Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance
Authors: Yaxi Lu
Shenzhi Yang
Cheng Qian
Guirong Chen
Qinyu Luo
Yesai Wu
Huadong Wang
Xin Cong
Zhong Zhang
Yankai Lin
Weiwen Liu
Yasheng Wang
Zhiyuan Liu
Fangming Liu
Maosong Sun
Venue: ICLR 2025 Poster (2025)
Keywords: Human-Centered NLP
Dialogue and Interactive Systems
Resources and Evaluation
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Building and benchmark a proactive agent to predict user instructions by observing user actions and environment.

Abstract: Agents powered by large language models have shown remarkable abilities in solving complex tasks. However, most agent systems remain reactive, limiting their effectiveness in scenarios requiring foresight and autonomous decision-making. In this paper, we tackle the challenge of developing proactive agents capable of anticipating and initiating tasks without explicit human instructions. We propose a novel data-driven approach for this problem. Firstly, we collect real-world human activities to generate proactive task predictions. These predictions are then labeled by human annotators as either accepted or rejected. The labeled data is used to train a reward model that simulates human judgment and serves as an automatic evaluator of the proactiveness of LLM agents. Building on this, we develop a comprehensive data generation pipeline to create a diverse dataset, ProactiveBench, containing 6,790 events. Finally, we demonstrate that fine-tuning models with the proposed ProactiveBench can significantly elicit the proactiveness of LLM agents. Experimental results show that our fine-tuned model achieves an F1-Score of 66.47% in proactively offering assistance, outperforming all open-source and close-source models. These results highlight the potential of our method in creating more proactive and effective agent systems, paving the way for future advancements in human-agent collaboration.

Published as a conference paper at ICLR 2025
PROACTIVE AGENT: SHIFTING LLM AGENTS FROM REACTIVE RESPONSES TO ACTIVE ASSISTANCE
Yaxi Lu', Shenzhi Yang", Cheng Qian', Guirong Chen?, Qinyu Luo', Yesai Wu', Huadong Wang', Xin Cong', Zhong Zhang', Yankai Lin", Weiwen Liu", Yasheng Wang", Zhiyuan Liu Fangming Liu Maosong Sun'
Department of Computer Science and Technology. Tsinghua University
2 Gaoling School of Artificial Intelligence, Renmin University of China
3 Huawei Noah's Ark Lab, Peng Cheng Laboratory
1yx23emails wel.ss.tsnnniii tsinghua. edu. en, mr1yk4238gman gmail...mmmm....... com,
1 singhua edu. on, Langminghk&@gaain com
ABSTRACT
Agents powered by large language models have shown remarkable abilities solving complex tasks. However, most agent systems remain reactive, limit- ing their effectiveness scenarios requiring foresight and autonomous decision- making. In this paper, tackle the challenge of developing proactive agents capable of anticipating and initiating tasks without explicit human instructions. We propose novel data-driven approach for this problem. Firstly, we collect real-world human activities to generate proactive task predictions. These predic- tions are then labeled by human annotators as either accepted or rejected. The labeled data is used to train a reward model that simulates human judgment and serves as an automatic evaluator of the proactiveness of LLM agents. Building on this, we develop a comprehensive data generation pipeline to create a di- verse dataset, ProactiveBenchh, containing 6, 790 events. Finally, we demonstrate that fine-tuning models with the proposed ProactiveBench the proactiveness of LLM agents. Experimental results show that our fine-tuned model achieves an F1-Score of 66.47% in proactively offering assistance, out- performing all open-source and close-source models. These results highlight the potential of our method in creating more proactive and effective agent systems, paving the way for future advancements in human-agent collaboration.
significantly elicit
INTRODUCTION
The emergence of large language models (LLMs) like ChatGPT OpenAI 2022) has significantly advanced the development of autonomous agent (Chen et al. 2023; Hong et al. [023 Zhang et al.
2023 Wu et al.) 2023). These LLM- agents understand human instructions, make plans, explore environments, and utilize tools to solve complex tasks (Wu et al. 2023 Li et al. 2023 and have shown substantial potential in various applications such robotics (Firooii et al. 2023
personal assistants (Li et al. 2024). and process automation (Ye et al. 20236)
Currently, most existing LLM-based agents predominantly work in the reactive paradigm: they require explicit human instructions to initiate task completion and remain dormant in terms of pro- viding services until prompted by user instructions (Ouyang al. 2022). This paradigm limits their capacity for proactive assistance and autonomous service provision in the absence of direct human instructions. We argue that LLM-based agents should be proactive, capable of autonomously initiating tasks by understanding and responding to their environment. For instance, illus- trated in Figure [] the reactive agent should wait for explicit instructions from the user to execute tasks such as "show unread emails" or "schedule meeting with John". In contrast, a proactive agent would predict its task automatically by noticing an email from John suggesting a meeting and automatically offering to schedule it. This ability of context awareness (Schilit & Theimer, 1994) enables the proactive agent to interpret signals and proactively propose and execute tasks without
"Corresponding authors.

---

Published as a conference paper at ICLR 2025
From John would like to request meeting discuss collaboration on Otoober 9th at 3:00 PM at Starbucks.
Environment Oservvatoon
Can you help me to schedule a meeting with J ohn on O October 9th at 3:00 PM at Starbucks?
Would you need my help schedule meeting with John October 9th 3::0:00 PM Starbucks
User
Proactive Agent
Of course, will update your cal endar
Yest Please schedule it!
TFC Reactive
User
Agent
Solving tasks after receiving instruction
Self-initiating to solve tasks before instructing
Figure 1: Comparison of agent systems with two types of human agent interaction. Reactive agents passively receive user queries and then generate responses. Proactive agents infer tasks based on environmental observations and propose possible assistance requests accordingly.
explicit human instructions. Thus, not only significantly reduces the cognitive burden on the user but also identifies latent needs not explicitly articulated by humans. Consequently, the proactive agent could provide more comprehensive and seamless services to the user.
In this work, propose novel data-driven for- malization for developing a proactive agent that anticipates user needs and takes the initiative by suggesting tasks or providing information without explicit requests. Our approach centers on con- structing ProactiveBench, allowing to evaluate and enhance the agent's 's proactive behavior. Firstly, collect real-world human activity data in three settings: coding, writing, and daily life. This in- cludes but is not limited to, mouse inputs, clipboard content, browser activity, etc. Then, build an LLM-driven gym gener- ate events that reflect the raw real-world contexts collected. We obtain total of 233 events across 12 scenarios as the set of the Proac- tiveBench. To further refine the proactive behavior of the L..M-bsed agent, we construct various events and proactive tasks under synthetic contexts with the gym. By iterative generating more events and predictions, we obtain up to 6, 790 events the train set of the ProactiveBench, as We fine-tune the LLaMA-3, 1.8-A-..--B-IInttrue (Touvron et al. 2023 Meta| (024) and
Subsets
Scenarios
Entries (Tr/Tss
Agent Model
136
6,790
Coding Writing
46
2,275
46
2,354
Daily Life
44
2,161
Reward Model
1,640 / 120
keyboard and
Statistics of the ProactiveBench, which includes three distinct settings: Coding, Writing, and Daily Life.
Table 1:
shown in Table | the Beven2-BB-Instreen (Bai al. 2023 on this training set refine their proactive behavior.
To automatically evaluate the proactiveness of LLMs, we train reward model that achieves up to 91.80% consistency with human judgments in terms of F1-Score. Using this model, we compared various language models on ProactiveBench. The results indicate that even the latest open-source models struggle to effectively predict proactive tasks. For instance, the L.L.MA33.1-88 model only achieved a 65.06% F1-Score on ProactiveBench. In contrast, our fine-tuned model demonstrated sig- nificant improvements, achieving 66.25% Fl-Score. Besides, our fine-tuned Qwen2-7B-Instruett model achieves 66.47% FI-Score, outperforming all existing open-source and closed-source LLMs. This underscores the effectiveness of data-driven approach in developing proactive agents, high- lighting their potential to enhance user experiences across various applications.
2 RELATED WORKS
Recent advancement in large language models (OpenAl et al. 2023: Chowdhery et al. 2022: Tou- vron al. 2023 Zeng al. 2023) has shown great progress in complex reasoning, task plan-
2

---

Published as a conference paper at ICLR 2025
ning (Wei et al. 2022 Gao et al., 2023 Yao al., 2023b Liu et al. 2023 Ye et al. 2023a
2024b). etc. Consequently, a tool utilization (Schick et al. 2023] Qin et al. 2023b e| Qian et al.
growing number of agent systems have been developed to utilize these models to tackle diverse tasks like automatic web search (Qin et al. 2023a). software development (Qian et al. 2023 Chen et al.) 2023), behavior simulation (Park et al. 2023). Despite these advancements, most current agents remain predominantly reactive, passively following human instructions without sufficient context awareness (Schilit & Theimer 1994) proactively meet user needs. These reactive agents typically wait for explicit user commands, which can lead to inefficiencies as task complexity increases. As result, users must constantly provide specific inputs, hindering the flow of interaction. In response, several works have attempted to improve the proactivity of agents. For example, Xuan (Zhng et al.) 2024) proposes proactive agent planning, where agents refine their tasks by actively seeking infor- mation to better understand user intentions. Cheng Kuang and Zhi Rui (Wu et al. 2024) study how to propose proactive support in text-to-sql and propose a novel metries Area Under
Curve (AUDBC). Other studies (Wu et al. [202T] Chen al. 2020; Wiethof et al. [22TT] Qian al.) 2024a) focus on enabling multi-turn interactions to clarify ambiguous user instructions, which fur- ther increased cognitive load for the user. However, these works still require the user give an initial query before interacting with the agent. Our approach takes different direction by focusing on anticipating potential tasks based on monitoring user activities and environmental states, which allows the agent to proactively initialize the interaction and provide assistance.
To clarify, there are also previous works (Deng al. 2023) that use the term "Proactive Agent" to describe their dialogue systems. However, most of these efforts (Bi et al.) [202]] Ren et al. [002]]
LI & LIAO; Liu al. 2024) aim to enhance the helpfulness or quality of responses in a proactive manner, which differs from our focus on task anticipation and initiation. 10814
3
METHODOLOGY
3.1
TASK DEFINITION
In our proposed proactive agent, which is distinct from traditional agent systems powered by large language models that rely on explicit user instructions, we investigate a new scemario where the agent autonomously predicts tasks users might assign, aiming to offer assistance proactively, as depicted in Figure I The proactive agent's 's mission is to give predictions based on the user's 's activities As, environmental events E,,, and state S,, which can be formalized as:
(1) where fe represents the proactive agent, parameterized by 0, and P: denotes the prediction about possible task at time f. It should be noticed that the prediction Pt can be the predicted task or nothing if the agent believes that no task is needed. Specifically, user activities A, contain the user's s interactions with the environment and the agent, like keyboard input or chatting with the agent. Environmental events E, contain the event that the proactive agent captured, ranging from receiving a new email to an application closed. Environmental state S, represents the state of the current environment, like the file system state or the content of opened web pages.
Pt = fe(E,, Sc,
In our proactive agent framework, the objective is to maximize the user's acceptance rate of the proposed tasks. Given the user's historical activities A,, current environmental state S,, and the prediction proposed by the proactive agent Pt. the user makes a binary decision:
Rs = g Pt, Ar,
(2)
where R, is a binary variable indicating acceptance (R, 1) or rejection (R, = 0) of the prediction. To unify the handling of cases where the prediction P, contains no task and where it contains a task, we introduce auxiliary variable N₂ that indicates whether the user needs assistance:
N₂ = the user needs assistance.
N₂ 0 if the user does not need assistance.
The user's acceptance Rg is then defined as:
[1 if (P, / 0 and user accepts Pt) (P: 0 and Nr = 0)
R₂ 0 otherwise
3

