PrepBench is a benchmark showing that state-of-the-art LLMs still struggle with natural-language-driven data preparation involving disambiguation, code generation, and workflow translation.
Dabstep: Data agent benchmark for multi-step reasoning
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SGKR uses function-call dependency graphs to retrieve structured code knowledge, improving LLM correctness on multi-step data reasoning benchmarks over similarity baselines.
KAIROS reduces power by 27% on average (up to 39.8%) for agentic AI inference by using long-lived context to jointly manage GPU frequency, concurrency, and request routing across instances.
Finch is a new benchmark with 172 composite workflows and 384 tasks from real enterprise data that shows top AI models like GPT-5.1 Pro pass only 38.4% of workflows under human evaluation.
Presents a new question-based evaluation framework for LLMs on aggregated social media text and reports that performance declines with input scale, task complexity, and numerical operations beyond 500 instances.
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KAIROS: Stateful, Context-Aware Power-Efficient Agentic Inference Serving
KAIROS reduces power by 27% on average (up to 39.8%) for agentic AI inference by using long-lived context to jointly manage GPU frequency, concurrency, and request routing across instances.