Singapore

In data-scarce settings, learning accurate and interpretable
models for tabular classification remains a fundamental chal-
lenge. We propose a novel pipeline for few-shot tabular clas-
sification that distills the predictive power of large founda-
tion models into globally interpretable student models. Given
a tabular dataset, we generate synthetic data using CTGAN
to approximate the underlying distribution. We then train
high-capacity teacher models (TabLLM, TabPFN, TabM) on
a small number of labeled examples and use them to pseu-
dolabel the synthetic data. Finally, we train student explainer
models (i.e., XGBoost, logistic regression, decision trees,
Generalized Linear Rule Model (GLRM), and TTnet) on
this pseudolabeled synthetic dataset. These student models
are globally and exactly interpretable, yielding logical de-
cision rules (e.g., disjunctive normal form) that fully repro-
duce their predictions. Across 15 tabular binary classification
benchmarks, our distilled models generally outperform base-
lines trained directly on the few-shot data, with improved
ROC AUC scores in the low-shot (4-16 samples) to mid-
and higher-shot settings (up to 256 samples). In particular,
TabPFN-based distillation achieves strong and reliable im-
provements across the entire shot spectrum, not only in heavy
data scarcity but also in broader low-data regimes. This work
demonstrates that foundation models can be effectively lever-
aged as teachers to produce small, transparent, and high-
performing classifiers. Our approach advances the goal of re-
liable and interpretable machine learning in real-world set-
tings where labeled data is limited.

AAAI 2026

Towards Globally Interpretable Few-Shot Tabular Learning: Distilling Foundation Models

tabular foundation models

interpretable ai

distillation

few-shot learning

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.

workshop paper

'LLM-as-a-judge,'' which utilizes large language models (LLMs) as evaluators, has proven effective in many evaluation tasks.
However, evaluator LLMs exhibit numerical bias, a phenomenon where certain evaluation scores are generated disproportionately often, leading reduced evaluation performance.
This study investigates the cause of this bias.
Given that most evaluator LLMs are aligned through instruction tuning and preference tuning, and that prior research suggests alignment reduces output diversity, we hypothesize that numerical bias arises from alignment.
To test this, we compare outputs from pre- and post-alignment LLMs, and observe that alignment indeed increases numerical bias.
We also explore mitigation strategies for post-alignment LLMs, including temperature scaling, distribution calibration, and score range adjustment.
Among these, score range adjustment is most effective in reducing bias and improving performance, though still heuristic.
Our findings highlight the need for further work on optimal score range selection and more robust mitigation strategies.

Exploring the Effects of Alignment on Numerical Bias in Large Language Models

Adaptations are one of the most prominent methods that enable the utilization of large backbone models for those who have limited computational resources. Yet, even in their last resort of using adaptations, many face a dead end in training when adaptation data are insufficient. Since the acquisition of adaptation data is arduous, the situation frequently appears, daunting without a particular solution. Often, even with extra training or random data augmentation, the performance of adaptations does not improve. To address this, we organize a solid probabilistic background and sampling method that focuses on the enormous amount of backbone data used for pre-training. We name the method, Backbone Augmented Training (BAT). First, we formulate and prove two key probabilistic propositions: one establishes the validity of BAT, while the other identifies a condition under which BAT benefits adaptation. Furthermore, we introduce an original data sampling scheme that satisfies these propositions and present ALgorithm for BAT (ALBAT) to efficiently implement this approach. ALBAT effectively enhances adaptation training even when other methods meet performance stalemate due to adaptation data scarcity.

BAT: Backbone Augmented Training for Adaptations

This paper investigates how the degree of group fairness changes when the degree of individual fairness is actively controlled. As a metric quantifying individual fairness, we consider generalized entropy (GE) recently introduced into machine learning community. To control the degree of individual fairness, we design a classification algorithm satisfying a given degree of individual fairness through an empirical risk minimization (ERM) with a fairness constraint specified in terms of GE. We show the PAC learnability of the fair ERM problem by proving that the true fairness degree does not deviate much from an empirical one with high probability for finite VC dimension if the sample size is big enough. Our experiments show that strengthening individual fairness degree does not always lead to enhancement of group fairness.

Impacts of Individual Fairness on Group Fairness from the Perspective of Generalized Entropy

Semi-supervised object detection (SSOD) has achieved notable progress through advances in self-training frameworks. However, most existing approaches still rely solely on classification confidence to select pseudo-labels, overlooking the localization uncertainty. This results in pseudo-labels that lack sufficient localization information, since classification confidence alone does not adequately represent the accuracy of spatial localization. In this paper, we address this limitation from two complementary perspectives: edge-level reliability estimation and instance-level pseudo-label selection. From the former perspective, we quantify localization confidence using a simple statistic — the maximum adjacent probability sum derived from the Distribution Focal Loss — and aggregate edge-level scores into a box-level reliability via the minimum over edges. This suppresses boxes with at least one uncertain boundary, improving spatial precision with negligible computational overhead. From the latter perspective, we integrate this localization-confidence-aware criterion into Efficient Teacher, a YOLO-oriented semi-supervised object detection framework, to adaptively filter pseudo-labels during self-training. Experiments on the KITTI dataset using YOLOv8-m demonstrate consistent improvements over baselines and improved training stability. Further analysis reveals that our approach achieves higher recall of pseudo-labels while maintaining precision, validating the effectiveness of localization confidence modeling in SSOD.

Localization-Confidence-Aware Pseudo-Label Selection for YOLO-based Semi-Supervised Object Detection

Eye movements during reading provide valuable data for studying cognitive processes and text properties. While eye-tracking data can enhance language models, its application is limited by data scarcity, particularly for low-resource languages. Although synthetic scanpath generation offers a promising solution, current models are largely confined to English and Chinese. This work explores multilingual model for scanpath generation, demonstrating that a single model trained on 13 typologically diverse languages outperforms monolingual counterparts across most metrics. By leveraging shared linguistic patterns, our approach mitigates data scarcity for individual languages and facilitates synthetic gaze data creation for broader multilingual NLP applications.

