# When Algorithmic Exploration Becomes Cheap: A Case Study of Agentic Research in EDA

arXiv: https://arxiv.org/abs/2610.10129 · HTML: https://mlsanity.com/paper/2610.10129/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 59 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Placed in

- LLM agents and tool use / [Educational agents and math problem solving](https://mlsanity.com/agents/educational-agents-and-math-problem-solving/)

## Builds on (most cited first, 12 of 12)

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- [VerilogEval: Evaluating Large Language Models for Verilog Code Generation](https://mlsanity.com/paper/2309.07544/) (2023)
- [ChipNeMo: Domain-Adapted LLMs for Chip Design](https://mlsanity.com/paper/2311.00176/) (2023)
- [Mathematical exploration and discovery at scale](https://mlsanity.com/paper/2511.02864/) (2025)
- [OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis](https://mlsanity.com/paper/2110.11292/) (2021)
- [Rethinking the Value of Agent-Generated Tests for LLM-Based Software Engineering Agents](https://mlsanity.com/paper/2602.07900/) (2026)
- [First Proof Second Batch](https://mlsanity.com/paper/2606.18119/) (2026)
- [Agent Skills Can Be Harmful: An Empirical Study of Skill-Induced Failures in LLM Agents](https://mlsanity.com/paper/2608.11888/) (2026)
- [Provably Good Prim-Dijkstra Revisited: New Theory and a Practical Algorithm for a Classical VLSI Routing Problem with LLMs](https://mlsanity.com/paper/2607.17005/) (2026)
- [Rethinking Logic Optimization Operators: Theory-Derived Operator Compression via Agentic Source Analysis](https://mlsanity.com/paper/2607.23672/) (2026)
- [Batch Before You Time: Decision-Scoped Proxy Execution for Timing-Aware Logic Rewriting](https://mlsanity.com/paper/2609.02470/) (2026)

## Abstract (arXiv)

As EDA researchers, we conducted eight deliberate trials of agentic algorithm exploration, selecting several topics outside our areas of depth. One faculty member and seven students participated, including students without publication experience. With limited intervention in the algorithms, agents developed mathematical constructions, analyzed existing tools, and implemented improvements; some efforts fell short of their practical goals. We also used AI to collect, classify, and analyze 8,420 papers from four EDA conferences and two journals over 2022-2026. Among 2,380 primary-core papers, we classified 97.7% from titles and abstracts as computationally closed, including work on new formulations. Together, these observations suggest that much of EDA offers an executable environment for increasingly accessible algorithm research. We see an opportunity for tool developers to investigate ideas they previously lacked time to pursue. We also ask how EDA should validate and reward research when results become easier to produce than to examine, and what papers and venue labels will continue to tell us about a contribution.