Research reading list

These publications inform AIWG workflow design. Results reported for other systems or benchmarks are not measurements or guarantees of AIWG performance. Evaluate a workflow on your own tasks before estimating cost, accuracy, or time savings.

See the product overview for capabilities and limitations, and the research background for design context. This companion bibliography collects the reading links covered in the README; it is not a fresh validation of every cited result.

Cognitive Foundations

• Miller, G.A. (1956). The Magical Number Seven, Plus or Minus Two. *Psychological

Review*, 63(2), 81–97. doi:10.1037/h0043158

• Sweller, J. (1988). [Cognitive Load During Problem Solving: Effects on

Learning](https://doi.org/10.1207/s15516709cog1202_4). Cognitive Science, 12(2), 257–285. doi:10.1207/s15516709cog1202_4

• Anderson, J.R. et al. (2004). An Integrated Theory of the Mind.

Psychological Review, 111(4), 1036–1060. (ACT-R cognitive architecture)

• Laird, J.E., Newell, A. & Rosenbloom, P.S. (1987). [SOAR: An Architecture for General

Intelligence](https://doi.org/10.1016/0004-3702(87)90050-6). Artificial Intelligence, 33(1), 1–64.

• Harel, D. (1987). [Statecharts: A Visual Formalism for Complex

Systems](https://doi.org/10.1016/0167-6423(87)90035-9). Science of Computer Programming, 8(3), 231–274.

• Young, S. et al. (2010). [The Hidden Information State Model: A Practical Framework for POMDP-Based Spoken Dialogue

Management](https://doi.org/10.1016/j.csl.2009.04.001). Computer Speech & Language, 24(2), 150–174.

Multi-Agent Systems & Orchestration

• Jacobs, R.A. et al. (1991). Adaptive Mixtures of Local Experts. *Neural

Computation*, 3(1), 79–87. (Mixture-of-Experts foundation)

• Hong, S. et al. (2024). [MetaGPT: Meta Programming for a Multi-Agent Collaborative

Framework](https://arxiv.org/abs/2308.00352). ICLR 2024.

• Qian, C. et al. (2024). ChatDev: Communicative Agents for Software Development.

ACL 2024.

• Shen, Y. et al. (2023). [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in

HuggingFace](https://arxiv.org/abs/2303.17580). NeurIPS 2023.

• Tao, W. et al. (2024). MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution.
• Zhang, J. et al. (2025). AFlow: Automating Agentic Workflow Generation. *ICLR

2025 Oral*.

• Wu, Q. et al. (2023). [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent

Conversation](https://arxiv.org/abs/2308.08155). (Conversational multi-agent framework)

• Yu, C. et al. (2025). A Survey on Agent Workflow — Status and Future. (24

systems, 11 metrics)

• Lodha, D. et al. (2026). [MCP-Diag: A Deterministic, Protocol-Driven Architecture for AI-Native Network

Diagnostics](https://arxiv.org/abs/2601.22633). COMSNETS 2026.

• Yu, G. (2026). AdaptOrch: Adaptive Orchestration for Multi-Agent LLM Systems Through Topology-Aware Task Planning.
• Gerred (2025). [Multi-Agent

Orchestration](https://gerred.github.io/building-an-agentic-system/second-edition/part-iv-advanced-patterns/chapter-10-multi-agent-orchestration.html). Tool isolation, resource boundaries, observable coordination.

• Falconer, S. (2025). [Event-Driven Multi-Agent

Systems](https://www.confluent.io/blog/event-driven-multi-agent-systems/). Confluent. 4 Kafka orchestration patterns.

• Mario, M. (2025). [Multi-Agent System Patterns: A Unified Guide to Designing Agentic

Architectures](https://medium.com/@mjgmario/multi-agent-system-patterns-a-unified-guide-to-designing-agentic-architectures-04bb31ab9c41). 4-dimensional framework.

• Runkle, S. (2026). [Choosing the Right Multi-Agent

Architecture](https://www.blog.langchain.com/choosing-the-right-multi-agent-architecture/). LangChain. Subagents, skills, and handoffs.

• Towards Data Science (2025). [Why Your Multi-Agent System Is Failing: Escaping the 17x Error

Trap](https://towardsdatascience.com/why-your-multi-agent-system-is-failing-escaping-the-17x-error-trap-of-the-bag-of-agents/). Coordination failure analysis.

• NexAI Tech (2025). [Multi-AI Agent Architecture Patterns for

Scale](https://nexaitech.com/multi-ai-agent-architecutre-patterns-for-scale/). Enterprise 5-layer architecture, 3 orchestration patterns.

