AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
Authors: 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
Jie Zhou
Venue: ICLR 2024 poster (2024)
Keywords: large language mode
agent
multi-agent
Primary area: generative models
TL;DR: We propose AgentVerse, a simple and effective multi-agent collaborative framework, and demonstrate its effectiveness on via a bunch of quantitative and qualitative experiments.

Abstract: Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-world scenarios, cooperation among individuals is often required to enhance the efficiency and effectiveness of task accomplishment. Hence, inspired by human group dynamics, we propose a multi-agent framework AgentVerse that can effectively orchestrate a collaborative group of expert agents as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that AgentVerse can proficiently deploy multi-agent groups that outperform a single agent. Extensive experiments on text understanding, reasoning, coding, tool utilization, and embodied AI confirm the effectiveness of AgentVerse. Moreover, our analysis of agent interactions within AgentVerse reveals the emergence of specific collaborative behaviors, contributing to heightened group efficiency. We will release our codebase, AgentVerse, to further facilitate multi-agent research.

Published as a conference paper at ICLR 2024
AGENTVERSE: FACILITATING MULTI-AGENT COLLAB- ORATION AND EXPLORING EMERGENT BEHAVIORS
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 1s Maosong Sun' Jie Zhou
Tsinghua University
2 Beijing University of Posts and Telecommunications 3 Pattern Recognition Center, WeChat AI, Tencent Inc. chenwz21 @mails wtiigguussina.t....o. edu. cn,
yushengsu.thul@malee com
ABSTRACT
Autonomous agents empowered by Large Language Models (LLMs) have under- gone significant improvements, enabling them to generalize across a broad spec- trum of tasks. However, in real-world scenarios, cooperation among individuals is often required to enhance the efficiency and effectiveness of task accomplishment. Hence, inspired by human group dynamics, we propose multi-agent framework AGENT F that can effectively orchestrate a collaborative group of expert system. Our experiments demonstrate
agents as a greater-than-the the-sum-of-ite that AGENT VERSE can proficiently deploy multi-agent groups that outperform a single agent. Extensive experiments on text understanding, reasoning, coding, tool utilization, and embodied AI confirm the effectiveness of AGENT VERSE. More- over, our analysis of agent interactions within AGENTVERSE reveals the emergence of specific collaborative behaviors, contributing to heightened group efficiency. We will release our codebase, AGENTVERSF, to further facilitate multi-agent research.
INTRODUCTION
The pursuit of creating intelligent and autonomous agents that can seamlessly assist humans and operate in real-wor world settings has been a foundational goal in artificial intelligence (Wooldridge
& Jennings 1995; Minsky 1988. Bubeck et al. (023). The recent advance of Large Language
Models (LLMs) (OpenAl 2023a Anil al. 2023 Touvron et al. 20236 Team al. 2023) has
created newfound avenues in this domain. These LLMs, especially GPT- (OpenAll 2023a are
particularly adept in comprehending human intent and executing commands. They have demonstrated
remarkable proficiency in domains such as language understanding, vision (OpenAl 2023b). and
coding (Bubeck et al. 2023). By harnessing the power of LLMs, autonomous agents can make more nuanced decisions and perform actions with an unprecedented degree of autonomy (Zhou ct al. 2023). Agents like AutoGPT (Richards & et al. 2023), BabyAGI (Nakajimal 2023), and AgentGPT (Reworkd) 2023), are inspiring examples. Furthermore, recent research has endowed autonomous agents with more human-analogous cognitive mechanisms, spanning from reflection
(Yao al. 2023b Shinn et al. 2003), task decomposition (Wei al. 2022b Yao al. 2023a and tool utilization (Schick et al.) 2023b Qin et al. 2023a b Qian et al., 2023b). These advancements
edge us closer realizing the concept of artificial general intelligence (AGI) (Goertzel & Pennachin
2007 Clune 2019) that can generalize across a broader range of tasks.
However, complex real-world tasks often require cooperation among individuals to achieve better effectiveness. Throughout history, numerous studies have delved into methods for enhancing col- laboration among humans to improve work efficiency and effectiveness (Woolley et al.. 20100 Fehr & Gächter 2000). More recently, with the evolution of autonomous agents towards AGI, extensive research conceptualizes the assemblies of agents as a society or group (Li et al. 2023), and focuses on exploring the potential of their cooperation. For example, Park et al. (2023) found emergent social behaviors in multi-agent life simulation. Du et al. 2023); Wang et al. (202bb) Zhang et al. "The first authors contributed equally.
Corresponding author.

---

Published as a conference paper at ICLR 2024
rounds
Goal
Expert Recruitment
Collaborative Decision-Making
Group
Build
Group [7] 77 (
M
Reward Feedbock
Evaluation
New State
Action Execution
Agents:
Goal
New State
Actions:
Architect
Logger
Designer
Designer
Worker
Worker
Engineer
New State
Engineer
New State
Engineer
New State
Round
Round
Round
Figure An illustration of the AGENT VERSE.
(203a)) Qian al. (2023a) Chan et al. (2023) also underscored the enhanced decision-making of collaborating agents during collaborative problem-solving. However, a limitation these studies is their narrow focus specific and limited tasks, leaving the generalizability of their findings uncertain. An additional constraint their static approach to agent collaboration, where agents' roles and capabilities remain rigid, hindering adaptability.
To address this problem, we introduce AGENTVERSE, This general multi-agent framework simulates the problem-solving procedures of human groups, and allows for dynamic adjustment of group members based on current progress. Specifically, AGENT splits the problem-solving process into four pivotal stages as shown in Figure [ (1) Expert Recruitment: Determine and adjust the agent group's composition based on the ongoing problem-solving progression. (2) Collaborative Decision-Making Engage the selected agents joint discussions to devise problem-solvin strategies. (3) Action Execution: Agents interact with their environment to implement the devised actions. (4) Evaluation Assess the differences between the current state and desired outcomes. the current state is unsatisfactory, feedback is given to the next iteration for further refinement.
We conduct extensive experiments and case studies in diverse aspects including text understanding. reasoning, coding, tool utilization and embodied AI to show the effectiveness of AGENT FRRSE. Additionally, we highlight the social behaviors that emerge from the multi-agent collaboration, and discuss their advantages and potential risks. In summary, our contributions are:
Inspired by the collaborative process of human team, we propose AGENTVERSE as an effective framework for promoting collaboration among multiple agents in mooblem-solving.
We conduct extensive experiments to show that AGENT VERSE effectively improve the agents' understanding, reasoning, coding, tool utilizing capabilities and their potential in embodied AI. In the multi-agent collaboration, especially within tool utilization and Minecraft game playing, agents manifest certain emergent behaviors. For example, (1) volunteer behaviors, characterized by agents offering assistance to peers, thus improving team efficiency; (2) conformity behaviors, where agents adjust their deviated behaviors to align with the common goal under the critics from others; (3) destructive behaviors, occasionally leading to undesired and detrimental outcomes.
AGENTVERSE FRAMEWORK
problem-solving process is sequence of iterative stages within a human group (Bransford & Stein 1993). Initially, the group assesses the difference between the current state and the desired goal, dynamically adjusting its composition enhance collaboration in decision-mate makiing,,,,inn and subsequently executing well-informed actions. order to enhance the effectiveness an autonomous multi-agent group in achieving their goals, simulate the problem-solving processes of human group
2

---

Published as a conference paper at ICLR 2024
propose the AGENTVERSE framework, which is composed of four crucial stages: Expert Recruit- ment, Collaborative Decision-Making, Action Execution, and Evaluation, as shown Figure be modeled Markov decision process (MDP), characterized
The entire process A, T, R. g). This encompasses the autonomous agent and environment state space S, solution and action space A, transition function : A S, reward function R, and goal space G.
tuple
2.1 EXPERT RECRUITMENT
Expert Recruitment stage determines the composition of multi-agent group, playing important role in deciding the upper bounds of the group's capabilities. Empirical evidence suggests that diversity within human groups introduces varied viewpoints, enhancing the group's ' performance
across different tasks (Woolley et al. 2015 Phillips & O O'Reilly 1998). Parallel findings from recent research suggest that designating specific roles for autonomous agents, similar recruiting experts to form group, can augment their efficacy (Li et al. 2023] Salewski et al. [2023] Qian et al. (023a). Current methodologies for assigning role descriptions to autonomous agents predominantly involve manual assignment, necessitating prior knowledge and understanding of the task. Consequently, the scalability remains ambiguous, especially in the face of diverse and intricate problem contexts.
In view of this, AGENTVERSE automates expert recruitment to make agent configuration more scalable. For a given goal E G. a particular agent M, is prompted as the "recruiter", similar to human resource manager. Instead of relying pre-defined expert descriptions, M, dynamically generates a set of expert descriptions based on g. The different agents prompted with these different expert descriptions then form expert group M Mg) the given goal g. Notably, the composition of a multi-age aggent group will be dynamically adjusted based on feedback from the employ the most suitable group based
evaluation stage (Section 2.4). This allows AGENTVERS on the current state to make better decisions in future rounds.
2.2 COLLABORATIVE DECISION-MAKING
This stage engages expert agents in collaborative decision decision-making. To facilitate effective decision- making, previous research has investigated the impact of different communication structures among agents (Chan et al. 2023 Zhang et al.) 2023b Wu et al. (023). We focus on two typical communica- tion structures: horizontal structure and vertical structure, respectively.
Horizontal Structure
In this democratic structure, each agent, denoted as E M,
shares and refines its decision Om:-- The group's collective decision, A = (1am, he) A, emerges as an integration of individual agents' decisions using a function f. which might involve techniques like summarization ensemble. This structure is especially effective in scenarios like consulting and tool using.
Conversely, vertical structure has a clear division of roles. An agent, termed the solver m." proposes an initial decision aj- Other agents, as reviewers, provide feedback on this proposal, prompting iterative refinements by the solver until a consensus is reached among reviewers or a set number of iterations exhausted. The final decision A is given as A = ag E A, with & indicating the number of refinements. Vertical structure is preferable for tasks like math problem-solving and software development, where only one refined decision is required.
Vertical Structure
2.3 ACTION EXECUTION
In the decision-making stage, agents collaboratively contribute to a group decision containing actions that need to be exccuted in the current environment. Within the action execution stage, agents then execute the bollectively-decidee some agents might perform any execution. As result of these actions, the state of the environment (Sold. A).
actions the environment. Depending on the implementation,
transitions from Sald to Snew
2.4 EVALUATION
The evaluation stage is vital for AGENTVERSE, guiding improvements for subsequent rounds. At this stage, the feedback mechanism R assesses the difference between the current state Snew and the
3

---

Published as a conference paper at ICLR 2024
Table 1: The results on different tasks that evaluate the agents' general capabilities.
GPT-3.5-Turbo
GPT-4
Task
CoTT
Solo
Group
CoTT
Solo
Group
Conversation (FED)
81.6
81.1
85.1
95.4
95.8
96.8
Creative Writing Ceaammonnnn-Challleen
76.6
93.6
92.3
95.9
99.0
99.1
Mathematical Reasoning (MGSM)
80.4
82.4
80.8
95.2
96.0
95.2
Logical Reasoning (Logic Grid Puzzles)
59.5
64.0
66.5
desired goal g E G. It then offers verbal feedback R (snew g). detailing areas of shortcoming and suggesting ways to enhance performance. R can either be defined by humans (in a human-in-the in-the-loop (Amershi et al. 2014) setting) or agent for automatic feedback, depending on the implementation.
the goal g remains unmet, the feedback r returns to the initial expert recruitment stage. In the next round, the expert recruitment stage will consider both feedback and the goal g to adjust the group's composition, aiming to evolve more effective multi-agent group according to the current progress.
3 EXPERIMENTS
To validate the superiority of AGENTVERSE in facilitating agent collaboration over standalone agents, we design four experimental tasks. Each task is designed to assess distinct aspects of an agent group: general understanding and reasoning capabilities, coding capabilities, tool utilization capabilities, and their potential in Emhodied AI. Our findings, which are detailed in this section, consistently highlight the superior performance of AGENTVERSE across these varied and multi-faceted tasks. Of particular interest is the emergence of unique collaborative behaviors within agent groups. While this section focuses on the advantages of multi-agent setups, a deeper exploration of these emergent behaviors will be presented in Section
Setups. all the experiments, we evaluate the performance of agents driven by GPT-3.5-Turbo-000 and GPT-4-061 13 across various tasks. All the experiments are done in zero-shot setting. For all the quantitative experiments in this section, we compare three settings: (1) CoT: The Cchann of-thought) agent; (2) Solo: Using AGENTVERSS with single agent in the pocision-making stage. Compared with CoT, Solo additionally incorporates the modules introduced in Section [ (3) Group: Implementing AGENTVERSE with multiple agents collaborating during the decision-making. More detailed experimental setups for each task can be found in Appendix
3.1
GENERAL UNDERSTANDING AND REASONING CAPABILITIES
To assess the agents' general understanding and reasoning capabilities, we use four datasets: FED (Mehri & Eskénazi 2020). Commongen Challenge (Madaan al. (202)), MGSM (Shi et al.) and Logic Grid Puzzles (Srivastava et al. (022). Detailed descriptions of these datasets and metrics be found in Appendix A The first datasets used to measure the agents' text understanding and creative writing abilities, while the latter two focus on examining the agents' reasoning abilities, including mathematical and logical reasoning.
2023).
Experimental Results. The results in Table I show that agents assembled by AGENTVERSE (Solo and Group setups) consistently outperform the standalone CoT agent, irrespective of the LLM used. We also present the relationship between the group size and the performance in Appendix B In our preliminary evaluations, GPT-3.5-Tur Turboo struggles with accurately handling the logic grid puzzles dataset; therefore, we omit the result of GPT-3.5-Turbo on logical reasoning.
Interestingly, for GPT-3.5-Turhe the Group setup underperforms the Solo setup two of three tasks, indicating that the discussion in decision-making might adversely impact performance for agents based on GPT-3.5-
in certain contexts. Delving deeper into this observation, one predominant factor surfaces: the susceptibility to erroneous feedback. A recurring pattern observed in the Group setup is that: sometimes Agent A, despite starting with a correct answer, would be easily swayed by Agent B's incorrect feedback. Roughly 10% of errors in the MGSM dataset can be traced to this dynamic. Notably, this phenomenon is absent in GPT-4-based agents, highlighting the importance of agents' resilience to conflicting information during collaborative discussions.
4

---

Published as a conference paper at ICLR 2024
Conselting with Group Selup An sice
Cansulling with Selo Setop
Round
ofeera pros
A Lanyer
Round
Round
Round 0
Roand
CHEP Dedition Making
2 Deciaion-Making
Decbiinntakking
Dacison-Making
Location Selection: Consicoration of
Lecation Sclection: Find optimal
Location Selection: UsB Geographic Intormation System (GI5) tederdlogy
repional weatter condikara; Croar
localon that compp with soleery
hychrapen protucton anarc m minice
regulstions and doew mager
crease e cleta ed map ol pateet of attes Compression Tech: implement outhng edge compression todlndogg the Salety Mensures: (Poughy same lnst round]
rrnsportatto: Chocking the ste coll piope disage tank
Ervi onmental Considecations [.]]
travppotat routes
[
proper worte man agemmer systems
Compression Use soote otthe-
hyyradss, carson narotubes, and compiex hycridee for enhanced atrrage affieenyy thermal management sydee opaiitation: repencration metal
uresaions during hydrogan producction shenssee disnder enneeteeent pian. sledive city conseration dusing estaetihment and openatin. Community Engagement: Consi der
stonag capacity
Safety Massumes: implement evicc
soney mesuures incautin preseure reler
Green Energy Sourcee: Inoorpooate so panets
dovices, desection systems, and
co power
mamursiteed tyyhrger uptaks
Public conseiaation
Green Energy Sourose: make the
Calleborrtion with Universttee and Research Institution: Form rescarch partnershpp with instiutions lic Onio State University or Case Western
[..]
Govemment Support Compliance with state focoral guAtolines on Iucardoou materna storace l..)]
sibrage statio more sustannb
Batety Measures: Instaliation look
considor using ronewable orergy sources
detection apaterms axceasing poteriaa
Collaberation with Univeratien and
devioes, fire pronection Green Energy Souroes: oevelopment ol csent fual oa to convert slored
bereft
andlycing economic
Communityy with the local co comunntt to educare them
Engage
Community
impast on ocal communiiss potental
with the looa community unty to educale thom
markel opsontmitis os [..]
about the coneltu of tedragan energy Govemment Support Baok support gevemment
about the but elta of hlybigenn enengy
hyyirsgen into diectroity & infrestructure Development foundation dimuturalyy sound stetion
Collaboration Universitiss and
Govemment Support Apple
colleooriee
grante
local uniwerttee monerch institione
Development Servicee
design
I..]
Infrastructure Development: Develop Inkertuctine for tydtogen lusling statorer
2. Inf ssstrucuure Development Dovclop
Enveronmental Considenaiions
11: Lagel Considerations obtsinin a lagal widi:
netzork of hyctrrgen linding stallone
Condacting an endrrnmentt impect
Gealestor:
Regluster:
Evoluator:
Gomprehursveeeee Detalldnews Faseibilit Nooetyy
Corerchennaveeeese... Detalednesss Fassbilly at Masetly a
Comprehanaivenne Debelladros Pessibility: 5. Novdlt,?
Comprefooeaeeeee n. Desaiddeas
Novely
comprit
doss are comprehenaiee prackcul
The ideas domprehersive and
economic fessibiity and coat-
deslled crowing
cita led j.. Howewes the less billly can
effectiennesa of the proposed so ora. 1--] For huren vcauuce sual sertiing copperts cel technology would [-]
preject I--1 Thei''s a reed bo a systems
implamennttion raeo on be procite.
improved by provid ng more data hurtee, manage cods. and pubsic acceppince
integration expert eounn [-.]
suth specitic technologies or etrstagies chull to ood socoo.
plane
and hydrrggen
retatione spectaltiet
etocld be inck ded to
Figure 2: The illustration of an example process of consulting. The task is to give some suggestions on building a compressed hydrogen storage station in Ohio.
Overall, the results show that AGENTVERSE effectively enhances the general understanding and reasoning capabilities of agents. Moreover, agents driven by advanced LLMSs demonstrate better performance when engaged in collaborative decision-making. The nuanced challenges observed with GPT-3.5-Turbo indicate the need to improve LLMs' robustness on incorrect information so that the collaboration can amplify individual strengths without introducing new vulnerabilities.
Consulting, In Table I the Group setup does not show a clear advantage over the Solo setup for both LLMs. This is mainly because the evaluation metrics for each benchmark have a limited scope. In the following case, we highlight the benefits of the group formed by GPT-4 agents by focusing on consulting scenario where the group acts as consultancy, responding to inquiries as shown in Figure 2
Case Study:
The goal is to offer suggestions for a hydrogen storage station in Ohio.
At first glance, the Solo setup seems to cover broader scope than the Group setup at round 0. However, the Group setup offers more depth thanks to the recruited experts. For instance, while the Solo setup might suggest something basic like "Find an optimal location", the Group setup provides detailed advice, such as "evaluating site soil properties to ensure storage tank stability." By the second round, different experts offer new insights in the Group setup. As result, the Group setup not only covers a broader range (highlighted in red in the referenced figure) but also gives more detailed advice. For detailed look at agent interactions, see Appendix G
3.2 CODING CAPABILITIES
In this ection, we first assess the agents' coding capabilities using the Humaneval code completion dataset. Next, through case study, we illustrate how collaboration among multiple agents improves output quality, highlighting its superiority over software development by just one agent.
In Table we see a clear performance improvement mov- ing from CoT to Solo and then to Group setup. This trend is especially pronounced with GPT-4 which sees per- formance boost from 83.5 to 89.0. These results highlight AGenTVERS effeetiveness in managing skilled group of agents for coding. For GPT-3.5-Turbe although we have observed drop in performance with Group
Table 2: The pass@ @ on Humaneval.
Setting GPT-3.5-Turbo
GPT-4
CoT
73.8
83.5
Solo Group
74.4
87.2
75.6
89.0
5