Cross-Lingual Transfer Learning for Enhanced Synthetic Scanpath Prediction in Reading

Scene Text Editing (STE) is the task of modifying text content in an image while preserving its visual style, such as font, color, and background. While recent diffusion-based approaches have shown improvements in visual quality, key limitations remain: lack of support for low-resource languages, domain gap between synthetic and real data, and the absence of appropriate metrics for evaluating text style preservation. To address these challenges, we propose STELLAR (Scene Text Editor for Low-resource LAnguages and Real-world data). STELLAR enables reliable multilingual editing through a language‑adaptive glyph encoder and a multi‑stage training strategy that first pre‑trains on synthetic data and then fine‑tunes on real images. We also construct a new dataset, STIPLAR(Scene Text Image Pairs of Low-resource lAnguages and Real-world data), for training and evaluation. Furthermore, we propose Text Appearance Similarity (TAS), a novel metric that assesses style preservation by independently measuring font, color, and background similarity, enabling robust evaluation even without ground truth. Experimental results demonstrate that STELLAR outperforms state-of-the-art models in visual consistency and recognition accuracy, achieving an average TAS improvement of 2.2% across languages over the baselines.

STELLAR: Scene Text Editor for Low-Resource Languages and Real-World Data

View selection is critical in active 3D neural reconstruction as it impacts the contents of training set and resulting final output quality. Recent view selection strategies emphasize the visibility when evaluating model uncertainty in active 3D reconstruction.
However, existing approaches estimate visibility only after the model fully converges, which has confined their application primarily to non-continuous active learning settings. This paper proposes Surface-Based Visibility field (SBV) that successfully estimates the visibility-guided uncertainty in continuous active 3D neural reconstruction. During learning neural implicit surfaces, our model learns rendering uncertainties and infers surface confidence values derived from signed distance functions. It then updates surface confidences using a voxel grid, robustly deducing the surface-based visibility for uncertainties.
This approach captures uncertainties across all regions, whether well-defined surfaces or ambiguous areas, ensuring accurate visibility measurement in continuous active learning. 
Experiments on benchmark datasets—Tanks and Temples, BlendedMVS, Blender, DTU—and the newly proposed imbalanced viewpoint dataset (ImBView) show that view selection based on SBV-guided uncertainty improves performance by up to 11.6% over existing methods, highlighting its effectiveness in challenging reconstruction scenarios.

Surface-Based Visibility-Guided Uncertainty for Continuous 3D Active Neural Reconstruction

Large Language Models (LLMs) can be conditioned with explicit personality prompts, yet their behavioral realization often varies depending on context. This study examines how identical personality prompts lead to distinct linguistic, behavioral, and emotional outcomes across four conversational settings—ice-breaking, negotiation, group decision, and empathy tasks. Results show that contextual cues systematically influence both personality expression and emotional tone, suggesting that the same traits are expressed differently depending on social and affective demands. This raises an important question for LLM-based dialogue agents: whether such variations reflect inconsistency or context-sensitive adaptation akin to human behavior. Viewed through the lens of Whole Trait Theory, these findings highlight that LLMs exhibit context-sensitive rather than fixed personality expression, adapting flexibly to social interaction goals and affective conditions.

Personality Expression Across Contexts: Linguistic and Behavioral Variation in LLM Agents

Personality traits are increasingly incorporated into AI agents in applications involving human interaction. However, it remains uncertain whether LLMs truly manifest the intended traits when prompted. In this paper, we evaluate six LLMs: GPT-4o Mini, Llama 3.2, Mistral NeMo, Gemini 2.0 Flash Lite, Gemma 2, and Claude 3 Haiku by prompting high and low configurations of the Big Five personality traits and as- sessing both overall trait alignment and item-level inconsis- tencies through asking the prompted LLM to take a person- ality survey. Our analysis reveals pronounced differences in how these LLMs express prompted personalities, with Llama 3.2, Mistral NeMo, and Claude 3 Haiku struggling to reflect specific trait items in low configurations, and GPT-4o Mini, Gemma 2, and Gemini 2.0 Flash Lite exhibiting strong per- sonality alignment under high-configuration settings. These findings show a personality misalignment, where LLMs do not necessarily express the intended traits as expected after personality prompting.

Evaluating LLM Alignment under Big Five Personality Prompting

In online chat platforms, multiple dialogues are often entangled in a complex manner. Disentangling them into coherent dialogues is essential for understanding conversations. Supervised models have long dominated this task. The application of large language models (LLMs) to this problem remains underexplored; however, preliminary studies have shown their performance to be substantially inferior to that of machine-learning methods. This raises fundamental questions: Do LLMs inherently lack the capability for dialogue disentanglement, and what approaches are necessary to enhance their performance? We answer these questions by introducing two novel methods: dialogue-level assignment (DLA), which tasks LLMs with assigning an utterance to a dialogue, and subsequent context (SC), which provides subsequent context as auxiliary evidence. Experiments on the benchmark IRC dataset show that our method, equipped with DLA+SC, achieves new state-of-the-art results across all metrics. These results prove that LLMs possess a strong capability for the studied task. Furthermore, our ablation study on open-source models investigates the effectiveness of DLA and SC, revealing that it is model- dependent. Nevertheless, it also demonstrates that both methods are key factors in improving performance. This research marks a paradigm shift for dialogue disentanglement, transitioning from machine-learning approaches to applying LLMs.

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Exploring the Effects of Alignment on Numerical Bias in Large Language Models

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