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Current multimodal agents can build sophisticated perception-control pipelines but fail to reliably execute physical tasks, losing object states and mistaking unfinished goals for completion.
→ This benchmark provides a critical empirical foundation for identifying and addressing the significant capability gaps preventing current AI agents from reliably operating in the physical world.
Irrelevant text distractors cripple mixed-modality retrieval performance more than images, revealing a text modality preference that degrades performance in a V-shaped curve.
→ This work provides a critical understanding of modality biases in multimodal retrieval, paving the way for more robust and equitable information access across diverse data types.
LLMs can be steered to reliably interpret complex genomic variants by grounding their reasoning in deterministic biological computations and evidence verification, avoiding hallucinated TF binding changes and fabricated experimental support.
→ This work offers a robust framework for unlocking the functional significance of vast numbers of noncoding variants, accelerating genomic medicine discovery.
AI agents are now building climate models that outperform published research and offer novel hypotheses for long-standing scientific debates.
→ This benchmark enables robust evaluation of AI agents in scientific discovery where ground truth is unknown, potentially accelerating scientific progress.
ACT3 reveals that integrating carbon cost into silicon design can transform how we approach semiconductor architecture, making sustainability a key performance metric.
→ This tool could fundamentally shift the semiconductor industry towards prioritizing sustainability, influencing both research directions and practical design choices.
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