Tootfinder

Investigating the Robustness of Retrieval-Augmented Generation at the Query Level
Sezen Per\c{c}in, Xin Su, Qutub Sha Syed, Phillip Howard, Aleksei Kuvshinov, Leo Schwinn, Kay-Ulrich Scholl
https://arxiv.org/abs/2507.06956

Investigating the Robustness of Retrieval-Augmented Generation at the Query Level
Large language models (LLMs) are very costly and inefficient to update with new information. To address this limitation, retrieval-augmented generation (RAG) has been proposed as a solution that dynamically incorporates external knowledge during inference, improving factual consistency and reducing hallucinations. Despite its promise, RAG systems face practical challenges-most notably, a strong dependence on the quality of the input query for accurate retrieval. In this paper, we investigate th…

Fair Data Exchange with Constant-Time Proofs
Majid Khabbazian
https://arxiv.org/abs/2506.14944 https://arxiv.org/pdf/2506.14944

Fair Data Exchange with Constant-Time Proofs
The Fair Data Exchange (FDE) protocol introduced at CCS 2024 offers atomic pay-per-file transfers with constant-size proofs, but its prover and verifier runtimes still scale linearly with the file length n. We collapse these costs to essentially constant by viewing the file as a rate-1 Reed-Solomon (RS) codeword, extending it to a lower-rate RS code with constant redundancy, encrypting this extended vector, and then proving correctness for only a small random subset of the resulting ciphertexts…