arXiv:2602.15997v4 Announce Type: replace-cross Abstract: Neural networks gain capabilities during training, but the internal changes that precede capability acquisition are not well understood. In particular, the relationship between geometric change and behavioral change, and the effect of task difficulty and model scale on that relationship, is unclear. We track geometric measures and linear probes across […]
MiCA Learns More Knowledge Than LoRA and Full Fine-Tuning
arXiv:2604.01694v1 Announce Type: cross Abstract: Minor Component Adaptation (MiCA) is a novel parameter-efficient fine-tuning method for large language models that focuses on adapting underutilized subspaces of model representations. Unlike conventional methods such as Low-Rank Adaptation (LoRA), which target dominant subspaces, MiCA leverages Singular Value Decomposition to identify subspaces related to minor singular vectors associated with […]
MIRAGE: The Illusion of Visual Understanding
arXiv:2603.21687v3 Announce Type: replace Abstract: Multimodal AI systems have achieved remarkable performance across a broad range of real-world tasks, yet the mechanisms underlying visual-language reasoning remain surprisingly poorly understood. We report three findings that challenge prevailing assumptions about how these systems process and integrate visual information. First, Frontier models readily generate detailed image descriptions and […]
Retrieval-Augmented Question Answering over Scientific Literature for the Electron-Ion Collider
arXiv:2604.02259v1 Announce Type: cross Abstract: To harness the power of Language Models in answering domain specific specialized technical questions, Retrieval Augmented Generation (RAG) is been used widely. In this work, we have developed a Q&A application inspired by the Retrieval Augmented Generation (RAG), which is comprised of an in-house database indexed on the arXiv articles […]
Understanding visual attention beehind bee-inspired UAV navigation
arXiv:2507.11992v4 Announce Type: replace Abstract: Bio-inspired design is often used in autonomous UAV navigation due to the capacity of biological systems for flight and obstacle avoidance despite limited sensory and computational capabilities. In particular, honeybees mainly use the sensory input of optic flow, the apparent motion of objects in their visual field, to navigate cluttered […]
Rare-Aware Autoencoding: Reconstructing Spatially Imbalanced Data
arXiv:2604.02031v1 Announce Type: cross Abstract: Autoencoders can be challenged by spatially non-uniform sampling of image content. This is common in medical imaging, biology, and physics, where informative patterns occur rarely at specific image coordinates, as background dominates these locations in most samples, biasing reconstructions toward the majority appearance. In practice, autoencoders are biased toward dominant […]
A Self-Improving Architecture for Dynamic Safety in Large Language Models
arXiv:2511.07645v2 Announce Type: replace-cross Abstract: Context: Large Language Models (LLMs) rely on static, pre-deployment safety mechanisms that cannot adapt to adversarial threats discovered after release. Objective: To design a software architecture enabling LLM-based systems to autonomously detect safety failures and synthesize defense policies at runtime, without retraining or manual intervention. Method: We propose the Self-Improving […]
Finite-Time Analysis of Gradient Descent for Shallow Transformers
arXiv:2601.16514v2 Announce Type: replace-cross Abstract: Understanding why Transformers perform so well remains challenging due to their non-convex optimization landscape. In this work, we analyze a shallow Transformer with $m$ independent heads trained by projected gradient descent in the kernel regime. Our analysis reveals two main findings: (i) the width required for nonasymptotic guarantees scales only […]
A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability
arXiv:2406.09031v5 Announce Type: replace-cross Abstract: Graph pooling has gained attention for its ability to obtain effective node and graph representations for various downstream tasks. Despite the recent surge in graph pooling approaches, there is a lack of standardized experimental settings and fair benchmarks to evaluate their performance. To address this issue, we have constructed a […]
Meta-Learning at Scale for Large Language Models via Low-Rank Amortized Bayesian Meta-Learning
arXiv:2508.14285v3 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) with low-rank adaptation (LoRA) is a cost-effective way to incorporate information from a specific dataset. However, when a problem requires incorporating information from multiple datasets – as in few shot learning – generalization across datasets can be limited, driving up training costs. As a consequence, […]
Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interpretable and Tractable Approach
arXiv:2601.11016v2 Announce Type: replace-cross Abstract: In this paper, we introduce a framework for contextual distributionally robust optimization (DRO) that considers the causal and continuous structure of the underlying distribution by developing interpretable and tractable decision rules that prescribe decisions using covariates. We first introduce the causal Sinkhorn discrepancy (CSD), an entropy-regularized causal Wasserstein distance that […]
An Object Web Seminar: A Retrospective on a Technical Dialogue Still Reverberating
arXiv:2603.26203v2 Announce Type: replace-cross Abstract: Technology change happens quickly such that new trends tend to crowd out the focus on what was new just yesterday. In this paper the peak popularity of the confluence of Object Technologies with early Web adoption is explored through the content of a seminar held in 1999. Distributed architectures were […]