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VALG autonomously navigates the complexities of ML theory research, producing viable theorem candidates while preserving mathematical integrity across proof attempts.
Teacher-student mismatch can lead to flawed outputs, but TIDE's innovative correction method boosts reasoning accuracy by over 200% in challenging scenarios.
LLMs get stuck in their ways: even explicit corrections can't break their rigid adherence to initial (incorrect) reasoning paths in multi-turn interactions, but a novel RL approach can fix it.
