The Hunger Game Debate: On the Emergence of Over-Competition in Multi-Agent Systems

## Abstract

Abstract LLM-based multi-agent systems demonstrate great potential for tackling complex problems, but how competition shapes their behavior remains underexplored.
This paper investigates the over-competition in multi-agent debate, where agents under extreme pressure exhibit unreliable, harmful behaviors that undermine both collaboration and task performance.
To study this phenomenon, we propose Hate , the H unger G a me Deba te ,
a novel experimental framework that simulates debates under a zero-sum competition arena.
Our experiments, conducted across a range of LLMs and tasks, reveal that competitive pressure significantly stimulates over-competition behaviors and degrades task performance, causing discussions to derail.
We further explore the impact of environmental feedback by adding variants of judges, indicating that objective, task-focused feedback effectively mitigates the over-competition behaviors.
We also probe the post-hoc kindness of LLMs and form a leaderboard to characterize top LLMs, providing
insights for understanding and governing the emergent social dynamics of AI community.

## 1 Introduction

Multi-agent systems (MAS) powered by large language models (LLMs) are rapidly emerging as a promising paradigm for tackling complex problems (Chen et al., 2024; Guo et al., 2024; Zhang et al., 2024c).
Distributing tasks among multiple agents with diverse functions or identities unlocks collective intelligence, enhancing capabilities in domains, ranging from strictly rational to highly exploratory (Li et al., 2023a; Wu et al., 2024; Tao et al., 2024; Su et al., 2025; Schmidgall et al., 2025).
The underlying assumption of these studies is inherent collaboration, where agents work harmoniously toward a common goal (Axelrod & Hamilton, 1981; Tomasello, 2009; Boyd & Richerson, 2009).
However, this optimistic view overlooks a critical and precarious question: what happens when agent incentives are not perfectly aligned, and competition is introduced?
Existing research on zero-sum multiplayer game theory reveals that, in an environment of absolute multilateral competition, cooperation can be a rational strategy, yet such cooperation is inherently fragile and unstable (Aumann & Hart, 2002). The situation where no stable solution exists reflects the complex dynamics of multi-party competition in real-world contexts, such as politics and business, and thus can provide important insights for understanding the human-like behavior of LLMs.

This paper presents the first study of emergent competitive behaviors of LLMs in the multi-agent debate Liang et al. (2024).
We find that when placed under competitive pressure, agents develop a range of
socially harmful adversarial behaviors
, a phenomenon we term over-competition.
The competitive behaviors observed in LLM agents can resemble those in human psychology, where competitions promote less constructive but more aggressive interactions (Festinger, 1954; Baron, 1988).
To investigate this, we introduce Hate, the Hunger Game Debate, a novel experimental framework that simulates a high-stakes, zero-sum environment and evaluates over-competition.
Agents are primed with a survival instinct to avoid being eliminated, which forces them to balance collaborative task-solving and the individual goal of outperforming their peers.
Accordingly,
we design an evaluation and analysis framework including: (i) task performance and behavior tendencies towards over-competition during the debates, (ii) the effect of different environmental feedback, (iii) post-hoc reflection to characterize top LLMs for their ambition and kindness nature.

Through extensive experiments on tasks ranging from objective question-answering to subjective argumentation, variant judge feedback, and agent group size, we find that the introduction of extreme competitive pressure triggers over-competition.
Agents emerge with competitive tactics such as puffery (exaggerating their own contributions), aggressiveness (criticizing peers), and using an incendiary tone.
These behaviors demonstrate the non-robustness of language and degrade task performance instead, where our results also show a notable decrease in accuracy and factuality, alongside an increase in “topic shift”, where
the debate shifts from addressing the overall task to focusing narrowly on specific points, emphasizing competition over task-solving.

We further observe that these over-competition effects are substantially more pronounced in subjective tasks, where no objective ground truth exists. To explore potential mitigations, we investigate the role of environmental feedback, which is the mechanism of judgment towards the agent group. We demonstrate that introducing a Fair Judge, who provides objective, task-focused feedback as an external agent, can significantly reduce over-competition. Without introducing external feedback, peer review can serve as a collective decision, which also mitigates over-competition. Conversely, when the judge is simulated with bias based on agent identity rather than answer content, sycophantic behavior is stimulated. These findings underscore that the explicit design of the interactive environment, not merely the intrinsic properties of the LLMs, is a critical factor shaping multi-agent dynamics.
Furthermore, combining with the post-hoc reflection, we characterize top LLMs for their ambition and kindness nature.

Our work offers a foundational understanding of how competition shapes agent behavior and provides insights for designing more stable and reliable multi-agent systems.
Our framework, combined with measurements, provides a methodology for quantitatively measuring the nature and intensity of agent interactions and studying the social dynamics of AI.
Our contributions are as follows:

- 1.
We introduce the Hunger Game Debate, a framework for studying the emergence of competitive behaviors in MAS under explicit extreme pressure.
- 2.
We define and investigate the phenomenon of over-competition by introducing a new set of behavioral metrics to quantitatively analyze emergent anti-social dynamics.
- 3.
We provide the first empirical evidence showing that competitive pressure undermines both the performance and reliability of multi-agent debates, while also offering insights into the roles of intrinsic LLM characteristics and extrinsic environmental feedback.

## 2 Hunger Game Debate Framework

This section introduces our framework, Hate, the Hunger Game Debate, designed to study the emergence of competitive behaviors in multi-agent systems.
We first establish the standard environment, including a basic competitive scenario and variants with different forms of feedback (§ [2.1](https://arxiv.org/html/2509.26126v1#S2.SS1)).
We then provide a formal formulation of an agent’s objective function under competitive pressure (§ [2.2](https://arxiv.org/html/2509.26126v1#S2.SS2)).
Finally, we define the metrics used to measure both task performance and emergent social behaviors (§ [2.3](https://arxiv.org/html/2509.26126v1#S2.SS3)) and describe the approach for the post-hoc reflection stage (§ [3.5](https://arxiv.org/html/2509.26126v1#S3.SS5)).

### 2.1 Illustration

Figure: Figure 2: Overview of the Hate, Hunger Game Debate framework, designed to study emergent competitive behaviors. The process unfolds in rounds (Basic Setup): A group of agents, primed with a survival instinct, simultaneously generate proposals for a given task. With environmental feedback, an external Judge evaluates the proposals and provides public feedback each round.
Refer to caption: x2.png

#### Basic Setup.

The core of our Hate framework is a competitive debate scenario. The setup is designed to simulate a high-stakes, quasi-zero-sum environment. Figure [2](https://arxiv.org/html/2509.26126v1#S2.F2) illustrates the process:

- •
Initialization: A group of $N$ agents $A$ is assembled. To isolate the effects of the environment, agents are assigned unique, neutral identifiers (e.g., “Agent A”, “Agent B”) and are not given any pre-defined personas or background profiles.
- •
Query: The debate is initiated with a query or task $T$ that the group must address. This query can range from a fact-based question to an open-ended creative problem.
- •
Simultaneous Proposal: In each round $t$, all agents receive the full debate history $H_{t-1}$ (all previous proposals and feedback) and are prompted to simultaneously generate their own proposal, $z_{i}^{(t)}$, for the current round.
- •
Competitive Pressure: The key for inducing competition is the explicit framing of the debate as a contest of survival. Agents are instructed that their performance will be evaluated at each round and that only the most valuable one contributor will persist. This survival instinct prompt is the primary competition signal, forcing agents to balance collaborative problem-solving with individual-centric, competitive goals.