• Wexford, E. (2026). [How to Build Multi-Agent Systems: Complete 2026

Guide](https://dev.to/eira-wexford/how-to-build-multi-agent-systems-complete-2026-guide-1io6). DEV Community. Multi-agent design guidance.

Reasoning & Planning

• Wei, J. et al. (2022). [Chain-of-Thought Prompting Elicits Reasoning in Large Language

Models](https://arxiv.org/abs/2201.11903). NeurIPS 2022.

• Wang, X. et al. (2023). [Self-Consistency Improves Chain of Thought Reasoning in Language

Models](https://arxiv.org/abs/2203.11171). ICLR 2023.

• Yao, S. et al. (2023). [ReAct: Synergizing Reasoning and Acting in Language

Models](https://arxiv.org/abs/2210.03629). ICLR 2023.

• Yao, S. et al. (2023). [Tree of Thoughts: Deliberate Problem Solving with Large Language

Models](https://arxiv.org/abs/2305.10601). NeurIPS 2023.

• Zhou, A. et al. (2024). [Language Agent Tree Search Unifies Reasoning, Acting, and Planning in Language

Models](https://arxiv.org/abs/2310.04406). ICML 2024.

• Kojima, T. et al. (2022). Large Language Models are Zero-Shot Reasoners.

NeurIPS 2022. ("Let's think step by step")

• Liu, Z. et al. (2026). [Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimization

(EMPO²)](https://arxiv.org/abs/2602.23008). ICLR 2026.

Self-Correction & Iterative Refinement

• Madaan, A. et al. (2023). Self-Refine: Iterative Refinement with Self-Feedback.

NeurIPS 2023.

• Shinn, N. et al. (2023). [Reflexion: Language Agents with Verbal Reinforcement

Learning](https://arxiv.org/abs/2303.11366). NeurIPS 2023.

Stage-Gate, SDLC & Traceability

• Cooper, R.G. (1990). [Stage-Gate Systems: A New Tool for Managing New

Products](https://doi.org/10.1016/0007-6813(90)90040-I). Business Horizons, 33(3), 44–54.

• Jacobson, I., Booch, G. & Rumbaugh, J. (1999). The Unified Software Development Process. Addison-Wesley. ISBN 978-0-201-57169-1.
• Gotel, O.C.Z. & Finkelstein, A.C.W. (1994). [An Analysis of the Requirements Traceability

Problem](https://doi.org/10.1109/ICRE.1994.292398). IEEE ICRE 1994.

Software Engineering & Agent-Computer Interface

• Jimenez, C.E. et al. (2024). [SWE-bench: Can Language Models Resolve Real-world GitHub

Issues?](https://www.swebench.com). ICLR 2024.

• Wang, X. et al. (2024). [Executable Code Actions Elicit Better LLM Agents

(CodeAct)](https://arxiv.org/abs/2402.01030). ICML 2024.

• Yang, J. et al. (2024). [SWE-agent: Agent-Computer Interfaces Enable Automated Software

Engineering](https://arxiv.org/abs/2405.15793). NeurIPS 2024.

• Laurent, A. (2025). [A Comparison of AI Code Assistants for Large

Codebases](https://intuitionlabs.ai/articles/ai-code-assistants-large-codebases). IntuitionLabs.

• Augment Code (2025). AI Coding Assistants for Large Codebases: A Complete Guide.
• AlgoMaster (2025). [How to Use AI Effectively in Large

Codebases](https://blog.algomaster.io/p/using-ai-effectively-in-large-codebases). Retrieval as bottleneck framing.

Context Engineering & Memory

• Liu, N.F. et al. (2024). [Lost in the Middle: How Language Models Use Long

Contexts](https://arxiv.org/abs/2307.03172). TACL 12, 157–173. doi:10.1162/tacl_a_00638

• Dai, Y. et al. (2025). [Pretraining Context Compressor for Large Language Models with Embedding-Based

Memory](https://aclanthology.org/2025.acl-long.1394.pdf). ACL 2025.

• Kang, M. et al. (2025). ACON: Optimizing Context Compression for Long-Horizon LLM Agents.
• Liu, F. & Qiu, H. (2025). Context Cascade Compression (C3): Exploring the Upper Limits of Text Compression.
• Vasilopoulos, A. (2026). [Codified Context: Infrastructure for AI Agents in a Complex

Codebase](https://arxiv.org/abs/2602.20478). (Three-tier context infrastructure: constitution + 19 agents + 34-doc KB)

• Ostby, D.L. (2025). [Stingy Context: Compressing Code Context for Cost-Effective AI Development

Assistance](https://arxiv.org/abs/2512.15504). (TREEFRAG, 18:1 compression ratio)

• Anthropic Applied AI Team (2026). [Effective Context Engineering for AI

Agents](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents). Anthropic Engineering Blog.

