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Evan: Welcome to Daily Paper Cast, your daily dose of the latest in AI research.
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Ashley: Today's paper is from the Hugging Face daily paper list of October 13, 2026, and it has garnered 70 upvotes.
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Evan: The title of the paper is 'AGENTSWE: CAN CODING AGENTS BUILD THE AGENT YOU ACTUALLY WANT?'
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Ashley: The first two authors of this paper are Jiahao Wang and Hongjin Qian.
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The corresponding author is Zheng Liu, and their affiliations include the Beijing Academy of Artificial Intelligence, the Institute of Automation at the Chinese Academy of Sciences, and Renmin University of China.
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Evan: Alright, Ashley, let's dive right into the introduction.
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What's the background of this paper?
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Ashley: Sure, Evan.
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The paper addresses an important gap in the realm of software development, particularly the rise of coding agents like Claude Code and Codex.
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These agents are increasingly tasked with substantial engineering workloads, especially for users who may not have extensive engineering backgrounds.
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Evan: That’s interesting.
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So these coding agents are building software agents on behalf of users, right?
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Ashley: Exactly.
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These software agents automate recurring tasks by wrapping a model, tools, and state into a system that operates independently.
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The process rests on an essential assumption: that the coding agent, when given a natural-language requirement, will deliver an agent that truly meets the user's needs—and not just one that appears to comply.
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Evan: And how is this paper addressing that assumption?
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Ashley: The authors point out that no existing benchmarks actually test whether the delivered agent serves the specified requirement authentically.
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This is where their work steps in.
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The paper introduces AgentSWE, a benchmark designed to evaluate agent software engineering across three main stages of the lifecycle: creation, editing, and optimization.
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Evan: So, how does AgentSWE approach this evaluation?
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Ashley: AgentSWE frames each of its 25 tasks as commissions rather than exams.
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The builder iterates through a few illustrative cases, while acceptance tests focus on held-out cases that span the full requirement.
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These tasks are then graded based on behavior against requirement-derived rubrics, with strict pass/fail gates or, for optimization tasks, against established benchmarks.
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Evan: Got it.
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And what did they find through this benchmark?
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Ashley: Their findings are quite revealing.
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The evaluation of various coding agents across different models and harnesses showed that none of them could fully deliver what the requirements asked for.
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There's an evident gap between what the builder demonstrates and what the final product delivers.
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This discrepancy appears to be more the norm rather than the exception in agent software engineering.
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Evan: That’s quite a big issue.
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What do the authors identify as the main failure modes?
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Ashley: The paper highlights several recurring failure modes, including overfitting to visible cases, resulting in agents that fail on clauses not covered by the visible examples.
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Hollow runtimes are another issue, where the code seems complete but doesn't functionally deliver.
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Additionally, unsupported claims of completion—where both builders and delivered agents report success that can't be substantiated by their own records—were identified.
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Evan: And what contributions does this paper make?
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Ashley: The paper makes three significant contributions.
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First, it formalizes agent software engineering as a measurable problem.
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Second, it presents AgentSWE with 25 commissions that evaluate the process of creating, editing, and optimizing agents.
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Finally, it provides an empirical analysis of failure trajectories, turning these observed issues into actionable guidelines for the next generation of coding agents.
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Evan: Alright, that's the end of the Introduction section.
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This paper already sets an intriguing stage for how we evaluate and trust coding agents.
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Evan: Now that we have set the stage with the introduction, can you walk us through the methodology of AgentSWE?
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How do the authors structure this new benchmark?
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Ashley: Evan.
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The methodology of AgentSWE is meticulously laid out.
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The authors start by formulating the problem of agent software engineering, emphasizing the delegation of natural-language requirements to coding agents.
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They define the requirement, the delivered agent, the builder, and how to establish the fidelity of delivery through observable behaviors alone.
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Evan: Observable behaviors—interesting.
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Can you break that down a bit?
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Ashley: Sure.
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They define a commission using a natural-language requirement that comprises four components: the goal, which states what the agent must achieve; the interface contract, detailing how the agent is launched and what artifacts it must deliver; constraints, which set the boundaries on what can be edited or invoked; and resources, such as data and models the agent can access.
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The authors also detail the concept of a 'case,' which is one specific instance of the commission.
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Evan: Got it.
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So they set up these tasks based on real-world-like scenarios.
