Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Besides, we suppose that the guide does not know the initial position of the tourist, so the guide not only has to give guidance to the tourist but also confirm the location of it.

The review comment to be evaluated is:

The paper is not clearly written and some sections are hard to understand. The related works do capture how the framework proposes new challenges that are not already captured in previous work. Besides a similar work that also uses communication between agents [2] perform a similar analysis and comparisons to that work would be help in highlighting the difference with the proposed framework.

Besides the scope of the proposed framework is limited as it can only handle artificial messages that have a much smaller length, vocabulary, and sources of variation. In contrast, previous work have already proposed similar benchmarks that involve real images with natural language descriptions [1].

[1] de Vries et al 2018. Talk the Walk: Navigating New York City through Grounded Dialogue.

[2] Singh et al. 2019. Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks.

The evidence to be evaluated is:Generally, this task involves humans, an artificial agent, and the environment.

The agent may receive requests or instructions and ask for more guidance from the human. It can navigate and interact with the environment to get more information or complete corresponding task requested by the human.

Generally, the goal of these VLN tasks is to help the agent find the target place known only by the human side.

However, this kind of work only gives the instructions at the beginning, so multi-turn communication can not be generated.

* Oracle guidance: In the process of navigation, the agent can request additional natural language guidance to get more information Chi et al. (2019). But in most cases, the oracle can only respond with simple words like "turn left" and "turn right".

However, talk-the-walk only focuses on the localization part, where the guide finds where the tourist is. But ignore the guidance part, in which the guide makes a route plan and gives guidance to the tourist, and the tourist makes corresponding actions.

As pictured in Figure 1, agent A can only see the objects around it, while agent B has the floor plan and the target place but doesn't know where A is.

Agent B needs to find where A is, according to the message given by A describing the objects around it.

* In the localization task, agent A takes in the room's vector where it is and gives out the messages of step t consisting of \(M\) tokens \(m_{t}=\{v_{0},v_{1},...,v_{M}\},v_{i}\in V\). While agent B collects history dialogue \(h_{t}=(m_{0},m_{1},..,m_{t})\) and tries to guess where A is.

* The only difference between guidance and localization task is that the guidance task switches the role of agents A and B, and agent A does not require the graph \(G\).

With the message coming from agent A in this turn, and the history sentences from A, agent B makes its guess by making use of the topological structure of rooms \(\theta_{B}(h_{t},G)\).

And B encodes the history sentences with another GRU and computes the similarity between this encoded history \(e_{m}\), with each node representation \(e_{s}^{i}\) calculated above to find the most possible position of agent A. Similarly, the model for the guidance task also includes an encoder, a language generation, and a language understanding module used for giving instructions to agent A about its next step.

In the training process, the input of the localization part is the surroundings of agent A, and the output of it is the guessing position made by agent B. However, the input of the route plan part in the next timestep is the actual position of A, instead of the guessing one, preventing the error from localization influencing other modules.

Input: Figure 1: The navigation task involves three components: the tourist (agent A), the guide (agent B), and the environment. Agent A and B have unequal abilities. While agent A can only see its surroundings and describe them to B, agent B has the global perspective, can make a route plan, and give instructions to A. Agent A and B cooperate to lead A to the target place.

Input: Figure 3: Model Structure. There are three sub-modules here, including localization, route plan, and guidance. In the localization part, by calculating the similarity score between messages given by A and room feature embeddings incorporating structural information, agent B tries to find where A is. Then we use the Floyd algorithm to find the shortest path between the guessed place and the target one. And in the guidance module, A could find its path with history dialogue with B and its own observations.

Input: Figure 2: Navigation Case. The navigation task consists of three sub-tasks: Localization, Route Plan, and Guidance. At every turn, agent A first gives a description of its surroundings in the room it is located. With the current messages and the history messages given by A, agent B tries to guess where A is using the topological structure of the rooms. Then, based on this guess, agent B makes a route plan for A to get to the target place, and gives an instruction to A describing the next step A should take.

Input: Figure 6: The effect of (a) sentence length and (b) the shortest route length between the start and target places on the task performance

Input: Figure 4: The change of accuracy over epoch in different tasks. (a) localization task. (b) navigation task

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Based on my review of the claim and evidence:

The claim that "the guide does not know the initial position of the tourist, so the guide not only has to give guidance but also confirm the location" is well-stated and accurately reflects the evidence. This is supported by:

1. Figure 1 and 2 clearly show the asymmetric information setup where Agent B (guide) has the floor plan but doesn't know Agent A's (tourist) location
2. The evidence explicitly states "agent B has the global perspective...but doesn't know where A is"
3. The localization module in Figure 3 shows how Agent B must first determine A's location before providing guidance
4. The review comments, while critical of implementation aspects, don't dispute this fundamental setup

The claim directly matches the system's core functionality as demonstrated in the evidence without exaggeration.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Our contributions can be summarized as follows:1. From the view of emergent language, we study the language with multiple turns.

The evidence to be evaluated is:The agent communicates with humans in natural language. The agent may receive requests or instructions and ask for more guidance from the human.

While humans should give requests or guidance to help the agent complete the task.

However, this kind of work only gives the instructions at the beginning, so multi-turn communication can not be generated.

* Oracle guidance: In the process of navigation, the agent can request additional natural language guidance to get more information Chi et al. (2019).

By putting the VLN task into multi-agent settings, the problems listed above can be alleviated or solved. To our knowledge, talk-the-walk de Vries et al. (2018) is the only work applying the VLN task with multiple agents and is very similar to our task settings. However, talk-the-walk only focuses on the localization part, where the guide finds where the tourist is. But ignore the guidance part, in which the guide makes a route plan and gives guidance to the tourist, and the tourist makes corresponding actions. Talk-the-walk models this part with a random walk and only implements a minimal baseline, making the VLN task with multi-agent settings incomplete.

Emergent language is an unplanned language that comes up in interactions between the students, and/or the teacher and the students.

Most of those part of works are studying single message pass, with one agent only having a language generation part, and the other one having a language understanding module. Still, there are some works putting attention to the multi-turn form Cao et al. (2018); Evtimova et al. (2017). But the former study focuses on a semi-cooperative case rather than a fully cooperative one like us, while the latter one uses the bag of words, and generates 0 or 1, with 1 indicating this token is in the generated sentence.

* In the localization task, agent A takes in the room's vector where it is and gives out the messages of step t consisting of \(M\) tokens \(m_{t}=\{v_{0},v_{1},...,v_{M}\},v_{i}\in V\). While agent B collects history dialogue \(h_{t}=(m_{0},m_{1},..,m_{t})\) and tries to guess where A is.

* In the route plan task, agent B uses the room graph \(G\), the guessed position of A \(\arg\max(\theta_{B}(h_{t},G))\), and the target position \(r_{o}\) to get the next step A should take \(\arg\max(\theta_{A}(h_{t+1},D))\).

With the message coming from agent A in this turn, and the history sentences from A, agent B makes its guess by making use of the topological structure of rooms \(\theta_{B}(h_{t},G)\).

We also show the case when agents can pass continuous messages, giving us a ceiling performance when the communication channel is not restricted.

We suppose that good language tends to give similar expressions describing a similar object. So we use topographic similarity \(\rho\) as our metric, which is the Spearman correlation between the message similarity sequence and the room vector similarity sequence. And our method outperforms the discrete VAE largely.

In this work, we propose a navigation task in an emergent communication scenario.

* Kajic et al. (2020) Ivana Kajic, Eser Aygun, and Doina Precup. Learning to cooperate: Emergent communication in multi-agent navigation. _arXiv preprint arXiv:2004.01097_, 2020.

Towards situated communication in multi-step interactions: Time is a key pressure in communication emergence.

