# Paper: Agentic multi-fidelity learning of quasiparticle and excitonic properties

---
type: paper
title: Agentic multi-fidelity learning of quasiparticle and excitonic properties
authors: Arnab Neogi, Aaron Forde, Christopher A. Lane, Sergei Tretiak, Jian-Xin Zhu
year: 2026
venue: arXiv
url: https://arxiv.org/abs/2606.07836
code_url:
source: arxiv
collected_at: 2026-07-08
published_at: 2026-06-05
updated_at: 2026-06-05
status: queued
relevance: high
topics:
  - agent-evaluation
  - computer-use
  - rag
  - workflow-agent
  - world-model
methods:
  -
benchmarks:
  -
models:
  -
datasets:
  - cond-mat.mtrl-sci
  - cond-mat.stat-mech
  - cs.AI
  - physics.comp-ph
  - quant-ph
related_concepts:
  -
related_jobs:
  -
related_experiments:
  -
related_projects:
  -
collection_score: 13
collection_queries: agent-evaluation
---

## One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

## Why Collected

- matched queries: agent-evaluation
- inferred topics: agent-evaluation, computer-use, rag, workflow-agent, world-model
- arXiv categories: cond-mat.mtrl-sci, cond-mat.stat-mech, cs.AI, physics.comp-ph, quant-ph
- collection score: 13

## Review Checklist

- Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
- Does it include a benchmark, dataset, code, or reproducible experimental setup?
- Should it be promoted from `queued` to `skimmed` or `summarized`?

## Links

- arXiv: https://arxiv.org/abs/2606.07836