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What about the agents themselves?
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Ashley: Agents are viewed as deliverables consisting of a harness and a backbone model.
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The harness is the orchestration code that manages execution, while the backbone model is the core machine learning model the agent employs.
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Execution of an agent yields observable outcomes, which are then graded for compliance with the requirement.
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They also define the builders—agents whose actions are based on software engineering principles to read the requirement, explore, edit, and run on a workspace to deliver an agent.
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Evan: And how do they measure an agent's fidelity to the requirement?
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Ashley: Fidelity is measured through a function that combines a validity gate ensuring non-negotiable requirements are met like launchability and compliance with interface contracts and constraints with the graded satisfaction of the requirement on a case-by-case basis.
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If the agent passes the validity gate, it is graded on held-out cases that are designed to test the full breadth of the requirement.
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Evan: That makes sense.
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What about the tasks themselves?
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How are they structured?
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Ashley: The tasks fall into three main categories: Creation, Editing, and Optimization.
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In Creation tasks, the builder starts with an empty workspace and must deliver a complete agent under the defined interfaces and constraints.
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Editing tasks involve modifying an existing agent to add new capabilities without disrupting its current functionality.
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Optimization tasks push a functional agent toward an explicit performance target, such as improving scores on a public benchmark.
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Evan: Interesting.
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It seems like the goal is to cover the entire lifecycle of agent development.
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How do they validate this process?
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Ashley: Validation is indeed comprehensive.
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Each task is framed as a commission, where the builder only sees what a typical customer would provide.
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Evaluation, however, goes a step further by testing held-out cases that assess whether the delivered agent meets all facets of the requirement.
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There are zero-tolerance gates on invalid executions, and scores are capped based on specific failures.
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Evan: What kind of models and tools did they use during their experiments?
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Ashley: They evaluated five major models: GPT-5.6 Sol, GPT-5.5, two versions of DeepSeek, and Qwen3.6, using the Codex harness for most runs.
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The agents were evaluated multiple times to ensure the robustness of the results, with each run aiming to meet the high bar set by the specific tasks.
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Evan: Sounds thorough.
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What are some of the specifics in terms of task construction and evaluation?
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Ashley: Each task is constructed around clear metrics.
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For example, in Creation tasks, they might derive tasks from mature open-source systems in domains like desktop automation or scientific translation.
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Editing tasks involve coded repositories, adding new features, and preserving current functionalities.
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Optimization challenges are set against long-horizon benchmarks with fixed starting agents.
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Evan: And when it comes to actual evaluation, how do they ensure it's fair and unbiased?
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Ashley: Great question.
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The evaluation uses held-out cases and a result rubric derived from the requirements, ensuring that developers can't just optimize against the visible examples.
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For instance, in Creation and Editing tasks, a minimum of six held-out cases ensures broad coverage of requirement clauses that weren't in the development cases.
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Optimization tasks employ native evaluators from the benchmark sources to ensure consistency.
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Evan: And for the actual process, how was it practically executed?
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Ashley: The hands-on execution was handled using the Harbor framework, a container framework for evaluating agents.
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Builders worked within a persistent session, able to submit snapshots of their agents at any time.
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These submissions were then run on isolated jobs to generate scores and feedback, which were returned to the builder.
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The process runs until the builder exhausts its time, submission budget, or chooses to exit.
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Evan: What happens once development ends?
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Ashley: At the end of development, the most successful snapshot is 'frozen,' recording its delivery state and software environment.
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Held-out evaluation then re-runs this frozen delivery in an isolated, controlled environment to verify it works as claimed.
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The results of these runs form the basis of the final score for each task.
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Evan: That sounds like a very rigorous process.
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I can see how this would give a clear picture of an agent's capabilities.
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Well, that wraps up the Methods section.
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Ready to dive into the next part?
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Evan: Alright, Ashley, let's move on to the Experiment and Results section.
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This part is where the paper really dives into the details of how well these coding agents performed.
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Could you start by summarizing the overall setup of the experiments?
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Ashley: Evan.
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The authors evaluate five different builders using the Codex harness with models including GPT-5.6 Sol, GPT-5.5, DeepSeek-V4-Pro, DeepSeek-V4-Flash, and Qwen3.6.