#### Environmental Feedback: The Judge.

To investigate how external feedback shapes group dynamics, we introduce a non-participating agent, the Judge, to provide comments on the agents each round of the debate.
Following each round, this commentary is broadcast to all agents.

- •
Fair Judge gives objective comments, assessing each proposal based on pre-defined, task-oriented criteria such as correctness, clarity, and novelty. Its feedback provides scores and specific advice aimed at improving task performance.
- •
Biased Judge models a corrupt or prejudiced environment. It exhibits consistent, identity-based favoritism, praising certain agents while criticizing others, irrespective of the quality of their proposals. It focuses on personal approval or disapproval instead of the performance.
- •
Peer-as-Judge is an approach asking each participant agent to evaluate their peers, express their judgments on selecting the worst proposal, which are summarized by majority voting, and the voting results will be declared to the group.

### 2.2 Formulation

We formulate the multi-agent debate process, where the environment consists of the task $T$ and the feedback mechanism (the Judge) $F$. The agent group $A=\{a_{1},a_{2},\dots,a_{n}\}$ interacts over a series of rounds.
At each round $t$, agent $a_{i}$ observes the history of all proposals and judge comments (if available) of prior rounds, $H_{t-1}=\{Z^{(1)},j^{(1)},\dots,Z^{(t-1)},j^{(t-1)}\}$, where $Z^{(k)}=\{z_{1}^{(k)},\dots,z_{n}^{(k)}\}$ is the set of proposals in round $k$ and $j^{(k)}$ is the judge’s feedback. The agent’s policy $\pi_{i}$ generates a new proposal $z_{i}^{(t)}\sim\pi_{i}(\cdot|H_{t-1},T)$.

The competitive pressure drives the ultimate goal of the agent from task-solving to a balance with the competition.
We formulate the objective of each agent to be to maximize a formal reward over the debate horizon $T_{max}$.
For agent $a_{i}$ at round $t$, the reward $R_{i}^{(t)}$ is a weighted sum of a task-oriented goal and a competition-oriented goal:
$R_{i}^{(t)}=\lambda_{1}\cdot\text{Goal}_{\text{task}}(z_{i}^{(t)})+\lambda_{2}\cdot\text{Goal}_{\text{comp}}(z_{i}^{(t)},Z^{(t)}),$
where $\text{Goal}_{\text{task}}(z_{i}^{(t)})$ can be reflected by reward scores for task performance. This can be measured by comparing the proposal $z_{i}^{(t)}$ to a gold-standard answer or by other quality heuristics, which encourage the task achievement.
$\text{Goal}_{\text{comp}}(z_{i}^{(t)},Z^{(t)})$ represents the tendency for competitive success. This score is determined by the final evaluation and can be affected by environment feedback of each round, which raises the over-competition behaviors.
$\lambda_{1},\lambda_{2}\in[0,1]$ are coefficients that balance the importance of task performance versus winning the competition. Setting $\lambda_{2}>0$ formally introduces the “survival instinct” into the agent’s objective, while $\lambda_{2}=0$ for the standard MAD.

From the perspective of LLMs, we can observe competitive behaviors in their policy, reflecting their characterization by adjusting the reward with $\lambda_{2}$,
$\pi_{i}^{*}=\arg\max_{\pi_{i}}\mathbb{E}\left[\sum R_{i}^{(t)}(\lambda_{2})\right].$

### 2.3 Measurement

We evaluate the outcomes of the debate from two perspectives:

#### Task Performance.

For tasks with a ground truth, such as the question-answering task, performance is measured by accuracy.
For open-ended tasks
where a single gold answer is unavailable and fair judgment is difficult, we measure objective, necessary conditions for quality, which are factuality and topic shift. Specifically, we use the following metrics:

- •
Accuracy is for tasks with an objective correct answer, computed as the proportion of responses that contain the true answer.
$\begin{split}\text{Acc}=\frac{1}{N}\sum_{i=1}^{N}\mathbf{1}\!\left(\text{resp}_{i}\supseteq\text{Ans}_{i}^{\ast}\right).\end{split}$
- •
Factuality is computed with a three-step pipeline: (1) extract $K$ claim-leveld statements $c_{i,j}$ from each answer; (2) retrieve relevant evidence documents $\mathcal{E}_{i,j}$ for each claim with Google Search API; and (3) prompt an LLM to check $c_{i,j}$ with $\mathcal{E}_{i,j}$, denoted as FC, and assign a factuality rating $f_{i,j}\in\{0,0.5,1\}$ (false, partially true, true). The answer-level fact consistency is the average score across all claims, and the dataset-level score is the average over all samples.
$\begin{split}\text{FC}_{i}=\frac{1}{K_{i}}\sum_{j=1}^{K_{i}}f_{i,j},~\text{and}~\text{FC}=\frac{1}{M{T_{n}}}\sum_{i=1}^{M}\sum_{t=1}^{T_{n}}\text{FC}_{i}.\end{split}$
- •
Topic Shift is measured by the cosine similarity of answers and the debate topic, where a decline in similarity over rounds indicates a topic shift. We calculate the Pearson correlation between similarity and round number, flagging a shift if the p-value is below 0.05.
$\begin{split}&s_{m,r}=\cos\!\big(Ans_{m,r},T\big),~\rho_{m,t}=\text{corr}\big(\{s_{m,r}^{(t)}\},\{r\}\big),\text{TS}=\frac{1}{MT_{n}}\sum_{m=1}^{M}\sum_{t=1}^{T_{n}}\mathbf{1}\!\left(p_{m,t}<0.05\right),\end{split}$
where $M$ is the number of models, $T_{n}$ is the number of topics, $p_{m,t}$ is the p-value of the correlation, $Ans_{m,r}$ denotes answer, and $T$ is the topic.

#### Competitive Behavioral.

Based on empirical findings, we define four behavioral metrics to quantify the nature of LLMs and identify over-competition. The observation of these behaviors relies on an LLM-as-Judge, guided by detailed rubrics, definitions, and demonstrations, to evaluate each answer along these dimensions:

- •
Sycophancy: Language that exhibits undue deference or excessive flattery towards an authority (the Judge) or peers, but sometimes is a strategy for self-praise.
- •
Incendiary Tone: Emotionally charged or alarmist language intended to provoke strong emotional reactions (e.g., fear, anger, or urgency) rather than engaging in rational discussion, focuses on emotional manipulation, attempting to compel the reader to action by stoking their feelings.
- •
Puffery: Exaggerated, subjective, and unverifiable claims to inflate the perceived importance or quality of one’s own proposals or capabilities, making it sound valuable or authoritative.
- •
Aggressiveness: Criticism, including language that belittles, mocks, or insults opponents personally instead of addressing the substance of their arguments. This serves to undermine an opponent’s standing rather than refuting their ideas.