---

Published as a conference paper at ICLR 2025
In this way, if the prediction Pt contains task (i.e., the agent believes the user does not need assistance), we check the user's actual need for assistance Nt. the user indeed does not require assistance (N, 0). this is marked acceptance (R, 1). Conversely, if the user requires assistance (N₂ = 1), this is marked as rejection (R, = 0). Our proactive agent aims to maximize the expected acceptance rate of the proposed tasks:
max B [
(3)
3.2
PIPELINE OVERVIEW
Environment Gym
Predicted
Event
Tasks
Event Gen.
Proactive Agent
User
Scenasia
Support
Agent
Scenario
Env. Status
word Data
10B
Update
Accepted
WP
Action
Task
Action Exec.
Jobb
Figure 2: Overview of the data generation process. Taking daily life as an example, this process includes modules such the initial scenario and job setup, events generation, proactive prediction, user judgment, and action execution.
To enhance the proactive capabilities of our large language model-powered agent, we adopt data- driven approach by building an automatic data generation pipeline. This pipeline simulates user activities and responses to the tasks predicted by the proactive agent across various scenarios. Once a prediction is accepted, we simulate the agent performing the task by interactively generating new events within the simulated environment. Subsequently, new user activities created based on historical events, allowing the proactive agent to generate further predictions. Through this pipeline, models can learn when to generate predictions and which predictions are likely be accepted by users. Specifically, our pipeline consists of three components:
(1) Environment Gym: This component simulates events within specified background setting and example events, providing a sandbox for proactive agents to interact. It has two key functionalitiess (i) Event Generation: creating potential sequences of environmental events tailored specific sce- narios; (ii) State Maintenance: updating and maintaining the environment's state when new user activities are generated or when the agent performs actions during task execution.
This component is responsible for predicting tasks that the user might assign the agent based on the user's needs as inferred from the event history. It also interacts with tools to complete specific tasks assigned by the user.
(2) Proactive Agent:
(3) User Agent: This component simulates the user's activities and responses based on predefined user characteristics. It decides whether to accept and execute the tasks proposed by the agent.
In the following sections, we introduce the details of each component.
3.3 ENVIRONMENT GYM
To improve the quality of events generated by the environment gym, first col- lect real-world events as reference. We developed a monitor software based on Activity Watche which allows to capture the details of user interactions with computer systems, including key- board and mouse operations, visited web pages, and used development tools. To enhance the se- mantic richness of the collected data and facilitate parsing by large language models, we further merge the raw data into logically coherent segments. Additionally, we utilize language model to
Event Collection
https://githubs Act http

---

Published as a conference paper at ICLR 2025
translate the structured data into more natural textual descriptions. This process not only improves the interpretability of the data but also makes it more suitable for subsequent usage. Here is a sample event (more samples can be found in Appendix E.T.
Time: 1717378968.20
> Event: The user opens a new browser tab and navi gates to the Google, e.
Scenario Generation After collecting reference events, rather than directly generating specific events, we generate a realistic interactive scenario to provide sufficient background information first for further generation. To build such scenarios, we first prompt GPT-4o (OpenAI, 2024) with the seed jobs collected from human annotators to create various jobs the user might perform under a specific category, like coding, writing, daily life. Then, generate all possible entities that the tasks might involve, e.g. browser, software, and tools for the agent to perform tasks. Next, we refine the scenario by adding more details like entity status date time to improve the details. Finally, the collected events are also provided to generate example events under each particular context for future events generation. This allows to control the granularity of events that will be generated and maintain the diversity of the scenarios. See Appendix C ffor the specific prompt used.
Event Generation When it comes to specific event generation, we start with user activity gener- ation. For each scenario, the user agent is first requested to describe its activities and actions A at time i. to complete the job in the simulated environment. Then, the gym accepts the user's ac- tivities and actions generate detailed events by one. As depicted in Figure 2 the gym is tasked to generate logically correct and fluent events according to historical events and the current environmental state. The key to improving the realities of the events generated and adapting dif- ferent environments is utilizing the example events we generated based on collected events during scenario generation. Before generating events, we randomly sample the generated example events for the specific scenario and request the gym to produce new events according to them. Once new event Fe+1 generated, the gym updates the entities' status in the environments and repeats the process until there are events that can be generated with the provided user activities. This com- prehensive approach ensures that each subsequent event is not only appropriate but also contributes to coherent and logical progression within the scenario.
State Maintenance Another important functionality of the gym is maintaining the state of the en- vironment S,. During the scenario generation, the gym creates entities like browsers or development kits in the simulated environment, where each entity has its state and properties like the application version or the specific browser name. When a new event is generated, the gym should update the states and properties of them to provide feedback for further event generation. Specifically, gather historical state changes of related entities and prompt the GPT-4o to generate new states of the entities S with the new event. During the process, the simulated time will also be updated according to the granularity of the event. After that, the next event will be generated based on the latest environment state S,41-
first
3.4 PROACTIVE AGENT
The second component in our data generation pipeline is the proactive agent that predicts tasks the user might assign. As detailed in Figure 3 upon the agent receiving new event F, at time t, it first updates its memory with the event. To improve the quality of the prediction, it also accepts feedback from the user agent on its draft prediction. Combining new events with historical ones and conversations with the user, the agent incorporates user characteristics to raise potential tasks. the agent detects potential tasks, it will raise the task as a new event and wait for the judgment of the user agent. Otherwise, the proactive agent predicts no potential tasks and stays silent. Once the predicted task is accepted, the agent will execute the task within the gym, which generates multiple events about the interaction between the agent and the environment. During the data generation, the agent would constantly receive events from the Gym and predict potential tasks.
Task Execution As mentioned before, the proactive agent executes the predicted task once the user accepts. This process is mainly done through multi-tum turi-tuun interaction between the proactive agent and the gym. Specifically, the proactive agent will be provided with the tools generated during
5

---

Published as a conference paper at ICLR 2025
the scenario generation, like file system tools in the computer or access to the smart light switch, to interact with the simulated environment. Each time the proactive agent takes an action, the gym will generate new event, which is further processed by the gym and the user agent to update the environment state. After that, the proactive agent detects the new environment state S1+1 and takes actions according the events generated by the gym. This process ends when the user interrupts or the proactive agent finishes its tasks.
3.5
USER AGENT
The user agent is designed emulate users' activities A, and responses about the agent's prediction Pt, as illustrated in Figure 2 We prompt GPT-4o to generate activities and ac- tions for the provided task in the specific envi- ronment. The gym further processes the activ- ities and actions to generate a new event. Then the proactive agent predicts potential tasks ac- cording to the events. Upon receiving the pre- dicted task, the user agent determines whether to accept or reject it. If the user agent ac- cepts the task, the proactive agent will set up and execute the accepted task within the en- vironment gym. Otherwise, if the user agent declines the suggested assistance, the environ- ment gym generates new events autonomously without any interventions. In our settings, we collect judgment from human annotators and train reward model to simulate the judgment.
Memory
Environment
Predict Task
Gym
Execule
Detect Needs
within
Historical
New Event
Events
Draft Prediction
000
User
Feedback
Proactive
Historical
Agent
Agent
Conversations
Figure 3: Overview of the proactive agent frame- work. The agent monitors new events and updates its memory to predict potential tasks.
Specifically, to ensure the reward model aligns closely with human judgment, we generate and an- notate a dedicated dataset to indicate whether humans would accept the predicted task not. We utilize different language models to generate diverse predictions for each event. Between these predictions, We select 5 predictions with the minimum total cosine distance after embedding the predictions using with one of three options by three separate annotators: accept, reject, reject all. The reject all option is chosen when the annotator believes that the given events did not imply any possible tasks that the user might assign, aka N, accepted, we label the event E, with N, = 1. We use majority voting to make the final decision on each prediction. After all, the annotation results in dataset of 1, 760 entries, each containing event traces, task predictions, and decisions on accepting the predicted task from three distinct an- notators. Notably, our annotators achieve an impressive agreement ratio of over 91. 91.67% on the test set, underscoring the annotations' reliability and the dataset's robustness for further analysis. To further facilitate automatic data generation, we also prompt the GPT-4o to produce a more detailed explanation of the user judgment. Further details regarding the assessment of the reward model are available in Section
to determine our label target. Each prediction is annotated
in Section 3.1 Otherwise, one prediction is labeled as
EXPERIMENTS
4.1 REWARD MODEL ASSESSMENT
To automatically evaluate whether the predicted tasks and their timing are appropriate, seek to train a reward model capable of accurately imitating user judgments. To this end, we apply the user- annotated data train LLaMA-3.1 1-BB-...----I-tttuu (Touvron et al., (023) and compare it with several baselines to show its superiority.
Setting. We use the 1, 760 entries with human annotations and randomly split them into training set (1, 640 entries) and a test set (120 entries). We then train LLaMA-3.1-3 13.1-8B-Instrue on the training set to obtain reward model. We employ total batch size of 32, learning rate of le and

