Epi: Towards a Persistent, Self-Improving Agent for Autonomous Research

By Huang, Xu, Luo +16Oct 18, 2026

Score████░░░░░░4.0

Key numbers

84-fold narrower interval for the
5.4% higher total benchmark score
64% of its model cost

Caveats

Preprint; not yet peer reviewed.

VerdictWorth a reader's time today.

Read the original PDF Code

Abstract

We present Épi, an autonomous research agent that sustains investigations across model sessions and evolves its methods through accumulated experience. Épi combines persistent research history, hierarchical memory, branching and independent review to maintain research continuity, learn from experiments, and explore and revisit alternative approaches. Its experiments run across session boundaries, and a fixed verifier evaluates their outputs. Épi produced 120 record-setting results, comprising 20 numerical bound records across 12 families and 100 construction records across eight families. These include an approximately 84-fold narrower interval for the n-queens constant and a more than 3.6-million-fold narrower rigorous interval for the quartic Feigenbaum attractor dimension. Exact dual certificates establish that the Cohn-Elkies linear-programming bound cannot be sharp in dimensions 10 and 11. Beyond numerical improvements, Épi gives a computer-assisted proof of Tuza's conjecture for split graphs with an eight-vertex clique part and at most three triangle-active neighborhood types, with arbitrary multiplicities. We extend this research process to algorithmic coding with Épi-turbo, a lightweight configuration that develops executable solvers for tasks including vehicle routing, scheduling and production planning. On 30 FrontierOR tasks, Épi-turbo achieves a 5.4% higher total benchmark score than Codex at 64% of its model cost, using the same underlying model and reasoning effort. Épi is designed to support human understanding by connecting computational results to explicit methods and mathematical arguments that researchers can examine and build upon.

Yue Huang, Zhangchen Xu, Xiaonan Luo, Zheyuan Liu, Lichi Li, Yiping Wang, Peiyang Song, Eric Gabriel Ramos, Enze Xie, Olexandr Isayev, Paul Liang, Nouha Dziri, Huan Sun, Jianfeng Gao, Ying Liu, Misha Sra, Xiangliang Zhang, Alex Pentland, Zichen Chen

The editor's rubric

Heuristic review

Leverage3 / 5: A method or resource many groups across the field will adopt within a year.; 24% of this desk’s rubric
Magnitude3 / 5: Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.; 18% of this desk’s rubric
Evidence3 / 5: Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.; 14% of this desk’s rubric
Novelty2 / 5: A new combination of known ideas.; 16% of this desk’s rubric
Trajectory2 / 5: Some room to improve with obvious engineering.; 18% of this desk’s rubric
Stakes3 / 5: Meaningful benefit to many people within a few years.; 10% of this desk’s rubric

Dimension	Level	Weight	What that level means
Leverage	███░░ 3	24%	A method or resource many groups across the field will adopt within a year.
Magnitude	███░░ 3	18%	Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
Evidence	███░░ 3	14%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	██░░░ 2	16%	A new combination of known ideas.
Trajectory	██░░░ 2	18%	Some room to improve with obvious engineering.
Stakes	███░░ 3	10%	Meaningful benefit to many people within a few years.

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose); breadth (many tasks); gains (x-fold); verification (multiple benchmarks, independent replication); stakes (global scale, general AI).

How the score was computed

rank-2026-10-18

Score████░░░░░░4.0

Score = 10 × (65% × adjusted merit / 10 + 25% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit: 5.3 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.6 / 10; Shrunk toward the desk prior by editor confidence (44%).
Attention: 24%; Citations, upvotes, points, mentions.
Freshness: 38%; Half-life decay since publication.

Hugging Face upvotes7 (reference 25, via hf-daily, Oct 21, 2026, 13:37 UTC)
GitHub stars9 (reference 250, via hf-daily, Oct 21, 2026, 13:37 UTC)

The record

Reviewed by heuristic-v7 on Oct 20, 2026, 13:49 UTC. Paper type: method.
Categories: cs.CL, cs.AI
BRIEF, No.9 in the Artificial Intelligence edition of October 21, 2026.