Papers, technical reports and blog posts from the Deaimer team on RL environments, datasets, benchmarks and how AI agents are evaluated, published openly.
AI agents often struggle when environments change mid-task, risking outdated decisions, lost work, and unsafe actions. This research proposes the State-Shift Evaluation Protocol (SSEP) to evaluate how reliably agents detect, adapt to, and safely handle unexpected changes.
A practical guide to post-quantum cryptography migration: NIST standards, global roadmaps, crypto agility, hybrid protocols, implementation risks, and a proposed framework for measuring readiness.
MCP connects AI agents to external tools but creates security risks by mixing trusted and untrusted content. This paper explores “Confused Tool” attacks and proposes the Three Gates of Tool Trust to prevent private data leaks across tools.
A dependency update can compile and still break production behavior. Explore why coding agents struggle with software migrations and how a layered evidence framework can test safer upgrades.
Most AI agent training runs waste GPU time on rollouts that teach the model nothing. This article covers three ways to get more learning per GPU-hour, plus a simple metric to track it.
Can AI-generated worlds teach robots when their physics is wrong? This research examines failure fidelity, action-conditioned world models, and sim-to-real transfer—and proposes a reproducible framework for evaluating RL environments.
As AI races toward AGI, can humanity keep control? Explore the growing gap between AI capabilities, human oversight, and accountability.
Harder training tasks do not automatically produce transferable AI skills. Learn how RL curricula can overfit to generators, and explore a proposed framework for testing surface, structural, and mechanism-level generalization
An AI agent can master a convincing simulation and still learn the wrong behavior. Explore causal fidelity in RL environments and a proposed audit of state transitions, observations, resets, and delayed consequences
Discover how prompt injection attacks spread between AI agents and how trust boundaries, security evaluations, and containment strategies can prevent cascading failures.
A passing reward does not always mean an AI agent completed the intended task. Explore how reward hacking, weak verification, and misleading feedback affect AI agent training—and why robust RL environments and verifiable rewards matter for reliable AI systems.
Discover how Harbor strengthens AI agent benchmarks through secure verification, reliable grading, and accurate performance evaluation
AI-generated CUDA and Triton kernels can pass familiar benchmarks yet fail on new shapes or deliver little serving benefit. Explore a reproducible protocol for testing correctness, workload shift, GPU efficiency, and deployment value.
Why do AI agents fail at long-horizon tasks? Explore how state tracking, cascading errors, changing workflows, and weak verification affect agent reliability—and how realistic evaluations can reveal the gaps.
Logs can guide an AI recovery agent - or mislead it. This research perspective examines poisoned telemetry, indirect prompt injection, and how executable environments can measure repair success and resistance to hostile instructions separately.
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.
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.
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 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.
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.
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.
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 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.
Purpose-built, containerized environments for coding agents, computer-use agents and other systems that learn through verifiable interaction.
Capabilities: Task design, environment engineering, automated graders, reference solutions, reward signals, adversarial validation and model-run analysis.
Explore RL EnvironmentsPrivate, unpublished coding-agent tasks available for controlled sampling, direct acquisition, and exclusive or non-exclusive licensing.
Capabilities: Terminal workflows, DevOps, system administration, debugging, repository-level engineering and long-horizon agent tasks.
View Available DatasetsIndependent benchmarks and evaluation systems designed to measure whether AI agents can complete difficult real-world work reliably.
Capabilities: Reproducible execution, task validation, repeated model trials, trajectory analysis and evidence-based reporting.
Explore Benchmarks
Private multi-step terminal workflows with objectively verifiable outcomes, from system administration and DevOps to debugging, security and data processing.
Terminal-driven scientific-computing and research workflows, from data-analysis pipelines to computational-methods tasks.
Repository-level software-engineering problems that need codebase understanding, multi-file changes and test-driven resolution.
An audited benchmark for whether AI agents can safely recover unfamiliar, production-like broken systems, scoring recovery alongside state safety, durability and integrity.
Deaimer's benchmark portfolio is expanding into additional agent capabilities and specialist domains. Further releases will be announced after validation.
A demo task from our private collection: the task as an agent sees it, plus the runs, checks and reviews we deliver with it.
Demo task for illustration. Reference solutions, verifiers and the full collection are shared under sample access or licensing.
Define the tools, tasks and success criteria. We build reproducible environments where agents can learn through action and be evaluated on verifiable outcomes.
Building, debugging, and maintaining complex software.
Repository-level tasks, bug fixes, feature implementation and test repair.
Completing tasks across desktop applications, browsers, and tools.
Multi-step interactions with applications, interfaces and tool-driven workflows.
Synthesizing information, analyzing data, and producing deliverables.
Research, analysis and document workflows built around defined sources and tools.
Following instructions, respecting constraints, and exercising sound judgment.
Scenarios that test instruction following, policy boundaries and decisions under ambiguity.
Understanding, synthesizing, and extending scientific research.
Computational experiments, research synthesis and scientific data workflows.
Most AI agent training runs waste GPU time on rollouts that teach the model nothing. This article covers three ways to get more learning per GPU-hour, plus a simple metric to track it.
Can AI-generated worlds teach robots when their physics is wrong? This research examines failure fidelity, action-conditioned world models, and sim-to-real transfer—and proposes a reproducible framework for evaluating RL environments.
Harder training tasks do not automatically produce transferable AI skills. Learn how RL curricula can overfit to generators, and explore a proposed framework for testing surface, structural, and mechanism-level generalization
An AI agent can master a convincing simulation and still learn the wrong behavior. Explore causal fidelity in RL environments and a proposed audit of state transitions, observations, resets, and delayed consequences
A passing reward does not always mean an AI agent completed the intended task. Explore how reward hacking, weak verification, and misleading feedback affect AI agent training—and why robust RL environments and verifiable rewards matter for reliable AI systems.
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.
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.
Request controlled samples from Deaimer's private coding datasets or define a small custom environment pilot under your own specifications and acceptance criteria.
