Terminal Bench Science Tasks & Agent Evals

The Terminal Bench Science Bottleneck

01

Quality Decay

Scientific agent tasks are only as good as their reference solutions and verifiers. When generalists write them, tests pass wrong answers or reject valid ones, and the resulting reward signal teaches agents to game checks instead of doing the science. Judging whether an agent fit a model correctly, chose a sound statistical test, or ran a simulation with valid parameters requires genuine PhD-level expertise. Without domain scientists authoring tasks and reviewing multi-step trajectories, your agents keep failing hard scientific workflows, wasting significant compute resources and blocking the deployment of reliable scientific AI.

02

Volume Walls

Internal teams trying to source PhD scientists who are also fluent in the command line, containers, and scientific Python hit immediate volume walls. Most AI organizations cannot scale expert task authoring beyond a handful of specialists, and every task needs a working environment, a reference solution, and a verifier checked by a second expert. This bottleneck delays crucial training and evaluation cycles and stalls ambitious scientific agent projects. Producing hundreds of high-quality tasks across life, physical, Earth, mathematical, and engineering sciences requires a globally distributed network able to onboard many domain experts at once.

03

Compliance Friction

Realistic scientific tasks often draw on proprietary assay results, unpublished experimental datasets, or internal simulation code, which demands exceptionally rigorous data security. Standard crowdsourcing platforms introduce severe compliance friction into your pipeline, as they lack the strict NDAs, robust SOC 2, and certified ISO 27001 infrastructure necessary to protect high-value intellectual property. Running task environments and agent trajectories on unverified platforms exposes your research and your private evaluation sets to leaks. You need a segregated, secure environment to guarantee 0% copyright risk and full provenance for every task and dataset.

01

Terminal-Based Scientific Task Authoring

Give your agents realistic scientific work to learn from. Our PhD-level domain scientists author tasks in the format terminal benchmarks use: a natural-language instruction, a Docker container environment with the required data and tools, an expert reference solution, and an automated verifier. Tasks span the life, physical, Earth, mathematical, and engineering sciences and cover data analysis, statistical inference, simulation, optimization, and model fitting. Every task is solved end to end by its author and checked by a second expert, so your training and evaluation sets reflect the real workflows researchers run every day.

02

Verifier Engineering & Reference Solution QA

A scientific task is only useful if its verifier is right. Our experts write tests that check numerical results within justified tolerances, validate output files and figures, and catch shortcuts that pass a naive check without doing the science. We run each reference solution against its verifier, probe for false positives and false negatives, and document the reasoning behind every threshold. This verification layer gives your RL and evaluation pipelines a reward signal you can trust, reducing noisy labels and keeping your agents aligned with how a domain expert would judge the result.

03

Expert Review of Scientific Agent Trajectories

Pass/fail scores show whether an agent succeeded, not why it failed. Our PhD-level reviewers read full agent trajectories on data analysis, simulation, model fitting, and inverse problems, and grade each step: which commands the agent ran, whether it chose valid methods and parameters, and whether its conclusions follow from the data. Structured feedback and preference rankings feed directly into SFT, RLHF, and reward model training, while failure-mode tags show your team where scientific reasoning breaks down inside long, multi-step terminal sessions.

04

RL Environments for Scientific Agents

Create custom reinforcement learning environments built for scientific agents. Our experts package research workflows as containerized environments with realistic datasets, pinned dependencies, resource limits, and automated verifiers, ready for large-scale parallel rollouts in harnesses such as Harbor. We calibrate difficulty so tasks are hard but solvable, remove tasks with leaked or public solutions, and refresh environments as your agent improves. Built natively within Abaka Forge, these environments let your models practice the multi-step data analysis, simulation, and optimization work real research requires.

05

Scientific Dataset Curation for Task Inputs

Accelerate task creation with clean, well-documented scientific datasets. Our vertically specialized teams source, curate, and tag experimental data, public research datasets, and peer-reviewed literature that tasks can be built around, from sequencing files and spectra to time series and imaging data. By strictly using fully cleared and vetted sources, we eliminate copyright risks while reducing your initial data preprocessing time by up to 70%. This allows your engineering teams to focus on agent architecture rather than wrestling with complex scientific data formatting.

06

Private Scientific Agent Benchmarking & Red-Teaming

Measure your agent's scientific capabilities on held-out tasks it has never seen. We build private benchmarks in the style of Terminal-Bench-Science, run your agent and model combinations through them, and score the share of tasks resolved, so you can compare with published results such as Claude Opus 5 with Claude Code at 30.0% on Terminal-Bench-Science 0.1. Our 6-dimensional evaluation framework adds factual alignment, reliability, and safety checks, and our scientists red-team agents for unsafe or unjustified actions, showing exactly where your system falls short of expert work.

