# CAREER: Adaptive Knowledge Synthesis for Language Model Reasoning

> **NSF 01002627DB NSF RESEARCH & RELATED ACTIVIT** · Washington University (MO) · $596,521

## 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 systems can perform multiple searches, they typically rely on a single, general-purpose model that struggles to form a structured plan or adapt its expertise to different phases when investigating a problem in depth. This project addresses this critical gap by creating a new way for AI systems to actively seek out and synthesize knowledge. By empowering models to formulate step-by-step research plans and adapt to each part of a problem, this project promotes the progress of science and advances national prosperity through the creation of highly reliable and transparent decision-making tools. These new capabilities will directly benefit evidence-based fields such as legal analysis and scientific discovery. In addition to these technological benefits, the project supports education by creating new university courses that teach students how to critically analyze AI systems. The investigator will also lead hands-on outreach activities for local school students to inspire the next generation of researchers.

The technical goal of this award is to establish a proactive, efficient, and transparent reasoning framework for knowledge-intensive tasks. The research activities are organized into three integrated thrusts. The first thrust develops the Adaptive Knowledge Synthesis framework, which trains LLMs to formulate structured reasoning plans through hypothesis-driven decomposition. Within this framework, a conductor model dynamically reconfigures a base LLM to create specialized expert

## Key facts

- **NSF award ID:** 2541822
- **Awardee organization:** Washington University (MO)
- **SAM.gov UEI:** L6NFUM28LQM5
- **PI:** Jiaxin Huang
- **Primary program:** 01002627DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), CAREER-Faculty Erly Career Dev
- **Estimated total:** $596,521
- **Funds obligated:** $417,565
- **Transaction type:** Continuing Grant
- **Period:** 10/01/2026 → 09/30/2031

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2541822

## Citation

> US National Science Foundation, Award 2541822, CAREER: Adaptive Knowledge Synthesis for Language Model Reasoning. Retrieved via AI Analytics 2026-07-03 from https://api.ai-analytics.org/grant/nsf/2541822. Licensed CC0.

---

*[NSF Awards dataset](/datasets/nsf-awards) · CC0 1.0*