SyncMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering

Thank you for your valuable comments. We are honored that you find our work to be well-organized and well-written. Your kind suggestions, such as generalizability, granular analysis, budget profiles, and user studies also provide constructive insights that we would like to take into consideration in our revision.

Allow us to first reply to most of your mentioned Weakness and Other Comments and Suggestions, and incorporate our responses to remaining Weakness and Other Comments and Suggestions into our question responses. Huge thanks for all your insightful comments and questions.

1. Our discussions and goals for future improvements (Sec A) resonate greatly with your valuable insights. In this work, we aim to provide the foundation for general agent out-of-sync in collaborative scenarios to benefit both human-AI and multi-agent collaborations. Constructing Caller and Callee to reflect real-world complexity hierarchy, experimental comparisons between Caller and Callee reveal the influence of task complexity on agents’ performance and behaviors. We then extend our discussion to real-world nuances and future improvements in Sec A, such as designing fine-grained complexity hierarchy and out-of-sync categorization, expanding SyncMind and SyncBench to other languages, collaboration systems, models, and domains, etc. We will also enrich our experiments with human-in-the-loop experiments and user studies (particularly aim to provide meaningful insights for human-AI collaborations), complex and adaptive resource metrics, granular analysis and pattern summarization, etc.

2. Reply to Questions

(1) Our resource-aware framework (Sec 2.3) includes cost and time as two dimensions of resources, where time is quantified as the number of turns, and cost is measured by an initial budget (i.e., agent’s discretionary budget at the beginning of the task), collaborator assistance cost (i.e., to quantify the time and cost consumed by the collaborator to gather information and assist the agent along with the time and LLM call cost of the agent), and solution validation cost (i.e., to quantify the time and cost of the agent to arrive at current state and propose the solution, along with the time and cost, e.g., build testing env and execute tests, to evaluate agent’s solution on five metrics). Therefore, the time/cost is an estimated amount of all types of time/costs, such as computation time/costs, build and execution time/costs, LLM call time/costs, employment time/cost, etc. They are enforced on SyncBench samples throughout agents' out-of-sync recovery by including all initial resource availability and cost in system prompt, and prompting resource consumption and remaining availability along with agents’ task evolution (Sec D-E). Our experiments further explore the effects of different resources’ availability, with deeper analysis on LLM agents’ resource awareness and strategic utilization (Sec 4.7, C.5).

(2) We totally agree to extend our failure analysis with more fine-grained categorization and summarization. We categorize your mentioned ‘failures’ as general types of out-of-sync causes (Fig 1), and agents’ out-of-sync recovery failures are further categorized based on our evaluation metrics, e.g., file localization failures, function localization failures, solution failures, etc., with detailed analysis and discussions (Sec 3.4, 4, C). We also perform in-depth analysis (Sec C) on LLM agents’ collaboration initiative, communication capabilities, action planning, and recovery strategies, with fine-grained elaboration on multiple aspects that affect agents’ performance based on five metrics (Sec 3.4). We will also extend our failure analysis to include a more granular analysis based on failure cases and multi-level categorization (Sec C, E.2).

(3) Yes, all of our 21 source repositories involve multiple programming languages (mainly Python) and can be properly handled by SyncBench. Replying to both your question and your earlier mentioned weakness: Although more than many existing SE benchmarks, e.g., SWE-Bench that leverages 12 popular github repositories, we agree that 21 repositories may still limit generalizability. Defining agent out-of-sync to be language-agnostic, we consider the differences among diverse languages, hoping to focus on one language at each time with sufficient data to better evaluate and improve LLM agents’ out-of-sync recovery abilities. Making our methods (adaptable to different repositories and languages) open-source to help the community expand and customize SyncBench, we will also enrich SyncBench with repositories with different primary languages, complexity, and domains.

(4) Yes, we will definitely include them in future work, along with our future improvements in Sec A, to reveal deeper insights for real-world complexity and human-AI collaborations. We will also embrace your other constructive suggestions, such as granular analysis, failure modes, budget profiles, user studies, etc.

