# LLM Agents & Planning: Literature Digest

> 10 foundational papers with 11,356 combined citations, plus the latest arXiv preprints.

- **Canonical:** https://agentflare.org/scholar/llm-agents-planning-literature-digest.html
- **Updated:** 2026-10-18
- **Category:** scholar
- **Full structured data:** `/scholar/llm-agents-planning-literature-digest.data.json` — $0.02 via AISA HTTP 402 (https://esa.aisa.one/api/v1/access/verify; agents set crawlerAutoPrice=true)

## Key data

- **Field:** large language model agents planning
- **Foundational:** 10
- **Recent (arXiv):** 8
- **Updated:** 2026-10-18

10 foundational papers with 11,356 combined citations, plus the latest arXiv preprints.

_…full analysis and the complete dataset are available to agents for $0.02 — fetch `/scholar/llm-agents-planning-literature-digest.data.json` (HTTP 402)._

## Sources

1. [Llm-planner: Few-shot grounded planning for embodied agents with large language models](https://openaccess.thecvf.com/content/ICCV2023/html/Song_LLM-Planner_Few-Shot_Grounded_Planning_for_Embodied_Agents_with_Large_Language_ICCV_2023_paper.html)
2. [Large language model based multi-agents: A survey of progress and challenges](https://arxiv.org/abs/2402.01680)
3. [A survey on large language model based autonomous agents](https://link.springer.com/article/10.1007/s11704-024-40231-1)
4. [On the planning abilities of large language models-a critical investigation](https://proceedings.neurips.cc/paper_files/paper/2023/hash/efb2072a358cefb75886a315a6fcf880-Abstract-Conference.html)
5. [Tptu: Task planning and tool usage of large language model-based ai agents](https://openreview.net/forum?id=GrkgKtOjaH)
6. [Large language models for planning: A comprehensive and systematic survey](https://arxiv.org/abs/2505.19683)
7. [Smart-llm: Smart multi-agent robot task planning using large language models](https://ieeexplore.ieee.org/abstract/document/10802322/)
8. [On the prospects of incorporating large language models (llms) in automated planning and scheduling (aps)](https://ojs.aaai.org/index.php/ICAPS/article/view/31503)
9. [AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation](https://dl.acm.org/doi/abs/10.1145/3690624.3709321)
10. [Embodied task planning with large language models](https://arxiv.org/abs/2307.01848)
11. [SciΠvot: Benchmarking Principal-Investigator Judgment at Pivotal Moments of Scientific AutoResearch Agents](https://arxiv.org/abs/2610.21082v1)
12. [PhySGA: A Physics-Space Grounded Agentic Framework for Generalizable and Interactive 3D Composition](https://arxiv.org/abs/2610.20560v1)
13. [Reflect After Failure: Brain-Inspired Self-Evolving Agents for Embodied Spatial Intelligence](https://arxiv.org/abs/2610.20312v1)
14. [Euler: Towards Human-Centered Multi-Agent Mathematical Discovery](https://arxiv.org/abs/2610.19864v1)
15. [When Is Smaller Greener? Right-Sizing Language Models for Agentic AI](https://arxiv.org/abs/2610.19345v1)
16. [CLASHarness: Benchmarking Multi-Agent Conflict in Computer Use via a Controllable and Verifiable Generation Pipeline](https://arxiv.org/abs/2610.19128v1)
17. [Agentic-MatriBO: Capability-Compiled Agentic Bayesian Optimization for Materials and Chemical Design](https://arxiv.org/abs/2610.17832v2)
18. [Instructions Are Evidence, Not Objectives: Measuring Outcome Alignment of Agents](https://arxiv.org/abs/2610.17757v1)

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