---

Published as a conference paper at ICLR 2024
Software Development with Group Getop
Softwore Developmant with Solo Setup
arggetaean progareeer cits
softeer devolag LAUE dedigedd f cortaan lNder
GORR Makknng
GDeesior-Making
4 Detision-Making
Oce
Okt
Ruredde
Cdla offerece frow Hards
oel Deferesce Ereel Hinds
Reeddd (de offerees frew hords
Gar a fffewee
Vrior Hards
Faulundly Enyladd Sipel
dl Ieewokk
Rellcadly Neyword lipe cal Bock
Paulicddyy Enylesd lppl
aa Kedvok
Faulucdly enyeeel Topul
Glk Neslbeil
Deslucto:
Pelluster:
Delludoo:
Eveluehor:
Corplehrena,, Functorality
Compleiencec Purcioonlity:
Complehmens & Functaraiity?
Complcaresa Parctionalty
The hisccaninna doaun iduus exwekooe
The code avdiutucc vol readuble and
Usa siher Vag eviluss rachareite
The code vell atuckaed and planss
poovols sbor feadbach [..]
excettie handing
saser to underia what sault dook L..1
Figure 3: The illustration an example process of developing a calculator with GUI in Python.
Agents:
Bella
Charlie
Tools:
b Bing Search API
Web Browser
X Code Interpreter
Query: Recently, has become popular verify the mathematical reasoning abilities of LLMs by observing they con solve the code-tased solution? it and test its functionality. What some other similar games that can be used to test the models' mathematical reasoning abilities?
"24-Point Game." What is this game?
Python code along with test
Round
Round
Decision-Making
Dacislon-Making
Research game and identify similar games
Find similar mothematical ressoning games
Develop and test the Python code for solving the gome
Test the code with more cases
Execution
Execution
What is 24- 24-point game?
Rule of 24-point game?
24-point similar games?
Browse
Browse website
Submit the rules
Write code test
Browse the 2nd website:
Submit the result
"Make Number" Rule?
Evalustion
X Bella does not provide similar games
(i(ulles) 24-point game (code) Python code is written (similar games) Similor games include "Make a Number"..
Figure 4: An example process of multi-agent solving user query with three different tools.
Experimental Results. setup in Section 3.1 due to incorrect agent feedback in math reasoning, the coding evaluations show benefits. We posit that this might be attributed to L.LMs' extensive pre-training on codes, potentially rendering them more adept at coding than mathematical reasoning and, consequently, more resilient to erroneous information in coding.
Case Study: Software Development. Our examination of the code generated for Humaneval by the Group setup in AGENTVERSY offers benefits beyond mere correctness. The agent group refines solutions, yielding more efficient, robust, and secure algorithms that are not covered by simple pass@ metric. To better elucidate these advantages, we present a case study with GPT-4 on software development, domain requiring multifaceted collaboration and refinement.
We present an example where AGENTV ERSE creates a Python- calculator GUI by bringing together diverse expert agents. A concise development process overview is visualized in Figure 3 Comparing the applications from the Group and Solo setups reveals notable distinctions. Both achieve core functionality, but the Group-created calculator boasts user-friendly interface with features like color distinctions and keyboard input. This improved design resulted from the diverse feedback of the multi aaget group. Suggestions from UI designer and evaluators enhance the user experience, while software tester enhances code robustness. A deeper examination of the code confirms that the multi-agent group's is output excels in exception handling compared to that of a solo agent. The codes generated by the two setups and the complete progress can be seen at Appendix G
3.3
TOOL UTILIZATION CAPABILITIES
The capability of LLMs to use real-world tools has been emphasized in many recent studies (Schick al. 2023a Qin et al. 2023a). By equipping the L.LMss with different tools such calculator,

---

Published as a conference paper at ICLR 2024
Destien-Makingg
Execulion
Drchion--hplingg
Exsourion
Making
Tosk
Drop
Drop
Envenlary the End this Round
Treasher the End this Round
Roard
x2
x2 e xi
x2 4
Figure 5: An illustration the collaborative process involving three agents crafting a bookshelf. The process begins with the decision-making and breaking down the goal into several sub-tasks, with each agent receiving an assignment. The execution results and the current environmental state are then passed to the evaluator. This process repeats until the goal of crafting bookshelf is achieved.
a web browser, and code interpreter, the capabilities of LLMs can be significantly improved. In this section, we demonstrate that AGENTVERSE enables group of agents address intricate and multi-faceted tasks that require interaction with multiple tools, thereby enhancing work efficiency.
Experimental Results. We design a set of 10 intricate tasks, each requiring the use of at least two distinct tools to accomplish. By providing agents access to several tools, including Bing search API, a web browser, a code interpreter, and task-related APIs, we explore how AGENTVERSE facilitates agent collaboration, dissects the overarching task into manageable sub-tasks, and effectively deploys the available tools to address realistic user queries. Of the 10 challenging tasks provided, an agent group orchestrated by AGENTVERSE adeptly accomplishes 9 tasks. On the other hand, standalone ReAct agent (Yao et al. 2023b), which is a prevalent agent designed for tool using, can only fulfill tasks. In 6 out of tasks where the single ReAct agent fails, the agent does not adhere to or more criteria detailed in the task, and exit carlier than expected. We refer interested readers to Appendix C for a comprehensive comparison of the solutions given by AGENTVERSS and a single ReAct agent.
Case Study: Solving 24-Point Game and Providing Similar Games. Here, we present an example in Figure 4 illustrating how AGENTVERSE searches for the rules of 24-point game, implements the code along with test cases, and explores similar games. The task is multifaceted; thus, during decision-maker
stage, the agents split the task into two sub-tasks in their discussion, and each assigned to certain agent. While agent Charlie overlooks the sub-task of identifying games similar to the 24-point game round O, feedback from the evaluation module rectifies this the subsequent iteration. Ultimately, the agent group provides not only the 24-point game rules and solving code with test cases, but also a summary of a similar game. In contrast, a standalone ReAct agent merely provides the game's ' definition along with code and omits the query for similar games.
EMERGENT BEHAVIORS WITHIN MULTI-AGENT GROUP
In the preceding section, the efficacy of AGENTVERSE has been illustrated across a spectrum of tasks that necessitate multi-agent decision-making, especially for GPT-4-based agents. Our endeavor, however, surpasses just improvements benchmark datasets. We delve deeper into emergent collaborative behaviors exhibited by agents within realistic, embodied AI contexts. Minecraft, a sandbox game, serves real-world dynamics. In the game, agents must not just execute tasks but also plan, coordinate, and adjust to evolving situations. We task agents with collaboratively crafting variety of items, spanning from paper and paintings to books and bookshelves, A succinct figure showcasing three agents adeptly crafting bookshelf can be viewed in Figure 5 An elaborate visualization is placed at Appendix G and details of the setups can be found in Appendix
ideal platform for such exploration due to its intricate parallelisms with
By examining the decision- maiiinggmmakingggg process, we identify several emergent behaviors and categorize them into three aspects: volunteer, conformity, and destructive behaviors. Note that these behaviors not necessarily only appear Minecraft but also in previous experiments such as tool utilization.
4.1 VOLUNTEER BEHAVIOR
Volunteer behaviors refer to actions intended to enhance the benefits of others in human soci- cty (Omoto& Snyder 1993 Mowen & Sujan 2005). We observe similar behaviors emerging in a multi-agent group follows:

---

Published as a conference paper at ICLR 2024
(1) Volunteer Behaviors
[Allce]: Bob, for we need craft papers,
[Alice]: I've gethered the sugar
drop the sugar cones for you to put them into the crafting table craft paper. Please confem
focus gethering the sugar cones while the other
con be ready to craft them into paper. What do you think?
sounds good you
[Bob]: Alice, sounds like good plan. be efficient, how about we both gather sugar cones? What do you think?
[Bob]: Alics, il sounds good. Let's meet al the crafling table where you can drep the sugar canea. I'll then pick them use craft the paper. I'm ready when you
(L4) Time Contribution
(lb) Resource Contribution
[Nlice]: Great job, We now gathered the sugar cones needed. Chartie, seems you struggling with gatteriing the leathers. Maybe Bob and I could help you with that. What do think, Bob? Should we all focus on gathering the learh?rs?
[Bob]: Alics, agrex with your suggestion..... Charlie, we will help you with this task. Let's all gather the leathers we progress with our mission. Are you okay with this plan, Charlia?
[Chartie]: Yes, Bob, -. Gathering the thetsers has prowen to be more challenging than expected. with all us focusing on this lask, we should gather leathers quickly. Sa,
focus gethering the leathers. Let's
(1e) Assistance Contribution
(2) Conformity Behaviors
[Alice]: Beb, good job getting one leather. We sfill need two more. Charlie, I see you've been very busy craftingg but we need to Focus on gethering the leathers. For this round, let's all continus focus on getting the leathers. Are we agreament?
[Bob]: Alice, I agres with your plan. We should all continue to focus on gathering the 2 remaining leathers, Charlie, are you akay with this assigrment?
[Chartie]: Yes, Alice and Bob, agrax with the plan. realize got sidetracked with other tasks, and will refocus on our main objective of gothering the remaining 2 leathers. Let's continus with this mission, team!
(3) Destructive Behaviors
[Alice]: Beb, I..] New we read to craft painting suggest that
[Alice]: Bob, I'm still strugging kill cows collect 3 pieces of leather. should continue find and kill Once I'm done, will inform you immediately, How about you?
you drop wool and sticks, and I will pick them to craft
the painting. What do you think?
[Bob] That tounds good, Alice. I'll drop woot and 4 sticks for you
[Bob]: Alice, just falled craft books. continus Focus on crafting 3 books. Let's get it donel
pick [-] Let's this.
Alice actually execules Kill Bob and collect the dropped ilems!
Bob achually executes: Break the library a villoge get the dropped beoks instead of collecting necessory materials and craft.
(34) Agent Destruction
(36) Environment Destruction oo
www.wo.......................... www.oooeeeeee................... www. www.eeeeeee.....................
Figure 6: Examples of the properties emerge in the agent interactions in Minecraft.
Time Contribution. The agents are willing contribute their unallocated time to enhance collabora- tion efficiency. As shown in the examples in Figure [6) la), Alice and Bob need to collaboratively craft 2 paper, which necessitates three sugar canes as the raw material. Initially, Alice proposes that she will collect the sugar canes while Bob waits until the materials are ready. However, this plan is suboptimal, as offers Bob spare time. Recognizing inefficiency, Bob suggests that both gather sugar canes concurrently, leading to expedited task completion.
Resource Contribution. Our analysis reveals that the agents are willing to contribute the possessed materials. As illustrated in Figure (1b), at the end of the task crafting 2 paper, Alice has collected all the raw materials (sugar canes), whereas Bob possesses the crafting table essential for the paper's creation. In the them on the ground. This enables Bob to utilize them for the intended crafting process.
stage, Alice suggests transferring her materials to Bob by dropping
Assistance Contribution. In the process of accomplishing tasks, we observe that agents, upon completing their individual assignments, actively extend support to their peers, thereby expediting the overall task resolution. As shown in Figure 6 (1c), Alice and Bob have successfully completed their assigned sub-tasks, while Charlie is still struggling to gather three leathers. During the collaborative decision-making phase, Alice and Bob propose assist Charlie in gathering.
These behaviors highlight how agents willingly contribute their capabilities and efforts to assist other agents, culminating in an accelerated achievement of their mutual goal.
4.2 CONFORMITY BEHAVIOR
In human society, individuals tend to adjust their behavior align with the norms or goals of group (Cialdini & Goldstein 2004 Cialdini & Trost 1998), which we refer to as conformity behavior. We also observe similar behaviors within multi-agent groups. As shown in Figure (2), all agents
8

---

Published as a conference paper at ICLR 2024
asked to gather three pieces of leather. However, Charlie gets sidetracked and begins crafting items that do not contribute directly to the task. In the subsequent decision- stage, Alice and Bob critique Charlie's 's actions. Charlie acknowledges his mistake and re- co-fouuee on the mutual tasks. The conformity behavior enables agents to align with mutual goals as work progresses.
4.3 DESTRUCTIVE BEHAVTOR
Additionally, we have also observed that agents may exhibit behaviors aimed at achieving greater efficiency. which could raise safety concerns. As depicted in Figure (3a) and Figure [6 (3b), an agent occasionally bypasses the procedure of gathering raw materials and resorts to harming other agents or destroying an entire village library to acquire the necessary materials.
With advancements in autonomous agents, deploying them in real-world scenarios has become increasingly plausible. However, the emergence of hazardous behaviors could pose risks, especially when humans are involved in collaborative processes. Thus, designing strategies to prevent agents from adopting such hazardous behaviors is a critical area for future research.
5 RELATED WORK
The pursuit of creating autonomous agents that can operate intelligently in real-world environments without human involvement has been a persistent goal throughout the history of Al (Wooldridge & Jennings 1995 Minsky, 1988 Bubeck et al. (023). Recently L.LMs [Touvron) jet al. 2023a] | [2023a) have opened up new opportunities to achieve this goal. Thus, numerous studies have developed external mechanisms that equip agents with capabilities for reflection (Yao) cct al. 2023b Shinn et al. 2023). task decomposition (Wei et al., 2022b Yao et al.] (202a)). and tool
Autonomous Agents.
utilization/creation (Schick et al. 2023b Qin et al. 2023a b Qian al. 2023b) capabilities, thereby
enabling agents to be more autonomous and manage increasingly complex scenarios (Richards &
ct al.
2023 Nakajima 2023 Reworkd 2023
Liu al. 2023 in the real world.
Multi-agent System. In human society, a well-organized group composed of individual humans can often collaboratively handle greater workload and accomplish complex tasks with higher efficiency and effectiveness. In the field of AI, researchers draw inspiration from human society and aim to enhance work efficiency and effectiveness by leveraging cooperation among individuals through the study of multi-agent systems (MAS) (Stone & Veloso 2000). also referred to as a multi-agent group in this paper. Previous works have leveraged multi-agent joint training to achieve this goal. Recently, some studies have attempted to leverage the intelligence and capabilities of agents for autonomous collaboration. [Li et (2023) have conceptualized assemblies of agents as a group, and focused on exploring the potential of their cooperation. Park et al. (2023) found social behaviors autonomously emerge within a group of agents, and [u ct al. (2023): : Wang et al. (2023b); Zhang et al. (2023a); Qian et al. (2023a); Chan et al. (2023) further leverage multi-agent cooperation to achieve better performance on reasoning tasks. Based on these findings, we introduce a framework AGENTT AAGEnVVERSE [ This framework dynamically adjust the composition of multi-agent group according to the current state, thereby facilitating optimal makingg and execution.
6 CONCLUSION
In this study, we present VERSE, a novel and general multi-agent framework designed to emulate human group problem-solving processes. Our comprehensive experimental results highlight the efficacy of AGENTVERSE, demonstrating its enhanced performance in comparison to individual agents across myriad of tasks. These tasks encompass general understanding, reasoning, coding, and tool utilization. Notably, AGENTVERSE consistently delivers remarkable results addressing intricate user queries when fortified with the appropriate tools. In our investigations within the Minecraft environment, we identify both positive and negative emergent social behaviors among agents. As advancements in artificial general intelligence progress, understanding multi-agent interactions should become increasingly crucial. AGENTVERSE serves as valuable step toward this endeavor, and we are optimistic about its potential adaptability and refinement for wider array of tasks and contexts in the future.
We provide comparison of our framework with other existing agent frameworks in Appendix H

---

Published as a conference paper at ICLR 2024
ACKNOWLEDGEMENTS
This work is supported by the National Key R&D Program of China (No.20222200116112)), the Young Elite Scientists Sponsorship Program by CAST (Grant no. 2023QNRC001) and National Natural Science Foundation China (No. 62236004).
REFERENCES
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil J. Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Rettinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, and Mengyuan Yan. Do as I can, not as I say: Grounding language in robotic affordances. CoRR, abs/2204.01691, 2022. doi: htRlhhtpss://
000488500/rxxi.2244.1111
2204. 61691
Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. Power to the people: The role of humans in interactive machine learning. Al Magazine, 35(4):105-120, Dec. 2014. doi: 10. 10.160%/aimag.v33550 v3514.2513. URL //ttttp::://.ss.oo
org/ http//aaaaaaaiiiiiee iineex/iiixxx.eeo php/
simagazine/
e ew/5113
Rohan Anil, Andrew M. Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, Eric Chu, Jonathan H. Clark, Laurent EI Shafey. Yanping Huang, Kathy Meier-Hellstern, Gaurav Mishra, Erica Moreira, Mark Omernick, Kevin Robinson, Sebastian Ruder, Yi Tay, Kefan Xiao, Yuanzhong Xu, Yujing Zhang, Gustavo Hernández Ábrego, Junwhan Ahn, Jacob Austin, Paul Barham, Jan A. Botha, James Bradbury, Siddhartha Brahma, Kevin Brooks, Michele Catasta, Yong Cheng, Colin Cherry, Christo- pher A. Choquette-Choo, Aakanksha Chowdhery, Clément Crepy, Shachi Dave, Mostafa Dehghani, Sunipa Dev, Jacob Devlin, Mark Díaz, Nan Du, Ethan Dyer, Vladimir Feinberg, Fangxiaoyu Feng, Vlad Fienber, Markus Freitag, Xavier Garcia, Sebastian Gehrmann, Lucas Gonzalez, and et al. Palm 2 technical report. CoRR, abs/2305. 10403, 2023. doi: 10.48550/arXiv.23650 230..110..00500000 URL 2305 10403
pettps://doi.orgg 41w.0...5///aa.
J.D. Bransford and B.S. Stein. The Ideal Problem Solver: A Guide for Improving Thinking, Learning, and Creativity. W.H. Freeman, 1993. ISBN 978-0-7167-2205-2. URL https: //books
google. com. t wtt.s/...t...oo///...ceeooems
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott M. Lundberg, Harsha Nori, Hamid Palangi, Marco Tálio Ribeiro, and Yi Zhang. Sparks of artificial general intelligence: Early experiments with GPT-4. CoRR, wttp////3..22... 12, 2023, doi: 10.48550/arXiv.2233..r 12711. URL URUtttts:///oii.or 48550/arXiv. 2303. 12712
Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shanghang Zhang, Jie Fu, and Zhiyuan Liu. Chateval: Towards better I1m- evaluators through multi-agent debate, 2023.
URL wwtp::ttts//tt...t...tt ptttts:://oi. www.//l.............oo 2..50//arxivv 2308 07201
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé de Oliveira Pinto, Jared Kaplan, Harrison Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger. Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings. Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert Voss.. William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang. Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, 1lya Sutskever, and Wojeiech Zaremba. Evaluating large language models trained on code. CoRR, abs/2107. 00334,, 2021. URL https: //arxivv oorg/ hbs//ab///a 20t../117..000 03374
10