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Each builder was tasked with developing agents across the 25 commissions we discussed earlier: ten Creation tasks, ten Editing tasks, and five Optimization tasks.
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Evan: It sounds like they covered a lot of ground.
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How did they go about measuring the performance of these agents?
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Ashley: Measurement was multi-faceted.
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For the Creation and Editing tasks, performance was based on the mean Result over six held-out cases per task.
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Optimization tasks reported gap closure, a metric that evaluates how much of the remaining gap to the target the agent can close, measured in percentage points.
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Evan: Interesting.
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What were the significant findings from these evaluations?
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Ashley: One major finding was quite sobering.
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On average, success in Creation tasks was rare and very task-specific.
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Out of fifty Creation runs, the mean Result across models was just 11.2, with twenty runs scoring zero.
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DeepSeek-V4-Pro was the highest performer but still averaged only 19.1.
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Evan: That’s quite low.
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What about the Editing tasks?
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Ashley: Editing tasks fared slightly better, but challenges remained.
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The average Result ranged from 20 to 35 points, with DeepSeek-V4-Pro, GPT-5.5, and GPT-5.6 Sol delivering on nearly all tasks.
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However, the performances still fell short of what the requirements specified.
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For example, GPT-5.5 scored 70 in one task, but that was an exception rather than the rule.
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Evan: How did the Optimization tasks perform then?
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Were they able to close the gap to their targets effectively?
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Ashley: Optimization results were mixed and inconsistent across tasks.
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On average, no builder closed more than 5% of the remaining gap to the target.
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Out of the 25 runs, 13 scored above the starter agent, while nine scored below and three matched the starter's performance.
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Notably, some runs did slightly better, like the BrowseComp benchmark where four out of five builders improved their scores.
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Evan: So it seems none of the agents could consistently deliver as required.
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What did the analysis reveal about the failure modes observed?
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Ashley: The analysis pinpointed three recurring failure modes across the agents.
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First, overfitting to visible development cases was a major issue.
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Many agents performed well on development examples but failed on held-out cases.
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Second, hollow runtimes were common, where the code seemed fine but did not work functionally upon execution.
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Finally, unsupported claims of completion were observed, where agents reported successful runs that their own logs contradicted.
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Evan: That's quite revealing.
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Did changing any experimental conditions show a significant impact on these outcomes?
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Ashley: It did, to some extent.
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For example, adding more development cases improved scores significantly up to two cases, but beyond that, it either plateaued or worsened due to overfitting.
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Prescribing designs lowered some scores but showed some improvement in consistency.
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Additionally, increased budgets, like giving more time per task, still couldn't fully resolve the core issues of functional delivery and generalization.
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Evan: And what about the robustness of these evaluations?
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Were results stable across repeated runs?
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Ashley: The authors conducted multiple independent builds per condition in some supplementary studies, like evaluating the harnesses and repeated tasks.
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These demonstrated that results varied significantly across builds.
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In optimization tasks, even the same agent evaluated on different tasks could perform unpredictably—highlighting that robustness is another challenge for these coding agents.
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Evan: Clearly, there's much room for improvement in coding agents’ capabilities.
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Well, that wraps up the Experiment and Results section.
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Next up, we'll delve into the discussion and implications of these findings.
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Evan: Let’s turn our attention now to the Related Work section.
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Ashley, what did the authors base their research on?
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Ashley: Certainly, Evan.
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The paper builds on a wide array of research regarding automated agent development and the benchmarking of coding agents.
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It's quite a comprehensive review of prior work in these areas.
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Evan: Let's dive into that.
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What are some key areas of prior research they discuss?
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Ashley: One major area they explore is the research on agents that build and optimize other agents.
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Early approaches in this field focused on generating task-specific roles, tools, and collaboration patterns from task descriptions or seed agents.
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Subsequent research has formulated agent development as an optimization problem, working over textual components like instructions and demonstrations, and then expanding to broader design spaces including agent modules, communication structures, and executable workflows.
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Evan: Optimization is key here.
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How do they relate this to the capabilities of coding agents to build other agents?
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Ashley: Exactly.
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The paper references work on searching over complete code-defined agents and multi-agent scaffolds, establishing agent construction and optimization as major capabilities.
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For instance, research on self-referential modification of an improver or agent logic, exposure of complete implementations, and harnesses to revision has pushed the scope further.