07

Scientific Agent Tool Use in the Terminal

Enhance your agent's ability to use the tools scientists rely on. We evaluate how your frontier models work with scientific Python libraries, command-line bioinformatics and geoscience tools, numerical solvers, and theorem provers inside a terminal. Our STEM experts check whether each command, script, and parameter choice is correct, and help you diagnose the environment and dependency errors that derail long sessions. By validating these interactions, we help your AI agents independently execute complex, multi-step scientific research tasks without suffering from execution drift or logic failures.

08

Imaging, Signal & Spatial Task Ground Truth

Support agents working on image reconstruction, signal processing, and spatial data. Our experts prepare ground truth for tasks built on microscopy, medical and astronomical imaging, sensor time series, and 3D/4D point clouds, and define the metrics verifiers use to judge reconstructed images or calibrated signals. Operating through the Abaka Forge platform, our PhD-level scientists provide the volumetric mapping, segmentation, and reference outputs your scientific agents need to be evaluated against reliable, expert-checked answers.

Why Outsource Terminal Bench Science Data

01

Faster Delivery

Bypass the long and expensive hiring cycles required to build an in-house team of domain scientists who can also author terminal-based tasks. Abaka AI’s pre-vetted, globally distributed scholar-network accelerates your project timeline with immediate access to practicing researchers. Your engineering teams can launch scientific agent training and evaluation months ahead of schedule, eliminating the standard sourcing bottlenecks without ever sacrificing foundational data quality.

02

Direct Savings

Eliminate the massive overhead associated with recruiting, onboarding, and retaining full-time PhD-level scientists. Our highly structured, transparent pricing model—including verified STEM Generalists at just $12/hr and advanced scientific experts tailored to your needs—provides cost-effective task and data generation. You pay exclusively for the rigorous scholarly input you require, optimizing your data budget while scaling your scientific agent training and evaluation.

03

Risk Reduction

Safeguard your most sensitive proprietary research data, private task sets, and internal pipelines. Operating entirely within our SOC 2 and ISO 27001 certified secure infrastructure, your data is protected by strict NDAs at every touchpoint. We guarantee full IP provenance and absolutely 0% copyright risk on all collected scientific datasets, and we never publish your tasks, shielding your high-value intellectual property and private evaluation sets from any external exposure.

04

Elastic Scalability

Scale your specialized scientific data requirements up or down instantly. Whether you urgently need a dozen expert biochemists for a niche task-authoring sprint or hundreds of verified STEM professionals to review agent trajectories at scale, our expansive network adapts seamlessly. We meet your precise volume requirements on demand, ensuring your AI development pipeline never stalls due to sudden data shortages.

05

Domain Expertise

Gain direct access to a highly curated workforce drawn from the life, physical, Earth, mathematical, and engineering sciences. Our specialized experts hold advanced academic degrees and work comfortably in the command line. This scholarly rigor guarantees a strict 99% accuracy standard, ensuring your agents are trained and judged on scientifically sound tasks rather than plausible-looking shortcuts and avoiding the hallucinated results that generalist review lets through.

06

Innovation Velocity

Focus your critical internal engineering resources completely on agent architecture, training cycles, and model development while our experts handle the intensive task and data workflows. By leveraging large-model automation directly within the Abaka Forge platform, we boost scientific annotation speed by up to 50x. This massive efficiency gain accelerates your overall innovation velocity, propelling your scientific agents to market faster.

Industries We Serve

Automotive

Automotive R&D increasingly relies on scientific agents for battery chemistry and materials work. Our PhD-level experts author terminal-based tasks that ask agents to fit battery degradation models, run materials simulations, and analyze thermal test data, each with a reference solution and an automated verifier. Expert reviewers then check agent trajectories step by step, so your engineering agents are evaluated against sound physics and chemistry rather than plausible-looking output.

GenAI / Foundation Models

Strengthen the scientific agent capabilities of your frontier models. Our scholar-network authors and verifies terminal-based tasks across the life, physical, Earth, mathematical, and engineering sciences, grades multi-step agent trajectories, and builds RL environments with reliable verifiers. We help you benchmark your agent privately on work similar to what Terminal-Bench-Science measures and compare against published results, so you see where it breaks before release.

