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Lax bug reproduction tests can lead to plausible but incorrect patches, but a new iterative framework boosts repair success by refining both tests and fixes.
Achieving over 134% accuracy gains with just 1,000 trainable parameters reveals a game-changing approach to enhancing spatial reasoning in vision language models.
TreeSeeker's branch-and-return mechanism enables agents to navigate complex search spaces more effectively, outperforming traditional methods by leveraging structured uncertainty management.
Forget hand-crafted templates: DUET learns to generate user and item profiles jointly, boosting recommendation accuracy by better aligning textual representations.
Stop obsessing over state prediction accuracy in text-based world models: aligning them with *behavior* yields better long-term planning and evaluation.
Ditch the army of task-specific models: AdNanny shows a single, reasoning-centric LLM can handle diverse offline advertising tasks with improved accuracy and reduced manual effort.
