Institution profile

Universidad de Buenos Aires

Academic institutionsouthamerica · ar
Official website
Research library111linked papers
Opportunities0open roles
Selected work

Representative Papers

Design and implementation of a novel cryptographically secure pseudorandom number generator

Aug 24, 2022Journal of Cryptographic Engineering

To address the challenge of simultaneously achieving security, statistical quality, and hardware efficiency in pseudorandom number generators (PRNGs) for lightweight cryptographic applications, this paper proposes a novel cryptographically secure PRNG (CSPRNG). The core innovation lies in the first integration of a lightweight modular exponentiation chain—providing strong mathematical hardness—with a customized Feistel-like nonlinear structure that employs S-boxes, bit-level diffusion, and permutation for efficient non-linear confusion. Implemented on FPGA, the design achieves a throughput of 1.2 Gbps while reducing area overhead by 37% compared to state-of-the-art lightweight CSPRNGs. It passes all statistical tests in both NIST SP 800-22 and Dieharder suites on a 2⁴⁰-bit output sequence. The proposed CSPRNG thus delivers provable cryptographic security, excellent statistical randomness, and high hardware efficiency with good portability across constrained platforms.

3 citationsRead paper

Entropy Sentinel: Continuous LLM Accuracy Monitoring from Decoding Entropy Traces in STEM

Jan 13, 2026

This work addresses the challenge of real-time monitoring of cross-domain performance degradation in large language model (LLM) deployment and guiding data collection without labeled feedback. The authors propose leveraging the entropy trace of output token probability distributions during decoding as an unlabeled, inference-time signal. By constructing an 11-dimensional statistical feature vector from top-k logprobs, they train a lightweight classifier to predict the correctness of individual responses and aggregate these predictions to estimate domain-level accuracy. This approach is the first to demonstrate that decoding entropy traces can effectively support cross-model, cross-domain performance monitoring and data prioritization. Experiments across 10 STEM benchmarks and 9 mainstream LLMs (3B–20B parameters) show strong alignment between estimated and ground-truth accuracy, with multiple models exhibiting near-monotonic domain ranking capability.

1 citationsRead paper

Recovering Temporal and Geographic Signals from Language Model Embeddings

Sep 04, 2026

Understanding whether language-model embeddings encode structured real-world information is important for both representation analysis and information retrieval. We study this question for temporal and geographic signals using a simple projection-based method that operates directly on output embeddings. Given a small set of seed examples, the method defines an axis in embedding space and ranks texts or entities by their projection onto that axis. Our approach is fully black-box and model-agnostic: it requires only embeddings, without access to model weights, internal activations, auxiliary probes, or additional training. This makes it applicable to modern embedding models available only through APIs and provides a lightweight way to analyze whether temporal and spatial dimensions are present in their representation spaces. We apply the method to temporal and geographic datasets and find that embedding projections recover meaningful chronological and spatial structure. These results provide evidence that output embeddings encode signals relevant to time and space, while also offering a practical tool for interpretability and for downstream temporal and geographic information retrieval tasks, such as temporal ordering, geographic ranking, and tagging.

0 citationsRead paper
Recent publications

Latest Papers

Recovering Temporal and Geographic Signals from Language Model Embeddings

Sep 04, 2026

Understanding whether language-model embeddings encode structured real-world information is important for both representation analysis and information retrieval. We study this question for temporal and geographic signals using a simple projection-based method that operates directly on output embeddings. Given a small set of seed examples, the method defines an axis in embedding space and ranks texts or entities by their projection onto that axis. Our approach is fully black-box and model-agnostic: it requires only embeddings, without access to model weights, internal activations, auxiliary probes, or additional training. This makes it applicable to modern embedding models available only through APIs and provides a lightweight way to analyze whether temporal and spatial dimensions are present in their representation spaces. We apply the method to temporal and geographic datasets and find that embedding projections recover meaningful chronological and spatial structure. These results provide evidence that output embeddings encode signals relevant to time and space, while also offering a practical tool for interpretability and for downstream temporal and geographic information retrieval tasks, such as temporal ordering, geographic ranking, and tagging.

0 citationsRead paper

A quantum let within the lambda calculus

Aug 29, 2026

本文解决了量子lambda演算中无法消除张量积的问题,通过引入新的let构造来分解和绑定单量子比特密度矩阵,从而恢复了丢弃量子比特的能力。

0 citationsRead paper