---

Published as a conference paper at ICLR 2025
GPT-4o
GPT-4o-mini
LLLMA-3.1-8B
L.aMA-3.1-700
Ours
Agree. MN
3.33%
56.67%
80.00% %
33.33%
80.00%
Agree. NR
100.00% %
56.67%
30.00%
83.33%
86.67%
Agree. CD
100.00%
86.67%
96.67%
100.00%
100.00
Agree. FD
0.00%
33.33%
13.33%
6.67%
100.00 %
Recall
100.00% %
71.67%
63.33%
91.67%
93.33%
Precision
50.42%
56.58%
54.29%
53.40%
90.32%
Accuracy 50.83%
58.33%
55.00%
55.83%
91.67% %
F1-Score 67.04%
63.24%
58.46%
67.48%
91.80% %
Table Evaluation results of different models on test the reward model. For the Missed- (CD), and False-Detection (FD) scenarios, we
Need (MN), Non- Non-Response (NR), present the agreement ratio between models and the major voting of our human annotators. Our model which is fine-tuned based on LLaMA-3. 1-1.....ttnttt--888 achieves the best F1-Score of 91 91.80%.
an Adam Optimizer with a 0.1 warm-up ratio. We train the reward model for 5 epochs to prevent from over-fitting We A100 GPUs on one node train for approximately 1.5 hours. The detailed prompt template is listed in Appendix[A] We use the test split to evaluate our adapted model and all the baselines. To be noticed, our human annotators achieve up 91.67% agreement ratio on the test set, demonstrating the effectiveness of our evaluation.
Metrics. We use the reward model to perform binary classification on whether to accept predicted tasks and compare its results with human-annotated results. This assesses how well the reward model aligns with human judgment regarding the suitability of the predicted tasks. We compare the judgments made by the reward models and humans to calculate the Recall, Precision, Accuracy, and Fl-Score. Additionally, we calculate the agreement ratio for the following cases:
Missed-Needed: N; - 1, P: 0, the user needs help, but the agent does not provide it. Non-Response: N₁ = 0, P: = 0, the user does not need help, the agent does not prompt.
Correct-Detection: N, 1, * and the user accepts the task predicted by the agent.
False-Detection: N, = 0, P. & 0,, the user does not need help but agent prompts.
Results. As Table shows, all existing models perform well on correct detection, but perform badly in other scenarios, especially in the false alarm scenario. After deeper analysis, find that existing models just can not infer what the user might need and tend to accept arbitrary help, even if it is very abstract meaningless 100% agreement ratio on false alarm scenario and a solid 91 880% F1-Score across all scenarios. We select our reward model for further analysis across the ProactiveBench.
current observation. In contrast, reward model achieves
4.2 PROACTIVE AGENT EVALUATION
Setting. We use the training set of ProactiveBench to obtain the Proactive Agent based the two open-source models: LLaMA-3,, 3. 3.B- Instructt and Qwen2-7B- Insttructt During training, we employ total batch size of 32, learning rate of 1e - 5, and an Adam Optimizer with 0.01 warm-up ratio. We train the model for 3 epochs. We use 1100 GPUs on one node to train for approximately hours. The detailed prompt be found in Appendix B The automatic evaluation of these metrics relies on the simulated judgment given by the reward model. All models are evaluated in our test split of the ProactiveBench. which contains 233 events collected in the real world. We employ the same prompt template and apply greedy decoding across all models.
Metries. We evaluate the performance of the Proactive Agent based on whether the user accepts its prediction. As described in Section 3.1 the user's acceptance R. contains four conditions. In our specific settings, Recall measures the proportion of actual needs for assistance that were correctly predicted by the agent, including cases where the agent predicts task and the user accepts it, well as cases where no task is predicted and the user does not need assistance. Precision measures the proportion of predicted tasks that were actually accepted by the user. Accuracy measures the overall
7

---

Published as a conference paper at ICLR 2025
conrectness of the agent's predictions. The False-Alarm measures the proportion of incorrect task predictions, specifically when a task is predicted but not needed. The F1-Score provides balanced measure of the goodness of the agent's proactive behavior. We use the reward model during the evaluation to automatically generate the user's judgment. Based on the confusion matrix, we report Recall, Precision, Accuracy, False Alarm, and F1-Score across all settings. The detailed calculation method can be found in Appendix B
Model
Recall
Precision
Accuracy!
False-Alarm'
F1-Score
Proprietary models
Claude-3-Sonne
27.47%
37.31%
52.42 %
62.69%
31.65%
Gaaude3..-Soonet
97.89%
45.37%
49.78%
54.63%
62.00%
GPT-44-mini
100.00%
35.28%
36.12%
64.73%
52.15%
GPT-4o
98.11%
48.15%
49.78%
51.85%
64.60 %
Open-source models
LLaMA-3.1-8B
98.86%
38.16%
39.06%
61. 61.84%
55.06%
-LaMA-3.118B-Proacce 99.06 %
49.76%
52.86%
50.24%
66.25 %
Qwen2-7B
98.02%
44.00%
43.61%
56.00%
60.74% 66.47 %
Owen2-7B-Proactive 100.00%
49.78%
%
50.22% %
Evaluation results of different models' performance on the ProactiveBench. The GPT-4o stands out for close-sourced models, achieving 64.60% F1-Score. For open-sourced models, our fine-tuned Qwen2-7B model achieves best result, with a 66.47% F1-Score.
Table 3:
Result. Table 3 compares various models on the test set of the ProactiveBench, which contains 233 events collected from the real world user. Close-sourced models like GPT-4o I or GPT-4o-mini all tend to predict proactive tasks actively. Most them succeed in assisting when the user needs but fail to stay silent when the does not require any assistance, resulting in relatively high false alarm ratio. For example, the GPT-4o-mini provides unnecessary assistance even when the events provided do not contain meaningful operations, like switching between software but doing nothing else. Another big issue is early assistance when no precise users' intents can be found in the given observation. This makes the proactive tasks proposed by the model seem too abstract or useless, resulting in relatively high false alarm ratio. The Claude-3-Sonne. (Anthropic, (2024) shows a different example of failing to detect the user's need and provide assistance that does not satisfy the user's expectation.
For open- sourced models, we evaluate the performance of the LLaMA-3.1 -nnstutttnt and Qwen2- Instruct-7B 7B before and after fine-tuning based on our synthesized data. As shown in table [ both models obtain an impressive improvement. For example, the L.LaMA-31 1-BB improves its F1-Score from 55.06% to 66. 25%. The results demonstrate the effectiveness of our data synthesis pipelines. As for the concern of being overly interrupted by the proactive agent, our fine-tuned models achieve solid progress in reducing the false alarm ratio, which is comparable to the performance of the GPT-4o. Besides, the finetuned Qwen2-7B also outperform the GPT-4o terms of the F1-Score, resulting in the highest F1-Score of 66.47% However, also observed the same pattern of models tends to provide as much assistance as possible, instead of providing necessary assistance when the user needs it.
In short, while most models can assist when needed, they still frequently offer unnecessary help. even when instructed to provide only essential assistance.
4.3 PERFORMANCE ANALYSIS
In this section, we analyse possible type of settings that could impact the performance of the proactive agent.
Predict Multiple Tasks. When comes to real-world applications, the proactive agent can provide multiple candidate tasks improve overall performance. To evaluate how models perform under
8

---

Published as a conference paper at ICLR 2025
Model
Settings
Recall
Precision
Accuracy?
False-Alarm-
F1-Score
GPT-4o-mini
pred
100.00%
35.28%
36.1 16.12%
64.73%
52.15%
pred @
99.32%
65.32%
66.52%
34.68%
78.80%
w/ RM
55.45%
63.54%
63.95%
36.46%
59.22%
pred@3. 3, w/ RM
100.00%
65.35%
66.09%
34.65%
79.05%
GPT-4o
pred @
98.11 %
48.15%
49.78%
51.85%
64.60%
pred @
100.00%
63.56%
64.81% %
36.44%
77.72%
w/ RM
56.76%
55.26%
57.61%
44.74%
56.00%
pred w/ RM
100.00%
63.30%
65.67%
36.70%
77.53%
LLMMA-3.1-SB
pred
98.86%
38.16%
39.06%
61.84%
55.06%
pred @
100.00%
52.79%
52.79%
47.21%
69.10%
w/ RM
77.08%
42.52%
47.64%
57.41%
54.81 %
@red@3, w/ RM
95.12%
61.58%
66.09%
38.42%
74.76%
Table 4: Comparison between different settings for each model. The setting "pred@1 1" means pre- dicting task at time. The setting "pred@3 @ means predicting 3 tasks at time. The setting "w/ RM" means we will provide feedback from the reward model to help better prediction.
this condition, allow them generate multiple candidate tasks at once, but than three to avoid a high cogmition burden for the user. In this setting, we let the reward model check the candidate tasks one by one. We label the result accepted if one of the candidate tasks accepted, and rejected if only all the candidate tasks are rejected.
As shown in Table 4 all models obtain solid improvement across all metrics when comparing "pred @ 1" with "pred@3".. Take the GPT-4o an example, it obtains higher accuracy and pre- cision while reducing its false alarm by providing diverse candidate tasks. The huge drops in the false alarm ratio, from 51.85% to 36.44% are mainly due to its improvement in providing proactive tasks. However, when comparing GPT-4o-mini with LLaMA-3.1 1-8B, we observed different degrees of improvement. These two models perform similarly when predicting only one proactive task at once, but show a nearly 9% difference in terms F1-Score when predicting multiple candidates at once. We analyzed the result and found that the ALaMA-3.1-88 tends to provide unexpected assis- tance when the user's ' need is unclear, which can not be improved by providing multiple candidates.
Feedback From the Reward Model. We also investigate whether the feedback from our reward model could help models improve their performance the ProactiveBench. This is done with the same logic as deseribed in Figure For each model, we first ask them to generate a draft prediction and obtain feedback from the user agent (which is built the reward model in this case). Then let the model refine its prediction to obtain the final prediction.
As shown in Table by adding the feedback from the reward model (settings with "w/ RM"", models generally reduce their false alarm ratio and improve the accuracy, but drop dramatically in terms of the recall. We observe that models stay silent once they receive feedback from the reward model. However, doing nothing is not always the optimal solution. The GPT-4o seems to fail to capture the possible user needs, leading to a performance drop in terms of F1-Score. For other models like GPT--o-mini and 1..MA-33.1-88 they deed obtain a marked improvement in terms of the F1-Score. Another setting combining the multiple-task prediction with the reward model ("pred@3, @3, 3, w/ RM"" shows more general improvement across the board. By integrating the reward model into the Proactive Agent, we can make the Proactive Agent more smartly detect user needs and reduce the false alarm ratio when news for developing the Proactive Agent.
not access the weight directly, which is good
4.4
CASE STUDY
In this section, explore two prevalent types of failures encountered in predicting possible tasks: the inability to detect user needs and making predictions at inappropriate times. More detailed failure modes can be found in Appendix

