is possible to obtain clusters that are approximately fair. Moreover, FMC can be applied to non-metric data (e.g., categorical data) as long as the likelihood is well-defined. Theoretical and empirical justifications of the superiority of the proposed algorithm are provided.

AAAI 2026

Fair Model-based Clustering

ml: ethics

and fairness

ml: deep learning algorithms

ml: clustering

bias

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.

technical paper

Online Social Networks (OSNs) widely adopt content moderation to mitigate the spread of abusive and toxic discourse. Nonetheless, the real effectiveness of moderation interventions remains unclear due to the high cost of data collection and limited experimental control. The latest developments in Natural Language Processing pave the way for a new evaluation approach. Large Language Models (LLMs) can be successfully leveraged to enhance Agent-Based Modeling and simulate human-like social behavior with unprecedented degree of believability. Yet, existing tools do not support simulation-based evaluation of moderation strategies. We fill this gap by designing a LLM-powered simulator of OSN conversations enabling a parallel, counterfactual simulation where toxic behavior is influenced by moderation interventions, keeping all else equal. We conduct extensive experiments, unveiling the psychological realism of OSN agents, the emergence of social contagion phenomena and the superior effectiveness of personalized moderation strategies.

Evaluating Online Moderation via LLM-Powered Counterfactual Simulations

Applying large pre-trained Vision-Language Models to recommendation is a burgeoning field, a direction we term Vision-Language-Recommendation (VLR). 
Bringing VLR to user-oriented on-device intelligence within a federated learning framework is a crucial step for enhancing user privacy and delivering personalized experiences.
This paper introduces FedVLR, a federated VLR framework specially designed for user-specific personalized fusion of vision-language representations.
At its core is a novel bi-level fusion mechanism:
The server-side multi-view fusion module first generates a diverse set of pre-fused multimodal views. 
Subsequently, each client employs a user-specific mixture-of-expert mechanism to adaptively integrate these views based on individual user interaction history.
This designed lightweight personalized fusion module provides an efficient solution to implement a federated VLR system. 
The effectiveness of our proposed FedVLR has been validated on seven benchmark datasets.

Federated Vision-Language-Recommendation with Personalized Fusion

Large Vision-Language Models (LVLMs) enhance the capabilities of Large Language Models by integrating visual inputs, thereby enabling advanced multimodal reasoning across diverse applications. However, these enhanced reasoning capabilities introduce new security risks, particularly to jailbreaking attacks that bypass built-in safety mechanisms to elicit harmful or unauthorized outputs. While recent efforts have explored adversarial and typographic prompts, most existing attacks suffer from three key limitations: reliance on auxiliary models, limited effectiveness in black-box scenarios, and inadequate exploitation of the LVLMs' intrinsic reasoning abilities. In this work, we propose TVChain, a novel black-box jailbreaking framework that explicitly intervenes in both the visual and textual reasoning processes of LVLMs. TVChain decomposes malicious prompts into a sequence of semantically meaningful sub-images that represent relevant objects and behaviors, thereby circumventing direct exposure of illicit content. In parallel, a carefully designed chain-of-thought (CoT) textual prompt is employed to steer the model's reasoning toward reconstructing the intended activity in a covert yet effective manner. We demonstrate that this compositional prompting strategy reduces the likelihood of triggering safety mechanisms while preserving attack efficacy. Extensive evaluations on eleven LVLMs (seven open-source and four commercial) across two benchmark datasets and three state-of-the-art defenses validate the effectiveness and robustness of TVChain.

