3 papers
cs.CL2026
Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale
Siddharth Gollapudi, Nilesh Gupta, Prasann Singhal +1
Language models (LMs) raise an intriguing alternative to vector-based retrieval: conditioning on an in-context corpus and directly generating a relevant answer. However, prior work…
cs.LG2025
Improved Approximations for Hard Graph Problems using Predictions
Anders Aamand, Justin Y. Chen, Siddharth Gollapudi +2
We design improved approximation algorithms for NP-hard graph problems by incorporating predictions (e.g., learned from past data). Our prediction model builds upon and extends the…
cs.LG2025
Learning-Augmented Frequent Directions
Anders Aamand, Justin Y. Chen, Siddharth Gollapudi +2
An influential paper of Hsu et al. (ICLR'19) introduced the study of learning-augmented streaming algorithms in the context of frequency estimation. A fundamental problem in the st…