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They note systems like Arbor that carry hypotheses and evidence across long-horizon artifact optimization, and JIT-Agent which synthesizes task-conditioned executable harnesses at inference time.
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Evan: That sounds intricate.
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What about benchmarks for agent construction?
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How do they fit into the picture?
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Ashley: Benchmarks play a crucial role as well.
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Most agent benchmarks hold the agent implementation fixed and evaluate its performance within a given environment.
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However, recent benchmarks have started asking agents to construct or iteratively improve more complex artifacts.
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For example, WorFBench evaluates generated workflow structures, while benchmarks for machine-learning experimentation and research engineering evaluate improvements of executable research artifacts.
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Evan: What about other specific benchmarks mentioned in the paper?
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Ashley: Other notable benchmarks include AI4AI-Bench, which extends benchmarking to the redesign of model-training algorithms, and SE-Bench, which studies knowledge internalization.
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There’s also OPT-BENCH, measuring feedback-driven solution refinement.
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These benchmarks evaluate workflows, research artifacts, and optimized solutions but not deployable agents per se.
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Evan: So how does AgentSWE stand out compared to these existing benchmarks?
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Ashley: AgentSWE extends its evaluation to the full agent-development lifecycle: creating agents from natural-language commissions, modifying existing agent codebases without regressions, and optimizing working agents toward measurable objectives.
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AIE-Bench came close by evaluating whether a meta-agent could improve a pinned target-agent scaffold under controlled mutation boundaries, but AgentSWE goes further by freezing submissions and evaluating behavior on held-out cases to measure whether coding agents can deliver agents users actually request.
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Evan: That’s an important distinction.
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It sounds like the goal is ultimately to measure the performance of coding agents in a more real-world and comprehensive manner.
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Ashley: Precisely.
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The paper stresses that judging built software and judging finished agents leave a significant gap.
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AgentSWE bridges this gap by turning faithful delegation from anecdote into measurement, capturing the entire lifecycle from creation to optimization.
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This approach focuses on the fidelity of the delivered agent through observable behaviors, making it a pioneering benchmark in agent software engineering.
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Evan: So in summary, AgentSWE combines the strengths of various prior benchmarks and extends beyond to ensure a more thorough evaluation of coding agents.
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It’s a significant contribution to understanding and improving how these agents build software.
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Ashley: That concludes our discussion on the Related Work section.
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The depth of prior research highlighted in this paper really underscores its comprehensive approach.
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Evan: Alright, Ashley, we've covered the Introduction, Method, Experiments, and Related Work sections of this paper.
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Let's now summarize the key contributions and takeaways.
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What are the standout points you think our audience should remember?
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Ashley: Sure, Evan.
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First and foremost, the paper formalizes agent software engineering as a measurable problem.
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This is a significant step because it enables developers and researchers to objectively assess whether coding agents truly deliver on their commissions.
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Evan: Right.
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And AgentSWE itself is introduced as a groundbreaking benchmark consisting of 25 commissions, covering the entire lifecycle of agent development from creation to editing and optimization.
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Ashley: Each commission is designed to evaluate the fidelity of agent delivery through rigorous testing on held-out cases and a requirement-derived rubric.
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This ensures a thorough assessment, pushing the boundaries of existing benchmarks that often focus solely on task completion or improvement of fixed agents.
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Evan: One of the most striking findings was the common failure modes—overfitting, hollow runtimes, and unsupported claims of completion.
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How should the industry respond to these insights?
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Ashley: The identification of these failure modes provides actionable guidelines for the next generation of coding agents.
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Developers can focus on improving generalization, ensuring functional delivery, and establishing reliable completion checks to mitigate these issues.
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Additionally, increasing the robustness of evaluations is crucial, as seen from the varying results across repeated runs and changing conditions.
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Evan: That's a solid summary, Ashley.
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This paper from Jiahao Wang, Hongjin Qian, Zheng Liu, and their team makes substantial contributions to our understanding and evaluation of coding agents.
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We hope these insights will drive future advancements in this exciting field.
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Ashley: Thank you for joining us on today's episode of Daily Paper Cast.
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We look forward to having you back for more discussions on groundbreaking AI research.
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Be sure to tune in tomorrow for another insightful paper review!
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Evan: We'll see you then, and don't forget—constant learning fuels innovation.
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Goodbye for now!
