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RECONTEXT boosts long-context reasoning in LLMs by effectively reusing evidence from the input, leading to superior performance without the need for retraining.
A unified model that seamlessly integrates text and graph learning outperforms traditional methods by up to 3.9 points on benchmark tasks.
Current multimodal LLMs still struggle to integrate information and reason critically when assessed on real scientific papers, despite progress on isolated tasks.