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ColdStart and the Science of Reliable Autonomous Recovery
ColdStart examines whether AI agents can recover unfamiliar broken systems while preserving data, security, and persistent behavior—bringing reliable recovery into focus as a measurable capability.
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Knowing When to Ask: Uncertainty, Abstention, and Evidence-Seeking in AI Agents
When should an AI agent act, inspect, ask, or abstain? This paper connects confidence calibration with the cost of error and the value of evidence, proposing a research framework for measuring uncertainty-aware decisions in executable environments.
Guardrails at the Point of Action: A Systems Framework for Tool-Using AI Agents
AI guardrails must govern what agents can do, as well as what they say. This paper proposes an action-centered framework for tool authorization, runtime enforcement, and oversight, with a research protocol for measuring safety and useful task completion.
Beyond the Context Window: Why AI Agents Need Memory That Works
AI agents need memory that helps them remember, revise, and resume work. Explore the research behind persistent memory and how custom datasets and environments can reveal whether agents use past experience wisely when conditions change.
The Experience Layer of AGI: How Real-World Tasks Shape More General AI
The path toward more general AI depends on the experiences agents learn from. Explore how diverse tasks, custom environments, and domain expertise support adaptation and transfer - and how Deaimer contributes through datasets and executable workflows.
From Coding Agents to Scientific Agents: Why Domain Expertise Matters
Working code is only the beginning of scientific AI. Explore why domain knowledge, data semantics, numerical judgment, and custom learning environments matter when developing agents for real research workflows.
Beyond Benchmark Scores: Why Realistic RL Environments Matter for Reliable AI Agents
Explore how realistic reinforcement learning (RL) environments improve AI agent training, evaluation, and reliability beyond benchmark scores. Discover why verifiable rewards, diverse tasks, and real-world testing matter for building capable AI agents.
Termetheus and the Foundations of Environment Based AI Learning
Explore how Termetheus turns custom terminal tasks into executable learning environments for AI agents, supporting post-training, domain-specific skills, and evaluation on unseen problems.
ColdStart and the Science of Reliable Autonomous Recovery
ColdStart examines whether AI agents can recover unfamiliar broken systems while preserving data, security, and persistent behavior—bringing reliable recovery into focus as a measurable capability.
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