Input: Figure 3: Model Structure. There are three sub-modules here, including localization, route plan, and guidance. In the localization part, by calculating the similarity score between messages given by A and room feature embeddings incorporating structural information, agent B tries to find where A is. Then we use the Floyd algorithm to find the shortest path between the guessed place and the target one. And in the guidance module, A could find its path with history dialogue with B and its own observations.

Input: Figure 1: The navigation task involves three components: the tourist (agent A), the guide (agent B), and the environment. Agent A and B have unequal abilities. While agent A can only see its surroundings and describe them to B, agent B has the global perspective, can make a route plan, and give instructions to A. Agent A and B cooperate to lead A to the target place.

Input: Figure 2: Navigation Case. The navigation task consists of three sub-tasks: Localization, Route Plan, and Guidance. At every turn, agent A first gives a description of its surroundings in the room it is located. With the current messages and the history messages given by A, agent B tries to guess where A is using the topological structure of the rooms. Then, based on this guess, agent B makes a route plan for A to get to the target place, and gives an instruction to A describing the next step A should take.

Input: Figure 6: The effect of (a) sentence length and (b) the shortest route length between the start and target places on the task performance

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find that studying language with multiple turns is well-demonstrated in this work. The evidence shows:
- A multi-turn communication system between Agents A and B with back-and-forth messaging
- Clear demonstration of multiple message exchanges through localization, route planning, and guidance phases
- Detailed model structure showing how messages are processed over multiple turns
- Analysis of how sentence length affects performance over multiple exchanges
- Explicit comparison to prior work that only used single message passing

The claim appears well-stated and accurately reflects what the research accomplished without exaggeration.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Our contributions can be summarized as follows:1. From the view of emergent language, we study the language with multiple turns.

The review comment to be evaluated is:

Strengths:
* The proposed setting is novel.

Weaknesses:
* While novel, the proposed setting is poorly motivated. Why studying this setting is significant? What would it enable?
* The description of the environment is very nebulous. Especially, the complexity of the visual and language components are highly under-specified. How is the input observation represented (image or feature-based)? How many object categories? How large is the agent's vocabulary? How are the environment graph generated? It is thus difficult to comprehend the significance of solving this environment.
* The plots are missing confidence intervals. Each experiments are not conducted multiple times with different random seeds. This seriously weakens the empirical claims.

The evidence to be evaluated is:The agent communicates with humans in natural language. The agent may receive requests or instructions and ask for more guidance from the human.

While humans should give requests or guidance to help the agent complete the task.

However, this kind of work only gives the instructions at the beginning, so multi-turn communication can not be generated.

* Oracle guidance: In the process of navigation, the agent can request additional natural language guidance to get more information Chi et al. (2019).

By putting the VLN task into multi-agent settings, the problems listed above can be alleviated or solved. To our knowledge, talk-the-walk de Vries et al. (2018) is the only work applying the VLN task with multiple agents and is very similar to our task settings. However, talk-the-walk only focuses on the localization part, where the guide finds where the tourist is. But ignore the guidance part, in which the guide makes a route plan and gives guidance to the tourist, and the tourist makes corresponding actions. Talk-the-walk models this part with a random walk and only implements a minimal baseline, making the VLN task with multi-agent settings incomplete.

Emergent language is an unplanned language that comes up in interactions between the students, and/or the teacher and the students.

Most of those part of works are studying single message pass, with one agent only having a language generation part, and the other one having a language understanding module. Still, there are some works putting attention to the multi-turn form Cao et al. (2018); Evtimova et al. (2017). But the former study focuses on a semi-cooperative case rather than a fully cooperative one like us, while the latter one uses the bag of words, and generates 0 or 1, with 1 indicating this token is in the generated sentence.

* In the localization task, agent A takes in the room's vector where it is and gives out the messages of step t consisting of \(M\) tokens \(m_{t}=\{v_{0},v_{1},...,v_{M}\},v_{i}\in V\). While agent B collects history dialogue \(h_{t}=(m_{0},m_{1},..,m_{t})\) and tries to guess where A is.

* In the route plan task, agent B uses the room graph \(G\), the guessed position of A \(\arg\max(\theta_{B}(h_{t},G))\), and the target position \(r_{o}\) to get the next step A should take \(\arg\max(\theta_{A}(h_{t+1},D))\).

With the message coming from agent A in this turn, and the history sentences from A, agent B makes its guess by making use of the topological structure of rooms \(\theta_{B}(h_{t},G)\).

We also show the case when agents can pass continuous messages, giving us a ceiling performance when the communication channel is not restricted.

We suppose that good language tends to give similar expressions describing a similar object. So we use topographic similarity \(\rho\) as our metric, which is the Spearman correlation between the message similarity sequence and the room vector similarity sequence. And our method outperforms the discrete VAE largely.

In this work, we propose a navigation task in an emergent communication scenario.

* Kajic et al. (2020) Ivana Kajic, Eser Aygun, and Doina Precup. Learning to cooperate: Emergent communication in multi-agent navigation. _arXiv preprint arXiv:2004.01097_, 2020.

Towards situated communication in multi-step interactions: Time is a key pressure in communication emergence.

Input: Figure 3: Model Structure. There are three sub-modules here, including localization, route plan, and guidance. In the localization part, by calculating the similarity score between messages given by A and room feature embeddings incorporating structural information, agent B tries to find where A is. Then we use the Floyd algorithm to find the shortest path between the guessed place and the target one. And in the guidance module, A could find its path with history dialogue with B and its own observations.

Input: Figure 1: The navigation task involves three components: the tourist (agent A), the guide (agent B), and the environment. Agent A and B have unequal abilities. While agent A can only see its surroundings and describe them to B, agent B has the global perspective, can make a route plan, and give instructions to A. Agent A and B cooperate to lead A to the target place.

Input: Figure 2: Navigation Case. The navigation task consists of three sub-tasks: Localization, Route Plan, and Guidance. At every turn, agent A first gives a description of its surroundings in the room it is located. With the current messages and the history messages given by A, agent B tries to guess where A is using the topological structure of the rooms. Then, based on this guess, agent B makes a route plan for A to get to the target place, and gives an instruction to A describing the next step A should take.

Input: Figure 6: The effect of (a) sentence length and (b) the shortest route length between the start and target places on the task performance

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find this claim is moderately overstated. While the evidence does show they study multi-turn language interactions between agents (as shown in Figures 1-3 where agents engage in back-and-forth communication), the claim presents this as a novel contribution without acknowledging prior work that also studied multi-turn emergent language (the evidence explicitly mentions papers by Cao et al. and Evtimova et al. that previously explored multi-turn communication). Additionally, the reviewer points out that the setting's significance and motivation are poorly explained.

<score>4</score>

Justification:
1. The paper does demonstrate studying multi-turn language, as evidenced by:
- The model architecture showing multiple message exchanges between agents
- Figures showing back-and-forth communication between agents
- Description of localization and route planning requiring multiple turns

2. However, the claim is overstated because:
- Prior work on multi-turn emergent language is acknowledged in the evidence (Cao et al. and Evtimova et al.)
- The reviewer notes the setting is "poorly motivated" and its significance is not well explained
- The evidence shows this is more of an application of existing multi-turn concepts rather than a novel study of multi-turn language itself
- Missing experimental details and confidence intervals noted by reviewer weaken the empirical support


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Our contributions can be summarized as follows:1. From the view of emergent language, we study the language with multiple turns.

The review comment to be evaluated is:

While this paper goes in a good direction by moving away from the Lewis game in the language emergence communication setting, it falls short as there is little scientific analysis. The authors mainly designed an artificial navigation task, handcrafted an observation space and reward, designed a neural network, trained it, and reported the results. Those results are not put into perspective with a detailed researched perspective. Indeed, the point of Emecom in ML is not to create artificial tasks for the sake of solving it, but to design tasks that could help understand how language evolves with a human intervention as possible (or being generic).

