Inner Speech as Behavior Guides: Steerable Imitation of Diverse Behaviors for Human-AI coordination
Authors: Rakshit Trivedi
Kartik Sharma
David C. Parkes
Venue: NeurIPS 2025 spotlight ()
Keywords: Imitation Learning
Inner Speech
Cognitive Processes
Diverse Behaviors
Steerable Imitation
Human Demonstrations
Human-AI coordination
Primary area: social_and_economic_aspects_of_machine_learning
TL;DR: MIMIC leverages language-based inner speech mechanisms as a mediational framework between perception and action to enable both diverse imitation of human behavior and fine-grained steering of artificial agents during inference time.

Abstract: Effective human-AI coordination requires artificial agents capable of exhibiting and responding to human-like behaviors while adapting to changing contexts. Imitation learning has emerged as one of the prominent approaches to build such agents by training them to mimic human-demonstrated behaviors. However, current methods struggle to capture the inherent diversity and non-Markovian nature of human behavior and  lack the ability to steer behavior at inference time. Drawing inspiration from the theory of human cognitive processes, where inner speech guides action selection before execution, we propose MIMIC (Modeling Inner Motivations for Imitation and Control), a framework that uses language as an internal representation of behavioral intent. MIMIC employs the novel use of vision-language models as linguistic scaffolding to train a conditional variational autoencoder capable of generating inner speech from observations. A diffusion-based behavior cloning policy then selects actions conditioned on  current observations and the generated inner speech. MIMIC enables fine-grained steering of behavior at inference time by conditioning the agent on behavior-specific speech. Experiments across robotic manipulation tasks and human-AI collaboration games demonstrate that MIMIC significantly enhances both behavior diversity and fidelity to human demonstrations while enabling nuanced behavioral steering without training on additional demonstrations.

Inner Speech as Behavior Guides: Steerable Imitation of Diverse Behaviors for Human-AI coordination
Rakshit Trivedi Massachusetts Institute of Technology triverêmit.
Kartik Sharma"
Georgia Institute of Technology
ksartik@gatech, edu
David C. Parkes
Harvard University
parkes harvard.
Abstract
Effective human-Al coordination requires artificial agents capable of exhibiting and responding to human-like behaviors while adapting to changing contexts. Imitation learning has emerged training them to mimic gomman-demonstradd struggle capture the inherent diversity and non-Markovian nature of human be- havior and lack the ability to steer behavior at inference time. Drawing inspiration from the theory human cognitive processes, where inner speech guides action selection before execution, we propose MIMIC (Modeling Inner Morivations for Imitation and Control), framework that uses language as an internal representa- tion of behavioral intent. MIMIC employs the novel use of rision-languaal models as linguistic scaffolding to train a conditional variational autoencoder capable generating inner speech from observations. A diffusion- behavior cloning policy then selects actions conditioned on current observations and the generated inner speech. MIMIC enables fine- aineggrained steering of behavior at inference time by conditioning the agent on behavior-specife speech. Experiments across robotic manipulation tasks and human-Al AI collaboration games demonstrate that MIMIC significantly enhances both behavior diversity and fidelity to human demonstra- tions while enabling nuanced behavioral steering without training additional demonstrations. We open source our code and provide pre-trained MIMIC agents and qualitative demos at: https: /tt:///mmiiiii/ee///////////iia/ research, gi github..e
of the prominent approaches build such agents by
behaviors. However, current methods
Introduction
Human-Al AI collaboration in complex settings requires artificial agents that can anticipate. understand, and appropriately respond to the full spectrum human behavior. This capability appears important in ensuring AI safety and alignment with human values and expectations [5, 7, 28]. One direction towards progress is to develop artificial agents which are able mimic human behavioral patterns. Through in silico surrogates for the richness of human behavior, deployment of AI technologies involving human-Al collaboration Dollabrration-emaliin comprehensive pre- deployment testing and validation across diverse interaction scenarios that would otherwise be impractical or unsafe to assess through direct human participation.
hope to support the safe
"Equal contribution CCorespooning author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Baaas deseedttiion
Imitation learning (IL) presents a promising paradigm for developing these human-like agents by enabling behavioral acquisition di- rectly from demonstrations. However, the ef- fective design of such agents imposes several requirements: (I) capture multimodal distribu- tions of human behaviors reflecting diverse mo- tivations and skill levels; (2) generate contextu- ally appropriate novel behaviors beyond those demonstrated; (3) provide mechanisms for con- trolled behavior generation; (4) process visual inputs characteristic of realistic environments; and (5) operate without requiring environment interactions during training.
Tuisky
Aped
Sknaks
Aeiters
Sennwamed
de Cogaaine
Figure I: Paradigm comparison. (a) direct state- to-action mapping. (b) inner speech m, mediates between perception and action. Extended discus- sion available in Appendix B.1.
In this work, we focus on behavior cloning
(BC) [34], which uses supervised learning to model human demonstrations. Despite its theoret- ical limitations [38], BC offers a simple, efficient, and offline approach to IL that has demonstrated remarkable efficacy across different domains [33]. Recent advances in BC extend beyond standard im- plementations to explicitly address behavioral diversity through the use of transformer models [39, 24] and diffusion- behavior policies [33, 36]. While achieving state-of-art results, these approaches still leave significant room for improvement with respect to distributional realism. Further, these approaches exhibit fundamental constraints in their capabilities: some lack support for controlled generation entirely [39, 33], while others restrict themselves to goal-conditional generation [24, 36). This limitation stands in contrast to our Dbjectiv-enaabling steerable imitation and generation of novel behaviors through designer-specified control at inference time-a more general paradigm that subsumes goal-conditional generation while offering significant flexibility in behavior synthesis.
A typical IL model assumes that the human takes her decision given the state as St ++ 04 and aims to directly approximate this conditional probability distribution aa 8) R pM(a | s). However, this state-to-action mapping overlooks crucial insight from psychological and cognitive science literature [44, 42, 6]: human decisions are influenced by intrinsic motivations and inner speech that mediate between perception and action, even when not explicitly tied to task objectives. Cognitive science research on inner speech [44, 42], conceptualizes inner speech as an internalized form of language that serves mediational mechanism' between environmental perception and action selection. This inner speech provides a cognitive framework that explains how identical environmental stimuli can produce diverse behavioral responses across different individuals. Figure 1 illustrates this paradigm shift: whereas traditional IL treats behavior as direct state-to-action mapping (8, - az), the cognitive upproach introduces inner speech as a mediational layer (s; + m, - az), enabling behavioral diversity through internal deliberation. Based on this theoretical foundation, we posit that effective imitation learning should model both the conditional action policy PH (a s, m) and the inner speech generation process Pa (m s) to better capture how humans act in a given scenario:
Proposition (Imitating with Inner Speech m). Instead of directly learning the human action distribution conditioned on the environment's 's state, To(a s) = pH(a 8), we propose to model human behavior through inner speech mediation: P=(a 8) = pPa( 8, s.m))p(m s)dm. Here, inner speech m is: (1) an internal representation that mediates task performance while potentially encompassing broader motivations, (2) represented in natural language, and (3) internal to the agent and not directly observable from demonstrations.
To this end, we formalize "inner speech as behavior guides", a computational framework building on Vygotsky's mediational theory [44] and Sokolov's empirical observations [42]. We then introduce MIMIC (Modeling Inner Motivations for Imitation and Control). a novel imitation framework that operationalizes this theoretical foundation through three key components: (1) an inner speech
'A mediational mechanism is cognitive process that transforms environmental stimuli into behavioral responses through an intermediate representation. In this framework, inner speech serves as the intermediate layer that interprets observations before determining actions.
"W note that MIMIC draws computational inspiration from cognitive theory rather than claiming biological plausibility. Similar to how convolutional networks leverage principles of visual processing without replicating neural mechanisms, our framework operationalizes functional properties of inner speech through computational architectures without asserting neurobiological correspondence.

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conditioned behavior cloner implemented using conditional diffusion- policy; (2) behavior guided inner speech generator utilizing conditional variational autoencoder (CVAE) that captures the stochastic and semantically condensed nature of inner speech; and (3) vision-language model serving as linguistic scaffolding that provides external language input to train the inner speech generator. During simulation, the agent generates its own inner speech based on ongoing behavior and uses this to condition its policy. MIMIC enables designer control through two mechanisms: (i) accepting textual descriptions of desired behavior as initial inner speech, with the agent continuing to generate its own inner speech as needed, and (ii) allowing designers to set a polling window that determines inner speech generation frequency, thereby serving the dual purpose of finetuning the amount of inner speech and facilitating behavior corrections at regular intervals a feature particularly valuable in addressing behavior cloning's ' brittleness.
