CoT-Core: Accelerating LLM Evaluation via CoT-Aware Coreset Selection
arXiv:2608.00014v1 Announce Type: new Abstract: Evaluating Large Language Models LLMs incurs prohibitive computational overhead during continuous development processes.
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arXiv:2608.00014v1 Announce Type: new Abstract: Evaluating Large Language Models LLMs incurs prohibitive computational overhead during continuous development processes.
arXiv:2608.00015v1 Announce Type: new Abstract: Both optimization modeling and constraint modeling are nontrivial problems requiring deep domain expertise and proficiency in modeling formalism languages.
arXiv:2608.00017v1 Announce Type: new Abstract: Selfimproving LLM agents increasingly learn from experience without updating any weights. Each episode is stored in an external memory, scored, and retrieved for similar future tasks to shape later behavior.
arXiv:2608.00026v1 Announce Type: new Abstract: Batched LLM serving improves throughput but complicates energy accounting. GPU power telemetry is aggregate, whereas sustainability reporting, chargeback, and workload analysis often require requestlevel energy charges.
arXiv:2608.00027v1 Announce Type: new Abstract: Efficient longsequence modeling remains a central challenge for large language models, as selfattention scales quadratically with sequence length.
arXiv:2608.00029v1 Announce Type: new Abstract: The performance of deep learning models at scale relies heavily on how effectively highlevel mathematical operations are mapped to underlying physical hardware.
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