I am listing here a few leads to improve the paper in the long run:
 - The introduction should frame the research problem into the past literature, ask a scientific question, and explain how the paper help answers it. Here, there is no citation; the VLM task is only said to be more natural (no reference to the embodiment, situated learning, or going beyond the signaling game)
 - The related work is extremely short. 18 citations in total for a paper about language emergence, and the navigation task is just inherently incomplete. On a positive note, the most relevant citations kajic/Kanilowska/DeVries are present
- The paper is 7 pages long, and page 3 is barely empty
- The model is designed to solve the task, e.g., the graph networks are specific to the observation space. I would recommend either picking a minimal architecture or reproducing the architecture that is classic in VLM.
- having 4 losses in an RL task is generally a bad sign as it is a symptom of human intervention. cf The bitter lesson by Sutton
 - Results are not compared to any baselines (VAE vs your network is not really a baseline...), and simply reporting the success rate is not sufficient. Examples of suitable ablation include: the correlation between language and environment complexity. Qualitative experiments to translate language and change in the environment (e.g., how changing the graph impacts the language), what are the external constraints that shape language, reward ablation, etc.

I would recommend the authors to re-work their paper and present it to a workshop to have further guidance. The initial idea is good, but the realization lacks scientific maturity.

The evidence to be evaluated is:The agent communicates with humans in natural language. The agent may receive requests or instructions and ask for more guidance from the human.

While humans should give requests or guidance to help the agent complete the task.

However, this kind of work only gives the instructions at the beginning, so multi-turn communication can not be generated.

* Oracle guidance: In the process of navigation, the agent can request additional natural language guidance to get more information Chi et al. (2019).

By putting the VLN task into multi-agent settings, the problems listed above can be alleviated or solved. To our knowledge, talk-the-walk de Vries et al. (2018) is the only work applying the VLN task with multiple agents and is very similar to our task settings. However, talk-the-walk only focuses on the localization part, where the guide finds where the tourist is. But ignore the guidance part, in which the guide makes a route plan and gives guidance to the tourist, and the tourist makes corresponding actions. Talk-the-walk models this part with a random walk and only implements a minimal baseline, making the VLN task with multi-agent settings incomplete.

Emergent language is an unplanned language that comes up in interactions between the students, and/or the teacher and the students.

Most of those part of works are studying single message pass, with one agent only having a language generation part, and the other one having a language understanding module. Still, there are some works putting attention to the multi-turn form Cao et al. (2018); Evtimova et al. (2017). But the former study focuses on a semi-cooperative case rather than a fully cooperative one like us, while the latter one uses the bag of words, and generates 0 or 1, with 1 indicating this token is in the generated sentence.

* In the localization task, agent A takes in the room's vector where it is and gives out the messages of step t consisting of \(M\) tokens \(m_{t}=\{v_{0},v_{1},...,v_{M}\},v_{i}\in V\). While agent B collects history dialogue \(h_{t}=(m_{0},m_{1},..,m_{t})\) and tries to guess where A is.

* In the route plan task, agent B uses the room graph \(G\), the guessed position of A \(\arg\max(\theta_{B}(h_{t},G))\), and the target position \(r_{o}\) to get the next step A should take \(\arg\max(\theta_{A}(h_{t+1},D))\).

With the message coming from agent A in this turn, and the history sentences from A, agent B makes its guess by making use of the topological structure of rooms \(\theta_{B}(h_{t},G)\).

We also show the case when agents can pass continuous messages, giving us a ceiling performance when the communication channel is not restricted.

We suppose that good language tends to give similar expressions describing a similar object. So we use topographic similarity \(\rho\) as our metric, which is the Spearman correlation between the message similarity sequence and the room vector similarity sequence. And our method outperforms the discrete VAE largely.

In this work, we propose a navigation task in an emergent communication scenario.

* Kajic et al. (2020) Ivana Kajic, Eser Aygun, and Doina Precup. Learning to cooperate: Emergent communication in multi-agent navigation. _arXiv preprint arXiv:2004.01097_, 2020.

Towards situated communication in multi-step interactions: Time is a key pressure in communication emergence.

Input: Figure 3: Model Structure. There are three sub-modules here, including localization, route plan, and guidance. In the localization part, by calculating the similarity score between messages given by A and room feature embeddings incorporating structural information, agent B tries to find where A is. Then we use the Floyd algorithm to find the shortest path between the guessed place and the target one. And in the guidance module, A could find its path with history dialogue with B and its own observations.

Input: Figure 1: The navigation task involves three components: the tourist (agent A), the guide (agent B), and the environment. Agent A and B have unequal abilities. While agent A can only see its surroundings and describe them to B, agent B has the global perspective, can make a route plan, and give instructions to A. Agent A and B cooperate to lead A to the target place.

Input: Figure 2: Navigation Case. The navigation task consists of three sub-tasks: Localization, Route Plan, and Guidance. At every turn, agent A first gives a description of its surroundings in the room it is located. With the current messages and the history messages given by A, agent B tries to guess where A is using the topological structure of the rooms. Then, based on this guess, agent B makes a route plan for A to get to the target place, and gives an instruction to A describing the next step A should take.

Input: Figure 6: The effect of (a) sentence length and (b) the shortest route length between the start and target places on the task performance

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence and claim "From the view of emergent language, we study the language with multiple turns", I find this claim to be moderately overstated.

While the paper does implement a multi-turn communication system between agents (as shown in Figures 1-3 where agents exchange multiple messages), the reviewer correctly points out that the scientific analysis of the emergent language aspects is limited. The evidence shows implementation details but lacks deep analysis of how the language emerges and evolves across turns. The paper focuses more on the navigation task mechanics rather than studying emergent language properties specifically.

<score>3</score>

Justification:
1. The model does enable multiple message exchanges between agents (supported by model architecture in Figure 3)
2. The review comment notes "little scientific analysis" of the language aspects
3. Limited evidence showing analysis of emergent language properties across turns
4. More focus on task completion than language emergence study
5. Reviewer points out lack of qualitative experiments to analyze language evolution


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Our contributions can be summarized as follows:1. From the view of emergent language, we study the language with multiple turns.

The review comment to be evaluated is:

The paper is not clearly written and some sections are hard to understand. The related works do capture how the framework proposes new challenges that are not already captured in previous work. Besides a similar work that also uses communication between agents [2] perform a similar analysis and comparisons to that work would be help in highlighting the difference with the proposed framework.

Besides the scope of the proposed framework is limited as it can only handle artificial messages that have a much smaller length, vocabulary, and sources of variation. In contrast, previous work have already proposed similar benchmarks that involve real images with natural language descriptions [1].

[1] de Vries et al 2018. Talk the Walk: Navigating New York City through Grounded Dialogue.

[2] Singh et al. 2019. Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks.

The evidence to be evaluated is:The agent communicates with humans in natural language. The agent may receive requests or instructions and ask for more guidance from the human.

While humans should give requests or guidance to help the agent complete the task.

However, this kind of work only gives the instructions at the beginning, so multi-turn communication can not be generated.

* Oracle guidance: In the process of navigation, the agent can request additional natural language guidance to get more information Chi et al. (2019).

By putting the VLN task into multi-agent settings, the problems listed above can be alleviated or solved. To our knowledge, talk-the-walk de Vries et al. (2018) is the only work applying the VLN task with multiple agents and is very similar to our task settings. However, talk-the-walk only focuses on the localization part, where the guide finds where the tourist is. But ignore the guidance part, in which the guide makes a route plan and gives guidance to the tourist, and the tourist makes corresponding actions. Talk-the-walk models this part with a random walk and only implements a minimal baseline, making the VLN task with multi-agent settings incomplete.

Emergent language is an unplanned language that comes up in interactions between the students, and/or the teacher and the students.

