Evan: Welcome to the Daily Paper Cast.
Ashley: Today, we’re bringing you a paper from the Hugging Face daily paper list of August 27, 2026, with 115 upvotes.
Evan: The title of the paper is 'FrontierChallenge: Evaluating Scientific Workflow Completion.'
Ashley: The first two authors are Liangcai Su and Zhaopeng Feng.
The corresponding author is Xinyu Wang from the Apodex Team.
Evan: Alright, let’s dive into the Introduction section.
Ashley, get us started on the background of this study.
Ashley: Sure thing, Evan.
Language models have significantly evolved over time.
Initially known as text generators, these models are now becoming agents that can plan, call tools, execute code, and modify persistent files.
Evan: That sounds like a leap forward.
Can you explain this evolution a bit more?
Ashley: Of course.
The advances in agent scaffolding and continual pre-training are paving the way.
This evolution means that beyond isolated tasks like literature retrieval or text translation, these agents can now coordinate multi-stage research workflows with inspectable outputs.
Evan: Coordinate multi-stage research workflows?
How does that shift the definition of success for scientific tasks?
Ashley: Great question.
Success now means more than just producing a plausible conclusion.
An agent must inspect heterogeneous inputs, select and run analyses, validate intermediate results, and deliver consistent code, tables, figures, and prose.
So, complete delivery is paramount.
Evan: Got it.
How does FrontierChallenge come into play within this context?
Ashley: FrontierChallenge is a benchmark designed to evaluate whether an agent can independently complete a specified scientific workflow from input processing to final deliverables, ensuring the complete task contract is satisfied.
Evan: Interesting!
What makes this benchmark different from existing ones?
Ashley: Well, many existing benchmarks focus on a final answer, an interaction trace, or a single program.
FrontierChallenge, however, targets cross-domain scientific workflows requiring multiple analytical stages and several deliverables, offering a more comprehensive evaluation.
Evan: I see.
What are some specific domains and tasks that FrontierChallenge covers?
Ashley: The benchmark includes 300 end-to-end scientific workflows grouped into domains such as quantum chemistry, molecular dynamics, materials characterization, analytical chemistry, life science, and electrochemistry/environment.
For this study, 97 tasks across these domains have been evaluated and publicly released.
Evan: You mentioned earlier that the benchmark focuses on complete task delivery.
How is this evaluated?
Ashley: Exactly.
The primary metric is Pass Rate, which measures the fraction of tasks for which the complete task contract is satisfied.
There is also the Avg.
Score, which captures partial progress, although it doesn’t count as a pass if the task is not fully completed.
Evan: How did the frontier models perform on these tasks?
Ashley: The results were quite revealing.
Despite high Avg.
Scores in some cases, the best-performing configurations only completed 20 of the 97 tasks, yielding a Pass Rate of 20.6%.
This gap is especially pronounced in fields like analytical chemistry and electrochemistry/environment, where high partial scores don’t translate into complete delivery.
Evan: That’s a significant finding.
It seems like there's a lot of room for improvement.
Ashley: Definitely.
The key takeaway here is that high partial scores and confident claims of task completion don’t necessarily indicate that a scientific task has been fully delivered.
Reliable evaluation requires looking at the end-to-end execution and the completeness of the scientific deliverables.
Evan: Indeed.
And I take it that this brings us to the end of the Introduction section of the paper.
Evan: Now, let’s move on to the methods used in the study.
Ashley, can you start with how the tasks were collected and curated for FrontierChallenge?
Ashley: Evan.
The task collection process focused on gathering realistic workflows from various scientific and engineering settings.
These tasks were derived from domain practices such as analysis, computation, simulation, and research delivery, rather than expanded question answering or isolated coding exercises.
Evan: So, it’s ensuring that these tasks are representative of real-world scenarios.
What were the criteria for screening and quality control?
Ashley: Right.
The curation adhered to four key design principles.
First, representativeness, meaning the workflows, software, and methods must reflect plausible professional practice.
Second, complexity, requiring an end-to-end, dependency-aware process resulting in a substantial deliverable.