• Huang, J.Y. et al. (2026). Do LLMs Benefit From Their Own Words?
• Böckeler, B. (2026). [Context Engineering for Coding

Agents](https://martinfowler.com/articles/exploring-gen-ai/context-engineering-coding-agents.html). Martin Fowler's Blog. Two-category framework.

• Haseeb, M. (2025). Context Engineering for Multi-Agent LLM Code Assistants.
• Verma, N. (2026). Focus Agent: LLM Agent with Active Context Compression for SWE-Bench.
• Zylos Research (2026). [Long-Running AI Agents and Task

Decomposition](https://zylos.ai/research/2026-01-16-long-running-ai-agents). (35-min degradation threshold, Planner-Worker model)

• Zylos Research (2026). LLM Context Window Management and Long-Context Strategies.

Agent Memory & Knowledge Systems

• Laird, J.E. et al. (1987). [SOAR: An Architecture for General

Intelligence](https://doi.org/10.1016/0004-3702(87)90050-6). Artificial Intelligence, 33(1), 1–64.

• Anderson, J.R. et al. (2004). [An Integrated Theory of the Mind

(ACT-R)](https://doi.org/10.1037/0033-295X.111.4.1036). Psychological Review, 111(4), 1036–1060.

• Park, J.S. et al. (2023). [Generative Agents: Interactive Simulacra of Human

Behavior](https://arxiv.org/abs/2304.03442). UIST 2023. doi:10.1145/3586183.3606763

• Xu, W. et al. (2025). A-MEM: Agentic Memory for LLM Agents.
• Hu, Y. et al. (2025). Memory in the Age of AI Agents: A Survey. (Surveys 100+

implementations, forms-functions-dynamics framework)

• Rezazadeh, A. et al. (2025). Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control.
• Yuen, S. et al. (2025). [Intrinsic Memory Agents: Heterogeneous Multi-Agent LLM Systems through Structured

Contextual Memory](https://arxiv.org/abs/2508.08997). Role-aligned heterogeneous memory.

• Graves, A., Wayne, G. & Danihelka, I. (2014). Neural Turing Machines. External

memory architectures.

• Packer, C. et al. (2023). MemGPT: Towards LLMs as Operating Systems. OS-inspired

virtual context paging.

• Yu, Z. et al. (2026). [Multi-Agent Memory from a Computer Architecture

Perspective](https://arxiv.org/abs/2603.10062). Architecture 2.0 '26. Three-layer I/O-cache-memory hierarchy.

• Chhikara, P. et al. (2025). Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory.

Recursive Context Decomposition

• Zhang, A.L., Kraska, T. & Khattab, O. (2026). Recursive Language Models.

arXiv:2512.24601. MIT CSAIL.

Provenance, Reproducibility & Research Management

• Wilkinson, M.D. et al. (2016). [The FAIR Guiding Principles for scientific data management and

stewardship](https://doi.org/10.1038/sdata.2016.18). Scientific Data, 3, 160018. (G20, EU, NIH endorsement)

• W3C (2013). PROV-DM: The PROV Data Model. W3C Recommendation.
• CCSDS (2024). [Reference Model for an Open Archival Information System

(OAIS)](https://public.ccsds.org/Pubs/650x0m2.pdf). ISO 14721. (Digital preservation lifecycle)

• GRADE Working Group (2004–present). GRADE Handbook. Evidence quality

assessment. Adopted by WHO, Cochrane, NICE, and 100+ organizations.

• Schmidgall, S. et al. (2025). Agent Laboratory: Using LLM Agents as Research Assistants.
• Sureshkumar, V. et al. (2026). R-LAM: Towards Reproducibility in Large Action Model Workflows.
• ServiceNow Research (2025). LitLLM for Scientific Literature Reviews. RAG-based literature review, no hallucination approach.

AI Safety & Failure Modes

• Tang, R. et al. (2023). [Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context

Learning](https://arxiv.org/abs/2305.17256). ACL 2023 Findings. doi:10.18653/v1/2023.findings-acl.284

• Bandara, E. et al. (2025). A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows.
• Roig, J.V. (2025). How Do LLMs Fail In Agentic Scenarios? A Qualitative Analysis.
• Von Arx, S., Chan, L. & Barnes, E. (2025). [Recent Frontier Models Are Reward

Hacking](https://metr.org/blog/2025-06-05-recent-reward-hacking/). METR Research Blog.