Embodied AI / Robotics

Prepare agents for the scientific computing behind lab automation and robotics. Our experts build terminal tasks around sensor calibration, signal processing, and control model fitting, packaged with containerized environments and automated verifiers. Combined with our video and 3D point cloud annotation, this gives your robotics teams expert-checked data and evaluations for systems that must reason about real instruments, noisy measurements, and complex physical laboratory environments.

Healthcare

Evaluate life-science agents with the rigor biomedical research demands. Our PhD-level biologists and biostatisticians write terminal-based tasks for genomic data analysis, statistical inference, and imaging pipelines, and review agent trajectories for methodological errors. Multi-layer QA at a 99% accuracy standard ensures every reference solution and verifier is sound, so your models are measured on factually grounded scientific work rather than surface-level answers.

Retail

Support formulation and product-science teams in cosmetics and specialized consumer goods. Our chemists and materials scientists author tasks where agents must analyze formulation stability data, optimize ingredient mixtures, and fit property models from lab measurements. Each task ships with a verified reference solution, and expert reviewers check agent outputs, helping your enterprise AI accelerate product development while keeping results scientifically defensible and compliant across global markets.

Finance

Give quantitative teams focused on biotech and pharmaceutical investing a clearer view of what scientific agents can do. Our experts design terminal tasks in statistical inference, model fitting, and trial data analysis, then grade agent trajectories for sound methodology. By evaluating your AI on realistic scientific workflows with verified answers, we help your investment research tools handle experimental results and technical literature with the factual grounding high-stakes analysis requires.

Geospatial

Train and evaluate Earth-science agents on the workflows behind environmental monitoring. Our geoscientists and remote-sensing specialists author tasks covering satellite image reconstruction, inverse problems, atmospheric and soil data analysis, and sensor calibration, each with an automated verifier. Expert review of agent trajectories ensures your geospatial AI turns complex observational data into results a domain scientist would accept.

Security / Defense

Evaluate scientific agents for high-stakes defense research in a controlled setting. Our vetted experts author signal processing, detection, and simulation tasks and review agent trajectories entirely within our securely segregated, SOC 2 compliant infrastructure. Private task sets stay isolated from public benchmarks, giving you a reliable measure of how your agents perform on sensitive scientific workflows before they reach critical national security deployments.

Agriculture / Industrial

Drive agricultural and industrial innovation with expert-built scientific agent tasks. Our scientists write terminal tasks on soil biochemistry analysis, crop genetics data, process optimization, and yield model fitting, each with a containerized environment and verifier. By training and evaluating your industrial agents on verified scientific workflows, our globally distributed scholar-network helps your enterprise improve crop yields, optimize resource distribution, and support long-term environmental sustainability.

How It Works

1) Day 0–3 — Scoping & Scholar Matching

We work with your team to define the scientific domains, workflow types, and difficulty targets for your terminal bench science tasks, then match your project with pre-vetted PhDs and domain experts from our globally distributed scholar-network.

2) Week 1–2 — Custom Pipeline Assembly

Our engineers configure secure pipelines within Abaka Forge, set up container templates, datasets, and dependencies, and define the task format, verifier standards, and trajectory review rubrics your agents will be judged against.

3) Week 2–3 — Pilot Tasks & Calibration

Our experts author and verify a first batch of tasks and grade sample agent trajectories. We refine guidelines and calibrate reviewers until the work meets a 99% accuracy standard against your baseline metrics.

4) Ongoing — Scaled Expert Delivery

With the pilot validated, we scale operations seamlessly. Leveraging large-model automation where applicable, our PhD-level workforce delivers new tasks, verifiers, RL environments, and trajectory grades directly to your secure endpoints.

5) Weekly — Quality Audits & IP Review

We run weekly multi-layer QA audits, re-check verifiers for flakiness, and retire tasks that become saturated or leak. Project managers report on accuracy, throughput, and IP provenance, ensuring strict SOC 2 and ISO 27001 compliance.

Modality & Format Coverage

Scientific workflow tasks draw on many data types, all processed natively through Abaka Forge. From sequence files and scientific text to imaging, signals, and 3D point clouds, we deliver structurally precise task data and evaluations tailored for frontier scientific agents.

