Deaimer designs reproducible, verifiable environments and evaluation tasksets for training and testing coding agents. Our work covers terminal operations, software engineering, DevOps, system administration, debugging, scientific computing, security, and long-horizon workflows.
Task environments that spin up clean and reset deterministically between runs.
Terminal operations and repository-level tasks that mirror real engineering work.
Hidden tests, graders, and reward functions designed for verifiable outcomes.
Vetted solution paths that anchor grading and downstream training.
Every task validated against a working solution and known-failing baselines.
Environment and dependency verification so tasks run the same way every time.
Adversarial review for shortcuts, data leakage, and reward-hacking exposure.
Repeated model trials with trajectory-level review of how agents actually solve tasks.
Through third-party engagements, Deaimer's teams have produced and reviewed coding-agent tasks associated with Terminal-Bench 2.0, 2.1 and 3.0, Terminal-Bench Science, and SWE-bench-style evaluations. Our experience includes environment construction, task authoring, verifier development, reference solutions, model-run analysis, and independent quality assurance.
Deaimer is an independent contributor to select third-party benchmark programs. Deaimer does not own, operate, sponsor, or hold an official partnership with Terminal-Bench or SWE-bench, and does not display their marks without permission.
Deaimer operates a supervised in-office coding team, supported by a network of 400+ computer-science, AI, mathematics, physics, and engineering specialists. Every task passes through separate author and reviewer workflows before it is accepted.
Define the agent capabilities and task categories the environment needs to cover.
Build the containerized environment, task setup, and supporting assets.
Confirm the task is solvable and correctly graded via a working reference solution.
A separate reviewer probes for shortcuts, leakage, ambiguity, and reward hacking.
Multiple model runs to confirm difficulty, stability, and signal quality.
Delivery with version history and a report documenting how each task was validated.
Training environments and tasksets for post-training coding agents.
Verifiable-reward environments built for RLVR and RL training pipelines.
Confidential evaluation suites for internal model benchmarking.
End-to-end construction of new coding-agent benchmarks.
Adversarial tasks that probe agent behavior at the edges of its capability.
Scaling an existing task suite into new domains and difficulty tiers.
Deaimer can produce or independently audit a small batch of coding-agent tasks under your specifications, security requirements, and acceptance criteria.