Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI (2026 · 5 citations)
Earlier work suggested that people can barely tell LLM output from human writing. Most of that evidence came from English text, GPT-3.5-turbo, and small samples. This paper tests whether the claim holds for newer models, other languages and expert readers, aiming to estimate the ceiling of human detection ability.
FAID: Fine-grained AI-generated Text Detection using Multi-task Auxiliary and Multi-level Contrastive Learning (2026 · 3 citations)
Detecting machine-written text is harder when humans and LLMs co-write, and most detectors only output a human vs.
ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning (2026)
Research on test-time compute (TTC) scaling for LLM reasoning is scattered. Methods are tested under different protocols, model setups and budgets. Most papers report accuracy but not cost, and released code is rarely ready for production. ThinkBooster addresses this with a single open-source framework (MIT license)…

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Papers of our library cite their works 5 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated 1 November 2026