21 citations · 49 across the 24 of their papers we have counts for
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cs.AI2026
Learning to Reason Efficiently with A* Post-Training
Andreas Opedal, Francesco Ignazio Re, Abulhair Saparov +3
Many applications of large language models (LLMs) require deductive reasoning, yet models frequently produce incorrect or redundant inference steps. We frame natural language infer…
cs.AI2025
Test of Time: Rethinking Temporal Signal of Benchmark Contamination
Terry Jingchen Zhang, Gopal Dev, Ning Wang +8
Post-cutoff performance decay of LLMs has been widely interpreted as a temporal signal for benchmark contamination, where public information released before the training cutoff may…