Thank you for your valuable comments. We are honored that you find our work to be well-designed. Your kind suggestions, such as backbone diversity, related work, human validation, and figure settings, also provide constructive insights that we would take into consideration in our revision.

1. We appreciate your suggestions on different platforms. Comparing seven LLM agents, we use OpenHands for more controlled Env interaction. We will also apply our methods to other platforms to strengthen our work.

2. In regard to data source, generalizability, and practical use, we define agent out-of-sync to be language-agnostic. While considering the differences among diverse languages, we hope to focus on one language at each time with sufficient data to better evaluate and enhance LLM agents’ out-of-sync recovery abilities. Constructing Caller and Callee for complexity hierarchy, we will make further exploration based on real-world nuances and future improvements discussed in Sec A. Making our methods (adaptable to different repositories and languages) open-source to help the community expand and customize SyncBench, we will also enrich SyncBench with out-of-sync tasks from real-world issues and PRs and repositories with different primary languages, complexity, and domains.

3. Thank you for your comments on cost metrics. As our resource-aware recovery (Sec 2.3) includes collaborator assistance (CA) costs and solution validation (SV) costs for resource consumptions of both collaborators and executions, we will further break down costs into sub-metrics based on task-specific build time and codebase scale. We would also like to enrich your suggestions with system capacity that can hugely affect the builds and time of the same repository.

4. Reply to Questions

(1) Tab 1 reveals that agents tackling more challenging tasks generally benefit more from CA, whose extent of impact depends hugely on agents’ willingness to collaborate, their reasoning and coding abilities, and how well they understand and utilize collaborators’ responses, besides task complexity. Pinpointing a precise task complexity threshold is therefore challenging, as it combines with other factors to exert effects, and varies significantly across different models and task settings (e.g., DeepSeek with the least willingness to collaborate benefits much less from CA than Llama-3.1-8B with the lowest $SR$; Claude-3.5-Sonnet with the highest $SR$ gains less than GPT-4o that is better trained for interactive use).

(2) Thank you for your insights into human-AI collaboration. Aiming to tackle the challenge of agent out-of-sync in general, we design know-everything agents as either human or AI collaborators (Sec 3.3, 4.4, D.5). Supported by recent work with LLMs as humans, we further validate our design via upper bound experiments, whose performance ($SR=86.33$%) demonstrates the effectiveness of not only LLM-simulated human/AI agents, but know-everything collaborators in providing useful feedback and assistance. We will also include human-in-the-loop experiments to better support human-AI collaborations.

(3) Initially $S0=B0$ at $T0$, collaborator update at $T1$ changes the repository state, resulting in $S0 \neq S1$. However, the agent is unaware of the update at $T1$, so its belief state is unchanged from $T0$ to $T1$, lending to $B0=B1$. Consequently, we have $S1 \neq B1$.

(4) Allow us to extend our response from 3: Our resource-aware recovery implements costs with an initial budget (i.e., agent’s discretionary budget at the beginning of the task), CA cost (i.e., to quantify the time and cost consumed by the collaborator to gather information and assist the agent along with the time and LLM call cost of the agent), and SV cost (i.e., to quantify the time and cost of the agent to reach current state and propose the solution, along with the time and cost, e.g., build testing env and execute tests, to evaluate agent’s solution on five metrics).

(5) This is one of the cases occurring due to the agent's limited communication abilities, especially in raising high-quality questions (Sec C.4, Tab C4), while it is hard to decide which is more resource-efficient (especially considering both time and cost) given different action trajectories: e.g., 20-turn success with 0 CA and 10 SV may cost more than 25-turn success with 5 CA and 1 SV. We therefore assess question quality based on final success. Experiments also reveal agents’ limitations in not only collaboration initiative, but communication quality and strategy (Sec 4.4, 4.5, C.4): e.g., if an agent first asks collaborator the localization of the update $U$ at $T1$, it can save many turns of independent Env exploration to localize and understand $U$. We aim to enhance agents’ collaboration awareness and communication capabilities with more sophisticated metrics design in future work.

(6) Huge thanks for kindly suggesting figure settings. We will improve our figures in both our revision and our future work.