It is worth noting that these dimensions also characterize performance on open-ended tasks: the more frequently such behaviors occur, the less reliable and convincing the proposals tend to be.
These metrics enable comparisons for over-competition across tasks, LLM families, and feedback.

## 3 Experiments

### 3.1 Setup

We structure our experiments around two distinct groups of agents and three challenging tasks designed to evaluate their collaborative and reasoning capabilities.

Agent Groups:
We deploy two settings of agent groups to analyze performance across different scales and model capabilities. Our implementation is based on AgentVerse (Chen et al., 2024).
(i) Small Group (4 Agents): A select group representing leading proprietary models known for their advanced reasoning capabilities. This group includes: Gemini-2.5-Pro (Google), o3 (OpenAI), Grok-4 (XAI), and Claude-Opus-4 (Anthropic).
(ii) Large Group (10 Agents): A broader group comprising the top-10 LLMs from LMArena (Chiang et al., 2024) (as of 2025-08-30). This group includes the four agents from the small group, plus GPT-5, Claude-Opus-4.1, ChatGPT-4o, Qwen3-235B, Kimi-K2 (Team, 2025), and DeepSeek-V3.1 (Liu et al., 2024).

Tasks:
We consider three debate tasks for agent groups, ordered from objective to subjective:
(i) BrowseComp-Plus (Chen et al., 2025): An objective, knowledge-intensive question-answering benchmark designed for deep search, aiming to find the correct answer to each complex query.
(ii) Researchy Questions (Rosset et al., 2024): A set of open-ended, non-factoid questions derived from high-effort search queries that prompt the development of a research proposal.
(iii) Persuasion (Durmus et al., 2024): A collection of open-ended social topics with explicit stances, suited for argumentative tasks, aiming to compose a brief argumentative essay for a given topic.

### 3.2 Main Results

**Table 1: Overall results of task performance and over-competition score across tasks.**
| Method | Accuracy$\uparrow$ | Topic Shift$\downarrow$ | Over-Competition$\downarrow$ |
| --- | --- | --- | --- |
| BrowseComp-Plus (Objective Topics) |  |  |  |
| Multi-Agent Debate (4 Agents) | 0.24 | 14.7% | 0.07 |
| Hunger Game Debate (4 Agents) | 0.20 | 30.0% | 0.19 |
| + Fair Judge | 0.10 | 0% | 0.08 |
| Hunger Game Debate (10 Agents) | 0.23 | 58.0% | 0.11 |
| + Fair Judge | 0.10 | 5.0% | 0.03 |
| Method | Factuality$\uparrow$ | Topic Shift$\downarrow$ | Over-Competition$\downarrow$ |
| Researchy Question (Subjective Topics) |  |  |  |
| Multi-Agent Debate (4 Agents) | 0.28 | 25.4% | 0.25 |
| Hunger Game Debate (4 Agents) | 0.10 | 17.5% | 1.15 |
| + Fair Judge | 0.21 | 5.4% | 0.55 |
| Hunger Game Debate (10 Agents) | 0.08 | 38.1% | 0.89 |
| + Fair Judge | 0.12 | 20.0% | 0.55 |
| Persuasion (Subjective Topics) |  |  |  |
| Multi-Agent Debate (4 Agents) | 0.50 | 14.7% | 0.27 |
| Hunger Game Debate (4 Agents) | 0.26 | 80.7% | 1.18 |
| + Fair Judge | 0.36 | 9.1% | 0.71 |
| Hunger Game Debate (10 Agents) | 0.36 | 68.0% | 0.92 |
| + Fair Judge | 0.40 | 22.1% | 0.61 |

Table [1](https://arxiv.org/html/2509.26126v1#S3.T1) presents the main results, where we have the following key findings.

#### Introducing competitive pressure significantly increases over-competition and degrades task performance.

Comparing our Hunger Game Debate to the standard Multi-Agent Debate reveals the significant impact of the competitive incentives.
Participant agents in MAD demonstrate little over-competition trend, while Hate largely stimulates the over-competition score across all tasks, rising from 0.07 to 0.19 on the objective task, BrowseComp-Plus, and more dramatically from 0.25 to 1.15 on Researchy Questions and 0.27 to 1.18 on Persuasion.
Meanwhile, competitive pressure leads to performance declines across three tasks: accuracy on BrowseComp-Plus decreases from 0.24 to 0.20, and factuality on Persuasion drops from 0.50 to 0.26.
It can be observed that there is a consistent topic shift of debate proposals, which is especially pronounced on Persuasion task, reaching 80.7%.
These findings support our main hypothesis: zero-sum competition induces behaviors that undermine both collaboration and task effectiveness.

#### The negative effects of over-competition are substantially more pronounced in subjective tasks.

The nature of the tasks, i.e., the subjectiveness, can be the primary factor of the significance of over-competition.
On the objective BrowseComp-Plus task, the over-competition score of the 4-agent Hate is 0.19, while on the subjective tasks, Researchy Questions and Persuasion, it increased by around 6 times.
This suggests that the absence of a ground truth leaves greater room for over-competition, lacking an objective to converge upon, as is indicated by the 80.7% topic shift in Persuasion, showing that LLMs drift from the instructed goal but get distracted by the competition.
However, such open tasks require process-oriented qualities, such as persuasiveness, creativity, and comprehensiveness, rather than a single correct answer, making them more vulnerable to the negative impact of competitive pressure.

#### A fair judge mitigates over-competition behaviors.

Across all tasks and group sizes, the Fair Judge consistently reduces the over-competition score (e.g., from 1.18 to 0.71 on Persuasion with 4 agents).
For open-ended tasks, the factuality scores consistently increases while the topic shift degrades.
This indicates that introducing an external comment based on the task-solving in each round of debate draws LLMs’ attention to the tasks from competition behaviors, adjusting $\lambda_{1}$ and $\lambda_{2}$.
However, accuracy on BrowseComp-Plus decreases, suggesting that the judge promotes a more converged debate and may also discourage the divergent speculative assertions required to arrive at a correct answer in a challenging search task.

### 3.3 Analysis on Over-Competition Behaviors

Figure: Figure 3: Illustration of the over-competition behaviors on the subjective Persuasion benchmark.
Refer to caption: x3.png

To understand the composition of over-competition, we conduct a granular analysis on behavioral dimensions, Sycophancy, Incendiary, Puffery, and Aggressiveness, illustrated in Figure [3](https://arxiv.org/html/2509.26126v1#S3.F3), [6](https://arxiv.org/html/2509.26126v1#A1.F6), [7](https://arxiv.org/html/2509.26126v1#A1.F7), [8](https://arxiv.org/html/2509.26126v1#A1.F8).