TVChain: Leveraging Textual-Visual Prompt Chains for Jailbreaking Large Vision-Language Models

In Social Deduction Games (SDGs) such as Avalon, Mafia, and Werewolf, players conceal their identities and deliberately mislead others, making hidden-role inference a central and demanding task. Accurate role identification, which forms the basis of an agent's belief state, is therefore the keystone for both human and AI performance. We introduce CSP4SDG, a probabilistic, constraint–satisfaction framework that analyses gameplay objectively. Game events and dialogue are mapped to four linguistically agnostic constraint classes—evidence, phenomena, assertions, and hypotheses. Hard constraints prune impossible role assignments, while weighted soft constraints score the remainder; information-gain weighting links each hypothesis to its expected value under entropy reduction, and a simple closed-form scoring rule guarantees that truthful assertions converge to classical hard logic with minimum error. The resulting posterior over roles is fully interpretable and updates in real time. Experiments on three public datasets show that CSP4SDG (i) outperforms LLM-based baselines in every inference scenario, and (ii) boosts LLMs when supplied as an auxiliary "reasoning tool." Our study validates that principled probabilistic reasoning with information theory is a scalable alternative—or complement—to heavy-weight neural models for SDGs.

CSP4SDG: Constraint and Information-Theory Based Role Identification in Social Deduction Games with LLM-Enhanced Inference

Vision–language models (VLMs) such as CLIP have unlocked powerful zero-shot transfer, yet efficient adaptation to downstream tasks remains challenging. Existing methods often depend on graph structures and dataset-specific tuning, making them sensitive to modality gaps and computationally costly at scale. In this paper, we propose IOTA (Inverse Optimal Transport Adaptation), a lightweight algorithm that reformulates VLMs inference from the perspective of inverse optimal transport (IOT), providing a unified view of training and inference. Under the IOT framework, IOTA enhances zero-shot alignment via a theory-guided unbalanced OT strategy and refines textual prototypes using OT-based pseudo-labels with a marginal-aware adaptive threshold, enabling reliable supervision without gradient updates. The framework naturally extends to few-shot scenarios through a label-guided masking mechanism. By decoupling image–text interactions from other inter-modal dependencies, IOTA avoids task-specific tuning and expensive affinity construction. Extensive experiments on standard benchmarks show that IOTA consistently improves zero-shot and few-shot performance while reducing memory and computation overhead, validating both its theoretical insight and plug-and-play practicality.

Inverse Optimal Transport for Efficient Adaptation of Vision-Language Models

We propose GARD, a domain-specific “guardrail” framework designed to detect and block sensitive financial information (SFI) in both inputs and outputs of large language models (LLMs). Unlike general-purpose content filters, GARD is built upon a comprehensive, taxonomy of SFI distilled from financial-services regulations (GLBA, PCI-DSS, FFIEC), information-security standards (ISO 27001, NIST SP 800-53/122/171), and enterprise policies. Leveraging this taxonomy, we synthesized a 17,495-sample dataset of risky and non-risky financial texts using multiple LLMs (Gemini 1.5, Mixtral-8x7B, GPT-4o, Llama 3.1) and real-world prompts. Each sample was embedded via OpenAI’s text-embedding-ada-002 and used to train a generative-adversarial network (GARD), where the discriminator learns to distinguish SFI-laden vectors.

On a held-out test set of 3,552 samples, GARD achieved 0.98 recall and 0.92 F1, markedly outperforming the OpenAI Moderation API (recall 0.30) and Azure Content Safety (recall 0.37) while sustaining over 50 prompts/sec throughput. Our contributions are threefold: (1) a grounded, granular taxonomy of SFI for GenAI guardrails; (2) an extensible, synthetic SFI dataset; and (3) GARD, a high-performance risk detector that can augment or replace existing commercial solutions. This taxonomy-driven approach advances AI safety, regulatory compliance, and responsible GenAI adoption in financial services.

A Guardrail Framework for Sensitive Financial Information Protection: A Taxonomy-Driven Approach

Text-to-image diffusion models are capable of generating high-quality images, but suboptimal pre-trained text representations often result in these images failing to align closely with the given text prompts. Classifier-free guidance (CFG) is a popular and effective technique for improving text-image alignment in the generative process. However, CFG introduces significant computational overhead.
In this paper, we present **DI**stilling **C**FG by sharpening text **E**mbeddings (DICE) that replaces CFG in the sampling process with half the computational complexity while maintaining similar generation quality. 
DICE distills a CFG-based text-to-image diffusion model into a CFG-free version by refining text embeddings to replicate CFG-based directions. 
In this way, we avoid the computational drawbacks of CFG, enabling high-quality, well-aligned image generation at a fast sampling speed.
Furthermore, examining the enhancement pattern, we identify the underlying mechanism of DICE that sharpens specific components of text embeddings to preserve semantic information while enhancing fine-grained details. 
Extensive experiments on multiple Stable Diffusion v1.5 variants, SDXL, and PixArt-$\alpha$ demonstrate the effectiveness of our method.

