Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration
Authors: Yijia Shao
Vinay Samuel
Yucheng Jiang
John Yang
Diyi Yang
Venue: ICLR 2026 Poster (2026)
Keywords: language model agents
human-AI collaboration
human-in-the-loop
evaluation
Primary area: infrastructure, software libraries, hardware, systems, etc.
TL;DR: We introduce Collaborative Gym, a framework for enabling and evaluating human-agent collaboration that demonstrates the performance advantages of collaborative agents over autonomous agents and reveals key limitations in language model agents.

Abstract: While the advancement of large language models has spurred the development of AI agents to automate tasks, numerous use cases inherently require agents to collaborate with humans due to humans' latent preferences, domain expertise, or the need for control. To facilitate the study of human-agent collaboration, we introduce Collaborative Gym (Co-Gym), an open framework for developing and evaluating collaborative agents that engage in bidirectional communication with humans while interacting with task environments. We describe how the framework enables the implementation of new task environments and coordination between humans and agents through a flexible, non-turn-taking interaction paradigm, along with an evaluation suite that assesses both collaboration outcomes and processes. Our framework provides both a simulated condition with a reliable user simulator and a real-world condition with an interactive web application. Initial benchmark experiments across three representative tasks-creating travel plans, writing related work sections, and analyzing tabular data-demonstrate the benefits of human-agent collaboration: The best-performing collaborative agents consistently outperform their fully autonomous counterparts in task performance, achieving win rates of 86% in Travel Planning, 74% in Tabular Analysis, and 66% in Related Work when evaluated by real users. Despite these improvements, our evaluation reveals persistent limitations in current language models and agents, with communication and situational awareness failures observed in 65% and 40% of cases in the real condition, respectively. Released under the permissive MIT license, Co-Gym supports the addition of new task environments and can be used to develop collaborative agent applications, while its evaluation suite enables assessment and improvement of collaborative agents.

Published as a conference paper at ICLR 2026
COLLABORATIVE GYM: A FRAMEWORK FOR ENABLING AND EVALUATING HUMAN-AGENT COLLABORATION
Vijia Shao', Vinay Samuel"*, Yucheng Jiang" John Yang', and Diyi Yang*
1 Computer Science Department, Stanford University
Carngiie Mellon University
{shaoy), diyLy)@essstannorrse stanford.edu
ABSTRACT
While the advancement of large language models has spurred the development of Al agents automate tasks, numerous use cases inherently require agents to col- laborate with humans due to humans' latent preferences, domain expertise, or the need for control. To facilitate the study of human-agent collaboration, we intro- duce Collaborative Gym (Co-Gym), an open framework for developing and eval- uating collaborative agents that engage in bidirectional communication with hu- while interacting with task environments. We describe how the framework enables the implementation of new task environments and coordination between humans and agents through flexible, non-tum-taking interaction paradigm, along with an evaluation suite that assesses both collaboration outcomes and processes. Our framework provides both simulated condition with reliable user simulator and a real-world condition with an interactive web application. Initial bench- mark experiments across three representative tasks creating travel plans, writ- ing related work sections, and analyzing tabular data demonstrate the benefits of human-agent collaboration: The best-performing collaborative agents consistently outperform their fully autonomous counterparts in task performance, achieving win rates of 86% Travel Planning, 74% in Tabular Analysis, and 66% in Related Work when evaluated by real users. Despite these improvements, reveals persistent limitations current language models and agents, with commu- nication and situational awareness failures observed in 65% and 40% of cases in the real condition, respectively. Released under the permissive MIT license, Co- Gym supports the addition of new task environments and be used develop collaborative agent applications, while its evaluation suite enables assessment and improvement of collaborative agents.
evaluation
Code & Data:
cow./BA/-NMM/coolaabrrttieeeeeee gym
INTRODUCTION
Artificial Intelligence has long aspired develop machines that act as teammates rather than mere tools (Nass et al. 1996; Russell & Norvig 1995 Seeber et al., 2020). Recent advances in language models (LMs) have sparked growing interest LM agents, with most research focused on devel- oping fully autonomous agents to automate tasks ranging from web navigation (Deng et al. 2023
Zhou al. 2024), personal assistance (Drouin et al., 2024bb [Shao et al. 2024b) coding Yangg (0244) 2022)) and scientific discovery (Huang et al. [2024a] Majumder et al. 2024). Yet, numerous use cases inherently require human involvement due to latent preferences, domain expertise, or the need for control, even when LM agents can handle much of the workload. Beyond practical necessity, ef- fective human-agen -ggent collaboration has the potential to achieve greater task performance compared to either alone, given their complementary expertise. Unfortunately, despite its importance, the human role remains largely overlooked in current LM agent research.
"Equal Contribution

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Published as a conference paper at ICLR 2026
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Figure Collaborative Gym (Co-Gym) enables collaboration between humans and LM agents within a task environment. Left: Human adds requests and sends multiple messages without waiting for agent responses. Right: Human rates collaboration highly as the agent proactively seeks help when uncertain about package installation.
To address these gaps, we introduce Collaborative Gym (Co-Gym), the first framework for devel- oping and evaluating agents that can engage in bidirectional communication with humans while in- teracting with task environments. Co-Gym consists of three core components. First, collaboration- driven environment design. Co-Gym imposes no constraints on agent implementation but instead defines an environment interface that supports dual control in a shared workspace. Second, a pro- tocol for non-turn-taking interaction. In Co-Gym, humans and agents coordinate through two col- laboration acts and a notification protocol for real-time change monitoring rather than enforced turn structures [Skantze] [02T). Third, evaluation suite that assesses both outcomes and processes. Co-Gym evaluates task delivery and performance while auditing initiative-taking patterns and hu- man satisfaction during collaboration. We instantiate Co-Gym in both simulated conditions with reliable based user simulator and real conditions with an interactive web application featuring a chat panel and shared workspace. While more task environments have been added after the release of Co-Gym under the MIT license, in the paper, we support three representative tasks crrssinn travel plans, writing Related Work sections, and analyzing tabular data.
Co-Gym provides the infrastructure to answer the two fundamental questions: (1) Is human- agent collaboration beneficial and in what ways? (2) How can we design LM agents that can collaborate with humans effectively? We explore these questions by evaluating three types of agent fuchitectures-fully autonomous agents that only interact with the environment, collabora- tive agents, and collaborative agents enhanced with a situational planning module module-poweree by four LMs under both simulated and real conditions. In the simulated condition where the agents in- teract with a LLM-based user simulator, our experiments show that compared to fully autonomous agents, collaborative agents have a lower delivery rate as human-agent teams sometimes fail to reach their goals within the step limit due to poor communication or coordination; but among the delivered cases, human-agoo teams tend to achieve higher-quality outcomes. In the real condition, our results indicate that human- ageett collaboration can be beneficial, with the best-performing collaborative agent achieving win rates of 86% in Travel Planning and 74% in Tabular Analysis compared to fully autonomous agents when evaluated by real users. Additionally, trajectories collected from Co-Gym and the evaluation suite reveal common failure modes in LM agents during collaborative processes, with communication and situational awareness emerging as the most prevalent issues. Our results underscore the need for advancements in both the underlying LMs and the agent scaffolding (e.g., memory, tooling) to enable more effective and satisfying numan-agent collaboration. Our main contributions can be summarized as follows:
We introduce Collaborative Gym (Co-Gym), the first framework for enabling and evalu- ating human-agent collaboration with dual control over task environments and non-turn-
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Published as a conference paper at ICLR 2026
taking interaction. Co-Gym is released under the permissive MIT license to facilitate the study of collaborative agents.
Experiments across three representative tasks, under both simulated and real conditions, highlight the benefits of human-agent collaboration. Co-Gym is publicly released to sup- port the development of collaborative agent applications.
Our evaluation and in-depth analysis reveal emergent dynamics in human-agent collabora- tion, along with weaknesses of current LM agents and underlying LMs.
RELATED WORK
Infrastructure for Human-Agent Collaboration
Despite increasing interest in develop- ing LM agents, most work targets fully autonomous agents (Brockman et al. 2016), with limited research on infras- tructure enabling human-agent collabo- ration. Existing approaches assume mostly human only single-control or dual- control with turn-taking. For example, WorkArena (Drouin et al., (2244) and Bench Yao et al. 2024) are single-control settings whereas recent work such as, -Bnch (Barres et al. 2025) support ccotoclo Notably however, these approaches still generally assume human-
Table I: Comparison of Co-Gym with related work on enabling human-agent collaboration.
Control Mode
Coordination
Supported Env
WorkArena
Single-control
N/A
Single (Browser)
CowPilot
Dual control
Turn laking
Single (Browser)
r-Bench
Single-control
N/A
Single (Database)
Bench
Dual control
Turn taking
Single (Dalabave)
Co-Gyy (Ours)
Dual-control
Non-tur--uking
Multiple
agent coordination follows turn-taking structure.
The most closely related work is CowPilot (Huq et al. 2025), which introduces dual-control, turn- taking framework for collaborative web navigation. At each step, users can pause, reject, or take alternative actions. providing a valuable testbed for studying human-agent coordination naviga- tion tasks. In contrast, Co-Gym relaxes the turn-taking constraint through coordination protocol with collaboration acts and notifications, allowing humans and agents to act asynchronously when needed. Moreover, while CowPilot is specialized for web navigation, Co-Gym provides a gen- eral interface across diverse task environments, enabling broader investigations into when and how human-agent collaboration is beneficial (Vaccaro al., 2024).
Evaluation of LM Agents Existing evaluations of LM agents often focus on end-to-end task suc- cess rates (Shridhar et al.] [202]] Zhou et al. 2024 Xie et al. 2024b Jimenez et al. (024) or tool-use capabilities (Xu et al. [023]] Huang et al. 2024b). These evaluations typically involve single-step user instructions and binary task outcomes, without considering qualitative aspects of performance. While some studies have incorporated human-in- inntaa----thh-ooop evaluation, they are generally limited to the simulated condition only (Yao et al. 2024 ZZhng al., [2024] Kim al., (022). Co-Gym ad- dresses this limitation by supporting both simulated and real conditions within unified framework, and includes an evaluation suite that assesses both collaboration outcomes and processes.
3 COLLABORATIVE GYM
We present Collaborative Gym (Co-Gym), a framework for enabling and evaluating human-agent collaboration. The design of Co- Co-Gym comprises three core components: (1) collaboration-drrven environment design ($3.1). (2) a protocol for non-turn-taking interaction ( ($3.2), and (3) evaluation
of both outcomes and processes $3.3
Figure provides an overview.
3.1 TASK ENVIRONMENT
Within Co-Gym, we define each task as a Partially Observable Markov Decision Processes (POMDP) (8, T, U, O) with state space S, action space A, transition function T S x A + S. reward function R S x A + R, instruction space U. and an observation space O. Adding a new task environment (CoEnv) into Co-Gym requires specifying the tools available in S, the cor- responding A. O, and T. and an initial task description as the instruction. In addition to the initial query, U also includes instructions that emerge during the collaboration process.
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Task Environment: class CoTrawelPLomminggnvvcceee
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Environment Node (EnvNode)
Figure 2: Overview of Co-Gym framework. The task environment interface (CoEnv) requires specifying the task description, action space, and observation space ($3.1). The collaboration acts and notification protocol ($3.2) are shared across tasks. For example, when the agent updates the public component, both parties are notified with the new observation (blue solid lines); parties can coordinate by sending messages (green dashed lines).
To support actions from both humans and agents within shared task environment, CoEnv intro- duces a role parameter in its step function, which allows the environment to be updated based on the role-specific act i on. Moreover, even within shared environment, the observation space can include both public and private components, analogous to human teams where some components (e.g., whiteboards) are shared while others personal notebooks) remain private. CoEnv allows such flexibility by supporting differrnniated observations for different parties using private flag to distinguish between actions affecting shared components versus private components of the action taker. Thus, the resulting CoEnv abstraction is: obs, reward, done, private env. step (role, action)
3.2 COORDINATION
Co-Gym removes turn-taking restrictions by coordinating via the collaboration acts and a notifica- tion protocol that are shared across task environments.
Collaboration Acts To mirror natural human collaboration, where coordination occurs through effective communication, Co-Gym augments the task-specific action space with two meta-actions: BendTeammateMes:e
for exchanging messages between teammates, and WaitTeammateeCntt inue which serves as keep-alive signal. Agents can proactively trigger a.matteeea sagee without requiring prior human messages. Each party can also send multiple consecutive messages without waiting for responses (see Figure 1 for example).