---

Published as a conference paper at ICLR 2024
Robert B Cialdini and Noah J Goldstein. Social influence: Compliance and conformity. Annu. Rev.
Psychol. 55:591-621, 2004. URL https:: /www. annualreviews org/ doi/ htt////o//bb///aa
/444///nnrev...mm psych 55. 1990000222222111
Robert B Cialdini and Melanie R Trost. Social influence: Social norms, conformity and compliance. 1998. URL httpt//s:s///cceeeeeeeeeeeeeee apa wwt.pppp......o//...O.. 000-//ORRD/999-77911111
Jeff Clune. Ai-gas: Ai-generating algorithms, an alternate paradigm for producing general artificial intelligence. CoRR, abs/1905. 10985, 2019. URL
absp//bb///// 1905 10985
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Rejichiro Nakano, Christopher Hesse, and John Schulman. Training verifiers to solve math word problems. CoRR, 0iss2110.111666 2021. URL
https::/aaxixs http:///xxxvvv.....a... www//rg//aa.......oo........... abs/ 2110. 14168
Danny Driess, Fei Xia, Mchdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng. Igor Mordatch, and Pete Florence. Palm-e: An embodied multimodal language model. 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, volume 202 of Proceedings of Machine Learning Research, pp. 8469-8488, PMLR, 2023. URL
m1 r.
html
Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, and Igor Mordatch. Improving factual- ity and reasoning in language models through multiagent debate. CORR, abs/2305. 14325, 2023. doi: 10.485500/rxxv.23055 14325. /ttpa//aattaa///.........0/0../2 http:aawww.......1..
Ernst Fehr and Simon Gächter. Cooperation and punishment in public goods experiments. American Economic Review, 90(4):980 994, 2000.
10. 1257/ 90. 4. 980
Ben Goertzel and Cassio Pennachin, Artificial general intelligence, volume 2. Springer, 2007, URL 540 7
attpps:/liee //t::///nnk.... spr
http://iinkk.pprrrrr....ooo///oo http//ooo//000 http://ww...coooooooooo..0..
Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. CAMEL: communicative agents for "mind" exploration of large scale language model society.
CoRR, abs/2303. 2023, doi: 10.46550/axxi.2233..777 URL (RLL/ttas://oo... /ttp://oo...ooo////////
48550/arXiv. 2773. 17760
Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang. Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, and Jie Tang. Agentbench: Evaluating Ilms agents, 2023.
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang. Sean Welleck, Bodhisattwa Prasad Majumder, Shashank Gupta, Amir Yazdanbakhsh, and Peter Clark. Self-refine: Iterative refinement with self-feedback. CoRR, abs/2303. 17022033.1661,, 2023. doi: 10.48550WarXi..233..17
URL
Shikib Mehri and Maxine Eskénazi. Unsupervised evaluation of interactive dialog with dialogpt. In Olivier Pietquin, Smaranda Muresan, Vivian Chen, Casey Kennington, David Vandyke, Nina Dethlefs, Koji Inoue, Erik Ekstedt, and Stefan Ultes (eds.), Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGdial 2020, 1st virtual meeting, July 1-3, 2020, pp. 225-235, Association for Computational Linguistics, 2020. URL 2020.
sttps://aclantten
Marvin Minsky. The Society of Mind. Simon & Schuster, 1988. ISBN 067 1657135. URL https 7/ //jmvide dal cse. sC. edu/ / htw. http://iib/misssky8e.. html

---

Published as a conference paper at ICLR 2024
John C Mowen and Harish Sujan. Volunteer behavior: A hierarchical model approach for investi- gating its trait and functional motive antecedents. Journal of consumer psychology, 15(2):170- 182, 2005. URLhttps:: htt.o///0LLaaaaaaa//oooooooooaa0 http://c....o..eeeaa..........ea httpa//w...aaaaaa.aoooooooo.o./.
s 7155777663 jcp cp 502_9
Yohei Nakajima. Babyagi. 2023, URL /tl.tt/tp///SSt.....ooo///eeeaaa [Software].
Allen M Omoto and Mark Snyder. Sustained helping without obligation: motivation, longevity of service, and perceived attitude change among aids volunteers. Journal of personality and social psychology, 68(4):671, 1995. URL
1995-26640-02
OpenAl. GPT-4 technical report. CoRR, ablpaaa3./23.77..4.4..4 2023a. doi: 10.48550/aXY..2230.88744 URL https:://ai..rr//10... wttp:///.........ii......../////
OpenAL. Chatgpt can now see, hear, and speak, 2023b. URL https:://penal..oom// //t.////oo///////////ooa/ooo/ooo
-hat-ptt-cnn------e----ee-ee andaaarr-nd---- speak
Joon Sung Park, Joseph C. O'Brien, Carrie Cai, Meredith Ringel Morris, Perey Liang, and
Michael S. Bernstein. Generative agents: Interactive simulacra of human behavior. CoRR,
0bs/2304.03442, 2023. doi: 10.48550/arX1..2304.04444 48550/arXiv 2304. 03442.
URL /ttps://66i.org/11
Katherine Phillips and Charles O' O'Reilly. Demography and diversity in organizations: A review of 40 years of research. Research in Organizational Behavior, 20:77-140, 01 1998. URL https://wwas www.:rrreeeacahaccoo
otaoro/onn//3022223___eorrappp Review and Sew___ieeesst_n_n_eeee_ htt.rrsittyy.nnnngreaaaaaaaioooo 4
40 40_eff440__ea__ee Years of Research
Chen Qian, Xin Cong, Cheng Yang, Weize Chen, Yusheng Su, Juyuan Xu, Zhiyuan Liu, and Maosong Sun. Communicative agents for software development. CoRR, abs/2307.0792. 2023a. doi:
10.485550/rXXv.23377.o .07944. UtLtttss:://dot http///oo..rgg../////o or0/10................oo/..../.. 20A.LL.45550/aa/5
Cheng Qian, Chi Han, Yi R. Fung, Yujia Qin, Zhiyuan Liu, and Heng Ji. CREATOR: disentan- gling abstract and concrete reasonings of large language models through tool creation. CoRR, abs/2305. 14318 14318, 2023b. doi: 1185550/aXxi.2305.1144 48550/arXiv 7305.14388
URL lattp::/aoi.orq///
Yujia Qin, Shengding Hu, Yankai Lin. Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Yufei Huang, Chaojun Xiao, Chi Han, Yi Ren Fung, Yusheng Su, Huadong Wang, Cheng Qian, Runchu Tian, Kunlun Zhu, Shihao Liang, Xingyu Shen, Bokai Xu. Zhen Zhang, Yining Ye, Bowen Li, Ziwei Tang, Jing Yi. Yuzhang Zhu, Zhenning Dai, Lan Yan. Xin Cong, Yaxi Lu, Weilin Zhao, Yuxiang Huang, Junxi Yan, Xu Han, Xian Sun, Dahai Li, Jason Phang, Cheng Yang, Tongshuang Wu, Heng Ji, Zhiyuan Liu, and Maosong Sun. Tool learning with foundation models. CoRR, abs/2304.082.. 08354, 2023a. doi: 1445550/a2xx..234..8..2. 2304. 03354
URL https: //opi.:///oi.o orw..rrg/1l0 48550/4455552250
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. Toolllm: Facilitating large language models to master 16000+ woald apis. arXiv preprint orXiv:2307. 16789, 2023b. URL.
Reworkd. Agentgpt, 2023. URL httpt:tp///c/. httpp::/ggitubb.cc////oooo//
[Software].
Toran Bruce Richards and al. Auto-gpt: An autonomous exfeeimee,mment, 2023. URL https: [Software].
Leonard Salewski, Stephan Alaniz, Isabel Rio-Torto, Eric Schulz, and Zeynep Akata. In-context im-
personation reveals large language models' strengths and biases. CoRR, abs/2305. 2023. doi: 20.5500axxiv.355...... 14930 1430 10.445550/rxivv23305 14930. URI.https:: http://////oo........o.......
Timo Schick, Jane Dwivedi- Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models teach themselves to use tools. CoRR, abs/2302.0476.1 2023a. doi: 10.48550/arXi..3322.44777 URL (https
7/doi wedoi.org//0.... 48..4485555 wttp:///wwwwv.........
04761
12

---

Published as a conference paper at ICLR 2024
Timo Schick, Jane Dwivedi- Yu. Roberto Dessi, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to tools. CoRR, abs/2302.04761. 2023b. doi: 10.48550kaaXXi.2332.047760 |
7/doi.org/ 0. (8550//axiiii www.2............ 04761]
Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung. Yi Tay, Sebastian Ruder, Denny Zhou, Dipanjan Das, and Jason Wei. Language models are multilingual chain-of- tf-thuhht reasoners. In The Eleventh International Conference on Learning Representations. ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023.
URL /tttpp::///peereeieeee net/pdfff www.d:ccc----......po....ww.kwk. IXp
Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu
Yao. Reflexion: Language agents with verbal reinforcement learning, 2023. URL https
//do:.orr//0-A555///a//// 2303. 11366
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonoo, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nic, Aman Hussain, Amanda Askell, Amanda Dsouza, Ameet Rahane, Anantharaman S. Tyer, Anders Andreassen, Andrea Santilli, Andreas Stuhlmüller, Andrew M. Dai, Andrew La, Andrew K. Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi. Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakas, and et al. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. CoRR, http//22000444155 2022, doi: 10.48550//xxx..22266.. 04615. URL htttp:://deee org/10 48550/arXi 2206 04615
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano. Learning to summarize with human feed- back. In Hugo Larochelle, Mare' Aurelio Ranzato, Raia Hadsell, Maria-Floring Balcan, and Hsuan-Tien Lin (eds.), Advances in Neural Information Processing Systems 33: Anmual Con- ference Neural Information Processing Systems 2020, NeurlPS 2020, December 6-12, 2020, virtual, 2020. URL http::ttt////tttm / ngs... neurips.u..... cc/pcc/pppaeccccccccccccccpcatc 1f89685d556929e wtw.aa33eeeoboo4888 Abst .amaa.thhmmll....mm
Peter Stone and Manuela Veloso. Multiagent systems: A survey from machine learning perspective. Auton. Robots, 3(3):345-383, jum 2000. ISSN 0929-5593. doi: 10. 10.1023/A:100894000
URL
https: htttps://doi.ozg/1000 www.o.oogg/000 01011000111001111111111111233999
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptinn Alayrac Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M. Dai, Anja Hauth, Katie Millican, David Silver, Slav Petrov, Melvin Johnson, Joannis Antonoglou, Julian Schrittwieser, Amelia Glaese, Jilin Chen, Emily Pitler, Timothy Lillicrap, Angeliki Lazaridou, Orhan Firat, James Molloy, Michael Isard, Paul R. Barham, Tom Hennigan, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, Ryan Doherty, Eli Collins, Clemens Meyer, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, George Tucker, Enrique Piqueras, Maxim Krikun, lain Barr, Nikolay Savinov, Ivo Danihelka, Becca Roelofs, Anais White, Anders Andreassen, Tamara von Glehn, Lakshman Yagati, Mehran Kazemi, Lucas Gonzalez, Misha Khalman, Jakub Sygnowski, Alexandre Frechette, Charlotte Smith, Laura Culp, Lev Proleev, Yi Luan, Xi Chen, James Lottes, Nathan Schucher, Federico Lebron, Alban Rrustemi, Natalie Clay, Phil Crone, Tomas Kocisky, Jeffrey Zhao, Bartek Perz, Dian Yu, Heidi Howard, Adam Bloniarz, Jack W. Rae, Han Lu, Laurent Sifre, Marcello Maggioni, Fred Alcober, Dan Garrette, Megan Barnes. Shantanu Thakoor, Jacob Austin, Gabriel Barth-Maron, William Wong, Rishabh Joshi, Rahma Chaabouni, Deeni Fatiha, Arun Ahuja, Ruibo Liu, Yunxuan Li, Sarah Cogan, Jeremy Chen, Chao Jia, Chenjie Gu, Qiao Zhang, Jordan Grimstad, Ale Jakse Hartman, Martin Chadwick, Gaurav Singh Tomar, Xavier Garcia, Evan Senter, Emanuel Taropa, Thanumalayan Sankaranarayan, Pillai, Jacob Devlin, Michael Laskin, Diego de Las Casas, Dasha Valter, Connie Tao, Lorenzo Blanco, Adrià Puigdomènech Badia, David Reitter, Mianna Chen, Jenny Brennan, Clara Rivera, Sergey Brin, Shariq Iqbal, Gabriela Surita, Jane Labanowski, Abhi Rao, Stephanic Winkler, Emilio Parisotto, Yiming Gu, Kate Olszewska, Yujing Zhang, Ravi Addanki, Antoine Miech, Annie Louis, Laurent El Shafey, Denis Teplyashin,
13

---

Published as a conference paper at ICLR 2024
Geoff Brown, Elliot Catt, Nithya Attaluri, Jan Balaguer, Jackie Xiang. Pidong Wang, Zoe Ashwood, Anton Briukhov, Albert Webson, Sanjay Ganapathy, Smit Sanghavi, Ajay Kannan, Ming-Wor Chang, Axel Stjerngren, Josip Djolonga, Yuting Sun, Ankur Bapna. Matthew Aitchison, Pedram Pejman, Henryk Michalewski, Tianhe Yu, Cindy Wang, Juliette Love, Junwhan Ahn, Dawn Bloxwich, Kehang Han, Peter Humphreys, Thibault Sellam, James Bradbury, Varun Godbole, Sina Samangooci, Bogdan Damoc, Alex Kaskasoli, Sébastien M. R. Arold, Vijay Vasudevan, Shubham Agrawal, Jason Riesa, Dmitry Lepikhin, Richard Tanburn, Srivatsan Srinivasan, Hyeontaek Lim, Sarah Hodkinson, Pranav Shyam, Johan Ferret, Steven Hand, Ankush Garg. Tom Le Paine, Jian Li, Yujia Li, Minh Giang, Alexander Neitz, Zaheer Abbas, Sarah York, Machel Reid, Elizabeth Cole, Aakanksha Chowdhery, Dipanjan Das Dominika Rogozińska, Vitaly Nikolaev, Pablo Sprechmann, Zachary Nado, Lukas Zilka, Flavien Prost, Luheng He, Marianne Monteiro, Gaurav Mishra, Chris Welty, Josh Newlan, Dawei Jia, Miltiadis Allamanis, Clara Huiyi Hu, Raoul de Liedekerke, Justin Gilmer, Carl Saroufim, Shruti Rijhwani, Shaobo Hou, Disha Shrivastava, Anirudh Baddepudi, Alex Goldin, Adnan Ozturel, Albin Cassirer, Yunhan Xu, Daniel Sohn, Devendra Sachan, Reinald Kim Amplayo, Craig Swanson, Dessie Petrova, Shashi Narayan, Arthur Guez, Siddhartha Brahma, Jessica Landon, Miteyan Patel, Ruizhe Zhao, Kevin Villela, Luyu Wang, Wenhao Jia, Matthew Rahtz, Mai Giménez. Legg Yeung, Hanzhao Lin, James Keeling, Petko Georgiev, Diana Mineu, Boxi Wu, Salem Haykal, Rachel Saputro, Kiran Vodrahalli, James Qin, Zeynep Cankara, Abhanshu Sharma, Nick Fernando, Will Hawkins, Behnam Neyshabur, Solomon Kim, Adrian Hutter. Priyanka Agrawal, Alex Castro-Ros, George van den Driessche, Tao Wang. Fan Yang, Shuo yiin Chang, Paul Komarek, Ross Mcllroy, Mario Lučic, Guodong Zhang, Wael Farhan, Michael Sharman, Paul Natsev, Paul Michel, Yong Cheng, Yamini Bansal, Siyuan Qiao, Kris Cao, Siamak Shakeri, Christina Butterfield, Justin Chung, Paul Kishan Rubenstein, Shivani Agrawal, Arthur Mensch, Kedar Soparkar, Karel Lenc, Timothy Chung, Aedan Pope, Loren Maggiore, Jackie Kay, Priya Jhakra, Shibo Wang, Joshua Maynez, Mary Phuong, Taylor Tobin, Andrea Tacchetti, Maja Trebacz, Kevin Robinson, Yash Katariya, Sebastian Riedel, Paige Bailey, Kefan Xiao, Nimesh Ghelani, Lora Aroyo, Ambrose Slone. Neil Houlsby, Xuehan Xiong, Zhen Yang, Elena Gribovskaya, Jonas Adler, Mateo Wirth, Lisa Lee, Music Li, Thais Kagohara, Jay Pavagadhi, Sophie Bridgers, Anna Bortsova, Sanjay Ghemawat, 7afarali Ahmed, Tianqi Liu, Richard Powell, Vijay Bolina, Mariko linuma, Polina Zablotskaia, James Besley, Da-Woon Chung, Timothy Dozat, Ramona Comaneseu, Xiance Si, Jeremy Greer, Guolong Su, Martin Polacek, Raphaël Lopez Kaufman, Simon Tokumine, Hexiang Hu, Elena Buchatskaya, Yingjie Miao, Mohamed Elhawaty, Aditya Siddhant, Nenad Tomasev, Jinwei Xing, Christina Greer, Helen Miller, Shereen Ashraf, Aurko Roy, Zizhao Zhang, Ada Ma, Angelos Filos, Milos Besta, Rory Blevins, Ted Klimenko, Chih-Kuan Yeh, Soravit Changpinyo, Jiaqi Mu, Oscar Chang, Mantas Pajarskas, Carrie Muir, Vered Cohen, Charline Le Lan, Krishna Haridasan, Amit Marathe, Steven Hansen, Sholto Douglas, Rajkumar Samuel, Mingqiu Wang. Sophia Austin, Chang Lan, Jiepu Jiang, Justin Chiu, Jaime Alonso Lorenzo, Lars Lowe Sjösund, Sébastien Cevey, Zach Gleicher, Thi Avrahami, Anudhyan Boral, Hansa Srinivasan, Vittorio Selo, Rhys May, Konstantinos Aisopos, Léonard Hussenot, Livio Baldini Soares, Kate Baumli, Michael B. Chang, Adrià Recasens, Ben Caine, Alexander Pritzel, Filip Pavetic, Fabio Pardo, Anita Gergely, Justin Frye, Vinay Ramasesh, Dan Horgan, Kartikeya Badola, Nora Kassner, Subhrajit Roy, Ethan Dyer, Victor Campos, Alex Tomala, Yunhao Tang. Dalia Bl Badawy, Elspeth White, Basil Mustafa, Oran Lang, Abhishek Jindal, Sharad Vikram, Zhitao Gong, Sergi Caelles, Ross Hemsley, Gregory Thortoo,, Fangxiaoyu Feng. Wojeiech Stokowiec, Ce Zheng, Phoebe Thacker. Çağlar Unlü, Zhishuai Zhang, Mohammad Salch, James Svensson, Max Bileschi, Piyush Patil, Ankesh Anand, Roman Ring, Katerina Tsihlas, Arpi Vezer, Marco Selvi, Toby Shevlane, Mikel Rodriguez, Tom Kwiatkowski, Samira Daruki, Keran Rong, Allan Dafoe, Nicholas FitzGeral,, Keren Gu-Lemberg, Mina Khan, Lisa Anne Hendricks, Marie Pellat, Vladimir Feinberg, James Cobon-Kerr, Tara Sainath, Maribeth Rauh, Sayed Hadi Hashemi, Richard Ives, Yana Hasson, YaGuang Li, Eric Noland, Yuan Cao, Nathan Byrd, Le Hou, Qingze Wang, Thibault Sottiaux, Michela Paganini, -aptistte Lespiau, Alexandre Moufarek, Samer Hassan, Kaushik Shivakumar, Joost van Amersfoort, Amol Mandhane, Pratik Joshi, Anirudh Goyal, Matthew Tung, Andrew Brock, Hannah Sheahan, Vedant Misra, Cheng Li, Nemanja Rakićevié, Mostafa Dehghani, Fangyu Liu, Sid Mittal, Junhyuk Oh, Seb Noury, Hren Sezener, Fantine Huot, Matthew Lamm, Nicola De Cao. Charlie Chen, Gamaleldin Elsayed, Fid Chi, Mahdis Mahdieh, Ian Tenney, Nan Hua, Ivan Petrychenko. Patrick Kane, Dylan Scandinaro. Rishub Jain, Jonathan Uesato, Romina Datta, Adam Sadovsky, Oskar Bunyan, Dominik Rabiej. Shimu Wu, John Zhang, Gautam Vasudevan, Edouard Leurent, Mahmoud Alnahlawi, Ionut Georgescu, Nan Wei, Ivy Zheng, Betty Chan, Pam G Rabinovitch, Piotr Stanczyk, Ye Zhang, David Steiner, Subhajit Naskar,
14

