wuyang
64 lines
1.4 KiB
Markdown
64 lines
1.4 KiB
Markdown
# Paper: Empirical Grounding Improves the Realism of LLM Agents Simulating Human Behavior During Disruptions
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---
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type: paper
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title: Empirical Grounding Improves the Realism of LLM Agents Simulating Human Behavior During Disruptions
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authors: Chen Xia, Zexi Kuang, Yuqing Hu
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year: 2026
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venue: arXiv
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url: https://arxiv.org/abs/2607.17437
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code_url:
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source: arxiv
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collected_at: 2026-07-27
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published_at: 2026-07-19
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updated_at: 2026-07-19
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status: queued
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relevance: high
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topics:
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- agent-evaluation
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- memory
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- planning
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- rag
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- reasoning
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- world-model
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methods:
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benchmarks:
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models:
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datasets:
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- cs.AI
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related_concepts:
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related_jobs:
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related_experiments:
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related_projects:
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collection_score: 19
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collection_queries: llm-agent, planning-agent
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---
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## One-line Takeaway
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Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.
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## Why Collected
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- matched queries: llm-agent, planning-agent
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- inferred topics: agent-evaluation, memory, planning, rag, reasoning, world-model
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- arXiv categories: cs.AI
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- collection score: 19
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## Review Checklist
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- Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
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- Does it include a benchmark, dataset, code, or reproducible experimental setup?
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- Should it be promoted from `queued` to `skimmed` or `summarized`?
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## Links
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- arXiv: https://arxiv.org/abs/2607.17437
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