Research & Deep Dives
Research papers, reports, scenarios, and technical findings translated into practical implications for builders and operators.
Open Research & Deep DivesA paper argues that AI automation depends less on isolated task exposure and more on whether AI-capable steps sit next to each other in a workflow. The workflow graph becomes the right unit for estimating exposure, redesign, and value.
A paper argues that enterprise AI should use language models as bounded interfaces while knowledge, rules, and repeatable computation live in explicit systems. The design keeps outputs auditable, deterministic where needed, and easier to govern.
A paper argues that repeated agent workflows should be compiled into reusable MCP blueprints instead of re-reasoned from scratch every time. Compilation reduces repeated inference while preserving validation, portability, and a path back to source intent.
Diffusion language models promise faster generation, but a paper shows why speed does not yet translate into reliable planning, tool use, or agent behavior. The gap comes from weak sequential control, state management, and verification under iterative work.
AlphaFund's whitepaper reframes recursive self-improvement as a measurable capital-allocation loop: create more edge than you lose, then reinvest the gains. The framework turns improvement into an investment decision governed by measurable returns and downside.
Recursive Language Models argue that very long prompts should become an external environment the model can inspect, decompose, and recursively call itself over. Recursion converts context management from passive storage into an active reasoning process.
An agent architecture argues that the append-only event log should be the runtime's source of truth, not an observability layer bolted on afterward. That choice makes recovery, replay, observability, and policy enforcement part of one architecture.
FinePhrase shows that high-quality synthetic pretraining data comes from prompt recipes, mix-in strategy, output diversity, and data infrastructure, not simply larger generator models. The result is a repeatable engineering discipline for controlling quality, coverage, and cost.