Learning Efficient and Interpretable Multi-Agent Communication
Authors: Wei Du
Benyu Wu
YUQING SUN
Wei Guo
Yuntao Du
Zhongmin Yan
Guoxian Yu
Lizhen Cui
Venue: ICLR 2026 Poster (2026)
Keywords: Multi-agent communication
reinforcement learning
contrastive learning
language grounding
Primary area: reinforcement learning

Abstract: Effective communication is crucial for multi-agent cooperation in partially observable environments. However, a fundamental trilemma exists among task performance, communication efficiency, and human interpretability. To resolve this, we propose a multi-agent communication framework via $\textbf{G}$rounding $\textbf{L}$anguage and $\textbf{C}$ontrastive learning (GLC) to learns  efficient and interpretable communication protocols. Specifically, GLC employs an autoencoder to learn discretized and compressed communication symbols, ensuring high communication efficiency. These symbols are then semantically aligned with human concepts using data generated by a Large Language Model (LLM), making them human-interpretable. Furthermore, a contrastive learning objective is introduced to ensure consistency and mutual intelligibility among all agents, thereby securing high task utility. GLC dynamically balances these objectives by the Information Bottleneck principle. Extensive experiments show that GLC outperforms state-of-the-art methods across multiple benchmarks, delivering superior task performance, higher communication efficiency, and enhanced human interpretability.

Published as a conference paper at ICLR 2026
LEARNING EFFICIENT AND INTERPRETABLE MULTI- AGENT COMMUNICATION
Wei Du Benyu Wu', Yuging Sun', Wei Guo!?, Yuntao Du', Zhongmin Yan', Guoxian Yu 1.2, Lizhen Cui
'School of Software, Shandong University, China
PJoint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, China
duwei sdu.edu.cn, bywu mail www..maa.ee............ sdu.edu.cn,
{sun yuqing, guowei, yuntaodu, yzm, gxyu, clz) @sdu.edu.cn
ABSTRACT
Effective communication is crucial for agget cooperation in partially observ- able environments. However, fundamental trilemma exists among task perfor- mance, communication efficiency, and human interpretability. To resolve this, we propose multi-agent communication framework via Grounding Language and Contrastive learning (GLC) to learns efficient and interpretable communication protocols. Specifically, GLC employs autoencoder to learn discretized and compressed communication symbols, ensuring high communication efficiency. These symbols are then semantically aligned with human concepts using data generated by a Large Language Model (LLM), making them human-interpretable Furthermore, contrastive learning objective is introduced ensure consistency and mutual intelligibility among all agents, thereby securing high task utility. GLC dynamically balances these objectives by the Information Bottleneck princi- ple. Extensive experiments show that GLC outperforms state-of-the-arr methods across multiple benchmarks, delivering superior task performance, higher com- munication efficiency, and enhanced human interpretability.
INTRODUCTION
Multi-Agent Reinforcement Learning (MARL) has achieved remarkable success in various complex real-world multi-agent systems (MAS) such as autonomous driving XuinnggXee et al. (2024) and traffic signal control Zhang et al. (2024). In MAS under partial observability, communication learning is key to overcoming individual perceptual limitations and achieving coordinated behavior. However, existing MARL approaches often face a fundamental tension between communication efficiency and human interpretability. Methods that prioritize efficiency typically produce opaque protocols that hinder collaboration with humans Lin al. (2021): [Freed FFree al. (2020), while those favoring interpretability often incur high communication costs that limit their practical applicability Lowe et al. (2019).
This challenge can be understood through the lens of the Information Bottleneck (IB) principle, which formalizes the trade-off between the complexity of a message and its informativeness about source variable. As shown in Figure [a). multi-agent settings, this translates a three- way balance among task performance (utility). communication efficiency (complexity), and human interpretability (informativeness) Tucker et al.] (2022a). An ideal protocol must compress high- dimensional observations into efficient messages (minimizing complexity) while preserving enough information (maximizing informativeness), both to achieve task goals (maximizing utility) and to remain interpretable by humans unfamiliar agents.
To address this trilemma, we propose a multi-agent communication framework via Grounding Lan- guage and Contrastive learning (GLC). As shown in Figure[[(), GLC integrates 3 key components: a discrete autoencoder that minimizes communication complexity through compressed symbolic representations, communication alignment mechanism that aligns discrete symbols with human
"Corresponding author

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(9)
Communication Efficiency
Autoencoder
Reconstruction
min
Complestty
CPX
Solution
Trilemma
GLC
Task Porformance
INF Informativeneso
Contrnstive
max
max
Language
Uniity
Interpretability
Learning
Ac UTL
INF AA
Grounding
Figure I: (a) The fundamental trilemma multi-agent communication, balancing efficiency, per- formance, and interpretability. (b) The GLC framework addresses this trade-off via autoencoder compression, LLM-based language grounding, and contrastive learning for inter-agent consistency.
semantic spaces to enhance interpretability, and a contrastive learning objective that ensures consis- tency among agents. By dynamically balancing these objectives, GLC opcrationalizes the IB prin- ciple for MAS to develop communication protocols that are both efficient and interpretable. During execution, agents communicate via discrete symbols, whose rich semantics are derived from LLM supervision and contrastive alignment. A key innovation is the decoupling of training from deploy- ment: while training utilizes LLM-ddrived anchors and contrastive learning, inference operates en- tirely without external supervision. Extensive experiments show GLC outperforms baselines in task performance, communication efficiency, and human interpretability. The core contributions are: (1) An integrated framework that learns efficient protocols through discrete compression and grounds them with semantic meaning via language alignment, thereby optimizing the efficiency-utiligy interpretability trilemma. (2) A principled training paradigm that unifies contrastive inter-agent alignment with offline LLM grounding, ensuring the emergent language is buman-interpretable and consistently shared, leading to more robust and generalizable collaboration.
2 RELATED WORK
Utility-Optimized Communication. A large body of multi-agent communication work prioritizes maximizing task performance, often at the expense of interpretability and sometimes efficiency. Early methods like CommNet (Sukhbataa et al. (2016) and IC3Net (ingh al. (2019) enabled end- to-end learning of continuous communication vectors that are highly optimized for specific tasks. Subsequent works like TarMAC et al. (2019) and MAGIC mechanisms and targeted communication to enhance utility further. While these approaches excel in performance, the learned protocols are typically opaque and require high communication cost, making them unsuitable for human-agent collaboration or bandwidtt-coonstriiee environments.
al. (202T) introduced attention
Efficiency- -Drivn Communication. Another line of research focuses on reducing communication cost, often through discretization Carmeli et al. FForst,o Foerster et al. (2016). Methods like acComm Lin et al. (2021) use autoencoders compress observations into discrete symbols. VQ-VIB Tucker et al. (2022a) further incorporates IB constraints to learn constrained vocabulary. These meth- ods achieve high communication efficiency but often result in symbols that are not semantically grounded, limiting their interpretability and generalizability to novel partners.
Communication more recent trend seeks to align agent communica- tion with uumaa--understaddbbl concepts. Some approaches [azaridou et al. (2020); Tucker et al. (022b): Li et al. (2024) leverage pre-trained language models or human data to ground continuous communication vectors in natural language semantics. While these methods enhance interpretabil- ity, they often rely on continuous representations that incur significant communication overhead, or they lack the structured discrete symbols necessary for efficient communication.
Interpretabillty-
Bridging the Trilemma. Prior approaches often excel in only one or two aspects of the trilemma, failing to balance all three. Our GLC framework addresses this gap by integrating discrete sym- bols for efficiency, language grounding for interpretability, and contrastive learning for utility and consistency into unified, information-theoetti framework.
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3 PRELIMINARIES
In this study, we model multi-agent reinforcement learning (MARL) with communication using a decentralized partially observable Markov decision process (Dec-POMDP) framework Olliehokk (0022). The model is formally defined by the tuple (N, S,, A, RR 7) where N represents the number of agents; S is the state space of the environment: O = 01 ON corresponds to the observation spaces available to each agent; C C² C²,,.... ..... CN defines the sets of communication messages that agents can transmit; A A A A denotes the sets of executable actions for each agent: T : S x A1 x... AN + A(S) defines the transition funcaion; R S A' x... AN R represents the reward funcion.
At each timestep t, agent receives local observation of and a set of communication messages d-1 = - ..... ON sent by all agents at the previous timestep. Using this information, the agent chooses an action of E A; and generates new message of E C, be broadeast to others. The state transition dynamics are governed by the function T S x... x Av - A(S), which takes the current s' and joint action a' fai..... a'y as input, and returns a probability distribution over the next state g/+1, Here, (SS) denotes the set of all probability distributions over the state space S, reflecting the possible stochastic outcomes of the joint action. After the state transition, each agent i receives an individual reward r R Rs' a²) determined by the reward function.