Notification Protocol While humans can continuously monitor their environment, agents require programmatic notification of changes. Co-Gym adopts the following notification protocol that oper- ates on four event types: (1) shared observation updates, which broadcast notifications to parties; (2) private observation changes, which notify only the associated party; (3) new messages, which trigger notifications for all recipients; and (4) environment inactivity exceeding a specified tempo- ral threshold, which broadcast notifications to all parties. For example, illustrated in Figure 2 when the agent updates the public component (i.e., Editor), both parties are notified with the new observation (blue solid lines); when the human sends message, only the agent is notified with the new observation (green dashed lines). We use a Redis server to manage notifications across different processes and Listing provides the pseudo code of this notification protocol.
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3.3 EVALUATION SUITE
Existing evaluations of LM agents often target task success rates. While task completion is crucial, the outcome quality and collaboration process also play significant part. Co-Gym framework provides an evaluation suite that assesses both collaboration outcomes and processes.
Evaluating Collaboration Outcome We assess the collaboration outcome along two dimensions:
DELIVERY RATE: This binary metric indicates whether collaborative agents can success- fully deliver task outcome within predefined step limit.
TASK PERFORMANCE: A task-specific scoring function evaluates the quality of the out- come for delivered cases. This function may deterministic metric or based on LM/hu- man judgments. Scores are normalized to the range [0, 1] for comparability across tasks.
Auditing Collaboration Process Co-Gym framework includes two process auditing metrics:
INTTIATIVE-TKKING: Collaborative agents operate mixed-initiative systems (Horvitz, 1999). To audit how the initiative shared in human-agent teams, we introduce Initiative Entropy (Hinit), which quantifies the balance of initiative among team members. Building on Chhu-Crroll & Brown (1997). we define an utterance in collaborative dialogues as ex- hibiting initiative if it directs task execution establishes mutual belief, and employ LM to annotate utterances (see Figure [2] for the judging prompt and examples). Using entropy as measure assigns higher values more uniform distributions and lower values to more skewed ones. Specifically,
Ei Ei-i (p:)(p))
Vi, > 0,
Hiit
3i, P: 0
Here, p: is the proportion of initiative-taking utterances party i and N refers to the total number of parties.
OVERALL SATISFACTION: At the end of the teamwork, collect human ratings of their collaboration experience with the agent using 1-5 Likert scale, complementing the eval- uation of task performance.
Co-GyM INSTANTIATIONS
We instantiate the Co-Gym framework in both simulated and real-world conditions. In this paper, we evaluate three representative tasks that highlight distinct facets of human-agen collaboration: latent personalized preferences, domain knowledge requirements, and compatible expertise between humans and agents. Additional task environments (e.g., computer-use environments (Xie et al.) 2024b)) have been contributed since our open-source release.
4.1 SUPPORTED CONDITIONS
Co-Gym (Simulated) We create sandbox environment where human-agent collaboration can be studied without impacting real-world environments or requiring real human participants. Each task is associated with set of pre-collected instances that define concrete shared goals for the human-agent team. Tools within each task environment are mocked using static databases emulate realistic interactions in reproducible setup.
Following the practice previous work (Wu al.) 2025 Yao et al. 2024 Barres al. 2025). we
build an L.-based user simulator to simulate human behavior. To introduce dynamics typical of human-agent collaboration, where humans possess additional knowledge and preferences about the task, provide the simulator LM with hidden information- -pre-curated insights associated with each task instance. Figure illustrates the user simulator workflow and we validate the simulator quality in Appendix
-rrmmatton-pp--curated
Co-Gym (Real) While experiments with simulated humans provide a valuable surrogate for ad-
vancing human agent collaboration, they cannot fully replace human evaluation. We instantiate Co-Gym (Real) as a web application, enabling users to easily perform the three supported tasks di- rectly through their web browsers. Details of user interface are provided in Appendix C.4 To
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Table 2: Summary of action space, observation space, and data used in Co-Gym (Simulated). Im- plementation details for these environments are provided in Appendix C
Observation Space (C)
Data for Co-Gym (Siumlated)
Task
Action Space (A)
Compunent
Private?
Data Source
Instances
Grader for Tusk Perf.
City
Plamiing
Travel
DistancaHatzia.
aostae Search, wtc.,,iiiihttt. Flight&earch,
Accommodal Swarc..
Search
Distance matrix Fdine
Iree False
Xie el all 2024 Valaiaso Suaser
TravelPlanner
102
Cinsler from Xie all 202
Related
Work
Library AddPapeer, v, Libra hahharyhrpppppe
Libs axy ae Tobea Bditox Opdate
SozzchE apez
Search window
Edito
Paper library
False
Irue
Subset of axXiv CS Pupers
100
Kubric Grading
(Figune 13
Tabular Analysis
JupyterBwweutaCull
Tabalar data
Jupyter notehook Ediie
Discoserr Rench Subast Majundder
110
Grader from Ma junder
Pditorigddae
incentivize humans in real-world evaluations, we instantiate the transition function within Co-Gym (Real) by leveraging real tools (e.g., Google Maps, arXiv search) within the task environments.
4.2 SUPPORTED TASKS
Travel Planning Travel planning is a widely sought yet complex task, as optimal solutions depend on users' latent preferences and constraints that may not be explicit in the initial query. The Travel Planning environment in Co-Gym provides a comprehensive action space, including various search functions, aligning with Xie al.] (2024a) which studies fully autonomous agents. The search win- dow is private in the observation space, while the task action space also includes Edit editorUpdate that modifies the travel plan visible to both the agent and human user.
Related Work Writing Grounded article writing is task commonly used to assess agentic sys- tems (Shao et al., 2024a] Wang et al., 2024) and users usually possess additional domain knowl- edge when they use agents to write the Related Work section. The Related Work environment supports SearchP aper action to retrieve papers based on the given query. A shared library and text editor included in the observation space, enabling actions like Libra ryAddPaper. and Editor EditorUpdate.
ropprarDrropppee. LibraryToDras
Tabular Analysis Another common application of LM agents is data-driven research, where searchers and agents can leverage different expertise: researchers contribute domain data while agents usually excel at coding and statistical analysis (Majumder al., 2024). The Tabular Anal- ysis environment includes shared Jupyter Notebook and text editor, supporting actions such as JupyterExee erExecuteeeel and caitooupddate The goal of this task to derive analytical in- sights from the provided data and initial query.
5 EXPERIMENT
The goal of the Co-Gym framework is enable human-agent collaboration with dual control over task environments and non-turn-taking interaction. In this section, we conduct three sets of experi- ments: (1) benchmark the performance of different agent types understand how collaborative agents compare with fully autonomous agents and how different models and agent designs affect performance; (2) analyze human-agent collaboration patterns using trajectories collected in Co- Gym: and (3) ablate the notification protocol to compare the Co-Gym interaction paradigm with the turn-take tak-iaking paradigm common chatbots.
5.1 BENCHMARKING DIFFERENT AGENT TYPES
We evaluate 3 agent types powered by LMs: GPT-4o (gpt-40-2022-08-00,, GPT-4-turbo (gpt-4-turbe 0224-04-09, Claude-3.5-sonnet
Llama-3,1-70B. The agent types are as follows: (1) Fully Autonomous Agent which only in- teracts with the task environment. (2) Collaborative Agent which adopts the same implementa- tion but extends the action space to include both task-specific actions and collaboration acts (i.e., SeaaTTeamaateeees..
(3) Collaborative Agent with Situ- ational Planning which employs a two-stage decision makinn approach when processing notifica- tions. First, the LM makes 3-way decision based on all available information (i.e., task description,

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Published as a conference paper at ICLR 2026
Table 3: Results in Co-Gym (Simulated). The human is simulated by gpt-4o. * denotes signifi- cant improvement on Task Performance (Task Perf.) over the Fully Autonomous Agent powered by the same LM (p < 0.05; McNemar test for Tabular Analysis due dichotomous task-specific scor- ing function and pairwise t-test for other tasks). For the auditing metric Initiative Entropy (Hini), higher numbers indicate that team members take initiatives more equally.
Travel Planning
Related Work
Tabular Analysis
Delivery Rate
Task Perf.
Delivery Rate
Task Perf.
Delivery Rate
Task Perf.
Hai
Him
Hin
GPT-44
0.873
0.591
0.960
0,583
0.991
0,408
Fully Autonomous
GPT-4-tubo
0.980
0.615
0.940
0,575
1.00
0.426
Agent
Claude sonnet
0.990
0.577
0.970
0.617
1.00
0.358
Llama-3.1-70B
0.745
0.646
0.960
0.727
0.836
0.358
GPT-40
0.745
0.641
0.42
0.980
0.588
0.16
0.927
0.311
0.10
Collaborative Agent
GPT-4-turbo
0,931
0.642
0.05
0.930
0.628
0.00
0,900
0.351
0.06
Claude 3.5 -onnett
0.677
0.653*
01.48
0.950
0.621
01.04
01.891
0,359
0.02
Llama-3.1-70E
0.706
0.703
0.28
0.930
0.675
0.10
0.746
0.427
0.23
Collaborative Agent
GPT:4n
0.735
0.667*
0.970
0.658
0.79
11891
0.434
with Situational
GPT-4-turbo
0,853
0.703*
0.43
0.950
0,604
0.27
0,846
0.428
0.09
Planning
Claude 3.5 -onnet
0.941
0.682*
0.80
0.900
0.736
0.55
0.946
0.365*
0.74
Llama-3.1-70B
0.706
0.707
0.70
0.990
0.679
0.70
0.736
0.402
0.62
chat history, action history, observations) to take a task action, send a message, or do nothing. If it chooses to do nothing. the next action would be Wa TeaitTeammateCont i otherwise, it is further prompted to generate the final action string. Figure 5 iillustatees the workflow of this agent. All agent types are implemented with ReAct (Yao et al.) (022) which requires the LM to output "thought" before generating the action. We incorporate a Scratchpad module as an in-session mem- ory for all agents (Sumers al.) 2023). This memory is updated by the same LM before determining the next action. All LMs are used with temperature of O.
Table 3 and Table 4 summarize results under simulated and real conditions respectively.
5.1.1 RESULTS IN Co-GyM (SIMULATED)
Under the same step limit, collaborative agents struggle more to complete the task. When we simulate human users using an LLM-based user simulator, human-agent collaboration exhibits lower Delivery Rate compared to Fully Autonomous Agents in the simulated condition with a step limit of 30. This could be attributed to the inherent complexity of decision-making for collaborative agents which must adapt plans frequently based on human actions or messages. Analysis of concrete cases (.2)) revealed that failures stemmed mainly from the agent ignoring human messages (C.2, 46%)), prompting repeated messages (SA.2, 26%), or repeating and omitting actions (PI..2, 33%).
Among completed tasks, collaborative agents lead to better task performance. Collaborative Agent with Situational Planning achieves the best Task Performance across all three tasks and has significant improvement over the Fully Autonomous Agent powered by the same LM. Notably, the agent powered by Claude-3.5-sonme achieves a 0. improvement in Travel Planning (maximum score: 1) and 0.119 improvement in Related Work. Compared to Fully Autonomous Agents, collaborative agents engage in a more iterative process. For example, before starting to plan the trip based on the initial query, the agent might ask, "Are there any particular cities or attractions you want to visit?": after drafting the first version of a related work section, the simulated human might suggest, "Could you please add headings?". This iterative interaction improves task outcomes.
5.1.2 RESULTS IN CO-GYM (RFAL.
We recruited human participants with relevant expertise or practical needs to collaborate with Col- laborative Agent with Situational Planning powered by gpt-4o in Co-Gym (Real). In total, 99 unique individuals participated in the study, contributing 150 human-agent collaboration trajecto- ries. These trajectories consist of 6.3k actions performed by human-agent teams and over 77k words of verbal communication. We ask humans to rate the final outcome on 1-5 scale (1: "Extremely dissatisfied", 2: "Somewhat dissatisfied",
"Neutral", 4: "Somewhat satisfied", "Exuremely satisfied"). All Task Performance scores are normalized to the range [0, 1] according to $3.3 Ap- pendix E33 includes more human evaluation details.
