arXiv Machine Learning
Aug 18
Koopman early warning signals for bifurcation and rate-induced tipping
arXiv:2608. 14716v1 Announce Type: cross Abstract: Abrupt transitions in complex systems are often preceded by early warning signals.
Retrieval-augmented generation
Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.
arXiv Machine Learning
Aug 18
A Unified Geometric Framework for Developmental Analysis of Spatial Transcriptomic Data
arXiv:2608. 15306v1 Announce Type: cross Abstract: High-throughput single-cell and spatial transcriptomic technologies provide high-resolution snapshots of heterogeneous cellular states, but their destructive nature prevents repeated measurements of the same cells over time.
arXiv AI
Aug 18
Shape Operator PCA: Curvature-Aware Projections for Geometric Machine Learning
arXiv:2608. 15313v1 Announce Type: cross Abstract: In this paper, we propose SHOPCA (Shape Operator-based Principal Component Analysis), a novel method for unsupervised metric learning and dimensionality reduction that incorporates differential geometric information into the covariance structure of classical PCA.
arXiv AI
Aug 18
Adding Voice Cloning to Text-to-Audio-Video Models with a Single Zero-Initialised Layer
arXiv:2608. 15690v1 Announce Type: cross Abstract: Text-to-audio-video (T2AV) generation models produce a video and its soundtrack from a textual description, but offer no control over whose voice speaks in the output.
arXiv AI
Aug 18
Beyond Visual CoT: Internalized Visual Thinking for Proactive Video Reasoning
arXiv:2608. 15869v1 Announce Type: cross Abstract: Multimodal large language models increasingly use visual chain-of-thought (Visual CoT) to reason about spatial, temporal, and embodied environments.
arXiv AI
Aug 18
RAGas: Retrieval-Augmented Gas Optimization for Smart Contracts with Continuous Knowledge Integration
arXiv:2608. 15857v1 Announce Type: new Abstract: Ethereum is now integral to mission-critical sectors, including finance, healthcare, and supply chain management.
arXiv AI
Aug 18
Beyond Asking: A Pipeline for Personalized Game Generation that Reads Players from Behavior
arXiv:2608. 16196v1 Announce Type: new Abstract: Personalized game generation requires inferring a player's abilities and behavioral style from how they play.
arXiv AI
Aug 18
OTel: Building Domain-Specialized Telecom LLM Foundations for Intelligent Networks
arXiv:2608. 15436v1 Announce Type: new Abstract: Frontier AI models have advanced rapidly, but they still struggle with telecom-specific tasks.
arXiv AI
Aug 18
Stop Indexing at Full Precision: Revisiting Clustering for Vector Embeddings
arXiv:2608. 14648v1 Announce Type: cross Abstract: In this study, we revisit three widely used techniques in vector search and utilize them to optimize vector embedding indexing through clustering: dimensionality reduction, quantization, and dimension pruning.
arXiv AI
Aug 18
Prompting is not enough: supervised baselines and leakage control for measuring shared decision-making with LLMs in pediatric encounters
arXiv:2608. 14792v1 Announce Type: cross Abstract: Objectives: To determine whether zero-shot prompting of a large language model (LLM) is sufficient to detect shared decision-making (SDM) behaviors in real clinical encounters, and whether supervised learning adds value under patient-grouped, nested evaluation.
arXiv Machine Learning
Aug 18
LLMs for Zero-Shot Threat Detection via Structured Risk Indicators
arXiv:2608. 16508v1 Announce Type: cross Abstract: We propose a two-stage large language model (LLM) framework for zero-shot detection of insider threats and advanced persistent threats (APTs) from heterogeneous security logs.
arXiv AI
Aug 18
Domain-Specific Text Embedding Models for Entity Resolution
arXiv:2608. 16161v1 Announce Type: cross Abstract: General-purpose text embedding models are designed to capture semantic similarity but are not optimised for distinguishing entity records that represent the same real-world business or person.
arXiv Machine Learning
Aug 18
BERTopic-Virality Prioritisation: A Scalable Framework for Thematic and Comparative Analysis of COVID-19 and Monkeypox Misinformation on Twitter
arXiv:2608. 15691v1 Announce Type: cross Abstract: Health misinformation circulating during pandemics can gain traction rapidly, creating harmful narratives that compete with public health guidance.
arXiv Machine Learning
Aug 18
POI Recommendation with LLM-Augmented Multi-Graph Learning and Contrastive Alignment
arXiv:2608. 16407v1 Announce Type: cross Abstract: Point-of-interest (POI) recommendation models based on graph neural networks achieve strong performance by propagating collaborative signals over user-item interactions, yet they struggle with the cold-start problem, where items with few or no interactions are not represented.
arXiv AI
Aug 18
OGX: An Open-Source, Vendor-Neutral Generative AI Application Server
OGX (Open GenAI Stack) is an open‑source AI application server and Python library that implements the APIs of major frontier labs such as OpenAI, Anthropic, and Google, allowing developers to build agentic AI applications against a single API surface. It supports a wide range of inference providers, vector store backends, and safety backends, and includes a Kubernetes Operator for production deployment. OGX is used as the self‑hosted, model‑agnostic backend for AI‑powered developer tools like Claude Code, Codex CLI, OpenCode, and OpenHands, and has garnered significant community engagement with over 8,400 GitHub stars, 242 contributors, and 4,000 commits.
arXiv AI
Aug 18
ATLAS: Scaffold-Free Algorithm Synthesis by LLMs via Embedding-Guided Quality-Diversity Search
ATLAS is an embedding‑guided quality‑diversity framework that enables scaffold‑free synthesis of full algorithms for combinatorial optimization using large language models. It allows the LLM to freely choose, restructure, and control algorithm components while automatically detecting and repairing execution, interface, and feasibility failures. Across four NP‑hard problems, ATLAS outperforms state‑of‑the‑art component‑synthesis methods and remains competitive with strong human‑designed algorithms, demonstrating that a larger design space can be practically searched.
arXiv AI
Aug 18
Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152
arXiv:2608. 16394v1 Announce Type: new Abstract: Generating regulation-compliant test scenarios is essential for validating safety-critical automotive systems, yet Large Language Models (LLMs) struggle to ground outputs in long, hierarchical standards.
arXiv AI
Aug 18
Cost Scales with Change, Not Corpus Size: Incrementally Maintaining an Evolving Semantic Substrate
arXiv:2608. 16621v1 Announce Type: new Abstract: Retrieval-augmented and agentic question-answering systems increasingly re-derive the meaning of a corpus at query time.
arXiv AI
Aug 18
Hypergraph-based Multimodal Retrieval-Augmented Generation with Incremental Refinement
arXiv:2608. 16628v1 Announce Type: new Abstract: Modern Multimodal Retrieval-Augmented Generation (M-RAG) systems are fundamentally limited by the binary connectivity paradigm of traditional simple graphs, which fails to capture the intricate, high-order correlations among heterogeneous entities, such as the N-ary relationships between a visual chart, its scattered textual descriptions, and underlying numerical data.
arXiv AI
Aug 18
SAPE: Sandwich Adapters for Parameter Efficiency in Large Language Model Fine-Tuning
arXiv:2608. 15360v1 Announce Type: cross Abstract: While Parameter-Efficient Fine-Tuning (PEFT) has substantially reduced the hardware cost of adapting Large Language Models (LLMs) by decreasing the number of trainable parameters, recent studies have sought to further improve PEFT through parameter sharing.