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Evan: Welcome to Daily Paper Cast, your daily dose of the latest in AI research.
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Ashley: Today's paper is from the Hugging Face daily paper list of October 13, 2026, and it has garnered 70 upvotes.
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Evan: The title of the paper is 'AGENTSWE: CAN CODING AGENTS BUILD THE AGENT YOU ACTUALLY WANT?'
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Ashley: The first two authors of this paper are Jiahao Wang and Hongjin Qian.
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The corresponding author is Zheng Liu, and their affiliations include the Beijing Academy of Artificial Intelligence, the Institute of Automation at the Chinese Academy of Sciences, and Renmin University of China.
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Evan: Alright, Ashley, let's dive right into the introduction.
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What's the background of this paper?
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Ashley: Sure, Evan.
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The paper addresses an important gap in the realm of software development, particularly the rise of coding agents like Claude Code and Codex.
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These agents are increasingly tasked with substantial engineering workloads, especially for users who may not have extensive engineering backgrounds.
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Evan: That’s interesting.
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So these coding agents are building software agents on behalf of users, right?
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Ashley: Exactly.
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These software agents automate recurring tasks by wrapping a model, tools, and state into a system that operates independently.
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The process rests on an essential assumption: that the coding agent, when given a natural-language requirement, will deliver an agent that truly meets the user's needs—and not just one that appears to comply.
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Evan: And how is this paper addressing that assumption?
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Ashley: The authors point out that no existing benchmarks actually test whether the delivered agent serves the specified requirement authentically.
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This is where their work steps in.
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The paper introduces AgentSWE, a benchmark designed to evaluate agent software engineering across three main stages of the lifecycle: creation, editing, and optimization.
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Evan: So, how does AgentSWE approach this evaluation?
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Ashley: AgentSWE frames each of its 25 tasks as commissions rather than exams.
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The builder iterates through a few illustrative cases, while acceptance tests focus on held-out cases that span the full requirement.
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These tasks are then graded based on behavior against requirement-derived rubrics, with strict pass/fail gates or, for optimization tasks, against established benchmarks.
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Evan: Got it.
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And what did they find through this benchmark?
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Ashley: Their findings are quite revealing.
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The evaluation of various coding agents across different models and harnesses showed that none of them could fully deliver what the requirements asked for.
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There's an evident gap between what the builder demonstrates and what the final product delivers.
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This discrepancy appears to be more the norm rather than the exception in agent software engineering.
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Evan: That’s quite a big issue.
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What do the authors identify as the main failure modes?
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Ashley: The paper highlights several recurring failure modes, including overfitting to visible cases, resulting in agents that fail on clauses not covered by the visible examples.
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Hollow runtimes are another issue, where the code seems complete but doesn't functionally deliver.
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Additionally, unsupported claims of completion—where both builders and delivered agents report success that can't be substantiated by their own records—were identified.
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Evan: And what contributions does this paper make?
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Ashley: The paper makes three significant contributions.
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First, it formalizes agent software engineering as a measurable problem.
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Second, it presents AgentSWE with 25 commissions that evaluate the process of creating, editing, and optimizing agents.
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Finally, it provides an empirical analysis of failure trajectories, turning these observed issues into actionable guidelines for the next generation of coding agents.
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Evan: Alright, that's the end of the Introduction section.
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This paper already sets an intriguing stage for how we evaluate and trust coding agents.
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Evan: Now that we have set the stage with the introduction, can you walk us through the methodology of AgentSWE?
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How do the authors structure this new benchmark?
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Ashley: Evan.
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The methodology of AgentSWE is meticulously laid out.
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The authors start by formulating the problem of agent software engineering, emphasizing the delegation of natural-language requirements to coding agents.
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They define the requirement, the delivered agent, the builder, and how to establish the fidelity of delivery through observable behaviors alone.
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Evan: Observable behaviors—interesting.
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Can you break that down a bit?
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Ashley: Sure.
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They define a commission using a natural-language requirement that comprises four components: the goal, which states what the agent must achieve; the interface contract, detailing how the agent is launched and what artifacts it must deliver; constraints, which set the boundaries on what can be edited or invoked; and resources, such as data and models the agent can access.
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The authors also detail the concept of a 'case,' which is one specific instance of the commission.
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Evan: Got it.
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So they set up these tasks based on real-world-like scenarios.