Third, diversity, ensuring the tasks vary in scientific knowledge, workflow type, and difficulty rather than merely changing inputs or parameters.
Lastly, verifiability, where outputs must be assessable through files, numerical values, quantitative measures, or explicit acceptance criteria to support repeatable automated evaluation.
Evan: That’s a thorough approach.
How were the tasks then standardized and packaged for evaluation?
Ashley: Once curated, each task was organized as a self-contained package with five elements.
These include the task description, defining the scientific objective; fixed inputs, providing data and necessary context; the software and tools available in the execution environment; an output contract listing the required deliverables; and an evaluation procedure defining successful completion.
Evan: Could you give us a practical example of what these packages might entail?
Ashley: Sure.
For instance, one task might involve an image-analysis workflow using microscopy images.
The package would include the images, required processing software, specific deliverables like segmented images and statistical tables, and a detailed procedure for evaluating the results.
Each task is designed to be rerun under fixed conditions and scored consistently across different systems.
Evan: Great.
Moving on to the dataset statistics, what can you tell us about the overall structure of FrontierChallenge's task set?
Ashley: The full collection consists of 300 scientific workflows, all of which passed quality control.
For this study, 97 tasks were selected and publicly released, while the remaining 203 tasks were retained as an internal held-out set.
These tasks span six reporting domains: quantum chemistry, molecular dynamics, materials characterization, analytical chemistry, life science, and electrochemistry/environment.
Evan: And how are these tasks distributed in terms of difficulty?
Ashley: Out of the 97 tasks, 74 are considered hard, and 23 are medium.
These tasks use inputs sourced from public empirical data, public sequence or structural resources, and scientifically constrained synthetic data.
Evan: Understood.
Let’s delve into the evaluation metrics.
What primary metrics were used to assess the performance of the agents?
Ashley: The evaluation primarily used two metrics: Pass Rate and Avg.
Score.
Pass Rate is the fraction of tasks for which the complete task contract is satisfied, serving as the primary measure of success.
Avg.
Score reflects the mean task score, capturing partial completion across task-specific scientific rubrics.
Evan: So, Pass Rate is based on whether the task was fully completed, while Avg.
Score accounts for partial progress.
How were these metrics calculated?
Ashley: Indeed.
The Pass Rate is calculated by determining the full-completion indicator for each task, which is set to 1 if the task is 99.9% complete or more, and then averaging these indicators across all tasks.
The Avg.
Score is simply the average of the task scores.
Evan: Interesting.
I see there’s a mention of an evaluation study.
What systems and models were evaluated in this study?
Ashley: The study evaluated twelve frontier models using three advanced agent scaffolds: Codex, Claude Code, and Frontier Agent.
For example, Codex was used with models like GPT-5.6 Sol and GPT-5.6 Terra (max), while Claude Code served as the common scaffold for several models, including Grok 4.6 and Kimi K3.
Frontier Agent was evaluated with Apodex 1.1 in the Agent Team configuration.
Evan: Could you elaborate on the task-specific evaluation?
Ashley: Each task had a task-specific Grader that checked the required files, numerical results, formats, figures, code execution, and cross-artifact consistency.
A full-completion indicator was set based on whether the task score met or exceeded 99.9.
The Pass Rate and Avg.
Score metrics were then computed across the tasks.
Evan: It sounds like a rigorous process.
How did the models perform overall?
Ashley: Strict completion remained rare across all configurations.
The Pass Rate ranged from 3.1% to 20.6%, while Avg.
Score ranged from 67.5 to 87.9.
For instance, GPT-5.6 Sol achieved the highest Avg.
Score of 87.9 but had the same Pass Rate of 20.6% as Grok 4.6 using Claude Code.
This showed a significant gap between partial progress and complete scientific delivery.
Evan: That’s a notable finding.
What about the performance variation across different domains?
Ashley: Performance varied significantly by domain.