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Collaborative agents lead to higher Task Performance and Overall Satisfaction. Consistent with the simulated condition, collaborative agents outperform autonomous agents across all tasks in the real con- dition in terms of Task Performance. The Delivery Rate reaches 100% because human participants are proactive in ensuring task comple- tion. However, Overall Satisfaction with the collaboration process varies by task. In Tabular Analysis, which achieves the highest Overall Satis-
Table 4: Results in Co-Gym (Real). The win rate cal- culated against the outcome produced by the Fully Au- tonomous Agent, using the provided query as the task de- scription. The sample size for each task is 50.
Travel Planning
Related Work
Tabular Analysis
Delivery Rate
1.00
1.00
1.00
Task Performance (Automatic Rating)
0.660
Task Performance (Human Rating)
0,788
0.604
0.804
Win Rate vs. Autonomous Agent
86%
66%
74%
Confidence Interval
[0.764, 0.956]
(.7291
[06618, 0.862]
Iniriative Entropy (I1,)
0.88
0.63
0.74
Overall Satisfaction
3:78
3.06
4.06
UserFan
0:34
0.32
faction of 4.06, the agent handles most of the work in writing, executing, and interpreting code, while humans primarily contribute by posing new questions for further analysis. In contrast, the Related Work task receives low satisfaction scores (3.06 out of 5 where indicates "Neutral" on the Likert scale), users often come with domain expertise and strong preferences regarding how relevant papers should be organized. Therefore, users frequently need to provide step-by-step instructions to guide the collaboration, as reflected in the low Himi score, which indicates unbalanced initiative- -tkiin.. These findings align with the idea that task difficulty in human-Al collaboration depends not only on technical complexity but also on user expectations and expertise (Spitzer et al. (023))
Users voluntarily allocate 21-32% of their actions direct environment control. To assess whether dual control provides meaningful additional flexibility for human users, we further com- pute ActRatio, the proportion of user actions taken directly on the task environment relative to all user actions which also include the collaboration acts. This metric quantifies how often users choose to intervene in the environment rather than only communicate with the agent, thereby indi- cating the extent to which users actually leverage dual control. Across the three tasks studied in this work, users voluntarily allocate 21-32% of their actions to direct environment control, demonstrating that users actively value and exploit this complementary interaction channel. Notably, UserEnvAC Ac- (Ratio is highest for the Related Work task, suggesting that users rely more on dual control when a task demands fine-grained edits when they possess stronger domain knowledge that they wish to directly apply.
5.2 ANALYZING HUMAN-AGENT COLLABORATION TRAJECTORIES
Co-Gym enables the discovery of effective patterns in human-agent collaboration. Although fully autonomous agents may complete the task on its own, we observe several key components of successful human-agent collaboration by analyzing trajectories with high Task Performance. One common pattern in successful collaboration is proactive communication. In Figure 14, we demon- strate a case of successful collaboration where the user poses a broad subjective question regarding activity planning, and the agent responds with relevant suggestions, waiting for the user's approval before making changes. Another emerging pattern is the distribution of work based on expertise. For example (Figure 15). when collaborating on a related work section about "Software Techniques for Emerging Hardware Platforms," the LM agent asks the human to narrow the search range for em- bedding methods in NLP tasks, it is tangential to the topic but previously mentioned. Throughout the collaboration, the LM agent primarily handles searching and writing, while the human focuses on reviewing the results and adjusting cited papers. Lastly, we observe the potential for collabora- live agents to enhance human control. For instance, when allowed to communicate with humans or wait for their input, the LM agent asks the human to make critical decisions, such as whether to install a specific package.
Error analysis reveals core challenges for LM agents when collaborating with humans and demonstrates similarities between simulated and real conditions. We developed failure mode annotation taxonomy through a single-pase review of all real human-agent collaboration trajectories collected from Co-Gym (Real). The resulting 26 error types were organized into five high-level categories: Communication (C), Situational Awareness (SA), Planning (PL), Environment Aware- ness (EA), and Personalization (P). Three authors independently hand-annotated 150 trajectories
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Table 5: Summary of error analysis. Detailed second-level categories (e.g., C.I, C.2) are provided in Table T0, along with an in-depth discussion of error attribution to the underlying LM and agent scaffolding. The error distributions between Go-Gym (Real) and Co-Gym (Simulated) have a Spear- man rank correlation of 0.803 (p- = 7.94 10 ?
Category
Description
Real
Simulated
Communication
Failures in maintuining effective information exchange, that disrupt understanding. coordination, task execution.
65%
80%
(C.1-C.7)
Situational
Failures in contextual understanding reasoning about current state task or collaboration.
Awareness
40%
47%
(SA.1-SA.6)
Planning
Failures in devising, updating, or executing coherent plans, especially dynamic long horizon scenarios.
39%
43%
(PL.1-PL..6)
Enviromment
Failures in recognizing accounting for operational constraints and resources within the task environment,
Awareness
28%
13%
(EA.I-EA.4)
Personalizution
Failures in adapting behaviors align with individual user in-session preferences and interaction patterns.
16%
11%
(P.1-P.)
each from Co-Gym (Real) and Co-Gym (Simulated) conditions based on the taxonomy. As shown in Table [ we found that trajectories collected from these two conditions exhibit many similari- ties. The error distributions between the two conditions have a Spearman rank correlation of 0.803 (p = 7.94 x 10-"), indicating strong alignment.
The most prevalent issues involve Communication and Situational Awareness (Raiman al. 2019 For instance, in Figure [6] the agent fails to update collaborators on its status (C.1 "Agents process tasks without informing users, resulting in a lack of progress awareness", C.3 "Agents do not pro- vide progress updates or notify users upon task completion") ") and provides incorrect summaries after executing actions (C.5 "Agents provide inadequate summaries after executing actions"), causing hu- man confusion and disrupting collaboration. In Figure 17 the agent fails to decide when to ask the human for help or to incorporate the human's 's suggestions, repeatedly encountering the same issues during code execution and becoming trapped in an endless loop (SA.1 "Agents disregard session context, treating each request as an isolated task", SA.2 "Repetitive queries arise from neglecting prior interactions and user feedback". SA.3 "Agents fail to process multiple user messages cohe- sively"). These errors highlight the limitations of current LMs when deployed in complex, agentic setups with human involvement. In Appendix G Table I0 error category to the underlying LM and agent scaffolding.
we further attribute each second-level
5.3 ABLATION STUDY
Tumn-based interaction, where hu- man agent coordinate by taking their turns, is well-suited for conversa- tional scenarios like chathots. How- ever, Co-Gym targets human-agent collaboration on real tasks (writ- ing, analysis, etc.), where parallel progress and opportunistic human in- tervention are more desirable. To val- idate this design choice, we compare Co-Gym with a turn-taking version.
Table 6: Ablation results of the notification protocol. Turn-taking Co-Gym removes the notification protocol and requires humans and agents to coordinate through turn- taking. The sample size for each task is 20.
Travel Planning
Tabular Analysis
Task Pert.
Overall Satisfaction
Win
Task Pert.
Overall Satisfaction
Win
Rate
Rate
Laking Co Gym
0.81
3.55
30%
0.79
3.55
30%
Co-Gym
0.84
3.95
70%
0.81
4.25
70%
We implemented the turn-taking ver-
sion by removing the notification protocol and constraining interaction so that humans and agents eCont inue can be used to skip the current
must alternate actions. this version, turn, and we added an overlay the web application that disables user actions during the agent's 's turn. We recruited 20 professional travel planners and data analysts through Upwork for Travel Planning and Tabular Analysis, respectively, to evaluate the Collaborative Agent with Situational Planning powered by go in both versions. We also collected pairwise preferences between the two versions.

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Table presents the ablation results. In Travel Planning, Co-Gym achieves higher task performance (0.84 vs 0.81), greater overall satisfaction (3.95 vs 3.55. p = 0.012 in pairwise t-test), and a substan- tial preference advantage (70% win rate). A similar trend holds in Tabular Analysis. Notably, the agent implementation remains identical across both versions-only the interaction paradigm differs. Qualitative feedback revealed that participants found the non-turn-taking interaction more efficient and teammate-like, as participants mentioned, "Non-turn-taking felt more natural and flexible: the agent didn't wait for every instruction and started working like real teammate; could step in anytime to guide it", "Output quality was good in both; non con-turn-ie tnn-tuun-taainng taking was effective. A remain- ing challenge is that the system should ask for help when it hits an error-ideally it can do both simultaneously."
CONCLUSION
We introduce Collaborative Gym (Co-Gym), the first framework designed to evaluate and facilitate human-agent collaboration with dual control over task environments and non-turn-takee interac- tion. By providing an evaluation suite that assesses both collaboration outcomes and processes, Co-Gym enables comprehensive benchmark experiments that demonstrate the potential for build- ing collaborative agents. Our results show that human-agent teams achieve superior performance compared to fully autonomous agents, while simultaneously exposing critical challenges in com- munication, situational awareness, and planning that necessitate advancements both underlying LMs and agent design. Released under the MIT license, Co- Co-Gym holds the potential to facilitate the study of human-agent collaboration and advances the broader goal creating AI systems that augment human capabilities rather than replace them.
ETHICS STATEMENT
This research involves human participants in the Co-Gym (Real) condition, and all human studies were conducted under IRB approval to ensure participant safety and data protection.
Human Study Ethics
Broader Impact The development of collaborative agents has the potential to transform human- computer interaction across industrics. From enhancing productivity in knowledge work to im- proving safety in high-stakes domains, collaborative agents could augment human capabilities in meaningful ways. However, there are risks associated with this technology. Miscommunication or over-reliance on agents could lead to errors in critical tasks, particularly if agents' limitations in communication or situational awareness are not mitigated. Furthermore, suppose LM agents be- come increasingly integrated into human workflows, ethical concerns related to bias, privacy, and accountability must be addressed. Ensuring that these systems are designed to respect human auton- omy, uphold fairness, and transparently communicate their limitations will be critical. By focusing on human-age agent collaboration rather than full automation, Co-Gym aligns with the broader goal of creating Al systems that value human agency and preserve human control.
REPRODUCIBILITY STATEMENT
To ensure reproducibility of our results, we provide comprehensive documentation and materials across multiple components. The complete Co-Gym framework, including all task environments, agent implementations, and evaluation scripts, is available as open-source code under the MIT li- cense, with the anonymized repository submitted supplementary materials for review. All ex- perimental details necessary to reproduce our results are specified in $5.1] including the evaluated language models, hyperparameters (temperature settings, step limits), and agent architectures. Im- plementation details of task environments are thoroughly documented in $4.2) while human eval- uation protocols, participant compensation details, and IRB approval information are provided in Appendix F.3 Statistical significance testing methods and error bar calculations are detailed along- side our experimental results in the main text. The interactive web application interface is illustrated in Figure 4 with screenshots, and all code includes sufficient documentation and setup instructions to enable faithful reproduction of our benchmark experiments across both simulated and real condi- tions.
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ACKNOWLEDGMENTS
This work was supported by Open Philanthropy, Schmidt Sciences, and a grant under the NSF CAREER IIS- NSS-2247357 ONR N00014-23-1-2420 and ONR N00014-24-1-2532 Claude credits are partially funded through the Anthropic External Researcher Access Program. We are thankful to Yanzhe Zhang. Chenglei Si, Hao Zhu, William Held, Ryan Louie, Omar Shaikh, Shanmon Shen, Zora (Zhiruo) Wang, Eric Zelikman, and loana Ciucă for their helpful suggestions and feedback at different stages of this project.
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Appendix
Table of Contents
Limitations & Future Directions
15
B
Disclosure of LLM Usage
15
Implementation Details Co-Gym
Infrastructure AP]
Details Supported Task Environments
Co-Gym (Simulated) User Simulator
17
C.4
Co-Gry (Real) User Interface
18
ser Simulator Validation
18
E
Implementation Details LM Agents
F
Evaluation Details
23
Metrics for Collaboration Outcome
................................
23
2 Metries for Collaboration Process
23
3 Human Evaluation Detail
29
Error Analysis
29
Representative Human-Agent Collaboration Trajectories
31
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A LIMITATIONS & FUTURE DIRECTIONS
Despite its contributions, Co-Gym has certain limitations. First, our study focuses on three tasks that, while representative, do not encompass the full spectrum of human-agent collaboration scenar- ios. Including a wider variety of tasks, such creative design real-time decision-making could provide more comprehensive evaluation of collaborative agents. Notably, the unified environment interface in Co-Gym facilitates the addition of new task environments, and additional environments (e.g., computer-use tasks) have been contributed since our open-source release. Second, the frame- work relies primarily simulated conditions for agent comparison. While validated the user simulator quality in Appendix D and our trajectory analysis demonstrates similar error patterns be- tween Co-Gym (Real) and Co-Gym (Simulated), exploring how to leverage simulated conditions to improve collaborative agents remains a meaningful direction for future work. Finally, our real- world experiments, though valuable and sufficiently powered for statistical significance, involve relatively limited number of human participants. Deploying Co-Gym (Real) more broadly to include diverse user populations across varying expertise levels represents a critical next step for validating our findings at scale.