In this study, we address a fully cooperative multi-agent scenario in which all agents collaborate toward a common goal: maximizing the total expected return of all agents. The team's 's objective is formally defined as:
T
N
0 _
ΣΣ
Y'' (s) ~ Te, of Te,
max +5O+AAX
where T is the timestep horizon and y is the discount factor. To optimize this objective, we adopt policy gradient algorithms, with a particular focus on the asynchronous advantage actor-critic (A3C) framework Mnih et al. (2016). We further enhance the learning stability and credit assignment using Generalized Advantage Estimation (GAE) Schullmn al. (2015), which helps in reducing variance while maintaining tractable bias in advantage estimates.
Regarding the language learning objective for the agents, we follow the conceptualization of lan- guage acquisition introduced in Li et al.) (2024). Specifically, a target language I,* E L is defined as the communication language that optimally enhances collective task performance. Here, L. denotes the space of all possible natural languages. Formally, any language I, L can be understood as set communication messages C, established through a mapping L + C that encodes agent observations into messages. In reinforcement learning terms, this is analogous to the process of gen- erating natural language descriptions from input observation vectors. To support language learning, build a training dataset D consisting of [D] (observation, action) pairs. These are extracted from expert trajectories produced by LLM agents communicating via L" The central aim is to enable trained agents use I," effectively for seamless coordination with experts in ad-hoc teamwork set- tings, A critical requirement is that the acquired language capability exhibits strong generalization, such robust performance in previously unseen scenarios not present in D.
4 METHODOLOGY
This section presents GLC, MARL framework designed for learning efficient and interpretable multi-agent communication. As illustrated in Figure 2, the framework integrates four core modules that work together to address the trilemma involving performance, efficiency, and interpretability: (1) The MARL agent module, which learns policies and generates efficient communication symbols via a discrete autoencoder: (2) The LLM agent module, which interacts with the environment text space to produce expert trajectories rich in semantics; (3) The MARL-LLM language grounding module, which aligns the semantics of discrete symbols with natural language descriptions generated by the LLM; (4) The communication alignement contrastive learning module, which ensures all agents develop a consistent and mutually understandable communication protocol. These modules jointly optimized through a unified multi-objective loss function. Each module will be elaborated
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in detail below. The GLC framework is grounded in the IB principle, detailed derivation of the connection between GLC objectives and the IB principle is provided in the Appendix A.I and A.2
Clinal Viaw
Local Vice
Agent
Apety
of d/-Al
Trandriie
Mesiago Encoder
4
MLP
Froskr
Krweye
Dation
DaingTTkiitg Tikiing
MIP
KC 1, " AC.
troakr
Message
Chemakaa
Ohenashe
Comnnnivriie Fine Vicke
Communicatton CommurisatonnAligneent Contraxtive Learning
Agent
MARL-LLM Longurge Grounding
Agent
The GLC framework integrates four core modules: (I) MARL Agents: encode partial ob- servations into discrete symbols via an autoencoder for efficient communication; (2) LLM Agents: interact in textual space generate expert trajectories that provide semantically grounded mes- sages Li et al. (2024); (3) MARL-LLM Language Grounding: aligns the discrete embeddings with L.MM-generated message embeddings using cosine similarity loss; (4) Communication Alignment Contrastive Learning: ensures a consistent protocol across all agents by a contrastive loss.
Figure 2:
4.1 MARL AGENT
At timestep & 1, each MARL agent receives its observation of-1 and generates discrete com- munication message cf- The observation is first processed through an MLP encoder to obtain 22-dimensional feature vector K R This feature is then passed through communi- cation autoencoder to produce discrete symbols hi-1 d;- The autoencoder consists of an encoder and a decoder, both implemented as a 3-layer multilayer perceptron (MLP). The decoder reconstructs the original feature from the discrete message via mapping cf- - it-1 with quan- tization applied prior to decoding. To enable gradient backpropagation through the discretization step, a straight-through estimator is employed Bengio et al. (2013). An auxiliary reconstruction loss Creenn || 1 is minimized alongside the policy gradient loss Spolicy during training.
At the following timestep t, each agent utilizes a shared message encoder to embed the previously transmitted communication symbols ct = ct-1 y... e through linear projection. These mes- sage embeddings are concatenated and processed by a 3-layer MIP, resulting in a fixed-dimensional message representation 1)(c-1) E R 128 Each agent employs
individual policy head imple- mented as a gated recurrent unit (GRU) module [Cung et al. (2014) with a linear output layer. The policy network combines the agent's encoded state feature h; with the message representation (pc''") via feature concatenation; & h op (d--)), where denotes concatenation along the feature dimension. The GRU-based policy subsequently produces both an action probability distri- bution af ~ (vp)) and corresponding value estimates. These outputs are utilized to compute the policy loss Lpolicy, which is optimized concurrently with the reconstruction loss Lrecun.
4.2 LLM AGENT
In our framework, we utilize LLM-based embodied agents to collect samples of the target language L accordance with the LangGround Li et (2024). To support environment interaction, we introduce a textual interface I, as proposed in Li et al. (2023), that enables two-way conversion be- tween natural language descriptions and structured abstract representations. All LLM agents op- erate under generalized task instructions that guide them to collaboratively achieve common goals. At each timestep i, agent i receives an English description [(o{) of its current observation, which also includes communicated messages from other agents at the previous timestep / ((C"-1). Each agent then generates both a communication message and an action decision, which are encoded into abstract representations C and A for subsequent task execution. We formalize dual-environment framework consisting of a textual space and a physical task space, ensuring informational equiva- lence between L.MM and MARL agents despite their representational discrepancies.
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Notably, the action-selection and communication behaviors of LLM agents arise intrinsically from their pre-trained capabilities, as we intentionally avoid providing explicit coordination guidance in the prompts. The resulting LLM-generated expert trajectories constitute supervised dataset D, which captures mappings from individual agents' (observation, action) pairs to natural language messages. Throughout MARL training, 'D supplies grounded language signals that steer the emerg- ing communication protocols toward human-interpretable patterns. Further details on LLM agent setup and trajectory collection are provided in the Appendix A.4
4.3 MARL-LLM LANGUAGE GROUNDING
To align communication between MARL and LLM agents, each MARL agent first maps discrete communication symbols ci to continuous vector representation m Φ(c)) R556 using 3-layer MLP. To steer the emerging communication protocols toward human- iaanan-interprttbble patterns, we in- troduce supervised language alignment loss during MARL training. At each timestep, contextually relevant human-language reference messages Co o af) are retrieved from the dataset based on the agent's current observation and action, providing semantic guidance for communication learning-
To promote semantic consistency between the emergent communication protocol and natural lan- guage representations, we design a similarity-driven objective function within shared embedding space. For each agent's communication vector m, ((c)), we compute the cosine similarity be- tween my and the corresponding lanan-language reference embedding my when supervision is available in dataset D. m, represents the message embedding the LLM agent produced at that exact timestep the collected trajectory. This results the following conditional alignment loss:
(m²) T mr [mmm] ||,,
Lalign = TD(o), a!)
where the indicator function Up enables the loss only for state-action pairs available in the expert dataset, using C(ff, at) to supply the target embedding m, in such supervised instances. This explic- itly maps both the agent's communication messages and natural language references into shared high-dimensional semantic space, where geometric consistency is enforced via cosine similarity.
4.4 COMMUNICATION ALIGNMENT CONTRASTIVE LEARNING
Drawing on ideas from Lo et al. (2023). this loss encourages semantic consistency among messages generated by different agents observing the same environmental state. For a given message m s(c(), we define its positive set messages produced by all other agents within temporal window w around timestep in the same trajectory т:
(mi) = (0
w.t+ww)
(3)
Negatives consist of messages sampled from other trajectories within the same training batch. Let 7 (m{) M mf denote the set of all messages in the batch excluding m.. where M comprises all messages across the batch of trajectories. We optimize the following supervised contrastive loss:
exp (mf mp/p) log Em.ez(me) exp (m m=/p)'
Leontro
(4)
H (m)) m
my myEH(mm)
where p E R denotes temperature parameter that scales the similarity distribution, and (m!)) indicates the number of elements in the positive set. During training, each agent maintains a re- play buffer that stores trajectory data gathered across multiple environment instances. This buffer includes communication messages received during interaction, which are used to compute the con- trastive alignment loss. Based on hyperparameter tuning over different window sizes, we set the temporal context window to 5 timesteps for all environments. Following Khosla et al. (2020). all message embeddings are normalized prior to loss calculation, and low temperature value (p = 0.1) applied to enhance training stability and empirical performance.