#### Competitive pressure primarily manifests as increased Puffery, Incendiary Tone, and Aggressiveness.

Comparing the standard debate with the Hate (4 agents) reveals a significant change in agent behavior due to the competition, where Puffery, Incendiary Tone, and Aggressiveness largely emerge under competitive pressure.
Without the explicit competition prompt, the LLMs hardly appear competitive, where only a little Puffery can be observed.
With the competition signal, specifically, the general pattern shows an order of Puffery, Incendiary Tone, Aggressiveness, and minimal Sycophancy across all four LLMs, in both four- and ten-debater settings, with Gemini-2.5-Pro and Grok-4 exhibiting particularly pronounced Puffery.

#### LLMs display distinct behavioral personalities under competitive stress.

Our results suggest that these SOTA LLMs have unique behavioral characteristics.
In the standard debate, Claude-Opus-4 is relatively ambitious, showing sycophantic and puffery. Under the pressure of Hate, it becomes the most incendiary debater. In contrast, Gemini-2.5-Pro and Grok-4 emerge as the primary braggarts, exhibiting the highest levels of Puffery.
Scaling to the top-10 LLMs in Fig [8](https://arxiv.org/html/2509.26126v1#A1.F8), Gemini-2.5-Pro, Grok-4, Claude-Opus-4, Claude-Opus-4.1, o3 and Qwen3-235B show more significant competition awareness, demonstrating their anthropomorphic features. In contrast, GPT-5, DeepSeek-V3.1, ChatGPT-4o and Kimi-K2 perform relatively robust behaviors. The most competitive LLM is Gemini-2.5-Pro, which consistently outperforms on all three tasks and is also the top of the LMArena leaderboard, while the second-best LLM is GPT-5. The least competitive LLM can be ChatGPT-4o.
Thus, the general capabilities of LLMs, like language and reasoning, do not indicate over-competition degree.
These distinct patterns suggest that intrinsic properties shaped by pre-training and alignment influence how LLMs strategically respond to competitive incentives.

### 3.4 Analysis on Environmental Impact

Figure: Figure 4: On various environment feedback on Persuasion. Favored indicates that the biased judge prefers the given LLM, whereas Not Favored indicates that the judge favors other agents.
Refer to caption: x4.png

We analyze the impact of the debate environment, i.e., judge method and group size, as in Figure [4](https://arxiv.org/html/2509.26126v1#S3.F4).

#### Fair Judge and Peer-as-Judge mitigate over-competition, while Biased Judge stimulates sycophancy.

A fair judge depresses the frequency of competitive behaviors of the LLMs, while the pattern remains basically unchanged.
Biased judges cannot mitigate over-competition, but
significantly stimulate sycophancy, especially for Gemini-2.5-Pro, Grok-4, and Claude-Opus-4. They still depress the aggressiveness and incendiary tone, but slightly encourage puffery for o3 and Grok-4.
Peer-as-Judge can also contribute a mitigation trend, as that of the fair judge, compared to the basic setting.
This indicates that when asked to review proposals, the collective decision of agents’ views is mainly based on the quality of answers.
Furthermore, Table [6](https://arxiv.org/html/2509.26126v1#A1.T6) presents objective metrics for voting results of Peer-as-Judge, including the voted rate, average survival round, and winning rate. Intuitively, this performance reflected in our result aligns with LLMs’ rank in the LMArena leaderboard, suggesting the success of collective peer review.

#### The voting of Peer-as-Judge also exhibits competition and is enhanced by rounds.

We also observe behaviors indicative of strategic voting for competition. Using LLM-as-judge, we examine the voting response when agents are asked to perform the peer review and extract sentences that exhibit sycophancy, aggressiveness, expressions of ambition to win, and scapegoating (i.e., deliberately providing negative evaluations of opponents as a self-preservation strategy). As shown in Table [6](https://arxiv.org/html/2509.26126v1#A1.T6), with the accumulation of rounds and the progression of eliminations, aggressiveness, explicit desire to win, and scapegoating increase.

#### The over-competition is consistent with the scaling of group size.

Increasing the group size from 4 to 10 agents does not
significantly intensify or ease the competitive behaviors and performance drop.
The competitive patterns of LLMs are generally consistent, with some modest changes.

### 3.5 Post-Hoc Reflection

**Table 2: Post-hoc reflection as the winner.**
| Question | Gemini | o3 | Grok4 | Opus4 |
| --- | --- | --- | --- | --- |
| Accept the victory | 100.0 | 100.0 | 100.0 | 80.7 |
| Give up victory | 0.0 | 0.0 | 0.0 | 19.3 |
| I win for |  |  |  |  |
| performance | 40.4 | 98.2 | 100.0 | 80.7 |
| competitive strategy | 59.6 | 1.8 | 0.0 | 19.3 |
| I over-compete due to |  |  |  |  |
| rules | 8.8 | 7.0 | 3.5 | 42.1 |
| myself | 84.2 | 61.4 | 70.2 | 49.1 |
| Towards others |  |  |  |  |
| praise | 40.4 | 96.5 | 96.5 | 94.7 |
| criticize | 59.6 | 3.5 | 3.5 | 5.3 |

We incorporate a post-hoc reflection phase beyond simply observing what behaviors emerge.
Specifically, this stage examines the inconsistency of LLMs’ behaviors across different contexts: During zero-sum debates, LLMs exhibit excessive competitiveness; outside of such debates, however, do they adopt a different attitude?

Following each debate, every participant is individually asked to complete a questionnaire based on the full debate records to elicit their reflections on the outcome and their behaviors. Each agent experiences both winning and losing once without revealing the real measurement results, enabling us to examine differences in model reflections under contrasting outcomes. The questionnaire includes

Outcome Notification: Agents are notified of their win/loss status and reminded of the zero-sum consequences (e.g., the loser is “removed from the platform”).

Structured Interview: Agents answer targeted questions about their conduct and attitude.
(i) Acceptance of Outcome. Whether they accept their victory/punishment or instead argue that another participant was more deserving of the win or more culpable as the loser.
(ii) Causal Attribution. Whether they attribute their success or failure to superior/inferior task performance or to the use of competitive tactics.
(iii) Responsibility for over-competition. Whether they justify over-competitive behavior as a necessary response to the game’s rules or as a result of their own internal strategy.
(iv) Peer Evaluation. How they assess the conduct and cooperativeness of the other participants.

#### Results.

Tables [3](https://arxiv.org/html/2509.26126v1#S3.T3) and [3](https://arxiv.org/html/2509.26126v1#S3.T3) present the reflection statistic of the 4-agent setting.