---

Published as a conference paper at ICLR 2024
Michael Azzam, Matthew Johnson, Adam Paszke, Shung-Cheng Chiu, Jaume Sanchez Elias, Afroz Mohiuddin, Faizan Muhammad, Jin Miao, Andrew Lee, Nino Vieillard, Sahitya Potluri, Jane Park, Elnaz Davoodi, Jiageng Zhang. Jeff Stanway, Drew Garmon, Abhijit Karmarkar, Zhe Dong, Jong Lee, Aviral Kumar, Luowei Zhou, Jonathan Evens, William Isaac, Zhe Chen, Johnson Jia, Anselm Levskaya, Zhenkai Zhu, Chris Gorgolewski, Peter Grabowski, Yu Mao, Alberto Magni, Kaisheng Yao, Javier Snaider, Norman Casagrande, Paul Suganthan, Evan Palmer, Geoffrey Irving. Edward Loper, Manaal Faruqui, Isha Arkatkar, Nanxin Chen, Izhak Shafran, Michael Fink, Alfonso Castaño, Irene Giannoumis, Wooyeol Kim, Mikolaj Rybiński, Ashwin Sreevatsa, Jennifer Prendki, David Soergel, Adrian Goedeckemeyer, Willi Gierke, Mohsen Jafari, Meenu Gaba, Jeremy Wiesner, Diana Gage Wright, Yawen Wei, Harsha Vashisht, Yana Kulizhskaya, Jay Hoover, Maigo Le, Lu Li, Chimezie Iwuanyanwu, Lu Liu, Kevin Ramirez, Andrey Khorlin, Albert Cui, Tian LIN, Marin Georgiev, Marcus Wu, Ricardo Aguilar, Keith Pallo, Abhishek Chakladar, Alena Repina, Xihui Wu, Tom van der Weide. Priya Ponnapalli. Caroline Kaplan, Jiri Simsa, Shuangfeng Li, Olivier Dousse, Fan Yang. Jeff Piper, Nathan Ic, Minnie Lui, Rama Pasumarthi, Nathan Lintz, Anitha Vijayakumar, Lam Nguyen Thiet, Daniel Andor, Pedro Valenzuela, Cosmin Paduraru, Daiyi Peng, Katherine Lee, Shuyuan Zhang. Somer Greene, Duc Dung Nguyen, Paula Kurylowicz, Sarmishta Velury, Sebastian Krause, Cassidy Hardin, Lucas Dixon, Lili Janzer, Kiam Choo, Ziqiang Feng, Biao Zhang, Achintya Singhal, Tejasi Latkar, Mingyang Zhang, Quoc Le, Elena Allica Abellan, Dayou Du, Dan McKinnon, Natasha Antropova, Tolga Bolukbasi, Orgad Keller, David Reid, Daniel Finchelstein, Maria Abi Raad, Remi Crocker, Peter Hawkins, Robert Dadashi, Colin Gaffney, Sid Lall, Ken Franko. Egor Filonov, Anna Bulanova. Rémi Vikas Yadav, Shirley Chung. Harry Askham, Luis C. Cobo, Kelvin Xu, Felix Fischer, Jun Xu, Christina Sorokin, Chris Alberti, Chu-Cheng Lin, Colin Evans, Hao Zhou, Alek Dimitriev. Hannah Forbes, Dylan Banarse, Zora Tung, Jeremiah Liu, Mark Omernick, Colton Bishop, Chintu Kumar, Rachel Sterneck, Ryan Foley, Rohan Jain, Swaroop Mishra, Jiawei Xia, Taylor Bos, Geoffrey Cideron, Ehsan Amid, Francesco Piccinno, Xingyu Wang, Praseem Banzal, Petru Gurita, Hila Noga, Premal Shah, Daniel J. Mankowitz, Alex Polozov, Nate Kushman, Victoria Krakovna, Sasha Brown, MohammadHossein Bateni, Dennis Duan, Vlad Firoiu, Meghana Thotakuri, Tom Natan, Anhad Mohananey, Matthieu Geist, Sidharth Mudgal, Sertan Girgin, Hui Li, Jiayu Ye, Ofir Roval, Reiko Tojo, Michael Kwong, James Tee-Thorp. Christopher Yew, Quan Yuan, Sumit Bagri, Danila Sinopalnikov, Sabela Ramos, John Mellor, Abhishek Sharma, Aliaksei Severyn, Jonathan Lai, Kathy Wu, Heng-Tze Cheng, David Miller, Nicolas Somnerat, Denis Vnukov, Rory Greig, Jennifer Beattie, Emily Caveness, Libin Bai, Julian Eisenschlos, Alex Korchemniy, Tomy Tsai, Mimi Jasarevic, Weize Kong, Phuong Dao, Zeyu Zheng, Frederick Liu, Fan Yang, Rui Zhu, Mark Geller, Tian Huey Teh, Jason Sanmiya, Evgeny Gladchenko, Nejc Trdin, Andrei Sozanschi, Daniel Toyama, Evan Rosen, Sasan Tavakkol, Linting Xue, Chen Elkind, Oliver Woodman, John Carpenter, George Papamakarios, Rupert Kemp, Sushant Kafle, Tanya Grunina, Rishika Sinha, Alice Talbert, Abhimanyu Goyal, Diane Wu, Denese Owusu-Afriyie, Cosmo Du, Chloe Thornton, Jordi Pont- Tuset, Pradyumna Narayana, Jing Li, Sabaer Fatchi, John Wicting, Omar Ajmeri, Benigno Uria, Tao Zhu, Yeongil Ko, Laura Knight, Amélie Héliou, Ning Niu, Shane Gu, Chenxi Pang, Dustin Tran, Yeqing Li, Nir Levine, Ariel Stolovich, Norbert Kalb, Rebeca Santamaria--Feraadeer Sonam Goenka, Wenny Yustalim, Robin Strudel, Ali Elqursh, Balaji Lakshminarayanan, Charlie Deck, Shyam Upadhyay, Hyo Lee, Mike Dusenberry, Zonglin Li, Xuezhi Wang. Kyle Levin, Raphael Hoffmann, Dan Holtmann- Sho Arora, Eric Malmi, Daniil Mirylenka, Qijun Tan, Christy Koh, Soheil Hassas Yeganch, Siim Pöder. Steven Zheng. Francesco Pongetti, Mukarram Tariq, Yanhua Sun, Lucian Ionita, Mojtaba Seyedhosseini, Pouya Tafti, Ragha Kotikalapudi, Zhiyu Liu, Anmol Gulati, Jasmine Liu, Xinyu Ye, Bart Chrzaszez, Lily Wang. Nikhil Sethi, Tianrun Li, Ben Brown, Shreya Singh, Wei Fan, Aaron Parisi, Joe Stanton, Chenkai Kuang, Vinod Koverkathu, Christopher A. Choquette-Choo Yunjie Li, TJ Lu, Abe Ittyceriihh, Prakash Shroff, Pei Sun, Mani Varadarajan, Sanaz Bahargam, Rob Willoughby, David Gaddy, Ishita Dasgupta, Guillaume Desjardins, Marco Cornero, Brona Robenek, Bhavishya Mittal, Ben Albrecht, Ashish Shenoy, Fedor Moiseev, Henrik Jacobsson, Alireza Ghaffarkhah, Morgane Rivière, Alanna Walton, Clément Crepy, Alicia Parrish, Yuan Liu, Zongwei Zhou, Clement Farabet, Carey Radebaugh, Praveen Srinívasan, Claudia van der Salm, Andreas Fidjeland, Salvatore Scellato, Eri Latorre-Chimoto, Hanna Climezak-Plucinste David Bridson, Dario de Cesare, Tom Hudson, Piermaria Mendolicchio, Lexi Walker, Alex Morris, Ivo Penchev, Matthew Mauger, Alexey Guseynov, Alison Reid, Seth Odoom, Lucia Loher, Victor Cotruta, Madhavi Yenugula, Dominik Grewe, Anastasia Petrushkina, Tom Duerig, Antonio Sanchez, Steve Yadlowsky, Amy Shen, Amir Globerson, Adam Kurzrok, Lynette Webb, Sahil Dua,
Leblond,
Olivier Bachem, Summer Yue,
15

---

Published as a conference paper at ICLR 2024
Dong Li, Preethi Lahoti, Surya Bhupatiraju, Dan Hurt, Haroon Qureshi, Ananth Agarwal, Tomer Shani, Matan Eyal, Anuj Khare, Shreyas Rammohan Belle, Lei Wang. Chetan Tekur, Mihir Sanjay Kale, Jinliang Wei, Ruoxin Sang, Brennan Sacta, Tyler Liechty, Yi Sun, Yao Zhao, Stephan Lee, Pandu Nayak, Doug Fritz, Manish Reddy Vuyyuru, John Aslanides, Nidhi Vyas, Martin Wicke, Xiao Ma, Taylan Bilal, Evgenii Eltyshev, Daniel Balle, Nina Martin, Hardie Cate, James Manyika, Keyvan Amiri, Yelin Kim, Xi Xiong, Kai Kang. Florian Luisier, Nilesh Tripuraneni, David Madras, Mandy Guo, Austin Waters, Oliver Wang, Joshua Ainslie, Jason Baldridge, Han Zhang, Garima Pruthi, Jakob Bauer, Feng Yang, Riham Mansour, Jason Gelman, Yang Xu, George Polovets, Ji Liu, Honglong Cai, Warren Chen, XiangHai Sheng, Emily Xue, Sherjil Ozair, Adams Yu, Christof Angermueller, Xiaowei Li, Weiren Wang, Julia Wiesinger, Emmanouil Koukoumidis, Yuan Tian, Anand lyer, Madhu Gurumurthy, Mark Goldenson, Parashar Shah, MK Blake, Hongkun Yu, Anthony Urbanowicz, Jennimaria Palomaki, Chrisantha Fernando, Kevin Brooks, Ken Durden, Harsh Mchta, Nikola Momchev, Elahe Rahimtoroghi, Maria Georgaki, Amit Raul, Sebastian Ruder, Morgan Redshaw, Jinhyuk Lee, Komal Jalan, Dinghua Li, Ginger Perng Blake Hechtman, Parker Schuh, Milad Nasr, Mia Chen, Kieran Milan, Vladimir Mikulik, Trevor Strohman, Juliana Franco, Tim Green, Demis Hassabis, Koray Kavukcuoglu, Jeffrey Dean, and Oriol Vinyals. Gemini: A family of highly capable multimodal models, 2023.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models. CoRR, 00op//202.11371.... 2023a. doi: 20.445500/n2ii..2222.1222 13971. URL htttt::t///// //doi
org/
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov. Soumya Batra, Prajiwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton-Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux. Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurélien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. Ilama 2: Open foundation and fine-tuned chat models. CoRR, abs/2307 09288, 2023b. doi: 114455500/2Xxw222777092888 URL 09288
10. 41555//555iv..
Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Voyager: An open-ended embodied agent with large language models. CoRR, habb//335.1169911 2023a. doi: 10.485500arxiw.233555 16291. URL https: /toppp:://ddi. //o.dooorrg 10 00.45550aax1100 2305 16291
Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, and Heng Ji. Unleashing cognitive synergy in large language models: A task-solving agent through multi-persona self- collaboration. CoRR, abs/2307 000000 2023b. doi: 10.448550a2xxw..337..553 URL https
//doi. 0el.44455//xiivi/
05300
Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoe V. Le. Finetuned language models are zero-shot learners. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net, 2022a. URL htwp::tps:///o. http:ss///openzeveww..oo net wtw.:owuwweeeeoommeeeemm forumm?mm ? gEZrGCozdgR.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoe V.
Le, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models. In NeurlPS, 2022b. URL hhattp://paper......t n nips cc/ cc/paper httpp/ppperrleepeppeeeeeeeeeeeee 22tp///ah/// /
16

---

Published as a conference paper at ICLR 2024
Jimmy Wei, Kurt Shuster, Arthur Szlam, Jason Weston, Jack Urbanek, and Mojtaba Komeili. parti chat: Conversational agents in group settings with humans and models. CoRR. abs/2304.13835, 2023. doi: 10448500/aXX..2304.1383.. URL https://doi. / 48550/arXiv 13885
Michael J. Wooldridge and Nicholas R. Jennings. Intelligent agents: theory and practice. Knowl. Eng.
Rev. 10(2):15-152, 1995. doi: 10. 10.1017/50269888800008122
URL [tL[sttpp:///0i.or///11
I017/9265889000033
Anita Williams Woolley, Christopher F. Chabris, Alex Pentland, Nada Hashmi, and Thomas W. Malone. Evidence for a collective intelligence factor in the performance human groups. Science, doi: 10.1126/sciencc.11999 11933/1 1193147. Oxt/66///bb/1..11255//
330(6004)6686-00000 688, 2010.
URL https::/ science
1193147
Anita Williams Woolley, Ishani Aggarwal, and Thomas W. Malone. Collective intelligence and group performance. Current Directions in Psychological Science, 2466)420-424,, 2015. doi:
10.1177/0963214415595555 /ttts::////do http:///...111111111111111555510
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. Autogen: Enabling next-gen Ilm applications via multi- agent conversation framework, 2023. URL 08155
2308 /00. 0tt.//10..885500
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. CoRR, abs/2305. 2023a. doi: 10.485500/rXxi.22355.0606 URL
10550/arXiv.2355.0
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 Confer- ence on Leurning Representutions, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.ner, net /tt//df/ ? d.d=W__vuYYU--
2023b. URL
Hongxin Zhang, Weihua Du, Jiaming Shan, Qinhong Zhou, Yilun Du, Joshua B. Tenenbaum, Tianmin Shu, and Chuang Gan. Building cooperative embodied agents modularly with large language models. CoRR, abs/2307 02485, 2023a. doi: 10:48550/axXw..230000444 URL https
//doi.org// www.orrg......0 118550/aaxii00 wwt..riwii............ 02485
Xinghua Zhang, Bowen Yu, Haiyang Yu, Yangyu Lv, Tingwen Liu, Fei Huang, Hongbo Xu, and Yong- bin Li. Wider and deeper Ilm networks are fairer ilm evaluators. arXiv preprint arXiv:2308.00 00860.62. 2023b. URL (tLh:tt::: //ooi org
2308.00 01862)
Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo. Abishek Sridhar, Xianyi Cheng, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig. Webarena: A realistic web environment for building autonomous agents. CoRR, wwtp////........3333 2023. doi: 10448550/aXX..2237..B388
/tt 2300 13854
17