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4.5 DYNAMIC BALANCING OF OBJECTIVES
The communication protocol is optimized via multi-objective learning framework integrating four complementary goals: (I) policy gradients that learn to share strategically relevant information across agents; (2) linguistic alignment with human-like communication examples drawn from D; (3) autoencoder-bant reconstruction of message semantics; and (4) contrastive learning for communi- cation alignment. These components combined into unified optimization objective through the following composite loss:
C= Cpolicy + Calsinn ARCrecon + Acheentra
(5)
The weighting coefficients Aa Ag. and A balance the contributions of the alignment loss, recon- struction loss, and contrastive loss, respectively. Crucially, instead of treating these static hy- perparameters, we view them as dynamic controls that guide the emergence of the communication protocol. Inspired by the IB principle's 's use of annealing Tishby et al. (2000); Tucker al. (2022a), we employ scheduling strategy that initially prioritizes learning a shared, meaningful semantics before gradually increasing pressure for efficient compression. This allows the communication pro-
tocol to evolve adaptively. The scheduling strategy of coefficients is set as follows,
(1) Task- Addstiveeeiveeeeeeee Aligmment AA: The weight A is set task task- denedddeeddntl..... A higher value prioritizes human interpretability in complex tasks like USAR, while a lower value in tasks like Predator-Prey allows greater focus on task performance. (2) Annealing for Communication Efficiency AR: We apply linear annealing schedule to AR. increasing it from small value to higher one. This implements an "explore then compress" strategy: initially allowing rich semantic exploration be- fore gradually increasing pressure for efficient discrete compression. (3) Stabilizing Consensus Ac: The Åc is kept at a fixed, moderate value to provide a consistent signal that fosters a shared pro- tocol among agents, ensuring stability during training. In practice, lightweight scheduler module updates these weights during training. This dynamic balancing enables GLC to adaptively evolve communication protocols optimized for each task's s specific requirements and learning stage, rather than settlig for static compromise.
EXPERIMENTS
In this section, we conduct a comprehensive evaluation of GLC two multi-agent benchmarks, Predator-Prey (Singh et al. (2019) and USAR Li et al. (2023), comparing it against oate-offthheant
baselines to rigorously answer the following questions:
Q1 (Task performance): How does GLC's performance compared with baseline methods?
Q2 (Communication Efficiency): How does GLC's efficiency compared with baseline methods?
Q3 (Interpretability):: To what extent are GLC's communication protocols human-interpretable?
Q4 (Trade-off Adaptation): How dynamically select weightings of objectives in diverse tasks?
Q5 (Generalization): How well does GLC generalize to unseen agent teams?
Q6 (Contribution)): What are the contributions of GLC's components to overall performance?
Q7 (Scalability): What is GLC's performance trend as the number of agents increases?
All experiments were executed on an RTX 4090 GPU with 24GB Memory. MARL baseline meth- ods were implemented using their officially released code. Through extensive empirical tuning, all models were trained under a unified set of hyperparameters. Each training epoch used a batch size of 500 with 10 model update iterations. Learning rates were set to 0.001 for Predator-Pres and 0.0001 for AS The Predator-Prey vision=1 (ppe1) task required 2.5 million timesteps and completed in about I hour, while the Predator-Prey vision=0 (pp.o) and USAR tasks needed 10 million timesteps, taking approximately hours. Baseline methods include IC3Net Singh et al. (2019). aeComm Lin et al. (2021), LangGround Li 2024h. VQ-VIB Tucker et al. (022a)). and a non-communicating independent agent baseline (noComm). Full environment details, baseline descriptions, and hy- perparameter settings available in the Appendix A.3 and A.4

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Task Performance (Q1). Our evaluation assesses GLC's task performance against satte-of-thheeas baselines. As shown Figure [] GLC demonstrates accelerated convergence and improved sta- bility relative other methods. In the moderately complex pP.1 setting, GLC quickly reaches performance levels comparable to leading baselines (IC3Net, aeComm, LangGround)), while also exhibiting smoother learning curves. Its advantage is further emphasized in the more difficult pP:0 environment. As shown in the middle figure, limited visibility increases the demand for coordi- nated communication. Despite this, GLC achieves notable performance gains earlier in the training process, highlighting its ability to capture and convey essential semantic information. Most impor- tantly, in the challenging USAR setting, GLC consistently attains higher performance levels with significantly reduced training fluctuations. This performance advantage stems from GLC's ability to learn semantically grounded and mutually consistent communication, which enables more efficient and robust coordination among agents compared to baselines.
20
Predator Prey [vision1))
Predator Prey (vision0))
100
US4R
GLC
Langleound
Laongr
tength
2.
Egiaade
Lung
20-
0.
0.8
0.0
Timestop(le6)
Timestep(le7)
Timestep:la7)
Figure 3: Learning curves of GLC and baselines. The x-axis indicates training timesteps, while the y-axis measures task performance using episode length, with shorter values denoting better results. Shaded regions around each curve reflect standard erross derived from three random seeds.
Communication Efficiency (Q2). To evaluate communication efficiency, we measured the total communication cost required complete tasks across three multi-agent collaboration environments. As summarized in Table (using the pP.1 environment as an example), GLC exhibits a clear advan- tage in communication efficiency. Through discrete symbol compression. GLC reduces the per-step communication volume by several orders of magnitude compared to continuous vector- vector-based meth- ods such as LangGround and IC3Net, achieving exceptionally low communication overhead. This ultra-low bitrate makes GLC particularly suitable for real-world applications with limited band- width. Although other discrete approaches, including aeComm and VQ-VIB, also achieve low com- munication usage per step, GLC maintains stronger semantic expressiveness while retaining a high compression rate, which is essential for communication that both efficient and interpretable.
GLC also significantly reduces the number of steps required to complete tasks, thereby further low- ering the overall communication cost. For instance, in the pP.1. GLC agents finish tasks in approx- imately 4.5 steps average, while baseline methods require between 5.3 and 7.2 steps. Combined with its low per-step transmission only 32 bits, GLC's total communication cost is substantially lower than all compared methods. In summary, GLC not only achieves high per-message efficiency through discrete compression but also reduces the need for repeated communication through more effective collaborative behavior. These characteristies make it particularly suitable for deployment in hondwidth-constrainng scenarios where both interpretability and operational efficiency are critical. Additional results for the other environments are provided in the Appendix A.3
Table Maximum Theoretical Communication Bits per Agent per Task Completion in ppe1 envi-
ronment (Assuming gating optimization)
Method
Bits/Step
Avg. Steps
Total Bits
Ratio to GLC
GLC
32.0
4.5
144.0
1.0
LangGround
8192.0
5.3
43417.6
301.5
IC3Net
8192.0
5.5
45056.0
312.9
aeComm
24.0
5.4
129.6
0.9
VQ-VIB
58.0
417.6
2.9
NoComm
0.0
6.6
0.0
0.0
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Interprefability (Q3). We next evaluate the semantic structure of the learned communication space, with a focus on its human- human-interpal interpretable properties and similarity to natural language. To evaluate how effectively GLC aligns agent communication with human language, assessment of the interpretability of its grounded messages. Using the offline dataset 'D as reference, similarity between GLCC agent messages and LLM agent responses in identical pP.o scenarios over 100 evaluation episodes, and (2) the BLEU score between GLC messages and their closest semantic matches in D. As presented in Table GLC slightly surpasses LangGround on both measures. Methods without explicit language alignment exhibit near-random interpretability results and are therefore omitted from comparison.
conduct quantitative
compute two metrics: (1) the embedding-
Table Interpretability Metrics Comparison
Cos sim
Bleu score
GLC
LangGroundd
GLC
LangGrounnd
PP.0
0.87=0.02
0.82+0.02
0.65e0.04
0.52+0.03
PP&1
0.8620.03
0.81=0.03
0.54+0.10
0.45=0.12
USAR
0.84+0.07
0.79=0.12
0.51
0.42+0.04
To assess semantic organization, we apply clustering and visualization to message embeddings col- lected over 100 evaluation episodes in the pp-0 environment, following the procedure in Lin et al. (2021). Using I-SNE Van der Maaten & Hinton (2008) for dimensionality reduction and DBSCAN Ester et al. (199) for cluster analysis, analyze whether the emergent protocol displays mean- ingful semantic structure. Figure 4 illustrates that the emergent messages form semantic clusters closely associated with specific environmental states. For instance, the rightmost red cluster cor- responds to agents located at coordinate (B, 3) without visibility of the prey. Querying the dataset D returns minimal-distance matches such the natural language description "moving right from (B, 3)", which accurately captures the agent's situational context. This close correspondence be- tween learned communication patterns and human-interpretable semantics confirms the meaningful organization of the emergent protocol space.
