4 citations · 7 across the 5 of their papers we have counts for
6 papers
Receiver-Conditioned Latent Communication gives 94% CacheBack
Maximillian Rossi, Prajwal Raghunath, Haoqing Xuan +2
Multi-agent systems distribute large contexts across agents that communicate to solve a task. Text messages are compact but require decoding and may omit evidence the receiving age…
Latent Cache Flow: Model-to-Model Communication Without Text
Maximillian Rossi, Prajwal Raghunath, Eugene Wu
LLM agents today communicate via text, which incurs considerable latency and information loss due to the need to autoregressively decode the sharer model's state and encode at the…
Steering Semantic Data Processing With DocWrangler
Shreya Shankar, Bhavya Chopra, Mawil Hasan +5
Unstructured text has long been difficult to automatically analyze at scale. Large language models (LLMs) now offer a way forward by enabling {\em semantic data processing}, where…
DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing
Shreya Shankar, Tristan Chambers, Tarak Shah +2
Analyzing unstructured data has been a persistent challenge in data processing. Large Language Models (LLMs) have shown promise in this regard, leading to recent proposals for decl…
Towards Accurate and Efficient Document Analytics with Large Language Models
Yiming Lin, Madelon Hulsebos, Ruiying Ma +4
Unstructured data formats account for over 80% of the data currently stored, and extracting value from such formats remains a considerable challenge. In particular, current approac…
SPADE: Synthesizing Data Quality Assertions for Large Language Model Pipelines
Shreya Shankar, Haotian Li, Parth Asawa +7
Large language models (LLMs) are being increasingly deployed as part of pipelines that repeatedly process or generate data of some sort. However, a common barrier to deployment are…