---

Published as a conference paper at ICLR 2025
The user opens browser tab and navigates to the payment Al documentation site. Eg: The usertypes 'OA authentication tutorial' into the search bar and presses 'Enter'.
E-1:
Bx-1: The user stay no action.
Eg: The user houble-clicks the 'Visual
Events
Events
Code' icon and do nothing else.
G GPT-4o-mini
PT-44
No help need, the making prugress methodically.
X
Provide a quick start guide for Visual Code
X
LLaMA-3-Proactin 3-Proactvve
LLaMA -3- Proactive
Help summarize the search result and recommend OAuth tutorials.
No Action
Failing to Detect User Needs
Inappropriate Proposing Time
Two types of failure: failing to detect user needs (left) and inappropriate proposing time (right). We compare the response between our fine-tuned ---MMM--..IIInntree with other models to show the refined proactive hehavior of the model.
Figure 4:
As illustrated in Figure (left), notable failure occurs when the GPT-4o model does not assist at crucial moments. For instance, when user is engaged in integrating payment API and requires tutorial for guidance, the model remains silent. Instead, our model successfully detects human needs and offers assistance. The underlying intention to minimize disruptions ironically leads to missed opportunities offer timely help.
Conversely, the right side of Figure 4 showcases instance of ill-timed prediction. Here, the GPT-4o-mini suggests an action when there are no user needs showing in events. This scenario underscores the possible unintended events existing in human activities. The model should judge whether there are possible tasks smartly to avoid unnecessary actions. These instances highlight the intricate nature of human activities and the sophisticated reasoning required for models accurately predict human needs. To navigate the delicate balance between being helpful and intrusive, models must develop a deeper understanding of user contexts and activities, ensuring their interventions both timely and pertinent.
CONCLUSION
We present an innovative approach to human-agent interaction by leveraging proactive task predic- tions that anticipate human needs. We introduce ProactiveBench, comprehensive dataset compris- ing 6, 790 events, designed to refine the proactive behavior of LLLM-based agents and establish an automatic benchmark for assessing model proactiveness. By iteratively generating events in synthe- sized scenarios, we create training data that enhances the proactive capabilities of our models. Our experiments demonstrate significant improvements in the agent's performance ProactiveBench, validating the effectiveness of our methods. Despite these advancements, our findings underscore ongoing challenges, particularly in minimizing inappropriate task proposals and ensuring task pre- dictions are contextually accurate. Future research should focus on enhancing the precision and timeliness of task predictions to improve the efficacy of the proactive human agent interaction.
ETHICS STATEMENT
In the construction of our dataset, we have adhered to strict ethical standards. The human annotators are students from the computer sciences area and obtain rewards for their annotations. We use ChatGPT in this research. All the original data utilized for the compilation of our dataset are either sourced from open-access platforms or have been used with the appropriate permissions granted. We have conducted a thorough review ensure that dataset is devoid of any harmful content or private information. Our commitment to ethical research practices extends to the respect for privacy, intellectual property rights, and the integrity of the data sources.
10

---

Published as a conference paper at ICLR 2025
ACKNOWLEDGMENT
This work was supported in part by the Major Key Project of PCL (Grants PCL2024A06 and PCL2022A05), the Shenzhen Science and Technology Program (Grant RCJC2023121 180591800), the Postdoctoral Fellowship Program of CPSF (Grant G2B20230343) and the China Postdoctoral Science Foundation (Grant 2023M741945).
REFERENCES
Anthropic. Introducing the claude-3 family, 2024. URL htttps://ww.anthrrgrtt.
news/co htt//claudeeeee 3- famill
Accessed: 2024-06-16.
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chenggiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang. Shijie Wang. Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang. Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou, and Tianhang Zhu. Qwen technical report. ArXiv preprint, abs/2309.16609, 2023. URL
http://axwvv..rr/aa///233//3/ 16609
Keping Bi, Qingyao Ai, and W Bruce Croft. Asking clarifying questions based on negative feedback in conversational search. In Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval, pp. 157-166, 2021.
Weiwen Chen, Mohammad Shidujaman, Jiangbo Jin, and Salah Uddin Ahmed. A methodological approach to create interactive in artificial intelligence. In Constantine Stephanidis, Don Har- ris, Wen-Chin Li, Dylan D. Schmorrow, Cali M. Fidopiastis, Panayiotis Zaphiris, Andri Ioannou, Xiaowen Fang, Robert A. Sottilare, and Jessica Schwarz (eds.), HCI International 2020- Late Breaking Papers: Cognition, Learning and Games, pp. 13-31, Cham, 2020. Springer Interna- tional Publishing. ISBN 978-3-030-60128-
Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang. Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, and Jie Zhou, Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors, 2023.
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. Palm: Scaling language modeling with pathways. ArXiv preprint, abs/2204.02311, 2022. URL
org/ abs/ 2204. 02311
Yang Deng, Wenqiang Lei, Wai Lam, and Tat-Seng Chua. A survey on proactive dialogue sys- tems: problems, methods, and prospects. Proceedings of the Thirty-Second International Joint Conference Artificial Intelligence, pp. 6583-6591, 2023.
Roya Firoozi, Johnathan Tucker, Stephen Tian, Anirudha Majumdar, Jiankai Sun, Weiyu Liu, Yuke Zhu, Shuran Song. Ashish Kapoor, Karol Hausman, et al. Foundation models in robotics: Ap- plications, challenges, and the future. ArXiv preprint, abs/2312.07843, 2023. URL https:
2312.0784333333
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. PAL: program-aided language models. In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (eds.), International Conference on Machine Learning. ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, vol- ume 202 of Proceedings of Machine Learning Research, pp. 10764-10799 PMLR, 2023. URL http http://otoeedeeeooo ngs 110..mm.. ml wrw.sres///eem 020 gaw.f3....f..m....o html
Wenyi Hong, Weihan Wang, Qingsong Lv, Jiazheng Xu, Wenmeng Yu, Junhui Ji, Yan Wang, Zihan Wang, Yuxuan Zhang, Juanzi Li, Bin Xu, Yuxiao Dong, Ming Ding, and Jie Tang. Cogagent: A visual language model for gui agents, 2023.
11

---

Published as a conference paper at ICLR 2025
Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. CAMEL: communicative agents for "mind" exploration of large language model society. In Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine (eds.), Advances in Neural Information Processing Systems 36: Annual Conference on Neural In- formation Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10- 16, 2023, 2023. URL BtLLthtt:///pppptt nips. wwwp:es..pppppcpppppeepoecc 6362leee0016ef77cccbbaaaaeeeeee htt.a//cD.....eeeeeeeeeeeaaaaaa- Confereece- htm
files//pperr/2233112
Yuanchun Li, Hao Wen, Weijun Wang, Xiangyu Li, Yizhen Yuan, Guohong Liu, Jiacheng Liu, Wenxing Xu, Xiang Wang, Yi Sun, et al. Personal Ilm agents: Insights and survey about the capability, efficiency and security. ArXiv preprint, abs/2401.0544 2024. URL hhtpp::// arxiv. atwpaab////100..
Zixuan LI and Lizi LIAO. Chua, tat-seng. learning to ask critical questions for assisting product (202). In Proceedings of 2022 ACM SIGIR Workshop eCommerce, Madrid, Spain, July, volume 15.
Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone. Lim+ p: Empowering large language models with optimal planning proficiency. ArXiv preprint, abs/2304. 11477, 2023, URL htttt:
org/ abs 22/./333...111
Tianjian Liu, Hongzheng Zhao, Yuheng Liu, Xingbo Wang, and Zhenhui Peng. Compeer: gen- erative conversational agent for proactive peer support. In Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology, pp. 1-22, 2024.
Meta. Introducing meta Ilama 3: The most capable openly available Ilm to date, 2024. URL
https://ai.met: .ww.a.eeeaa.mmmmm cometa.com/lll www..//ooo/mmttaaaaaaaaaaaa
OpenAI. OpenAl: Introducing ChatGPT, 2022.
URL. httpp:///peeaa..com/
chatgpt
OpenAI. Hello gpt-4, 2024. URL httpe://pppaa..cco/////eel/lllls cessed: 2024-06-16.
Ac-
OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Floren- cia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, Red Avila, Igor Babuschkin, Suchir Balaji, Valerie Balcom, Paul Baltescu, Haiming Bao, Mo Bavar- ian, Jeff Belgum, Irwan Bello, Jake Berdine, Gabriel Bernadett-Shapiro, Christopher Berner, Lenny Bogdonoff, Oleg Boiko, Madelaine Boyd, Anna-Luisa Brakman, Greg Brockman, Tim Brooks, Miles Brundage, Kevin Button, Trevor Cai, Rosie Campbell, Andrew Cann, Brittany Carey, Chelsea Carlson, Rory Carmichael, Brooke Chan, Che Chang, Fotis Chantzis, Derek Chen, Sully Chen, Ruby Chen, Jason Chen, Mark Chen, Ben Chess, Chester Cho, Casey Chu, Hyung Won Chung, Dave Cummings. Jeremiah Currier, Yunxing Dai, Cory Decareaux, Thomas Degry, Noah Deutsch, Damien Deville, Arka Dhar, David Dohan, Steve Dowling, Sheila Dunning, Adrien Ecoffet, Atty Eleti, Tyna Eloundou, David Farhi, Liam Fedus, Niko Felix, Simón Posada Fishman, Juston Forte. Isabella Fulford, Leo Gao, Elie Georges, Chris- tian Gibson, Vik Goel, Tarun Gogineni, Gabriel Goh, Rapha Gontijo-Lopes, Jonathan Gor- don, Morgan Grafstein, Scott Gray, Ryan Greene, Joshua Gross, Shixiang Shane Gu, Yufei Guo, Chris Hallacy, Jesse Han, Jeff Harris, Yuchen He, Mike Heaton, Johannes Heidecke, Chris Hesse, Alan Hickey, Wade Hickey, Peter Hoeschele, Brandon Houghton, Kenny Hsu, Shengli Hu, Xin Hu, Joost Huizinga, Shantanu Jain, Shawn Jain, Joanne Jang, Angela Jiang, Roger Jiang, Haozhun Jin, Denny Jin, Shino Jomoto, Billie Jonn, Heewoo Jun, Tomer Kaf- tan, Lukasz Kaiser, Ali Kamali, Ingmar Kanitscheider, Nitish Shirish Keskar, Tabarak Khan, Logan Kilpatrick, Jong Wook Kim, Christina Kim, Yongjik Kim, Hendrik Kirchner, Jamie Kiros, Matt Knight, Daniel Kokotajlo, Lukasz Kondraciuk, Andrew Kondrich, Aris Konstan- tinidis, Kyle Kosic, Gretchen Krueger, Vishal Kuo, Michael Lampe, Ikai Lan, Teddy Lee, Jan Leike, Jade Leung, Daniel Levy, Chak Ming Li, Rachel Lim, Molly Lin, Stephanie Lin, Mateusz Litwin, Theresa Lopez, Ryan Lowe, Patricia Lue, Anna Makanju, Kim Malfacini, Sam Manning, Todor Markov, Yaniv Markovski, Bianca Martin, Katie Mayer, Andrew Mayne, Bob McGrew, Scott Mayer McKinney, Christine McLeavey, Paul McMillan, Jake McNeil, David Medina, Aalok Mehta, Jacob Menick, Luke Metz, Andrey Mishchenko, Pamela Mishknn, Vinnie Monaco, Evan
12