Most of those part of works are studying single message pass, with one agent only having a language generation part, and the other one having a language understanding module. Still, there are some works putting attention to the multi-turn form Cao et al. (2018); Evtimova et al. (2017). But the former study focuses on a semi-cooperative case rather than a fully cooperative one like us, while the latter one uses the bag of words, and generates 0 or 1, with 1 indicating this token is in the generated sentence.

* In the localization task, agent A takes in the room's vector where it is and gives out the messages of step t consisting of \(M\) tokens \(m_{t}=\{v_{0},v_{1},...,v_{M}\},v_{i}\in V\). While agent B collects history dialogue \(h_{t}=(m_{0},m_{1},..,m_{t})\) and tries to guess where A is.

* In the route plan task, agent B uses the room graph \(G\), the guessed position of A \(\arg\max(\theta_{B}(h_{t},G))\), and the target position \(r_{o}\) to get the next step A should take \(\arg\max(\theta_{A}(h_{t+1},D))\).

With the message coming from agent A in this turn, and the history sentences from A, agent B makes its guess by making use of the topological structure of rooms \(\theta_{B}(h_{t},G)\).

We also show the case when agents can pass continuous messages, giving us a ceiling performance when the communication channel is not restricted.

We suppose that good language tends to give similar expressions describing a similar object. So we use topographic similarity \(\rho\) as our metric, which is the Spearman correlation between the message similarity sequence and the room vector similarity sequence. And our method outperforms the discrete VAE largely.

In this work, we propose a navigation task in an emergent communication scenario.

* Kajic et al. (2020) Ivana Kajic, Eser Aygun, and Doina Precup. Learning to cooperate: Emergent communication in multi-agent navigation. _arXiv preprint arXiv:2004.01097_, 2020.

Towards situated communication in multi-step interactions: Time is a key pressure in communication emergence.

Input: Figure 3: Model Structure. There are three sub-modules here, including localization, route plan, and guidance. In the localization part, by calculating the similarity score between messages given by A and room feature embeddings incorporating structural information, agent B tries to find where A is. Then we use the Floyd algorithm to find the shortest path between the guessed place and the target one. And in the guidance module, A could find its path with history dialogue with B and its own observations.

Input: Figure 1: The navigation task involves three components: the tourist (agent A), the guide (agent B), and the environment. Agent A and B have unequal abilities. While agent A can only see its surroundings and describe them to B, agent B has the global perspective, can make a route plan, and give instructions to A. Agent A and B cooperate to lead A to the target place.

Input: Figure 2: Navigation Case. The navigation task consists of three sub-tasks: Localization, Route Plan, and Guidance. At every turn, agent A first gives a description of its surroundings in the room it is located. With the current messages and the history messages given by A, agent B tries to guess where A is using the topological structure of the rooms. Then, based on this guess, agent B makes a route plan for A to get to the target place, and gives an instruction to A describing the next step A should take.

Input: Figure 6: The effect of (a) sentence length and (b) the shortest route length between the start and target places on the task performance

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, I find that the claim "From the view of emergent language, we study the language with multiple turns" is reasonably well-stated with some minor overstatement.

The evidence shows the authors do study multi-turn communication between agents, as demonstrated in the model architecture (Figure 3) and navigation case descriptions showing back-and-forth exchanges. However, the reviewer points out limitations in the scope and novelty. The evidence also indicates previous works like Cao et al. (2018) and Evtimova et al. (2017) have studied multi-turn communication, though in different contexts.

The claim itself is relatively modest but could better acknowledge these prior works and limitations. Therefore, I would rate it as slightly overstated.

<score>2</score>

Justification:
1. The evidence supports that they study multi-turn language (shown in model architecture and navigation task design)
2. The reviewer points out scope limitations regarding message length and vocabulary
3. The evidence reveals similar prior work in multi-turn communication, though in different settings
4. The claim could better qualify its contribution relative to existing work
5. The statement is fundamentally accurate but needs minor refinement to be fully well-stated


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

To give a suitable scenario for multi-turn conversations, we provide a navigation task adapted from the vision-language navigation(VLN) task.

The evidence to be evaluated is:Navigation tasks with vision and natural language information have attracted much attention in the last few years. Generally, this task involves humans, an artificial agent, and the environment. The agent communicates with humans in natural language. The agent may receive requests or instructions and ask for more guidance from the human. It can navigate and interact with the environment to get more information or complete corresponding task requested by the human. While humans should give requests or guidance to help the agent complete the task. Generally, the goal of these VLN tasks is to help the agent find the target place known only by the human side.

From the view of language format, we can divide those tasks into three categories Gu et al. (2022):* Initial instructions: The agent is given instructions for the whole navigation episode.

However, this kind of work only gives the instructions at the beginning, so multi-turn communication can not be generated.

* Oracle guidance: In the process of navigation, the agent can request additional natural language guidance to get more information Chi et al. (2019).

By putting the VLN task into multi-agent settings, the problems listed above can be alleviated or solved. To our knowledge, talk-the-walk de Vries et al. (2018) is the only work applying the VLN task with multiple agents and is very similar to our task settings. However, talk-the-walk only focuses on the localization part, where the guide finds where the tourist is. But ignore the guidance part, in which the guide makes a route plan and gives guidance to the tourist, and the tourist makes corresponding actions. Talk-the-walk models this part with a random walk and only implements a minimal baseline, making the VLN task with multi-agent settings incomplete.

This task is inspired by a real-world scenario where two agents with unequal abilities, agents A and B, cooperate to help agent A find the target place. As pictured in Figure 1, agent A can only see the objects around it, while agent B has the floor plan and the target place but doesn't know where A is. To win this game, they have to cooperate to help A reach the target place with limited rounds.

Here, we divide the navigation task into three steps: First, localization. Agent B needs to find where A is, according to the message given by A describing the objects around it. Second, route plan. Having the position of A and the target place, agent B has to find the shortest path for A to get there. Third, guidance.

Formally, this navigation task is constructed as follows: There are houses, containing rooms \(R=\{r_{0},r_{1},...,r_{N}\}\) each. Every time, one house is randomly chosen from those \(K\) candidates. Then two rooms are drawn from the selected house, and one of them is the target room \(r_{o}\), and the other is the start room \(r_{s}\). Every room has several gates \(D_{i}=\{d_{ij},j\in Neighbor(i)\}\). There is a graph for each house, to model the relationship between rooms. We use \(G=(R,D)\) to denote a house, where \(R\) is the vertex set, and \(D\) is the edge set. There are two players called agent A, and agent B parameterized by \(\theta_{A},\theta_{B}\) separately.

A case is shown in Figure 2, where Agent A starts from room 0 and wants to get to room 5. Firstly, the information about the house containing features of a set of rooms \((r_{0},r_{1},..,r_{N})\) is given. Agent A encodes the target room information \(r_{o}\) including objects and gates and then uses the encoded vector to generate corresponding emergent language with its language generation module \(e_{s}^{o}=GRU(MLP(r_{o}))\).

Similarly, the model for the guidance task also includes an encoder, a language generation, and a language understanding module used for giving instructions to agent A about its next step.

Finally, we put those sub-tasks together to complete the navigation task.

We construct the dataset with 6 rooms for each navigation task. And every object in this dataset has 3 optional colors and 3 optional shapes. Also, some features not relevant to this task are added, like the gate color which we suppose to be the same among all rooms in the same house.

In this work, we propose a navigation task in an emergent communication scenario. This task is adapted from the vision-language navigation task proposed in human-machine communication. However, compared with the traditional VLN task, the application of multi-agent communication in the VLN task makes the communication policy more flexible and can generate a more complex

Vision-and-language navigation: A survey of tasks, methods, and future directions. In _Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_. Association for Computational Linguistics, 2022. doi: 10.18653/v1/2022.acl-long.524. URL [https://doi.org/10.18653%2Fv1%2F2022.acl-long.524](https://doi.org/10.18653%2Fv1%2F2022.acl-long.524).

