# An Interactive Agent for Requirement-Driven Candidate Sourcing

arXiv: https://arxiv.org/abs/2608.23501 · HTML: https://mlsanity.com/paper/2608.23501/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 21 of its 48 references that are in the map.
- First posted on arXiv 24 Aug 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 21 of 21)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents](https://mlsanity.com/paper/2504.12516/) (2025)
- [AutoCodeRover: Autonomous Program Improvement](https://mlsanity.com/paper/2404.05427/) (2024)
- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019)
- [Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information](https://mlsanity.com/paper/1805.04655/) (2018)
- [Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG](https://mlsanity.com/paper/2406.11147/) (2024)
- [Requirements Engineering for Machine Learning: Perspectives from Data Scientists](https://mlsanity.com/paper/1908.04674/) (2019)
- [Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs](https://mlsanity.com/paper/2310.13976/) (2023)
- [MARE: Multi-Agents Collaboration Framework for Requirements Engineering](https://mlsanity.com/paper/2405.03256/) (2024)
- [Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening](https://mlsanity.com/paper/2401.08315/) (2024)
- [Leveraging LLMs for the Quality Assurance of Software Requirements](https://mlsanity.com/paper/2408.10886/) (2024)
- [DeepFact: Co-Evolving Benchmarks and Agents for Deep Research Factuality](https://mlsanity.com/paper/2603.05912/) (2026)
- [LLMREI: Automating Requirements Elicitation Interviews with LLMs](https://mlsanity.com/paper/2507.02564/) (2025)
- [The State-of-Practice in Requirements Elicitation: An Extended Interview Study at 12 Companies](https://mlsanity.com/paper/2102.11556/) (2021)
- [Requirements Elicitation Follow-Up Question Generation](https://mlsanity.com/paper/2507.02858/) (2025)
- [ReqElicitGym: An Evaluation Environment for Interview Competence in Conversational Requirements Elicitation](https://mlsanity.com/paper/2602.18306/) (2026)
- [REAgent: Requirement-Driven LLM Agents for Software Issue Resolution](https://mlsanity.com/paper/2604.06861/) (2026)
- [Evaluating AI Recruitment Sourcing Tools by Human Preference](https://mlsanity.com/paper/2504.02463/) (2025)

## Built on by (most cited first, 1 of 1)

- [From Matching Models to Recruiting Agents: A Systematized Narrative Review of AI Recruitment Systems, Evaluation, and Governance](https://mlsanity.com/paper/2609.04286/) (2026)

## Abstract (arXiv)

Finding people from a natural-language description (``ML engineers transitioning to research roles in biotech'') is increasingly delegated to LLM agents and framed as information retrieval. We argue that it is fundamentally a requirements engineering task: such a request is an under-determined requirement with implicit constraints, many valid answers, and no acceptance criterion, so useful answers require eliciting, validating, and verifying the requirement before search can matter. We present sys, to our knowledge the first interactive, requirements-driven candidate-sourcing agent (it elicits, validates, retrieves, and verifies a vague people-request into a justified slate through bounded elicitation, workflow templates, a two-stage commit protocol, and bidirectional termination guards) and bench, a benchmark that runs the requirements lifecycle (criteria-anchored validation, multi-model evidence-grounded oracle construction, and cost-aware verification). Across 21 systems and all 691 requirements, sys dominates breadth (100% coverage at 2.5× the yield) and is near-orthogonal to the field, with 90% of the people it returns are surfaced by none of 20 strong LLM-plus-web baselines combined. Beyond breadth, an evidence-grounded judging of every system shows sys recalls the most relevant real people: 0.241 of the union pool, 1.9× the next system, with a bootstrap 95% interval disjoint from every baseline. sys is thus the strongest sourcing engine (the deepest real, reachable candidate pool), while precision-ranking LLMs serve as~complementary verifiers.