Papers, technical reports and blog posts from the Deaimer team on RL environments, datasets, benchmarks and how AI agents are evaluated, published openly.
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.
Six practice areas that work together beautifully — so you get one partner across the entire data lifecycle, not six vendors and a headache.
Reproducible, verifiable environments and evaluation tasksets for training and testing coding agents.
High-volume acquisition across global regions with targeted participant recruitment and dependable production oversight.
Human-led enrichment for training, alignment, prompting, and fine-tuning workflows.
Accuracy, QA, validation, and structured conversion before it ever reaches you.
Expert talent, managed teams, strict compliance — built for enterprise scale.
Proprietary tech powering secure delivery and reporting across every project.
We've run hundreds of engagements — so we know the steps that matter. Every project runs on the same tight playbook.
We translate your model's goals into a concrete spec — volumes, quality thresholds, demographic coverage, compliance requirements.
Vetted contributors matched by language, domain, and specialization. Leads and QA placed within 72 hours.
Workflows execute on our portals. Every item tracked, sampled, audited. Live visibility into progress.
Structured, validated, formatted to spec. Export to S3, GCS, Azure, or direct-to-pipeline with full QA reports.
From first brief to first delivery in under a week. No long onboarding, no hand-holding — just a team that already knows what great data looks like and exactly how to get it to you.
No long contracts. Flex up or down at any point.40,000+ vetted contributors across 38 regions — so your datasets reflect the world your models actually operate in.
Our custom-built portals give project managers, annotators, and clients a single source of truth. No spreadsheets, no blind handoffs, no guesswork.
Request controlled samples from Deaimer's private coding datasets or define a small custom environment pilot under your own specifications and acceptance criteria.