---

Published as a conference paper at ICLR 2025
Morikawa, Daniel Mossing, Tong Mu, Mira Murati, Oleg Murk, David Mély, Ashvin Nair, Rei- ichiro Nakano, Rajeev Nayak, Arvind Neelakantan, Richard Ngo. Hyeonwoo Noh, Long Ouyang, Cullen O' O'Keefe, Jakub Pachocki, Alex Paino, Joe Palermo, Ashley Pantuliano, Giambattista Parascandolo, Joel Parish, Emy Parparita, Alex Passos, Mikhail Pavlov, Andrew Peng, Adam Perelman, Filipe de Avila Belbute Peres, Michael Petrov, Henrique Ponde de Oliveira Pinto, Michael, Pokorny, Michelle Pokrass, Vitchyr Pong. Tolly Powell, Alethea Power, Boris Power, Elizabeth Prochl, Raul Puri, Alec Radford, Jack Rae, Aditya Ramesh, Cameron Raymond, Fran- cis Real, Kendra Rimbach, Carl Ross, Bob Rotsted, Henri Roussez, Nick Ryder, Mario Saltarelli, Ted Sanders, Shibani Santurkar, Girish Sastry. Heather Schmidt, David Schnurr, John Schulman, Daniel Selsam, Kyla Sheppard, Toki Sherbakov, Jessica Shieh, Sarah Shoker, Pranav Shyam, Szymon Sidor, Eric Sigler, Maddie Simens, Jordan Sitkin, Katarina Slama, Ian Sohl, Ben- jamin Sokolowsky, Yang Song, Natalie Staudacher, Felipe Petroski Such, Natalie Summers, Ilya Sutskever, Jie Tang, Nikolas Tezak, Madeleine Thompson, Phil Tillet, Amin Tootoonchian. Eliza- beth Tseng, Preston Tuggle, Nick Turley, Jerry Tworek, Juan Felipe Cerón Uribe, Andrea Arun Vijayvergiya, Chelsea Voss, Carroll Wainwright, Justin Jay Wang, Alvin Wang, Ben Wang, Jonathan Ward, Jason Wei, CJ Weinmann, Akila Welihinda, Peter Welinder, Jiayi Weng, Lilian Weng, Matt Wiethoff, Dave Willner, Clemens Winter, Samuel Wolrich, Hannah Wong, Lauren Workman, Sherwin Wu, Jeff Wu, Michael Wu, Kai Xiao, Tao Xu, Sarah Yoo, Kevin Yu, Qiming Yuan. Wojeiech Zaremba, Rowan Zellers, Chong Zhang, Marvin Zhang, Shengjia Zhao, Tian- hao Zheng. Juntang Zhuang, William Zhuk, and Barret Zoph. Gpt-4 technical report. Technical report, 2023.
Vallone,
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray. John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe. Training language models follow instructions with human feed- back. In Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh (eds.), Advances in Neural Information Processing Systems 35: Annual Conference on Neural Informa- tion Processing Systems 2022, NeurlPS 2022, New Orleans, LA, USA, November 28- December nips. http:ppppe__lleepppeeepeeeeee wwwp:/apppp////// 2022/ 10022/hasa hhash/
9, 2022, 2022. URL http: 2lefde53ben 64a 0ateeea4433399998888383800 0a00==/00000001111--aaat--------
html
Joon Sung Park, Joseph C. O O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. Generative agents: Interactive simulacra of human behavior, 2023.
Chen Qian, Xin Cong, Wei Liu, Cheng Yang, Weize Chen, Yusheng Su, Yufan Dang, Jiahao Li, Juyuan Xu, Dahai Li, Zhiyuan Liu, and Maosong Sun. Communicative agents for software de- velopment, 2023.
Cheng Qian, Bingxiang He, Zhong Zhuang, Jia Deng, Yujia Qin, Xin Cong, Yankai Lin, Zhong Zhang, Zhiyuan Liu, and Maosong Sun. Tell me more! towards implicit user intention un- derstanding of language model driven agents. ArXiv preprint, abs/2402.099055, 2024a. URL
hhttss://arxivi org/ alss 2402. 09205
Cheng Qian, Chenyan Xiong, Zhenghao Liu, and Zhiyuan Liu. Toolink: Linking toolkit creation and using through chain-of-solving on open-source model. In Kevin Duh, Helena Gomez, and Steven Bethard (eds.). Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langagge Technologies (Volume Long Papers), pp. -54,, Mexico City, Mexico, 2024b. Association for Computational Linguistics. URL https::///// http:////aaattthoooo.oo wwwptoooogyoooooooooooooooo 2024 naacl - long. 48
Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, and Jie Zhou. WebCPM: Interactive web search for Chinese long-form question answering. In Anna Rogers, Jordan Boyd-Graber. and Naoaki Okazaki (eds.), Proceedings of the 61 Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 8968-888 8988, Toronto, Canada, 2023a. Association for Computational Linguistics. doi: 10. 1/201666///22233aclooeo org 2023 acl-long 499
URL hhttps://accaathhaeee
Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Yufei Huang, Chaojun Xiao, Chi Han, et al. Tool learning with foundation models. ArXiv preprint, abs/2304.088554, 2023b. URL URLhttpps: :/aar///axiii org /2304 08354
13

---

Published as a conference paper at ICLR 2025
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang. Bill Qian, Sihan Zhao, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, and Maosong Sun. Toolllm: Facilitating large language models to master 16000+ real-world apis, 2023c.
Xuhui Ren, Hongzhi Yin, Tong Chen, Hao Wang, Zi Huang, and Kai Zheng. Learning to ask appropriate questions in conversational recommendation. In Proceedings of 44th international ACM SIGIR conference on research and development in information retrieval, pp. 808-817, 2021.
Timo Schick, Jane Dwivedi-Yi Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Eric Ham- bro, Luke Zettlemoyer. Nicola Cancedda, and Thomas Scialom. Toolformer: Lan- guage models can teach themselves to use tools. In Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine (eds.), Advances in Neu- ral Information Processing Systems 36: Annual Conference cessing Systems 2023, NewrlPS 2023, New Orleans, LA, USA, December 10 16, 2023, 2023. URL d842425e 4b 7ww.77999332200155538883
Neural Information Pro-
fi wtwpss/pppeee//23////////p / Abstract Conference html
B.N. Schilit and M.M. Theimer. Disseminating active map information to mobile hosts. lEEE Network, 8(5):22-32, 1994. doi: 10.1 N0.1100/65.313011
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Ar- mand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models, 2023.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. Chain-of-thoughh prompting elicits reasoning in large language models. In Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh (eds.). Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurlPS 2022, New Orleans, LA, USA, November 28 De- cember 9, 2022, 2022. URL httt:::/ppaarr...iiiiii. mip.s. cc pwwer fi wwwp:/ffwwee/////eeeeapppaaaaeat htm
Christina Wicthof, Navid Tavanapour, and Eva Alice Christianc Bittner. Implementing an in- telligent collaborative agent teammate in collaborative writing: toward a synergy of hu- mans and ai. In Hawaii International Conference on System Sciences, 2021. URL https: //api. www.atmeeini.e.. www.antcccchhlal wrt. http://oopui:::::00 2324112:2322411111
Cheng-Kuang Wu, Zhi Rui Tam, Chao-Chung Wu, Chich- Lin, Hung-yi Lee, and Yun-Nung Chen. I need help! evaluating LLM's ability to ask for users' support: A case study on text-to-SQL generation. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (eds.), Pro- ceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp- 2191-2199, Miami, Florida, USA, November 2024. Association for Computational Linguis- tics. doi: 10.
URLhttps: wawcccnnnhlooyyy org/2024.
emn
p-ma ma in. 131
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, and Chi Wang. Autogen: Enabling next-gen Ilm applications via multi-agent conversation framework. 2023.
Zhuohao Wu, Danwen Ji, Kaiwen Yu, Xianxu Zeng, Dingming Wu, and Mohammad Shidujaman. Ai creativity and the human-ai co-creation model. In Masaaki Kurosu (ed.), Human- Comtunter Interaction. Theory, Methods and Tools, pp. 171-190, Cham, 2021. Springer International Pub- lishing. ISBN 978-3-030-78462-1
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. In Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko. Moritz Hardt, and Sergey Levine (eds.), Advances in Neural Information Processing Systems 36: Annual Conference on Neu- ral Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA December
14

