Research & Motivation
FABIABox is not built on hype. It is built on a stack of peer-reviewed findings that show why local, fine-tuned, agentic AI is the right architecture for the next generation of companies.
Why Local Models Compete with the Cloud
Cloud APIs centralize data, expose IP, and create lock-in. Keeping models on-premise or on-device removes the network as a threat surface and makes inference reproducible and auditable.
Differentially Private Fine-tuning of Language Models
Yu et al. • 2021
Private fine-tuning lets institutions adapt large models without leaking training data — a hard requirement for GDPR and regulated workflows.
Read PDFOn-device Federated Learning with Flower
Mathur et al. • 2021
Training and inference can stay on the device while still learning collectively. Data never has to leave the owner's control.
Read PDFHow Fine-Tuned Models Beat General-Purpose Cloud APIs
A smaller model trained on the right data routinely outperforms a giant generic model on the target task. The key is not scale; it is alignment, domain coverage, and efficient adaptation.
LoRA: Low-Rank Adaptation of Large Language Models
Hu et al. • 2021
Parameter-efficient fine-tuning makes it feasible to specialize a model with a fraction of the compute and storage of full fine-tuning.
Read PDFFine-tuning and Utilization Methods of Domain-specific LLMs
Jeong • 2024
Survey of how domain-specific LLMs are built and why they beat general models on vertical tasks.
Read PDFThe False Promise of Imitating Proprietary LLMs
Gudibande et al. • 2023
Distilling GPT outputs into an open model does not close the capability gap. Real performance comes from domain-specific training, not imitation.
Read PDFLIMA: Less Is More for Alignment
Zhou et al. • 2023
A small set of high-quality examples can align a model surprisingly well. This lowers the data barrier for founders building specialized agents.
Read PDFDirect Preference Optimization
Rafailov et al. • 2023
Models can be aligned to human preferences without the complexity and cost of reinforcement learning from human feedback.
Read PDFThe Agentic Infrastructure Stack
Agents need memory, tools, planning, and multi-agent coordination. These papers define the primitives that FABIABox packages into a single workstation.
ReAct: Synergizing Reasoning and Acting in Language Models
Yao et al. • 2022
Interleaving reasoning traces with actions lets an agent explain what it is doing while it uses tools to solve problems.
Read PDFToolformer: Language Models Can Teach Themselves to Use Tools
Schick et al. • 2023
Models can learn when and how to call external tools — search, code, calculators — without hand-crafted pipelines.
Read PDFGenerative Agents: Interactive Simulacra of Human Behavior
Park et al. • 2023
Agents with memory, reflection, and planning can sustain believable long-horizon behavior — the blueprint for persistent AI co-workers.
Read PDFA Survey of Multi-Agent Deep RL with Communication
Zhu et al. • 2022
How multiple agents coordinate through communication — foundational for agent squads that split work and check each other.
Read PDFPOLARIS: Governed Execution for Agentic AI
Moslemi et al. • 2026
Enterprise back-office agents need policy-aware, auditable orchestration — exactly what a sovereign local box can enforce.
Read PDFAI Agentic Workflows and Enterprise APIs
Tupe & Thube • 2025
Current enterprise API architectures are built for humans. Autonomous agents require new interaction patterns and controls.
Read PDFHow the Agentic Economy Becomes Real
Agentic systems are not just faster software. They change the unit economics of work by automating end-to-end workflows and introducing digital labor as a new factor of production.
Evolving the Productivity Equation
Farach, Cambon & Spataro • 2025
Argues that AI systems doing cognitive work should be treated as a new factor of production — digital labor.
Read PDFAgentic AI and Occupational Displacement
Gupta & Kumar • 2026
Extends task-exposure analysis to entire workflows, showing where autonomous agents substitute occupational labor.
Read PDFOn the Opportunities and Risks of Foundation Models
Bommasani et al. • 2021
Foundational survey of capabilities, applications, and societal impact of foundation models — the technology layer beneath the agentic economy.
Read PDFThe True Cost of Running Your Own Agents
Cloud AI bills scale with usage, tokens, and employees. A local, specialized deployment converts variable inference costs into a fixed hardware cost and avoids the carbon/energy overhead of undifferentiated giant models.
Energy and Policy Considerations for Deep Learning in NLP
Strubell et al. • 2019
Quantifies the hidden dollar and carbon cost of large-model training and inference — the core TCO argument for efficient, local models.
Read PDFGreen AI
Schwartz et al. • 2019
Makes the case for efficiency as a first-class metric and argues against the "bigger is always better" assumption in AI.
Read PDFReady to build with sovereign AI?
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