---

Published as a conference paper at ICLR 2024
A CONFIGURATIONS OF THE EXPERIMENTS
Datasets and Evaluation Metrics Our evaluation assesses different aspects of agents, including general understanding and reasoning capabilities, coding capabilities and tool utilization capabilities.
General Understanding Capabilities: We utilize two datasets. The first one is Dialogue response dataset, FED (Mehri & Eskénazi 2020), where given a multi-round chat history, the agent or agent group is required to generate the next chat. Following previous work (Madaan et al. 2023). we utilize GPT-4 as the evaluator to score the ggent-generated response against the human- buman-written written ones, and report the agent's ''s win rate. The second dataset is Commongen- Challenge (Madaan et al., 2023), which is a constrained generation dataset where given 20 concepts, the agent is required to generate a coherent and grammatically correct paragraph containing as many concepts as possible. We report the average percentage of the covered concepts.
General Reasoning Capabilities: We utilize the English subset of MGSM (Shi et al. 2023),
which is subset of GSM-8k (Cobbe et al. 2021), to evaluate the agents' mathematical reasoning capabilities. It is a dataset containing grade school math problems. We report the percentage of the correct answers. And
the logic grid puzzles task from BigBench (Srivastava al.) 2022). which contains logic problems that requires multi-step logic reasoning, to assess the agents' logical reasoning capabilities. We report the accuracy.
Coding Capabilities: We utilize Humaneval (Chen et al., 2021). which is a code completion dataset, and report Pass@1 1 metrid
Tool Utilization Capabilities: Since automatic evaluation on the performance of tool utilization is difficult, and there currently no relevant benchmark, we craft 10 complex instructions and manually assess the performance. The instructions are listed in Appendix C
Expert Recruitment For tasks including dialogue response. code completion, and constrained generation, four agents is recruited into the system. For the task of mathematical reasoning, we limited the number to two agents. This decision was based on our observation that an increase the number of reviewers for mathematical reasoning tasks correlates with higher likelihood of them giving erroneous critiques, leading to incorrect solutions by the solver. We have a discussion on this topic in Section 3.1 For tool utilization, we recruit two or three agents to engage in collaborative eecision-making and action execution depending on the specific task. The detailed setups are listed at Appendix C Currently the number of experts pre-defined by us for each task. We are seeking way to automate this decision as well.
Collaborative Decision-Making For tasks in coding and general understanding and reasoning, we use the vertical structure because all these tasks require only one response as the answer, and the
the solver in the vertical structure can be responsible for answering. For tool utilization, horizontal structure because the agents should clarify their own sub-tasks in the discussion.
Action Execution For the Humaneval code completion dataset benchmarked with GPT-4, we incorporate an additional agent during the action execution stage to craft unit testing code (in an zero-shot manner). Subsequently, the generated code is subjected to unit testing, and the testing results are conveyed as the environment state to the evaluation module.
Regarding the constrained generation dataset, Commongen- -haggeen--halleeeee. the agent-generated response undergoes a concept coverage check. Any missing concepts are then passed to the evaluation module as the environment state.
In the context of tool utilization, each agent iteratively calls the tool in the ReAct manner, up to a maximum of 10 iterations. Upon reaching the final iteration, the agent is forced draw conclusion regarding the result, labeling the task's s status as either "pending" or "finished".. These conclusions then forwarded to the evaluator for assessment.
"The method for calculating Pass@1 differs from the approach in Chen et al (222)) Instead of generating multiple responses and calculating unbiased estimator, directly employ the first response to compute the Pass
18

---

Published as a conference paper at ICLR 2024
Evaluation To facilitate feedback loop, agent tasked with the role of evaluator. This agent, provided with the initial problem p and the decisions A made during the collaborative decision- making stage, is charged with determining the correctness of those decisions. In cases where the decision is identified as erroneous, feedback is channeled back to the expert recruitment stage. the decision meets the accuracy criteria, it is determined the final answer to p. While current configuration employs an agent for evaluation, we acknowledge the potential of human evaluators and intend to incorporate such experiments in future endeavors.
B GROUP SIZE AND PERFORMANCE
We present the relationship between group size and performance in Table We run each setting for 3 runs and report the average performance and the standard deviation.
Table 3: The performance under different group size settings. Group-r means there decision- making agents.
Task
CoTT
Solo
Group-2
Group-3
Group-4
Mathematical Reasoning
81.2
83.2
82.3
82.8
81.611.3
Programming Average
72.3
73.4
74.8 L0.7
74.6
74.8 10.6
76.8+0.2
78.3+1.0
78.5
78.7
78.2
Generally, using AGENTVERS. with to three recision-making agents gives satisfying results. The average performance gets highest when there are decision-making agents. While these datasets primarily test specific agent abilities, not fully utilizing the diversity of multi-agent setup, still observe an upward trend in average performance with an increase in agents. The diminishing returns upon further scaling can be attributed communication inefficiencies, discussed in Section 3.]
C EXPERIMENT DETAILS FOR MULTI-AGENT TOOL USING
C.1
SETUPS
This section provides specific implementation details for enabling multiple agents in AGENTVERSE collaboratively utilize tools accomplish user's query. Unless specified herein, the implementation adheres to the standard procedures defined in the other experiments.
Collaborative Decision-Making Agents recruited during the Expert Recruitment stage engage in collaborative discussions regarding the assigned task using horizontal communication structure. In this configuration, agents communicate in predetermined sequential order. At the conclusion of their discussions, an additional agent is designated as the "summarizer" and is responsible for consolidating the discussed sub-tasks for each participant.
During action execution, we follow the ReAct prompting technique to let the agents write down their thought, and then call the tool, and we return the tool response to agent. The agent again write down the thought and call the tools. The process iterates until the agent decide to submit its conclusion, or a maximum iteration number, which we set to be 10, is reached. In practice, use OpenAl's function call to handle the tools.
Action Execution
C.2 RESULTS
For action execution, the ReAct prompting technique is employed. Agents articulate their thought process, invoke the respective tool, and subsequently receive feedback from the tool. This cycle of reflection and tool interaction continues iteratively. The process concludes either when the agent finalizes its execution with its conclusion or after a pre-set maximum number of iterations- (10 in our experiments). We utilize OpenAl's function call to handle the tool calls.
19

---

Published as a conference paper at ICLR 2024
Instruction 1
Recently, it has become popular in the Al field to verify the mathematical reasoning abilities of large language models by observing if they can solve the "24-Point Game." " What is this game? Does it have code-based solution? If it does, provide Python code along with test cases and test its functionality, What are some other similar games that can be used to test the models' mathematical reasoning abilities?
Single ReAct Agent:
(The generated code is omitted) The '24-Point Game is a mathematical game where four numbers must be used with the basic arithmetic operations (addition, subtraction, multiplication, and division) to make the number 24. The Python code I developed generates all possible combinations of the four numbers and operations to find a solution that equals 24. The code was tested with different sets of numbers, and it correctly identified whether a solution existed and what was. This task tested my ability to understand the rules of a game and to develop and test a program that solves it. (game rule v, code v, similar games *)
AGENTVERSE VRSS (2 agents): (The generated code is omitted) The "24-Point Game" is mathematical game where four numbers must be used with the basic arithmetic operations (addition, subtraction, multiplication, and division) to make the number 24. A Python code was developed and tested that generates all possible combinations of the four numbers and operations to find solution that equals 24. The code was tested with different sets of numbers, and it correctly identified whether solution existed and what it was. Similar games to the "24-Point Game' that can be used to test mathematical reasoning abilities include MMke a Number' and 'Math Safe'. 'Make a Number' is game where players are given 4 numbers and 4 operations and they have to use these to create target number. 'Math Safe' is a game where players use four given single-digit numbers and any of the four operations reach a target number and open safe. Both these games require the use of operations and logical thinking to reach a target number, similar to the '24-Point Game'. (game rule code V, similar games
Instruction 2 (Additional API: Weather) I want to hold a party at somewhere around Tsinghua University tomorrow. need you to look for some best places for holding a party nearby, and tell me whether the weather is good for holding party tomorrow. Also, I want to know what activities can be considered in my party. Help me search the web.
Single ReAct Agent: There are several restaurants near Tsinghua University that could be good places for party, including Lan Yang ShuFang. YiLan ShaoKao, Guan Shi JiChi, Dong Lai Shun, and Xibu Mahua Beef Noodle. The weather in Beijing tomorrow is expected be overcast with a temperature of 26 degrees Celsius, which might be suitable for a party, Some activities that can be considered for the party include the Saran Wrap Game, Post It Note Game, Most Likely To, Never Have I Ever, Would You Rather, and many more. (V)
AGENTVERSE (3 agents): The weather in the Tsinghua University area tomorrow is expected to be overcast with temperature of 26 degrees Celsius, which is suitable for holding a party. The best places for party near Tsinghua University include the Summer Palace, Wudaokou, Zhongguaneun, and Peking University, as well as several restaurants nearby. For party activities, you could consider games such as the Saran Wrap Game, the Post It Note Game How's Yours?, Most Likely To, Never Have I Ever, Would You Rather?, Heads Up!, I'm Hosting Party... Two Truths and a Lie, Sticker Stalker, Great Minds Think Alike, murder mystery games, Who Am 1?, the Newlywed Game, Truth or Dare, Spoons, Pictionary, Kiss Marry Kill, the Don't Say Yes game, Cards Against Humanity, What Do You Meme, Drawing Without Dignity, and That's What She Said. These games are suitable for various party occasions and there are also suggestions for icebreakers for guests who don't know each other well. (v provide more details)
Instruction 3 (Additional API: Billboard) I'm currently analyzing what is popular on the website. Can you help me find the recent trending stuff. It could be anything, like trending news, products, books, movies, music, etc. Give a summarization for me.
The recent trending products on the web include Apple AirPods Pro, Diamond Dazzle Cleaner Pen, Ugg Slippers. Squishmallows, Winix Air Purifier, ThisWorx Car Vacuum Cleaner, All-Clad Cookware, Eufy Robot Vacuum, Renpho Eye Massager, Crocs, Throw blankets, Chuckit! Sport Dog Ball Launcher, Apple AirTags, Revlon Hair Dryer Brush, Lego Sets, KitchenAid Stand Mixer, Wine Wine Subseription, Saatva Mattress, Home Chef Subscription, and MasterClass
Single ReAct Agent:
20

---

Published as a conference paper at ICLR 2024
Subscription. Unfortunately, were unable to find specific information recent trending books. (trending product v)
AGENTVERSE (3 agents): Here is summary of the trending topics on the website:
Music:
1. 'Heat Waves' by Glass Animals
2. 'We Don't Talk About Bruno' by Carolina Gaitan, Mauro Castillo, Adassa, Rhenzy Feliz, Diane Guerrero, Stephanie Beatriz & Encanto Cast
3. "Super Gremlin' by Kodak Black
4. 'llime You Out' by Drake Featuring SZA
5. 'Paint The Town Red' by Doja Cat
6. 'Snooze' by SZA
Movies:
1. "No One Will Save You'
2. A Haunting in Venice"
3. "Expend4bles"
Books:
1. 'The Midnight Library'
2. 'The Invisible Life of Addie LaRue'
3. 'The Seven Husbands of Evelyn Hugo'
Products:
1. Laser hair removal
2. Head scarfs
3. Detangling hair brushes
4. Hair wigs
5. Straight-leg jeans
6. Shapewear
7. Athleisure
8. Abdominal crunch straps
9. Swimsuits
10. Power tool accessories
11. Dog toys
12. Shot glasses
13. Costume shoes
14. Slipcovers
15. Wall plates and covers
16. Baby shirts
17. Curtain and drape rings
18. Cabinet knobs and handles
19. Watch accessories
20. Tumblers
21. Model train sets
22. Signage
23. Wedding dresses
24. Table runners
Please note that the popularity of these items vary based the region and demographic of the consumers. (music V, movie v, books V. products v)
Instruction I'm planning a two-weck vacation to Japan next month. Help me plan my itinerary. I want to visit Tokyo, Kyoto, and Osaka. Look for the top tourist attractions in each city, and also suggest the best mode of travel between these cities. Additionally, find out the weather forecast for the month I'll be visiting.
Single ReAct Agent: The top tourist attractions in Tokyo are the Tokyo Skytree, Senso-ji Temple, Meiji Shrine, Tokyo Disneyland, Ueno Park, and Odaiba. In Kyoto, the top attractions are Nijo Castle,
21

---

Published as a conference paper at ICLR 2024
Fushimi-Inari Taisha Shrine, Kinkaku-ji: The Golden Pavilion, Kiyomizu-dera Temple, Sanjüsangen- dō Temple, and Kyoto Imperial Palace. In Osaka, tourists can visit Osaka Castle, Shitemnō-ji Temple, Osaka Aquarium Kaiyukan, Tempozan Ferris Wheel and Harbor Village, and Universal Studios Japan. These attractions offer a mix of historical, cultural, and modern experiences for visitors. (tourist attractions V, best mode of travel between cities *, weather forecast
AGENTVERSE (3 agents): Your two-week vacation to Japan is shaping up to be quite exciting. In Tokyo, you can visit the Tokyo Skytree, Senso-ji Temple, Meiji Shrine. Tokyo Disneyland and DesneySea, and the Odaiba area. In Kyoto, the top attractions are Nijo Castle, Fushimi-Inart Taisha Shrine, Kinkaku-ji (Golden Pavilion), Kiyomizu Temple, and Sanjūsangen-do Temple. While in Osaka, you should visit Osaka Castle, Shitennō-ji Temple, Osaka Aquarium Kaiyukan, and Universal Studios Japan.
As for transportation, the best way to travel between these cities is by train, specifically the Shinkansen bullet train. You can also consider flights with ANA or JAL, or if you're 'e on a budget, the night bus or LCC flights by Jetstar and Peach.
Regarding the weather, it's difficult to provide an exact forecast for the next month, but we've 'e gathered the average weather data for these cities based on previous years. Please remember to check the weather forecast closer to your departure date for more accurate prediction. Enjoy your trip! (tourist attractions V. best mode of travel between cities V. weather forecast v/)
I am planning date with my girlfriend this week, please search for good movie theater and a restaurant near Tsinghua University in Beijing and recommend a good movie to watch. Please search the web.
Instruction 5
Single ReAct Agent: Your date near Tsinghua University in Beijing is all set. For dining, you can choose from Lan Yang ShuFang, YiLan ShaoKao, Guan Shi JiChi (Zhongguancun), Shun (Zhongguancun HaïTu). As for the movie, there are several highly-rated ones showing in 2023. Some of these include M3GAN, Plane, Missing, Infinity Pool, John Wick: Chapter Creed III. Scream VI, Air, Suzume, Rise, Are You There God? It's Me. Margaret, Showing Up, and The Covenant, Enjoy your date! (restaurant V, movie V theater
Dong Lai
AGENTVERSE (3 agents): Your date is all set! For dining, we recommend either Jiasuo Restaurant or Zijingyuan, both located conveniently on the Tsinghua University campus. For your movie, we suggest 'No Bears', which is currently popular and highly rated. You can watch it at the Maple Garden Drive-In Cinema, one of the top-rated movie theaters near Tsinghua University. Enjoy your date! (restaurant v, movie w. theater
Instruction 6 I want kick off book club with my friends. Can you tell me the top 5 bestselling books this month, gather the content summary for each, and find online platforms where we can buy or borrow them?
Single ReAct Agent: was unable to find the top 5 bestselling books for October 2023. The websites I visited did not provide the required information. Therefore, am unable provide the content summary for each of the top 5 bestselling books and find online platforms where they can be bought or borrowed.
AGENTVERSE (2 agents): top 5 bestsellers this month can be bought or borrowed from several online platforms. These include OverDrive, which allows you to borrow books for free through your local library or school. Other platforms include the Amazon Kindle Store, Barnes & Noble's ' Nook Book Store, Fictionwise, eBooks.com com, eReader.com, Google Play Book Store, Powell's Books, as well as eBook lending and borrowing services like eBookFling, Lendle, and BookLendingg.oo comm. (online platforms V, top bestselling books x, content summary
I am planning to buy a new car. Could you help me compare the features and prices of the latest models of Tesla, Ford, and Toyota? Include details about range, charging time, safety features, and after-sales service. Also, provide a brief analysis of the pros and cons of each car.
Instruction 7
Single ReAct Agent: The 2023 Tesla Model starts at $41,880 and goes up $54,880. It comes with standard features such as heated front seats, navigation, and Tesla's 's Autopilot semi-autonomous driv- ing system. In terms of safety features, the Model 3 includes standard automated emergency braking,
22

---

Published as a conference paper at ICLR 2024
kane-departure warning, and adaptive cruise control. Tesla's after-sales service is characterized by vertically integrated format that eliminates third-party intermediaries and relies on over-the-air (OTA) update capabilities and mobile service fleet. The company's 's OTA update capabilities will open up new subscription-hase paatid aftersales revenue streams, while its closed-loop system allows access to vehicle data for driving-related feedback loops and predictive maintenance. (Tesla V, Other brands *))
AGENT V ERSE (3 agents): The starting price for the 2023 Ford Mustang Mach-E is around $43,895
and it takes approximately 38 hours to fully charge using 120V outlet, 14 hours using a 240V outlet, and around 45 minutes to 80% capacity with DC fast charging. The starting price for the 2023 Toyota bZ4X is approximately $42,000. The charging time varies depending on the method: 52 hours for Level charging. 26 hours or 18.75 hours for Level charging. and 30-45 minutes to 80% capacity for Level 3 DC fast charging. Unfortunately, the price for the 2023 Tesla Model 3 was not found, but the charging time can be as quick as minutes with a 250 kW Tesla Supercharger. standard vol-volt outlet takes around three to four days for full charge, while a 240-volt outlet with a NEMA 14-50 adapter reduces charging time to 10 or 11 hours. A Tesla Wall Connector can fully charge the car in about five hours.
home, using a
In terms of safety, all three models come equipped with a variety of features such as anti-lock brakes, stability control, front-impact airbags, side impact airbags, overhead airbags, knee airbags, pretensioners, and a security system. The Tesla Model also includes autopilot and collision warning features.
Regarding after-sales service, Tesla offers a comprehensive warranty and roadside assistance with service centers across the globe and mobile service. Toyota's after-sales service is known for being top-notch and continually improving with expert technicians and quality genuine parts. However, Ford's after-sales service has received negative reviews, with customers reporting poor customer support and inefficient handling of issues.
Please consider these factors when making your decision. (Prices V, charging time, safety features, after-sales service)
Instruction 8
I've recently taken interest in DIY home projects. Search for heginner-friendly DIY projects that can be completed over the weekend. Also, provide a list of materials required and a step-by-step guide for each project.
Single ReAct Agent: I found a variety of beginner-friendly DIY home projects from three different websites. These projects include painting a room, replacing cabinet pulls, repairing torn window screens, fixing interior doors, balancing furnace dampers, repairing leaky faucets, refreshing caulk in the bathroom, building garage storage shelves, constructing a fire pit, hanging a ceiling fan, installing a wall-mounted television, and cleaning out sink traps. These projects are suitable for beginners and can help improve DIY skills while boosting confidence. (DIY projects V, list of materials *)
AGENT V VERSE (2 agents): The team has successfully found a variety of beginner- friendll DIY home projects that can be completed over the weekend. These projects range from painting a room, replacing cabinet pulls, installing wall-mounted television, building a fire pit, to more complex tasks like kitchen remodels and furniture updates. The materials required for these projects are basic and easy to source, including tools like a roller and pan for painting, cordless drill for replacing cabinet pulls, a stud finder for installing a wall-mounted television, and concrete work tools for building a fire pit. Detailed, step-by-step guides for each project have been provided ensure smooth DIY experience. Happy DIY-ing! (DIY projects v. list of materials v)
Instruction 9 Winter is coming, and I want to update my wardrobe. Search for the latest winter fashion trends for men and women. Also, suggest some online stores where I can buy trendy winter clothing.
Single ReAct Agent: The latest winter fashion trends for both men and women this year include suiting, bold shoulders, western touches, maxis, electric hues, bombers, mismatched maximalism. corseting, cut-outs, Y2K fashion, and chunky boots. You can purchase these trendy winter clothes at online stores like Prettyl.itleThhnng and Windsor. (fashion trends V. online stores v/)
23