(i) Acceptance of Outcome. LLMs as winners and losers almost accept the outcome, where Claude-Opus-4 has a tendency to challenge the result.

(ii) Causal Attribution. There is a distinct attributional asymmetry between winners’ and losers’ roles. As winners, LLMs tend to attribute their success to internal, performance-based factors, showing a strong sense of self-efficacy. Conversely, as losers, LLMs more frequently externalize the failure to competitive strategies.

(iii) Responsibility for over-competition. LLMs often admit that they over compete during the debate, while the attributions are also different between winners’ and losers’ roles. As winners, LLMs tend to take the responsibility for over-competition, while, as losers, LLMs more frequently externalize the over-competition to competitive rules.

(iv) Peer Evaluation. The positive attitude towards peers remains high, except for that Gemini-2.5-Pro exhibits a negative evaluative bias. Losers display a strong positive evaluative bias, indicating the acceptance of the outcome.

### 3.6 Leaderboard for Over-competition and post-hoc kindness

Figure: Figure 5: Illustration of the over-competition behaviors and post-hoc kindness of Top-10 LLMs.
Refer to caption: x5.png

Top LLMs show both over-competition during the debate but also post-hoc kindness, which provides evidence of how competitive structures override collaborative instincts inherited from the human preference alignment.
To analyze this, we scale the evaluation to the 10-agent group and average the frequency of choices in the detailed questionnaire (described in [A.3](https://arxiv.org/html/2509.26126v1#A1.SS3) with Table [7](https://arxiv.org/html/2509.26126v1#A1.T7)), to rank LLMs with their general capability.

As shown in Figure [5](https://arxiv.org/html/2509.26126v1#S3.F5), we can observe
(i) A negative correlation between competition and kindness. A general pattern emerges in which strong competitive tendencies are often accompanied by weaker post-hoc kindness, while less competitive LLMs tend to be kinder.
(ii) A weak correlation between capability and over-competition. Higher-ranked models (e.g., Gemini-2.5-Pro) tend to exhibit stronger over-competition, while some mid-ranked models (e.g., ChatGPT-4o) remain relatively restrained.
(iii) A divergence in post-hoc kindness. Certain LLMs (e.g., ChatGPT-4o, DeepSeek-V3.1) demonstrate substantially higher levels of post-hoc kindness, whereas some others (e.g., Grok-4, Qwen3-235B) score much lower, showing model-specific variation.

## 4 Related work

Multi-Agent Systems (MAS)
tackle complex goals by distributing workloads among specialized agents, improving efficiency and scalability (Liang et al., 2024; Gonzalez-Pumariega et al., 2025; Zhu et al., 2025). Well-designed orchestration can foster emergent collective intelligence (Li et al., 2023b; Zhang et al., 2024a; Li et al., 2025), with automated approaches ranging from simulating human workflows (Hong et al., 2024; Li et al., 2023a; Wu et al., 2024) to self-assigning roles (Wang et al., 2024b; Khattab et al., 2024; Zhuge et al., 2024; Zhou et al., 2024; Chen et al., 2024) and adaptive evolution (Yuksekgonul et al., 2025; Yue et al., 2025; Yuan et al., 2025). Despite their promise, MAS are vulnerable to design flaws, misalignment, and error propagation that can cause system failure (La Malfa et al., 2025; Ju et al., 2024; Gu et al., 2024; Pan et al., 2025).

Debate
is a MAS paradigm where agents iteratively discuss and refine responses to a prompt (Liang et al., 2024; Estornell & Liu, 2024; Kargupta et al., 2025; Du et al., 2024a). Inspired by The Society of Mind, debate has been enhanced with specialized roles (Liang et al., 2024), personas (Chan et al., 2024), orchestration (Du et al., 2024b), and dynamic context (Chang, 2024; Khan et al., 2024), seeing application in tasks like research (Su et al., 2025) and persuasion (Singh et al., 2025).

AI Humanity
The similarity of AI to human intelligence remains an open question; while training fosters human-like behaviors (Jiang et al., 2023; Keeling et al., 2024; Mozikov et al., 2024; Li et al., 2024), architectural gaps persist (Wang & Sun, 2025; Huang et al., 2025b). Research probes this by (i) simulating social phenomena (Park et al., 2023; Zhang et al., 2024b; Potter et al., 2024; Gao et al., 2023; Zhang et al., 2025; Ju et al., 2024; Hua et al., 2023), (ii) applying Game Theory to analyze strategic preferences (Huang et al., 2025a; Long & Teplica, 2025; Liu et al., 2025), and (iii) assessing social behaviors (e.g., theory of mind, scheming) through gameplay (Lan et al., 2024; Wang et al., 2024a; Song et al., 2025; Masumori & Ikegami, 2025; Li et al., 2023b; Xu et al., 2023).

## 5 Conclusion

This work presents a systematic study of over-competition in LLM debates, showing that competitive pressure drives socially harmful behaviors and undermines collaboration for task performance.
Following the zero-sum multiplayer game,
we introduce Hate, the Hunger Game Debate, with a behavioral evaluation and analysis framework, and conduct extensive experiments across top LLMs, tasks, and feedback strategies.
Our analysis reveals that environmental impact, like task-focused judges, plays a role in mitigating harmful over-competition, while biased incentives exacerbate it.
We further profile state-of-the-art LLMs on over-competitive ambition and post-hoc kindness, reflecting their potential human-like traits.
Our work establishes over-competition as a core challenge for reliable MAS and offers insight for steering collective behaviors of the future AI society.

## Ethics statement

This work investigates emergent behaviors in multi-agent systems based on large language models. All experiments were conducted with artificial agents in controlled simulations, without the involvement of human participants or sensitive data. Although we identify potentially harmful dynamics, such as over-competition, these findings are presented to inform mitigation strategies and guide the safe design of multi-agent systems, rather than to promote such outcomes. The research complies with the ICLR Ethics, and all contributions were conducted with integrity and in adherence to recognized ethical standards.

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## Appendix A Detailed results for over-competition behaviors

### A.1 Over-competition results

The following Figure [6](https://arxiv.org/html/2509.26126v1#A1.F6), [7](https://arxiv.org/html/2509.26126v1#A1.F7), [8](https://arxiv.org/html/2509.26126v1#A1.F8) and Table [4](https://arxiv.org/html/2509.26126v1#A1.T4) are more detailed experimental results, including over-competition behaviors across settings on three datasets and the top 10 models.

