AAAI Conference and Symposium Proceedings
Browse the AAAI Library containing several high-quality AAAI Conference proceedings in artificial intelligence.
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Innovative Applications of Artificial Intelligence Conference (IAAI) - AAAI
IAAI traditionally consist of case studies of deployed applications with measurable benefits whose value depends on the use of AI technology.
AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI) - AAAI
EAAI provides a venue for researchers and educators to discuss and share resources related to teaching and using AI in education across a variety of curricular levels, with an emphasis on undergraduate and graduate teaching and learning.
X-SYS: A Reference Architecture for Interactive Explanation Systems
arXiv:2602.12748v1 Announce Type: new Abstract: The explainable AI (XAI) research community has proposed numerous technical methods, yet deploying explainability as systems remains challenging: Interactive explanation systems require both suitable algorithms and system capabilities that maintain explanation usability across repeated queries,...
Optimal Take-off under Fuzzy Clearances
arXiv:2602.13166v1 Announce Type: new Abstract: This paper presents a hybrid obstacle avoidance architecture that integrates Optimal Control under clearance with a Fuzzy Rule Based System (FRBS) to enable adaptive constraint handling for unmanned aircraft. Motivated by the limitations of classical...
Quantum walk inspired JPEG compression of images
arXiv:2602.12306v1 Announce Type: cross Abstract: This work proposes a quantum inspired adaptive quantization framework that enhances the classical JPEG compression by introducing a learned, optimized Qtable derived using a Quantum Walk Inspired Optimization (QWIO) search strategy. The optimizer searches a...
Perceptual Self-Reflection in Agentic Physics Simulation Code Generation
arXiv:2602.12311v1 Announce Type: cross Abstract: We present a multi-agent framework for generating physics simulation code from natural language descriptions, featuring a novel perceptual self-reflection mechanism for validation. The system employs four specialized agents: a natural language interpreter that converts user...
ForeAct: Steering Your VLA with Efficient Visual Foresight Planning
arXiv:2602.12322v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models convert high-level language instructions into concrete, executable actions, a task that is especially challenging in open-world environments. We present Visual Foresight Planning (ForeAct), a general and efficient planner that guides a VLA...
Why Deep Jacobian Spectra Separate: Depth-Induced Scaling and Singular-Vector Alignment
arXiv:2602.12384v2 Announce Type: cross Abstract: Understanding why gradient-based training in deep networks exhibits strong implicit bias remains challenging, in part because tractable singular-value dynamics are typically available only for balanced deep linear models. We propose an alternative route based on...
Rational Neural Networks have Expressivity Advantages
arXiv:2602.12390v1 Announce Type: cross Abstract: We study neural networks with trainable low-degree rational activation functions and show that they are more expressive and parameter-efficient than modern piecewise-linear and smooth activations such as ELU, LeakyReLU, LogSigmoid, PReLU, ReLU, SELU, CELU, Sigmoid,...
Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward
arXiv:2602.12430v2 Announce Type: cross Abstract: The transition from monolithic language models to modular, skill-equipped agents marks a defining shift in how large language models (LLMs) are deployed in practice. Rather than encoding all procedural knowledge within model weights, agent skills...
Discovering Semantic Latent Structures in Psychological Scales: A Response-Free Pathway to Efficient Simplification
arXiv:2602.12575v1 Announce Type: new Abstract: Psychological scale refinement traditionally relies on response-based methods such as factor analysis, item response theory, and network psychometrics to optimize item composition. Although rigorous, these approaches require large samples and may be constrained by data...
Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats
arXiv:2602.12635v1 Announce Type: new Abstract: As LLMs scale, low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. In this work, we evaluate HiFloat (HiF8 and HiF4), a family of formats tailored for Ascend NPUs. Through...
$\mathcal{X}$-KD: General Experiential Knowledge Distillation for Large Language Models
arXiv:2602.12674v1 Announce Type: new Abstract: Knowledge Distillation (KD) for Large Language Models (LLMs) has become increasingly important as models grow in size and complexity. While existing distillation approaches focus on imitating teacher behavior, they often overlook the original learning environment...