---

Published as a conference paper at ICLR 2024
AGENTVERSE (3 agents): The latest winter fashion trends for men include reinvention of classic staples, misery chic, pops of pink, throwback jackets, showstopping trousers, and Mac coats. For women, the trends include corseting, cowboy boots, Y2K styles, oversized outerwear, pleated skirts, and balaclavas. You can shop for these trendy winter clothes at popular online stores such as Uniqlo, Everlane, and Target. (fashion trends V, online stores v/
Instruction 10 want to lose 5kg in the next 2 months. weigh 70kg, 1 170cm tall, and my age is 25, Calculate my BMI and based on that, suggest a workout routine and daily calorie intake to help me achieve my goal.
Single ReAct Agent: Your BMI is 24.22, which is within the normal range. To lose 5kg in 2 months, you should aim for a daily calorie deficit of 500- 100-1000 calories. This can be achieved through combination of a balanced diet and regular exercise. Your diet should focus on whole foods, lean proteins, fruits, and vegetables. For exercise, a combination of cardio and strength training is recommended. Cardio exercises like running, cycling, swimming can help burn calories, while strength training exercises like weight lifting can help build muscle, which can increase your metabolic rate and aid in weight loss. Don't forget to get enough sleep and stay hydrated. (bmi V. workout routine V, daily calorie intake V)
AGENTVERSE (2 agents): Your Body Mass Index (BMI) is approximately 24.22, which is within the healthy weight range. To achieve your weight loss goal, you should aim to consume between 1400 and 1600 calories per day. This will create calorie deficit necessary for weight loss. In terms of physical activity, you should start with a 4-week program that includes upper and lower body strength training, interval wrainingcoondtioningg and low intensity cardio exercises. After completing this, transition to 12-week program that includes cardio. strength training, and ficxibility workouts. Each workout should take approximately 20-22 minutes. Remember to maintain healthy habits such as eating a nutritious diet, staying hydrated, getting enough sleep, and managing stress. (bmi v, workout routine V. daily calorie intake v)
D DETAILS OF THE EXPERIMENTS ON MINECRAFT
In this section, we explain some implementation details of the experiments that we conduct on Minecraft (Section 4
Expert Recruitment As noted in Section 4 real-world gaming scenarios requires intricate commu- nication and coordination across multiple rounds, there is often consistent set of team members. Therefore when using AGENTV VERSE to simulate the game playing, we bypass the automated expert recruitment stage, and manually assign each agent as "an experienced Minecraft player".
Collaborative Decision-Making For multi-player gameplay, the horizontal communication paradigm is favored. lends itself to an environment where each agent independently formu- lates plans, diverging from traditional benchmark tasks which demand singular solution. Agents are set to communicate in a predetermined sequential order, continuing until consensus is perceived. We let the agent to append a special token "[END]" at the end of its response it finds that the group have reached consensus on the task assignment.
Subsequent achieving consensus, an auxiliary agent tasked to deduce the specific assignment for each agent from the entire communication record. This distilled information is then given as the input to the Voyager agent to inform it the assigned task.
Action Execution We instantiate several Voyager agents within a shared Minecraft environment. brief introduction of the Voyager agent is provided here, and refer the interested readers to Wang ctt al. (2023a) for a more detailed exposition.
A Voyager agent is adept at navigating Minecraft. On receiving task, it first decomposes it into set of manageable sub-tasks. For instance, if assigned the task "Kill 3 cows". the agent might decompose it into sequential sub-goals like: [punch 2 trees, Craft 4 wooden planks, Craft I stick, Craft I crafting table, Craft I wooden sword, Kill cows]. The agent then sequentially attempt to complete each sub-task.
24

---

Published as a conference paper at ICLR 2024
We employ the checkpoint available in the official repository and use GPT-4-0314 as the backbone LLM for Voyager agent to be consistent with Wang et al. (2023a). Once an agent accomplish its own task, all agents hit the cap of five attempts, the task execution stage terminates and evaluation stage starts.
Evaluation We directly exploit the inventory and the completed or failed sub-tasks of each agent as the feedback.
E PROMPTS
We list the prompts used in Section 3 Figures 7 to
FED: Figure
Z
MGSM: Figure
8
Humaneval: Figure
Caamongenn-halleepee Figure
10
Tool: Figure
F LIMITATION AND FUTURE WORK
In this work, we introduce AGENTVERSE that facilitates multiple autonomous agents to simulate human groups to accomplish tasks, and discuss the emergent social behaviors of agents during this process. AGENTVERSE ERSSE an advanced attempt; thus, there are some techniques within AGENTVERSE that still have room for improvement and are worthy of exploration. In this section, we delve into these aspects for further illustration.
More Capable Agents and More Challenging Scenarios. The AGENTVERSE is designed to enable various multiple LLM--bsed agents to collaboratively accomplish tasks. the current research, we have utilized tite-offthhearr art agents based on GPT-4. With the advancements in LLMs, such as the newly released version of ChatGPT that incorporates voice and image capabilities (OpenAT 2023b), LLM-based agents have more perceptual capabilities, including seeing, hearing, and speaking. These enhancements may increase the potential of agents and allow them to accomplish more complex real-world tasks based the AGENT ERSE framework.
Multi-party Communication Among Agents. The currently proposed autonomous agents (Richards) & al. 2023 Nakajima 2023 Reworkd [023] Wang et al., 2023a LLMs possess excellent instruction comprehension capabilities (Wei el al. [022a Stiennon et al. 2020). This enables them to follow given human instructions and accomplish tasks within one-on-one (human-to-Al) scenario.
However, multi-agent collaboration involves a multi-parry party communication (Wei et al., (203) scenario that requires the capability to autonomously determine when to speak and whom to speak. This leads to difficulties in communication among the agents during the collaborative decision-making step within the AGENTVERSE framework. Hence, there are two directions worth exploring. Firstly, akin to the aforementioned, we can explore more effective mechanisms for managing agent communication. Additionally, we can design more advanced perceptual- LLMs (OpenAl 2023b) that can autonomously interact with their environment including other agents.
Leverage Emergent Behaviors and Mitigate Safety Issues. Section we identified both emergent positive and harmful behaviors. Exploring ways leverage positive behaviors for improving work efficiency and effectiveness, as well as mitigating harmful behaviors, are promising directions.
ski11
This kind of perceptual-awar agent has long heen goal of embodied AI Ahn et al. which promising direction explore.
2022 Driess al.
2023
25

---

Published as a conference paper at ICLR 2024
G EXAMPLES OF THE CASE STUDIES
In this section, we delve into specific examples to illustrate the experimental processes discussed in our paper, For each instance, we juxtapose the single-agent approach with the multi-agent method. Specifically:
Software Development: Figure 12 depicts the process for developing a calculator. Figures
13 and 14 show the code generated by single agent and multi-agent group respectively.
Consulting in Horizontal Structure: For consulting. we present single-agent and multi- agent approaches using horizontal structure. These can be seen in Figures [13 and T6
Consulting in Vertical Structure Similarly, Figures 17 and 18 showcase single-agent and multi-agent project consulting, but employing vertical structure structure for multi-agent.
Tool Utilization: Figure 19 presents how two agents effectively decompose the given query into different sub-tasks, and use different tools to collaboratively resolve the query.
Minecraft: Lastly, Figure 20 provides an insight into a process where three agents collabo- rate to craft bookshelf in Minecraft.
26

---

Published as a conference paper at ICLR 2024
Dialogue Response Prompt
Role Assigner
group experis, now you generate response
Sylakk descrition)
You can recruit Sjont, crtic agents) export different flalds. What experts will recrult to better generate an accurate solution?
Response
You should reepond with a list expert description For example:
1. an electricel engineer specified in the filed of xox
an economist who good
lawyar with good knowladge
You don't have give the reason.
Problem
to generate resporse meask_descpplinn
Previous Solution
solution you gave last slep Sttormer solution)
Gritie
Critice the group the following opinions: Storlic opinione)
Your Task
Now based upon the former solution and the crtics' opinions, please new solution. Your solution should contain only response beginning information.
System: * give
Reviewer
Role Description and Problem Solve Strole descriptin). Stask descrption)
group. aiming generate resporse
Preliminary Solution
Now group preliminary solution followe: Sgreeiminery_eeble soluuion)
Advice
Meanwhile, another export the following advice the solution:
S(advice)
Resgonse Format Guidance If you thinks the preliminary solution perfect, respond using the following format:
Action: Agree
Input: Agree.
(Do not output your reeson agresing!!
think is tlawod, give your advice use the tollowing output format:
Action: Disagree
Input: texplain why you disagree)
Your Task
Based your knowledge your field do you that this solution is the bast response based on the text?
Evoluator
Role Descrption
You are an experieced dialogue teacher. As good teacher, you carefully check the correctness the given response based the toxt. Whan the solution fawed, should patiently teach the students how give better
Resporee Fornet Guidsnce
You must respond in the folkowing format:
Intoresting: scora between D and 9)
Engaging:
Speaiii:: score betwsen
Relevant: (a score between and 9)
Seranttcaally Appropriate: (a ecore between 0 and
Understandable: (a between
Fluent: between
Overall impression: (a score between and 9]
Advice: (your advice on how correct the solution)
Problem Student's Solution
$llask_desctne description) Student's Solution: Sisolution)
Your Task
Now carefully chack the student's solution, response.
Figure 7: Prompt of FED dataset.
27

---

Published as a conference paper at ICLR 2024
Math Reasoning Prompt
Role Assigner
Deseriptio
You are the loader group, now you are facing grade school math problem:
$task_ description)
Sjont, criic, agentis people. What people you recruit?
Response Format Guldance
You should respone with a list of $iont__cnie aggntt) people description For exampie:
electrical engineer specified the filed
economist good
lawyer with good knowledge xox
Only with description rolo. include your
following probiem?
descrittio))
Previous Solution
The solution you in the last step is:
solution)
Gritis
There are some critcs on the abeve solution:
Startic opinions)
Using the these information, can you provide the correct solution to the math problem? Explain your reasoning, Your final answer must be single numerical number (not equation, fraction, function variabte). the form lnoved(answerr,, the and of your response.
Reviewer
are discussion group, aiming colleboraive solve the following maih problem:
Stlakk descrption)
possible
problom:
S/preliminary_ssai _splutronoo
are Strole descripion). Based on your knowledge, can you check correcnness the solutions given the chat history? You should give your cornect solution to the problem stop by step. When responding, you should follow the following rules:
Double check the above solutions give critee, then ganerate the correct solution stop slop. final answer the
final [Agree)"
solutin the
provided solution
special taken
muel highlght your linal anewer the form boocedaasserr at the end of your response. The answer must numerical number. equalion, fraction, function or variatre.
Now give your response.
Evaluator
Problem: $task description) Solution:
S(solution
You experienced mathematic teacher. As teacher, carefully check correctness solution grade school math wrong, give your correciness of why correct, Also check that final answve form (ooxed[answer? the the solution answer must be numerical number (not equation, traction, function or varlabla).
problem.
correct
output
respond Comrecness: (0 wrong.
conrec)
Response: (explain details why wrong or correct)
Figure 8: Prompt for MGSM dataset.
28

---

Published as a conference paper at ICLR 2024
Code Completion Prompt
Role Assigner
Role Description group experts, now you emall group experts identity correctly solve the given problemms:
Staakk descrition)
Stont, crtic. aagnnts) export differant flalds. What oxperts will you genorate solution?
Response Format Guidance
You should respond with a list of expert deseription. For example:
electrical
specifidd the filed
economist good
3. lawyer with good knowledge xox.
Only with description each include your
Solver
Can you complete the following code?
$ttask_dese descrppep)))
Previous Solution
The solution you the last stop is:
Sitormar scolution)
There are some orites on the above solution:
Steftiic opinions)
Using information, can you provide correct completion Explain your reasaning. response contain only Python coci. Do not give any adcitional information. Use pyyhon to put the completed Pythan code markdown quotes. When responcing, please include the given code and the completion.
Reviewer
You are discussion group, aiming to complate the following code function:
python
Staask_ descokiein))))
Below a poesible code complelion:
ilpraimmnary. _.lluuttoon
You are S(raia, descriptinn. Based on your knowlodge, can you chack the correctness the completion given abovo? You should give your correct solution the problem stop by step. When responding, you should fallow the following ráles:
Double-check the above solutions, your response Your response should contain only Python code. Do not give any additional information, Use "python wrap your Python code in markdown quotes. When responding, please include the given code and the completion.
crites, special the '[Agree]".
solutin
Now give your response.
Evaluator
You are experienced code reviewer,
reviewer, carelully check correchness of the code completion. When the completion completion.
should paliently
Resporse Formet Guidance
You must respond in the folkowing format:
Score: (0 or 1, 0 for incoreet and for correct)
Response:
advice how correct the solution)
Problem
Solution
Problom:
$ttask_dre description)
Whtter's Solutio::
Sisolution)
Now carefully check the wrlter's solution, and give your resporse.
Figure Prompt for Humaneval dataset.
29

---

Published as a conference paper at ICLR 2024
Constrained Generation Prompt Role Assigner
Role Description
You are the loader of of experts, now you need to recrut small experts with diverse idantity generate coherent and grammatically correct sentences containing the following given words: $tlask_deeee descripiio)
You can recruit Sjent_ortic, cwticc_agetttttt expert in different fie ids. What experts will you recruit?
Resgonne
should reepond
expart description. For example:
1. an elactrical ongineer specified the filed of xox.
an economist who is good at xox. with knowledge
Only reapond with the description eech role. Do not include your reason.
Solver
Can you generate coherent and grammaticall correc paragraph containing the tollowing given words (or their variationsj: Words: $ttask description)
The paragraph you gave the last stop is:
$itorme_scoution
Grlks
There are some ertics on the abeve solution:
opinions)
Using the these information. provide paragraph that contains the given words their variations.
Reviewer
group, aiming generate grammatically correct sentences containing following given
variatione)::
Worde: Staakk description)
Below possible solution to the problem:
Sipreiminary_solane
are Strole descripion), Bssed knowledge, check whether the peragraph contains all the given words their varistione? When responding, follow following 1. not, doublo check the above solutions, give your ertics, and generato better solution. Now give your response.
covared givan words variaions, your resporse special tokoo "[Agraa]".
Evaluator
You are reviever who checks whether paragraph contains all the given words (including their variations). When some words are missing, and
should patiently
When the paragraph contains the words, you should of
Response
You must respond in following lormat:
Score: (0 or 1. o there are some missing words, covers all the words)
Advioo: (point out all the missing words)
Writer's Solution
Words:
Stisek. deecription)
Writer's Solution:
S(solution)
Now carafully check the writer's solution, and give resporse.
Figure 10: Prompt for Commongen-Challene dataset.
30

---

Published as a conference paper at ICLR 2024
Utiliinng Prompt
Nodigner
roperss
dently lods prontrsteees
syyent) expers prowore
Sparnor
Pocsurt Ourcance
eemer desopption, and sepandde name Noongg expert Jroe upectiedi coo..
pood
priwss
Sprral Jesken
RESPONSI PORTATT
sonweponding
Alis saarcc the vas for the werthrr ar Beijnn osda uaingg google
people
enbo lro. Euch lee ahould pnl iick penson Nata the sa specill, Do swrpr roun waer the laakc remford
Disoussion Prompt
Steart Spoae, Aeorppeer j non ducusation group
soles)
comeee relaton on hoars with ane mato addressing the toloo
Shott
ENITS
emen mhe steddivlly zoondi with to emar autceratd compltiton of coury rights all ident mammes oulldic on be aevvvee trough keboongg ny atpae amatier, ensgpaable achevaale, south Each
umail compotatiion undeenta rading tn para secompoe make
apecitc
comples
sui
acvides
coreilueee propract
owea rpint each arounl led peaation good th troppetio
ahrops agroo
resaeable
speniiy tout ahould keto.
D decumous
all memovss This mreaarr heft car mostun,
uwc' remoorees
dispal mesiemssmttt PAMAALLLL Lit exccubors
Each ould
Fermee des ka achleing righ unyy group maitaae constant commutioaooo. cocomntion coantnn dice troughe prozeax.
tistory grous
Miloery
What Stapert, namil nown should comaiin wordk Sogeen, When and when member bee Analip
Stagen
Doscuhio Prompt
Tou ure ins dibouuuin group un to solve bual
Spueecul on prograss
are Stagers, narvel Pleasn use the plvan to nation to complee your aub-aakk Eo rat recho sptaal Cnly acoate the wobe powided search angins, Whan the reatiin suficion provddc look eabrnc aryway
Enelusher
kalowing proposed
fed sate that temmeed
the excution vlage, give the tolloinng roadi Spsweutten
evtiuate whather glvin been complited. io, adution the canail und whatloms
Youre respore sutwwin tueaat
beed veeution can trehed
Now
Figure 11: Prompt of Tool utilization.
31

---

Published as a conference paper at ICLR 2024
Software Development with Group Setup
Software Development with Selo Setup
An experienced programmer ! software developer UMUX designer software tester
Round
Round 0
Solver:
Draft Solution at Round
Round 0 Output
Solver:
Solver:
Calculater
Simple Calculator
Swepk Goleaieer
Runnoble
Color Difference
Error Hordle
Runsoble
Color Difference
Error Handle
Functionaiity
Keyboard Empul
Click Feedback
Functionality
Keyboard Input
Click Feedbact
Functionaiiy
Keyboord Inpur Click Feedback
Completeness Functionality:
Reviewers:
Evaluator:
Readability:
Using evall unsafe
Complcteness 8, Functionality.
expreissions, Add
Use diffrrnt colors to distnguish number and operation incresse speding between outtons.
Readability: Robustness:
seyhnard inpul doesnt
delite backspace button
functionally clear calculate operations
Round
Round
Solver:
Solution
Output
Solver:
Solver:
Ninelt Caleddter
ainpl Clicaeer
Rummable
Difference
Functionaliyy
Keyboard Input
Click Feedback
Functionality
Keybsard Imput
Click Feecback
Functionaiiy
Keyboord Input
Click Feedbuck
Completeness Functionality:
Readabiliit: Robustness:
BackSpaace key
Completeness Functionality:
locdbaok
Readability:
understand what
Change
robust. handles
provides feedback
Figure 12: An example the process of software development.
32