* Jain et al. (2019) Vihan Jain, Gabriel Magalhaes, Alexander Ku, Ashish Vaswani, Eugene Ie, and Jason Baldridge. Stay on the path: Instruction fidelity in vision-and-language navigation. In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_, pp. 1862-1872, Florence, Italy, July 2019. Association for Computational Linguistics. doi: 10.18653/v1/P19-1181. URL [https://aclanthology.org/P19-1181](https://aclanthology.org/P19-1181).

Beyond the nav-graph: Vision-and-language navigation in continuous environments, 2020. URL [https://arxiv.org/abs/2004.02857](https://arxiv.org/abs/2004.02857).

* Thomason et al. (2019) Jesse Thomason, Michael Murray, Maya Cakmak, and Luke Zettlemoyer. Vision-and-dialog navigation, 2019. URL [https://arxiv.org/abs/1907.04957](https://arxiv.org/abs/1907.04957).

* Zhu et al. (2021) Fengda Zhu, Xiwen Liang, Yi Zhu, Xiaojun Chang, and Xiaodan Liang. Soon: Scenario oriented object navigation with graph-based exploration, 2021. URL [https://arxiv.org/abs/2103.17138](https://arxiv.org/abs/2103.17138).

Input: Figure 1: The navigation task involves three components: the tourist (agent A), the guide (agent B), and the environment. Agent A and B have unequal abilities. While agent A can only see its surroundings and describe them to B, agent B has the global perspective, can make a route plan, and give instructions to A. Agent A and B cooperate to lead A to the target place.

Input: Figure 2: Navigation Case. The navigation task consists of three sub-tasks: Localization, Route Plan, and Guidance. At every turn, agent A first gives a description of its surroundings in the room it is located. With the current messages and the history messages given by A, agent B tries to guess where A is using the topological structure of the rooms. Then, based on this guess, agent B makes a route plan for A to get to the target place, and gives an instruction to A describing the next step A should take.

Input: Figure 3: Model Structure. There are three sub-modules here, including localization, route plan, and guidance. In the localization part, by calculating the similarity score between messages given by A and room feature embeddings incorporating structural information, agent B tries to find where A is. Then we use the Floyd algorithm to find the shortest path between the guessed place and the target one. And in the guidance module, A could find its path with history dialogue with B and its own observations.

Input: Figure 6: The effect of (a) sentence length and (b) the shortest route length between the start and target places on the task performance

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, the claim appears well-stated as it accurately describes how the navigation task is adapted from VLN to enable multi-turn conversations. The evidence thoroughly details the three-step navigation process (localization, route planning, guidance) and demonstrates how the task facilitates ongoing dialogue between agents A and B. The figures clearly illustrate the task setup, model structure, and communication flow. The claim is modest in scope and directly supported by the detailed implementation described in the evidence.

<score>1</score>

Justification:
- The evidence provides comprehensive technical details about the task adaptation, including formal mathematical notation and system architecture
- Figures 1-3 explicitly demonstrate the multi-turn conversation flow between agents
- The task components (localization, route planning, guidance) are well-defined and enable continuous communication
- The evidence shows experimental validation through performance graphs examining sentence length and route length effects
- The claim is measured in scope, simply stating this provides a "suitable scenario" rather than making grander assertions about capabilities or performance


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

To give a suitable scenario for multi-turn conversations, we provide a navigation task adapted from the vision-language navigation(VLN) task.

The review comment to be evaluated is:

Strengths:
* The proposed setting is novel.

Weaknesses:
* While novel, the proposed setting is poorly motivated. Why studying this setting is significant? What would it enable?
* The description of the environment is very nebulous. Especially, the complexity of the visual and language components are highly under-specified. How is the input observation represented (image or feature-based)? How many object categories? How large is the agent's vocabulary? How are the environment graph generated? It is thus difficult to comprehend the significance of solving this environment.
* The plots are missing confidence intervals. Each experiments are not conducted multiple times with different random seeds. This seriously weakens the empirical claims.

The evidence to be evaluated is:Navigation tasks with vision and natural language information have attracted much attention in the last few years. Generally, this task involves humans, an artificial agent, and the environment. The agent communicates with humans in natural language. The agent may receive requests or instructions and ask for more guidance from the human. It can navigate and interact with the environment to get more information or complete corresponding task requested by the human. While humans should give requests or guidance to help the agent complete the task. Generally, the goal of these VLN tasks is to help the agent find the target place known only by the human side.

From the view of language format, we can divide those tasks into three categories Gu et al. (2022):* Initial instructions: The agent is given instructions for the whole navigation episode.

However, this kind of work only gives the instructions at the beginning, so multi-turn communication can not be generated.

* Oracle guidance: In the process of navigation, the agent can request additional natural language guidance to get more information Chi et al. (2019).

By putting the VLN task into multi-agent settings, the problems listed above can be alleviated or solved. To our knowledge, talk-the-walk de Vries et al. (2018) is the only work applying the VLN task with multiple agents and is very similar to our task settings. However, talk-the-walk only focuses on the localization part, where the guide finds where the tourist is. But ignore the guidance part, in which the guide makes a route plan and gives guidance to the tourist, and the tourist makes corresponding actions. Talk-the-walk models this part with a random walk and only implements a minimal baseline, making the VLN task with multi-agent settings incomplete.

This task is inspired by a real-world scenario where two agents with unequal abilities, agents A and B, cooperate to help agent A find the target place. As pictured in Figure 1, agent A can only see the objects around it, while agent B has the floor plan and the target place but doesn't know where A is. To win this game, they have to cooperate to help A reach the target place with limited rounds.

Here, we divide the navigation task into three steps: First, localization. Agent B needs to find where A is, according to the message given by A describing the objects around it. Second, route plan. Having the position of A and the target place, agent B has to find the shortest path for A to get there. Third, guidance.

Formally, this navigation task is constructed as follows: There are houses, containing rooms \(R=\{r_{0},r_{1},...,r_{N}\}\) each. Every time, one house is randomly chosen from those \(K\) candidates. Then two rooms are drawn from the selected house, and one of them is the target room \(r_{o}\), and the other is the start room \(r_{s}\). Every room has several gates \(D_{i}=\{d_{ij},j\in Neighbor(i)\}\). There is a graph for each house, to model the relationship between rooms. We use \(G=(R,D)\) to denote a house, where \(R\) is the vertex set, and \(D\) is the edge set. There are two players called agent A, and agent B parameterized by \(\theta_{A},\theta_{B}\) separately.

A case is shown in Figure 2, where Agent A starts from room 0 and wants to get to room 5. Firstly, the information about the house containing features of a set of rooms \((r_{0},r_{1},..,r_{N})\) is given. Agent A encodes the target room information \(r_{o}\) including objects and gates and then uses the encoded vector to generate corresponding emergent language with its language generation module \(e_{s}^{o}=GRU(MLP(r_{o}))\).

Similarly, the model for the guidance task also includes an encoder, a language generation, and a language understanding module used for giving instructions to agent A about its next step.

Finally, we put those sub-tasks together to complete the navigation task.

We construct the dataset with 6 rooms for each navigation task. And every object in this dataset has 3 optional colors and 3 optional shapes. Also, some features not relevant to this task are added, like the gate color which we suppose to be the same among all rooms in the same house.

In this work, we propose a navigation task in an emergent communication scenario. This task is adapted from the vision-language navigation task proposed in human-machine communication. However, compared with the traditional VLN task, the application of multi-agent communication in the VLN task makes the communication policy more flexible and can generate a more complex

Vision-and-language navigation: A survey of tasks, methods, and future directions. In _Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_. Association for Computational Linguistics, 2022. doi: 10.18653/v1/2022.acl-long.524. URL [https://doi.org/10.18653%2Fv1%2F2022.acl-long.524](https://doi.org/10.18653%2Fv1%2F2022.acl-long.524).