We evaluate MIMIC's efficacy across three dimensions: (i) fidelity in imitating diverse behaviors, (ii) capability for steerable behavior generation during simulation, and (iii) performance enhancement in human-Al collaborative contexts. For the first two dimensions, we conduct experiments on the D3IL benchmark dataset [21], which encompasses diverse robotic manipulation tasks. Our results demonstrate that MIMIC surpasses state-of-the-art BC approaches in generating human-like behaviors, achieving higher entropy, higher fidelity and often higher success rates in its generated trajectories while enabling designer-specifien control over behavior generation. Next, we consider the Overcooked environment in a 2-player setting [5], where MIMIC agents evaluated in collaboration with human proxy models achieve consistently higher cooperative rewards than agents trained using other BC approaches. We substantiate these empirical findings with comprehensive qualitative analysis and architectural ablation studies. Our results highlight the potential of this inner speech based approach to create adaptive, human-like agents that act as effective human surrogates, facilitating the safe development and evaluation of Al agents before their deployment among humans.
2 Preliminaries and Related Work
Problem Setup. Let (S, A, P, r, 7, define a Markov Decision Process (MDP), where S and A represent state and action spaces, P : S x A x S + R is the transition probability distribution, r denotes the reward function, v: S IR is the initial state distribution, and 7 E (0, 1) is the discount factor. A stochastic behavior policy क. S x A + [0, 1] defines an agent's action selection.
Imitation learning focuses learning policy directly from demonstrations without access to reward signals. We consider expert policy gg as a mixture of experts { ...... represent- ing diverse tasks, space specializations, behavioral variations. A demonstration trajectory T' = {(5)) ab),.., ..., (shy, }} records a sequence of state-action pairs. Given dataset 'D = {r') of N trajectories, behavior cloning (BC) applies supervised learning over state-action pairs, maxi- r=1 log ne (uf]ss). Our technical approach employs diffusion models and conditional variational autoencoders (CVAEs) and refer interested readers to Appendix D for a background discussion on these models.
mizing action likelihood: LBC = maxe Σ Σ |Td
Imitation Learning. Recent advances in language-based based interfaces for IL include Thought cloning (TC) [18], which directly imitates human thoughts but differs from our method in that it requires access to annotation of actual human thought for each step in the demonstration trajectory. Further, its performance is highly tied with goal (mission) conditioning and degrades by almost 40% in our experiments once the goal (mission) condition is removed. Similarly, external speech has also been used to steer agent behaviors through action re-ranking [30] although with mechanisms that remain external to the agent. Closest prior is [46] that use "intra-agent speech" as cuni-supereieed captioning that is frozen and used as auxiliary supervision during BC to achieve zero-shot object-level generalization. We instead treat inner speech as a latent mediator that conditions the policy and is generated online, enabling steerable, diverse imitation.
Lunguage-Interfacee
AI Approaches to Modeling Cognitive Processes. Recent AI research has explored computational implementations of cognitive processes. [6] propose autotelic A/ which internalizes language for self- directed learning, emphasizing language as tool for goal generation and intrinsic motivation. While sharing our interest in cognitive foundations, autotelic systems focus on autonomous learning rather than behavioral diversity, using language primarily for goal generation rather than as mediational mechanism. [19] utilize large language models to simulate reasoning processes (chain of thought) before action selection, implementing serial, deterministic reasoning rather than the stochastic,
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parallel processing characteristic of inner speech in Vygotskian theory. Our framework models inner speech probabilistic process that generates diverse behavioral patterns, more closely aligning with cognitive theories of human behavioral diversity. [4] propose natural language as a latent space for reinforcement learning, using language to give a hierarchical structure behavior. While this approach shares our recognition of language as a cognitive tool, it focuses on decomposing complex tasks rather than generating behavioral diversity. Our framework uniquely combines the stochastic nature of inner speech with IL to capture human behavioral variation without explicit linguistic supervision. An extended related work discussion available in Appendix C.
3 MIMIC: Inner Speech as Behavior Guides
As discussed in Section I, existing IL techniques [18, 30, 39, 24, 46, 43] do not satisfy Proposition I, and struggle to fully capture the noisy, diverse, and non-Markovian nature human behavior. Here, we first present a theoretical formulation that connects inner speech and behavioral diversity, grounded in Vygotsky's 's characterization of inner speech as a mediational mechanism. We then present MIMIC, novel imitation framework that operationalizes this theoretical model to enable agents to generate inner speech and condition their action selection on these representations. Our framework enables steerable generation of diverse behaviors: by conditioning on user-specified speech and generating its own inner speech representations, the agent can adopt corresponding behavioral modes.
3.1
Theoretical Formulation of Inner Speech
The theoretical framework conceptualizes inner speech as a mediational mechanism for capturing behavioral diversity in imitation learning.
3.1.1
Inner Speech as Generative Mediating Process
Drawing from Vygotsky's s [44] theoretical characterization of inner speech as a cognitive mediator between perception and action, we formalize inner speech stochastic process that transforms environmental observations into linguistic representations before generating behavior. For an agent operating in environment ε, we define: S as the state space, representing environmental percepts; A as the action space, encompassing possible behavioral responses; Z as the inner speech representation space, latent embedding of verbalized cognition; fo S - Z as the inner speech generation function; and go S x Z + A as the action policy conditioned on observation and inner speech.
Vygotsky identified following structural properties characterizing inner speech that inform our computational design: (i) Predicativity: Inner speech emphasizes relationships and actions rather than entities. For example, an agent internally represents "moving left to coordinate" rather than "the box is on the left". (ii) Semantic Condensation: Complex strategic meanings compress into compact representations while preserving functional significance, e.g. multi-step coordination strategy becomes a brief internal directive. (iii) Regulatory Dynamics: As Vygotsky conceptualized inner speech as enabling organization of ceanvviorrrruultinng responses over time-we formalize this as temporal regulatory dynamics. Inner speech generation operates over extended timescales, conditioned on behavioral history rather than instantancous state, reflecting that strategic processing integrates information across temporal windows. This is consistent with empirical observations that inner speech intensifies during complex cognitive tasks [42]. These properties indicate that inner speech operates as a compressed, semantically enriched representation- reduced in dimensionality relative to perceptual input, yet preserving task-relevant strategic information and temporal context.
3.1.2 Mathematical Formulation
We formalize inner speech as a stochastic mediational process grounded in Vygotsky's 's theoretical characterization. The agent's policy decomposes through inner speech as a latent mediator:
p(a s) = -f p(a|s, m) m)p(mlssddm
(1)
where 8 E S is the environmental state, m E represents the inner speech embedding, [m]]s) generates inner speech from observations, and p(a]s, m) produces actions conditioned on both state and inner speech. This stochastic formulation captures behavioral diversity: different inner speech representations lead to different behavioral modes even when facing identical environmental states.

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We now provide mathematical formalizations of the three structural properties characterized above, showing how each property shapes the computational structure of inner speech generation.
Predicativity: Relational Structure Extraction. The emphasis on relationships over entities is formalized through relational encoding that generates inner speech distributions: p(m, (H,) fo(Hi; 1lrt), where Whel denotes parameters that prioritize relational features in the behavioral history H4. This ensures inner speech captures strategic patterns.
Information- -oooomattio--hoortiiii Formulation. The compression of complex meanings into compact representations is formalized through the information bottleneck framework: maxm [m; H4) - 5 DKL (p(m) (H)) [p(mm))) where [(m; Hi) measures mutual information be- tween inner speech and behavioral history, Dx (p(m)HH, (H4)) (p))))(m) enforces compression toward simple prior, and & controls the trade-off. Higher /3 enforces stronger compression, modeling the progression from elaborate external descriptions toward abbreviated mature inner speech.
Semantic Condensation:
Temporal Regulatory Dynamics. We formalize this through non-Markovian conditioning on behavioral history: pmm, H4), where H. (St-Wit, (t-W==13 where H. represents a window of length W encompassing recent states st and actions a- This formulation reflects that strategic processing emerges from accumulated experience rather than instantaneous perception.
3.1.3 Architectural Correspondence
We now describe how our computational architecture operationalizes the theoretical formulation presented above. Our framework explicitly implements predicativity, semantic condensation, and temporal regulatory dynamics through dedicated architectural components.
Transformer Attention as Predicative Processing. The emphasis relational structures is imple- mented through the transformer's 's multi-head attention mechanism that processes inner speech along-
side state and action representations: Attention Q, K, V) = softmax QK"
V, where Q, K, are vag
derived from the concatenated representation (,, Zm; Ra 2,] encoding state, inner speech, action history, and temporal information respectively. The cross-attention mechanism naturally focuses on relationships between inner speech zm and behavioral context, capturing task-relevant strategic patterns ("how to coordinate") rather than merely encoding object features ("what is present").
Variational Compression for Semantic Condensation. The Conditional Variational Autoencoder architecture directly instantiates the information bottleneck formulation. The encoder compresses behavioral history into latent bottleneck (2((4) = (Nss (HL), o (H,)), from which inner 4o (=)))) and decoded via py [m[]2, H₂) V dece (2, H4). The vari-
speech codes sampled ational objective realizes the information-theone
tradc-off: Lis = Eg (=/ [log pe [m|2, H:)] H.) corresponds to maximiz-
pa (m|] 3Dxr (go (=)H+))lp(())) where the reconstruction term [o ing [mm; H.) (preserving behavioral relevance), while the KL divergence term enforces compression toward a simple prior p(z). The annealing parameter 3 models the progressive shift from flexible external linguistic structure compressed autonomous inner speech generation.