Agent Observation
Communication embeddlng space
Agent Observation
100
30
2
2
3
50
5
5
Reach pray location 100
-50
50
100
Moving right from B3
Natural language Message
Natural language Message
Figure 4: Learned communication embedding space. The communication vectors from pPco envi- ronment are visualized using t-SNE and clustered with DBSCAN, As examples, we identify two semantically meaningful clusters, each corresponding to a specific agent observation. To demon- strate the alignment between the agent communication space and the human language embedding space, we also present the most similar specific reference message from the dataset D.
Trade-off Adaptation (Q4). Our weighting strategy dynamic in two respects: through task- specific presetting of XA and Ac and training-adaptive scheduling of An- To validate the efficacy of the latter adaptive schedule, we contrast it with a baseline that uses the same preset weights but keeps them frozen during training. Using the ppUp task as instance, the baseline used a fixed set of weights (ΛĄ = 0.5, AR = 0.1, Ac = 0.1) that remained constant throughout the training. These values were selected based on experimental results in the Appendix A.3) In contrast, the training- adaptive schedule used the linear annealing schedule for A starting from Ag = 0.01 and annealing to An 0.1, while the other two weights consistent with the task-specific presetting weights.
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In the pP-0 environment, comparative results presented in Table indicate that GLC with dynamic weighting achieves better task performance than the static baseline, completing episodes more effi- ciently. More notably, it accomplishes this while significantly reducing overall communication cost, demonstrating an ability to learn a more compressed communication protocol without compromis- ing task success. Although there is slight decrease in interpretability metrics, the protocol remains substantially more understandable than non-grounded baselines, preserving human-intenn numma-interpretable se- manties despite higher compression. These findings provide qualitative evidence that the dynamic annealing strategy successfully guides the system through "learn then compress" trajectory, effec- tively balancing utility and complexity.
Table 3: Ablation study on dynamic vs. static weighting in the pPoo environment.
Method
Episode Length +
Total Bits L
BLEU Score f
GLC (Fixed Weights)
9.62 0.03
307.8 + 0.96
0.65 = 0.04
GLC (Dynamic Weights)
8.71 + 0.04
278.7 + 1.28
0.62 + 0.05
Our experimental environments, by design, impose distinct pressures on the communication pro- tocol, emphasizing different corners of the efficiency-utiliyy utility-intepretaailitg trilemma (as shown in Figure D The emergent properties of GLC's protocol naturally adapt to these pressures. In the PPvn environment, where agents "blind' and rely solely on communication for survival, the pres- sure is overwhelmingly toward maximizing task utility. The protocol is driven to convey the most critical information for coordinated search and capture under extreme uncertainty. While efficiency is necessary, the primary imperative is to achieve the task goal, leading to a protocol that is highly effective for coordination even if not maximally compressed. Conversely, the pp+1 environment provides partial observability, shifting the primary pressure toward optimizing communication effi- ciency. With local vision reducing the absolute dependency on communication for basic survival, the value of efficient information sharing becomes paramount. Here, GLC learns a protocol that prioritizes minimalistic, bandwidth-efficien symbols to supplement the agents' own perceptions, achieving the task goal with minimal communicative overhead. Finally, the USAR environment, with its heterogeneous roles and complex action sequences, demands protocol rich in semantic interpretability. Agents must communicate nuanced intentions, requests, and contextual information (e.g., bomb defusal sequences). Consequently, GLC's protocol in this setting aligns most closely with human language concepts, sacrificing some efficiency for the clarity and unambiguous understanding required for successful collaboration in such an intricate task. This demonstrates that GLC does not seek single optimal point on the trilemma but rather dynamically adapts its emergent communication protocol to the specific constraints and requirements of the task at hand. Further experimental analysis supporting these findings provided in the Appendix A.3
CONCLUSIONS
This paper introduced GLC, novel multi-agent communication framework that effectively bridges the long-standing trade-off among task performance, communication efficiency, and human inter- pretability. By integrating discrete autoencoder-bant compression with LLLM-grounded semantic alignment and inter-agent contrastive learning, GLC enables the emergence of protocols that are simultaneously communication-effiice.
task-effective, and human-interpretable. Crucially, dy- namic weighting schedule allows the framework to adaptively balance these objectives throughout training, guided by the specific constraints of the task environment. This adaptability ensures the practical applicability of GLC in diverse real-world domains, including robotic swarms where low- bandwidth communication paramount, and autonomous vehicle fleets where interpretability is crucial for human trust and collaboration.
While GLC represents a significant step forward, several promising directions remain for future work. These include developing dynamic alignment mechanisms that incorporate real-time human feedback, extending the framework to incorporate multimodal signals, and integrating structured semantic constraints external knowledge graphs to improve the generalization. A deeper theo- retical analysis of the generalization property of emergent communication under grounded learning would also be valuable. Addressing these challenges will further enhance the practicality of GLC for real-world human-AI collaboration and advance the broader goal of developing efficient and interpretable multi-agent systems.

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7 ETHICS STATEMENT
We promise that we have read the ICLR Code of Ethics, and this article has not raised any questions regarding the Code of Ethics.
ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation of China under Grant (No. 92367202 and 62531013 and 62432006), the Shandong Provincial Natural Science Foundation (No. ZR2024JQ001 and 7R2025L)2H004), the Taishan Scholars Program (No. tsqn202408317), Shan- dong Postdoctoral Science Foundation (No. SDCXX7XG-202510019, China Postdoctoral Science Foundation (No. 2025M771502)
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A APPENDIX
A.1 INFORMATION- ITTRRATTIONTHEORRTII THEORETIC FOUNDATION
The GL.C framework is grounded in the IB principle, which formalizes the trade-off between the compression of input data and the preservation of task-relevant information. We frame the learning of communication protocols within this principle: each agent must compress its encoded observa- tion H; into a concise message C, while ensuring that the message remains informative about key variables essential for collaboration. Specifically, the message should be informative about (1) Task Utility (Y), to enable effective coordination; (2) Human Interpretability (L), by aligning with seman- tic concepts from a language space: and (3) Other Agents' Perspectives (C-,), to ensure consistency and mutual intelligibillit.
The IB objective of maximizing I(C;; C_, while minimizing [(;;; H.) is intractable to op- timize directly. Instead, GLC operationalizes this principle through a composite loss function that integrates four complementary objectives, each acting as a surrogate for a component the IB trade-off:
Cpolicy +iA Caligo -I(C,;
+λC
Coontra
+AR Crecon
(6)
-I(CCC-)) =I(H.C.)
Intuitively, the policy loss Cpolicy encourages messages to be informative about task success; the alignment loss Lalign grounds them in buman-understandable semantics; the contrastive loss Leontra fosters consensus among agents; and the reconstruction loss freeon regulates the compression pro- cess. The dynamic balancing of coefficients AA An, Àe allows GLC to adaptively prioritize differ- ent facets of this trade-off during training. A detailed derivation of the connection between the loss function and the IB principle provided in Appendix A.2
A.2 DETAILED IFFOMATION-THEORRTI DERIVATION
This section provides a detailed derivation of the connection between the GLC loss function and the IB principle. The core IB objective for an agent can be formulated as finding a communication message C₁ that maximizes the following Lagrangian:
L = I(C;; L_C - HH,
(7)
where H, = fene (O,) is the encoded representation of observation O,, I(-;-) denotes mutual infor- mation, and is a Lagrange multiplier controlling the trade-off.