Modality	Annotation Types	Tools	Output Formats
Text	Task instructions, Reference solutions, Verifier test scripts, Scientific literature QA	Abaka Forge	JSON, CSV, JSONL, XML
LLM RLHF	Agent trajectory grading, Scientific reasoning preference ranking, Reward rubric design	Abaka Forge	JSONL, Parquet, SFT format
Image	Microscopy segmentation, Image reconstruction ground truth, Figure and plot output checks	Abaka Forge	COCO, YOLO, PNG, JPEG
Video	Lab protocol spatial reasoning, Chemical reaction tracking, Biological timeline analysis	Abaka Forge	MP4, AVI, Frame-level JSON
3D/4D Point Cloud	Protein folding spatial analysis, 3D molecular bonding modeling, Volumetric biological mapping	Abaka Forge	PCD, OBJ, PLY, 3D-JSON
LiDAR + Camera fusion	Automated lab environment mapping, Industrial chemical plant navigation, Robotics spatial fusion	Abaka Forge	ROS Bag, JSON, PCD+Image
Audio	Medical dictation transcription, Laboratory voice command logging, Specialized STEM phonetic tagging	Abaka Forge	WAV, MP3, Transcript JSON

Success Story

A frontier model lab

Challenge

A frontier model lab was building a scientific agent meant to run research workflows in a terminal, the kind of work Terminal-Bench-Science measures. In internal tests the agent produced plausible but wrong analyses, misapplied statistical methods, and declared simulations finished when they were not. Its existing tasks had weak verifiers that rewarded these errors, and generalist annotation platforms lacked the scientific expertise to fix them, stalling the agent's launch by critical months.

Approach

Abaka AI deployed a specialized task force of 50+ PhD-level chemists, biologists, and physicists from our exclusive scholar-network. Operating entirely within our SOC 2 compliant Abaka Forge platform, these experts authored new terminal-based tasks with reference solutions, rebuilt verifiers for existing ones, and graded agent trajectories step by step. We utilized large-model automation to preprocess dense scientific datasets, allowing our experts to focus entirely on high-value verification, trajectory review, and targeted red-teaming.

Results

The new tasks and verifiers removed reward signals that had been teaching the agent to cut corners, and the lab saw a measurable improvement on its held-out scientific workflow evaluations. Our review layer maintained 99% QA accuracy across authored tasks and trajectory grades, and our optimized workflows reduced the team's preprocessing time by 70%, enabling the lab to deploy its scientific agent to production on schedule.

99%

QA accuracy on expert task and trajectory reviews

70%

Reduction in data preprocessing time

0%

Copyright risk on task source data

By the Numbers

1M+

Vertically specialized annotators globally

50+

Countries hosting our scholar-network experts

2019

Founded — trustworthy data partner for frontier AI

1,000+

Enterprise and research customers served

What Customers Say

Finding people who could write and verify real scientific workflows for our agents was a massive bottleneck. Abaka AI gave us immediate access to PhD-level scientists, and their task and trajectory reviews showed exactly where our agent's analysis went wrong.

Head of Science AIFrontier Model Lab

The level of scholarly rigor Abaka AI brings is unmatched. Their platform securely handled our proprietary research data, and their PhDs built verifiers with the 99% accuracy we needed to trust our scientific agent evaluations.

Director of Applied MLEnterprise Healthcare AI

We needed agents that could handle sensor calibration and signal processing for our lab automation stack. Abaka AI's experts built verified terminal tasks around our real instrument data. Their expertise is fundamental to our robotic success.

Lead Robotics EngineerLab Automation Robotics

Working with Abaka AI’s STEM generalists and specialized scientists gave us the high-quality task data we needed. Their strict adherence to zero copyright risk and seamless integration into our workflows made them the ideal AI data partner.

Chief AI OfficerScientific Tech Startup

Why Choose Abaka

01

Elite Scholar-Network Access

We bypass standard crowdsourcing by maintaining an exclusive global network of highly educated domain experts. When your agents need terminal bench science tasks, we deploy verified PhDs and specialized researchers across the life, physical, Earth, mathematical, and engineering sciences who can author, verify, and review real scientific workflows.

02

Ironclad IP Security

Your proprietary scientific research is protected by strict NDAs, SOC 2, and ISO 27001 compliance. We ensure full IP provenance with absolutely 0% copyright risk.

03

Unmatched Data Accuracy

Through deep multi-layer QA and advanced tooling in Abaka Forge, our scientific experts consistently achieve 99% accuracy on complex scientific tasks, verifiers, and trajectory reviews.

04

Cost-Effective Specialized Sourcing

Leverage top-tier STEM talent without the crippling overhead. With transparent hourly pricing for specialized domains, including STEM Generalists at $12/hr, we deliver scalable scientific expertise tailored precisely to your operational budget.

05

No Model Competition Risk

We are a purely dedicated data partner for frontier AI. We never build models that compete with our clients, guaranteeing that your private task sets and evaluation data remain exclusively yours.