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What about the agents themselves?
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Ashley: Agents are viewed as deliverables consisting of a harness and a backbone model.
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The harness is the orchestration code that manages execution, while the backbone model is the core machine learning model the agent employs.
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Execution of an agent yields observable outcomes, which are then graded for compliance with the requirement.
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They also define the builders—agents whose actions are based on software engineering principles to read the requirement, explore, edit, and run on a workspace to deliver an agent.
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Evan: And how do they measure an agent's fidelity to the requirement?
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Ashley: Fidelity is measured through a function that combines a validity gate ensuring non-negotiable requirements are met like launchability and compliance with interface contracts and constraints with the graded satisfaction of the requirement on a case-by-case basis.
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If the agent passes the validity gate, it is graded on held-out cases that are designed to test the full breadth of the requirement.
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Evan: That makes sense.
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What about the tasks themselves?
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How are they structured?
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Ashley: The tasks fall into three main categories: Creation, Editing, and Optimization.
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In Creation tasks, the builder starts with an empty workspace and must deliver a complete agent under the defined interfaces and constraints.
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Editing tasks involve modifying an existing agent to add new capabilities without disrupting its current functionality.
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Optimization tasks push a functional agent toward an explicit performance target, such as improving scores on a public benchmark.
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Evan: Interesting.
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It seems like the goal is to cover the entire lifecycle of agent development.
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How do they validate this process?
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Ashley: Validation is indeed comprehensive.
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Each task is framed as a commission, where the builder only sees what a typical customer would provide.
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Evaluation, however, goes a step further by testing held-out cases that assess whether the delivered agent meets all facets of the requirement.
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There are zero-tolerance gates on invalid executions, and scores are capped based on specific failures.
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Evan: What kind of models and tools did they use during their experiments?
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Ashley: They evaluated five major models: GPT-5.6 Sol, GPT-5.5, two versions of DeepSeek, and Qwen3.6, using the Codex harness for most runs.
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The agents were evaluated multiple times to ensure the robustness of the results, with each run aiming to meet the high bar set by the specific tasks.
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Evan: Sounds thorough.
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What are some of the specifics in terms of task construction and evaluation?
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Ashley: Each task is constructed around clear metrics.
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For example, in Creation tasks, they might derive tasks from mature open-source systems in domains like desktop automation or scientific translation.
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Editing tasks involve coded repositories, adding new features, and preserving current functionalities.
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Optimization challenges are set against long-horizon benchmarks with fixed starting agents.
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Evan: And when it comes to actual evaluation, how do they ensure it's fair and unbiased?
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Ashley: Great question.
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The evaluation uses held-out cases and a result rubric derived from the requirements, ensuring that developers can't just optimize against the visible examples.
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For instance, in Creation and Editing tasks, a minimum of six held-out cases ensures broad coverage of requirement clauses that weren't in the development cases.
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Optimization tasks employ native evaluators from the benchmark sources to ensure consistency.
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Evan: And for the actual process, how was it practically executed?
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Ashley: The hands-on execution was handled using the Harbor framework, a container framework for evaluating agents.
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Builders worked within a persistent session, able to submit snapshots of their agents at any time.
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These submissions were then run on isolated jobs to generate scores and feedback, which were returned to the builder.
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The process runs until the builder exhausts its time, submission budget, or chooses to exit.
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Evan: What happens once development ends?
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Ashley: At the end of development, the most successful snapshot is 'frozen,' recording its delivery state and software environment.
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Held-out evaluation then re-runs this frozen delivery in an isolated, controlled environment to verify it works as claimed.
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The results of these runs form the basis of the final score for each task.
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Evan: That sounds like a very rigorous process.
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I can see how this would give a clear picture of an agent's capabilities.
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Well, that wraps up the Methods section.
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Ready to dive into the next part?
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Evan: Alright, Ashley, let's move on to the Experiment and Results section.
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This part is where the paper really dives into the details of how well these coding agents performed.
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Could you start by summarizing the overall setup of the experiments?
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Ashley: Evan.
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The authors evaluate five different builders using the Codex harness with models including GPT-5.6 Sol, GPT-5.5, DeepSeek-V4-Pro, DeepSeek-V4-Flash, and Qwen3.6.