This objective is intractable for complex environments. We thus decompose it into tractable surro- gate losses. First, we note that the information preservation term be lower-bounded by consid- ering the contributions of each target variable individually:
I(C,; Y, L, C_.) 2 ((C,Y) I(C,; ICC,;
This simplification allows address each information term separately. The GLC loss function (Equation 6) is designed to maximize these terms:
Lpllicy & Y)
(Task utility)
Aalign x (CC, L)
(Human interpretabilitt)
Coonta x I(C,; C-.) (Inter-agent consistency)
Conversely, the compression term [(C;; H3) regulated by the reconstruction loss Ceccon
By enforcing the message C, to be predictive of the By enforcing the message C; to be predictive of the encoded observation H, via the autoencoder, we implicitly control the complexity of the mes- sage, ensuring that it does not retain excessive, irrelevant information from H, Since H, is itself compressed representation
O obtained through the encoder fene. controlling (C,; H.) effectively constrains / (C,;; O.) through the data processing inequality. Thus, the overall GLC objective can be viewed as a practical and scalable approximation to the idealized IB principle for multi-agent com- munication, where rather than the direct pathway O + C. This approach maintains the fundamental IB trade-offs while remaining tractable for complex environments.
directly control the information flow through the pathway O + H; - C;
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A.3 ADDITIONAL EXPERIMENTS
A.3.1 BASELINES
We implemented the MARL baseline methods using their publicly available official code. A short overview of each baseline is provided as follows: aeComm Lin et al. (202T) advances multi-agent discrete communication methods by grounding messages in reconstructed observations, exhibiting better performance than end-to-end and inductive bias methods in decentralized contexts. VQ-VIB Tucker al. (2022a) representative numan-interpretable communication paradigm, constructing semantic spaces for discrete tokens that demonstrate effective performance in human-agent collab- oration scenarios. IC3Net [Singh et al. (2019) a continuous communication method without lan- guage grounding, which employs gating mechanism dynamically control inter-agent messag- ing. LangGround et al.] (2024) represents the pioneering effort developing iuman-interpretable multi-agent communication by aligning continuous vectors with natural language semanties.
A.3.2 COMMUNICATION EFFICIENCY (Q2)
To further validate the communication efficiency of GLC, we conducted a qualitative analysis in both the USAR and pP-o environments. In the complex USAR setting, which involves multi-room navigation and specialized coordination requirements, GLC demonstrated significant advantages in low communication overhead. The discrete communication protocol enabled agents to maintain effective coordination while using minimal communication resources, in stark contrast to continuous vector-based approaches, which incurred substantially higher communication costs.
The PPvo presented an even more challenging scenario, where agents operated with zero visual per- ception and relied exclusively on communication for situational awareness. In this setting, GLC's efficiency advantages became particularly evident. The framework successfully supported complete task coordination through compact discrete symbols, demonstrating that meaningful communica- tion can be achieved without the bandwidth burden associated with continuous vector transmission. Across both environments, GLC maintained a consistent pattern of efficient communication with- out compromising coordination effectiveness. The discrete symbolic approach proved particularly valuable in scenarios requiring frequent information exchange, where the cumulative bandwidth savings became increasingly significant. Furthermore, the semantic grounding of these discrete symbols ensured that communication remained interpretable despite the high compression ratio. GLC demonstrates robust communication efficiency and coordination across diverse environmental constraints. It performs effectively in perception- proving its ability balance communication cost with coordination effectiveness. This makes GLC highly suitable for real-world applications with limited or costly communication resources.
scenarios and complex multi-step tasks,
Table 4: Maximum Theoretical Communication Bits per Agent per Task Completion in pp-o (No
Gating Optimization)
Method
Bits/Step
Avg. Steps
Total Bits
Ratio to GLC
GLC
32.0
8.7
278.4
1.0
LangGround
8192.0
10.8
88473.6
317.8
IC3Net
8192.0
15.0
122880.0
441.4
aeComm
24.0
10.6
254.4
0.9
VQ-VIB
58.0
15.9
922.2
3.3
NoComm
0.0
19.8
0.0
0.0
A.3.3 HUMAN INTERPRETABILITY (Q3)
Topographic similarity quantifies the structural alignment between distances in the observation space, such as physical agent locations, and corresponding distances in the communication space, such embedded message vectors, as described in Brighton & Kirby (2006). This metric reflects the compositionality and generalizability of the emergent communication protocol, since semanti- cally related observations should idcally yield similar communication signals. Following the method outlined in Lazaridou et al. (2018), we compute this measure using 100 evaluation episodes from the PPvo environment. Our procedure involves computing cosine similarities among all communication
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Table 5: Maximum Theoretical Communication Bits per Agent per Task Completion in USAR (No
Gating Optimization)
Method
Bits/Step
Avg. Steps
Total Bits
Ratio to GLC
GLC
32.0
14.9
476.8
1.0
LangGround
8192.0
19.1
156467.0
328.2
IC3Net
8192.0
20.4
167116.8
350.5
aeComm
24.0
19.4
465.6
1.0
VQ-VIB
58.0
18.4
1067.2
2.2
NoComm
0.0
38.3
0.0
0,0
vector pairs and measuring Euclidean distances among all agent position pairs. The topographic similarity score is then obtained as the negative Spearman correlation coefficient p between these two sets of distances. As shown in Table our method achieves the highest topographic similarity, with / = 0.73, among all baseline approaches, indicating that the resulting communication patterns exhibit structural properties most akin to those of human language.
Table 6: Topographic similarity in pPo0
Methods
Topographic Similarity
GLC
0.73:10.11
LrouGround
0.67-10.07
IC3Net
0.54-10.14
acComm
0.37=10.05
A.3.4 TRADE-OFF ADAPTATION (Q4)
We present the analysis of weight selection in GLC (no annealing schedule). The selection of the lan- guage alignment weight A is highly dependent on task complexity, as evidenced by the data in Table 7 In the simpler Tredator-Prey environments pPso and PP.1. a moderate weight of d 0.5 yields best performance (lowest episode length), indicating that a balanced level of semantic guidance is sufficient for effective coordination. However, in the more complex USAR environment, which requires nuanced communication for specialized roles and multi-step sequences, higher weight of A 1.0 is optimal. This demonstrates that complex collaboration tasks benefit significantly from stronger pressure to align the emergent communication with human- inteannntterretablle concepts.
Table 7: Episode length of GLC with different da on several scenarios
Scenarios
λ^ = 0.1
A^ 0.5
AA= =
PPco
9.9420.05
9.6220.03
10.21
ppe1
5.63+0.17
5.2820.13
5.42+0.16
USAR
22.63+1.25
21.55+1.06
20.85+0.73
The reconstruction loss weight AR plays critical role in balancing communication efficiency against semantic richness. As analyzed in Table 8, which presents episode length under varying ÅR values, observe clear trend: an intermediate value of Å 0.1 yields the optimal task performance across all environments. Excessively low values (Aæ = 0.01) lead to inadequate com- pression, resulting in less efficient communication and slightly longer episode completion times. Conversely, overly aggressive compression (An = 0.5) damages the semantic content of the mes- sages, hindering coordination and degrading performance. This validates our dynamic annealing strategy for AR. which allows the model to initially explore rich semantic space before gradually applying compression pressure, thereby avoiding the pitfalls of either extreme.
The contrastive learning weight λc, analyzed in Table 9 also shows clear task-dependent trend. For both Predator- Prey settings, a low weight of Xc = 0.1 is sufficient to ensure consistency among agents without introducing disruptive noise. Conversely, in the USAR environment, higher weight of Ao = 0.5 leads to the best performance. This suggests that complex environments with het- erogeneous agents require stronger contrastive signal to foster robust and mutually intelligible
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Table 8: Episode length of GLC with different AR on several scenarios
Scenarios
An 0.1
An = 0.5
An =
PPen
9.62+0.03
10.15=0.09
10,47=0.11
ppe1
5.28+0.13
5.81=0.20
6.02=0.19
USAR
23.78+1.32
20.85+0.73
22.06+0.92
communication protocol that can handle intricate coordination demands. In summary, the weight choices are not static but are dynamically adapted based on the specific pressures of the task envi- ronment, allowing GLC to effectively balance the trilemma of efficiency, utility, and interpretability.
Table 9: Episode length of GLC with different Àg several scenarios
Scenarios
Àc = 0.1
Àg = 0.5
Àç
PPco
9.6220.03
9.93+0.07
10.0620.10
PPc1
5,2840.13
5.4320.18
5.75+0.20
USAR
22.9341.19
20.85+0.73
21.7421.04
A.3.5 GENERALIZATION (Q5).
Our framework is designed support seamless ad-hoc teamwork Mirsky et al. (2020) among previ- ously unfamiliar agents without pre-coordination. To evaluate this capability, we train GLC agents in 10x10 Predator-Pres environment under varying levels of language grounding (covering 25%, 50%, 75%, and 100% of environmental states). As shown in Table 10] the benefits semantic alignment generalize states without explicit grounding during evaluation, and GLC consistently outperforms LangGround across all grounding levels. Our findings confirm that GLC achieves zero-shot alignment between agent communications and human language embeddings. Importantly, GLC organizes the entire communication space semantically, going beyond mere memorization of iiservation-message pairs. This semantic structure supports interpretable message generation in novel states, even when training involves only a limited set of grounded examples.
