agent/papers/items/2026-2607-00233-from-signals-to-structure-how-memory-architecture-drives-language-emergence-in-l.md

Paper: From Signals to Structure: How Memory Architecture Drives Language Emergence in LLM Agents

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type: paper title: "From Signals to Structure: How Memory Architecture Drives Language Emergence in LLM Agents" authors: Yashar Talebirad, Eden Redman, Ali Parsaee, Osmar R. Zaiane year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.00233 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-30 updated_at: 2026-06-30 status: queued relevance: high topics:

• memory
• rag
• tool-use methods:

benchmarks:

models:

datasets:

• cs.AI
• cs.CL
• cs.IT
• cs.MA related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 16 collection_queries: llm-agent

One-line Takeaway

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

Why Collected

• matched queries: llm-agent
• inferred topics: memory, rag, tool-use
• arXiv categories: cs.AI, cs.CL, cs.IT, cs.MA
• collection score: 16

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/2607.00233