Periodic Generation as Temporal Regulation. The Mar-Maakvi temporal structure is imple- mented through periodic inner speech generation fixed intervals: m, V e (Hee, He) if t mod W = O. and m = m otherwise. This W-st -tep update cycle captures the intermittent nature of strategic processing. The agent generates new inner speech every W steps based accumulated behavioral history, then conditions its actions on this inner speech until the next update.
3.2 Learning Inner Speech and Behavior Generation from Demonstrations
We now describe how MIMIC learns these components from demonstrations. The framework consists of two key elements- an inner speech-conditionel behavior cloner and rehavior-condition inner speech implemeetiiinl the theoretical formulation presented above (c.f. 2).
3.2.1 Inner Speech-condiiionee Behavior Cloner
Following Proposition 1, we learn the space of human actions in the presence of inner speech
m. Given dataset of demonstrations D = {(s a (1) | [1, [1,n]], we augment it with initial speech (*) to obtain PM {(m
| [1,7],i e [1,n]]. We then train an
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parameters
Activduuing training
during
Behavior-conditlone Inner speech generator
Human demonstrations
Inner speech-conditioedd
Behavior cloner
Discriminative
State
a
a'
desce plicess
Pre-traiied
sequence images
Environment
21
22
2
22
Predicted
OVAE decoder
State
Action
inner speach
DDPM
DDPM
DDPM
DDPM
NIO,
Inner
speech
OOVE: Cond toral Varational Autcencoder
'ODPM: Denoising Diffusion Probab listic Mode.
Figure 2: Overview of MIMIC: Agent inner speech is scaffolded by using a pre-traines VLM to discriminate different behaviors in human demonstrations, Next, we train DDPM-T (diffusion policy with transformer architecture) behavior cloner conditioned on this inner speech and a VAE-based inner speech generator conditioned on the history states. During simulation, the inner speech is periodically generated to influence the behavior cloner given the actions it generated in the past.
imitator (a m.) that models the probability of an action given the environment's state and the inner speech m of the agent. We implement the action policy go in the theoretical framework using diffissin--bat -based policy with transformer architecture (DDPM-T) [33]. This architecture trains transformer-basee based conditional denoising network ég (ā,, 8, m, T) to predict noise added to the action å at step given the current state and inner speech m. The diffusion process follows:
Forward diffusion:
a,+1 ~ NN(1 ,,,,,111
1IT))
m),, Reverse diffusion: å, N (T- (ār+1 1, B+1)
We train this network using a reconstruction Fisher divergence loss: (an as) = D.
m, - el/
(3)
The network is trained using classifier-free guidance, where m is randomly replaced with with probability during training [16]. This enables the model to operate both with and without inner speech conditioning. is transformer architecture that can take inputs of arbitrary size. We first encode the inputs m. å, and т using different encoders and then concatenate the representations Zy. Zm, Za, and z, together before passing into the transformer. The state is encoded using domain-specift encoders such as vision- based convolutional encoder for a vision environment or locomotion- busedd featufe encoder for a vision-free environment. Inner speech m will provided as latent representations in the natural language space and use trainable 2-layer MLP to obtain Z.n- and z, are obtained using standard linear encoding and cosine-based encoding, respectively.
3.2.2 Secaaior-Conditionee Inner Speech Generator
This component instantiates the inner speech generation function fo S - Z. We implement using conditional variational autoencoder (CVAE) architecture capture the stochastic and semantically condensed nature of inner speech. The variational nature of this implementation models the probabilistic nature of cognitive regulatgt,,,,,, allowing for the generation of diverse yet contextually appropriate inner speech representations.
Algorithm (Appendix A): Training and Vision-Language Model Scaffolding.
The training process for the inner speech generator implements a learning-based internalization of linguistic structure. Central to this process is the use of vision-language models (VLMMs) to provide external linguistic scaffolding. The VLMs generate initial descriptive characterizations of demonstrated behaviors, providing explicit linguistic structure that serves as training targets for the CVAE. We model the agent's 's inner speech m as linguistic descriptions of behavior. For each
demonstration in our dataset, we obtain sequence of T images converted into a GIF where the task horizon. We then use a VLM to generate descriptive external speech that characterizes the behavior by passing k=8 randomly picked GIFs with the prompt:
(I(2) I ....
... I(").. which are

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Innerse yourself the role of the (agent) that has enacted the actions the attached GIFs. (environment). Cenerate your inner thought process that helps dascribe the diatinctive behaviora shown the each of the GIF. generata those phrasea that differentiate the behavior adopted in different CIPs. YOU MIST return the thought proceaa each GIF in new 1ine.
This process generates linguistic descriptions e(t) of each demonstration's behavior, which we en- code using the CLIP [35] embedding model to ob- tain m Figure 3 shows the latent space of inner speech obtained from demonstrations, exemplify- ing the diversity captured by inner speech. These external descriptions serve as initial scaffolding that the model learns to compress and internalize, implementing the semantic condensation principle.
Figure 3: TSNE visualization of inner speech for Aligning dataset. (c.f. Appendix G for others.)
By using pre-trained VLMs, we leverage their rich linguistic knowledge to generate descriptions that capture behaviorally-reeevaa attributes and strategic patterns. Critically, this scaffolding approach circumvents the need for explicit human annotation of thought during demonstrations, making MIMIC more scalable and broadly applicable than approaches requiring verbalized thoughts each step [18].
For computational efficiency, we train a specific generative model V to generate realistic inner speech by only looking at a limited historical behavior. We use conditional variational auto-encoder (CVAE), where both encoder Vp Venc and decoder V are conditioned on the past image sequence, which is encoded by pooling over the convolutional representations. The CVAE is trained to generate the CLIP-encoded inner speech generated by the oracle VLM by minimizing the loss function:
T
(Dxr))
-
(6)
Veec (VeneVenn (m
W
w.) [ B AAx (Veno ( ((.,,)) (4)
i=1 I=W
where Axr. denotes the re-parameterized KL divergence and W denotes fixed window size. The regularization parameter 3 is annealed during training as described in the architectural correspondence, modeling the progressive shift from flexible external linguistic structure compressed autonomous inner speech generation. This loss is independent of the diffusion policy and can be trained in decoupled manner. Through this end-to-end optimization, the CVAE learns to fuse perceptual features, strategic intentions, and temporal context into compact representations a process that connects to Vygotsky's concept of agglutination, where multiple semantic components bind into unified cognitive structures.
This training procedure implements learning- internalization process inspired by Vygotsky's theory of internalization. The VL.M-generated descriptions serve as initial external linguistic targets, providing explicit structure that guides early training. Through continued training, the CVAE learns to autonomously generate compressed inner speech representations from behavioral patterns alone, implementing the structural transformation from elaborate external descriptions the compact autonomous representations characteristic of mature inner speech.
Algorithm 2 (Appendix A): Simulation. During simulation, the agent must generate its inner speech based on its ongoing behavior. To enable this, we use the CVAE decoder dee to generate the inner speech given the images of the past actions. We employ the W-step update cycle described in Section 3.1.3 for this purpose. We also wait for lo timesteps before the first generation. Thus, starting from null speech of every W timesteps starting from / t= lo- The framework enables explicit control over agent behavior through linguistic prompts, realizing the linguistic controllability aspect of the theoretical framework. By providing a natural language description B of the desired behavior, we override the initial inner speech from m +- to B. The agent then continues its periodic updates to maintain consistency with the trained inner motivation space.
0, a new inner speech is generated periodically using V after
Experiments
4.1 Setup
Datasets. We use three robotic control environments from the D3II. benchmark [21]: Aligning, Sorting, and Stacking. These datasets include images taken from top camera and an in-hand camera
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during the demonstration. For the human-Al coordination task, we use the Overcooked dataset [5] in three different layouts: Cramped room, Coordination ring, and Asymmetric Advantages. These include two agents (one with green and one with blue hat). Both vision-based observations (de- noted as " "-Vision") and feature-based observations are considered for the evaluation. For simulation, we consider the standard rollouts and trajectories in the D3IL benchmark, while for Overcooked we simulate 100 games to evaluate our agent.
Methods. We denote the proposed method as MIMIC while the state-of-art baseline behavior cloner denoted BC. Each uses the same diffusion- based architecture of DDPM-T for behavior policy.
Implementation. We use the Adam optimizer [22] with the learning rate tuned for each dataset (following Jia al. [21]) to train both our CVAE-based inner speech generator and the diffusion- based behavior cloner. The other hyperparameters are searched for the best performance, with first update step to {0. 1, W/2, W -1 }} and update window W {1, 10, 20, 50, 100} for low horizon tasks and W E (100, 200, 300} for higher ones. The optimal random dropout probability for the diffusion model training is found to be optimal E 0.13 depending on task, while the parameter 3 = 0.1 was found to be optimal through tuning for training the CVAE. To obtain the inner speech from the VLM, we fix the batch size of inner speech B 8. Further details on the training, computation, and other experimental setup can be found in Appendix E.
the standard metries of the D3IL benchmark during simulation, i.e., success rate or proportion of successful completions of the task, and behavioral entropy comparing the entropy of simulations with a categorical distribution of behavior descriptors. Higher values indicate accurate learning of the successful and diverse behaviors shown human demonstrations. To assess our objective of high fidelity imitation, also report the mean distance from the end- nnd-point in the Aligning task, and the Wasserstein distance between the generated and training state and completion time distributions wherever feasible, following [33]. For Overcooked, we evaluate the performance using the mean collective reward obtained when the trained agent plays along with a proxy human agent and Wasserstein distance between the discrete actions.
