agent/papers/items/2026-2606-07836-agentic-multi-fidelity-learning-of-quasiparticle-and-excitonic-properties.md

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

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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