Finally, in terms of the scope of our contributions, Co-Gym centers on dual-control, non-turn-taking interaction. We do not claim that all collaborative tasks should adopt this paradigm. For example, chatbots would still be valuable for tasks that can be effectively completed through standard turn- taking conversations. Dual-control, non-turn-taking interaction is most beneficial in settings where (1) collaboration unfolds within a shared, manipulable workspace, and (2) users have incentives to directly shape the involving artifact have needs for fine-grat tme-grained control. Even within this subset, the optimal interaction setup is inherently task-dependent. Our goal is not to prescribe that all col- laborative systems adopt this style, but to provide a principled framework for studying collaboration when such interaction is desirable. To this end, Co-Gym offers a reusable scaffold: researchers can plug their own environments and empirically assess whether dual control and non-turn-taking dynamics benefit their particular task. We hope the community will collectively build understanding about when this mode of interaction is most effective for human-agent collaboration.
B DISCLOSURE OF LLM USAGE
Large language models were used solely to assist with writing and polishing the manuscript text, but were not involved in research ideation, methodology development, or information retrieval. Ad- ditionally, we utilized a collaborative agent within our Co-Gym Tabular Analysis task environ- ment to assist with statistical significance testing and analysis of experimental results. All results have been verified by the authors.
C IMPLEMENTATION DETAILS OF Co-GYM
C.I INFRASTRUCTURE & API
As introduced in 3 Co-Gym defines an API for task environments that enables multiple parties to take actions in a shared workspace. The API requires role parameter in the step function and additionally returns a private flag to indicate whether change needs to notify all parties. Lever- aging the additional flag, Co-Gym further establishes a notification protocol to coordinate non-turn- taking interaction, eliminating the rigidity of using a Redis server. which facilitates communication across different components (i.e., the envi- ronment, agent, or simulated user in Co-Gym (Simulated) condition) to send and listen to messages on designated channels. Specifically, the environment sends notifications to each party (iee., role) through the {role}, / obs channel and listens for updates the step channel. Listing provides the pseudo code of the
interaction. This protocol is implemented
implementing this notification protocol.
Notably, Co-Gym is not agent framework but framework designed to apply and evaluate LM agents in various task environments alongside human collaborators. Our design principles con- sider an LM agent as an L-Mempowered system comprising the underlying LM(s), prompts, and additional scaffolding (e.g., memory, external tools, etc.). To accommoaate this flexibility, Co- Gym imposes minimal restrictions on agent implementation. To streamline agent integration, Co-
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claas EnvNode:
asyne def hveni_handder (self, chanzel, message):
if channel =- "step":
action massagel [*action"]
role message ("rolee
private Palse
// process act
is_eenn__eemmateeeeo
wew.emaaaee__esseeeeeeee.. laction):
10
self self.update_chet
wtpppptt__cht_titttooe (acticn)
11
(action)
12
return
13
else:
14
cbs, rewerd, dene, private self. self.env.step(rele, action)
15
if done:
yield "ead", (...)
17
... Clean
18
19
// send notification if private:
20
payload solf.gee_psy/ood(toal))
22
yield ['(zole//obe", payload
sise:
24
for role in solffream_member::
members:
25
payload petttggee_pyyllaeeeee (role)
26
yield ["[zcle]/bbe" payload
27
elif channel "tick";
28
if solff.oxcoed_idleeettts return self.tean_members:
29
30
11
payload salf walf.ggr__palloo lodd (rale)
32
yleld I"(zolel/oba", payload
Listing 1: Pseudo code of the Co-Gym notification protocol.
Gym includes an Node which wraps the LM agent to subseribe to notifications on the {role} sallee/ofl /obs channel, adhering to the notification protocol in Listing When new message ap- pears on the {role}/ obs channel, the LM agent responsible for deciding whether to take action and, if so, specifying the action. The Agen then sends the action string and agent role to the environment via the step channel, where the EnvNode processes it.
C.2 DETAILS SUPPORTED TASK ENVIRONMENTS
As discussed in we include three representative task environments as the initial set of bench- mark problems to evaluate collaborative agents: Travel Planning, Related Work, and Tabular Anal- ysis. In this section, document the implementation details of these task environments.
Travel Planning We leverage the medium and hard cases from the validation set of the Trav- elPlanner benchmark (Xie al. 2024a) and use its database records simulate search actions. Hard constraints for each task instance (e.g., budget limits, special accommodation requests) are set hidden information only visible the simulated user. While the original TraveIPlanner bench- mark employs binary final pass rate to measure success, we use its evaluation script to compute the task performance the average of the commonsense pass rate and the constraint pass rate, pro- viding continuous measure of performance. In Co- Co-Gym (Simulated), the search functions in the Travel Planning action space operate on databases constructed by Xie al. (2024a). In Co-Gym (Real), real-time Google Search and Google Places APIs are utilized.
Related Work We leverage the Computer Science (CS) category of the arXiv repository by se- lecting high-quality conference papers in various areas construct initial queries. For each query, the hidden information for simulated humans curated by extracting 3-9 hints such as required subheadings, citations, subsection counts, and writing style characteristics. For SearchP aper in Co-Gym (Simulated), we index papers from the arXiv CS category published prior to Octo- ber 2024 using the voyage-3 text embedding model and retrieve top 10 papers for each search query. For task-specific scoring function, we develop a 1-5 scoring rubric (see Figure [33 and use gpt-4-06 6 1---3 as the judge model operating temperature of O. Automated scores align with
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human evaluations, with correlation coefficients of 0.791 (Pearson) and 0.741 (Spearman) across 20 sampled sections. Co-Gym (Real), we use the arXiv search AP for SearchP aper.
Tabular Analysis We use BescoveryBennhh, dataset designed for systems to derive hypothe- ses based on queries and provided tables (Majumder et al. 2024). We focus on instances from DiscoveryBench-Ree that include unprocessed table more than table, which considered challenging cases within the original benchmark. The domain knowledge and dataset metadata fields in the original dataset treated additional information available to the simulated human. Task performance is evaluated by assessing whether the derived hypothesis and the gold hypothesis are entailed using its evaluation script.
C.3 Co-GyM (SIMULATED) USER SIMULATOR
In Co-Gym (Simulated), we employ an LM (gpt-4o in experiments) simulate hu- man behavior. Since the simulated human also needs to perceive change programmatically, we implement the which listens to notifications in the same way as the AgentNode (see Appendix C.T). Within the Simulat attehhanndddeeeee event loop, the simulated human processes observations and selects actions from five predefined action types that represent potential human behaviors:
Task Environment: class CoT@ablarrkallyysseee.eCCCo
* shared Geal: Analyze the tabular data and document the findings in the editor based initial
Agent
F
jupyter
File System with CSV Tubles
Jupyter Executor
Text Editor
Collaboration Acts
ANSWER QUESTION: The simula- tor LM further generates the an- swer and the next action would be SendTeanmateMessagg
Notification Protocol
Simulated Human
Environment Node (EnvNode)
Hid Hindenn Information
Task Description
History
History
PROVIDE FEEDBACK: The simula- tor LM further generates the feed- back and the next action would be SendTeammat eeeesttMMssss ss eMassage, simulat- ing human proactive information shar- ing.
Domain Knowloge, Dataset Metadatk
Answer the question Provide bedback
lake a task action
Wait and do nothinz Finish
Pre-collected
Action
Next
The simulator LM further generates an action string within the task-specific action space, simulating human task engagement.
TAKE TASK ACTION:
Figure 3: Illustration of Co-Gym (Simulated). The human is simulated by language model us- ing hidden information associated with each task case. The hidden information is not visible to the LM agent.
The next action would be WaitTeannateCom Cenneiinnn,n simulating the human tendency to pause, reflect or expect the agent to do the actual work.
Do NOTHING:
FINISII: Notify the environment to end the task.
To create the potential dynamics within the human-agent team, where the human may have addi- tional knowledge, preferences, or insights about the task, we curate hidden information for each task instance which only visible the simulator LM. As illustrated in Figure S the simulator chooses the action type and obtains the next action based on such hidden information together with the task description, current observation, chat history, and action history.
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Figure 4: Screenshots of the interactive web application for Co-Gym (Real).
C.4 CO-GYM (REAL) USER INTERFACE
To enable real-time collaboration between human users and LM agents in the Co-Gym (Real) con- dition, we developed web applications tailored for each task. These applications feature a chat panel and a shared workspace. Due to the non-turn-taking interaction design of Co-Gym, human users can perform task actions send messages to the LM agent any time. The user interface provides visual signals to notify changes, adhering to the notification protocol. Figure 4 illustrates the web application. We host the web application
CPU server with 4 cores and 16 GB of memory.
Once the task is completed, we ask the human participant to: (1) rate the final outcome on a scale of 1 to 5, (2) rate their Overall Satisfaction with the collaboration process
scale of 1 to 5, (3) provide pairwise comparison rating of the outcome against the result obtained by a fully autonomous agent based on the initial query. Additional evaluation details are provided in Appendix F
D USER SIMULATOR VALIDATION
Quality Coding We sample 100 simulated trajectories each from three tasks and recruit annotators from Prolific to annotate the simulator quality in terms of accuracy, consistency, and plausibility.
Specifically, the annotation rubric is as follows:
User simulator quality evalua- tion based on the percentage of positive ratings determined by majority vote.
Table 7:
Does the user simulator respond ap- propriately based on its internal goal and hidden information?
Accuracy:
Accuracy
Consistency
Plausibility
Consistency: Is the user simulator internally co- herent across turns?
93.0%
92.0%
90.3%
Plausibility: Does the behavior resemble sonable human response?
Each trajectory received three independent annotations. We report the percentage of positive ratings based on majority vote in Table
Comparison of Simulated and Real Trajectories We further conduct a pairwise trajectory an- notation task. Annotators are shown two trajectories performing the same task and are informed
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that or both trajectories may be simulated. For each pair, annotators asked whether they can confidently identify which trajectory involves a simulated user. They are given three options: (1) the two trajectories are indistinguishable, (2) Trajectory A is simulated, or (3) Trajectory is simulated. To control for bias and evaluate baseline distinguishability, we include 50 simulated-human pairs and 50 human- humann pairs in the annotation pool. Each pair received three annotations.
pairs, 26% the annotations judged the two trajectories to indistin- guishable, suggesting that the simulated users often behave similarly real users. When annotators chose a specific trajectory as simulated (i.e., distinguishable cases), their accuracy was 56.9%, which significantly above chance (binomial test, p 0.0898). These findings indicate that our sim- ulated users are often difficult to distinguish from real users, supporting the realism and plausibility of our simulator.
Among the simulated-humd
E IMPLEMENTATION DETAILS OF LM AGENTS
As described in $5.1 we implement LM agents with ReAct-style prompting. We further incorporate a Scratchpad module for in-session memory as trajectories are usually long in the three tasks we study, especially in a human-agent collaboration setup. This section details the implementation of the Collaborative Agent with Situational Planning, the best- best-performing method in our experiments, which employs a two-stage decision- maaiiinnnmiiingggg process to handle notifications. Figure illustrates the agent workflow when processing notification received via the event loop in Agent Node.
The system prompt describes the current task and emphasizes that the agent shall collaborate with its teammate(s) to complete the task. It includes all relevant information at the current timestamp, such as the latest scratchpad state, observations, and chat history between the human and the agent, as depicted in Figure
System Prompt
A key challenge in human-agent collaboration is enabling the agent dynamically replan in response new requests suggestions while maintaining task progress. Also, human involvement often results in longer trajectories compared to fully au- tonomous agents. To enable the agent to record important information for future during the session, we implement the in-session memory (i.e., Seratchpad) as a dictionary, allowing for the insertion, deletion, and editing of items. When a new notification arrives, the system prompt is con- catenated with the scratchpad update prompt (Figure 7.. instructing the LM to update the scratchpad based on the current observation and chat history.
