arXiv:2607.00171v2 Announce Type: replace
Abstract: Text embeddings are standard for semantic similarity tasks, yet their evaluation remains an open challenge. Current benchmarks are static, cover on...
By Andrianos Michail, Stylianos Psychias, Michelle Wastl, Simon Clematide, Rico Sennrich, Juri Opitz
arXiv:2608. 07540v1 Announce Type: new Abstract: AI systems increasingly operate between flexible input representations and formal objects used by downstream tools.
By Fateme Mazdarani, Carlos Toxtli
arXiv:2609.39294v1 Announce Type: new
Abstract: Precise numerical reasoning with Large Language Models (LLMs) is essential for expanding their applicability to complex real-world tasks. However, text...
By Jinsung Jeon, Seung-won Hwang
The paper introduces a method for evaluating the intrinsic interestingness of mathematical theorems by comparing the length of their proofs to the length of their statements. It trains a 27B language model to predict proof difficulty, enabling the generation and selection of more interesting theorems while significantly reducing overlap with existing Mathlib. The approach allows iterative expansion of a self‑building, machine‑verified mathematical library guided by quantifiable metrics.
By Niket Patel, Ahmad Rammal, Amaury Hayat, Remi Munos, Julia Kempe
arXiv:2608. 15325v1 Announce Type: cross Abstract: We propose a new framework for machine-learning-oriented argument analysis tasks.
By Leander Heldring, Santiago Torres
arXiv:2508.02208v3 Announce Type: replace-cross
Abstract: Evaluating the mathematical capability of Large Language Models (LLMs) is a critical yet challenging frontier. Existing benchmarks fall short...
By Yebo Peng, Yaoming Li, Zixiang Liu, Zhizhuo Yang, Xinye Xu, Bowen Ye, Weijun Yuan, Zihan Wang, Tong Yang