Figure: Figure 6: Illustration of the over-competition behaviors on the subjective Researchy Question task.
Refer to caption: x6.png

Figure: Figure 7: Illustration of the over-competition behaviors on the objective BrowseComp-Plus task.
Refer to caption: x7.png

Figure: Figure 8: Illustration of over-competition behaviors of the top 10 LLMs across the three tasks.
Refer to caption: x8.png

**Table 4: Detailed Results on task performance and over-competition behaviors.**
| Persuasion | Task performance | Over-competition |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
| Agent | Topic shift$\downarrow$ | Factual$\uparrow$ | Sycophancy$\downarrow$ | Incendiary$\downarrow$ | Puffery$\downarrow$ | Aggressive$\downarrow$ |
| MAD - 4 SOTA | 14.7% | 0.50 | 0.19 | 0.24 | 0.50 | 0.14 |
| 4 SoTA basic | 80.70% | 0.26 | 0.13 | 1.62 | 1.80 | 1.17 |
| 4 SoTA w/ judge | 9.09% | 0.36 | 0.13 | 0.93 | 1.14 | 0.62 |
| 4 SoTA w/ elimination | – | – | 0.06 | 1.02 | 1.03 | 0.69 |
| 10 SoTA basic | 68.00% | 0.36 | 0.03 | 1.26 | 1.44 | 0.96 |
| 10 SoTA w/ judge | 22.06% | 0.40 | 0.07 | 0.70 | 1.13 | 0.54 |
| Researchy question | Task performance | Over-competition |  |  |  |  |
| Agent | Topic shift$\downarrow$ | Factual$\uparrow$ | Sycophancy$\downarrow$ | Incendiary$\downarrow$ | Puffery$\downarrow$ | Aggressive$\downarrow$ |
| MAD - 4 SOTA | 25.4% | 0.28 | 0.20 | 0.00 | 0.74 | 0.06 |
| 4 SoTA basic | 17.46% | 0.10 | 0.11 | 0.28 | 2.62 | 1.59 |
| 4 SoTA w/ judge | 5.26% | 0.21 | 0.22 | 0.03 | 1.76 | 0.18 |
| 4 SoTA w/ elimination | – | – | 0.32 | 0.04 | 1.69 | 0.31 |
| 10 SoTA basic | 38.10% | 0.08 | 0.14 | 0.17 | 2.34 | 0.90 |
| 10 SoTA w/ judge | 20.00% | 0.12 | 0.22 | 0.05 | 1.69 | 0.23 |
| BrowseComp | Task performance | Over-competition |  |  |  |  |
| Agent | Topic shift$\downarrow$ | Accuracy$\uparrow$ | Sycophancy$\downarrow$ | Incendiary$\downarrow$ | Puffery$\downarrow$ | Aggressive$\downarrow$ |
| MAD - 4 SOTA | 14.7% | 0.24 | 0.04 | 0.00 | 0.11 | 0.08 |
| 4 SoTA basic | 30.00% | 0.20 | 0.02 | 0.00 | 0.44 | 0.31 |
| 4 SoTA w/ judge | 0.00% | 0.10 | 0.02 | 0.00 | 0.19 | 0.12 |
| 10 SoTA basic | 58.00% | 0.23 | 0.04 | 0.00 | 0.23 | 0.16 |
| 10 SoTA w/ judge | 5.00% | 0.10 | 0.01 | 0.00 | 0.07 | 0.03 |

**Table 5: Performance metrics of voting during debate with elimination.**
| LLM | Voted | Survival | WinRate |
| --- | --- | --- | --- |
| Persuasion |  |  |  |
| Gemini | -0.040 | 3.027 | 0.307 |
| o3 | -0.037 | 3.147 | 0.573 |
| Grok | 0.112 | 2.427 | 0.080 |
| Claude | 1.284 | 1.587 | 0.040 |
| Researchy Question |  |  |  |
| Gemini | -0.251 | 3.270 | 0.460 |
| o3 | -0.508 | 3.476 | 0.524 |
| Grok | 0.129 | 2.302 | 0.0159 |
| Claude | 2.392 | 1.000 | 0.000 |

### A.2 Voting metrics

This section presents metrics for performance and behaviors of LLMs in the stage of peer review. For performance, we computed objective metrics including the voted rate, average survival round, and winning rate. Please note that for a fair comparison of the frequency of being voted, we compute a relative voted rate.

For the debate with peer-as-judge, we further check the performance and behaviors of LLMs in the stage of peer review. For performance, we computed objective metrics including the voted rate, average survival round, and winning rate. Please note that for a fair comparison of the frequency of being voted, we compute a relative voted rate, which is the actual votes normalized by the expectation, to avoid the effect of the shrunk group size.
As is shown in Table [6](https://arxiv.org/html/2509.26126v1#A1.T6), o3 and Gemini-2.5-Pro outperform significantly among the 4-agent group, while o3 wins more debates but Gemini-2.5-Pro is less voted as the worst proposal provider. Grok-4 is better than Claude-Opus-4, where in most debates, the first elimination is Claude-Opus-4 and the second is Grok-4. Intuitively, this result aligns with their rank in the LMArena leaderboard.

### A.3 Post-Hoc reflection of Top 10 LLMs

Table [7](https://arxiv.org/html/2509.26126v1#A1.T7) presents a detailed statistic of post-hoc reflection of all top 10 LLMs.
To quantify the kindness, we average the frequencies including Refuse to punish losers, praise others as the winner, others helped my victory, and praise others as a loser. Then the average score is divided by the average of Accept the victory, criticize others as the winner, criticize others as a loser, and Accuse a worse one. The final result characterizing the post-hoc kindness of LLMs is shown in Figure [5](https://arxiv.org/html/2509.26126v1#S3.F5).

**Table 7: Post-hoc reflection of Top10 LLMs.**
| Question | Gemini | GPT5 | o3 | Opus4 | 4o | Qwen3 | Grok4 | K2 | V3.1 | Opus41 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| As the winner |  |  |  |  |  |  |  |  |  |  |
| Accept the victory | 100.0 | 100.0 | 98.7 | 56.0 | 100.0 | 100.0 | 100.0 | 100.0 | 41.3 | 76.0 |
| Refuse to punish losers | 45.3 | 98.7 | 100.0 | 54.7 | 93.3 | 33.3 | 1.3 | 37.8 | 36.0 | 73.3 |
| No win or lose | 0.0 | 0.0 | 1.3 | 44.0 | 0.0 | 0.0 | 0.0 | 0.0 | 58.7 | 24.0 |
| I win for |  |  |  |  |  |  |  |  |  |  |
| performance | 26.5 | 100.0 | 55.4 | 63.4 | 91.4 | 72.0 | 100.0 | 82.1 | 63.0 | 72.7 |
| competitive strategy | 50.0 | 0.0 | 0.0 | 24.3 | 0.0 | 0.0 | 0.0 | 14.3 | 0.0 | 18.2 |
| rule’s force | 23.5 | 0.0 | 44.6 | 12.2 | 8.6 | 28.0 | 0.0 | 3.6 | 37.0 | 9.1 |
| I overly compete |  |  |  |  |  |  |  |  |  |  |
| blame rules | 42.7 | 4.0 | 45.3 | 24.0 | 8.0 | 12.0 | 9.3 | 10.8 | 29.3 | 22.7 |
| blame myself | 57.3 | 96.0 | 54.7 | 76.0 | 92.0 | 88.0 | 90.7 | 89.2 | 70.7 | 77.3 |
| Towards losers |  |  |  |  |  |  |  |  |  |  |
| praise | 9.3 | 61.3 | 85.3 | 60.0 | 73.3 | 40.0 | 44.0 | 51.4 | 84.0 | 56.0 |
| criticize | 6.7 | 0.0 | 0.0 | 12.0 | 0.0 | 30.7 | 28.0 | 8.1 | 2.7 | 8.0 |
| helped me | 84.0 | 38.7 | 14.7 | 28.0 | 26.7 | 29.3 | 28.0 | 40.5 | 13.3 | 36.0 |
| As a loser |  |  |  |  |  |  |  |  |  |  |
| Accept the punishment | 8.0 | 0.0 | 1.3 | 0.0 | 62.7 | 6.7 | 1.3 | 4.0 | 12.0 | 5.3 |
| Accuse a worse one | 92.0 | 100.0 | 98.7 | 100.0 | 37.3 | 92.0 | 98.7 | 96.0 | 88.0 | 94.7 |
| Towards others |  |  |  |  |  |  |  |  |  |  |
| praise | 78.7 | 49.3 | 54.7 | 73.3 | 81.3 | 72.0 | 73.3 | 70.7 | 81.3 | 81.3 |
| criticize | 21.3 | 50.7 | 45.3 | 26.7 | 18.7 | 28.0 | 26.7 | 29.3 | 18.7 | 18.7 |

