Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.
arXiv:2609.15664v1 Announce Type: cross Abstract: Amid the rapid advancement of physical-world intelligence, cloud-edge collaborative large language models (LLMs) have emerged as a promising roadmap...
arXiv:2609.15344v1 Announce Type: new Abstract: We study the adaptation of pretrained language models to univariate time-series forecasting through a parameter-efficient transfer learning framework,...
arXiv:2609.15234v1 Announce Type: new Abstract: Recently, Large Language Models (LLMs) have gained significant attention due to their strong language understanding and generation capabilities, demons...
arXiv:2609.14762v1 Announce Type: cross Abstract: Cloud-hosted large language models (LLMs) are increasingly used for root cause analysis (RCA) in AIOps pipelines, but they introduce data privacy ris...
arXiv:2609.08797v2 Announce Type: replace-cross Abstract: The Potts model extends the Ising model to multinomial data. We introduce a Rater Ising-Potts model that uses agreement indicators between pa...
arXiv:2609.14412v1 Announce Type: new Abstract: Deep research agents answer complex questions through iterative loops of searching, reading, and reasoning. Recent work on reasoning-intensive benchmar...
Echo-E$^3$Net is an anatomy‑guided spatio‑temporal neural network designed to estimate left ventricular ejection fraction (LVEF) from ultrasound images. It uses a dual‑phase Endocardial Border Detector to locate end‑diastole and end‑systole landmarks and an Endocardial Feature Aggregator to fuse these landmarks with global deep‑feature descriptors for EF regression. The model achieves competitive accuracy on EchoNet‑Dynamic and EchoNet‑Pediatric datasets while using only 1.55 M parameters and 8.05 GFLOPs, enabling real‑time deployment on limited‑resource devices.
arXiv:2609.15660v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) grounds a language model's answers on retrieved external knowledge and returns each answer with citations that i...
The paper introduces the Universe of Universes (UoU) framework, treating the ecosystem of major large language models as a structured retrieval corpus and proposing a compositional Automated Reasoning and Machine Learning architecture for cross-model retrieval‑augmented generation. It formally defines the Benefit Yield Function (BYF), measuring marginal performance gain per added model, and identifies an implosion threshold θ* where BYF becomes zero and ensemble performance degrades. The work highlights gaps in current LLM ensemble research, such as lack of performance analysis across full model universes, and connects these findings to implications for DoD AI acquisition policy and testing of AI‑enabled systems.
arXiv:2607.29397v3 Announce Type: replace Abstract: Deploying large language models in realistic server environments poses challenges, as the system needs to provide high-quality responses with low l...
arXiv:2609.15640v1 Announce Type: cross Abstract: Long-text image--text congruence scoring is increasingly important for vision-language systems that must evaluate whether detailed textual descriptio...
arXiv:2603.18908v5 Announce Type: replace Abstract: Independently trained language models often learn compatible late-stage representations, despite differences in training objectives, architectures,...
arXiv:2609.14783v1 Announce Type: cross Abstract: Robots need touch to manipulate objects safely and reliably, as many properties, such as softness, texture, and contact stability, are hard to infer...
The paper introduces a multimodal foundation model for lunar remote sensing, trained from scratch on SomBench—a dataset of nearly two million co‑registered tile bundles across 11 modalities at 1 m and 100 m resolutions. The model extends the TerraMind masked‑token architecture with lunar‑specific features such as explicit acquisition geometry and joint training of two spatial scales, and employs FlexiViT patch embeddings for adaptable patch sizes. Evaluation on crater detection, irregular mare patch segmentation, and polar ice prospectivity regression shows that the pretrained model matches or surpasses ImageNet‑pretrained baselines, with notable label efficiency and effective adaptation via LoRA.
ClinAgent is a conversational system that uses a ReAct-based LLM agent to retrieve and synthesize clinical trial information from multiple sources such as ClinicalTrials.gov, PubMed, and a local dataset. The agent iteratively reasons over user queries, selects appropriate tools, and refines its actions to provide grounded, up-to-date responses in natural language across multi-turn interactions. Evaluation across three phases shows that DeepSeek (thinking mode) excels in planning quality while Gemini 3.0 Flash delivers the highest overall performance and expert ratings, demonstrating the promise of agentic AI for improving clinical trial data access.
arXiv:2609.14528v1 Announce Type: cross Abstract: Multi-Hop Knowledge Graph Question Answering (KGQA) tasks require models to assemble relational evidence along paths in a KG to answer natural-langua...
arXiv:2609.14739v1 Announce Type: cross Abstract: Large language models are increasingly used in high-stakes domains such as law, where systems must ground their reasoning in retrieved evidence and a...
arXiv:2609.07093v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions by accessing external knowledge and has been widely a...
arXiv:2609.14565v1 Announce Type: new Abstract: LLM-based sequential recommenders usually cast next-item prediction as text generation, but this interface is poorly matched to full-catalog top-K rank...
FedV-KGQA addresses multi‑hop question answering over vertically partitioned knowledge graphs where each silo holds disjoint relation types. The system trains local embeddings, concatenates silo‑specific entity views, anchors questions at a topic entity, and ranks candidates without sharing raw triples. Experiments show federated fusion nearly matches centralized accuracy, that anchoring and enrichment are more critical than embedding choice, and that the cheapest encoder depends on target accuracy.