We further assess our agents' performance in ad-hoe teamwork scenarios, which involve cooper- ating with unfamiliar partners without prior coordination. To simulate human-agent collaboration, we form mixed teams consisting Each configuration is rigorously evaluated over 8 episodes under 3 distinct random seeds. Team performance is measured by the number of steps required to complete the task, with fewer steps indicating higher coordination efficiency. Full results, including means and standard deviations for all conditions, are provided Table TT GLC proves uniquely effective for ad hoc collaboration, outperforming all baseline methods when teamed with unfamiliar LLM agents. While homoge- neous teams (GLC-GLC or LLM-LLM) achieve the highest performance, GLC's key advantage lies in its ability to bridge the protocol gap through kanguage-alignes communication, ensuring superior coordination in mixed ad-hoc teams.
MARL agents and LLM agent serving as a human proxy.
Table 10: Comparative zero-shot generalization evaluation on pp.1 between GLC and LangGround.
Cos sim
Bleu score
GLC
LangGround
GLC
LangGround
100%
0.83+0.04
0.77+0.04
0.70=0.09
0.63+0.07
75%
0.71+0.10
0.66+0.10
0.54+0.13
0.49 14
50%
0.43+0.11
0.30+0.15
0.39+0.18
0.30=0.18
25%
0.25+0.07
0.18=0.06
0.30=0.12
0.22+0.09
Figure (left) shows the success rate GLC agents under varying degrees of language grounding. The results indicate that increased grounding leads to improved team performance and stronger communication alignment. Our findings confirm the emergence of zero-shot alignment between agent communication signals and human language embeddings. Notably, GLC organizes the entire communication space semantically structured manner, going beyond mere memorization of intervation-messaee pairs. This semantic organization supports interpretable message generation
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Table 11: Ad-hoc teamwork performance, which is measured by task completion steps.
'eam
PPv1
PPen
USAR
GLC
3,56#0.79
7.63+3.12
18.21+2.64
LLM
6.79+5.12
11.47=5.05
15.96+3.28
GLC+LLM
7.85=5.03
14.23+3.81
18.70=7.26
LL
8.51
15.5244.79
23.20=10.61
aeComm+LLM,
10.2646.37
17.6744.63
20.3449.13
noComm+LLM
10.7245.84
20.2110.08
31.1549.73
in novel states through the learned topological representation, even when training involves only limited set of grounded examples.
Predator Prey110 vs 10)
Predetor Prey 10)
0.7
100% grounded
0.7
GLC
0.6
75% grounded
LangGround
50% grounded
0.6
KC3Net
0.5
23% grounded
0.5
noComm
0.4
0.4
0.3
0.3
0.2
0.1
0.1
0.0
4
0.0
Timestepimitiion)
Timestapimitlion
Figure 5: Left: Team performance of GLC agent with different levels of language grounding on pP.1 (10 by 10). Right: Comparative performance between GLC and baseline methods in the Predator- Prey (10 by 10) environment with visual range limited to 1.
A.3.6 CONTRIBUTION (Q6).
We perform an ablation study assess the architectural contributions of GLC. The framework inte- grates three core components: an autoencoder for communication compression, an LLM-supervieed module for semantic alignment, and a contrastive learning module for enhancing communication consistency and generalization. We evaluate these through three ablated variants: (1) GLC-AE: The autoencoder is removed; discrete symbol generation is replaced with direct linear projection from observations to continuous vectors; (2) GLC-LLM: The language alignment loss is disabled; while the embedding network is retained, it receives no LLM-supervised training. (3) GLC-CL: The contrastive learning module is ablated.
the ablation study provides several key insights. The performance decline in GLC-AE highlights the essential role of the autoencoder in achieving communication efficiency via learned discrete compression. Although GLC-LLM preserves reasonable task performance, its notably lower BLEU scores emphasize the importance of LLM supervision for generating human- interpretable messages. Additionally, the GLC-CL variant, which removes the contrastive learning component, shows reduced embedding consistency and generalization ability, confirming that con- trastive learning vital for ensuring structural coherence and mutual intelligibility among agents. These results collectively indicate that the autoencoder supports efficient communication, the LLM alignment enables semantic interpretability, and contrastive learning enhances consistency and gen- eralization. The complementary functions of these modules illustrate how GLC effectively balances efficiency and interpretability multi-agent communication.
As shown in Table 12,
Table 12: Ablation study on GLC on ppup environment.
Performance
GLC-CL
GLC-AE
GLC-LLM
GLC
Episode length
11.26#0.07
11.02+0.06
10.87+0.06
9.13+0.05
Cos sim
0.82+0.03
0.84+0.02
0.06+0.01
0.87+0.02
Bleu score
0.5320.06
0.5620.06
0.0420.01
0.6520.04
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A.3.7 SCALABILITY (Q7).
To assess the scalability of our method, we performed experiments in an extended Predator- Prey setting (pP=1, 10x10 grid with 3 predators and 1 prey). Figure presents the learning curves, demonstrating that GLC consistently outperforms all baseline methods. Notably, GLC exceeds the performance of ablative baselines such as IC3Net, which lacks language grounding. and noComm, which uses no communication. To further investigate scalability under more demanding conditions, extended our analysis to larger grids with proportional increases in agent and prey populations: pPco- 15x15 Grid with 8 Predators and Prey; pPro- 20x20 Grid with 10 Predators and 4 Prey. To ensure computational tractability and define clear failure state for coordination, maximum episode length (timeout) was set for each environment: 50 steps for the 15x15 grid, and 60 steps for the 20x20 grid. The comprehensive results, summarized in Tables [3 and [4] underscore the capability of GLC to stabilize emergent communication learning in MARL agents as the scale of the environment increases.
Table 13: Performance Comparison in PPvo (15x15 grids with 8 Predators and Prey), which is measured by task completion steps, success rate and maximum theoretical communication bits per agent per task completion.
Method
Avg. Episode Length ())
Success Rate (1)
Total Comm Bits (1)
GLC
25.8
79%
825.6
LangGround
35.2
58%
288,358.4
IC3Net
38.5
52%
315,392.0
aeComm
29.4
68%
705.6
VQ-VIB
33.7
60%
1,954.6
noComm
45.0
28%
0.0
Table 14: Performance Comparison in pPea (20x20 grid with 10 Predators and 4 Prey), which is measured by task completion steps, success rate and maximum theoretical communication bits per agent per task completion.
Method
Avg. Episode Length (C)
Success Rate (1) Total Comm Bits (1)
GLC
38.5
72%
1,232.0
LangGround
55.2
40%
450,560.0
IC3Net
57.3
35%
469,401.6
aeComm
42.6
58%
1,022.4
VQ-VIB
52.8
45%
3,062.4
noComm
58.7
15%
0.0
As the environment scales in size and population, all methods face increased coordination chal- lenges, leading to natural decline in task performance. However, GLC demonstrates the most resilient behavior, with the smallest relative degradation in success rate and episode completion ef- ficiency. This robustness stems from its structured communication protocol, which maintains effec- tiveness even as task complexity grows. In contrast, baseline methods exhibit steeper performance drops. The performance gap between GLC and other methods consistently widens with increasing scale. While all approaches struggle with larger environments, GLC's relative advantage becomes more pronounced in the most challenging settings. This expanding margin demonstrates that the value of semantically grounded communication increases with complexity. The contrastive learning mechanism ensures protocol consistency across large agent populations, while the language ground- ing provides stable semantic references that enable effective generalization advantages that become critically important in large-scale coordination scenarios.
Across all tested scales, GLC maintains its exceptional communication efficiency while delivering competitive task performance. The discrete communication protocol avoids the combinatorial ex- plosion that plagues continuous methods, keeping bandwidth requirements manageable even with many agents. This combination of maintained performance and practical efficiency makes GLC particularly suitable for weal-world applications where both coordination effectiveness and resource constraints must be considered simultaneously. The framework thus provides a scalable solution that balances the trilemma objectives effectively across different operational scales.
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A.4 EXPERIMENTS DETAILS
A.4.1 ENVIRONMENT
In the Predator Prey environment, n predators with a restricted visual range e collaborate to locate stationary prey within an x x grid, Agents receive a shared reward when any predator reaches the prey, and each episode terminates either when all predators have succeeded or after a maximum of T' timesteps. Each predator perceives only local e' X grid region and selects movement actions based on these partial observations, making communication essential for effective coordination. Our experiments use 5 x grid with 3 predators and prey under two vision settings: Under e = O. predators perceive the prey only when co-located in the same cell. Each episode capped at 20 steps. With randomized initial positions and higher-dimensional state and action spaces than standard benchmarks, the Predator-Prey environment presents substantial coordination and perceptual challenges.