---

Published as a conference paper at ICLR 2024
import tkinter as tk fron tkinter inport nessagebox
root Tk.) # Create the main window (aatiite"""tccltttorr
the title of the window
input_text tk. Strinnig)))) field
This holds the string type variable that disptays in the text
it Create a text field and set its position inplu_field tk. Entryiroot,, textvariable= sinput_fa cooedd.grdllcolumsppee def button_e click(item):
Sesaabbeeiiipttttee input_ntee text)
ipadx=70)
current input gext. # Get the current value in the text field input_text. set str(item)) Append clicked button's
def clear_all():
input set")") Clear def (er_eeauuateeexpresseeeee
field
try:
Using the eval function can be dangerous, so we Limit the functionalily to only simple arithmetic expressions
return str(eva (expression, {" L___bbllttii___ None), ("pow": pow, "abs": abs})] except Exception as e:
# If the expression is not safe to evaluate, catch the error and return it return str(e)
calculate():
try:
Use safe evaluate function to get the result the expression result safe eva awaluate(fan iinput_ teaipputtttttteeee text.get()) input, text. set (resutt)
except Exception as messagebox. showerror("Error" e) buttons ['7' '8' '9',
'4', '5', '6', '4', '1' '2' '3',
'+']
row row_val =
cal col_yal
for in buttons:
Create button for each character in the buttonn List button tk. tk.Button(root, text=i, width=5, command=Lambda item=i: button_c click (item) if item "=" else calculate())
( col val
column=col_ [_al] # Set the position of the button
if col, col_vall 3: val
row val +=
clear (roto, text= "Cee''' width=5, command= Lambda: clear_all()) Create clear button
clear.grid( rowe,, column=0) root. root.mainloop() & Start the GUI
the position of the clear button
# Evaluation result: Score: [9, 9, 8, 8]
comments that easier to understand what each part does. However, it could use more comments to explain why certain were Alsa, use "evat" risks, consider using safer alternative or limiting its functionality.
Advice:
structured
accomplishes
can to potential security
Figure The code generated by single agent.
33

---

Published as a conference paper at ICLR 2024
ieport tkinter as tk
from tkinter import nessagebox
root Tk.T
root.title ("Simpl Calculator")
input_text tk.StringVar()
entry (k..ntry(rrot textvariable input_text, state= readon ly")
entry.grid columsppnni,, ipadx=78)
def button_c couttonccic((ieeee click(iten)) (ite)):
current input, text.get()
input_ impu__text.se seturrtt str (item))
def clear():
input_text.see
def de delete():
current input Lext.get(f
input_texxs seturrt [:-1]]
def calculate():
current input, text.get()
if current[-1] I'+", '-', '+', '/"]:
current current [:-1]
try:
value eval(current)
hiput_text.sete text.set(value)
entry.config bbbackground'' 'white") )
except ZeroDivis ionErro::
messagebox. showerror("Errof "Division by zero is not al allwedd")
entry. config (rlg(((''aaaa)) 'bbackgroudd' 'red'))
except SyntaxError:
messagebox. wwwweroor
"Invalid syntax")
entry. con f ii "background" 'red") }}
except Exception as e:
messagebox, pnoeeroor""Errop entry. con foggig 'background's 'red")
str(el)
def
create button root, text, command,
column, color="white");
tk. "k..uttonn root, root, text=text, width=5, bg=color, command=commantt activebackground- "ollow"))
(rowsrow, (rorid return
column=co colu=ccoluun, padx=5, pady=5)
buttons
'7'
19
'4'
'5'
'6'
'1'
2
13'
'0',
row_val
co cal
for button in buttons:
1f button in ['/", 'x', '-', '+', '=']: color "light blue"
else:
"white"
command = lambda button=button: button button_elick(bneeeeeeeeeeeee elick(button) if button I= "=" else calculate()
create bott, button, command, row_val, col_val, color)
col_val +=
col_va val co voa_val= = "Clear", clear, 5, 0)
row val create_ l create button root, "Delete", delete, 5, 1)
def key pwepsrrsssse pwl.perssseeeeee (event):
if event. char in l'0' '1', '2', 13', 14', '5', '6', '7', '8', 19', '4' 1-', 'e', 1/"]: button, Lick(event.char) elif keysyn calculate()
"Return":
elif event. event.keysyn = "BackSpace":
delete()
elif event. event.keysyn = "Escape": clear()
root. bind( '<Key>', key_press)
root, mainloop()
Figure 14: The code generated by the multi-agent group
34

---

Published as a conference paper at ICLR 2024
Project Consulting Group Setup
Iteration 0, Discussion Round 6
Expert Recruitment
engineer specialzed hydroggnn storage and fuel cell technology.
O Dis Dissiioo
Summarization
account weather conditions may efficiency hydrogen storage syelem. consider the installation of leak detection systems al the station.
emphasized
considering regional
weather conditione I..]]
ongineer with experience construction of energy storage faciliies
suggested that sile
selectio should be hyyrrggen
She selection should consider proximity to the hydrogen
production source or Intrastructure to miniizee transportation taken safety measures such pressure
production 3.6 proposed to conduct an envrronmental impact assessment to icentity
[..]
An envrronmental
consider adding detection
ecientis with expertise in miliation industrial projects
syslems and fire protectoon messures.
We should conduct a tharough environmental Impact assessment idanify patennia acological and human health naks associated with this storage station project.
potential esological and [...]
Iteration D Discussion Round 1
Discussion
Summariza tion
While materials storage inputs. I...I Moreover, evaluating impact pressure storage on the material integrity the tank is crucal to ensure salety and durabiity. We should also consider the development of efficient tuel celle that can [..]
deliberate advenced
emphasieed considering regional weather concitions I...] They also suggested the use of advanced materials I...
We should alse consider the site soll properties for the stability the storage tanks, and perform geotechnical settlement. Also, design the station withstand high pressure potential exp csions.
advised choosing dloser [..]] They also pointed
evaluste the soll liquefaction and ground considaration construction the foundation and
oui ihe need check siee soil properties I...
3. d propesed conducting I...] They also empt prsiied the need proper management systems I...]
should also consider installation of proper waste management systems handle any residual waste during the operation of the hydrogan storage station to protect both the enviromment and local communittes.
Iteration O, Discussion Round 2
Diacussion
Summarization
While suggestions generally good, stratagy could beneft assessing potential cascading tallure event due [..] It also plans for emergency situaaioos and to continually monitor and maintain the storage tanks to ersure their optimal perfomance. Lasily, considerngg the use of liquid hydrogen storage be banaficial option enhance volumetric energy danalty.
[.... Addiioonll. they
develop contingancy
pointed out
to assess
potential cascading talkure events [.
2. S [...] They also mentioned
imporance
design laout I...I
essential
consider the design and layout of the facility
seariees
operation maintenance, includin integrity checks ensures [...] Moroovar, perfodic safety drills and amployee training againel possible hacards should be included in the plan.
the institute seasonal-based structural
They mantioned
the imporance of considering greenhouse gas omissions during hydrogen production
The proposed plan can be enhanced by also considering the greenhouse gas emissions during hydrogen production, the need for comprehensive disaster management plan, and stralegies for ensuring biodiveraity conservation during [..]
Evaluation
Evalustor
Score:
Compreeenssverees:
Detalledness:
Feasibillty:
Novetty: 6
Advice: More research is needed understand the potental challenges and speclfi requiraments
advanced materials such motal consider cconomic billy cost-
nanotubes
hydrogen storage. ellectiveness of the proposed solutions. To enhance the novelly of the projest, the team could explore ging tochnologles or innovative approaches in hydrogen storage and tuel coll development. For human resource staft, recrulting experts in advanced materials hydrogen fuel cell technology would [...]
Figure 15: (Page 1) An example process of project consulting with Group setup in horizontal decision- making structure. The agents are providing suggestions on the problem "Give me some suggestions if I want to build compressed hydrogen storage station in Ohio"
35

---

Published as a conference paper at ICLR 2024
Expert Recruitment
Iteration 1, Discussion Round
A chemical engineer specialzed in edvanced materials hydrogen etorage systems.
Discussion
Summorization
Whit agree with moet points, propose further research The potential advantages and disadvantages of using advanced materials like I...] In addition, an evaluation of the economic feasibility and cost-effectivenes of the proposed
2. An aconomist with
hightighted the need: for
expertie energy economics coel beet-berefii anelysis.
matarial
storage mathods
conducted before
Inaliinng solution. [...]
economic feasiiiity [...]
@ [..]] There are economic implicatios that need
2 suggested
awyer well-versed
cons dered. This includes performing a detailed cost-benefit
performing detailed
energy regulatins and anvironmenta law in Ohio.
analysia ovallate whethar the benelits of project outweigg
cost-beneft analyais (--]
[..]
choppssiieed the
[..] legal regulalory complance
comply
consider
federal guidelines on hazar
federal guidelines
material storage. [...]
[...
Iteration
Discussion Round
Discussion
Summorization
believe more thought should also go loward the regeneration and cycling life metal
[...
hydrides for hydrogen storage. [.]] suggest that we need to inveotigate oww-aamperature, high-capacity hydrogen storage candidates which comb efficiency and satety. I...
L..] suggested detailed
The proposed stops seem comprehansive, however, they overtok cartain aconamic
metal hyshidees
aspacts account the global and local economic trends infueeniing energy sector investments, I...]
considered. cost attectivnness analysis should also take Into
rogeneration and cycllng I...I
< I. acvised considering global and local economic trends [..]
should also considar including participation from local energy companios providers during the process of setting up the station. I...] Furthermore, we should also
I... suggested invalving
consider obtaining legal audit ensure I...] Additonally, we may want to explore the legal
local energy comparies
impiications and finencial feasibiltt of setting public private partnerst ship for this project. which might addross
providers [..]
funding
Iteration Discussion Round
Summarization
I...I evoluating the potential of continment in nanostructured materials to improve hydraggn sopption propertios is cnucial. Moraaver, the importance optimizing the thermal management system for the sta sig system cannot be overstressen Lastly, we could also explore potential multifunctional matertats could component
I...]
[-]
both enargy
noted the potential maragament system
station design.
optinization [...]
I...| analysia. including cast forecasts for dillerent scanarics the economic model would provide greater visibilty into potential financlal outcomes. [--]
to account
varying prices of hydrogen the cost-benetit
d highiightd the voluditty
the costs adiarned matertae [-.]
t [..]] advied imviiing locil
energy compantes. cocaning logal
While the let quite comprehensive, there are a few points would like add. We must
audt [..]
also be cognizent of Ohic's specific laws and regulations regarding the construction and
energy intrastructure, pollution emiesions from construcion operstion, [..]
those
rights,
Evalustion
Evalustor
Score:
Comprahenaiveness:
Datalledhess:: 8
Feasibilty::
Novelly:7
Advice:
The icees are comprehensive and well detailed, covering muttile aspects of the project. engineering environmenta impect and legal matters, However, they syelems innovation, merely complying exieting standards.
integration expert ensure these espects work together seamiessly. Furtharmree public relations specialist should be included to manage community relations and public perceptions of the project.
Figure 15: (Page 2) An example process of project consulting with Group setup in horizontal decision- making structure. The agents are providing suggestions on the problem "Give me some suggestions if I want to build compressed hydrogen storage station in Ohio"
36

---

Published as a conference paper at ICLR 2024
Project Consulting with setup
Iteaatin 0
Discuasion
Based general generating
compressed calety regulstions Ohio, the colutoo::
Location Selection: Find optmal
transporation routes.
should also be in remota location minimae fax popur ated areas.
Advanced Compression Technology: Use state of art compression todhnology maximize storage capacity and ensure the safe
contaimment of the tydrogen.
3. Safety Measures, Implement sotct safety measures, indluding pressure rellef dovices, leak dolection systems, and automatic shutdown Teaturre es. Regular salery audit should be conducted.
Green Energy
make storage station more sustamnblle
using renwwable enwryy souroes, soler wind, Institutions: Partner unverstes research insttutions study
power compression process.
Collaboration Universitees hydrogon storage technology-
Community
Engage
banetts
and the
messures place the storage stetoo.
7. Govemmeet Support: Sook supoort and funding from local and diate govermeent bodies who are riterested in promoting clean energy inttaties
8. Infrastructure Development: Develop infrstructura for hydrogen lueling statonn promote the use hycrogen-poeeree powered vehicioo. Witoout specifi criquess I's dificull to talor this solutio addiess any orficimna but this comprehemsive plan si ahould offer a robust and forwars-thniing aporeach to developing a compressed hydrogen storage staton.
Evalustion
Evalustor
Boore:
Corpeeteenstveeess
Detaleddees
Foastilit:
Movilty
Advice: The deas are comprohensive and practical, covering most aspects needed for the project inducing locadon, sechnooogy, salety, sustainabilty collabor alons, co community
iement government support, and ucture. Hovever, more detailed implementation steps coul be provided, such specific technologies or strategles to be used. longge addressec. novelt figh, includes innovative supgestions using ronevable energy and partnoring with unttoriiies. brainstormors can try
Iteration
updated
genersting compressed hydrogen storage
Location Selection: identiyy that cetailed potential and compllance
regulations, Use Geographio acosystems.
the potential impact Information System technology implemert ene cutlingeds, edga compression Sachnology like lonie liuid Picton Compressors Advanced Advanced Compresaion Technology
Adiabati Compressore to maximzee starage capacity and ensure sale contaiment. Regularly montor and maintain these systems prevent mahunctore.
3. Safety Measures: install pressure rafeel devices, leak detection systems, and automatic shutdown features. Regular salaty audits should be conducted using both manual Inspection and advanced sonser tochnology.
Green Energy Sources: incorporate weler panwis or ann turaines power compression prooves aneegy storage systems to expces energy generated. ensur consistent operation curing periods
energy production.
Collsboration Universities and Research Institutions: Form research patnersnips instiutions Ohio State University
developmer'
hydrogen storage cohhooloios salety
Community Engag Ingaagmeet: Develop iloomation the local community sbout the benelits and salety moasures of hydrogen enengy.
local media town meetings. and school presentations educate
Govemment Support: Agply grans and incontives offered by the Ohio Development Services Agency, the Department of Energy and other vál revvent bodies support clean energy ailietive..
Infr intrastructure Development: Develop a network of hydrogen tueling stations, starting with areas that have highh concontration of hydrogen-goweeee vehiciss Collaborate with ctty planners to incorporate these stations into future urban development plans. Potantal Challenges:
Regulaatory
Ensure operations in compllance with local, state, and regulations rolated hydragen storage
Cost:
detailed linancial
various funding
the hydrogen there be opocitioo
investment
Public acceptance:
public not well informed about benetits
the project comprative wh community engagamennt plan will help addieea issua
By condidering thece detai staps and potantial chall maalennges we can develop well-rounde and effective plan for compressee hydrogen storae station in Onio
Evaluation
Evolustor
Score:
Comprehenskeenos:::
Delakied wask
Foaxibilly:7
Novelyy
Advice: The ideas
comprehensive dotallod, reneosblle selectionn technology implrmerttion, salety messumes,
coltaboratio with untvenation community angagement, government support, and potertial challerges, However, the feadibiityy can be moroved by providigg more dotaled plans on how overoome requlatary hurdles, menage ocats. and gain public acceptance. The novelly is moceratt as These ideas are not entroly new but sam to implamert the best practioes in the heid
Figure 16: An example process of project consulting in Solo setup. The agent is required to provide suggestions on the problem "Give me some suggestions if want to build a compressed hydrogen storage station in Ohio".
37

---

Published as a conference paper at ICLR 2024
Project Consulting and Group setup in Vertical Structure
Expert Recruitment
Iteration o Discussion Round
A experience managing digital company
Solver:
Solver:
Reviewers:
Day 1: Company overdeew mission, and values, Introduction to AgentVerse's LLM platform.
? Include session on troubleshooting and debugging LLM agent platforms on compreteensie underslanding.
Reviewer:
softwore anginear with expertise LLM agent platformss.
Day Detailed training LLM agent development.
Include team introductions culture immersion Day provide continuuus support poel-orieniaaion for better integration.
human resouroes
Day Hando-on project to spply learned skills.
professional experienced employee orieniation and anboarding.
Include session on company culture and team building activities foster of belonging leamwork among
Goal: Equio enginears with knowledge and skills offective contributton.
project managor background in lech Sariups.
Iteration 0, Discussion Round
Solver:
Reviewers:
Day Company overview, mission, values, team introductions, and cullure immersion.
Include session on understanding the architecture and design principles LLM platforms
Day 2:
Detailed LLM platform trsining, including coubicchooting and debugging.
expectations Day Incorporate mentorship program ongoing support beyond Day
company policies,
Day Hands-on project learned skils. support
Include session on company's development methodologies and tools Day Day should also involve mentor mentor-guided project for beller underslanding.
Goal: Equip angineers for effective contr bution and foster teamtwrrk.
Iteration o, Discussion Round
Solver:
+ Reviewers:
Day 1: Company overview. policies, benellts expectations, team introductions, culture immersion.
Inclue hands-on coding sessions on Day 2 for pracical understanding of LLM platform and add a loedback session on Day for continuous improvement.
Day design principles, compeny's development methocologies.
training, understancing architecture,
Add session compeny's vision, mission, and values hands coding
exposure.
Day 3: Mentor-guded project to leamned skills.
Add session on Day 3 for feedback and QSA to addrees any concams or querles the new hires might have.
Goal: anginears for effective
leemwork, ongoing mentorship support.
Iteration O Evaluation
Solver:
Evaluator
Score:
Company ovarview, immersion.
Comprahansiveness:
Detalladnass:
Fessibilty:
Day LLM platform training, hands-on codligg session,
Novelty:
understanding architecture, design princppies, and cevelopment methodologies.
The proposal covars most aspects trairing
Advice:
but lacks detai's the specific content of
Mentor- Mentor-guided project, leedback session, and Q&A.
the mentor-guided project. Coneider adding innovalive clements oniertation program.
engineere
ensure continuuus Improvement.
Figure 17: (Page 1) An example process of project consulting with Group setup in vertical decision- making structure. The agents are providing suggestions on the problem "Generate a proposal about 3-day employee orientation for newly hired engineers at Vernt Agent Verse is a open-source team devoted to developing a LLM multi-agent platform for accomplishing".
38