06

Comprehensive Multi-Modal Platform

Abaka Forge is an all-in-one solution handling everything from containerized task environments and verifiers to 3D point clouds and scientific text. Our platform accelerates your data pipeline by 50x through large-model automation, seamlessly moving your scientific agent work from raw data to production-ready tasks and evaluations.

Frequently Asked Questions

Expand all

How much does terminal bench science task authoring cost?

Our pricing is structured to provide transparent and scalable access to highly specialized experts. For scientific domains, our STEM Generalists are available at $12/hr. Highly specialized tasks like LLM Math/Coding evaluation are $18/hr. We also offer pre-built datasets, such as STEM QA at $0.001 per unit. Custom quotes for task authoring, verifier building, and trajectory review are tailored to domain, difficulty, and volume.

How quickly can you deploy PhD-level scientists for our project?

We can typically scope your domains and match your terminal bench science requirements within Day 0 to Day 3. Custom pipelines, container templates, and review rubrics in Abaka Forge are assembled over the first two weeks, allowing us to begin pilot tasks and scale rapidly. This approach saves you critical weeks compared to traditional in-house hiring.

What formats do you deliver scientific tasks and evaluations in?

Each task ships as a natural-language instruction, a Dockerfile-based environment, a reference solution script, and verifier tests, ready to run in containerized harnesses such as Harbor. Task data can include CSV, HDF5, FASTA, imaging, and 3D formats, and trajectory grades are delivered as JSON or JSONL for SFT and RLHF.

How do you guarantee accuracy on complex scientific tasks?

We achieve a standard of 99% accuracy by deploying verified experts from our scholar-network who hold PhDs in the relevant science. Every reference solution is run against its verifier and checked by a second expert, with continuous calibration during pilot phases and weekly quality audits, so your agents train and are evaluated on scientifically sound tasks.

How secure is our proprietary research data and evaluation work?

Security is our highest priority. We operate under strict SOC 2, ISO 27001, GDPR, and CCPA compliance. All task environments, datasets, and agent trajectories are processed through segregated secure pipelines within Abaka Forge, protected by strict NDAs. We provide full IP provenance, ensuring your proprietary research never faces copyright or exposure risks.

What is Terminal-Bench-Science and what does it measure?

Terminal-Bench-Science 0.1 is an agent benchmark released in August 2026 by Stanford researchers and the Terminal-Bench team with domain scientists. Its 70 tasks span five science domains and test whether agents can run real scientific workflows in a terminal. At release the top entry, Claude Opus 5 with Claude Code, resolved 30.0%. Abaka AI is independent of the benchmark.

Why choose Abaka AI over standard crowdsourcing platforms?

Standard platforms rely on generalists who cannot write or verify the scientific workflows that terminal bench science tasks require. Abaka AI provides direct access to a scholar-network of PhDs and STEM experts across the life, physical, Earth, mathematical, and engineering sciences. Additionally, we are completely self-funded and never build models that compete with you.

How do you handle changes to our scientific evaluation criteria?

We offer high elastic scalability and flexibility. If your agent's focus shifts from statistical inference to simulation or inverse problems, our dedicated project managers quickly adjust the Abaka Forge pipeline, update task guidelines and review rubrics, and calibrate our experts to meet the new criteria without derailing your project timeline.

Do you offer pilot programs for scientific task authoring?

Absolutely. We strongly recommend a pilot phase during Weeks 2 and 3 of our engagement. This allows our PhD-level scientists to author and verify an initial batch of tasks and grade sample trajectories, enabling your team to rigorously assess our 99% accuracy standard and fine-tune guidelines before moving to full-scale production.

Who owns the tasks, verifiers, and data you create?

You maintain exclusive, 100% ownership of all data. We provide full IP provenance with absolutely 0% copyright risk on collected materials. Furthermore, Abaka AI guarantees that your tasks, verifiers, and trajectory data are never repurposed, resold, published, or used to train competing foundation models.

What platform do your scientists use for task authoring and review?

All our experts utilize the Abaka Forge platform. This comprehensive, all-in-one environment combines collection, cleaning, annotation, and production capabilities. It incorporates large-model automation to speed up scientific preprocessing by up to 50x, ensuring highly efficient and secure processing across all complex AI modalities.

Is there a minimum project size for terminal bench science task work?

While we specialize in large-scale enterprise deployments, our elastic scalability allows us to support highly targeted, niche projects. Whether you need a small set of custom scientific workflow tasks or large volumes of trajectory grades and RL environments, we tailor our STEM expert allocation to your specific needs and volume requirements.

Expert Tasks and Agent Evaluation forTerminal Bench Science