For instance, in quantum chemistry, the highest Pass Rates were achieved by Claude Code with Grok 4.6 at 60% and Codex with GPT-5.6 Sol and Claude Opus 5, both at 55%.
However, in domains like analytical chemistry and electrochemistry/environment, high Avg.
Scores coexisted with near-zero Pass Rates.
Evan: Can you give more details on domain-specific performance variations?
Ashley: In materials characterization, the Avg.
Scores reached 88.1, but the Pass Rate never exceeded 9%.
Analytical chemistry had an Avg.
Score of 87.6, but only one configuration, DeepSeek V4 Pro-0813, managed to complete any tasks, with a Pass Rate of 4%.
Electrochemistry/environment had an even starker contrast with an Avg.
Score of 94.9 but a Pass Rate of 0% for all configurations.
Evan: It's intriguing how performance can differ so widely across domains.
Any insights on the computational resources and execution times?
Ashley: Indeed.
Reported token use varied by more than sixfold across the configurations.
For instance, Grok 4.6 with Claude Code used around 2.183 million tokens per task, whereas Apodex 1.1 with Claude Code used 13.730 million tokens per task.
Execution times also varied significantly, with the mean execution time ranging from 21.8 to 112.8 minutes per task.
Evan: That’s quite a range.
Were there any particular patterns in how resources were used?
Ashley: Yes, for example, GPT-5.6 Sol used about 6.327 million input tokens and 23.1 thousand output tokens per task, while GPT-5.6 Terra (max) reported slightly higher input token usage but fewer output tokens.
Execution times showed long tails, with Frontier Agent (Agent Team) having the longest mean time of 112.8 minutes and Gemini 3.7 Flash averaging 28.4 minutes per task.
Evan: Wow, that’s detailed.
I assume these metrics also offered insights into failure modes?
Ashley: Failure mode analysis revealed that task-specific contract breaches, completion language in final messages, and raw tool errors varied by domain but were common across both passing and non-passing runs.
For example, only 1.5% of non-passing runs explicitly indicated ongoing work, despite 75.5% containing completion language.
Tool errors were frequent yet did not consistently predict success or failure.
Evan: So even if an agent claimed to complete a task or encountered errors, these weren’t reliable indicators of success.
Ashley: Exactly.
Reliable scientific agent handoffs will thus require more robust contract tracking, cross-artifact validation, and evidence-based completion checks.
Evan: This brings us to the end of the Method section.
Evan: Let's get into the heart of the research with the Experiment and Results section.
Ashley, what were the main research questions addressed in these experiments?
Ashley: The experiments were designed to address three primary research questions.
First, how reliably can current frontier models complete specified scientific workflows?
Second, how does scientific workflow performance vary across different domains?
And third, which observable contract breaches and trajectory behaviors most frequently accompany incomplete scientific handoffs?
Evan: Sounds comprehensive.
What agent scaffolds and models were used in these experiments?
Ashley: They used three advanced agent scaffolds: Codex, Claude Code, and Frontier Agent.
These scaffolds supported twelve frontier models, including GPT-5.6 Sol, GPT-5.6 Terra (max), Grok 4.6, Kimi K3, Claude Opus 5, Qwen 3.8 Max, DeepSeek V4 Flash-0731, DeepSeek V4 Pro-0813, Apodex 1.1, GLM-5.2, and Gemini 3.7 Flash.
Each model tackled the same 97 task objectives and task-visible inputs.
Evan: That’s a wide array of models.
How was the evaluation carried out for these tasks?
Ashley: Each task was evaluated using a task-specific Grader.
The Grader checked the required files, numerical results, formats, figures, code execution, and cross-artifact consistency.
These checks returned a native score for each configuration on each task, and this score was then used to compute the Pass Rate and Avg.
Score.
Evan: Right.
And what did the overall performance look like?
Ashley: Overall, the Pass Rate was quite low, ranging from 3.1% to 20.6%, showing that strict task completion remains challenging.
Avg.
Scores ranged from 67.5 to 87.9, indicating significant partial progress but highlighting the gap to full task completion.
Evan: Specifically, which models and configurations performed best?
