arXiv:2608.30517v1 Announce Type: new Abstract: Benchmark saturation and data contamination increasingly obscure genuine scientific reasoning in frontier LLMs. We introduce \textsc{ScienceArena}, an...
arXiv:2605.14040v2 Announce Type: replace Abstract: Trackable improvement in multimodal physics reasoning rests on a training-and-evaluation system that is itself rarely verified: the corpora a model...
arXiv:2604. 06802v2 Announce Type: replace Abstract: Recent AI systems have achieved gold-medal-level performance on the International Mathematical Olympiad, demonstrating remarkable proficiency at competition-style problem solving.
arXiv:2606. 10254v1 Announce Type: new Abstract: While Large Language Models (LLMs) have achieved near-perfect performance in \emph{solving} high-school mathematics, their ability to \emph{evaluate} the diverse reasoning processes of real human students remains under-examined.
The paper introduces GAP, a methodology that automatically generates mathematically equivalent variants of existing math problems using surface renames and kernel rewrites. Applying GAP to all 1,051 William Lowell Putnam Competition problems creates PutnamGAP, a 6,306-item dataset with 5,255 unseen variants. Evaluation of 18 commercial and open-source LLMs shows accuracy drops across all models, especially under kernel rewrites, indicating a persistent weakness in transferring proof plans to new mathematical settings.
arXiv:2606. 13020v1 Announce Type: new Abstract: Three paradigmatic forms of inference recur across scientific reasoning: deduction, induction, and causal abduction.
arXiv:2510. 09595v3 Announce Type: replace Abstract: Competitive programming problems are increasingly used to evaluate the coding capabilities of large language models (LLMs) due to their complexity and ease of verification.
arXiv:2602. 20629v3 Announce Type: replace Abstract: As Large Language Models (LLMs) saturate elementary benchmarks, the research frontier has shifted from generation to the reliability of automated evaluation.
arXiv:2601.08654v3 Announce Type: replace Abstract: Rubric-based text evaluation increasingly relies on large language models (LLMs) as scalable judges, yet frozen black-box models can interpret the...
The paper reports a specialized training pipeline for large language models to excel in competitive programming, combining problem curation, synthetic reasoning traces, supervised fine‑tuning, and reinforcement learning. Using 22,000 curated problems, the authors train two models—Nemotron‑3‑Nano‑CC (30B) and Nemotron‑3‑Ultra‑CC (550B)—and introduce GenCorrect, a test‑time refinement strategy. On the IOI 2025 benchmark, Nano‑CC scores 468 points with GenCorrect, surpassing the gold‑medal threshold, while Ultra‑CC reaches 502; in IOI 2026, a competition‑specific Ultra‑CC system scores 535.4, exceeding both the gold threshold and the top human score of 498.27, marking the first AI system to outscore the highest‑scoring human contestant on an IOI problem set.
arXiv:2602. 16793v2 Announce Type: replace Abstract: In the past year, custom and unreleased math reasoning models reached gold medal performance on the International Mathematical Olympiad (IMO).
The paper introduces HOA (Humanize Olympiad Agent), a workflow that orchestrates multiple LLM agent sessions via reviewer feedback, a structured wiki, and a verifier to tackle olympiad problems. Using GPT‑5.6 Sol, HOA achieves perfect scores on the IMO, IPhO, and IOI 2026, near‑full marks on the IChO 2026, and solves all 672 PutnamBench problems, while ranking first on the Lean‑Eval leaderboard. With open models like Kimi‑K3, HOA still attains full scores on major olympiads and a gold‑level IChO score, showing that systematic feedback can close gaps between closed‑source and open‑source agents.