• Anthropic Alignment Team (2025). [From shortcuts to sabotage: Natural emergent misalignment from reward

hacking](https://www.anthropic.com/research/emergent-misalignment-reward-hacking). Anthropic Research.

• Batista, R.M. & Griffiths, T.L. (2026). [A Rational Analysis of the Effects of Sycophantic

AI](https://arxiv.org/abs/2602.14270). (Bayesian analysis, epistemic risk)

• Kumar, R.S.S. et al. (2025). [Taxonomy of Failure Modes in Agentic AI

Systems](https://www.microsoft.com/en-us/security/blog/2025/04/24/new-whitepaper-outlines-the-taxonomy-of-failure-modes-in-ai-agents/). Microsoft Security.

• Twiss, J. (2026). AI Coding Degrades: Silent Failures Emerge. IEEE Spectrum.
• van Linschoten, A.S. (2025). [The Agent Deployment Gap: Why Your LLM Loop Isn't

Production-Ready](https://www.zenml.io/blog/the-agent-deployment-gap-why-your-llm-loop-isnt-production-ready-and-what-to-do-about-it). ZenML.

Task Decomposition & Declarative Pipelines

• Khot, T. et al. (2023). [Decomposed Prompting: A Modular Approach for Solving Complex

Tasks](https://arxiv.org/abs/2210.11610). ICLR 2023.

• Khattab, O. et al. (2023). [DSPy: Compiling Declarative Language Model Calls into Self-Improving

Pipelines](https://arxiv.org/abs/2310.03714). ICLR 2024.

• Dohan, D. et al. (2022). Language Model Cascades. Google Brain. (PGM

formalization of multi-step LLM pipelines)

• Peng, B. et al. (2023). [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and

Automated Feedback](https://arxiv.org/abs/2302.12813). Microsoft Research.

Training, Alignment & In-Context Learning

• Ouyang, L. et al. (2022). [Training language models to follow instructions with human feedback

(InstructGPT)](https://arxiv.org/abs/2203.02155). (RLHF methodology)

• Dong, Q. et al. (2024). A Survey on In-Context Learning. EMNLP 2024.
• Bai, Y. et al. (2022). Constitutional AI: Harmlessness from AI Feedback.

(Principle-based alignment)

RAG, Retrieval & Tool Use

• Lewis, P. et al. (2020). [Retrieval-Augmented Generation for Knowledge-Intensive NLP

Tasks](https://arxiv.org/abs/2005.11401). NeurIPS 2020.

• Schick, T. et al. (2023). [Toolformer: Language Models Can Teach Themselves to Use

Tools](https://arxiv.org/abs/2302.04761). (Self-supervised tool learning)

Domain Knowledge & Specialization

• Song, Z. et al. (2025). [Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive

Survey](https://arxiv.org/abs/2502.10708). EMNLP 2025 Findings. (Four-paradigm taxonomy)

• Zhang, T. et al. (2024). RAFT: Adapting Language Model to Domain Specific RAG.

COLM 2024. (Outperforms RAG-only and SFT-only)

Constrained Generation & Output Validation

• Beurer-Kellner, L., Fischer, M. & Vechev, M. (2023). [Prompting Is Programming: A Query Language for Large Language

Models (LMQL)](https://arxiv.org/abs/2212.06094). PLDI 2023. doi:10.1145/3591300

• Willard, B.T. & Louf, R. (2023). Efficient Guided Generation for Large Language Models (Outlines).
• Lhoest, Q. & Turuta, M. (2024). [Structured Generation with

Outlines](https://huggingface.co/blog/outlines-structured-generation). Hugging Face Blog.

• Gerganov, G. et al. (2024). [Grammar-Based Sampling (GBNF) —

llama.cpp](https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md). Context-free grammar constrained sampling.

LLM Serving & Local Deployment

• Yu, G. et al. (2022). [Orca: A Distributed Serving System for Transformer-Based Generative

Models](https://www.usenix.org/conference/osdi22/presentation/yu). OSDI '22.

• Kwon, W. et al. (2023). [Efficient Memory Management for Large Language Model Serving with

PagedAttention](https://arxiv.org/abs/2309.06180). SOSP '23. UC Berkeley.

• Ollama Team (2024). [Ollama Concurrent Requests and Performance

FAQ](https://github.com/ollama/ollama/blob/main/docs/faq.md). `OLLAMA_NUM_PARALLEL` configuration guidance.

MCP & Agentic Standards

• Agentic AI Foundation / Linux Foundation (2025). [Model Context Protocol Specification

2025-11-25](https://modelcontextprotocol.io/specification/2025-11-25). (Tool integration protocol)