Emergent Reasoning Patterns in Multi-Agent LLM Systems
D. Montgomery, S. Chen, M. Webb
Nature Machine Intelligence — 2026-01-15
A systematic study of emergent reasoning behaviors in multi-agent LLM architectures.
Scaling Laws for Retrieval-Augmented Generation in Enterprise Settings
S. Chen, D. Park, J. Liu
ICML 2026 — 2026-02-01
Scaling laws for RAG systems operating on enterprise document collections exceeding 10M tokens.
Bias Propagation in Fine-Tuned Language Models: A Longitudinal Study
E. Rodriguez, D. Montgomery
arXiv:2025.14892 — 2025-12-10
Tracking bias metrics across 15 fine-tuning iterations on proprietary datasets.
Context Window Degradation: Empirical Evidence from Production Deployments
M. Webb, A. Thompson
NeurIPS 2025 Workshop — 2025-11-20
Empirical evidence that reasoning quality degrades beyond 40K tokens in production LLM deployments.
The Cost of Intelligence: Token Economics in Production AI Systems
D. Park, S. Chen
Tangible Ideas White Paper — 2025-08-15
Analysis of real-world token costs across 50 production AI deployments.
Prompt Engineering as Software Engineering: Patterns and Anti-Patterns
D. Montgomery, M. Webb, S. Chen
ACL 2025 — 2025-07-01
Formalizing prompt engineering through the lens of software engineering with 12 reusable patterns.
Federated Fine-Tuning for Privacy-Preserving Model Customization
S. Chen, J. Liu
arXiv:2025.07231 — 2025-04-20
A federated fine-tuning protocol enabling organizations to customize LLMs on sensitive data.
A Framework for Responsible AI Deployment in Healthcare Settings
J. Liu, D. Montgomery, E. Rodriguez
Tangible Ideas Technical Report — 2025-10-05
A comprehensive framework for deploying AI systems in clinical environments.