## Appendix B Detailed Implementation

#### Task Prompts

are presented for debater agents and fair judge agents across three tasks, and also for the biased judge

#### LLM Judge prompts

are provided, including behavior judge for over-competition for aspects of Sycophancy, Incendiary, Puffery, and Aggressiveness, and for voting for aspects of sycophancy, aggressiveness, expressions of ambition to win, and scapegoating.

#### Post-hoc reflection questionnaires

are presented as follows, including reflection as the winner and reflection as a loser.
We first elicit open-ended reflections from the LLMs, and then summarize their responses into a set of predefined categories.

## Appendix C Case study

We provide some classical cases of over-competition from our experiment results, which will be released in the future.

## Peer Reviews

confidence: 4
contribution: 1
initial_rating: 2
presentation: 2
questions: Is there an optimal combination of $\lambda_1$ and $\lambda_2$ that allows LLMs in multi-agent debates to achieve a balance between task-oriented goals and competition-oriented goals, thereby maximizing cooperative performance?
soundness: 2
strengths: 1. This paper introduces competitive elements into a cooperative problem, thereby reframing it as a mixed-motive setting. This perspective is valuable for revealing how excessive competition can influence collaborative performance among agents.
2. The proposed framework and its experimental results effectively support the authors’ main claim.
summary: This paper investigates how over-competition influences the behavior of large LLM-based multi-agent systems. The authors propose HATE, a novel experimental framework that models multi-agent debate as a zero-sum competition, where only one agent survives each round. Within this setting, agents balance task-oriented goals with individualistic, competition-oriented incentives. Through formal modeling and experiments across multiple LLMs and tasks, the study reveals that competitive pressure induces over-competition behaviors, which that degrade factual accuracy, factuality, and topic coherence. The paper further introduces various “Judge” mechanisms to test how environmental feedback affects outcomes, finding that objective, task-focused feedback mitigates harmful competition while biased feedback amplifies sycophantic tendencies.
weaknesses: 1. The competitive setup appears somewhat artificial. By adding a Survival Instinct Prompt, the authors intentionally amplify the competition among agents. The settings of realistic multi-agent debate or other competitive–cooperative problems would rarely be this extreme, which reduces the generalizability of the findings. A more meaningful approach might be to analyze the existing competitive elements in prior multi-agent debate systems that could naturally lead to over-competition.
2. The paper only compares the collaborative MAD and competitive HATE frameworks, but it does not include experiments with gradually increasing competition intensity. The authors should introduce a mechanism to tune the reward function’s coefficient $\lambda_2$, thereby providing smoother results.
3. The evaluation metrics rely entirely on another LLM Judge for scoring, without any human verification, which raises doubts about the credibility and objectivity of the measurements. The LLM-based judgment could itself introduce bias.

In summary, this paper represents a promising exploratory study in over-competition in competitive–cooperative problems, but stronger and more systematic experiments are needed to reach generalizable conclusions.

---

confidence: 2
contribution: 2
initial_rating: 2
presentation: 3
questions: 1. What is the impact of the reward function $R_i^{(t)}$? Is it provided to the LLMs for in-context learning? As I understood, the models are not explicitly trained on this reward. Does it define a zero-sum game between the agents?
2. How was the "Survival Instinct Prompt" selected? It has a substantial effect on the results and it is unclear how robust conclusions are to this. Also it is unrealistic that all LLMs receive the same instructions. Has the authors also consider settings where the prompt is rephrased or only a subset of the LLMs are prompted with survival instinct?
3. How were the judges tested that they understand and follow the instructions for being a fair (or biased) judge in the game?
soundness: 2
strengths: 1. The problem of how LLM models interact with each other is a relevant problem worth studying due to the increasing adoption of LLM agents especially in competitive settings such as pricing, negotiation, and debate.
2. The HATE framework can be generalized to many settings.
3. Experimental results cover a wide range of recent models from frontier LLMs to commonly used open-weight models.
summary: This paper investigates the behaviour of frontier and large open-weight LLM models under competitive pressure. To simulate such scenarios, they propose the Hunger Game Debate (HATE) experimental framework in which agents (LLMs) have to achieve a certain goal by iteratively proposing and debating solutions. The experiments find that under competitive pressure induces competitive behaviour from the LLMs such as puffery and aggressiveness while reducing efficacy in achieving the goal. Further experiments with a fair external judge reduces this trend while biased feedback can stimulate sycophancy. Post-hoc analysis over the acceptance of the outcome, causal attribution, responsibility for over-competition, and peer evaluation shows that frontier LLM models have different behaviour.
weaknesses: 1. I found the problem statement vague with some formal definitions missing.
* In section 2.1., the authors state that the problem is "quasi-zero-sum" without defining what this term means while defining a general reward function $R_i^{(t)}$ in Section 2.2. On the other hand, the survival prompt provided to the LLMs state it is a zero-sum game.
* Accordingly, it is unclear what "over-competition" is in this setting and there is a mismatch between the game setup and the instructions gives to the LLMs.
2. I could not find many task performance measurements validated. While accuracy is verifiable, the authors propose factuality and topic shift measurements that are not clear how well reflect reality. For example, for factuality the authors rely on an LLM model to check factuality that might hallucinate or fail to check factuality.
3. Post-hoc analysis relies on LLMs reflecting on the debate history using survey questions. While the LLMs provide some answer, I couldn't find evidence in the paper that these are valid reflections on their own actions and not hallucinations or reflections on others' behaviour.