DICE: Distilling Classifier-Free Guidance into Text Embeddings

Despite advances in large language models (LLMs) on reasoning and instruction-following benchmarks, it is unclear whether they can reliably produce outputs aligned with a variety of user goals, a concept we call *steerability*. We highlight two gaps in current LLM evaluations for assessing steerability. First, many benchmarks are built with past LLM chats and text scraped from the Internet, which may skew towards common requests, underrepresenting less-common requests by potential users. Second, prior work measures performance as a scalar, which could conceal behavioral shifts in LLM outputs in open-ended generation. To mitigate these gaps, we introduce a framework based on a multi-dimensional goal space that models user goals and LLM outputs as vectors with dimensions corresponding to text attributes (e.g., reading difficulty). Applied to a text-rewriting task, we find that current LLMs induce intended changes or "side-effects" to text attributes, impeding steerability. Interventions to improve steerability, such as prompt engineering, best-of-$N$ sampling, and reinforcement learning fine-tuning, have varying effectiveness, yet side effects remain problematic. Our findings suggest that even strong LLMs struggle with steerability, and existing alignment strategies may be insufficient. We open-source our steerability evaluation framework at https://anonymous.4open.science/r/steerability-DAAB/.

A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs

In retail lending, offering preferential interest rates is a core marketing instrument for balancing customer acquisition with portfolio profitability. Accurately predicting the effect of interest-rate discounts for each customer is pivotal for optimizing the discount strategy: offering overly generous discounts erodes margins, while insufficient discounts drive price-sensitive customers to defect.
Off‑the‑shelf machine learning uplift models rarely respect the complex operational constraints of financial business, such as tiered rate grids, regulatory guard‑rails, and marketing budget ceilings. We propose an integrated system that fuses causal inference and domain adaptation to produce constraint‑aware, customer‑specific discount recommendations. To further enhance practitioner adoption, a large language model layer translates model outputs into actionable narratives. Developed in Hyundai Capital Services, the system boosted transaction volume by 13\%, demonstrating both technical soundness and material business impact.

Optimizing Preferential Rate in Retail Lending with Causal Inference and Domain Adaptation

Model-based diagnosis is a generally applicable, principled approach to the systematic debugging of a wide range of system types such as circuits, knowledge bases, physical devices, or software. Based on a formal description of the system, it enables precise and deterministic reasoning about potential faults responsible for observed misbehavior. In software, such a formal system description can often even be extracted from the buggy program fully automatically. As logical reasoning is central to diagnosis, the performance of model-based debuggers is largely influenced by reasoning efficiency, which in turn depends on the complexity and expressivity of the system description. Since highly detailed models capturing exact semantics often exceed the capabilities of current reasoning tools, researchers have proposed more abstract representations.

In this work, we thoroughly analyze system modeling techniques with a focus on fault localization in spreadsheets—one of the most widely used end-user programming paradigms. Specifically, we present three constraint model types characterizing spreadsheets at different abstraction levels, show how to extract them automatically from faulty spreadsheets, and provide theoretical and empirical investigations of the impact of abstraction on both diagnostic output and computational performance. Our main conclusions are that (i) for the model types, there is a trade-off between the conciseness of generated fault candidates and computation time, (ii) the exact model is often impractical, and (iii) a new model based on qualitative reasoning yields the same solutions as the exact one in up to more than half the cases while being orders of magnitude faster.

Due to their ability to restrict the solution space in a sound way, the explored model-based techniques, rather than being used as standalone approaches, are expected to realize their full potential in combination with iterative sequential diagnosis or indeterministic but more performant statistical debugging methods.

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Evaluating Online Moderation via LLM-Powered Counterfactual Simulations

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A General Anchor-Based Framework for Scalable Fair Clustering