* Jain et al. (2019) Vihan Jain, Gabriel Magalhaes, Alexander Ku, Ashish Vaswani, Eugene Ie, and Jason Baldridge. Stay on the path: Instruction fidelity in vision-and-language navigation. In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_, pp. 1862-1872, Florence, Italy, July 2019. Association for Computational Linguistics. doi: 10.18653/v1/P19-1181. URL [https://aclanthology.org/P19-1181](https://aclanthology.org/P19-1181).

Beyond the nav-graph: Vision-and-language navigation in continuous environments, 2020. URL [https://arxiv.org/abs/2004.02857](https://arxiv.org/abs/2004.02857).

* Thomason et al. (2019) Jesse Thomason, Michael Murray, Maya Cakmak, and Luke Zettlemoyer. Vision-and-dialog navigation, 2019. URL [https://arxiv.org/abs/1907.04957](https://arxiv.org/abs/1907.04957).

* Zhu et al. (2021) Fengda Zhu, Xiwen Liang, Yi Zhu, Xiaojun Chang, and Xiaodan Liang. Soon: Scenario oriented object navigation with graph-based exploration, 2021. URL [https://arxiv.org/abs/2103.17138](https://arxiv.org/abs/2103.17138).

Input: Figure 1: The navigation task involves three components: the tourist (agent A), the guide (agent B), and the environment. Agent A and B have unequal abilities. While agent A can only see its surroundings and describe them to B, agent B has the global perspective, can make a route plan, and give instructions to A. Agent A and B cooperate to lead A to the target place.

Input: Figure 2: Navigation Case. The navigation task consists of three sub-tasks: Localization, Route Plan, and Guidance. At every turn, agent A first gives a description of its surroundings in the room it is located. With the current messages and the history messages given by A, agent B tries to guess where A is using the topological structure of the rooms. Then, based on this guess, agent B makes a route plan for A to get to the target place, and gives an instruction to A describing the next step A should take.

Input: Figure 3: Model Structure. There are three sub-modules here, including localization, route plan, and guidance. In the localization part, by calculating the similarity score between messages given by A and room feature embeddings incorporating structural information, agent B tries to find where A is. Then we use the Floyd algorithm to find the shortest path between the guessed place and the target one. And in the guidance module, A could find its path with history dialogue with B and its own observations.

Input: Figure 6: The effect of (a) sentence length and (b) the shortest route length between the start and target places on the task performance

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Based on my review, this claim appears overstated for several key reasons:

1. The evidence shows significant limitations and underspecification of the proposed navigation task:
- The visual and language components are poorly defined (e.g., object categories, vocabulary size)
- The environment complexity is oversimplified (only 6 rooms, 3 colors, 3 shapes)
- Experimental validation lacks confidence intervals and multiple random seed trials

2. While the task is adapted from VLN, the claim of providing a "suitable scenario" is not well supported due to:
- Limited justification for why this specific setup enables better multi-turn conversations
- No comparative analysis with existing VLN frameworks
- Reviewer correctly points out poor motivation for studying this particular setting

The evidence suggests this is more of an initial proof-of-concept rather than a comprehensively validated "suitable scenario."

<score>4</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

To give a suitable scenario for multi-turn conversations, we provide a navigation task adapted from the vision-language navigation(VLN) task.

The review comment to be evaluated is:

While this paper goes in a good direction by moving away from the Lewis game in the language emergence communication setting, it falls short as there is little scientific analysis. The authors mainly designed an artificial navigation task, handcrafted an observation space and reward, designed a neural network, trained it, and reported the results. Those results are not put into perspective with a detailed researched perspective. Indeed, the point of Emecom in ML is not to create artificial tasks for the sake of solving it, but to design tasks that could help understand how language evolves with a human intervention as possible (or being generic).

I am listing here a few leads to improve the paper in the long run:
 - The introduction should frame the research problem into the past literature, ask a scientific question, and explain how the paper help answers it. Here, there is no citation; the VLM task is only said to be more natural (no reference to the embodiment, situated learning, or going beyond the signaling game)
 - The related work is extremely short. 18 citations in total for a paper about language emergence, and the navigation task is just inherently incomplete. On a positive note, the most relevant citations kajic/Kanilowska/DeVries are present
- The paper is 7 pages long, and page 3 is barely empty
- The model is designed to solve the task, e.g., the graph networks are specific to the observation space. I would recommend either picking a minimal architecture or reproducing the architecture that is classic in VLM.
- having 4 losses in an RL task is generally a bad sign as it is a symptom of human intervention. cf The bitter lesson by Sutton
 - Results are not compared to any baselines (VAE vs your network is not really a baseline...), and simply reporting the success rate is not sufficient. Examples of suitable ablation include: the correlation between language and environment complexity. Qualitative experiments to translate language and change in the environment (e.g., how changing the graph impacts the language), what are the external constraints that shape language, reward ablation, etc.

I would recommend the authors to re-work their paper and present it to a workshop to have further guidance. The initial idea is good, but the realization lacks scientific maturity.

The evidence to be evaluated is:Navigation tasks with vision and natural language information have attracted much attention in the last few years. Generally, this task involves humans, an artificial agent, and the environment. The agent communicates with humans in natural language. The agent may receive requests or instructions and ask for more guidance from the human. It can navigate and interact with the environment to get more information or complete corresponding task requested by the human. While humans should give requests or guidance to help the agent complete the task. Generally, the goal of these VLN tasks is to help the agent find the target place known only by the human side.

From the view of language format, we can divide those tasks into three categories Gu et al. (2022):* Initial instructions: The agent is given instructions for the whole navigation episode.

However, this kind of work only gives the instructions at the beginning, so multi-turn communication can not be generated.

* Oracle guidance: In the process of navigation, the agent can request additional natural language guidance to get more information Chi et al. (2019).

By putting the VLN task into multi-agent settings, the problems listed above can be alleviated or solved. To our knowledge, talk-the-walk de Vries et al. (2018) is the only work applying the VLN task with multiple agents and is very similar to our task settings. However, talk-the-walk only focuses on the localization part, where the guide finds where the tourist is. But ignore the guidance part, in which the guide makes a route plan and gives guidance to the tourist, and the tourist makes corresponding actions. Talk-the-walk models this part with a random walk and only implements a minimal baseline, making the VLN task with multi-agent settings incomplete.

This task is inspired by a real-world scenario where two agents with unequal abilities, agents A and B, cooperate to help agent A find the target place. As pictured in Figure 1, agent A can only see the objects around it, while agent B has the floor plan and the target place but doesn't know where A is. To win this game, they have to cooperate to help A reach the target place with limited rounds.

Here, we divide the navigation task into three steps: First, localization. Agent B needs to find where A is, according to the message given by A describing the objects around it. Second, route plan. Having the position of A and the target place, agent B has to find the shortest path for A to get there. Third, guidance.

Formally, this navigation task is constructed as follows: There are houses, containing rooms \(R=\{r_{0},r_{1},...,r_{N}\}\) each. Every time, one house is randomly chosen from those \(K\) candidates. Then two rooms are drawn from the selected house, and one of them is the target room \(r_{o}\), and the other is the start room \(r_{s}\). Every room has several gates \(D_{i}=\{d_{ij},j\in Neighbor(i)\}\). There is a graph for each house, to model the relationship between rooms. We use \(G=(R,D)\) to denote a house, where \(R\) is the vertex set, and \(D\) is the edge set. There are two players called agent A, and agent B parameterized by \(\theta_{A},\theta_{B}\) separately.

A case is shown in Figure 2, where Agent A starts from room 0 and wants to get to room 5. Firstly, the information about the house containing features of a set of rooms \((r_{0},r_{1},..,r_{N})\) is given. Agent A encodes the target room information \(r_{o}\) including objects and gates and then uses the encoded vector to generate corresponding emergent language with its language generation module \(e_{s}^{o}=GRU(MLP(r_{o}))\).