Metrics. For control tasks,
4.2 Results
4.2.1
Does MIMIC achieve high fidelity imitation of diverse behaviors on D3IL benchmark?
Table Comparison of MIMIC against BC with the DDPM-T architecture on the D3IL benchmark. "wass' denotes Wasserstein Distance metrics for the non- non-vision environments (infeasible in case of vision) where state Wasserstein distance is calculated using random rollouts.
Environment
Model
Success rale
Distance |
Entropy
State-wass
Time-wass 4
Aligning
BC
0.6645
0.1105
0.4743
0.6961
59.034
MIMIC-S
0.8021
0.0664
0.4184
0.0459
50.569
MIMIC-E
0.7229
0.0847
0.6148
0.0492
45.397
Aligning- Vision
BC
0.1833
0.1875
0.0895
MIMIC-S
0,2229
0,1885
0.0849
MIMIC-E
0.2083
0.1849
0.1473
Sorting-
Sorting-Vision Vision
BC
0.7972
0.3596
MIMIC-S
0.8417
0.3719
MIMIC-E
0.8083
0.4499
box 2 box
box 2 box / box
Stacking
BC MIMIC-S MIMIC-E
0.8027 / 00.4879
0.2058 0.1503 0.1049
9.43
336.51
0.8129 / 0.6074
0.1774 0.0737 0.0394
0.75
345.14 144
0.8213/ 0.5333
0.2115 0.1556 / 0.0878
13.69
336.51
Table shows that MIMIC is superior at generating human-like behaviors across four different benchmark D3IL environments, achieving higher entropy and higher success rate in its generated trajectories than the state-of-the-art DDPM--Tbbaed behavioral cloner. We consider two different variants of MIMIC: MIMIC-S and MIMIC-E, denoting the combination of hyperparameters that gives the highest success rate and highest entropy, respectively. In almost all cases, both variants of MIMIC improve the performance over the BC model. We improve significantly in the Aligning task, while in
'ee provide comparisons with additional behavior cloning approaches such as BESO [36] and BeT [39] in Appendix G.
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Success
Entrpy
Entropy
05
42-
0.
62
61
HIMIC
Randoon
kmanns
CLPS
CUIRE
MIMET
aPr44
akmini
Pwem
amrd
GPT UFT-p-mini
démim
Qownn
(a) Inner speech ablation
(b) Embeddings
(c) VLM MIMIC-S
(d) VLM MIMICEE
Figure 4: Effect on Success Rate and Entropy performance of MIMIC on changing various com-
ponents on the Aligning dataset: (a) removing inner speech during simulation, (b) changing the embedding model, (c-d) using different VI.Ms for training and scaffolding.
the more complex Stacking task, MIMIC-E achieves the best success rates and entropy for and 2 boxes. increasing the success rate by substantially. BC gives competitive values of entropy for the 3rd box, perhaps due to the actions being random since the success rate for the two boxes remains low. The gains of MIMIC remain consistent even in environments with vision-based observations, indicating that useful supplementary information is provided through inner speech. We further validate MIMIC's effectiveness in capturing diverse human behavior with high fidelity, showing significantly lower Wasserstein distance between the generated and training distributions of the state and completion time distributions (following [33]) in both aligning and stacking environments.
4.2.2 Do MIMIC agents serve effective in silico human surrogates, achieving successful coordination when evaluated with human proxy models in collaborative tasks?
Table 2: Comparison MIMIC against BC with DDPM-T on the Overcooked environments.
Table 2 shows that MIMIC significantly improves the collective reward achieved in three different settings of the Overcooked environment. The total reward achieved by the actions of MIMIC with the human agent is significantly higher than BC with the human agent in all cases, achieving an increase of up to 30. These results highlight the impact of using inner speech in improving human-Al coordination and demonstrate that modeling the inner speech of the agent consistently improves performance when evaluated against human proxy models. Additional results are provided in Appendix G.
Environment
Model
Collective reward
Cramped
BC
115.8 3.86
room
MIMIC
151.8 + 2.45
Cramped room- Visi-Vision
BC
73.6 + 6.18
MIMIC
108.8 4.84
Coordination
BC
113.0 2.21
ring
MIMIC
121.0 1.93
Asymmetric advantages
BC
215.8 + 3.04
MIMIC
227.6 2.69
4.2.3 How does the performance of MIMIC vary with different components?
Ablation on inner speech. To validate the importance of language as inner speech we compare MIMIC with other forms of inner speech such as completely random vector and clustered vector of the training trajectories. For clustering. we employ a K means algorithm with K and learn CVAE to generate the mean cluster representation for each action in the training set. These inner speech generators are used directly during simulation as described above. Figure 4a shows that MIMIC is significantly better than other formulations, with the worst being the K-means algorithm and random more effective than the hase BC model.
Embeddings. Figure 4b shows the effect of changing the CLIP model to encode the linguistic deseriptions with text-only MPNET model 4, We find that MIMIC-S with CLIP outperforms the MPNET variant in success rate, while MIMIC-E with CLIP outperforms it in entropy. This shows that a shared lasion-language representation space is useful to obtain effective inner speech.
http:::://hugggnggacccco///eeeee htt.reeeeeaaastttssslllllle--eee v2

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Table 3: GPT-40 evaluation.
+ denotes no update.
t
W
Success rale
Entropy
GPT 4o Eval (1-5)
Top
Inhand
Training conditions
0.6083
0.4690
4.03 + 0.63
4,05 + 0.51
10
20
0.7417
0.3815
3,98 = 0.68
3.98 + 0.81
49
50
0.6042
0,5156
3,88 + 0.72
4.05 +: 0.71
Valdation conditions
0.6687
0.5787
3,83 = 0.69
4,40 + 0.49
10
20
0,6479
0.2949
3.94 = 0.64
4.17 +: 0.37
Grip the box from the right and approach from diagonal top- rotate it counter-cloekwise to right angle, requiring swift rota- tion to align
0.7250
0.6357
4.30 0.46
achieve alignment
Figure 5: Conditional behavior generation. Figures in the right panel show generated behaviors for the specified condition as inner speech with no subsequent updates.
Vision-language model. We study the effect of changing the VLM from GPT-4o to 04-mini, 4o-mini, and Qwen (Qwen2.5-VLL722-Innntit Ins-tuutt.... Figures 4c and 4d show that GPT-4o gives the best success rate compared with other VLMs, followed closely by the open-source Qwen model. Surprisingly, we find 04-mini to lack in success rate even though its descriptions lead to the most diversity (highest entropy). We believe this is due to the CLIP model failing to distinguish the nuanced behaviors generated using 04-mini. More importantly, we find that in all cases, MIMIC outperforms the BC model in success rate and often increases the base entropy in all cases except Qwen. This is likely due to a lack of diversity in Qwen generated descriptions of the dataset.
4.2.4 Can MIMIC be used to generate desired behavior, thereby enabling steerable imitation?
We study the effectiveness of MIMIC in generating desired behaviors. Here, we consider using the behavioral descriptions generated by the VL. for the training and validation sets as the desired conditions. Then, we follow Section 3 to give the desired condition as the initial inner speech and generate successful behaviors that can match the desired description. We then use GPT-4o as a judge to evaluate how well the generated trajectory matches the desired description (full prompt provided in Appendix E). Table 3 shows that MIMIC can be used to generate successful and controllable behaviors in the Aligning dataset. Using three different combinations of simulation parameters, find that MIMIC generates highly successful and desired trajectories for both training and validation descriptions as conditions. Generating training behaviors is likely to need mediation since behaviors are already conditionally captured, so no updates work best for training, while periodic updates help in validation. Figure further shows examples of generated behavior with periodic updates. In the first case, we note how it grips the box from the right side and keeps rotating it to follow the condition, while in the second case, we find an approach from diagonal top-right angle for alignment. More examples are provided in Appendix G.
5 Conclusion
This paper introduces MIMIC, a framework that bridges cognitive science and imitation learning by operationalizing the theory of inner speech as mediational mechanism between perception and action. By formalizing inner speech as behavior guide, we address fundamental limitations in conventional behavior cloning approaches, which attempt to directly map states to actions. The empirical results validate the theoretical proposition that language-basee internal representations enable more faithful modeling of human decision-making, shown here to achieve superior imitation fidelity with higher entropy and success rates while enabling designer-spectnn discussion future directions, limitations, and the broader societal impact of our approach. Our findings establish the theoretical and practical viability of inner speech mechanisms as a computational foundation for imitation learning systems that can simultancously exhibit behavioral richness and remain controllable. This research further opens significant avenues for investigation on potentially transforming how AI systems internalize human-like decision processes while establishing new essential
control. Appendix B provides an extended
research trajectories in language-mediated mediated control and multi-agent collaboration- groundwork for systems that reliably collaborate across the full spectrum of human behaviors.
