arXiv:2509.15942v2 Announce Type: replace-cross Abstract: Climate projections have uncertainties related to components of the climate system and their interactions. A typical approach to quantifying these uncertainties is to use climate models to create ensembles of repeated simulations under different initial conditions. Due to the complexity of these simulations, generating such ensembles of projections is computationally […]
CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution
arXiv:2512.23880v2 Announce Type: replace Abstract: Large language model (LLM) agents currently depend on predefined tools or early-stage tool generation, limiting their adaptability and scalability to complex scientific tasks. We introduce CASCADE, a self-evolving agentic framework representing an early instantiation of the transition from “LLM + tool use” to “LLM + skill acquisition”. CASCADE enables agents […]
Learning Linearity in Audio Consistency Autoencoders via Implicit Regularization
arXiv:2510.23530v2 Announce Type: replace-cross Abstract: Audio autoencoders learn useful, compressed audio representations, but their non-linear latent spaces prevent intuitive algebraic manipulation such as mixing or scaling. We introduce a simple training methodology to induce linearity in a high-compression Consistency Autoencoder (CAE) by using data augmentation, thereby inducing homogeneity (equivariance to scalar gain) and additivity (the […]
Enhancing Model Defense Against Jailbreaks with Proactive Safety Reasoning
arXiv:2501.19180v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are vital for a wide range of applications yet remain susceptible to jailbreak threats, which could lead to the generation of inappropriate responses. Conventional defenses, such as refusal and adversarial training, often fail to cover corner cases or rare domains, leaving LLMs still vulnerable to more […]
LogogramNLP: Comparing Visual and Textual Representations of Ancient Logographic Writing Systems for NLP
arXiv:2408.04628v2 Announce Type: replace-cross Abstract: Standard natural language processing (NLP) pipelines operate on symbolic representations of language, which typically consist of sequences of discrete tokens. However, creating an analogous representation for ancient logographic writing systems is an extremely labor intensive process that requires expert knowledge. At present, a large portion of logographic data persists in […]
Orca: Browsing at Scale Through User-Driven and AI-Facilitated Orchestration Across Malleable Webpages
arXiv:2505.22831v2 Announce Type: replace-cross Abstract: Web-based activities span multiple webpages. However, conventional browsers with stacks of tabs cannot support operating and synthesizing large volumes of information across pages. While recent AI systems enable fully automated web browsing and information synthesis, they often diminish user agency and hinder contextual understanding. We explore how AI could instead […]
In-context Language Learning for Endangered Languages in Speech Recognition
arXiv:2505.20445v5 Announce Type: replace-cross Abstract: With approximately 7,000 languages spoken worldwide, current large language models (LLMs) support only a small subset. Prior research indicates LLMs can learn new languages for certain tasks without supervised data. We extend this investigation to speech recognition, investigating whether LLMs can learn unseen, low-resource languages through in-context learning (ICL). With […]
Understanding Post-Training Structural Changes in Large Language Models
arXiv:2509.17866v3 Announce Type: replace-cross Abstract: Post-training fundamentally alters the behavior of large language models (LLMs), yet its impact on the internal parameter space remains poorly understood. In this work, we conduct a systematic singular value decomposition (SVD) analysis of principal linear layers in pretrained LLMs, focusing on two widely adopted post-training methods: instruction tuning and […]
DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systems
arXiv:2505.19847v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) improves factuality by grounding LLMs in external knowledge, yet conventional centralized RAG requires aggregating distributed data, raising privacy risks and incurring high retrieval latency and cost. We present DGRAG, a distributed graph-driven RAG framework for edge-cloud collaborative systems. Each edge device organizes local documents into a knowledge […]
OPERA: A Reinforcement Learning–Enhanced Orchestrated Planner-Executor Architecture for Reasoning-Oriented Multi-Hop Retrieval
arXiv:2508.16438v3 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) and dense retrievers have driven significant progress in retrieval-augmented generation (RAG). However, existing approaches face significant challenges in complex reasoning-oriented multi-hop retrieval tasks: 1) Ineffective reasoning-oriented planning: Prior methods struggle to generate robust multi-step plans for complex queries, as rule-based decomposers perform poorly […]
Quantifying Fidelity: A Decisive Feature Approach to Comparing Synthetic and Real Imagery
arXiv:2512.16468v3 Announce Type: replace Abstract: Virtual testing using synthetic data has become a cornerstone of autonomous vehicle (AV) safety assurance. Despite progress in improving visual realism through advanced simulators and generative AI, recent studies reveal that pixel-level fidelity alone does not ensure reliable transfer from simulation to the real world. What truly matters is whether […]
Deep SPI: Safe Policy Improvement via World Models
arXiv:2510.12312v2 Announce Type: replace-cross Abstract: Safe policy improvement (SPI) offers theoretical control over policy updates, yet existing guarantees largely concern offline, tabular reinforcement learning (RL). We study SPI in general online settings, when combined with world model and representation learning. We develop a theoretical framework showing that restricting policy updates to a well-defined neighborhood of […]