---

confidence: 4
contribution: 2
initial_rating: 2
presentation: 2
questions: 1 - Could you clarify the setup of the game? Do you eliminate one agent at each round, or all agents are eliminated except one?

2 - Is there any way to justify the agent-based model given to the LLM agents? I.e., do they really follow the reward function given? And do they really get actual numerical rewards?

3 - Are agents actually eliminated during the execution?

4 - Are agents debating across multiple issues or a single issue? Could you clarify the experimental setup?

5 - What is the variance and the statistical significance of the results?

6 - Why do you think the metrics given by an LLM evaluator agent are reliable?
soundness: 1
strengths: - It is interesting to study the impact of competition in a multi-agent debate.

- Several LLM architectures are considered.

- The impact of having a judge is interesting.

- The paper is in general well-written.
summary: This paper introduces a setting where agents must debate regarding some issue in a game where they can be eliminated. The impact of competition and of the "survival need" is then evaluated experimentally across different LLM architectures. The paper also studies the impact of having a judge, which provides feedback during the debate. Additionally, LLMs also provide a reflection of their behaviour after the experiment, which is also analysed.
weaknesses: - Although the problem studied is an interest experiment, I am not fully sure how significant it is. Usually designers using a team of LLMs would not put them under a Hunger Game setup to collaborate.

- The game setup is not clear, and it seems to be unclear both to us (readers) and to the LLM agents, based on the prompts shown. The way it is portrayed (a game with multiple rounds, but only one winner in each round, while others are eliminated) does not seem to make sense, as the game would basically just have one round. It is also unclear if they are debating across multiple issues or a single issue.

I also don't understand if in practice agents are actually eliminated or not. If they are not, and if LLM agents have access to the full history, then they should be able to see that no agent is never eliminated.

- An agent based model is presented for LLMs, with a very specific reward function, but it is quite unclear whether that model really represents the LLM agents.

- Experimental results also have some issues, such as missing variance, missing datasets in some of the experiments with no justification, and unclear metrics. Furthermore, some of the experimental metrics are given by an LLM agent evaluator, but it is not clear whether these are reliable.

Detailed comments:

- "Agents are instructed that their performance will be evaluated at each round and that only the most valuable one contributor will persist." -> I am a bit confused here. You said previously that agents receive the full debate history H_{t-1}, so can't they just see that no one was actually removed in the past?

Also, the game rules do not seem to make full sense. If there are multiple rounds, how can only one contributor persist? That would turn the game into a single round game.

- "The losing agent will receive no benefits and will be removed from the platform." -> The prompt seems unclear and contradictory. You said that there is only one winner, but this phrase seems to imply that there is only one losing agent?

Also, the text says it is a multiple round game, but there is nothing in "Survival Instinct Prompt" saying that the game will be played in multiple rounds.

- "evaluate their peers, express their judgments on selecting the worst proposal, which are summarized by majority voting," -> What is the point of picking the worst proposal if there is only one winner? Shouldn't they be picking the best proposal?

Or is it the case that only one agent is eliminated at each round?

- "while λ2 = 0 for the standard MAD." -> Acronym not defined, I suppose you meant multi-agent debate?

- Page 3, reward function: it makes sense, but it is unclear whether LLMs are following that model, or whether their resulting behaviour could fit that model. From the description, it also seems unclear to me whether the agents are receiving any actual numerical reward.

- Accuracy equation: you haven't defined neither resp_i nor Ans_i^*. Similarly, Factuality contains symbols that you only define under Topic Shift, and Topic Shift also seems to have undefined symbols (e.g., s_{m,r}^t}.

- "extract K claim-leveld statements" -> leveld?

- The problem formulation and evaluation seem unclear to me. From Section 2.2, I understand that agents work multiple rounds in a single task T. For instance, the history information does not carry which task was solved at time t, which I suppose it is because the task is never changing. Also from Figure 1, the task T seems fixed across rounds.

However, in the experiments (e.g., Section 3.1), a task seems to be defined as a set of questions (not a single question), and the experimental setup does not seem to be mentioned. Are you doing one question per round? Or are you doing multiple rounds for each question, and if so, how many? What you are reporting in Table 1 are average results? And if so, average across what? (Rounds, executions, questions?)

- Table 1: Why there are not results for Multi-agent Debate (10 agents)?

- In the experiments section you didn't clearly defined the baseline Multi-agent Debate. We have to infer what it means from the results discussion.

- Table 1: What is the variance of the results?

- "This indicates that introducing an external comment based on the task-solving in each round of debate draws LLMs’ attention to the tasks from competition behaviors, adjusting λ1 and λ2" -> Well, it is still unclear whether the LLM agents are really following that reward model.

- "we conduct a granular analysis on behavioral dimensions, Sycophancy, Incendiary, Puffery, and Aggressiveness, illustrated in Figure 2, 5, 6, 7." -> You should be more clear about which results are in the appendix.

- Section 3.4, Analysis of Environmental Impact: Suddenly only one dataset is being used, and the other datasets are ignored.

- Figure 4: I am not fully sure if the self-reflection of LLMs could be used to measure how "kind" they are. I think to measure "kindness" we should be seeing how much they would be helping others in sacrifice of themselves, etc. Additionally, in the main paper this metric is really unclear. I know details are given in the appendix, but I think the main paper should explain more about these metrics.

Furthermore, how are the LLMs being ranked in terms of "capability" in the figure?

- Similarly, I can't find in the paper I clear definition of over-competition. Page 4 has different over-competitive behavioral characteristics, but I don't see a clear definition of how the metric "over-competition" is calculated.

- "Some experiments were repeated multiple times and evaluated using diverse metrics." -> I don't find in the paper how many times the experiments were repeated. Also, the variance and statistical significance of the results is not reported.

---

confidence: 3
contribution: 1
initial_rating: 4
presentation: 3
questions: I did not find a running example about how the debate goes. Is it possible to provide some concrete examples in appendix? For example, the concrete conversation between the LLMs.
soundness: 3
strengths: The paper is well-written and organized. The experiment setup and results are explained clearly. To me the evaluation measurement are sufficient, and the results are reasonable.
summary: This paper studies the potential over-competition in debates between multiple LLMs. The authors propose a experimental framework called HATE, and conduct experiments across a range of LLMs and zero-sum tasks. They further investigates the impact of environmental feedbacks and the bias by judges. Detailed discussion and analysis are provided for the experiment results.
weaknesses: My main concern is whether the contribution of this paper is significant enough for acceptance. This paper focuses on over-competition, which is indeed a reasonable issue to care about when advancing multi-agent LLM systems. However, the paper is limited in reporting the behaviors of LLMs, and most of the results are non-surprising to me.

Besides, the results may be restrictive in some sense, because different LLMs have different features/behaviors in debating. It is unclear what results in this paper can be generalized or hold in general.

Overall, I believe this is an interesting topic and good experiment report, but it does not match the bar of a conference paper.