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Question: Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods this research? Note that if the LLM used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality the research, declaration is not required.
Answer: [Yes]
Justification: The core method development in this research involves novel use of Vision- Language models and the corresponding prompts are clearly written (Section 3.2.2).
Guidelines:
The answer NA
that the method development in this research does involve LLMs as any important, original, or non-standard components.
Please refer to our LLM policy (https: : http://een..../eeee.e http://uurrs..ceeoeeeeeeeeeeeeee wwt.c/ooeeeeeccce//25//////////e for what should or should be described.
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Supplementary Material for Inner Speech as Behavior Guides
A
Algorithms for MIMIC Training and Simulation
This section provides the pseudocode for the algorithms described in Section 3.2.2.
Algorithm MIMIC: Training
Algorithm MIMIC: Simulation
Require: Set of human demonstrations D, Batch
Require: Initial state 8 of the environment, First update step lo, and update window W
size B, Hyperparameters for training
1: Obtain demonstration E DD as images (()
1: Inner speech m 4- 0
2: Inner speech (m(()) ...m((BB)) <- CLIP
1:2"
for 1. = T do
(VLM (II
1: D)).
3:
it't (mod W) = to then
1:7>
Past images I--wir from St-Wit Sample z ~ 10,
3: Construct DM by augmenting {m ( D.
5:
4: Train Te, V to minimize Laitt (DM) [Eq. 3]
Update m
Ic
and Lix (D [Eq. 4] respectively.
7:
end if
Generate the action at ~ To(: st. m)
9: Update the state 8t-1 +- E(si,
10: end for
B Discussions
B.1 Inner Speech Behavior Guide
MIMIC provides an alternative to the conventional behaviorist framework IL the form of mediated action selection framework that is grounded in cognitive science. The fundamental distinction between the behaviorist and cogmitive approaches to IL, as illustrated in Figure 6, represents more than technical architectural choice. It reflects competing theories of how intelligent behavior emerges.
The behaviorist paradigm (left panel) conceptualizes human action direct responses to environ- mental stimuli, where an agent learns a mapping function from states to actions (ss . 0z). This approach, while computationally elegant, treats the human mind a black box, assuming that behavioral patterns can be fully captured through observed input-output pairs. contrast, cognitive approach (right panel) recognizes that human actions mediated by internal mental pro- cesses-wiie cognitive science literature terms 'inner speech,' internalized linguistic structures that guide behavior. Here, the same environmental state can produce diverse actions because it is filtered through an intermediate cognitive layer (&t + my + az), where m represents the inner dialogue that shapes interpretation and response selection. This mediational architecture explains fundamental observation about human behavior: why different individuals, or even the same individual at different moments, can respond differently to identical situations.
In this way, the cognitive model seeks to eapture not just what humans do but to also approximates how they deliberate, through internal linguistic reasoning that weighs options, considers context, and reflects individual motivations. The computational instantiation of this cognitive framework leverages the latent space of inner speech principled mechanism for both behavioral diversity and designer control. The continuous latent representation m enables stochastic sampling during inference, naturally inducing behavioral variability that mirrors human decision-mas making heterogeneity, while simultaneously providing a semantic interface for control- designess can specify desired behaviors through natural language that constrains the latent distribution. Crucially, the vision- lision-language model (VLM) shown Figure serves as developmental scaffolding, transforming visual observations into external linguistic descriptions that bootstrap the inner speech generator during training.
For artificial agents intended to collaborate with humans, the distinction between these two frame- works is critical: while behaviorist approaches may achieve high task performance, they fail to
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Human demonstrations
Human demonstrations
Training
Training
Agent
seurce feg: socrllltt
syroch
Agent
Coguitine tnols
Bootstrap
Stimulus
Response
Stimulus
Response
gonerator
Inner speedh
D-30
Observations
Actions
Observations
Actions
Environment
Environment
(a) Behaviorist framework: Direct stimulus-rosponse mapping
(b) Cognitive framework: tion selection
Linguistically-mddiane mediated ac-
Figure 6: Contrasting theoretical frameworks for IL. (a) The behaviorist approach models human behavior as a direct mapping from environmental states to actions (s; * az), treating cognitive processes as opaque transformations. (b) The cognitive approach instantiated by MIMIC introduces
inner speech as a mediational layer (s; - my - 0z), where my represents linguistically-streeti internal deliberation that enables behavioral diversity and contextual adaptation.
Approach
Behavioral Diversity
Designer Control
Speech Type
Language Grounding
Language Annotations Required
Conditioning
Space
Type
Behavior Transformer [39]
(discrete modes)
N/A
X
No
Unconditional
Diffusion BC (33)
(continuous)
N/A
x
No
(implicit)
Unconditional
BESO [36]
(purtial)'
(goals only)
External
(goals)
(gouls)
Goal-cooditioned
Thought Cloning [18)
External
(houghts)
(per-step)
Unconditional
MIMIC (Ours)
(stochastic)
(goncral)
Internal
Cinner speach)
(CVAE)
linguistic
General
Table 4: Comparative analysis of IL approaches across key dimensions of behavioral modeling and control. Legend: / Full support; x = No support. Annotations: 'Generates diversity through diffusion but only within goal-constrained trajectories. "Conditions fixed human-provided thoughts, limiting emergent diversity. *Uses linguistic signals that remain external annotations rather than internally generated mediators. "Bootstraps from visual observations using VL.M-generated captions, eliminating the need for human language annotations.
generate the behavioral diversity and contextual adaptability that characterize human partners, lim- iting their effectiveness in real-world collaborative scenarios where understanding and predicting varied human responses is essential. Table 4 distills MIMIC's unique position as the only approach that achieves language-grounded control without requiring exhaustive human linguistic supervision, instead leveraging lasion-language models to automatically generate the necessary training signals from existing visual demonstrations.
B.2 Limitations and Future Extensions
While MIMIC represents significant advance in rant consideration for robust deployment across diverse contexts. First, the fidelity of inner speech generation remains contingent upon the quality of linguistic annotations produced by vision-language
II, several limitations war-
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models during training, creating dependency where advances in behavioral modeling are par- tially gated by progress in vision-language understanding- thougg notably, improvements in VLM capabilities will naturally enhance MIMIC's performance without architectural modifications.
Second, the temporal granularity of inner speech generation, controlled through the W parameter, requires careful calibration for different task domains, as excessive polling may induce behavioral instability through frequent re-planning while insufficient polling reduces the framework's capacity to correct for distributional drift.
Furthermore, while the CVAE's latent representation enables stochastic behavioral generation, the mapping between latent codes and semantic content remains opaque; post-hoc clustering or CLIP- based projection to natural language recovers partial interpretability but potentially loses nuanced behavioral intentions encoded in the continuous space.
Finally, the framework's s efficacy in complex multi-agent environments with heterogeneous behavioral patterns remains unexplored, and coordination complexity may scale non-linearly with agent count in scenarios beyond dyadic interaction.
Future directions could include exploring semi-supervised approaches leveraging limited human annotations to calibrate VLM -LM-ggnerated captions so as to mitigate data quality dependencies, po- tentially through active learning frameworks that identify high- high-uncertainty trajectories for targeted human review. Adaptive polling mechanisms that dynamically adjust temporal granularity based on task complexity or behavioral uncertainty metrics would provide more robust default behaviors while reducing practitioner burden. Developing disentangled latent representations or incorporating discrete latent variables with explicit semantic grounding could enhance interpretability without sacrificing behavioral diversity. For multi-agent scalability, hierarchical inner speech architectures that model group-level intentions alongside individual cognition present a promising direction, enabling agents to reason about collective dynamics while maintaining individual behavioral authenticity.
B.3 Beyond diffusion based behavior policy
While our current implementation employs a diffusion-based behavior policy (DDPM-T), the MIMIC
framework is fundamentally model-agnostic. The core insight-that inner speech serves as a stochas- tic mediator between perception and action-can be instantiated with any conditional behavior cloning architecture. The framework decomposes into two independent components: (1) inner speech generator pmm (mm(H)) that produces linguistic representations from behavioral history, and (2) a behavior policy p(a)s s, m) that conditions these representations. This modular design means the behavior policy can be implemented using transformers (e.g.. Behavior Transformer), flow-hased models, energy-based models, even standard supervised learning approaches any architecture capable of conditional generation.
The key requirement is that the base model accepts an additional conditioning signal. Since inner speech is represented as a continuous embedding m E Z, can be naturally incorporated through concatenation with state features, cross-attention mechanisms, FiLM conditioning, or additive condi- tioning depending on the architecture.
The periodic generation mechanism (parameter W) is similarly ecchtectuur-agnosiie as it operates the simulation level rather than within the model architecture. Any autoregressive policy can maintain fixed inner speech representation for W steps before regenerating, making this general inference-time control mechanism applicable across model families. Future work could explore this architectural flexibility to identify optimal policy architectures for different task domains while maintaining the core inner speech framework.