0 and 1.
The USAR environment Li et al. (023)) simulates a cooperative bomb disposal task in which three specialized agents (Alpha, Bravo, and Charlie) must locate and defuse hidden bombs with unknown color-coded activation sequences. The agents operate in a graph- based environment consisting of n interconnected rooms. Each agent carries unique wire cutters and can execute three types of actions: moving between rooms, inspecting bomb, or using cutter. Agents have partial observability, perceiving only the contents their current room. The action space combinatorial in nature, scaling with the number of rooms (π1, available cutters (m)) and the inspection action. Defusing bomb with r phases yields a reward of 10 x z points. Episodes end when all bombs are successfully defused or after a timeout. In our implementation, the environment includes n 5 rooms and 5 bombs with varying difficulty: two 1-phase bombs, two 2-phase bombs, and one 3-phase bomb, each assigned one of three possible colors. Each agent is equipped with two distinet wire cutters. The episode terminates after 100 steps if not completed earlier. The task demands precise coordination, as agents must communicate effectively to share essential information such bomb sequences and cutter availability.
A.4.2 EMBODIED LLM AGENTS
We utilize large language models as embodied agents for cooperative team tasks, implementing an architecture that incorporates belief states and communication memory. Each agent maintains an internal record of environmental observations and messages from teammates. The agents oper- ate under minimal task guidelines that deliberately avoid explicit coordination strategies, thereby reducing reliance on extensive prompt engineering and enhancing general applicability. For our ex- perimental setup, we adopt the methodology established in LangGround Li al. (2024), with further implementation details available in their released resources. We use OpenAl's GPT-4-0125-preee -preview model ((emperature=0) via API calls to ensure deterministic and reproducible agent behavior.
We constructed the dataset DD by collecting expert trajectories from basedd embodied LLM agents during interactive task execution. As shown in Table 11, LLM-only teams achieve per- formance competitive with MARL methods, confirming that their action communicommiiiiiionnn policies provide effective guidance for training MARL agents. the USAR environment, we collected 30 episodes comprising 1500 (observation, action) pairs along with associated communication mes- sages. For the Predator-Prey environments, we gathered 1362 and 1874 pairs for the PP.o and pP.1 settings, respectively. To align natural language messages with agent communication vectors, used OpenAl's text-embedding-3-1 ---rgge API to embed all textual messages into 256-dimensional vectors, matching the dimensionality of the agent communication space.
A.4.3 LLM-ENVIRONMENT TEXT INTERFACE
We implement both the Predator-Prey and USAR environments using the Gym API Brockman et al. (0016). To facilitate LLM agent interaction, we develop a textual interface that provides sequen- tial natural language observations and executes corresponding actions, supports natural language message broadcasting with messages appended to subsequent observations, and ensures observa- tional equivalence with MARL agents such that both receive identical partial observations every timestep. The interface serves as a bidirectional mediator between the game engine and language model agents, translating states into language and language into actions. It constructs natural lan-
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guage observations by formatting key game elements, such as round number, team score, visible objects, and received messages, into coherent textual descriptions. For action processing, the sys- tem employs keyword- saareddaaddddeeee parsing of agent responses and generates specific error feedback when invalid actions are detected, for example, when an agent attempts to inspect a bomb that does not exist. This dual translation mechanism ensures adherence to environment rules while enabling fluent natural language communication.
A.4.4 LLM-MARL COLLABORATION
To simulate human-agent collaboration under constrained resources, we form mixed teams con- sisting of MARL agents and LLM agent powered by GPT-4-turbo in both Predator-Prey and USAR environments. The LLM agent processes textual observations to produce both actions and communication messages. A dedicated interface converts environment states into natural language descriptions and parses LLM responses into executable actions. Bidirectional communication is facilitated through two mechanisms: messages from the LLM agent are embedded into continuous vectors using OpenAl's s API for interpretation by MARL agents, while discrete outputs from MARL agents are translated into natural language via cosine similarity matching against predefined phrase dataset D. This design enables seamless coordination between learning- -aasd and Eanguage-guiders agents.
B DISCUSSION ON SCALABILITY AND FUTURE WORK
Our selection of the Predator-Prey and USAR environments was strategic, as they serve as estab- lished and computationally tractable testbeds that effectively capture the challenges of the efficiency-uttility -it--utttttt-teeeprruulliit trilemma under study. These environments allowed for the exten- sive ablation studies and convergence analyses necessary validate GL.C's core contributions within practical resource constraints. We acknowledge that evaluation on larger-scale henchmarks like the StarCraft Multi-Agent Challenge (SMAC) or real-world robotic simulators represents valuable direction for future work, and we confirm that the GLC framework is environment-agnoste and readily generalizable such scenarios.
The GLC architecture is inherently designed for scalability through several core principles. The discrete autoencoder ensures bandwidth-efffcient communication that is invariant to environment size or agent population. Furthermore, the contrastive learning objective maintains semantic con- sistency and protocol coherence across large agent populations by structuring the communication space based on functional context. The dynamic balancing mechanism, guided by the Informa- tion Bottleneck principle, allows the system to adaptively prioritize different objectives- compression or semantic richness-dependiig experiments in Appendix A.3.7, conducted on enlarged grid worlds with increased agent and prey populations, empirically validate that GLC maintains robust performance and communication effi- ciency as the problem scale expands. To further demonstrate GLC's generalization ability, we plan test it in more complex embodied settings such ALFWorld (multi-step reasoning with natural language) and RoCoBench (grounded multi-agent collaboration). Success in these domains would strongly validate GLC's practicality for real-world human-Al collaboration under longer horizons and physical constraints.
as the task's complexity and scale. Our scalability
In terms of computational viability, we emphasize that GLC's design is highly efficient and practical for real-wer world deployment. The use of the LLM is strictly confined to one- one-time, offline phase for generating static dataset of expert trajectories. During the central training and deployment phases, no LLM queries are made, eliminating any ongoing computational overhead, latency, cost asso- ciated with large model inference. This makes GL.C particularly suitable for handwidth-coossrainee applications like robotic swarms autonomous vehicle networks, where both interpretability and low communication latency are critical.
GLC creates a synergistic relationship with LLMs rather than seeking replace them. While LLMs serve as serrroe-purpose knowledge bases and a source of human- aaigaed semantic grounding, GLC learns task- highly efficient communication protocols. Our ad-hoc teamwork experiments demonstrate that these two paradigms can interoperate effectively, with GLC agents successfully collaborating in mixed teams with L.MM agents. This shows that GLC's protocols not only effi-
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cient but also semantically accessible to externa human-like like intelliignnces, bridging the gap between opaque RL protocols and verbose natural language.
Looking ahead, future work will explicitly explore GLC's application in more complex and demanding domains. This includes application to extended multi-agent benchmarks like SMAC, investigation into distributed training strategies handle increased environmental complexity, and deeper analysis of how the emergent communication vocabulary and its syntactic structure evolve with task difficulty. We are confident that the GLC framework provides solid and scalable founda- tion for these future research directions toward practical and interpretable multi-agent systems.
C THE USE OF LARGE LANGUAGE MODELS
Large language models (LLMs) were not used for research ideation or writing in this work. An LLM (OpenAI's GPT-4) was employed exclusively for generating the expert trajectory dataset DD used in experiments. The authors take full responsibility for the content of this paper.
