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Visual Para-Thinker++ achieves remarkable improvements in visual reasoning accuracy by leveraging a multi-agent architecture that minimizes hallucination risks through parallel processing and effective output reconciliation.
SG-OPD achieves remarkable gains in mathematical reasoning by effectively aligning student and teacher models through innovative trust signaling techniques.
By jointly reinforcing informative visual tokens and suppressing irrelevant ones, DuCAR significantly reduces hallucinations in LVLMs, outperforming prior single-modality focused approaches.