B.4 Cognitive Inspiration vs. Biological Plausibility
Our framework draws computational inspiration from cognitive theory without claiming biological fidelity or neurobiological correspondence. This distinction is crucial: we operationalize functional properties from cognitive theories of inner ral regulation- computational mechanisms (CVAF, transformer attention, diffusion policy) rather than attempting to replicate neural substrates.
condensation, predicativity, and tempo-
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This approach follows productive tradition in Al where psychological theories inform architectural design without requiring neural isomorphism. Convolutional networks leverage principles of hier- archical visual processing without mimicking V1 neurons; attention mechanisms capture aspects of human focus without replicating neural attention circuits. Similarly, MIMIC extracts functional principles from inner speech theory to address behavioral diversity in imitation learning.
Our technical contributions lie in: (1) formalizing inner speech properties through information- theoretic and probabilistic frameworks (Section 3.1), (2) instantiating these properties through specific architectural choices (Section 3.2), and (3) empirically validating that these computational mechanisms improve behavioral fidelity and diversity. We make no claims about whether artificial agents experience phenomenological "inner speech" or whether our architectures replicate human cognitive processes at a mechanistic level.
The value of cognitive inspiration lies in generating testable hypotheses about computational mecha- nisms-in our case, that introducing linguistic mediation between perception and action can capture human behavioral diversity. Our empirical results validate this computational hypothesis while remaining agnostic about biological implementation.
B.5
Broader Impact
The development of cognitively groundedd artificial agents through MIMIC presents opportunities as well as ethical considerations for human-Al collaboration. The capacity to generate behaviorally- realistic human surrogates enables comprehensive pre-deployment safety validation, potentially preventing harmful interactions in high-stakes domains such as healthcare and autonomous systems. By incorporating iinguistically-mediin in AI decision- making while modeling cognitive diversity through stochastic inner speech gener- dtioonfacilitatinng more inclusive systems that account for varied cultural reasoning patterns and individual differences collaborative scenarios.
control mechanisms, MIMIC also enhances transparency
However, the same sophistication in replicating human-like behavioral patterns also introduces novel risks requiring careful governance. The ability to generate convincing behaviors could enable sophisticated social engineering attacks or deceptive Al personas designed to exploit human trust. When functioning correctly, such technology might enable unauthorized behavioral profiling: when producing incorrect outputs, it could generate inappropriate social behaviors violating cultural norms; and through intentional misuse, it could facilitate manipulative agents targeting human cognitive vulnerabilities. Additionally, biases embedded in usioo-languaae models used for bootstrapping could perpetuate societal inequities if generated inner speech reflects discriminatory patterns in training corpora.
Mitigation strategies should focus on balancing innovation with principles of transparency and accountability. Disclosure of artificial agency through technical markers in inner speech generation could prevent deceptive practices. Establishing auditable logs of cognitive mediation processes would enable post-hoc analysis supporting accountability in high-stakes applications. These kinds of mitigations would would help to promote further advances gggnitively-groundee Al enhancing rather than undermining human agency in increasingly automated societies.
C Extended Related Work
Imitation learning. II algorithms are commonly organized into behavior cloning, inverse- reinforcement learning, and distribution-matching families [20, 32, 14]. Classic behavior cloning (BC) regresses actions from expert states; the seminal ALVINN system kept a vehicle in its lane by copying recorded steering commands [34], Covariate-shift issues in BC motivated Dataset Aggre- gation (DAgger) [38], which iteratively queries experts on states visited by the learned policy to mitigate distribution mismatch. Inverse-reinge reinforceinno learning (IRL) infers a reward explaining the demonstrations [31, 11, while generative adversarial IL (GAIL) matches occupancy measures via an reward, linking it back to IRL [12]. Hierarchical IL discovers latent sub-policies that can be sequenced for long-horizon manipulation [11]. While these approaches model imitation as direct mappings from states to actions or through inferred rewards, MIMIC introduces inner speech as mediational
adversarial game [15]. The discriminator GAIL can interpreted as potential-
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mechanism between perception and behavior, enabling both distributional matching and designer control through linguistic intervention a capability absent in traditional IL paradigms.
Diverse behavior imitation: from single mode to multimodal. Human demonstrations are multi- modal. Recognizing this, InfoGAlL augments GAIL with an information term so a latent captures hidden styles [25], employing mutual information maximization to discover discrete behavioral modes within expert demonstrations.
Variational methods [45] learn conditional VAΣS to embed motor skills, allowing one-shot imitation by sampling different latent states. Such approaches encode behavioral diversity through continuous latent representations that can be manipulated to generate novel skill variations. A hierarchical VAE extends this idea to multi-scale variation [11]. decomposing complex behaviors into temporal hierarchies where higher-level latent representations control long-horizon strategies while lower-level latent representations capture execution details. Sequence models such Behavior Transformers okenize continuous actions and clone k distinct modes using prompt tokens [39], leveraging the transformer architecture's capacity for in-context learning to capture multimodal action distributions through discrete behavioral prototypes.
Score-based approaches fit diffusion models directly trajectories, reproducing the full joint-action distribution [33]. These methods model the entire behavioral manifold through iterative denoising processes, achieving high-fidelity reproduction of demonstration diversity. BESO shows the same mechanism can be goal-conditioned with only three denoising steps [36], demonstrating compu- tational efficiency while maintaining distributional expressiveness through accelerated diffusion sampling. These techniques broaden hehavioral variety, but explicit control over which behavior type will appear at test time remains limited. This is gap addressed by MIMIC's language-grounded inner speech. MIMIC builds on diffusion BC but conditions the denoising step on an inner-speech vector learned via a vision-language scaffold, enabling fine-grained linguistic control without retraining.
Imitation learning for studying Human-Al coordination. The deployment of Al agents in collab- orative human environments necessitates computational approaches that transcend purely algorithmic optimization to encompass the full spectrum of human behavioral patterns and coordination dy- namies. In multi-agent coordination domains, empirical evidence demonstrates the critical role of human behavioral modeling: in Overcooked, self-play agents confuse human partners due to convergence in self-play to non-human equilibria, whereas agents fine-tuned after first imitating human gameplay coordinate effectively [5]. Similarly, in Hanabi, monte-carlo search regularized with huean-be prior achieves high human-partner win rates by incorporating human-like suboptimalities and communication patterns. These systems employ a two-stage pipeline-learn a human model, then train a best response-yel yet this segregation introduces computational inefficiency and potential misalignment between the human model and the coordination policy.
The effectiveness of the above approaches is constrained by data availability: existing datasets exhibit significant limitations in capturing behavioral diversity. D4RL offers benchmark tasks but its demonstrations stem from synthetic experts, limiting stylistic variety [13]; RoboNet amasses large tele-operation sets yet focuses on narrow table-top primitives [8]: CALVIN provides language supervision but shows single canonical solution per goal [26]: and Overcooked traces are short and stylistically homogeneous [5]. D3IL deliberately captures multiple human strategies for each manipulation task, making a rare test-bed for diversity [21]. The BabyAl dataset is another exception, in providing explicit thought annotations for every action, enabling thought cloning [18] to learn from paired action-thought demonstrations. However, this kind of linguistic supervision is expensive and its availability is rare.
MIMIC addresses the architectural and data challenges: it unifies the two-stage pipeline as the diffusion policy trained by imitation already exhibits human-like variability and can serve directly as the partner model during new-agent optimization, while mitigating data bottlenecks by using automatically generated captions to train its inner-speech generator on existing video-only cor- pora- pora-effectivvly bootstrapping linguistic mediation from visual demonstrations without requiring the exhaustive human annotation.
Language Interfaced Imitation Learning. Thought cloning (TC) [18] discussed above directly imitates human thoughts but requires access to annotation actual human thought for each step in the demonstration trajectory. Further, the performance of TC is highly tied with goal (mission) conditioning and degrades by almost 40% in our experiments once the goal (mission) condition
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is removed. Similarly, external speech has been used to steer agent behaviors through action re- ranking [30], though these speech mechanisms remain external to the agent. [43] enforce linguistic bottlenecks through auxiliary tasks but through approaches that operate outside the II. paradigm. Closest prior [46], who frame intra-agent speech as semi-supervised captioning: a vision-language captioner is pretrained and then frozen to provide auxiliary caption and caption-matching supervision that improves behavior cloning and enables zero-shot object-level generalization with few additional captions. In contrast,
model inner speech as an explicit latent mediator that conditions the policy, i.e., p(a s) = [f p(a 8, m) p(m 8) dm, and we generate m online from recent history via conditional VAE. This shift from auxiliary language supervision mediational control yields steerable and distributionally realistic imitation ddesigner-promphe behaviors, periodic refresh) rather than only improved supervision signals.
