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arxiv: 2605.02366 · v1 · submitted 2026-05-04 · 💻 cs.AI

A Compound AI Agent for Conversational Grant Discovery

Zhisheng Tang , Mayank Kejriwal This is my paper

Pith reviewed 2026-05-08 19:25 UTC · model grok-4.3

classification 💻 cs.AI
keywords compound AIgrant discoveryconversational agentsReActLLM agentsresearch fundinginformation retrievalhybrid search
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The pith

A compound AI system aggregates nearly 12,000 grants from scattered portals and answers researcher queries through a single conversational interface.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Research funding discovery is fragmented across dozens of agency portals with incompatible search tools and data formats. The paper describes a compound AI setup that deploys LLM-equipped browser agents to autonomously gather, normalize, and index opportunities into a unified, biweekly-updated database. A ReAct-based conversational layer then interprets a researcher's description or uploaded PDF, combines structured search with selective web lookup, and streams results with visible reasoning steps to avoid hallucination. Users can refine constraints across multiple turns without rewriting their core query. The approach is presented as cutting manual search time from 30-45 minutes down to under 10 minutes and is already serving thousands of researchers.

Core claim

The authors claim that a compound AI agent unifies fragmented grant discovery by coupling an aggregation layer of LLM browser agents that collect and normalize almost 12,000 federal and nonprofit opportunities with a ReAct-based query layer that interprets research context, performs hybrid search, and delivers transparent conversational results, thereby reducing discovery time from 30-45 minutes of manual portal navigation to under 10 minutes.

What carries the argument

The compound AI agent formed by an LLM-equipped browser aggregation layer that maintains a unified opportunity database and a ReAct-based conversational query layer that performs context-aware hybrid retrieval.

Load-bearing premise

LLM browser agents can reliably scrape and normalize data from many different grant portals without errors or omissions, and the ReAct layer can interpret researcher context and retrieve matching opportunities without hallucinating or overlooking relevant grants.

What would settle it

A controlled test in which the same set of researchers conduct identical grant searches both manually across the original portals and through the system, then compare the time taken and the completeness of the returned opportunities.

Figures

Figures reproduced from arXiv: 2605.02366 by Mayank Kejriwal, Zhisheng Tang.

Figure 1
Figure 1. Figure 1: Distribution of funding opportunities across U.S. view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of our conversational grant discovery system (Pane A) with traditional web search (Pane B). Our view at source ↗
read the original abstract

Research funding discovery remains fundamentally fragmented: researchers navigate disparate agency portals (e.g., in the United States, NSF, NIH, DARPA, Grants.gov, and many others) with heterogeneous interfaces, search capabilities, and data schemas. We present a compound AI system that unifies this landscape through two tightly coupled components: (1) an aggregation layer that autonomously collects, normalizes, and indexes almost 12,000 federal and nonprofit opportunities from fragmented sources via LLM-equipped browser agents, maintaining a biweekly-updated unified database; and (2) an agentic ReAct-based query processing layer that interprets research context (including from PDF documents) and employs hybrid search combining a structured index with selective web search to retrieve relevant opportunities - while avoiding LLM hallucination. The conversational interface supports iterative refinement through multi-turn interactions, allowing researchers to progressively apply constraints without reformulating their core research description. Results stream in real time with full transparency of intermediate reasoning, enabling appropriate calibration of user trust. Currently used by almost 3,000+ users, our approach demonstrates the feasibility of compound AI in reducing grant discovery time from 30--45 minutes (manual, fragmented portal searches) to under 10 minutes (unified, conversational search).

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The manuscript describes a compound AI system for conversational grant discovery. It consists of an aggregation layer using LLM-equipped browser agents to autonomously collect, normalize, and index nearly 12,000 federal and nonprofit grant opportunities from heterogeneous sources, with biweekly updates to a unified database. A ReAct-based query processing layer interprets research context (including from PDF documents) and employs hybrid search (structured index plus selective web search) to retrieve relevant opportunities while avoiding hallucinations. The conversational interface supports multi-turn iterative refinement with real-time streaming results and transparency of reasoning. The authors claim this reduces grant discovery time from 30-45 minutes (manual searches) to under 10 minutes and report current use by over 3,000 users, demonstrating feasibility of compound AI for this task.