Ashley: Codex with GPT-5.6 Sol achieved the highest Avg.
Score of 87.9 and tied for the highest Pass Rate of 20.6% with Claude Code using Grok 4.6, which had an Avg.
Score of 86.6.
Other notable performances were by Claude Opus 5 and Kimi K3, both achieving a Pass Rate of 17.5%.
Evan: Interesting.
So, performance varied substantially across different domains as well.
Can you elaborate on that?
Ashley: Certainly.
In quantum chemistry, Claude Code with Grok 4.6 achieved the highest Pass Rate of 60%, followed by Codex with GPT-5.6 Sol and Claude Code with Claude Opus 5 at 55%.
Meanwhile, in molecular dynamics, GPT-5.6 Sol, GPT-5.6 Terra (max), and Grok 4.6 each attained a Pass Rate of 38%.
Evan: But there were domains where performance was particularly poor.
Tell us more about those.
Ashley: Sure.
In analytical chemistry and electrochemistry/environment, Avg.
Scores were high, reaching 87.6 and 94.9, respectively.
However, the Pass Rates were negligible, with only 4% in analytical chemistry and 0% in electrochemistry/environment.
This indicates that while progress was substantial, it didn’t translate into full task completion.
Evan: That’s quite a discrepancy.
How about resource use and execution time?
Ashley: Reported token use varied widely.
For example, Grok 4.6 with Claude Code used around 2.183 million tokens per task, whereas Apodex 1.1 with Claude Code used 13.730 million tokens per task.
Execution times also showed substantial variation.
Frontier Agent (Agent Team) had the longest mean execution time at 112.8 minutes, while GPT-5.6 Terra (max) had the shortest mean time at 21.8 minutes per task.
Evan: Was there a pattern in how these resources were utilized?
Ashley: Yes, for instance, GPT-5.6 Sol used about 6.327 million input tokens and 23.1 thousand output tokens per task, with a cache share of 98.8%.
In comparison, GPT-5.6 Terra (max) reported a slightly higher input token use but fewer output tokens.
Execution times also varied, with long tails observed for several models.
Evan: What about the failure modes?
Any insights there?
Ashley: The analysis showed that failure signatures like Judge-assessed artifact shortfalls were common, especially in materials characterization and analytical chemistry where they appeared in 95% and 97% of non-passing submissions, respectively.
Final messages often falsely claimed task completion, which highlights that completion language is not a reliable success indicator.
Similarly, raw tool errors were frequent but did not consistently predict task failure.
Evan: So, even though a model claims a task is complete or encounters errors, it doesn't necessarily reflect success or failure.
Where do we go next from here?
Ashley: The key takeaway is that to improve reliability, scientific agents will need robust contract tracking, cross-artifact validation, and better evidence-based completion checks to ensure full task delivery.
Evan: This brings us to the end of the Experiment section.
Evan: Next, let's explore the Related Work section of this paper.
Ashley, how does FrontierChallenge compare with other general ability and long-horizon benchmarks?
Ashley: FrontierChallenge builds on the foundation laid by benchmarks like Humanity’s Last Exam, which probes the limits of expert knowledge using difficult, checkable questions.
Other related benchmarks include AgentBench, GAIA, OSWorld, and SWE-bench, which extend evaluation to tool use, computer interaction, and software engineering.
Evan: So, these benchmarks assess a broad range of capabilities.
How does FrontierChallenge specifically add to this landscape?
Ashley: FrontierChallenge focuses specifically on cross-domain scientific workflows.
It complements the existing benchmarks by targeting the reliable completion of scientific tasks that involve multiple stages and require the delivery of heterogeneous artifacts.
Evan: Interesting.
Can you tell us more about the benchmarks that evaluate scientific knowledge and data analysis?
Ashley: Sure.
There are several, including LAB-Bench, which evaluates knowledge and reasoning needed for biological research.
BixBench, BioMysteryBench, and CompBioBench use biological data to construct open-ended or objectively verifiable problems.