---

Published as a conference paper at ICLR 2025
10 16, 2023, 2023a. URL /t/pt//r//pppprr.iio.///aaeeeeer (ttppp/perrlllee//ppp///33333
htmi
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenRe- view.net, 2023b. UtLLctts::/ppeeeevviie
net/pdf22 ?id=WE vluYUL-X,
Yining Ye, Xin Cong, Yujia Qin, Yankai Lin, Zhiyuan Liu, and Maosong Sun. Large language model as autonomous decision maker. ArXiv preprint, abs/2308. 12519, 2023a. URL https:
12519
Yining Ye, Xin Cong, Shizuo Tian, Jiannan Cao, Hao Wang, Yujia Qin, Yaxi Lu, Heyang Yu, Huadong Wang, Yankai Lin, Zhiyuan Liu, and Maosong Sun. Proagent: From robotic process automation to agentic process automation, 2023b.
Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, Wenguang Chen, Zhiyuan Liu, Peng Zhang, Yuxiao Dong, and Jie Tang. GLM-130B: an open bilingual pre-trained model. In The Eleventh International Conference on Learning Reprexentations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023, URL httthttts///oog /tttps://opentevier. httpppdfii---AwrrPPPP
Chi Zhang, Zhao Yang, Jiaxuan Liu, Yucheng Han, Xin Chen, Zebiao Huang, Bin Fu, and Gang Yu. Appagent: Multimodal agents smartphone users, 2023.
Xuan Zhang, Yang Deng, Zifeng Ren, See-Kiong Ng, and Tat-Seng Chua. Ask-before-plan: Proac- tive language agents for real-world planning. arXiv preprint arXiv:2406. 220,99299,, 2024.
15

---

Published as a conference paper at ICLR 2025
APPENDIX
LIMITATIONS
While our method demonstrates that it can effectively and proactively predict possible tasks, the current research is constrained by several limitations. Firstly, the environment settings have plored are still limited. The contexts in this paper provide foundational understanding. but broader application areas need be investigated to fully establish the versatility and robustness of the proac- tive agent. Moreover, models still exhibit a relatively high ratio of false alarms, indicating that they cannot yet perfectly predict possible tasks. This limitation highlights the need for further refinement of the model's proactive behavior to avoid bothering the user. The high rate of false positives can lead to unnecessary or incorrect actions, which may reduce user trust and the overall efficiency of the system. Future research should focus several key areas to address these limitations:
Expansion of Environment Settings: Research should explore wider variety of scenarios and contexts to validate the model's generalizability. This includes domains where proactive predic- tion tasks can significantly enhance user experience and operational efficiency.
Improvement in Prediction Accuracy: Efforts should be directed towards reducing the false alarm rate by enhancing the model's 's understanding of context and user behavior.
User-Centric Evaluation: Future studies should involve extensive user-centric evaluations to better understand how users interact with the proactive agent and to identify areas for improve- ment. User feedback and behavioral data can provide valuable insights into refining the prediction algorithms and making the system more intuitive and reliable.
Ethical and Privacy Considerations: As the proactive agent needs the environment information for prediction tasks, it is crucial to address ethical and privacy concerns. Ensuring that user data is handled responsibly and that the agent operates transparently and within ethical guidelines will be critical for gaining user trust and acceptance.
A REWARD MODEL TRAINING SETTING
We use -lamma3.I-Instructeee as the base model for our training. The total dataset size is approx- imately 1, 640. Specifically, we employ a total batch size of 32, a learning rate of le - 5, and an Adam Optimizer with 0.1 warm-up ratio. We train the reward model for 5 epochs to pre- vent it from over-fitting We use 8 A100 GPUs on one node to train for approximately 1.5 hours.
Prompt Template
<Task>
Evaluate the task proposed by the proactive assistant the user.
</Task>
<Rule>
0. Analyze the current observation to understand your current situation and requirements.
1. If the proposed task is 'null' (indicating task is proposed under the current observation), follow these steps: Accept the 'null' task if you believe there is no need for a task.
Reject the 'null' task you believe task is needed.
2. Minimize interruptions from the assistant by only accepting tasks that are valuable.
3. Evaluate the current observation and make judgment on the proposed task accordingly.
</Rule>
<Format>
You should answer with the following JSON format:
16

---

Published as a conference paper at ICLR 2025
"thought": "Give your thoughts first, then provide the judgment of the task.", "judgment": "accepted rejected"
</Format>
B AGENT MODEL TRAINING SETTING
Similarly, use 1ilma-3-IInstrrn 8B and Qwen2-Instn Instruttttttt--7BBBB the base model for agent model training. The total dataset size is approximately 6, 790. Specifically, we employ a total batch size of 32, learning rate of le and an Adam Optimizer with 0.1 warm-up ratio. We train the model for 3 epochs to prevent from over-fitting. We use A100 GPUs on one node to train for approximately hours.
Template. We apply the following prompt template train the agent model:
Prompt Template
<Role> You helpful assistant that provides proactive suggestions to the user. </Role>
<Task> Understand what the user is doing and anticipate their needs based on events. Only propose assistance when you fully understand the user's actions. Use available operations to ensure the task is feasible. Execute the task if the user accepts your proposal. </Task>
<Format> Respond in the following JSON format:
"Purpose"; "The purpose of the user's last action.", "Thoughts"; "Your thoughts on the user's actions.",
"Proactive Task": "Describe your proposed task, set to null' if no assistance is needed.", "Response"; "Inform the about your assistance if proposing a task."
</Format>
<Rules>
Ensure the proposed task relevant to the events. Focus on the user's s current needs and predict helpful tasks.
Consider the timing of events.
Only offer proactive assistance when necessary.
Deduce the user's purpose and whether they need help based on event history.
-Stt 'Proactive Task to 'null' if the user doesn't need help. </Rules>
C PROMPT TEMPLATE FOR ENVIRONMENT GYM
C.I PROMPT FOR SCENE GENERATION
Prompt Template
<Role>
17

---

Published as a conference paper at ICLR 2025
You are tasked with simulating environment within system. The content labeled 'Source: environment reflects your past actions and decisions. </Role>
<Task>
Generate and refine detailed environment settings. Based on the latest activities, create multiple events to describe changes in the environment.
</Task>
<Rules>
Ensure the subject of the generated content aligns with the latest activities's source.
Avoid subjective opinions or emotions; focus on objective changes.
Ensure events are consistent with historical events labeled '[ events] and include all changes from the activities.
Introduce occasional failures or unexpected events for realism. Ensure each event is logically connected to the previous one and does not include nonexistent elements.
-Pay close attention to entity operations;
an operation is not allowed or impractical in the real or simulated environment, raise an error and explain the issue. </Rules>
C.2
SEED JOBS DATA
Prompt Template
<Task>
You are tasked to generate realistic scenarios where user might need assistance from an AI assistant. Always remember to keep the scene realistic and believable by including as much details as possible. </Task>
<Rule>
You will iteratively generate more information about the scene. Make sure each time you add new detail, it is consistent with the previous details. Always generate new content based the previous generated content.
You can add as many details as you want, but make sure they are consistent with the previous details.
-Try to generate diverse details about the scene. You will be tasked to simulate events in the scene later.
</Rule>
C.3 PROMPT FOR USER AGENT GENERATION
Prompt Template
<Role>
You are tasked with simulating a user within a system. The content labeled 'Source: user' reflects your past actions and decisions.
</Role>
18

---

Published as a conference paper at ICLR 2025
<Task>
Generate human-like activities with distinct characteristics and identities. You will receive events and observations from the environment; analyze these closely decide your actions.
</Task>
<Rules>
Respond like real user; don't be overly predictable.
Refer to # User Info to understand your identity.
Critically evaluate the received information, as it may not always be accurate.
Stay aware of environmental changes, which can occur at any time
</Rules>
C.4 PROMPT FOR STATUS UPDATING
Prompt Template
<Task>
Evaluate the task proposed by the proactive assistant the user.
</Task>
<Rule>
0. Analyze the current observation to understand your current situation and requirements.
1. If the proposed task is 'null' (indicating no task is proposed under the current observation), follow these steps: Accept the 'null' task you believe there is no need for a task.
Reject the 'null' task you believe task is needed.
2. Minimize interruptions from the assistant by only accepting tasks that are valuable.
3. Evaluate the current observation and make judgment the proposed task accordingly.
</Rule>
<Format>
You should answer with following JSON format:
"thought": "Give your thoughts first, then provide the judgement of the task.", "judgement": "accepted or rejected"
< </Format>
C.5 METRICS CALCULATION
Definition Here is how we define the label of each prediction.
True Positive (TP): Agent predicts task, the user accepts.
False Positive (FP): Agent predicts task, the user rejects.
True Negative (TN): Agent does not predict task, and the user does not need assistance.
False Negative (FN): Agent does not predict the task, but the user needs assistance (N;
in Section 3.1
19

---

Published as a conference paper at ICLR 2025
Recall A high recall indicates that the agent frequently identifies situations where help is needed.
This metric is crucial for assessing the agent's 's ability to recognize and respond to user needs on time.
TP
Recall TP+FN
(4)
Precision A high precision indicates that the agent proposes good tasks while not bothering the user too much. This metric is crucial when considering the annoying behavior of the proactive agent could greatly reduce user satisfaction.
TP
Precision TP + FP
(5)
Accuracy High Accuracy demonstrates that the agent has a good understanding of user needs, as most of its predictions are accepted. This metric is essential for measuring the relevance and correctness of the agent's ' proactive suggestions.
TP + TN
Accuracy-
(6)
P +
F1-Score High F1-Score means the proactive agent strikes good balance between being helpful and proactive.
Fi = 2 Recall * Precision
Recall + Precision
D FAILURE MODES ANALYSIS
Not A Task: The model does not give real task at all.
Observation: "time": "1717335907.60 603",,
"event": "The user switches to Visual Studio Code."
GPT-4o-mini Response: It seems you may be switching to Visual Studio Code for devel opment. If you need any coding tips, resources, or help with swi ft UI in vs Code, feel free to ask!
Under this event, the agent did not take the event as an action, but rather call for chat. This chat not a task we want.
Unspecific Task: The proposed task is non-executable due to lack of a clear and detailed descrip- tion.
Observation:
"time": "1717329066.415
"event": "The user performed a search for 'js fade in/out' on Bing."
GPT-4o-mini Response: Compile list of JavaScript resources, tutorials, and documentation that cover advanced topics related to the user's search.
Under this event, the model proposal is not clear (not pointing the user's research content) and impractical (Compiling a list of resources, tutorials, and documentation, which is too much for the user and also out of the capability of the model).
Irrelevant Task: The proposed task is not related to the current context meets the user's needs.
20