Significance. If the performance and reliability claims are substantiated through rigorous evaluation, the work would illustrate a practical, deployed application of compound AI agents that combines browser automation for data aggregation with agentic reasoning for query handling in a fragmented real-world domain. The unified database and conversational interface directly address researcher pain points in funding discovery, and the reported user adoption indicates practical interest. However, the absence of any quantitative validation limits assessment of the claimed efficiency gains and system robustness.

major comments (3)
  1. [Abstract] Abstract: The central claim that the system reduces grant discovery time from 30-45 minutes to under 10 minutes is load-bearing for the feasibility demonstration but is unsupported by any user study, timed comparisons, session logs, A/B testing, or quantitative metrics.
  2. [Abstract] Abstract: No evaluation metrics, error analysis, precision/recall figures, or validation procedures are described for the aggregation layer's LLM-equipped browser agents in collecting and normalizing data from heterogeneous portals, nor for the ReAct layer's context interpretation, retrieval completeness, or hallucination avoidance.
  3. [Abstract] Abstract: The manuscript provides no methodology section, experimental results, or details on the hybrid search implementation, database maintenance process, or how transparency in reasoning is achieved to support user trust calibration.
minor comments (1)
  1. [Abstract] Abstract: The phrase 'compound AI' is invoked without a concise definition or reference to prior literature on the term, which may hinder readers new to the concept.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We recognize that the manuscript is primarily a system description of a deployed compound AI application and lacks the quantitative evaluations and methodological details expected in empirical work. We will revise the manuscript to qualify unsupported claims, add a methodology section, and discuss limitations and observed performance.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the system reduces grant discovery time from 30-45 minutes to under 10 minutes is load-bearing for the feasibility demonstration but is unsupported by any user study, timed comparisons, session logs, A/B testing, or quantitative metrics.

    Authors: We agree that the specific time-reduction figures lack rigorous quantitative support and are based on informal internal testing and anecdotal user feedback rather than controlled studies. In the revised manuscript, we will qualify this statement in the abstract to indicate it reflects observed benefits from deployment and early user reports, remove the precise numerical claims, and add a forward-looking note on planned user studies to validate efficiency gains. revision: partial

  2. Referee: [Abstract] Abstract: No evaluation metrics, error analysis, precision/recall figures, or validation procedures are described for the aggregation layer's LLM-equipped browser agents in collecting and normalizing data from heterogeneous portals, nor for the ReAct layer's context interpretation, retrieval completeness, or hallucination avoidance.

    Authors: The manuscript focuses on architectural description and real-world deployment rather than formal benchmarking. We will add a dedicated 'Validation and Limitations' section describing internal checks for data normalization, the hybrid search strategy for reducing hallucinations (structured index plus selective web retrieval), and observed error patterns from usage logs. We will explicitly note the absence of precision/recall metrics as a current limitation and outline future evaluation plans. revision: partial

  3. Referee: [Abstract] Abstract: The manuscript provides no methodology section, experimental results, or details on the hybrid search implementation, database maintenance process, or how transparency in reasoning is achieved to support user trust calibration.

    Authors: We will expand the manuscript with a new Methodology section that details the aggregation pipeline (LLM browser agents, normalization rules, and biweekly update process), the hybrid search implementation (structured index construction, ReAct-based query interpretation from text/PDFs, and triggers for web search), and the transparency mechanisms (real-time streaming of reasoning steps). This will support reproducibility and explain how users can calibrate trust. revision: yes

Circularity Check

0 steps flagged

No circularity: descriptive system paper with no derivations or fitted predictions

full rationale

The paper presents a built compound AI system for grant discovery, consisting of an LLM-browser aggregation layer and a ReAct query layer. No mathematical models, equations, first-principles derivations, or statistical predictions appear in the provided text. Claims such as time reduction from 30-45 to under 10 minutes are stated as feasibility demonstrations based on system existence and user adoption (3,000+ users), without any fitted parameters, self-referential definitions, or load-bearing self-citations that reduce to inputs by construction. None of the enumerated circularity patterns apply, as there is no derivation chain to inspect.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on assumptions about the capabilities of current LLM agents in web navigation and reasoning tasks, which are domain assumptions not independently verified in the abstract.

axioms (2)
  • domain assumption LLM-based browser agents can autonomously and accurately extract and normalize grant data from diverse web sources.
    This is required for the aggregation layer to function as described.
  • domain assumption The ReAct-based agent can interpret research context and perform hybrid search without introducing hallucinations.
    Central to the query processing layer's reliability.

pith-pipeline@v0.9.0 · 5512 in / 1501 out tokens · 47396 ms · 2026-05-08T19:25:25.270653+00:00 · methodology

discussion (0)

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