Cognitive Theories of Inner Speech and Behavioral Diversity. The relationship between inner speech and behavioral diversity has been extensively studied within cognitive psychology and neuro- science, providing rich theoretical foundations for our computational approach. Vygotsky's s seminal work [44] established inner speech as internalized social dialogue that mediates higher cognitive func- tions, proposing that the transformation of interpersonal communication into intrapersonal dialogue creates a mechanism for behavioral self-regulation. This theoretical framework was subsequently empirically observed by Sokolov [42], who characterized inner speech as possessing distinctive structural and functional properties that differentiate it from external communication.
Contemporary cognitive research has extended these foundations to explain behavioral diversity. Fernyhough's 's [10] dialogic theory posits that inner speech maintains the dialogical characteristics of interpersonal communication, suggesting that behavioral diversity emerges from the multiplicity of internalized perspectives. As Alderson-Dag Day and Fernyhough [2] note, "inner speech allows for the simulation of multiple action pathways before behavioral execution," providing a cognitive mechanism for generating diverse behavioral responses to identical environmental stimuli.
Neuroimaging studies [3] have identified neural correlates of inner speech, showing activation in both language production regions and motor planning areas during inner speech episodes. These findings are consistent with the hypothesis that inner speech serves as a cogmitive rehearsal mechanism for behavioral alternatives. Morin's 's [29] self relf-regulatory framework further proposes that inner speech functions as a behavioral selection mechanism, where verbalized thoughts act as "cognitive filters" that modulate action selection based on contextual factors beyond immediate environmental stimuli.
This theoretical perspective aligns with empirical observations in human imitation learning. Melt- zoff's [27] "like me" framework demonstrates that human imitation is not merely mimicry but rather an inferential process that reconstructs the intentions and mental states underlying observed actions. The diversity in imitative behavior derives from this reconstructive process, where different individ- uals generate different internal models of the demonstrator's cognitive states. Our computational architecture operationalizes this cognitive process, modeling how inner speech mediates between observation and action to produce diverse yet contextually appropriate behaviors.
AI Approaches to Modeling Cognitive Processes. Some Al research has explored computational implementations of cognitive processes. [6] propose "autotelic Al" that internalizes language for self-directed learning, emphasizing language as a tool for goal generation and intrinsic motivation. While sharing our interest in cognitive foundations, autotelic systems focus on autonomous learning rather than behavioral diversity, using language primarily for goal generation. [19] utilize large language models to simulate reasoning processes ("chain of thought") before action selection, imple- menting serial, deterministic reasoning rather than the stochastic, parallel processing characteristic of inner speech in Vygotskian theory. Our framework models inner speech as a probabilistic process generating diverse behavioral patterns from identical environmental states, more closely aligning with cognitive theories of human behavioral diversity. [4] propose natural language as a latent space for reinforcement learning, using language to structure behavior hierarchically. While this approach shares with us the adoption of language as cognitive tool, it focuses on decomposing complex tasks rather than generating behavioral diversity. Our framework uniquely combines the stochastic nature of inner speech with IL to capture a wide spectrum of human behavioral variation without requiring explicit linguistic supervision.
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D Technical Background
In this section, we provide a detailed background on diffusion models and conditional variational autoencoders, which constitute the backbone of MIMIC's architecture.
D.1 Diffusion Models
Diffusion models, specifically denoising diffusion probabilistic models (DDPMs), constitute class of generative models that transform noise distributions into target data distributions through iterative denoising processes. Their theoretical foundation derives from non-equilibrium thermodynamics and Markovian diffusion processes, establishing a principled approach to generative modeling through progressive noise injection and removal [17, 9, 40].
The diffusion framework comprises two fundamental stochastic processes operating in complementary directions. The forward diffusion process defines a Markov chain that incrementally incorporates Gaussian noise according to predefined variance schedule, systematically destroying the data structure. Given data Xo ~ (xx,, this process generates increasingly noisy latents through:
g(x//x+1) = -N(x; v -,x,
(5)
where { ]T= represents the noise schedule with 0 < 8r < 1. The noise schedule can follow various strategies including linear, cosine, or learned schedules, each affecting the quality-efficiene trade-off' during generation. This formulation admits tractable closed-form expression for any timestep:
gxx,|>x) xn) (x1; Vo.Xo, (1 T
where = 1 3r and ā = II= As T' - oc with appropriate schedule, Xy approximates an isotropic Gaussian distribution, effectively erasing information about the original data.
The reverse diffusion process recovers the original data distribution through learned denoising transformations, parameterized as:
pe (xx=1/x;)) Ho t), (x(xx,)) t)),
where (x(x)) ((x;; 0, I). Following established practice, the variance is typically fixed as Es(x;, (x,, t) = III, with of either learned or set to 3; or B = -- Br. The mean ( t) is parameterized through a neural network that predicts the noise component:
1 ( t) = Vai
3r
co (xx,
X;
(8)
VI
where co t) predicts the noise added during the forward process. This parameterization establishes a fundamental connection to score-based generative models, as the predicted noise is proportional to the score function Vx, log q(x.).
The training objective, derived from variational inference principles, minimizes the negative evidence lower bound (NELBO). However, empirical investigations demonstrate that a simplified objective yields superior practical results:
Siimle ((..
[||e ca(Vorxo + V1 0,6, 1)
This formulation enables efficient training through direct noise prediction across all timesteps simultaneously. while sampling necessitates iterative denoising from pure noise.
D.2 Conditional Variational Autoencoders
Conditional variational autoencoders (CVAEs) extend the traditional VAE framework by incorporat- ing conditional information into the generative process, thereby enabling controlled generation based on specified attributes, contextual constraints, or structural specifications [41, 23]. This conditional paradigm addresses the fundamental limitation of standard VAEs in providing explicit control over generated outputs, establishing CVAEs as particularly valuable for applications demanding targeted generation capabilities.
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CVAEs introduce a conditioning variable to model the conditional data distribution p(x|c) through a latent variable framework. The generative process is formulated hierarchically as:
c)po po(x)c) = f (x((x),,cpoo(llc))l (z)c)d.. c)dz.
(10)
The conditioning mechanism operates at multiple architectural levels: influencing the prior distribu-
tion pe (z c), the likelihood po (x(x/z, c), both components simultaneously. Different conditioning strategies yield distinct modeling capabilities, ranging from simple attribute control to complex structural generation tasks requiring sophisticated conditional dependencies.
Since direct computation of the posterior pa [z) x, e) remains intractable, CVAEs employ variational inference with approximate posterior qo (z c), yiclding the conditional evidence lower bound (ELBO): log po (x|c) (x/xxe) [log pe x (z, c)] DxL (g ())lpo(z)c))
(11)
The conditional prior (z(c)c can be parameterized
learned function of the conditioning variable, enabling the model to adapt the latent space structure based on conditional information. This adaptability fundamentally distinguishes CVAEs from simpler conditional generation approaches that merely concatenate conditions with inputs.
Neural networks parameterize both the encoder go(2)x c) as a conditional Gaussian distribution and the decoder py (x) zz c). which reconstructs inputs based on both latent variables and conditioning information. The encoder produces conditional distributional parameters:
qo (z/xx c) NZz (x, c), diag(o (o (x, c))).
(12)
The reparameterization trick facilitates gradient brasd optimization through:
z Ms (x, c) + o Ee e~ C~N(0,,1)
(13)
The complete training objective minimizes the negative conditional ELBO:
LeVAe
E (x|x,c) [log Po (x|z, c)] Dxz (gs (z) x, c) c)llpozz)c)
(14)
This objective balances reconstruction fidelity against latent space regularization while incorporating conditional constraints. The KL divergence term encourages the approximate posterior to remain proximate to the conditional prior, enabling meaningful interpolation within the conditional manifold and ensuring that latent representations respect the conditioning structure.
E Experimental Setup
E.1 Additional Environment details
Figure 7 (a,b,c) illustrates the D3IL environments used in experiments. We use the 4-box and vision-based setting for the Sorting environment, as we observed more stability and higher performance in the BC model under these settings. The following outlines some brief details those environments.
Aligning: The Aligning task requires the robot to precisely manipulate box such that it aligns with a target box within specified tolerances, with the constraint that colors must match for each side. The task admits two distinct behavioral modalities: pushing from the inside from the outside of the box configuration, thereby introducing controlled multi-modality in the action space. The state representation encompasses end-effector position in Cartesian space, pushing box position and quaternion, and target box position and quaternion, with actions represented as desired Cartesian velocities. This task exemplifies the challenge of precision control under multi-modal behavioral strategies, requiring policies to master fine-grained manipulation while maintaining behavioral diversity.
Sorting:
The Sorting task requires the robot to sort red and blue blocks into their color-matching target boxes, with task complexity scaling from 2 to 6 blocks. For the 6-block variant, the task exhibits
'https://githenn wtw.aa/.cco//AAabb//3/3333 (MIT License)
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20 distinct behaviors and demands complex manipulation sequences with high variation in trajectory lengths, challenging existing approaches. The state representation includes andeeffector position, all boxes' positions and tangent of Fuler angles along the 7-axis, with dimensionality scaling linearly with the number of objects. This environment tests an agent's capacity to handle combinatorial complexity and maintain closed-loop sensory feedback across extended manipulation sequences.