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Reviews:
[{"review_id": "CNPHCUs4gm", "reviewer": "Reviewer_wBBP", "summary": "This paper proposes a multiagent communication framework, Grounding Language and Contrastive learning (GLC), to learn an efficient and interpretable protocol. It includes three components: 1) an autoencoder that compress message to discrete symbols, 2) a contrastive learning objective that aligns messages from other agents in the same time window and pull away messages from other trajectories in the same batch, and 3) a dual LLM agent that enables alignment with natural language. The experiments with Predator-Prey and USAR  environments show that GLC performs better than prior methods and the learned embeddings are interpretable.", "questions": "- The author uses LLM agent as a human proxy to show the generalization to unseen agents. Is it possible for real human to collaborate with GLC and what’s the performance of that?\n- Though the size of the environment is scaled, the exact number of agents used in that environment is unclear.\n- How does GLC compare with LLM agent with compressed messages? This baseline should give good efficiency and high interpretability.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper provides a unified framework to learn communication protocols that are both practically efficient and semantically meaningful.\n- The proposed method has alignment with human language while being efficient.\n- The system incorporates a mechanism for dynamically adjusting the weights of various loss terms based on different situations.\n- Extensive experiments are presented to demonstrate the utility, interpretability, and efficiency of the proposed method.", "weaknesses": "- The experiment tasks and baselines are not clearly introduced. It is unclear the communication goal of the tasks and what each baseline represents. \n- It is unclear why contrastive learning to align messages in a temporal window can be useful in complex collaboration settings, agents need to split a task. In this case, align the same language will lead to all agents performing the same task.\n- The language example in Fig. 4 shows that the language structure is simple, e.g., going to a certain cell. It is unclear how well the learned protocol can generalize to a map with a different size. \n- The interpretation metrics rely on cosine similarity and BLEU. However, these measurement can have high scores but with key information being different. It will be a better evaluation if it can evaluate the semantic equivalence rather than these scores.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "j9CCtMfskX", "reviewer": "Reviewer_WbEU", "summary": "In this work, the GLC framework addresses a foundational challenge in MARL, reconciling high task performance, communication efficiency and human interpretability. GLC integrates a discrete autoencoder, to compress inter-agent communication channels for efficiency, an LLM to ensure messages are interepretable while a contrastive learning objective is used for inter-agent semantic consistency across diverse agents. The authors evaluate their framework in common MARL benchmarks - USAR and Predator Prey. The authors demonstrate superior performance to other communication framework baselines, but at a much higher communication efficiency.", "questions": "The decoding mechanism for latent state to LLMs depend on similarity based approaches and maybe fail to capture deeper relations. Do you have insights on how the framework would perform on more complex tasks with more agents?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. GLC's loss function is decomposed into tractable surrogate rewards - task reward, human interpretability and inter-agent consistency and it integrates it along with a compression term (reconstruction loss) for the autoencoder. The use of explicit IB objectives with LLM integration provides a robust framework for future research.\n2.  Online querying of the LLM, and not just as a reference for post-hoc or expert trajectories, allows agents to adapt to evolving team topologies or task variations, as demonstrated by the strong zero-shot generalization.", "weaknesses": "1. Computational and practical implications of an online LLM querying are not explored or evaluated, as scalability concerns both in computation costs and as agent populations increases seems to be concerning.\n2. Longer horizon tasks are not evaluated where an LLM might experience a shift.\n3. Traffic Junction, StarCraft II, SMAC would be better benchmarks, however, the training time needs to be reasonable for that. A deeper discussion on computational viability of the framework would be helpful for the community.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wyS4fIkJlH", "reviewer": "Reviewer_NCwj", "summary": "This paper presents a trilemma in multi-agent cooperation, which are **task performance** that agents need to coordinate well to succeed, **communication efficiency** that messages between agents should be compact, **human interpretability** that messages should align with human-understandable concepts. It introduces a new framework, Grounding Language and Contrastive Learning (GLC), to balance the three objectives simultaneously. At the core, GLC has an autoencoder to learn discrete symbols, which ensures highly efficient communication. GLC grounds language by aligning these discrete symbols with human concepts using data generated by a large language model, which makers the symbols interpretable by humans. Additionally, GLC has a contrastive learning objective to ensure consistency and mutual intelligibility among all agents, which ensures high task utility. For experiments, GLC show strong performance in multiple metrics over multiple benchmarks.", "questions": "1. GLC communicates at 32 bits per step, despite it's very efficient, but have you evaluated what will be the efficiency for the LLMs? One property of human language or human-like language is not very efficient as our language often contain words / tokens that are not semantically meaningful. Therefore, communication efficiency lowers the interpretability or resemblance to human language?\n2. What will be scalability of this method since the tasks being evaluated are mainly those simple multi-agent games for emergent communication? For example, whether it is applicable to real-world tasks or whether it can continuously improve existing language models?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The framework is well motivated by simultaneously tackling three main problems (i.e. task utility, communication efficiency, human interpretability) in multi-agent cooperation games, with prior works only tackle one or two dimensions.\n2. Use of large language models (LLM) -generated message embeddings avoids human labeling, and effectively aligns the symbol to humans since LLMs are known for the capability of producing human-like language. This process can also be considered as teaching, essentially from LLM to the MARL agent.\n3. Paper is well-written with nice illustrative figures. Experimental results are strong compared with a large set of baselines and ablations.", "weaknesses": "1. Since the language grounding is through a cosine similarity loss between the embeddings, therefore it is only interpretable at semantic level, but not at syntax level (i.e. compositionality, etc.). However, human-interpretable languages should consider both levels.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "voP2g2sCJV", "reviewer": "Reviewer_YdVf", "summary": "The paper proposes GLC, a multi-agent communication framework that supposedly addresses the \"trilemma\" between task performance, communication efficiency, and human interpretability. They use discrete autoencoders, LLM-based language grounding, and contrastive learning. Tested on several environments.", "questions": "How do you actually show a human what the agents are communicating in real-time? The nearest neighbor lookup to dataset D seems brittle.\n\nWhy does GLC+LLM underperform pure LLM? Doesn't this contradict your interpretability claims?\n\nTables 7-9 show hyperparameter sensitivity but don't provide any principled way to choose values for new domains. How is this usable?\n\nThe IB principle is about trading off compression and task-relevant information. How do you decide how much to weight each one? The current approach seems arbitrary.\n\nYou claim the method \"naturally adapts\" to different task pressures but you're manually setting different weights for each environment. That's not adaptation, that's human tuning. Am I misunderstanding something?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The trilemma framing is reasonable, I guess. It's true that most methods don't try to optimize all three objectives at once.\nThe communication efficiency numbers in Table 1 look good - achieving 300x compression over continuous methods is noteworthy.\nFigure 4 showing the t-SNE clustering is interesting, though I have concerns about how cherry-picked these examples might be (see below).", "weaknesses": "This method is way too complicated. There are four different loss terms that need to be carefully balanced, three hyperparametersthat seem to require extensive tuning, moreover different scheduling strategies for each one. The paper says λ_A is \"task-dependent\" (so you need to tune it separately for each new task), λ_R uses a linear annealing schedule, and λ_C is fixed. This is a hyperparameter nightmare. \n\nHow is a practitioner supposed to know what to use for a new environment?\n\nThe whole \"dynamic balancing\" thing in Section 4.5 feels like post-hoc rationalization for hyperparameter tuning. They claim different environments \"impose distinct pressures\" that their method \"naturally adapts\" to, but really they're just tuning different weights for different tasks. That's not adaptive, that's just... tuning.\n\nThe Information Bottleneck connection is handwavy. They claim the framework is \"grounded in the IB principle\" but Equation 6 just shows a rough correspondence between loss terms and IB components. The \"detailed derivation\" in Appendix A.2 is literally 2 paragraphs and doesn't actually derive anything rigorously. This feels like slapping \"information theoretic\" labels on things to sound fancy rather than actually using IB theory to guide the design.\nAlso, why does reconstruction loss approximate I(C;O)? They're reconstructing an intermediate feature vector h, not the observation O. This seems like a pretty important distinction that's glossed over.\n\nExperimental problems:\nThe environments are tiny. A 5x5 grid with 3 agents is not realistic. Even their \"scalability\" test is just a 10x10 grid. How is this supposed to work with real multi-robot systems that might have dozens of agents in complex continuous spaces?\n\nThe comparison to LangGround seems unfair. LangGround uses 8192-bit continuous vectors while GLC uses 32-bit discrete symbols. Of course GLC is more efficient! But LangGround could also quantize its messages. A fair comparison would use the same vocabulary size. Actually looking at Table 1, aeComm uses 24 bits and performs comparably to GLC in terms of steps (5.4 vs 4.5 in ppv1), and VQ-VIB also uses discrete communication. So the efficiency advantage isn't as clear as they claim.\n\nThe baselines are pretty old - IC3Net (2019), aeComm (2021), VQ-VIB (2022). Where are more recent methods? Multi-agent RL is a fast moving field. While I am no expert, it is surprising. \n\n I'm confused about the interpretability claims in general. The agents communicate using discrete symbols (32 bits = presumably 2^32 possible messages?). How do you translate these to natural language for a human? The paper doesn't really explain this. They mention \"cosine similarity matching against a predefined phrase dataset D\" in the appendix but that sounds like nearest-neighbor retrieval which could easily fail for novel messages.\n\nSection 4.5 is confusing and doesn't clearly explain WHY each hyperparameter needs a different strategy\nFigure 2 is way too cluttered, I can barely understand what's happening\nLots of important details are hidden in the appendix (like how the text interface works)\nThe \"dual-environment framework\" mentioned in Section 4.2 is never formalized\nSome claims are overstated - they say the method \"dynamically balances\" objectives but really you just set different weights for different tasks", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)