← Back to all news

Retrieval-augmented generation

Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.

6,375 stories · RSS feed

arXiv AI
Sep 11

The Menu Is an Execution Prior: State-Path Tool Menus for Online Agents

The paper introduces the State-Path Tool Menu, a method that presents agents with a short, ordered subset of tools before execution, ensuring that both the final action and its prerequisite tools are available in a usable sequence. By learning a pre‑execution route from the request state to the desired outcome, the menu acts as an execution prior, encoding which tools can run from the current state, how outputs satisfy later inputs, and recurring orders from training paths. Experiments on ToolBench show that this approach boosts online success from 0.737 to 0.898 and outperforms several baselines while covering more complete tool chains with fewer tools.

By Bo Yan, Weikai Lin, Song Wang
llmsragagents
More like this →
arXiv Machine Learning
Sep 11

Continuous Diffusion Scales Competitively with Discrete Diffusion for Language

The paper revisits the continuous diffusion language model Plaid and introduces RePlaid, aligning its architecture with modern discrete diffusion models. RePlaid achieves a compute gap of only 20× compared to autoregressive models, surpasses Duo with fewer parameters, and outperforms MDLM in over‑trained settings. On OpenWebText, RePlaid sets a new state‑of‑the‑art continuous diffusion perplexity of 22.1 and demonstrates superior generation quality, while theoretical analysis links likelihood‑based training to linear cross‑entropy over time and structured embedding geometries.

By Zhihan Yang, Wei Guo, Shuibai Zhang, Subham Sekhar Sahoo, Yongxin Chen, Arash Vahdat, Morteza Mardani, John Thickstun
llmsragdiffusionbenchmarks
More like this →
Hugging Face Trending Papers
Sep 10

RAG-Safety-Bench: Reliable Evaluation of Retrieval-Augmented LLM Safety

RAG-Safety-Bench is a benchmark designed to evaluate how retrieval-augmented generation (RAG) affects the safety of large language models (LLMs). It isolates safety impacts by testing four conditions: non-RAG, RAG with an oracle document, RAG with related but non-answer documents, and RAG with random safe documents. Results on five open-source LLMs reveal an inverse relationship between benign and unsafe capabilities, show that standard safety guardrails do not guarantee safety in RAG, and confirm that even benign documents can trigger unsafe outputs.

llmsragbenchmarkssafety
More like this →
Hugging Face Trending Papers
Sep 10

A Dynamic Fusion Large Language Model for Traffic Flow Prediction

The paper introduces DF-LLM, a Dynamic Fusion Large Language Model designed for traffic flow prediction. It combines a spatiotemporal embedding module, a fusion module that uses graph convolution to capture spatial topology and dynamic dependencies, and an LLM backbone with differentiated parameter adaptation and context aggregation attention. Experiments on four datasets show that DF-LLM outperforms existing methods in predictive accuracy.

llmsrag
More like this →
arXiv AI
Sep 10

US-JEPA: A Joint Embedding Predictive Architecture for Ultrasound

US-JEPA introduces a self‑supervised framework for ultrasound imaging that predicts masked latent representations instead of raw pixels, using a frozen, domain‑specific teacher to provide stable targets. This approach avoids the hyperparameter sensitivity and computational cost of traditional online teachers, enabling the student model to build upon the teacher’s semantic priors. The authors benchmark US‑JEPA against all publicly available ultrasound foundation models on UltraBench, showing competitive or superior performance across multiple organs and pathological conditions under linear probing.

By Ashwath Radhachandran, Vedrana Ivezi\'c, Shreeram Athreya, Corey W. Arnold, William Speier
ragbenchmarks
More like this →
arXiv AI
Sep 10

Knowledge-Guided Vision-Language Inference for Image-Based Urban Flood Depth Estimation

The paper introduces FloodVision, a knowledge-guided vision‑language framework that estimates urban flood depth from a single RGB image. It combines a general‑purpose vision‑language model with FloodKG, a domain knowledge base that encodes canonical object dimensions and component landmarks to promote component‑level reasoning. On 654 crowdsourced New York flood images, FloodVision cuts mean absolute error from 15.62 cm to 8.75 cm and median error from 14.35 cm to 7.75 cm, outperforming the VLM‑only baseline in 69.3 % of cases.

By Zhangding Liu, Neda Mohammadi, John E. Taylor
llmsragcomputer-visionmultimodal
More like this →