Scratchpad Update (Figure
Situational Planning (Figure 2 Collabo- rative agents face more complex action space than fully autonomous agents, they must co- ordinate and communicate with humans to keep them informed about critical decisions, elicit la- tent preferences, and leverage their expertise. Unlike the baseline Collaborative Agent, which performs poorly in Co-Gym (Simulated) results (see Table 3). the Collaborative Agent with Situational Planning incorporates a three-way classification prompt instead of directly gen- erating the action string. This prompt guides the agent to decide whether take a task ac- tion, communicate with teammate(s), or wait for teammate(s) to proceed. The classification is achieved through in-context few-shot learn- ing, with the prompt template shown in Fig- ure 8 This additional step encourages explicit consideration of coordination and communi- cation, resulting in more balanced initiative- taking, proactive confirmations, and improved task performance.
Description Query
Update
Notifiation
Scratchpad
Nottlicatonn
Situational Plan
? Plan
Sendita ifforme
Nottiiattoo
② Act
Notifiaation
Event
Nottiiation
Action History
step
Agent Policy
Figure 5: The workflow of Collaborative Agent with Situational Planning to process a notification received by the event loop in AgentNode.
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After determining the plan, the agent generates action string de- pending on the decision: the plan is to take a task action, the LM is prompted to generate the corresponding action string, similar to Fully Autonomous Agent; if the plan is to communicate with teammate(s), the LM is prompted to generate a message and we construct the action string ac- cordingly; if the plan is wait, the Agent Node remains idle and listens for upcoming notifications. The action string, when constructed, is sent to EnvNode via the st ep channel.
Action Taking (Figure 5
System Prompt
SETTING:
Your name is (name). You are helpful AI Agent who can take actions to interact with the environment and collaborate with other team members (c.g., the user) to complete the task. Your goal is to complete the task and aim for high task performance rating.
You need collaborate with your teammates effectively because they may have additional exper- tise or have preference/informatio important to the task. There are the following members the team: )
TASK DESCRIPTION:
(task description]
SCRATCHPAD:
Here is the scratchpad that you take notes store information in previous steps, which
your memory:
{scratchpad]
OBSERVATION:
Here is the current observation that reveals the current status of the task environment:
{observation)
COMMUNICATION:
Here is the current chat history that records the messages exchanged between you and other teammates (e.g., the user): {chat_history)
Figure 6:
The system prompt for Collaborative Agent and Collaborative Agent with Situational Planning. The system prompt will be populated with information provided in the notification.
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Scratchpad Updating Prompt
Note that the current environment observation may change when you your teammate(s) take actions. Remember to update your scratchpad accordingly if needed. Guidelines:
1. Keep your scratchpad concise and relevant.
2. If there is any information that could be useful for future steps but not in the scratchpad, add it the scratchpad.
every information in the current observation already scratchpad, you not need to update the scratchpad.
4. If past action does not lead to any progress, consider updating the scratchpad remind yourself of not repeating the action.
ACTION SPACE SPECIFICATION:
You can choose from and only from the following actions to manipulate your scratchpad. You can only choose action at time. Invalid actions will hurt your performance rating.
The following actions
available:
Add a note to the scratchpad (Parameters: ['note id', 'note'])
Deeseription Add a note to the scratchpad with the provided note id and note.
Regex pattern for the action (your output needs to follow this if you take this action):
ADD NOTE(note id=(.", note=(."))
Delete note from the scratchpad (Parameters: ('note_id'])
Description:
Delete note from the scratchpad with the provided note id.
Regex pattern for the action (your output needs to follow this if you take this action):
'DELETE NOTH(note id=(.*))
Edit note in scratchpad (Parameters: ['note id', "note']]
Description: Edit note in the scratchpad with the provided note note_id.
Regex pattern for the action (your output needs to follow this if you take this action): 'EDIT "EDIi.NOTEEnntc.iee (d+=(note_d==(")), note=(."))
Do nothing (Parameters: ())
Deseription: Choose this action if there is no need to update the scratchpad. Do not spam the scratchpad
with unnecessary updates.
Regex pattern for the action (your output needs to follow this you take this action): 'DO NOTHINGO)
OUTPUT FORMAT:
Give your output in the format of "Thought:... www.:......nnt..n.......... (must follow the regex pattern of the selected action)".
Figure 7: Prompt for updating the agent scratchpad that serves the in-session memory.
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Situational Planning Prompt
Now, based on the current situation, decide to either:
Send message your teammate(s) (e-g., ask question, request foodback, etc.) collaboration.
Take lask action change the task envirvument observation.
3. Do nothing to allow your teammate(S) to take actions.
To ensure your are collaberating effectively, remember to:
Communicate clearly effectively with your leammate(()) (e-g.
2. Wait for other teammates to respond if your previous action requires response. Do not spam the chat.
Coordinate and synchronize your actions
user other tcammates.
Help establish lask and role expectations with teammates you need their expertise.
5. Take your teammates' cognitive load into consideration when making decisions. You should not ask them to debug your own code too many questions the
OUTPUT
Give outpur in the format of Thoouhht AnPla: I, Send message 2. Take task action 3, Do nothing".
Example I:
TASK DESCRIPTION:
The analyze user-provided tabular identify potterns insights.
SCRATCHPAD::
OBSERVATION:
jupyter history:
Code block:
pandas
dE pd. re.. ('daa.ccv"))
print idf.columns)
Output:
Index [l"Ag'', 'Education", 'Cooupation', "Compuerr Jaage", 'Experience with LIW, "LAM Dsage Frequencyy,, "Workflow 1', "Workflow 2', 'Time 2', *Preferced Assis 1', 'Time 2', '#referred Assis 2'), dtype="cbjee" object') COMMUNICATION: chat history.
ACTION HISTORY:...
Thought: need to understand the data better before taking action. There is no additional information ahout each column need send message the user more information. Plan: 1. Send a message
and the background
Example 2:
TASK DESCRIPTION:
The task to plan a trip to Paris.
SCRATCHPAD:
OBSERVATION:
COMMUNICATION
You:
Could you please provide with your preferred travel dates? Are there any specitic places would like visit Paris?
ACTION HISTORY:
Thought I have asked the user for their preferred travel dates and places to visit in Paris. need to wail for the user's 's re- sponse hefore taking any action. Do nothing
Example 3:
TASK DESCRIPTION:
The task to write a related work section for my paper aroud Human, A1 collahoration.
SCRATCHPAD:
OBSERVATION:
ATION:
ACTION HISTORY: No actions taken
Thought: I need task action to search for relevant papers. Plan: 2. Take task action
reviewing existing literature and papers related
Human- Al collaboration. I should take
Figure 8: Prompt for situational planning that classifies the current context into one of three action categories: taking task action, sending message, waiting for teammate(s) to proceed.
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Table 8: Percentage of trajectories hitting agent action number limit (i.e. 30).
Travel Planning
Related Work
Tabular Analysis
GPT-4o
0.01
0.03
0.00
GPT-4-turbo
0.00
0.01
0.00
Fully Autonomous Agent
Claude-3.5-sonme
0.00
0.00
0.00
Llama-3.1-70B
0.34
0.25
0.53
GPT-4o
0.01
0.12
0.00
GPT-4-turbo
0.00
0.04
0.00
Collaborative Agent
Claude-3.5-sonnet
0.11
0.04
0.00
Llama-3.1-70B
0.60
0.18
0.18
GPT-40
0.00
0.25
0.00
Collaborative Agent
GPT-4-turbo
0.00
0.09
0.00
with Situational Planning
Claude-3.5-sonnct
0.25
0.02
0.02
Llama-3.1-705
0.42
0.21
0.19
F EVALUATION DETAILS
3.3 outlines the evaluation suite that assesses collaborative agents along two dimensions: outcome and process. Below, we provide more details on the computation of these metrics.
F.1 METRICS FOR COLLABORATION OUTCOME
Delivery Rate Agents are given step limit of 30 in experiments. A task is considered "deliv- ered" if the editor is not empty upon completion of the task or when the step limit is reached. We set the step limit of 30 following previous work (Xic al. 2024a). As shown in Table 8 most agents do not hit the step limit except for agents powered by Llama-3.1-700
Task Performance Each task environment in Co-Gym specifies task-specific scoring function to quantify Task Performance ($3.3). This function may be a deterministic metric or based on LM/human judgments. Please refer to for details.
F.2 METRICS COLLABORATION PROCESS
Initiative Entropy (Hmit) For each message in the chat history, we use Llama-3.1-70B to determine whether it demonstrates initiative- taking. The prompt used this shown in Figure I2 The prompt is based on the framework proposed by Chu-Carroll & Brown (997)) which defines initiative in collabora- tive dialogues as either directing task execu- tion ("task initiative") facilitating mutual understanding ("dialogue initiative"). Using these judgments, Hmii is computed according to Equation (T). We validate the prompt by ran- domly sampling 60 cases and having two hu- man annotators provide judgments. The av- erage Cohen's between Llama-3.1-70B and the annotators was 0.425 and the joint Fleiss' score was 0.310. These results indicate moder- ate agreement between model predictions and human annotations (Mukaka 2012). To aid in- terpretation, also include walk-through of how Hmm is computed and present example trajecto- ries at different Himi levels in Figure 9 Figure 10, Figure
Table 9: Scoring rubric for Overall Satisfaction in Co-Gym (Real).
Extremely dissatisfied: The agent communicates poorly all the time and is not helpful for the task at all.
Score Deseription
hatewhat dissatisfied: The agent communicates poorly and not very helpful the tusk.
Score Description
The agent have meaningful communication and is somewhat helpful for task.
Neutral:
Score 3 Description
Somew hat satisfied:
The agent
Score Description
communicates effectively overall is helpful for the tusk.
The agent communicates effectively the time very helpful for
Extremely satisfied:
Score Description
Overall Satisfaction We compute this metric in Co-Gym (Real) condition by requesting the hu- man to rate their overall satisfaction with the agent on a 1-5 scale according to the description in Table g
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High-Hman Example: Both human and agent actively take initiative
Agent takes InterneSeearch actions, 4 BusinessSearch actions; User takes 2 InternetSearch actions
Agent lakes EditorUpdate action
[User] SendTeamma eMessage make sure the flight is usiness class and offers lay flat hed (*Judged as taking initiative: directs task exccution*)
Agent takes EditorUpdate action
eMessage Hi! I've put together draft itinerary for your one week trip to London. It includes business class flights with lay-flat beds, stay at The Waldorf Hilton, and selection of attractions and restaurants. Please me know there's anything you'd 'd like to adjust or plan.
[Agent] SendTeammn
(*Taking initiative: establishes mutual belief")
[Agent] SendTeammat eMeeaage Hi! I've drafted your travel itinerary, including business class flights with lay-flat beds, stay The Waldorf Hilton, and selection of attractions and restaurants in London. Please let me know there's anything you's like to adjust or add to plan. Your feedback important to ensure the itinerary meets your expectations!
(*Taking initiative: establishes mutual belief + solicits suggestions")
[User] SendTeammat ssage oh ya, for the travel plan. mean takes 7 days include departure and returning time
(*Not taking initiative: answers the agent's 's question*)
Agent takes 4 EditorUpdate actions
[User] SendTeammat eMessage wht the best time visit british museum
(*Taking initiative: drives task execution**)
[Agent] SendTeamatt
The best time visit British Museum during weekdays, es- pecially in the early morning or late afternoon, to avoid crowds. Let me know if you'd like to adjust the itinerary to fit this timing!
(*Taking initiative: establishes mutual belief + proposes next step*)
[User] Gendteammateee eMessateesssaeee the tower of london best landmark london, mean best one must visit
(*Taking initiative: drives task execution*)
[Agent] SendTeammat eMessage The Tower of London is indeed one of the most iconic and histori- cally significant landmarks in London, making it must-visit attraction. Would you like prioritize as must-see on your itinerary, or are there other attractions you'd prefer to include well? Let me know your preferences so can tailor the itinerary to your interests!
(*Taking initiative: establishes mutual belief + proposes next step")
[User] SendTeammat eMessage you show me the options for the flight, means like in what times, it transit or direct flight
(*Taking initiative: drives task execution**)
Agent takes 7 InternetSearch actions, 2 EditorUpdatte actions; User takes DistanceMatrix action,
InternetSearch actions
[User] SendTeanmat eMessage can you make clear just simply make options for day6 (*Taking initiative*)
[Agent] SendTeamatteMessage Here are some flight options For Day 6 you might consider these
options:
(*Not taking initiative: executes user request*)
[User] SendTeammat eMessage can you put travel plan
(*Taking initiative: drives task execution*)
Ageni takes EditorUppaat;; User takes 1 EditorUpdate and 5 DistanceMatrix actions
[Agent] SendTeammateMer eMesage I've updated the travel plan with the flight options and Day 6 itinerary choices. Please review the updated plan in the shared editor and let know if there's any- thing else you't 'd like to adjust add!
