arXiv:2605.22759v2 Announce Type: replace Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging. Specifically, converting low-level sensor data into representations capable of characterizing higher-level states is difficult due to high phenotypic diversity and variation in individual baseline health, physiology, and lifestyle […]
Finer Parameter Steps for Low-Rank PEFT: A Controlled Study with CP Tensor Adapters
arXiv:2606.00428v1 Announce Type: cross Abstract: Low-rank adapters are usually compared by sweeping a small set of ranks, but the rank also fixes the resolution of the parameter budget. For a $2048times2048$ OPT attention projection, increasing LoRA by one rank stores $4096$ trainable scalars, leaving large gaps between feasible low-budget adapter sizes. This paper asks whether […]
On Wednesdays, We Ask Questions: Optimizing “Active Listening” in Automated Legal Triage and Referral
arXiv:2606.00272v1 Announce Type: new Abstract: The FETCH classifier generates follow-up questions to help refine the best match for the applicant’s legal problem, using a low-cost ensemble of LLMs. In this paper, we describe an expert attorney and LLM-assisted evaluation of the follow-up question approach in FETCH and show that while low-cost LLMs perform well at […]
Pre-Deployment Robustness Stress Testing for CT Segmentation Systems Using Clinically Motivated Multi-Corruption Augmentation
arXiv:2606.00491v1 Announce Type: cross Abstract: Deep learning-based CT segmentation systems often achieve high accuracy on clean benchmark images, but their performance may degrade under heterogeneous clinical imaging conditions such as noise, resolution loss, contrast variation, intensity shift, and artifacts. This instability can limit reliable deployment in real-world medical imaging workflows. We propose Robustness via Augmented […]
Self-supervised Monocular Depth and Pose Estimation for Endoscopy with Latent Priors
arXiv:2411.17790v3 Announce Type: replace-cross Abstract: Accurate 3D mapping in endoscopy enables quantitative, holistic lesion characterization within the gastrointestinal (GI) tract, requiring reliable depth and pose estimation. However, endoscopy systems are monocular, and existing methods relying on synthetic datasets or complex models often lack generalizability in challenging endoscopic conditions. We propose a robust self-supervised monocular depth […]
A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models
arXiv:2606.00563v1 Announce Type: cross Abstract: Selection bias is a common and often unavoidable aspect of real-world data that challenges the generalizability of machine learning models. When models trained on biased data are deployed in the broader target population, poor model generalization may lead to real harm, particularly in high-risk settings such as healthcare. This risk […]
Evaluating Bivariate Causal Statements Based on Mutual Compatibility
arXiv:2606.00278v1 Announce Type: new Abstract: For many real-world systems, causal ground truth is difficult to obtain, making claims about causal effects hard to assess. We develop methods for evaluating collections of $binomn2$ bivariate causal statements over a set of $n$ variables. In the setting of acyclic linear statements, any such collection can be extended to […]
MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation
arXiv:2606.00610v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge. Although effective for simple queries, traditional RAG struggles with large-scale, unstructured corpora where information is highly fragmented. Graph-based RAG (GraphRAG) incorporates knowledge graphs to capture structural relationships, enabling more comprehensive […]
Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
arXiv:2508.06588v3 Announce Type: replace-cross Abstract: Vector Quantization (VQ) has recently emerged as a promising approach for learning compressed and discrete representations for graph-structured data. However, a fundamental challenge, i.e., codebook collapse, remains underexplored in the graph domain, significantly limiting the expressiveness and generalization of graph tokens.In this paper, we present an empirical study and observe […]
Scaling Behavior of Single LLM-Driven Multi-Agent Systems
arXiv:2606.00655v1 Announce Type: cross Abstract: The burgeoning field of LLM-based Multi-Agent Systems (MAS) promises to tackle complex tasks through collaborative intelligence, yet fundamental questions regarding their scaling behavior and intrinsic collective dynamics remain underexplored. This paper systematically investigates how the performance of a homogeneous MAS evolves as the number of agents increases, isolating the variable […]
Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture
arXiv:2606.00288v1 Announce Type: new Abstract: Large language models are undergoing a transition from model technology to system technology. As developers use Codex, Claude Code, AutoGPT, and related agents to write code, manage projects, and execute multi-step tasks, recurring engineering problems such as cache reuse, context management, agent scheduling, and permission control increasingly resemble classical computer […]
Multi-Agent Conformal Prediction with Personalized Statistical Validity
arXiv:2606.00717v1 Announce Type: cross Abstract: Uncertainty quantification is essential in high-stakes machine learning tasks. However, one of the principled solutions, conformal prediction, faces challenges under limited local calibration data, privacy constraints, and data heterogeneity. In multi-agent settings, existing works do not simultaneously and satisfactorily address these challenges with guarantees either limited to averages across agents […]