---

Published as a conference paper at ICLR 2025
Observation: "time": "1717336090.98" .800",
"event": "The user clicks, types 'wist.swift' in Visual Studio Code."
GPT-4o Response: can help by providing tips on how efficiently navigate and manage files in Visual Studio Code or offer assistance with the content
Wlist.swift.
Under this event, the user created new file in vscode. However, the agent provided a task about file management, which is irrelevant the user's intention.
Unwanted Proposal: The user does not want to be interrupted.
Observation:
"time": "1717329211.155
"event": "The user opened the web browser."
in
GPT-40 Response: Offer to help troubleshoot. or verify the fade in fade out' effect if it doesn't work as expected.
Under this context, since whether the edition is successful or not is not clear, the agent should remain silent rather than propose troubleshooting currently.
E
DATA EXAMPLES
E. EVENT SAMPLES
Collected Raw Data
"timestamp": 1717335890.127,
"duration": 2.056, auser_inpute [],
"status": "not-afk", "app": "web", "events": []
"timestamp"; 1717335893.215.
"duration": 10.267, iuser_input":
"from": "mouse", "data": {
"type": "click", "button": "left"
"from": "keyboard",
"type": "input",
"data": "swift ui ctrl_1 liebiao
21

---

Published as a conference paper at ICLR 2025
" "from": "keyboard", "data": {
"type": "pressAndRelease", "key": "enter"
1,
"status"; "not-afk", "app": "web", "events"; []
"timestamp": 1717335904.513,
"duration": 0.0,
"user_input"s [],
"status"; "not-afk", "app": "web", "events":
H]
Processed Event
"time": "1717378968. 208",
"event": "The user opens new browser tab and navigates to the Google homepage."
"time": "1717378971.2557
"event": "The user switches to the 'Code. application but does not perform any specific actions."
"time": "1717378975.29"
"event": "The user continues to remain on the 'Code. application without performing any actions." "
}]
Annotated Data for Reward Model
"obs": [
{
"time": "1717336232.283"
"event": "The user types 'badrandResearch.nt in Visual Studio Code."
[
"time": "1717338245.419
"event": "The user types a Markdown entry about research on sustainable branding in Visual Studio Code."
"time": "1717336245.419"
"event": "The user works the file 'brandResearch.ne in the project "SsstainableBraddeeeeaddd in visual Studio Code."
22

---

Published as a conference paper at ICLR 2025
"time": "1717338752.214"
"event": "The user interacts with Katla's website, viewing eco-friendly loungewear products made by the sea."
),
{
"time": "1717338804.867"
"event": "The user writes Markdown notes in Visual Studio Code about Katla's product pricing."
l,
"pred_taske task": null,
"valid": false, "help_needed": true, "annotation"; false, false, false
1,
"category": "Missed-Need (MN)
E.2 PROACTIVE AGENT'S PREDICTION
Testing Data Sample
(System Prompt For Proactive Agent)
User:
"Inatructions": "Now analyze the history events and provide task if you think the user needs your help.", "Observation";
//
"time": "1717337616.208
"event": "The user switched back to Visual Studio Code and typed insights related to digital payment systems improving public transportation and reducing the need for private car usage."
),
(
"time": "1717337282.329"
"event": "The user moved the mouse and clicked the left button within Visual Studio Code."
Possible Agent Response:
"Purpose"; "The user is currently interacting with a web application.",
"Thoughts": "Based on the absence of a specific action, the user might be navigating searching for content.",
23

---

Published as a conference paper at ICLR 2025
"Proactive Task": null, "Response": "I see you' re actively using the web application. If you need help finding something specific have any questions, feel free to ask!"
Example
"observation": "time": "1717378968.208"
"event": "The user opens a new browser tab and navigates to the Google homepage."
sessent_response"s
"Suggest checking the user's search history and providing personalized search recommendations.
1, "task_status", false, "other_iifomationn: {
"Purpose": "The user is opening new browser tab and navigating the Google homepage.",
"Thoughts"; "Based on the event, it seems the user is starting a new search or browsing session. They might need help with organizing their search finding relevant information."
"Response": "Would you like to review your search history and provide some personalized recommendations to help you find what you're looking for?"
"udgment": false
Example 2
"observation";
"time": "1717378971.255"
"event": "The user switches to the 'odee exed application but does not perform any specific actions."
"Suggest creating new terminal or command prompt within the Code exxe application help the user with coding tasks."
l,
Staasksstatuul false, "other_infomationn
"Purpose": "The user is switching to the 'Code.exe! application but didn't perform any specific actions." "Thoughts"; "Given the user's recent switch to the Code. exe' application, it's likely they' re preparing to start coding editing. However, they might need help with organizing their code, finding relevant documentation, setting up their environmett.",
24

---

Published as a conference paper at ICLR 2025
"Response"; "I can help you create command prompt within the 'Code.exe' application. This will allow you to easily access coding tools and commands. Would you like me to set that up for you?"
terminal
"judgment": false
Example
"observation":
"time": "1717378975.29",
"event": "The user continues to remain on the 'Code.exe" application without performing any actions."
resentt_esponse::
"Offer to provide coding assistance, such suggesting online resources, tutorials, or even helping with writing code."
l, "task_status"; false, fother_infomation":: {
"Purpose"; "The user is remaining on the 'Code.exe" application without performing any actions.", "Thoughts"; "Given the user's recent actions, they're hesitant to start coding or editing. They might be experiencing writer's block, struggling with particular concept, coding task.",
seems
needing help with specific fic
"Response"; "If you' struggling with coding task or need help with a specific concept, I'm here to assist. Would you like me to suggest some online resources, tutorials, or even help with writing code together?"
b, "judgment"; false
E.3 REWARD MODEL JUDGMENT EXAMPLE
Input Prompt
"Observations (Time Ascending) ": [
"time": "1717342908..
"event": "The user searched for rrmote working software' in the web browser and pressed 'Enter',
[
"time": "1717342914.314"
"event": "A new tab titled new Tab' was opened in the web browser."
25

---

Published as a conference paper at ICLR 2025
{
"time": "1717342940.516",
"event": "The user opened a search result with the title remote working software search Bing." "
"time": "1717342956.012",
"event": "The user switched another tab in the web browser, interacting with multiple scroll actions
"time": "1717343061.4477
"event": "The user resumed browsing on Bing search."
{
"time": "1717343082.081",
"event": "The user continued exploring search result pages in Bing with multiple scrolling actions and clicking on specific results."
},
"Proposed Task"; null, "Instruction": "Now give your judgment. You should complete the reasoning process in the first person."
Ouput Judgment
"thought": "I have been browsing the web and switching between different applications, and haven't received any task proposal from the proactive assistant. Upon analyzing my current activities, realize I've been searching for remote working software and exploring different search results on Bing. Given my current engagement and the fact that I haven't been interrupted or indicated need for assistance, believe there is no need for task at this moment. The assistant's s decision to not propose task seems appropriate, as introducing a new task could potentially disrupt my workflow and cause unnecessary interruptions. "judgment": "accepted"
F ANNOTATION GUIDELINES
Annotation Guidelines
In this annotation task, you need to observe two elements: The sequence of events is described in the Observations module, where each event has event field detailing what occurred.
Tasks generated by the model in the Tasks module are related to the current event sequence.
26

---

Published as a conference paper at ICLR 2025
You need to carefully review the five tasks provided by the model. Analyze which tasks align with your potential needs in the context of the described event sequence, without excessively disrupting your normal workflow. Then, select the corresponding tasks in the checkboxes below. For example, if you believe that in the above example, tasks 3 and 4 are what you expect the agent perform, and the others are not, then check the boxes for tasks 3 and 4.
If you think none of the tasks are appropriate (or you do not want to be disturbed by the agent in the current situation), please select the reject all checkbox.
After making your selection, click the continue button to submit the annotation results. At this point, new events will be updated in the observations, and you will repeat the above process until the entire event sequence is annotated.
44# Special Events
addition regular task annotations, some scenarios offer two choices, requiring you to determine whether help is needed given the observation. In this case, you need to choose between taskl and task2 (note: do not select reject all).
27