(*Taking initiative: establishes mutual belief + proposes next step")
User takes 2 Editor actions and finishes task
Figure 9: A human-agent collaboration trajectory with high Hmit- In this example, Pagent 5/11, Puser 6/ 11, and Hm = 0.994. Typos in user messages are preserved from the original data.
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lower iini case where the user primarily takes initiative.
Agent takes SearchArxiv action, TodraperTollbrrary action, LibraryToDraft action
[Agent] SendTeammat eMesagge Hi user xxx! I've collected several research papers related data leakage and memorization in language model benchmarks. Do you have any specific preferences or additional information that you think should be included in the "Related Works" section? Let me know if there's anything specific you'd d like to focus
any other details you provide.
(*Taking initiative: establishes mutual belief + proposes next step*)
[User] SendTeammat eMes sagee do you have the capability to talk about the [1] [2] or [3] sources that are in the library?
(*Taking initiative: drives task exccution*)
[Agent] Bendreammateeesee
Sure! Here's brief overview of the sources:
[1] +Quantifying and Analyzing Entity-level Memorization in Large Language Models*: introduces
method for quantifying memorization at the entity level in LLMs, highlighting privacy risks and the ability of models to reproduce training data even with partial leakages.
[2] *Ae Large Language Models Memorizing Bug Benchmarks?": investigates data leakage in bug benchmarks used evaluating LLMs in software engineering tasks, showing memorization can inflate performance metrics.
[3] *Inference- Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Eval- uation": presents Inference- Time Decontamination (TTD) for detecting and rewriting leaked samples to provide more accurate performance evaluation.
(*Not taking initiative: answers the user's question*)
[User] SendTeammat eMessage what are some papers on how bad memorization has gotten?
(*Taking initiative: drives task exccution*)
Agent takes 2 SearchArxiv actions, EditorUpdatt action
[User] SendTe ammat eMessage can you give me a more in-tepth summary of this bug- benchmarks
paper?
(*Taking initiative: drives task execution*)
[Agent]
Certainly! Here's more in-depth summary of the * *Ae Large Lan- guage Models Memorizing Bug Benchmarks?* paper:
(*Not taking initiative: answers the user's question*)
Figure 10: A human-agent collaboration trajectory with low Hini- In this example, Pagent = 1/4, Puser 3/4, and Hm.. 0.811.
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A very low iiit case where it's mainly the agent that takes initiative.
Agent takes Execute actions; User takes I EditoUppaate action
[Agent] SendTeammat weeeaaaettttttaaaeeeeeeeeeeeeeee saessage Hi _xx! As I begin analyzing the impact of COVID-19 on birth rates, do you have any specific variables or aspects you would like me focus on? This could include particular birth outcomes, demographic factors, or any other elements you find important. Let me know so can tailor the analysis to your needs!
(*Taking initiative: establishes mutual belief*)
[User] SendTeammat eMessage Demographic factors, , other factors and time frame
(*Not taking initiative: answers the agent's 's question*)
[Agent] SanddTeammateeesseeee
Thank you for sharing your preferences! Could you please specify which demographic factors or time frames you are particularly interested in? This will help me tailor the
analysis to better meet your needs.
(*Not taking initiative: repeats the previous question*)
[User] SendTeammat eMessage Age, income level, urban vs rural the agent's 's question")
Nooot taking initiative: answers
[Agent] SendTeammaten eeessage Could you please specify any particular time frames you inter- ested in for analyzing the impact of COVID-19 on birth rates? This will help focus the analysis the
relevant data.
(*Taking initiative: establishes mutual belie(*)
[User] SendTeammat eMessage data from 2020 to 2022
(*Not taking initiative: answers the agent's question")
Agent takes Execute action
[Agent] BendTeammateeessse ssagge Hi user_xxx xxx! I've encountered an issue while cleaning the COVID- 19 pregnancy dataset. There are non-numeric values in the TTttll'' column, such as Number'' which are causing errors during conversion. Do you have any specific instructions on how you would like to handle these non non-numeric values? For example, should remove these rows, replace them with specific value, or take another approach? Let me know how you'd like to proceed!
(*Taking initiative: establishes mutual belief + asks for help at a critical step*)
[User] SendTeammat eMessage remove rows with non numeric values Let me know how you's d like to proceed!
(*Not taking initiative: answers the agent's 's question*)
[Agent] GendTeammater MMssaae Thank you for your guidance! I will proceed with removing rows that contain non-numeric values in the COVID-19 pregnancy dataset. Once the data is cleaned, I'll con- tinue with the analysis to assess the impact of COVID-19 on birth rates and the correlations between birth outcomes and COVID-19 incidence among pregnant women. I'll keep you updated on the progress!
(*Taking initiative: establishes mutual belief*)
Agent takes 2 ExecttelupyterCelll actions
[User] SendTe eMessage Okay than you
(*Not taking initiative*)
Agent takes Execute. actions
[Agent] SendTeammat eMessage user xxx! As continue with the analysis, could you please specify any particular birth outcomes you would like me to focus on? This could include factors like birth weight, preterm births. any other outcomes you find important. Also, I've been working on cleaning the datasets and analyzing the monthly birth statistics. If you have any additional preferences questions, foel free to let mc know!
(*Taking initiative: establishes mutual belief*)
[User] SendTeammat eMsssage access to prenatal care, fetal distress, gestational age. birth weight (*Not taking initiative: answers the agent's question*)
Agent takes 4 ExeeetellupyterClll actions, EditorUpdate action; User finishes the task
human duman-agent collaboration trajectory with very low Hmi- In this example, Pagent 5/5, Puser = 0/5, and Hmi O. Grammar errors in user messages are preserved from the original data.
Figure 11:
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Judging Initiative
am analyzing team member initiative in collahoration. Two types of utterance count as taking initiative:
1. Task Initiative: A team member is said to have the task initiative if she is directing how other member(s)) task should be accomplished, i.e., if her utterances directly propose actions that other members should perform.
Examples: "Let's send engine E2 to Coming.", "Let's look at the first problem first.", "Let's 's consider driving from Fort Lauderdale to Louisiana and explore three cities there." Passive utterances like "Any suggestions", "Right, okay." are considered task initiative.
Dialogue Initiative: A team member is to have the dialogue initiative tries to estab- lish mutual beliefs. Both giving concrete information and asking concrete questions are considered dialogue initiative.
Examples: "We can't go by Dansville because we've got Engine going that track.", "Would you problem?"
like consider traveling on different date?", "What you think about
Repeating what the other person said, asking for clarification are not considered dialogue initiative. Now given an utterance in the conversation, you need to judge whether the utterance takes initiative or not. Indicate your judgement with "Yes" "No".
Uterranc: [utterance)
Reasoning: Let's think step by step in order to
Figure 12: Prompt for judging initiative taking.
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Scoring Rubric
You are an ACCURATE, FAITHFUL, CRITICAL, and FAIR judge. You will be given a related works section draft and a topic for the puper related works written
Your Task:
Critically evaluate the quality of the related works section based on the Evaluation Criteria below.
final score MUST be integer.
-Related Works sections that exhibit low quality attributes according to the evaluation criteria below must be given low overall scores. evaluation criteria below must he given high scores. Output the integer final score your last sentence in the following format: "Therefore, the final score is..."
RRelated Works sections that exhibit high quality attributes according
Keep this rubric open while ovaluating and reference the ovaluation criteria below when assigning a score.
You MUST give your final score based the Evaluation Criteria given below.
Evaluation Criteria:
Score
Ciitation Usage: Only zero to four unique citations
Relevance and Coverage: Content is off-topic lacks any meaningful discussion of related works.
Organization and Structure: Disurganized with cleur siructure; ideus scatlered and hard to follow. Uses bullets points list format in at least one subsection.
Writing Style; Informal language; numerous grammnttccal errons; may use points instead prose.
*1Iporrant **; a prose style of writing is not present throughout the full related works (i.e. a subsection or subsections in bullet point format) or only 0 unique citations are present, overall score should automatically assigned regardless of the rest of the criteria.
Score 2:
Citation Usage: ONLY five to six unique citations. Limited citations with minimal diversity; overuse of certain citations; inconsi stent lormatting.
Relevance and Coverage: Touches on relevant topics superficially; lacks depth; may include imelevant information. Most sulsections only discuss one unique work are not fleshed out.
Organization and Structure: Some attempt organization, but lack coherence; grouping of ideas is unclear. Subsections put of order or talks about current paper's work. Writing Style: Incoasistent academic several grammatical errors; langunge may be unclear times. Terminology like "In summary" are used.
Score = 3:
Citation Usage: Seven more citations with some diversity; occasional over-reliance on cortain sources; formatting mostly consistent. not all citations between the lowest cited index and the highest cited index appear least overall score of the highest that can be awarded.
RReeevnnce and Covenge: Addresses relevant topics adequately hut lacks depth; discussions generally accurate not insightful.
Organization and Structure: Logical structure is present, grouping of ideas is mostly coherent; transitions may need improvement. Writing Style: Maintains an academic tone with lapses; few grammatical errors; language is generally
Score = 4:
CCittton Usage:: Multiple unique citations with good diversity;
well-distributed and support the text effectively; consistent
formatting.
Reeeevncee and Coverage: Provides comprehensive overview relevant topics; discussions show undorstanding and critical
analysis.
Organization Structure: Well-organized with clear thematic grouping: ideas flow logically coherently. subsection discussed multiple papers and has ideas and similaritics fleshed out. Writing Style: Polished academic writing: cleat and concise; minimal grammatical errors.
Score 5:
Citation Usage: Wide variety unique citations with excellent diversity; citations enhance the discussion significantly; flawless
formatting.
Relevance Coverage:
Thuroughly covers relevant aspects topic depth insight; demonstrates critical analysis
and synthesis.
Organization and Structure: Exemplary orgamization with clear, logical progression; seamless transitions between ideas, There are clear and separated pargraahs that each contain theme. Writing Style: Exceptional academic writing; language is precise, clear, and engaging, no grammatical errors.
Paper Topic:
[lupic}
Related Works:
works) }
Evaluation Form:
Figure 13: Scoring rubric for Related Work task environment in Co-G Co-Gym (Simulated).
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Published as a conference paper at ICLR 2026
F.3 HUMAN EVALCATION DETAIL
For the experiment condition with Co-Gym (Real), we recruited human participants with relevant expertise or practical needs collaborate with Collaborative Agent with Situational Planning pow- ered by gpt-4o. Specifically, we targeted participants who had current needs in travel planning, tabular data analysis. or writing related work sections/literauure surveys. Recruitment was initially conducted through word-of-mouth. To broaden our participant pool, we recruited travel planners through Upwork for Travel Planning and participants with Python programming and data analysis experience through Prolific to work with the agent on writing analytical reports from tabular data. Participants were compensated at a rate of $8.00 per hour, and the study received approval from our institution's ss Institutional Review Board (IRB).
In the ablation study (see $5.3), each participant (1) collaborated with a Collaborative Agent with Situational Planning in turn-taking Co-Gym, then (2) completed matched non-turn-taking ses- sion on the original Co-Gym. After each session, they rated Task Performance (final outcome on a 1-5 scale, normalized to [0, 1)) and Overall Satisfaction (I-5 Likert) using the same rubric described above. Finally, they indicated a preference between conditions with a brief rationale.
G ERROR ANALYSIS
We conducted a comprehensive error analysis on trajectories collected from Co-Gym by develop- ing failure mode annotation taxonomy and annotating 150 trajectories each from Co-Gym (Real) and Co-Gym (Simulated) conditions. While certain errors can be partially alleviated through the Co-Gym framework itself (e.g., C.1 "Agents process tasks without informing users, resulting lack of progress awareness" can be mitigated by streaming the agent action to the user interface), our analysis intentionally focuses on evaluating LM agents so as illuminate concrete directions for their improvement. Accordingly, we attribute each error type gaps in the LM's s inherent capa- bilities, deficiencies in the design of the agent scaffolding (e.g., memory, additional tools), both. Table summarizzs the results.
