AI systems now write long and difficult mathematical proofs, and checking their step-by-step validity is becoming a bottleneck. A learned verifier could check proofs cheaply, but training one requires a correctness label on every step, and such labels are hard to obtain for open-ended proofs. Existing methods take them...
Rodney Lafuente-Mercado, Salman Rahman, Jing-Yan Shen et al.· 0 citations
By making failure cheap to locate and correct, this work is a foundation for more trustworthy long-horizon agents that learn from their own mistakes, and a practical path to overseeing increasingly autonomous AI.
Salman Rahman, Y. Kim, Mihir Parmar et al.· 2 citations
Though absolute performance remains low, finetuning on NormViz-Train improves pair accuracy relatively by up to 125% and 36% Qwen3-VL 4B and 8B respectively, showing a path forward to teach models to connect visual perception to cultural significance.
Akhila Yerukola, Fabrice Y. Harel-Canada, Simran Khanuja et al.· 0 citations
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