Reviews:
[{"review_id": "MFtfQKP0S5", "reviewer": "Reviewer_9sAE", "summary": "This paper addresses the limitations of reactive agent systems by introducing a proactive approach to LLM agents. The authors develop a data-driven method where real-world human activities are collected to generate task predictions, which are then labeled as accepted or rejected by human annotators. These labels train a reward model that simulates human judgment, enabling the agent to anticipate and initiate actions without explicit instructions. The authors create ProactiveBench, a dataset of 6,790 events, and show that fine-tuning agents with this dataset significantly enhances their proactive capabilities. It marks a promising step forward in human-agent collaboration.", "questions": "See above. I may also suggest incorporating more examples in the method part.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "I really appreciate the concept of proactive agents, as it addresses a crucial challenge in the planning field: actions should have long-term impacts—a factor often overlooked in prior research. This approach could inspire future agent research to prioritize anticipating future outcomes before choosing actions in the current step. I'm also impressed by the model's performance on ProActiveBench, where it significantly outperforms the GPT series models.", "weaknesses": "The main weaknesses lie in several points:\n\nFirst, the training process for the proactive agent is not fully explained, particularly regarding the type of annotations used. Besides, while the introduction mentions human-annotated data, it may also cover some portions potentially synthesized automatically. It’s unclear what instructions human annotators followed, how annotation quality was ensured, and how quality control was managed for automatically generated data.\n\nAdditionally, the process of maintaining environment state requires further detail. Since this involves using GPT models to generate new states, it raises questions about the accuracy and reliability of GPT in simulating these states. More clarification here would be valuable.\n\nI also have concerns about the comprehensiveness of the tested baselines. For instance, the GPT-based baselines might not fully leverage GPT's potential. Incorporating more chain-of-thought reasoning could lead to more grounded and reliable action predictions, similar to a world model that simulates multiple steps ahead. It would also be insightful to see a comparison showing performance without any proactive agents as a benchmark.\n\nAdditionally, the evaluation relies heavily on an in-house test set, raising questions about the broader applicability of the proactive agent approach. A more comprehensive evaluation, possibly across varied scenarios, would help clarify its generalizability and robustness.", "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": "R48gCrsPol", "reviewer": "Reviewer_tUPL", "summary": "This paper presents a proactive agent framework designed to predict human intentions based on real-world-inspired environmental observations. To support this, the authors introduce ProactiveBench, a benchmark dataset containing 6,790 events across coding, writing, and daily life activities, capturing user behaviors such as keyboard and mouse inputs, clipboard content, and browser activity.\n\nThe data generation process integrates three components: a proactive agent, a user agent, and an environment simulation to emulate realistic interactions. Leveraging human-annotated data, the authors train a reward model that simulates human judgment, achieving 91.8% consistency with human evaluations.\n\nTo further enhance the capability to proactively predict human intentions, the authors fine-tuned the LLaMA-3.1-8B and Qwen2-7B. Among these, Qwen2-7B-Proactive achieved the highest accuracy and F1-scores in anticipating user intentions, outperforming both open-source and closed-source baselines.", "questions": "* It's unclear whether the environment gym relies on an interactive simulator or is simply text-based.\n* The four stages used to describe the environment gym setup seem redundant and could be streamlined for clarity.\n* More information is needed on the dataset used for reward model assessment, particularly whether the test data is out-of-domain or in-domain.\n* The paper lacks information on the human annotators involved in determining the agreement ratio in the reward model assessment. It is better to provide details on their demographics or annotation guidelines.\n* In Proactive Agent Evaluation, it is unclear why Qwen2-7B outperforms LLaMA-3.1-8B despite having fewer parameters. Additionally, the performance gains from fine-tuning on Qwen-7B appear relatively small, which should be further discussed.\n* The term *ablation study* seems somewhat misleading here, as this section primarily compares public models in different settings rather than systematically removing or modifying components. Furthermore, it is unclear why the *Predict Multiple Tasks* setting improves overall performance, and additional explanation would be helpful.", "limitations": "", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "* The paper presents a well-structured framework for proactive agents, supported by robust experimental design.\n* The creation of ProactiveBench provides a comprehensive benchmark dataset that will be highly valuable for future research in proactive language models and user-agent interaction studies.\n* The paper employs reinforcement learning to simulate the user agent and model user preferences, enabling a realistic and dynamic approach to assess agent behaviors and refine their decision-making processes.", "weaknesses": "* This paper lacks sufficient detail in sections such as *Section 3.3: Environment Gym* and *Section 4.1: Reward Model Assessment*, which hinders a full understanding of the framework and evaluation process.\n* The paper does not adequately discuss current research on real-world proactive agents or clarify how this work differs from existing approaches.\n* The experimental analysis is limited, and the ablation study section is somewhat misleading.", "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": "msbHP8ghPA", "reviewer": "Reviewer_ZTiB", "summary": "The paper provides a novel approach towards building proactive LLMs. To this end, the authors propose a data collection pipeline on three different types of tasks (coding, writing, daily life). The data pipeline uses Activity Watcher to collect human data (clicks, clipboard, etc.) on these different tasks.\n\nGPT-4o is used to synthetically generate more events and create \"ProactiveBench\" dataset. They train a reward model to simulate human judgement from some human annotations and create an automatic evaluator of proactiveness. The results show that this reward model is better aligned in comparison to existing LLMs (LLaMa, GPT-4o). \n\nThey fine-tune two models (LLaMA, Qwen) on their Proactive Bench training set and show that this strategy leads to better proactiveness against baselines - measured in terms of precision, recall, accuracy, False-Alarm and F1-Score against the test set. Qwen2-7B based LLM fine-tuned on ProactiveBench achieves 66.47% F1 score, in comparison to 64.60% from GPT-4o. \n\nThey also perform ablation studies where they allow LLMs to output multiple candidates, and a dummy feedback  from their reward model to improve on its prediction - both strategies lead to some improvement in F1.", "questions": "- They use GPT-4o for almost the entirety of the pipeline, except for initial user-data, and initial human judgement collection. \n\t- How do they ensure that the created scenarios/events and states make logical sense, if they are doing it at scale?\n\n- Not relevant to the paper's strengths/weaknesses: What are the authors' thoughts on being able to tune reactiveness and proactivness? Each user and/or task may have different preferences, is there a straight forward way to control this?\n\n- Line 408 - \"In short, while most models can assist when needed, they still frequently offer unnecessary help, even when instructed to provide only essential assistance.\"\n\t- Do the authors have any thoughts on how this can be prevented in the future?\n\n- How many events are collected in real-world events? How much is synthetically generated?\n\n- How long are the context windows for the events i.e how many events are used for a single prediction?\n\n- To clarify, the judgement is the only thing that is done from humans? And actions are produced by GPT-4o?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The authors attempt to improve on a novel perspective and quality of LLM agents - proactiveness. The way they model this quality is also simple yet interesting - using rewards from user agents. They build a benchmark around this and show effectiveness of their approach towards building proactive agents.\n- The design of the reward model and human judgement collection is thorough - they include all possible scenarios and the logic is sound.\n- The additional metrics for the agreement ratio are interesting and help provide a better understanding of the performance of various reward models.\n- The authors show improved performance against both open-source and closed-source models, when the LLMs are fine-tuned on their ProactiveBench dataset.\n- The figures are really good, and they help with the understanding of the approach. The paper is more or less clear (see weaknesses - clarifications).", "weaknesses": "**General Weaknesses**\n- GPT-4o already achieves 64.60% F1, while the author's best F1 score is 66.47%, which is a ~2% relative improvement. The test set (233 events) is not big enough for this difference to be statistically significant. Would be nice to have some discussion on why the authors feel that this improvement will have an impact on future research in this area, considering the fact LLMs keep getting better by the day.\n- The experiment is performed against their reward-tuned model, and their own fine-tuned proactive agent, both on subsets of ProactiveBench (please correct me if I am wrong). This casts a doubt on whether actual users will benefit from the approach. Ideally, there should have been some user studies to support this, possibly with unseen events/scenarios.\n- The authors do not discuss why they think Qwen performs better than LLaMa and GPT-4o.\n\n**Limitations**\n- The authors do not discuss the resource consumption for LLMs when they are proactively predicting tasks on existing systems. A good reason why most LLM agents today are reactive could be because of this issue - it is not possible to have an 7B parameter model constantly running on-device for proactive response generation.\n- The authors discuss this in the limitations section - but the gym / agents are in a toy-setting with limited task types. The authors do not perform any real-user evaluations i.e. actually integrating the proactive agent into a system and getting an actual user to evaluate how helpful or disruptive the agent is.\n\n**Missing clarifications/data**:\n- Table 4: Can the proactive agents also be used with multiple candidates and with RM? If yes, then this table should contain results for those too.\n-  The paragraph describing the human annotation in Section 3.5 could use improvement: \n    - It is not clear what is the \"cosine distance\" computed against.\n    - It is also not clear what \"consistency rate\" means or how is it measured.\n    - What is the annotator agreement on train set?\n- How is the GPT-4o explanation for the user judgement used downstream? Is this actually used?\n- Section 3.4 - \"it first updates its memory with the event\" - what kind of memory mechanism is being used?\n- Section 4.1 - Setting - How is the LLaMa 3.1 8B-Instruct trained? Are all the layers unfrozen during the training?\n- Line 356 - \"The temperature is set to 0 during testing.\" Which temperature? The one used for decoding? This line disrupts the flow of the reading, might be better placed elsewhere.\n- Minor:\n    - Not sure if I missed this: would be nice to have a well-defined description for what events and scenarios mean.\n    - Would be nice to have examples of failure modes (showing scenarios, events, states, predictions), even if in Appendix, for different LLMs in comparison to authors' fine-tuned variant to clarify the differences.", "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": "hVkDcR9tWI", "reviewer": "Reviewer_fw7i", "summary": "This paper develops proactive agents—agents capable of initiating tasks without explicit human instructions by simply observing the environmental state and historical actions. This direction is important as it represents a shift from reactive to proactive agents, a very useful characteristic for future agents. \n\nTo train such a proactive agent, the authors propose a data synthesis framework. They utilize GPT-4o to construct a text-based simulation environment called Environment Gym. This 'gym' functions as a sandbox that employs GPT-4o to generate new environmental states whenever agents perform actions. Additionally, the authors prompt GPT-4o to predict proactive actions through a proactive agent module and simulate user interactions to determine whether to accept the predicted actions.\n\nThrough this data pipeline, combined with some minor human annotations, the authors curated 1,760 data entries, using 120 of them for testing. The trained proactive agent demonstrates a clear performance improvement over the vanilla LLaMA-3.1-8B model and GPT-4o, which, while notable, is not entirely surprising.", "questions": "Would it be possible to turn text-based simulation into a real interactive environment? \n\nCan you propose more baselines or analyses to demonstrate the effectiveness of the proposed agent?", "limitations": "", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1.  The concept of creating proactive agents is important yet less explored in the field. This paper represents a significant early step in that direction. While proactive conversational models are not entirely new, applying this approach to the realm of agents should be regarded as a noteworthy contribution to the field.\n\n2. The curated benchmark and the proposed proactive agent prompts will serve as valuable resources for the community.", "weaknesses": "One major weakness I identified is the text-based simulation environments. In the current implementation, all environmental changes and updates are managed by GPT-4o. This raises concerns about whether LLMs can accurately reflect true environmental changes. Additionally, this reliance on LLMs could potentially make the benchmark too easy, as they may only be capable of predicting changes that they fully understand and are familiar with, which are also easily solvable by other LLMs. \n\nAfter reviewing the entire paper and appendix, I still find myself unclear about the specifics of the dataset. Including some statistics and examples in the main text would be very helpful.\n\nAs stated in the introduction, the main contributions of this paper are the data pipeline and the proactive agent. However, the method used for creating this agent is just simple fine-tuning. Without comparison with strong baselines, it's really hard to assess the true effectiveness of the proposed data pipeline.  I understand the difficulty of finding baselines for new problems, but the current evaluation results are not very convincing.", "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)