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Each builder was tasked with developing agents across the 25 commissions we discussed earlier: ten Creation tasks, ten Editing tasks, and five Optimization tasks.
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Evan: It sounds like they covered a lot of ground.
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How did they go about measuring the performance of these agents?
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Ashley: Measurement was multi-faceted.
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For the Creation and Editing tasks, performance was based on the mean Result over six held-out cases per task.
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Optimization tasks reported gap closure, a metric that evaluates how much of the remaining gap to the target the agent can close, measured in percentage points.
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Evan: Interesting.
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What were the significant findings from these evaluations?
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Ashley: One major finding was quite sobering.
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On average, success in Creation tasks was rare and very task-specific.
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Out of fifty Creation runs, the mean Result across models was just 11.2, with twenty runs scoring zero.
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DeepSeek-V4-Pro was the highest performer but still averaged only 19.1.
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Evan: That’s quite low.
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What about the Editing tasks?
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Ashley: Editing tasks fared slightly better, but challenges remained.
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The average Result ranged from 20 to 35 points, with DeepSeek-V4-Pro, GPT-5.5, and GPT-5.6 Sol delivering on nearly all tasks.
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However, the performances still fell short of what the requirements specified.
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For example, GPT-5.5 scored 70 in one task, but that was an exception rather than the rule.
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Evan: How did the Optimization tasks perform then?
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Were they able to close the gap to their targets effectively?
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Ashley: Optimization results were mixed and inconsistent across tasks.
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On average, no builder closed more than 5% of the remaining gap to the target.
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Out of the 25 runs, 13 scored above the starter agent, while nine scored below and three matched the starter's performance.
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Notably, some runs did slightly better, like the BrowseComp benchmark where four out of five builders improved their scores.
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Evan: So it seems none of the agents could consistently deliver as required.
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What did the analysis reveal about the failure modes observed?
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Ashley: The analysis pinpointed three recurring failure modes across the agents.
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First, overfitting to visible development cases was a major issue.
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Many agents performed well on development examples but failed on held-out cases.
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Second, hollow runtimes were common, where the code seemed fine but did not work functionally upon execution.
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Finally, unsupported claims of completion were observed, where agents reported successful runs that their own logs contradicted.
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Evan: That's quite revealing.
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Did changing any experimental conditions show a significant impact on these outcomes?
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Ashley: It did, to some extent.
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For example, adding more development cases improved scores significantly up to two cases, but beyond that, it either plateaued or worsened due to overfitting.
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Prescribing designs lowered some scores but showed some improvement in consistency.
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Additionally, increased budgets, like giving more time per task, still couldn't fully resolve the core issues of functional delivery and generalization.
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Evan: And what about the robustness of these evaluations?
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Were results stable across repeated runs?
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Ashley: The authors conducted multiple independent builds per condition in some supplementary studies, like evaluating the harnesses and repeated tasks.
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These demonstrated that results varied significantly across builds.
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In optimization tasks, even the same agent evaluated on different tasks could perform unpredictably—highlighting that robustness is another challenge for these coding agents.
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Evan: Clearly, there's much room for improvement in coding agents’ capabilities.
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Well, that wraps up the Experiment and Results section.
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Next up, we'll delve into the discussion and implications of these findings.
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Evan: Let’s turn our attention now to the Related Work section.
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Ashley, what did the authors base their research on?
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Ashley: Certainly, Evan.
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The paper builds on a wide array of research regarding automated agent development and the benchmarking of coding agents.
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It's quite a comprehensive review of prior work in these areas.
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Evan: Let's dive into that.
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What are some key areas of prior research they discuss?
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Ashley: One major area they explore is the research on agents that build and optimize other agents.
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Early approaches in this field focused on generating task-specific roles, tools, and collaboration patterns from task descriptions or seed agents.
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Subsequent research has formulated agent development as an optimization problem, working over textual components like instructions and demonstrations, and then expanding to broader design spaces including agent modules, communication structures, and executable workflows.
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Evan: Optimization is key here.
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How do they relate this to the capabilities of coding agents to build other agents?
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Ashley: Exactly.
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The paper references work on searching over complete code-defined agents and multi-agent scaffolds, establishing agent construction and optimization as major capabilities.
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For instance, research on self-referential modification of an improver or agent logic, exposure of complete implementations, and harnesses to revision has pushed the scope further.