BLADE focuses on benchmarking data-driven scientific decisions, while ScienceAgentBench assesses agents' capability to produce executable analyses derived from the scientific literature.
Evan: Right, and how do these benchmarks aim to increase scientific realism?
Ashley: They substantially increase the realism by requiring agents to engage in activities similar to those performed by human researchers.
However, many of these benchmarks evaluate an answer, a decision set, or a single, self-contained program rather than a complete set of deliverables.
Evan: How does FrontierChallenge stand out in the context of end-to-end scientific workflows?
Ashley: FrontierChallenge evaluates the capability to complete a specified scientific workflow after its objective, inputs, and required outputs are fixed.
It combines cross-domain coverage, multi-artifact outputs, and task-specific executable evaluation, which many other benchmarks do not focus on.
Evan: Could you give us some examples from the paper about other benchmarks that are closely related to end-to-end scientific workflows?
Ashley: Certainly.
CORE-Bench and PaperBench evaluate computational reproduction and full research replication.
ScienceBoard, SciAgentArena, and SciAgentGym assess scientific software use, interactive research environments, and multi-step tool use.
BioAgent Bench and BiomniBench evaluate end-to-end artifacts or processes in bioinformatics and biomedicine.
Evan: And how do these benchmarks contribute to the field?
Ashley: They all contribute by pushing the boundaries of what AI agents can achieve in scientific settings.
Each of these benchmarks addresses specific aspects of scientific problem-solving, be it software interaction, data analysis, or workflow execution.
Evan: So, FrontierChallenge builds on these approaches while focusing on the reliability of completing fixed scientific tasks across various domains?
Ashley: Exactly.
FrontierChallenge's emphasis is on verifying that an agent can accomplish the complete set of deliverables for end-to-end workflows.
This includes generating mutually consistent artifacts that meet predefined criteria, expanding on the crucial evaluation of complete task completion.
Evan: It's clear that a lot of thought has gone into placing FrontierChallenge within the existing ecosystem of benchmarks.
Anything else worth mentioning about the related work?
Ashley: One last point is that while these benchmarks help establish a rigorous framework for evaluating scientific agents, many focus on isolated functions or single-domain applications.
FrontierChallenge differs by integrating these aspects into a coherent, multi-domain, end-to-end workflow evaluation system.
Evan: That really highlights the unique position of FrontierChallenge.
And with that, we’ve reached the end of the Related Work section.
Evan: As we draw to a close, let’s summarize the key contributions and takeaways from this paper on FrontierChallenge.
Ashley: Sure, Evan.
First, FrontierChallenge presents a new, comprehensive benchmark specifically designed to evaluate the completion of end-to-end scientific workflows across multiple domains.
The benchmark includes 300 tasks, with 97 publicly released and evaluated in this study.
Evan: Second, FrontierChallenge emphasizes the need for reliable task completion by introducing metrics like Pass Rate and Avg.
Score.
These metrics highlight the gap between partial task completion and full delivery, underscoring the importance of evaluating complete artifact bundles.
Ashley: Additionally, the study's results show significant variation in performance across different models and domains.
For example, despite high Avg.
Scores, strict task completion remains low, particularly in domains like analytical chemistry and electrochemistry/environment.
Evan: Third, resource use and execution times play a critical role in evaluating performance.
The study highlights substantial variation in token use and execution times across different configurations, offering insights into the computational efficiency of these models.
Ashley: Finally, the paper emphasizes the need for more robust methods in scientific agent evaluation.
Insights from failure mode analysis suggest that completion language and tool errors aren't reliable indicators of successful task completion, which points to the need for improved contract tracking, cross-artifact validation, and evidence-based completion checks.
Evan: These contributions make FrontierChallenge a significant step forward in evaluating the capabilities of scientific agents in completing complex workflows.
Ashley: That wraps up today's episode.
Thank you for joining us on the Daily Paper Cast.
Evan: Be sure to tune in next time as we continue to explore more cutting-edge research from the world of AI and machine learning.
See you next time!
Ashley: Until then, stay curious and keep learning!