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Rensselaer Polytechnic Institute, Troy, New York, USA
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PerfAgent doubles the rate of expert-level code optimizations by leveraging profiler-guided feedback, revealing hidden performance bottlenecks that traditional methods miss.
Existing KB-VQA benchmarks mislead model evaluation, with flawed assumptions leading to overestimated reasoning capabilities in Visual Language Models.
MLLMs can significantly improve KB-VQA performance by first identifying entities from a limited candidate set before selecting evidence, leading to a more efficient and effective workflow.
Current red-teaming efforts miss the forest for the trees: ARES reveals that safety failures often stem from a systemic breakdown between the LLM *and* the reward model, not just the LLM itself.
A new lattice-based transaction scheme offers financial institutions a post-quantum secure and auditable distributed ledger solution that existing Ring-CT models can't provide.