Similarly, the model for the guidance task also includes an encoder, a language generation, and a language understanding module used for giving instructions to agent A about its next step.

Finally, we put those sub-tasks together to complete the navigation task.

We construct the dataset with 6 rooms for each navigation task. And every object in this dataset has 3 optional colors and 3 optional shapes. Also, some features not relevant to this task are added, like the gate color which we suppose to be the same among all rooms in the same house.

In this work, we propose a navigation task in an emergent communication scenario. This task is adapted from the vision-language navigation task proposed in human-machine communication. However, compared with the traditional VLN task, the application of multi-agent communication in the VLN task makes the communication policy more flexible and can generate a more complex

Vision-and-language navigation: A survey of tasks, methods, and future directions. In _Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_. Association for Computational Linguistics, 2022. doi: 10.18653/v1/2022.acl-long.524. URL [https://doi.org/10.18653%2Fv1%2F2022.acl-long.524](https://doi.org/10.18653%2Fv1%2F2022.acl-long.524).

* Jain et al. (2019) Vihan Jain, Gabriel Magalhaes, Alexander Ku, Ashish Vaswani, Eugene Ie, and Jason Baldridge. Stay on the path: Instruction fidelity in vision-and-language navigation. In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_, pp. 1862-1872, Florence, Italy, July 2019. Association for Computational Linguistics. doi: 10.18653/v1/P19-1181. URL [https://aclanthology.org/P19-1181](https://aclanthology.org/P19-1181).

Beyond the nav-graph: Vision-and-language navigation in continuous environments, 2020. URL [https://arxiv.org/abs/2004.02857](https://arxiv.org/abs/2004.02857).

* Thomason et al. (2019) Jesse Thomason, Michael Murray, Maya Cakmak, and Luke Zettlemoyer. Vision-and-dialog navigation, 2019. URL [https://arxiv.org/abs/1907.04957](https://arxiv.org/abs/1907.04957).

* Zhu et al. (2021) Fengda Zhu, Xiwen Liang, Yi Zhu, Xiaojun Chang, and Xiaodan Liang. Soon: Scenario oriented object navigation with graph-based exploration, 2021. URL [https://arxiv.org/abs/2103.17138](https://arxiv.org/abs/2103.17138).

Input: Figure 1: The navigation task involves three components: the tourist (agent A), the guide (agent B), and the environment. Agent A and B have unequal abilities. While agent A can only see its surroundings and describe them to B, agent B has the global perspective, can make a route plan, and give instructions to A. Agent A and B cooperate to lead A to the target place.

Input: Figure 2: Navigation Case. The navigation task consists of three sub-tasks: Localization, Route Plan, and Guidance. At every turn, agent A first gives a description of its surroundings in the room it is located. With the current messages and the history messages given by A, agent B tries to guess where A is using the topological structure of the rooms. Then, based on this guess, agent B makes a route plan for A to get to the target place, and gives an instruction to A describing the next step A should take.

Input: Figure 3: Model Structure. There are three sub-modules here, including localization, route plan, and guidance. In the localization part, by calculating the similarity score between messages given by A and room feature embeddings incorporating structural information, agent B tries to find where A is. Then we use the Floyd algorithm to find the shortest path between the guessed place and the target one. And in the guidance module, A could find its path with history dialogue with B and its own observations.

Input: Figure 6: The effect of (a) sentence length and (b) the shortest route length between the start and target places on the task performance

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Based on the review and evidence, I find this claim to be significantly overstated.

While the authors do present a navigation task inspired by VLN, several critical issues emerge:
1. The task adaptation lacks scientific rigor and proper contextualization within existing VLN literature
2. There's minimal comparison to existing baselines or analysis of how this adaptation advances VLN understanding
3. The evidence shows a relatively simple, artificial environment with basic room/object attributes
4. The reviewer correctly points out the lack of scientific maturity and analysis
5. The claim implies this provides a "suitable scenario" but the implementation appears quite limited

The evidence suggests this is more of an initial prototype rather than a well-developed VLN adaptation that would be truly "suitable" for multi-turn conversations.

<score>4</score>

Justification:
- The reviewer notes "little scientific analysis" and that the authors "mainly designed an artificial navigation task, handcrafted an observation space"
- The evidence shows very basic room configurations with limited object attributes (just "3 optional colors and 3 optional shapes")
- Missing critical comparative analysis and experimental validation
- Limited scientific grounding with only 18 citations noted by reviewer
- Claims suitability without demonstrating advantages over existing approaches


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

To give a suitable scenario for multi-turn conversations, we provide a navigation task adapted from the vision-language navigation(VLN) task.

The review comment to be evaluated is:

The paper is not clearly written and some sections are hard to understand. The related works do capture how the framework proposes new challenges that are not already captured in previous work. Besides a similar work that also uses communication between agents [2] perform a similar analysis and comparisons to that work would be help in highlighting the difference with the proposed framework.

Besides the scope of the proposed framework is limited as it can only handle artificial messages that have a much smaller length, vocabulary, and sources of variation. In contrast, previous work have already proposed similar benchmarks that involve real images with natural language descriptions [1].

[1] de Vries et al 2018. Talk the Walk: Navigating New York City through Grounded Dialogue.

[2] Singh et al. 2019. Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks.

The evidence to be evaluated is:Navigation tasks with vision and natural language information have attracted much attention in the last few years. Generally, this task involves humans, an artificial agent, and the environment. The agent communicates with humans in natural language. The agent may receive requests or instructions and ask for more guidance from the human. It can navigate and interact with the environment to get more information or complete corresponding task requested by the human. While humans should give requests or guidance to help the agent complete the task. Generally, the goal of these VLN tasks is to help the agent find the target place known only by the human side.

From the view of language format, we can divide those tasks into three categories Gu et al. (2022):* Initial instructions: The agent is given instructions for the whole navigation episode.

However, this kind of work only gives the instructions at the beginning, so multi-turn communication can not be generated.

* Oracle guidance: In the process of navigation, the agent can request additional natural language guidance to get more information Chi et al. (2019).

By putting the VLN task into multi-agent settings, the problems listed above can be alleviated or solved. To our knowledge, talk-the-walk de Vries et al. (2018) is the only work applying the VLN task with multiple agents and is very similar to our task settings. However, talk-the-walk only focuses on the localization part, where the guide finds where the tourist is. But ignore the guidance part, in which the guide makes a route plan and gives guidance to the tourist, and the tourist makes corresponding actions. Talk-the-walk models this part with a random walk and only implements a minimal baseline, making the VLN task with multi-agent settings incomplete.

This task is inspired by a real-world scenario where two agents with unequal abilities, agents A and B, cooperate to help agent A find the target place. As pictured in Figure 1, agent A can only see the objects around it, while agent B has the floor plan and the target place but doesn't know where A is. To win this game, they have to cooperate to help A reach the target place with limited rounds.

Here, we divide the navigation task into three steps: First, localization. Agent B needs to find where A is, according to the message given by A describing the objects around it. Second, route plan. Having the position of A and the target place, agent B has to find the shortest path for A to get there. Third, guidance.

Formally, this navigation task is constructed as follows: There are houses, containing rooms \(R=\{r_{0},r_{1},...,r_{N}\}\) each. Every time, one house is randomly chosen from those \(K\) candidates. Then two rooms are drawn from the selected house, and one of them is the target room \(r_{o}\), and the other is the start room \(r_{s}\). Every room has several gates \(D_{i}=\{d_{ij},j\in Neighbor(i)\}\). There is a graph for each house, to model the relationship between rooms. We use \(G=(R,D)\) to denote a house, where \(R\) is the vertex set, and \(D\) is the edge set. There are two players called agent A, and agent B parameterized by \(\theta_{A},\theta_{B}\) separately.