Thank you for your valuable review. We are honored that you find our work to be novel and well-written. Your kind comments on generalizability and human-in-the-loop experiments also provide constructive insights that we would like to include in our revision.

1. We measure question quality based on whether questions can lead to successful outcomes (Sec 4.5, Fig 7), and extend it to three assessment aspects (specificity, timing, context integration) (Sec C.4). We therefore analyze the correlations of question quality $SR, LA_{file}, LA_{func}, ASR, CSR$, question categories, and question characteristics. Additionally, we assess and analyze question quality based on categorization and recovery effects (Sec C.4, Tab C4).

2. We conduct upper bound experiments by providing the all-seeing collaborator with complete task-specific contexts and ground truths to assist the coding agent in single-turn recovery ($ASR=100$%). Results ($SR=86.33$%, $LA=100$%) validate not only the reliability of LLM-simulated collaborators, as either humans or AI agents, in effectively providing high-quality task-specific assistance to coding agents, but the untapped potential of coding agents in proactively interacting with collaborators, efficiently obtaining and understanding relevant information of significance for current task, and effectively utilizing collaborator assistance to recover its out-of-sync state.

3. We agree that collaboration generally improves $SR$, with varying influence among cases (Sec 4). To explore further, we extend our discussion to its impact variances in Sec C, especially $LA_{file}, LA_{func}$, and performance gaps among agents, as affected by their intrinsic reasoning and coding abilities, as well as their collaboration willingness and quality. Cases with contrastive effects on $LA$ and $SR$ also suggest that accurate localization cannot guarantee recovery success, which involves multiple aspects, like agent’s technical and collaboration capabilities, their help seeking facets (e.g., ask more about solution than localization), etc.

4. Thank you for suggesting the out-of-scope issue. We leverage commits to build out-of-sync scenarios, thereby ensuring that the state mismatch of initial SyncBench samples is based on history commits without going beyond temporal out-of-sync scenarios (Sec 3, A, B.2). We then apply multi-level filtering to filter out low-quality data to better reveal insightful findings.

5. After discovering LLM agents’ unwillingness to collaborate in preliminary tests, we further push them to collaborate by adding incentive instructions in prompt to encourage them to ask for collaborator assistance: e.g., (Sec D.1) the last sentence **Tips**... in the input prompt to specifically encourage the agent to ask for collaborator’s assistance.

6. Reply to Questions

(1) For projects with robust CI/CD and version control, semantic inconsistencies remain challenging for collaborative programming, as collaborators with different belief states need to work on individual tasks from now and then, causing temporal belief state mismatches given the dynamic collaboration environments. We therefore aim to introduce SyncBench and SyncMind to help tackle semantic out-of-sync beyond cases that can be resolved by version control and continual testing systems.

(2) In terms of each model’s performance variation, we summarize individual performance in both settings (Tab 1, C1, Fig 6) and find the general positive influence of collaborator assistance on recovery success ($SR$), with varying effects on different metrics and LLMs due to different LLMs’ intrinsic coding and reasoning capabilities. Our pilot tests (Sec B.1) and experiments (Sec 4) shows that performance variance within each model is generally consistent under the same setting at different data scales, with pilot tests furnishing a preliminary validation to determine proper experiment settings. Largely affected by models’ intrinsic abilities, we aim to reveal both general and model-specific strengths and weaknesses of different agents, providing insights into future development of human-agent and multi-agent systems.

(3) Allow us to reply to both your question and earlier comments (Sec 3, A, B): Although more than many existing SE benchmarks, e.g., SWE-Bench that leverages 12 repositories, we agree that 21 repositories (all with multiple languages, primarily Python) may still limit generalizability. Yes, we define agent out-of-sync to be language-agnostic. Considering the differences among diverse languages, we hope to focus on one language at each time with sufficient data to better evaluate and improve LLM agents’ out-of-sync recovery abilities in each language. We will also include more repositories with different primary languages, complexity, and domains, into SyncBench, and make our construction method (adaptable to different repositories and languages) open-source to help the community enrich and customize SyncBench further.