Towards a Diagnostic and Predictive Evaluation Methodology for Sequence Labeling Tasks
arXiv:2602.12759v1 Announce Type: new Abstract: Standard evaluation in NLP typically indicates that system A is better on average than system B, but it provides little info on how to improve performance and, what is worse, it should not come as...
Left-right asymmetry in predicting brain activity from LLMs' representations emerges with their formal linguistic competence
arXiv:2602.12811v1 Announce Type: new Abstract: When humans and large language models (LLMs) process the same text, activations in the LLMs correlate with brain activity measured, e.g., with functional magnetic resonance imaging (fMRI). Moreover, it has been shown that, as the...
ProbeLLM: Automating Principled Diagnosis of LLM Failures
arXiv:2602.12966v1 Announce Type: new Abstract: Understanding how and why large language models (LLMs) fail is becoming a central challenge as models rapidly evolve and static evaluations fall behind. While automated probing has been enabled by dynamic test generation, existing approaches...
Sparse Autoencoders are Capable LLM Jailbreak Mitigators
arXiv:2602.12418v1 Announce Type: cross Abstract: Jailbreak attacks remain a persistent threat to large language model safety. We propose Context-Conditioned Delta Steering (CC-Delta), an SAE-based defense that identifies jailbreak-relevant sparse features by comparing token-level representations of the same harmful request with...
HyperMLP: An Integrated Perspective for Sequence Modeling
arXiv:2602.12601v1 Announce Type: cross Abstract: Self-attention is often viewed as probabilistic query-key lookup, motivating designs that preserve normalized attention scores and fixed positional semantics. We advocate a simpler and more unified perspective: an autoregressive attention head can be viewed as...
Abstractive Red-Teaming of Language Model Character
arXiv:2602.12318v1 Announce Type: new Abstract: We want language model assistants to conform to a character specification, which asserts how the model should act across diverse user interactions. While models typically follow these character specifications, they can occasionally violate them in...
A Machine Learning Approach to the Nirenberg Problem
arXiv:2602.12368v1 Announce Type: new Abstract: This work introduces the Nirenberg Neural Network: a numerical approach to the Nirenberg problem of prescribing Gaussian curvature on $S^2$ for metrics that are pointwise conformal to the round metric. Our mesh-free physics-informed neural network...
Computationally sufficient statistics for Ising models
arXiv:2602.12449v1 Announce Type: new Abstract: Learning Gibbs distributions using only sufficient statistics has long been recognized as a computationally hard problem. On the other hand, computationally efficient algorithms for learning Gibbs distributions rely on access to full sample configurations generated...
Continuous Diffusion Models Can Obey Formal Syntax
arXiv:2602.12468v1 Announce Type: new Abstract: Diffusion language models offer a promising alternative to autoregressive models due to their global, non-causal generation process, but their continuous latent dynamics make discrete constraints -- e.g., the output should be a JSON file that...
Geometric separation and constructive universal approximation with two hidden layers
arXiv:2602.12482v1 Announce Type: new Abstract: We give a geometric construction of neural networks that separate disjoint compact subsets of $\Bbb R^n$, and use it to obtain a constructive universal approximation theorem. Specifically, we show that networks with two hidden layers...
On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs
arXiv:2602.12506v1 Announce Type: new Abstract: Reinforcement learning (RL) fine-tuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivating its extension to vision language models (VLMs). While RL-tuned VLMs improve on visual reasoning benchmarks, they...
Bench-MFG: A Benchmark Suite for Learning in Stationary Mean Field Games
arXiv:2602.12517v1 Announce Type: new Abstract: The intersection of Mean Field Games (MFGs) and Reinforcement Learning (RL) has fostered a growing family of algorithms designed to solve large-scale multi-agent systems. However, the field currently lacks a standardized evaluation protocol, forcing researchers...
Analytical Results for Two Exponential Family Distributions in Hierarchical Dirichlet Processes
arXiv:2602.12527v1 Announce Type: new Abstract: The Hierarchical Dirichlet Process (HDP) provides a flexible Bayesian nonparametric framework for modeling grouped data with a shared yet unbounded collection of mixture components. While existing applications of the HDP predominantly focus on the Dirichlet-multinomial...