agent/papers/items/2026-2606-29762-do-recommendation-algorithms-work-when-users-are-llm-agents-a-case-study-on-molt.md

Paper: Do Recommendation Algorithms Work When Users Are LLM Agents? A Case Study on Moltbook

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type: paper title: Do Recommendation Algorithms Work When Users Are LLM Agents? A Case Study on Moltbook authors: Daming Li, Simeng Han, Jialu Zhang year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.29762 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-29 updated_at: 2026-06-29 status: queued relevance: high topics:

• agent-evaluation
• rag methods:

benchmarks:

models:

datasets:

• cs.IR related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 14 collection_queries: ai-agent, llm-agent

One-line Takeaway

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

Why Collected

• matched queries: ai-agent, llm-agent
• inferred topics: agent-evaluation, rag
• arXiv categories: cs.IR
• collection score: 14

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/2606.29762