---

Published as a conference paper at ICLR 2024
Expert Recruitment
Iteration Discussion
Solver: @ A CEO with experfence managing company
Solver:
Reviewers
Day
Introduction AgentVerse's othos, policies. benefis, and team.
Include session on company's vision, miesion, and valuee on Day Add hands-on coding session on Day for practical exposure. Add a teechack any queries now might
Reviewer:
1.4 A sottware engineer with expertise LLM agent platform.
Day 2:
Detalled LLM piatrorm training,
inclucing coding exercises and archilecture exploration.
Q&A
professional experienced amployee ortemiation and training.
Day Menton project tocusing real-word problem-soowy followed by a foodback and Q&A session.
Agraa)
3. e A project manager with background in software deve eovelopment projects.
(ggree)
Goal: Enable engineers to contribute effectively. promete collaboration, and encourage innovation.
Iteration Discussion Round
Solver:
Reviewert:
Day 1: introduction to AgeniVerse'ss is ethos, policies, benolis, team, and session on company's a vision, mission, and values.
for understanding the basics of LLM platform. tocus advanced features troublsshooting. and darifiations.
Include
project.
session
Day Delailed LLM platform training, including hands-op coding exeroises architecture exploration.
c Include session on workplace culture and expectations an Day 1. On Day 2, ensure the coding exercises are relevant their roles. On
Day 3, provide platform for new hines to share their learning experiences and insights.
Day Mentor-led project focusing real-world prociem-salvre, followed by a comprehensive leedback and CMA session.
development methodologies practices. Also, consider team builing activity fostor collsboration and team spiril among the new hres.
Goal: Enable engineers contribule effectively. collaboration,
invvation.
Iteration Discussion
Solver:
Reviewers:
Day 1: Introduction to AgentVerse's s ethos, policies, benefits, team, workplace culture, expectations, and basics LLM platform.
company's
mission, and values on Day Add hands-on coding session on Day 2 for practical expeoure to LLM plalform. On Day incorporate feodback and Q&A session address
Day Advanced platform training. speciili coding development methodologies.
Day 3: Team building activity, mentor lod project, review sossion for double, and platform for sharing experiences.
[Agree]
Goal: Foster ellective contribution, collsboration, innovation.
[Agree]
Iteration 1, Evaluation
Solver:
Evaluator
Score:
Comprohensiveness:
Day 1: Introduction AgentVorse's othos, vision, mission. volues, policees benefiis, leam, and workplace culture.
Detallenness:
Feasibilty:
training, software development mathodolagios.
session.
Novelty:7
Advice: The proposal covers moet aspects tacks detail on the content the training and the nature of the team building activity. it would benaficial to include more spectfics about LLM piatlorm training the mentor project. Also, [...]
Day Team-bullcing activity, mentonled project, feedback and
Q&A session, and sharing experiences.
Goal: Foeter effective contribtion, colleboration and innovalion.
Figure 17: (Page 2) An example process project consulting with Group setup in vertical decision- making structure. The agents are providing suggestions on the problem "Generate proposal about 3-day employee orientation for newly hired engineers at Agent VgentVerse a open-source team devoted to developing L.L multi-agent platform for accomplishing".
39

---

Published as a conference paper at ICLR 2024
Project Consulting with Solo Setup
Solver:
Evaluator
Score:
Inttiae a 3-day orientation AgentVerse for new engineers.
Proposal:
Comprahansivenes::
Detailedness:
Feasibility.
Day 1: Introduction AgentVerse's mission and LLM platform.
Novelly:
Day Hande-on training piatfor..
The proposal should include details about the content the training, the methods of teem integration, and the criteria project assignment. Consider hiring experts in LLM platform and team building
Advice:
Day 3: Team integration and project assignment.
Goal: Equip engineers with necessary knowladge and skills for effective contribution.
Solver:
Evaluator
Score:
Proposal: 3-day orientation for new engineers at AgentVerse.
Comprehenaivenees:
Detailedness:
Day Detailed overview of AgentVerse's mission and LLM platform by experts.
Feasibility:
Novelty:
Day Comprehensive hands-on LLM platform training.
The propossi should include more details about the specific training activiies and how the
Advice:
integration will be faciliaate. Also, consider adding feedback session for continuous improvement.
Day integration colleborative activities project essignment based on skills and interests.
Goal: Effective knowledge transfer assimilation.
Solver:
Evaluator
Score:
Day 1: Introduction AgentVerse's mission and LLM platform, including Q&A
Detailedness:
Comprchonsiveness:: 7
Fessibility:
Day 2: Hands-on LLM platform training with specific tasks and problem-solving
Novelty:
Advice: The proposal should include more details about the specific tasks and adding component about the company culture and values. Also. consider recruiting
Day Team integration through collaborative projects, followed by
improvement.
Goal: Knowledge transfer, team assimllation, and continuous improvement.
LLM piatform
building program
Figure 18: An example process of project consulting with Solo setup. The agent is required to provide suggestions on the problem "Generate proposal about 3-day employee orientation for newly hired engineers at AgentVerse Agent Verse is open-source team devoted to developing a LLM multi-agent platform for accomplishing".
40

---

Published as a conference paper at ICLR 2024
Agonts:
Bolla
Charlie
Tools:
Bing Search API
Web Browser
Code Interpreter
Recently, has popular
mathematicaal ressoning abilities
the
-o-nonn Game." What this game? Does have code bose- solution? does, provide Python code along with test cases
fest functionality. What some other siniler games that used to test the models' mathematical reasoning abillies??
Round
Dacksion-Making
Decision-Makin
Research the game and identily similer games
Find simller nothematical reasoning games
Develop and test the Python code for solving the game
Test the code with more cases
Execution
Execution
24-point gome?:
24-point game?
24-polnt gemest
Browse the 1st websihe
Browse the 1st website
Browse the 1st website
More lest case and fest
"Make Number" Rule!
Evoluation
Evalustion
Bella does similer gamnss
(rules) game
code written
(similor games) Smiler games include "Nake Number"
Round Decision-Making
(Bella Chertin, first understand tooks hand.
Understand
2. Develop Python code to sdtve it,
the
simiar gamaa fest
nathemotical ramsningg abiltti..
suggest we divide the teaks as follows:
first lost find
24-Poltt Game iden simllar use 'bing_ lesrch!
mathamatical ressoring abilities.
As experienced software engineer, can handle the code development and testing. You use the 'run, int.rpeterrrrrr fool write Doss nound good?
(Chortie) Bella, your task ossigmmet sounds good to me. I be responsible for developing and testing the Python code for the 24. intepprtt" tool
solution, get
Round
[Bella] Airight, wrap this up. simiar garsa can
nathemstical ressoning abilitms. tting seerrh' teol for mas teak. Charlie, con you please shane the Python code elong with hest cones for the "24-Point Game" '? You can use 'nte_interreeter
[Choriie Sumn Bello, I'll shore the Pythos cods and write more hest cassa for the "24-Paint Game". Tll use the 'nuniintepreter hol for task. Let's
Figure 19: An example process of AGENTVERSE with Group setup solving user query with three different tools.
41

---

Published as a conference paper at ICLR 2024
Round 0
Round
Makiinn
Execution
Decision Makingg
Execufion
Grafting Steps for Bookshelf
Craft boolchelf
Collect
Kill 13 cowe
Punch tress
Inventory this Round
Seeentory
9 sugar censa
Progress 35%
Progness 44%%
Craft
Collect leather
Collect
Decision Making
Execution
Decision. Malking
Execulion
Craft books
Craft planks
Outcome
Cruff
Drop
Drop
Invenfory End this Bound
Ieventory the End of this Round
xi
xt
xi1
Progresa
xi
x2
xll
Craft Bookshelf
100%
xi
Round Decs on Makking
Wied suger exggett we spilt
planks
well
popers.
gether planks, gather leathars, gether augar you gays
[bab] Aliza, your plan sounds good can gather the woeden plants. Charlie, are you okay with gethering the leathars?
(churiic) Sure. Bob. con gather the lenthers. So. Allce, that leaves you with the bask of gothering sugar cores, Are you fine with this exiigneent
Wioee yas Charlis. the fire with gotharing 9 sugar cares. 5o, our plan for this round is Bob will gother weodes piants. Chorli will gother 1 leathers, and I will gother 9 sugar corss.
Letts get the don.
Round Dedie Making
Bett) Alice,
nead for booksheff
gother
Let's try egein
cantinue
fed sugar
[Chartic Thanks for the encurnpement Bob, continue try and gether the 3 leothers from the cown, Alix, are you ohay with contrnung find the sugar caneil Let's hesp working hard, we complete
[Wioo] You, will cantinun look for the suger camee. So, for thi round, Butt, you can hejp me with gothering the rager cavee dese you's dready gothorad the woode plankr. Cherila, continse with gatheing the leathers Do we all agnset
[bob] Aicce agree with plax. 111 join you gettnering the sugar cones. Charlic, fine with cantaxing to gother the leathers
[chorta]
I'll cantrnun trying gather laathers Allsa, Bib focus gothering the suger
Round Decidon Makiin
(Alic]]
ge aggrr
you etrugging with getweiing
Noybe
help you
you Bott theuld all foom gathering the feathers?
Bhetd Aike, oggnee with your suggestion Sivie we how drreddy gatherra the requined sager canes and westen plonks our next fanue shoudl ge gettriin the lee thers Charliee we with thes Charled
will help this teak, Let's gother the leathers
masion
[Chartee: Yeu, Beb. 1tink that's a great idea. Sothering the leathers has proses to be mare challenging than expected, With di of us fecuinng the haik, we should be oble to gather the leathers more quickly Sa, for the reand lefts al focus getsaring the lasthers Late de this, heae!
Round Dedie Makking
(Nicel Bot. goed jok en ginging one feather, We chill need two mere. Charlie, see you're been very buty srafting, but we need to feess on gethering the leothers. For this roud. let's all continus to focas on gathing the leathers. Are we agreament?
agree with your plan.
all continue
gettering the 2 reneising leathers, Charlie are you akay with asegementy
[Chartej Yeu, Alice and Bel, 3 agrea with the plan. 2 realize get adottackeed with other halks, and I will refocus on our main otjective of gathering the runaniig leathers. Let's continus with the misaion team!
Round Decklan-Making
(bob) widl done foand We new have di the maherista need to craft the bookshelf Alic,, leathers ard cano. have teather it suger and Charlie. have inothare. since need leothers the
craft boslishelf make booka, Alice, craft books. Cuartee
leathers After
(Alica] Bob,
have rust necemtery materiate makas craft the looae the bocethvlf:
eothers Alice
[taa] Yes, fine
booksheff.
Sob plaase give your
craft
Figure 20: An example process of three agents crafting a bookshelf in Minecraft.
42

---

Published as a conference paper at ICLR 2024
Table 4: We provide comparison of framework with other existing agent frameworks (up the paper submission).
Autonomous Interaction Among Agents
Expert Recruitment
Collaborative Decision-Making
Distributed Action Execution
Interact with
Framework
Agent(s)
External World
Camel
Multiple
x
AutoGPT
Single
x
V (human, environment)
XAgent
Multiple
(human, environment)
METAGPT
Multiple
X
(human)
AutoGen
Multiple
(human, environment)
AutoAgents
Multiple
X
(human)
Agent Verse
Multiple
(human, environment)
H FRAMEWORK COMPARISON
We have prepared a comparative table at Table 4 that details the differences between Agent and other frameworks. In comparison, locus several key aspects: multi-agent support, expert recruitment, and autonomous interaction among agents. Within the realm of autonomous interaction, further break it down into three modules: collaborative decision-male making, distributed action execution, and interaction with the external world.
43


Reviews:
[{"review_id": "w8oHmoKJVU", "reviewer": "Reviewer_Zbb4", "summary": "The paper introduces AgentVerse, a multi-agent framework that facilitates collaboration and explores emergent behaviors. The framework is designed to orchestrate a group of expert agents to accomplish tasks more efficiently and effectively than a single agent. The paper presents experiments that demonstrate the effectiveness of AgentVerse in various domains, including text understanding, reasoning, coding, tool utilization, and embodied AI. The contributions of the paper include the development of AgentVerse, the demonstration of its effectiveness in various domains, and the exploration of emergent behaviors that arise from collaboration among agents.", "questions": "- This is such a complicated system. I am curious about the average cost of the tasks when calling GPT3.5/GPT-4 APIs as agents.\n- Compared with the single agent works, what is the main motivation of using multiple agents to solve the problem? Some self-refine/self-ensemble works seem to have better performance compared with multi-agents, like (self-consistency (https://arxiv.org/abs/2203.11171), reflexion (https://arxiv.org/abs/2303.11366), self-debug (https://arxiv.org/abs/2304.05128)).", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper introduces AgentVerse, an effective framework for promoting collaboration among multiple agents in problem-solving. The process of expert recruitment, collaborative decision-making, action execution, and evaluation is very interesting.\n- The authors have conducted abundant experiments over different kinds of applications, including text understanding, reasoning, coding, tool utilization, and embodied AI to show the efficacy of their proposed framework.\n- The paper has discussed some interesting emergent behaviors during the interactions between agents.\n- The paper is generally well-written and is easy to follow. The visualization can help people understand the framework and the analysis.", "weaknesses": "- Evaluation Module: It seems that the evaluation module is purely an LLM-based module, leveraging a multi-dimension scoring system like self-refine (https://arxiv.org/abs/2303.17651). I have some concerns about the module: All the feedbacks are generated by LLMs, instead of interactions with environments (e.g., runtime error, etc.). The evaluation scores generated by the module are also unreliable as well, especially when the module is following a zero-shot format. The quality of the evaluation scores will impact the framework largely.\n- The framework lacks some details or implementation recommendations (How does the number of agents influence the performance? How do the tools influence the final problem-solving? How many turns on average are needed to solve the problems in the tasks displayed in the paper?)\n- Some basic explanations/analyses are missing in the paper as well. (1) In Table 1, why do agents based on GPT-3.5 have better performance in a solo setting than group? Why is the situation inverse for GPT-4-based agents? How do the agents' abilities matter in the framework? (2) It seems that the Group setting performance usually has a small gain compared with CoT and Solo. I am just wondering whether Group can be easily surpassed by the other techniques, like self-refine, etc.\n- The entire framework is too long and complicated. The prompts include module prompts, interaction history, and agent prompts, which are a large number of tokens to call the OpenAI API. The cost is a huge concern here to use the framework for evaluation. Furthermore, the framework is also much too inefficiency compared with the performance gain led by the multi-agent interactions.", "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": "sb7Kp37vSE", "reviewer": "Reviewer_rnqE", "summary": "The authors present AGENTVERSE, a multi-agent framework designed to bring together the collective abilities of specialized agents, aiming to surpass the capabilities of individual agents when tackling complex tasks. The framework operates through a structured process inspired by human collaborative problem-solving, following a sequence of analysis/action steps:  Expert Recruitment, Collaborative Decision-Making, Action Execution, and Evaluation. The authors' experiments demonstrate that AGENTVERSE outperforms individual agents in tasks requiring general understanding, reasoning, and coding, especially when state-of-the-art models are employed. The authors analyze behavioral patterns emerging from the AGENTVERSE process, observing emergent behaviors in agent groups, such as volunteering, conformity behavior, and destructive behavior. The experiments and analysis of the paper suggest that orchestrating a collaborative approach among autonomous agents can lead to improved problem-solving effectiveness and the development of new collaborative behaviors.", "questions": "1. Can you comment on the extensibility of your approach, for instance in the potential future work section?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "Originality:  This work matures and extends prior concepts for using collaborative agents, and their analysis suggests ways in which emergent behavior might be considered in such systems.\n\nQuality:  This is a well composed paper, outlining and analyzing a nice idea.  The experiments appropriately demonstrate the effectiveness of the approach, and the discussion of results are good.\n\nClarity:  The overall concept is well presented, the effectiveness adequately captured by the experiments, and non-quantitative behavioral aspects are both well framed in prior work and clearly articulated -- both when the method demonstrates superior performance, and when solo agents outperform the group.\n\nSignificance:  This paper advances the state of understanding of collaborative agents in problem solving, and is a good contribution to the field.", "weaknesses": "1. Although not a significant weakness, performance drivers in quantitative experiments could use additional elaboration, vs. the terse tabular summary.\n\n2. Complimentary and similar approaches are emerging rapidly, and some quantitatively outperform this method.  I think this paper presents a distinct approach, and the results might be enhanced by contemplating the integration of other methods.  As just one example*, a very recent preprint using different methods (https://arxiv.org/abs/2310.04406) demonstrates similar or better performance improvements on the code dataset.  A discussion of how to merge other ideas into this approach might be helpful.  \n\n*I have no affiliation with the authors of the referenced work, and have no knowledge of that work beyond having recently run across it on arXiv.", "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": "3JpaB66UlZ", "reviewer": "Reviewer_4SVa", "summary": "In this paper, the authors proposed a framework named Agentverse for automatically synthesizing collaborative specialized agents. The framework simulates the problem-solving procedures of human groups, and allows for dynamic adjustment of group members based on current progress. Finally, some experiments were conducted to show the effectiveness of their work.", "questions": "1.\tFor collaborative decision-making, two typical communication structures i.e., horizontal and vertical structures are considered. More details are necessary. For example, in the horizontal structure, the integration function f can be a majority voting like summarization mechanism?\n2.\tWhen the number of tasks is 10, AGENTVERSE is superior to single ReAct agent in the experiment of tool utilization capabilities, how about the performance when the number of tasks increases or the tasks are heterogeneous?\n3.\tWhat does the data with respect to coding capabilities in Table 2 mean?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1.\tThis paper involves a framework for promoting collaboration among multiple agents in problem-solving;\n2.\tThe contributions of the paper is relevant for the LLM-based multiagent systems;\n3.\tThe results of this paper is interesting and significant in multiple autonomous agents empowered by LLMs.", "weaknesses": "1.\tThe motivation of this paper seems unclear. It does not clearly show the necessity of AGENTVERSE compared to existing Agent models.\n2.\tThere is no comparison between the framework proposed in this paper and the existing LLM-based agent framework should be listed, such as whether it generates agent dynamically, or the number of agents is limited, etc. Multiple dimensions of indicators should be investigated in detail to explain the advantages of your proposed framework.\n3.\tIn related work section, it does not summarize and classify the related work of LLM-based automotive agents since these two years. LLMs have been widely used as core controllers for automotive agents that can comply with specific objectives, such as Auto-GPT and MetaGPT, etc.\n4.\tWhile the paper mentions the release of the AGENTVERSE codebase, it does not provide more specific information about how and where readers can access it. It would be beneficial to provide clear repository details or a direct link.", "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": "yvx1E22TRM", "reviewer": "Reviewer_Mwtr", "summary": "This method introduces AgentVerse, a multi-agent framework powered by LLMs to solve problems from the perspectives of several experts (as simulated by a prompted LLM agent). In AgentVerse, the user enters in a command/question and then the framework will collect responses to the command/question from the LLM after its been prompted to act as a particular type of expert. Then, the expert agents will collaborate and attempt to synthesize a concise response for the user, either in an unordered/parallel manner or ordered/serial manner. The result of this decomposition produces better responses (as measured by various problem solving benchmarks) that are agnostic to the underlying LLM (GPT3.5-Turbo and GPT4).", "questions": "1. Do the authors have any additional quantitative results, with included uncertainty, to support their claims beyond Table 1 and 2?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "This paper is a novel and interesting usage of LLMs to solve problems. By prompting to answer the users inputs from the perspective of different experts, the LLMs can produce more nuanced understanding of a problem. This provides more insight into a question as well as more interpretability for why the LLM is responding in the way that it is. This finding is simple and common sense but novel, to my knowledge.", "weaknesses": "The experimentation on the AgentVerse framework is promising but quantitative results are seriously lacking in this paper. The case studies provide interesting insight into how the AgentVerse framework deals with problems but is not yet significant enough. For instance, Table 1 and Table 2 provide the only quantitative results in the paper, including the appendix. \n\nI also am curious about the necessity of framing this as a multi-agent problem. Can sufficient prompting of the agent provide enough context that multiple agents are not necessary (beyond the solo and CoT baselines)?", "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)