CAREER: Adaptive Knowledge Synthesis for Language Model Reasoning — NSF Award to Washington University (MO, $417,565)
Large Language Models (LLMs) have shown impressive performance on pure reasoning tasks like math questions and logic puzzles. However, Artificial Intelligent (AI) systems with such LLMs still struggle with complex tasks that require finding and combining information from multiple external sources. For instance, answeri
| Award title | CAREER: Adaptive Knowledge Synthesis for Language Model Reasoning |
|---|---|
| Award ID | 2541822 |
| Awardee | Washington University |
| City | SAINT LOUIS |
| State | MO |
| Amount obligated | $417,565 |
| Principal investigator | Jiaxin Huang |
| Program | Info Integration & Informatics |
| Start date | 10/01/2026 |
| Abstract | Large Language Models (LLMs) have shown impressive performance on pure reasoning tasks like math questions and logic puzzles. However, Artificial Intelligent (AI) systems with such LLMs still struggle with complex tasks that require finding and combining information from multiple external sources. For instance, answering a complicated legal or scientific question often requires a step-by-step investigation where the answer to one question determines what to search for next. While current AI syst |
| Source | NSF Awards |
$799/mo
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