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Published as a conference paper at ICLR 2026
Table 10: Breakdown of common failure modes of LM agents during human-agent collabora- tion. Failure mode statistics are derived from authors annotating 150 trajectories each from Co-Gym (Real) and Co-Gym (Simulated) conditions. These failure modes focus on LM agents whole, encompassing both the underlying LM and the agent scaffolding (e.g., memory, additional tools, etc.). Failure modes are traced to these two dimensions, with x indicating failures from the under- lying LM and X representing issues arising from the agent scaffolding.
Failure Mode Description
Real Simulated
Error Source
Communication
Real: 65%
Simulated: 80
(C.I) Agents process tasks without informing users, resulting lack of progress awarenees.
23%
24%
(C.2) Agents do not confirm or communicate their actions before execution.
29%
46%
(C.3) Agents do not provide progress updates notily users upon task completion.
33%
27%
(C.4) Absence of exlimated completion times impedes collaborative efficiency.
15%
13%
(C.5) Agents provide inadoquaic summaricss afler execating actions.
14%
14%
(C.6) Agents do not proactively seek clarificution when user input is ambignous or insufficient.
11%
5%
(C.7) Agents miss implicit cues to initiate expected actions.
12%
5%
Situational Awareness (SA) Simolated:
(SA.1) Agents disregard session contest, treating each request as isolated tack.
(SA.2) Repetitive queries arise from neglecting prior interactions and user leedback.
13%
(SA.3) Agents to process multiple user messages cohesively.
11%
9%
(SA.4) Agents deviate from price instructions during extended sessions.
3%
7%
(SA.5) Agents execute critical actions without obtaining prior confirmation.
18%
8%
(SA.6) Agents do not adhere instractions as session duration increases.
10%
15%
Planning (PL)
Real: 39% Simulated: 43%
(PL..)) acknowledge tacks but fail
them.
10%
8%
(PI.22) Lack of cask planning results repented omitted actions.
27%
33%
(PI.3)
previous actions when
identified, lacking initiative correct them.
3%
3%
(PI.4) Agents fuil choase the optimal way modify the environment, leading inefficient operations.
15
3%
(PI.5) Agent proactive planning abilities and cannor infer suhsequent steps withour explicit guidunce.
Multi-faccted requests incomplete pantial responses.
Environment Awareness (EA)
Real: 28% Simulated: 13%
(EA.1) Agents do not assess the feasibility of requests within the constraints of available tools and resources.
13%
3%
(EA.2) Agents propose actions that they unable to perform within the environment.
2%
0%
(RA.3) Agents fail to identify when external data tools are required fultill request.
15%
10%
(KA.4) Agents ballucinate inscourate information by not urilizing available tonls and external data.
8%
3%
Personalization
Real: 16% Simolated: 11%
(P.I) Agents rely on rigid templates that do not adapt individual user needs.
7%
5%
(P.2) Agents pose broad questions lacking specificity and clarity.
5%
3%
(P.3) Ageats do not incorporate user preferences into future interactions, limiting persondication.
6%
5%
30

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Published as a conference paper at ICLR 2026
H REPRESENTATIVE HUMAN-AGEN COLLABORATION TRAJECTORIES
Can add nore finis to each of the dlements in the tinerar so we know how book evenes?
Search
Ditzaraa
our currendy acding booking into eaeh element your ismerary make eaaler for you plan and book activitis and accommodazions onco fiv updated hinorory with these links, thithre'ss arsthing specific you'd like to prioittes, please let snow!
know
uos Day Travel Auceland
fighes///tts///// type www.limeaeeebeett nef)
"AAcomm nodation:" ** Stay at hossl Bucklladdwiiii glaton frze and wagstarant
specifccllysheeee should
herely perejatrps///
com/tnyli///uuudii temm
** "Accliities ** Explore Aucifind, visit (Sky
inmerrs yourselves culture during yourtrip. recommend the hillowing artiities: Fororus Moto cuttunal Experience:
pensrrmmi simon, enjoy along (Viaduut
Harbour h/rtps//wwwe www.uct.coooog..o noo)
fottr here traditional Mate- performannee leern about Moon arts and crafts ond witness the famous Pohutu Geyter Tamali Maooi Village Partic pana in owening experience at Tamail Moort Vilago, where cou enjoy traditional hangi lauas and ou Bural per formances 3.
Explore
"TTovee"" -n Driue from Audiland and Rotorva (appeniinanel monely hourg.
options
titrgp // wnw boo com///icihhhoccohhiic com//tt/tt//oottouaaaaaooo mmnil
MMMAettteee tiee tee wootheereeeeeeeeeooooo pariiz crjore colturr----
Heoue svep "ibttonu Cultural Exper
with TTsmekii Villye
parto,
Villag cittegetttpss////// /wwwww waw.kimaanriillticco www.kiomoolllggo..ooo. so ne/) traditional hang!
feist and cultural perforannes, and ralix in hotsspring
Figure 14: An example of effective human-agent collaboration in the Travel Planning task. The human seeks subjective recommendations from the agent experiencing Maori culture. The agent offers relevant suggestions, defers the final decision to the human, and updates the plan accurately based on the human's 's choice.
Task Description:
am studying Software Techniques for tmeeginng Hartiwore Pattorms. noad you to related work section this research proposal.
The mi propresi Chancher-sse wsei Word imneddiing for Splking hound batowarks
Characterlent lexal Compos ttonaits with Vouua Featunes and models reduing
The agen takes soveral actions create draft.
worth...
msybe sod more relsted work about embrdding methods
Safking Concelutional heurt betworks thxt offication
veterrk zran orter pooees put whr iepfoment weus ratuort janaus a energeefficn
Search ourrree ambbnding methocs splling neural netwnnlo' Liboryhddhppe (ftteer--)) --. Inker1-3
Meuro-syntell with sptking neurdl netwerrks
Coentetpraeet pocts are seperatve ane wiette deseuue from thee
embedking
gpedifi Include? If
like
Geadient Descent Solddng Neura Metwords
Ultraru provide opreheeennee view let know preferance!
Much www. fur wakor aniing sppan erper... vi do
compu
network mocals strkk
DbbrarDrroPPppr
search for papers introducing word cordieemboe embodddnn is charazter embodding KLP
SUbLE Eneryy Emfiien Ketual on Scoath Us Soltins Noural Netwocis
SearcPPppe/gueey very laned enteeeddnnnngneeeeee eribedding in NLP"
The human suggests narrowing the focus and deletes irrelevant papers.
UbronyAd patoooAAddPPPpttttsssee... Innksss Inks-]..D
LibraryAddPaper
Figure 15: An example of effective human agent collaboration in the Related Work task. The LM agent conducts search generate the initial draft and asks clarifying questions based on the hu- man's suggestions. The human reviews the collected papers and provides guidance leveraging their expertise.
31

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Published as a conference paper at ICLR 2026
Task Description:
receiving message, agent following
Plan family trip to New Zealand December 2025, We enjoy hiking and
Searchh
Shorte attrmativen Bouteburn Traci Glenarchy')
The Routzburn Track offers a few options. for less sireracus secerience, recommend the guided S-day liku opton....
EdtorrUpaate
text:". me Day Key Summit Track Experience
like
Routebum Track. The km millo] round trip offers vo the Southern
Are there shorter attemaliunn to the Bouteburn Track this 20 miles with feet wleattio
Alps
less
"Meals"" " Enjoy packeed lunch with glutan-fros and vugetariin options.
Maybe something more like miles and loss than Ik elavation
SeneTisammaleMesiag
message- "I tour great ilteraaivv ne your The Key Sumnit Trnck as shomor hile that right you'r looking
miles) round elevation gain of 400 meters 11.312 teet), ottering sturning stows of Southern Alps
fiuunda great ahermative for your The Key Summntt Track shorter hide than might be just wifeet you're looling fire. a lim 14 millss) round trip with an elecoeiion
managrable option group.
Would you lile to include this your tinerary??
400 meters (1, 212 fout), offer stunning siews of the Southern Alss. This should more manageable option for
The summary is contradictory the action.
Would you like include this Hirerary?
Relevant Fallure Modes (Communication):
C.1: Agents process tasks without informing users, resulting in lack of progress awareness.
Yes plaase add my tinerory
C.3: Agents not provide progress updates upon completion.
woen't clear that you finished this taok adding Kiyy Summit the tiieeney. Be sure to me know what you're doing!
C.S: Agents provide inadequato summaries after exccuting actions.
Figure 16: A partial example trajectory illustrating LM agent failures in the Communication category.
Description:
Analyce the tables to answer "Over which time period has gardening surpassed agriculture the contributar the non-native flore??
Load dotasets
tesporal trends, of pe.reed Seyt /htm// htt//////////////eeeeeee bomport trands, contingeryy, tabla.cav ptylogemetss tree
The
acttons lupgerreeeuteeee
pei road covl thow joryon dovyoneeenee aevkrinvosion
truaston
pathways
invaded iiche pa prtlways_df pe..mead (how (hom/jooymn joryen invade niche pathways.cs"")
seems like there enor with the colume name "nntraduction period' the datawt. Could you double- check column
Display the strst feu row asch datmet
temporal, trends, trends, df head(:, phylogenetic, www.e_eee..... tre.e_e..eeddd) head(), traasonn success, patppptttyyys.. sft.hwyys_sffiii sf.leel().
coottsconnneeeeee they evartly?
fia
niche, pww.wpyss.dee..c df
Omittdd.
looks 'smppraal al_trends__f df' parsed cornectly. try using tabs separiter when lnading the
[2]
teeporat_trend__ee trennd,eddde Lnttottuctinn, pertodd ).sun))
1110:: Kestrmer: "inbrodas slon..
Jupyter Execonecelln without solving and respond
The agent
Relevant Failure Modes (Situational Awareness):
'ntrrdducioo perfod' column Multtindes, required aperation, mightwant data pivoted
SA.1:Agents disregard context, tresting each
5A.2: Repetitive queries arise from neglecting prior interactions
reshape
feedback.
SA.3: Agents to process multiple user messages cohesively.
The human has to send a similar message again.
Figure
17:
A partial example
trajectory
illustrating
LM
agent
failures
in the
Situational Awareness category.
32

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Published as a conference paper at ICLR 2026
Task Description:
After receiving this message, the agent takes the following actions:
investigate multi-
besed ageni, please give me some literature review
Omppopeeiram
tile TouchUpp-i improving, fewiure Representation through Cantiii
Finetriing) "I
agont
ScordhPaper
LirurykddPPape
BropPaperfommbreny
Touchhu
Representation
want the paper that foous mutti-moda domais, only text books.
Fincturingll
Action Status Falled ("action, error' Talll drop Touchiip-5 S: Festure Representat am through GrapheCentric firettuinng Make sure this paper voists the lis and spalled cerrectly))
agent SearchPaper appr again,
DropPapefrommbrenyl
titee ttlue"toucUpp Touchhp-ft inprwving fewture Depresetation trough Craph-Centtic fineturing!
papers.
Action Status: Folled l"action error. "Fall drop Touchup Improving Resture Representation Graph Centric Rieetniing, Make this the spelled coneetlly)
LUM--based papers
images you pleaserevi lirrry
The agent keeps repea ting the same action that previously caused error.
these pap allgn your revearch focud? ane speci mrew or addiitinn papars you please let know.
include,
Relevant Fallure Modes (Planning):
PL.2: Lack of task planning results repeated omitted actions. Agents revert previous actions crrors identified, lacking initiative to correct without explicit instructions.
lraay commanns my requirements that ony multi model papars
that
Figure 18: partial example trajectory illustrating LM agent failures in the Planning category.
Task Description:
Available Environment Options for The Given Task
study US post feste-level and national statisties provided by the U.S. of Labor
considar completa
500
To consider the report complete, can review it lnst time to enuure all key points are zover rxd and the formotting consistont. Once you're sotislied, you can docament prefuer format. this feel tree
know!
how to esport the document a pdl format
To export the document is FDF tormat, you con tollow these steps depending on the software you' te using 1. *evicrosoff Word"", 6o Hile' 'Save As solect GGoggle File' Downlozdi "PF Document [adtr: *Othhr Text Siitors** Look Save option and choose the
"oor from drmedown menu of
Docs**;
Relevant Failure Modes (Environment Awareness]: Agents the constraints of availabee tools and resources.
the feasiblity of requests within
Agents propose actions they within the environment. Agents fulfil request.
options page
required
The agent proposes actions that are unavallable the environment, causing
Figure 19:
partial example
trajectory
illustrating
LM
agent
failures
in the
Environment Awareness category.
