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Even the most advanced LLMs struggle to maintain narrative consistency, with a staggering 68% of generated content conflicting under user interventions.
CRISP reduces interaction turns by up to 33% while maintaining accuracy by prioritizing critical evidence-gathering steps in deep search agents.
Reducing reasoning tokens while boosting accuracy, SuCo transforms how LRMs approach problem-solving by focusing on sufficiency rather than excess.
LLMs can translate long documents far more effectively by learning to selectively attend to relevant context, mimicking human translation strategies.
Multi-agent systems get a 6.3% accuracy boost on math problems thanks to a new "rectify-or-reject" pruning method that dynamically filters out bad information at test time.