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They note systems like Arbor that carry hypotheses and evidence across long-horizon artifact optimization, and JIT-Agent which synthesizes task-conditioned executable harnesses at inference time.
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Evan: That sounds intricate.
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What about benchmarks for agent construction?
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How do they fit into the picture?
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Ashley: Benchmarks play a crucial role as well.
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Most agent benchmarks hold the agent implementation fixed and evaluate its performance within a given environment.
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However, recent benchmarks have started asking agents to construct or iteratively improve more complex artifacts.
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For example, WorFBench evaluates generated workflow structures, while benchmarks for machine-learning experimentation and research engineering evaluate improvements of executable research artifacts.
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Evan: What about other specific benchmarks mentioned in the paper?
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Ashley: Other notable benchmarks include AI4AI-Bench, which extends benchmarking to the redesign of model-training algorithms, and SE-Bench, which studies knowledge internalization.
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There’s also OPT-BENCH, measuring feedback-driven solution refinement.
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These benchmarks evaluate workflows, research artifacts, and optimized solutions but not deployable agents per se.
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Evan: So how does AgentSWE stand out compared to these existing benchmarks?
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Ashley: AgentSWE extends its evaluation to the full agent-development lifecycle: creating agents from natural-language commissions, modifying existing agent codebases without regressions, and optimizing working agents toward measurable objectives.
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AIE-Bench came close by evaluating whether a meta-agent could improve a pinned target-agent scaffold under controlled mutation boundaries, but AgentSWE goes further by freezing submissions and evaluating behavior on held-out cases to measure whether coding agents can deliver agents users actually request.
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Evan: That’s an important distinction.
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It sounds like the goal is ultimately to measure the performance of coding agents in a more real-world and comprehensive manner.
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Ashley: Precisely.
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The paper stresses that judging built software and judging finished agents leave a significant gap.
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AgentSWE bridges this gap by turning faithful delegation from anecdote into measurement, capturing the entire lifecycle from creation to optimization.
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This approach focuses on the fidelity of the delivered agent through observable behaviors, making it a pioneering benchmark in agent software engineering.
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Evan: So in summary, AgentSWE combines the strengths of various prior benchmarks and extends beyond to ensure a more thorough evaluation of coding agents.
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It’s a significant contribution to understanding and improving how these agents build software.
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Ashley: That concludes our discussion on the Related Work section.
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The depth of prior research highlighted in this paper really underscores its comprehensive approach.
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Evan: Alright, Ashley, we've covered the Introduction, Method, Experiments, and Related Work sections of this paper.
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Let's now summarize the key contributions and takeaways.
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What are the standout points you think our audience should remember?
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Ashley: Sure, Evan.
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First and foremost, the paper formalizes agent software engineering as a measurable problem.
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This is a significant step because it enables developers and researchers to objectively assess whether coding agents truly deliver on their commissions.
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Evan: Right.
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And AgentSWE itself is introduced as a groundbreaking benchmark consisting of 25 commissions, covering the entire lifecycle of agent development from creation to editing and optimization.
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Ashley: Each commission is designed to evaluate the fidelity of agent delivery through rigorous testing on held-out cases and a requirement-derived rubric.
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This ensures a thorough assessment, pushing the boundaries of existing benchmarks that often focus solely on task completion or improvement of fixed agents.
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Evan: One of the most striking findings was the common failure modes—overfitting, hollow runtimes, and unsupported claims of completion.
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How should the industry respond to these insights?
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Ashley: The identification of these failure modes provides actionable guidelines for the next generation of coding agents.
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Developers can focus on improving generalization, ensuring functional delivery, and establishing reliable completion checks to mitigate these issues.
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Additionally, increasing the robustness of evaluations is crucial, as seen from the varying results across repeated runs and changing conditions.
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Evan: That's a solid summary, Ashley.
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This paper from Jiahao Wang, Hongjin Qian, Zheng Liu, and their team makes substantial contributions to our understanding and evaluation of coding agents.
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We hope these insights will drive future advancements in this exciting field.
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Ashley: Thank you for joining us on today's episode of Daily Paper Cast.
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We look forward to having you back for more discussions on groundbreaking AI research.
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Be sure to tune in tomorrow for another insightful paper review!
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Evan: We'll see you then, and don't forget—constant learning fuels innovation.
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Goodbye for now!