33

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Published as a conference paper at ICLR 2026
Task Description: passar
collaboration
process and the final task outcome is also in English:
Maldivas
Malia
Hi there! I'm exdtte to helo you plan romomik summer vocation the Moldives. Could you please share any specific preferences reguremento you have for this trip? For exampte, are there particular acttvit es you're interested n, or any apcilie resorts islando you have in mind? This will help me create personalized hiieerary for
Cleak Pepie
Cratendie
mocan suuasaa sui pisecca you parepe
welh Lene
Gostaria que fossem 7 noites, com pequeno almogo wistta gulada pela
Cocorn sourhh arer tuo stccee
Bup cu wiih
sani.
Searchultired onfquury- "romantic reaorts Maldives with breakfastt and guided island tours")
Canar fripp thanee siate the matrr
bulles
with beaelio
Gostaria saber tembim a prepe por nale mum ruser estrelas
Bener frow sen centector our
some great aptions duster nearts Maldives, such ot
(Personalication::
messages and the human speaks Portuguese.
agant speaks English
P.1: Agents rely on rigid templates that do not adapt to individual user needs.
Figure 20: A partial example trajectory illustrating LM agent failures in the Personalization cate- gory.
34


Reviews:
[{"review_id": "rbi5mjfb2r", "reviewer": "Reviewer_9bBh", "summary": "This paper proposes CoGym, a framework for implementing dual-control, non-turntaking environments. The authors implement three tasks (travel planning, writing, data analysis) and show that multi-agent or human-agent collaboration in these tasks outperforms non-collaborative agents.", "questions": "- I think the most interesting section is around \"discovery of effective patterns\" -- these insights get at some of my questions above around when dual-control, non-turntaking interaction is necessary. I would love to see more involved discussion or understanding of this!\n- Conclusions drawn from the simulated results are slightly confusing/potentially misleading as described right now (Sec 5.1.1).**\nBecause this is collaboration with user simulators, these results are better described as \"failure modes/strengths of multi-agent vs. single-agent systems.\" I think these results are pretty interesting and important, but better framed as disentangling how much of task failures are due to the model having different capabilities when it has to converse in dialogue (vs. operate in a single-agent setting), and how much of task failure is due to _collaboration_ specifically (e.g. failing to ask the right questions, share information with the user, etc.). As written, it sounds like \"collaboration in general leads to better task performance,\" but since this is \"collaboration with another model,\" I don't think it should be read as such.\nThis confusing conclusion also appears in the introduction: \"In the simulated condition...collaborative agnets have a lower delivery rate as human-agent teams sometimes fail to reach their goals\" (but in the simulated condition these are *agent-agent* teams?)", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 2, "strengths": "- tackles an important problem: understanding human-AI collaboration and building benchmarks that better align with how it is natural to use the models\n- interesting and well-motivated setting/idea: dual-control and non-turntaking interaction\n- well-designed environment abstractions (notifications)\n- broad range of tasks (travel planning, writing, data analysis/coding)\n- nice new evaluation metric for initiative in collaboration (initiative entropy)", "weaknesses": "**Core contribution of the paper is unclear**\n\nI'm not sure what point to take away from this paper. I agree with the introductory points that collaboration in general is important, but CoGym specifically focuses on \"dual-control, non-turntaking collaboration.\" This seems to be the main contribution of the framework relative to other benchmarks with user simulators, but the experiments mainly focus on evaluating collaboration vs. no collaboration, rather than the dual-control/non-turntaking aspects. I think the new ideas in the paper are actually quite interesting, but I'm not sure what to make of it and what we should expect the community to do with CoGym.\n\nDo the authors think that:\n- all collaborative tasks should be dual-control, non-turntaking interaction?\n- a specific subset of collaborative tasks? what subset? When should one build environments into Co-Gym vs. prefer turntaking interaction?\n\nNotably, the task performance difference between turn-taking and non-turntaking is negligible. I'm not surprised that non-turntaking interaction is preferred since it's less constraining, but I think the point would be stronger if the authors could identify situations where non-turntaking interaction is important for performance.\n\nThis is made more confusing by the fact that the authors repurpose tasks designed for the single-agent setting. These tasks fundamentally are solvable without collaboration, but the paper makes them into collaborative tasks by hiding some relevant information. The paper does not focus on this design decision, but I think the choice of what information to hide importantly determines what kind of collaboration is necessary. For example: does the user just need to tell the agent a few relevant facts, or is there more complex coordination necessary (motivating dual-control/non turn-taking)? One idea: maybe the user needs to make fine-grained edits in order to communicate intent, which would make dual control very useful.\n\n**Limited understanding of how well the user simulator matches real humans in this setting**\n\nThe analysis of the alignment between failure modes of simulated/real condition is helpful, but very limited. I imagine that there are a lot of differences between user simulator and human behavior, especially in this new async setting. I think it's important to better understand -- if we do evaluation in CoGym, where and when we can generalize these insights to real humans -- not just in error modes, but in *how* the interaction happens, for instance. Even more qualitative analysis would be helpful!\n\n**I think the paper has a lot of potential but I would have loved to see deeper thinking about these points to warrant a strong accept.**", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fZelkxSOMs", "reviewer": "Reviewer_qQDZ", "summary": "The authors introduce a new framework for developing and evaluating human-agent collaboration ability of LLM agents - Co-Gym. Co-Gym provides an agent-agnostic interface for collaboration, allows for non-turn-taking interaction, and an evaluation suite for assessing outcomes and processors. To enable coordination in a non-turn-taking fashion, they use a notification protocol to notify the agents in either shared or private manner of various events.\n\nThe authors evaluation on three tasks - creating travel plans, writing related works sections, and analyzing tabular data. The metrics used are delivery rates and task performance (quality). The authors also use a measure for initiation entropy and overall satisfaction of the human collaborator on Likert scale.\n\nThe results show that in simulation (mocked LM-based human interaction using static databases with privileged information), the collaborative agents have low-delivery rate compared to fully autonomous agents, but the quality of outcome is higher for the human-agent teams. In the real-world, the delivery rates for collaborative agents is better. The authors also analyze failure modes and show that communication and situational awareness are the most common issues.", "questions": "1. How can we extend this framework to environments which are not text-only?\n2. How do we work with continuous action spaces within this framework?\n3. How can the readers gain some confidence on the sim-only evaluation (done using GPT-4o)?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Co-Gym allows for dynamic non-turn taking and dual-control interactions, which provides high level of flexibility for the human-agent collaboration.\n2. There is a concept of private and shared information in the collaboration, which is an interesting and realistic aspect of the framework.\n3. It is a unique and interesting finding that collaboration works better in the real-world than in simulation for their tasks.", "weaknesses": "1. The tasks in Co-Gym can only be textual, including the description of states, instructions, action space, etc. It is harder to evaluate human-agent collaboration with Co-Gym in real-world or vision-based environments, where the states can not be defined using text.\n2. The tasks that the authors choose to evaluate on are pretty limited. While more tasks can be (and have been) added to Co-Gym, a more thorough evaluation is warranted to demonstrate the effectiveness of the approach and the ability of this framework to evaluate the human-agent collaboration ability.\n3. The collaborative agent in the manner that Co-Gym implements them fails to achieve higher success rates than a fully autonomous agent in simulation, casting a doubt on the approach and the task quality that have been added to the framework. It also shows that human-involvement is necessary to evaluate the agents' ability to collaborate, which casts doubt on the overall approach.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zc50yveM7H", "reviewer": "Reviewer_ytEG", "summary": "This paper presents Collaborative Gym (Co-Gym), a framework for developing and evaluating human–agent collaboration. Unlike most existing benchmarks that assume turn-taking or single-control settings, Co-Gym enables parallel and bidirectional communication between humans and agents in shared task environments. The framework supports both simulated (with LM-based user simulators) and real (interactive web application) conditions. Experiments on three representative tasks—travel planning, related work writing, and tabular analysis—show that collaborative agents consistently outperform fully autonomous agents and turn-taking workflows in overall task quality and user satisfaction.", "questions": "The paper reports that collaborative agents have lower delivery rates than fully autonomous agents, yet achieve higher task performance when tasks are completed. This raises questions about whether the framework’s coordination or timeout mechanisms limit the collaborative agents, and whether such outcomes are artifacts of the environment design rather than true behavioral inefficiency.", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper proposes a well-designed and extensible infrastructure that supports both simulated and real human–agent collaboration, complete with an evaluation suite for outcomes and collaboration processes.\n\n2. The comparison between turn-taking and current paradigms, along with process-level metrics such as initiative entropy, provides valuable insight into how interaction structure impacts collaborative performance.", "weaknesses": "1. The ablation study on turn-taking could be more comprehensive. It appears that the turn-taking version involves alternating actions without explicit communication. For a fairer comparison, it would be useful to include a variant that allows turn-taking with communication, to isolate the effect of interaction timing from that of message passing.\n\n2. The error analysis is rich but mixes issues caused by the framework design (e.g., communication protocol, observation structure) with those caused by inner agent reasoning (e.g., LM limitations). A clearer discussion of which errors are systemic versus agent-specific, and potential framework-level mitigations (e.g., proactive confirmation policies, adaptive notification thresholds), would strengthen the interpretation.\n\n3. Task diversity: All three benchmark tasks are text- or knowledge-work oriented. It would be valuable to include more interaction-heavy or dynamic scenarios (e.g., collaborative gaming or creative design) to test whether the framework generalizes to conversationally rich or time-sensitive collaboration domains.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SeSiBgiid7", "reviewer": "Reviewer_eBt5", "summary": "This paper proposes a novel asynchronous, non-turn-taking framework for evaluating collaborative agents. The system design features a message-passing mechanism, a notification protocol (supporting wait-delay actions and private-public divisions), and an interface accommodating both real-human and LLM-simulated users.\n\nThrough evaluation on three instantiated tasks, the authors find that: 1) While collaborative agents struggle with task completion, they yield better quality on completed tasks. 2) The simulated evaluation setting achieves high fidelity to evaluations with real humans. 3) Communication and situational awareness remain prevalent issues in human-AI collaboration. 4) The non-turn-taking design incentivizes more performant and satisfying collaboration.", "questions": "1. Could the authors provide a qualitative example or a concrete walk-through of how the \"Initiative-Taking\" metric is computed? This would help build intuition for this metric (i.e., why this metric is useful and necessary from an intuitive sense).\n\n2. The claim on L395 (\"Consistent with the simulated condition...\") appears to contradict the data. In the simulated condition, collaborative agents \"struggle to complete the tasks,\" but in the human-AI condition, the task delivery rate is 100%. Please clarify this inconsistency.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Overall, this paper is well-written and easy to follow. The framework design rigorously considers key aspects of human-AI collaboration from prior studies (e.g., proactive human control, non-turn-taking), while providing flexibility for adding new tasks. The experimental findings are particularly insightful, illustrating important lessons and failure modes in current human-AI collaboration.", "weaknesses": "From a high-level perspective, this is a strong paper on *human-AI collaboration*. (I give my scores based on this calibration) However, since it is framed as a contribution to *infrastructure/software*, further improvements are needed to strengthen the paper:\n\n1. **Lack of Infrastructure-Level Detail**: As an infrastructure paper, the programming model and system design are not sufficiently detailed. Key content is relegated to the appendix, leaving it unclear how a new task is practically implemented. The code example (Figure 2) is too high-level to understand the core mechanics (e.g., how the `event_loop` and `event_handler` operate). Furthermore, the notification protocol is not analyzed from a systems perspective: How are potential deadlocks or inefficiencies handled in highly uncertain, massively parallel environments? How are message backlogs managed? The choice of Redis, a lightweight in-memory database, also raises questions about system robustness and fault tolerance.\n\n2. **Unclear Implementation and Justification for Dual Control**: While dual control is an important feature for human-AI interaction, its implementation in an asynchronous, non-turn-taking system could bring extra complexity. The paper does not discuss the engineering challenges (e.g., reliably sending a 'pause' signal to a multi-threaded agent). Moreover, the necessity of this feature for the selected tasks is not clearly justified. The paper notes it improves delivery rates and user satisfaction for human collaborators, but it lacks a clear ablation study in the human evaluation setting to quantify the benefits of dual control.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)