A case is shown in Figure 2, where Agent A starts from room 0 and wants to get to room 5. Firstly, the information about the house containing features of a set of rooms \((r_{0},r_{1},..,r_{N})\) is given. Agent A encodes the target room information \(r_{o}\) including objects and gates and then uses the encoded vector to generate corresponding emergent language with its language generation module \(e_{s}^{o}=GRU(MLP(r_{o}))\).

Similarly, the model for the guidance task also includes an encoder, a language generation, and a language understanding module used for giving instructions to agent A about its next step.

Finally, we put those sub-tasks together to complete the navigation task.

We construct the dataset with 6 rooms for each navigation task. And every object in this dataset has 3 optional colors and 3 optional shapes. Also, some features not relevant to this task are added, like the gate color which we suppose to be the same among all rooms in the same house.

In this work, we propose a navigation task in an emergent communication scenario. This task is adapted from the vision-language navigation task proposed in human-machine communication. However, compared with the traditional VLN task, the application of multi-agent communication in the VLN task makes the communication policy more flexible and can generate a more complex

Vision-and-language navigation: A survey of tasks, methods, and future directions. In _Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_. Association for Computational Linguistics, 2022. doi: 10.18653/v1/2022.acl-long.524. URL [https://doi.org/10.18653%2Fv1%2F2022.acl-long.524](https://doi.org/10.18653%2Fv1%2F2022.acl-long.524).

* Jain et al. (2019) Vihan Jain, Gabriel Magalhaes, Alexander Ku, Ashish Vaswani, Eugene Ie, and Jason Baldridge. Stay on the path: Instruction fidelity in vision-and-language navigation. In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_, pp. 1862-1872, Florence, Italy, July 2019. Association for Computational Linguistics. doi: 10.18653/v1/P19-1181. URL [https://aclanthology.org/P19-1181](https://aclanthology.org/P19-1181).

Beyond the nav-graph: Vision-and-language navigation in continuous environments, 2020. URL [https://arxiv.org/abs/2004.02857](https://arxiv.org/abs/2004.02857).

* Thomason et al. (2019) Jesse Thomason, Michael Murray, Maya Cakmak, and Luke Zettlemoyer. Vision-and-dialog navigation, 2019. URL [https://arxiv.org/abs/1907.04957](https://arxiv.org/abs/1907.04957).

* Zhu et al. (2021) Fengda Zhu, Xiwen Liang, Yi Zhu, Xiaojun Chang, and Xiaodan Liang. Soon: Scenario oriented object navigation with graph-based exploration, 2021. URL [https://arxiv.org/abs/2103.17138](https://arxiv.org/abs/2103.17138).

Input: Figure 1: The navigation task involves three components: the tourist (agent A), the guide (agent B), and the environment. Agent A and B have unequal abilities. While agent A can only see its surroundings and describe them to B, agent B has the global perspective, can make a route plan, and give instructions to A. Agent A and B cooperate to lead A to the target place.

Input: Figure 2: Navigation Case. The navigation task consists of three sub-tasks: Localization, Route Plan, and Guidance. At every turn, agent A first gives a description of its surroundings in the room it is located. With the current messages and the history messages given by A, agent B tries to guess where A is using the topological structure of the rooms. Then, based on this guess, agent B makes a route plan for A to get to the target place, and gives an instruction to A describing the next step A should take.

Input: Figure 3: Model Structure. There are three sub-modules here, including localization, route plan, and guidance. In the localization part, by calculating the similarity score between messages given by A and room feature embeddings incorporating structural information, agent B tries to find where A is. Then we use the Floyd algorithm to find the shortest path between the guessed place and the target one. And in the guidance module, A could find its path with history dialogue with B and its own observations.

Input: Figure 6: The effect of (a) sentence length and (b) the shortest route length between the start and target places on the task performance

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
I'll analyze whether the claim "To give a suitable scenario for multi-turn conversations, we provide a navigation task adapted from the vision-language navigation(VLN) task" is well-stated or overstated.

Based on the evidence, the claim appears somewhat overstated. While the paper does present a navigation task that enables multi-turn conversations between agents, several limitations noted in the review comments are valid:

1. The framework is limited to artificial messages with smaller length/vocabulary compared to existing work
2. Similar work like Talk the Walk already proposed comparable benchmarks using real images and natural language
3. The novelty is questionable as similar agent communication approaches exist

However, the evidence does show implementation of a structured multi-agent VLN system with localization, route planning and guidance components.

<score>3</score>

Justification:
- The evidence demonstrates a functional multi-turn conversation system but overstates its suitability/novelty
- Reviewer notes valid limitations regarding artificial nature of messages and similarity to existing work
- While the basic claim of providing a navigation task is true, calling it "suitable" without acknowledging limitations is an overstatement
- The task does enable multi-turn conversations but builds heavily on previous work rather than being entirely novel

Key supporting evidence:
- Detailed system architecture shown in figures
- Experimental results demonstrating functionality
- Acknowledgment of similar work like Talk the Walk
- Review comments highlighting limitations in scope and novelty


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Compared with methods with agents speaking the natural language, ours is cheaper with no expensive annotation, making it a more practical way for agents to communicate.

The evidence to be evaluated is:With the limitations of the message length and vocabulary size in discrete communication scenarios, discrete VAE may communicate less useful information compared with our model.

And our method outperforms the discrete VAE largely.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
I'll evaluate the claim that this method is "cheaper with no expensive annotation" and "more practical" compared to natural language methods.

Assessment:
The claim appears significantly overstated based on the provided evidence. The evidence only discusses performance comparison with discrete VAE models and message length/vocabulary limitations. There is no:
1. Cost comparison with natural language methods
2. Data on annotation requirements
3. Evidence of practicality advantages
4. Economic analysis of implementation costs

The evidence focuses solely on information content and performance relative to discrete VAE, which doesn't support claims about cost or practicality compared to natural language approaches. This represents a substantial overreach from the available data.

<score>5</score>

The claim makes broad assertions about economic and practical advantages that aren't supported by the presented evidence. The evidence only discusses performance aspects compared to discrete VAE models, not costs or practicality versus natural language methods. This disconnect between claim and evidence makes it highly overstated.


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Compared with methods with agents speaking the natural language, ours is cheaper with no expensive annotation, making it a more practical way for agents to communicate.

The review comment to be evaluated is:

Strengths:
* The proposed setting is novel.

Weaknesses:
* While novel, the proposed setting is poorly motivated. Why studying this setting is significant? What would it enable?
* The description of the environment is very nebulous. Especially, the complexity of the visual and language components are highly under-specified. How is the input observation represented (image or feature-based)? How many object categories? How large is the agent's vocabulary? How are the environment graph generated? It is thus difficult to comprehend the significance of solving this environment.
* The plots are missing confidence intervals. Each experiments are not conducted multiple times with different random seeds. This seriously weakens the empirical claims.

The evidence to be evaluated is:With the limitations of the message length and vocabulary size in discrete communication scenarios, discrete VAE may communicate less useful information compared with our model.

And our method outperforms the discrete VAE largely.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Let me assess the claim based on the provided information:

The claim that this method is "cheaper with no expensive annotation" and "more practical" appears significantly overstated based on several factors:

1. The evidence provided is extremely limited and doesn't directly address cost or practicality claims
2. The reviewer points out critical missing details about the environment complexity, input representations, and vocabulary size
3. The empirical validation is weak, with the reviewer noting missing confidence intervals and lack of multiple runs
4. While the method may outperform discrete VAE, this alone doesn't substantiate claims about cost or practicality
5. The comparison to "methods with agents speaking natural language" is not supported by any evidence

The claim makes broad statements about cost and practicality without sufficient experimental validation or detailed comparison with alternatives.

<score>5</score>