Stacking:
The Stacking task requires the robot to sequentially stack 1-3 blocks in designated yellow target zone, employing a parallel gripper and augmented reality control interface for enhanced dexterity. The state representation includes robot joint positions, gripper width, and boxes' positions with Euler angle tangents, while actions encompass both joint velocities and gripper width control. Success criteria demand not only lateral positioning within the target zone but also appropriate vertical heights confirming successful stacking. This task represents the pinnacle of manipulation complexity the D3II. suite, requiring precise grasp-place sequences, dynamic stability maintenance, and adaptive recovery from perturbations.
Figure (d.e.f) illustrates the Overcooked environments used in our experiments. Note: term "Greedy agent" to report results for Overcooked environments, however, this agent is the same as the human proxy agent (split of the trajectories collected from humans) as reported in [5].
We use the
Cramped Room: Agents must navigate a confined workspace while executing sequential cook- ing tasks. The constrained spatial topology induces frequent collision possibilities, necessitating real-time trajectory adaptation and implicit coordination protocols that emerge through embodied interaction rather than explicit communication. The environment's s space encompasses agent positions, object locations, and cooking progress indicators, with actions comprising discrete move- ment commands and object interactions. This layout operationalizes fundamental questions about emergent coordination strategies in spatially constrained multi-agent systems, where optimal policies must balance task efficiency against collision avoidance through anticipatory modeling of partner trajectories.
Asymmetric Advantages: The Asymmetric Advantages layout tests whether agents can develop high-level strategic reasoning that leverages differential access to resources, as players begin in distinct spatial regions with asymmetric proximity to cooking stations. This environmental structure necessitates role specialization and adaptive task allocation, where agents must infer and exploit comparative advantages based on spatial positioning and partner capabilities. The layout embodies game-theoretie coordination challenges where multiple Nash equilibria exist, each corresponding to different role assignments and workflow patterns. Success requires agents transcend myopic task completion toward globally efficient coordination strategies that emerge through iterated interaction and mutual adaptation.
The Coordination Ring layout presents a topologically constrained environment where the ring-like spatial structure forces agents establish and maintain directional conventions (clockwise or counterelockwise movement) to prevent deadlock scenarios. Agents must rapidly converge shared behavioral protocols without explicit communication channels. The circular topology creates a coordination game with multiple equilibria, where misaligned conventions result in systematic inefficiencies through blocking behaviors. This layout thus serves minimal testbed for studying how artificial agents can develop and adapt to emergent social conventions, mirroring fundamental processes in human social coordination where arbitrary but stable behavioral patterns facilitate collective action.
Coordination Ring:
E.1.1 Environment descriptions for the inner speech prompt
Aligning. The GIF(s) show {camera} view of your actions and your task was to move a box starting from different positions using robotic hand to align with the other box, which is fixed.
Sorting. The (s) show a {camera) view of your actions where your goal was to sort red and blue blocks their color-matching target box.
Stacking. The (IF show a (camera) view of your actions where your goal to stack blocks with different colors in (yellow) target zone.
httppr//ghhhaeee//mmmooppttttttt (MIT License)
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(a) Aligning
(b) Stacking
(c) Sorting
(d) Cramped-room
(e) Coordination-ring
(f) Asymm.
Advan-
tages
Figure 7: Environments used in our experiments. (a-c): D3IL environments Aligning, Stacking and Sorting environments, respectively. (d-f): Overcooked map layouts Cramped-room, Coorindation ring, and Asymmetric Advantage, respectively.
Overcooked: The goal is place three onions in pot (dark grey), take out the resulting soup on plate (white) and deliver it (light grey), as many times as possible within the time limit. The GIF(s) show how the {agent) moves and interacts with other agents in a cramped room.
E.2 Conditional evaluation
Evaluate the ability of the caption describe the overall motion the gif from 5.
Also give your reasoning. The caption is deliberately succinct and ignores the amall motion so do not consider these points while evaluating.
Caption: {caption)
GIF: attached Score:
E.3
Large language models
We use API version 2025-01-01-preeiee for all models and employ structured output API obtain the behavior descriptions corresponding to each GIF.
E,4 Computational environment
All experiments were conducted on gegh-performance computing server, equipped with a 64-core x86_6s 64 processor (128 threads) and 1007 GB of RAM, running Ubuntu 22.04 LTS (kernel 5. 5.15.0. 5.00- 153-generic). For GPU-accelerated computations, we utilized an NVIDIA A A100 80GB PCIe GPU with CUDA version 12.6. The experiments were implemented using Python 3.10.14 14 (conda-forge distribution) with PyTorch 2.7.0.
E.5 Extension to multi-agent speech
novel implication of MIMIC's flexibility arises in multi-agent settings where agent can be conditioned not only on its own inner speech but also on the inner speech of other agents, inspired by
hnttps:/ppattfrr: htt.://awaaaappppaaaooooooooo/// httpa///./../oo//...//////tttttt
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mirror neuron theory of social cognition [37]. This extension models how inner speech mediates social interaction, allowing agents to adapt their behavior based on their perception of others' cognitive states We denote it as MIMIC-MA in our experiments.
F Complexity Analysis
Training, First, generating inner-speech captions with GPT-4o is inexpensive: we make N/B API calls with average context length of ~ 100 for text and B (85 + 170n) for images (with n tiles of 512 x 512 px), totaling ~ N 170n tokens which is about $ 2 for over 400 trajectories, even with high resolution 2048 x 2048 images. The CLIP model (~~ 0.5 B parameters) likewise lightweight and can be efficiently used during training to generate the inner speech.
Inference. Let Tev AE and Tais denote one forward pass through the CVAE and diffusion models, respectively. Over a simulation horizon H with window size W, we perform H diffusion passes and H/W CVAE passes, yielding total complexity of O(HTaff + H/WTcV Since both condinncond with similar runtimes and H H/W, the diffusion term dominates. So, MIMIC adds no inference overhead.
G Additional Experiment Results
We first report the extended results in each environment along with the parameter configurations that correspond to the reported best performance. We then analyze the sensitivity of MIMIC to various hyper-parameters and different VLM models. We conclude with the visualization of behaviors obtained through designer specified control text for the Aligning and Sorting environments.
G.1 How is inner speech represented in the embedding space?
Figure shows the TSNE visualization of the CLIP-encoded inner speech of different environments, as generated using GPT-4o. We find that it tends to cluster together similar behaviors while separating distinct behaviors in this 2D space.
G.2 Which configurations maximize the efficacy of MIMIC?
Robotic Manipulation Task. Table reports the hyperparameters corresponding to the best perfor- mance reported in Table for D3IL. benchmark. Here, Pärop denotes the probability of randomly dropping the for Laitt. We note that higher update windows are preferred for long horizon environ- ments, such as Stacking and Sorting than Aligning, where smaller update windows (W) gives high performance. Initial steps show more variation while indicating that higher values are preferred in non-vision environments. This means that for the first few steps, the agent takes its action with no inner speech. On the other hand, random dropout probability of inner speech (Pdrop) is found to be important for the Aligning environment for higher performance while no such dropout more useful in others.
Overcooked Tasks. Table shows the hyperparameters along with the Wasserstein distance between the actions for the OverCooked environment. We also include the reward collected by the PPO prroyy model from [5]. This approach trains the PPO agent in partnership with the Hyrozy model, essentially giving it access ground truth. [5] calls this value the "gold standard" and reports only the PPO agent trained in the presence of an imitator reaching close to the gold standard. The results demonstrate that MIMIC already outperforms BC significantly and reaches closer to surpasses the gold standard performance, demonstrating the capability of MIMIC agent to collaborate effectively with human proxy model. The results further show that the best hyperparameters often include low initial step and high update window with a non-zero dropping of probability. The Wasserstein distance between the generated and training actions also small, following the trend in Table 1. This showcases the high fidelity of behavior imitation as compared to just task success that MIMIC achieves.
'In multi-agent settings, agents observe each other's behaviors and infer corresponding inner speech representations, or share inner speech explicitly cooperative scenarios where communication available. For other environments, it was difficult to visualize the behaviors via static images and so we defer them to the gif versions shared along with the source code at our project website.
31

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Aligning-Tog Top
(b) Aligning-Inhan Inhand
(c) Stacking- Top
(d) Stacking- Inhand
Sorting-Top
(f) Borting-Inhand
(g)
Cramped-room
(h) Cramped-room
Coordination-ring
(i) Coordination-ring
Blue hat
Green hat
Blue hat
Green hat
(k) Asymmetric advan- tages Blue hat
(I) Asymmetric advan- lages Green hat
Figure 8: TSNE visualization of CLIP-encoded inner speech generated using GPT-4o for the en- vironments used in our experiments. (a-f): Top and inhand cameras in D3IL: Aligning, Stacking and Sorting environments, respectively, (g-l): Blue and green hat agents in overcooked map layouts: Cramped-room, Coorindation ring, and Asymmetric Advantages, respectively.
We also study the setting using the other agent's speech (denoted as MIMIC-MA) and find the performance to slightly improve in Coordination-ring. layout where coordination becomes central the task. Using the green hat agent's speech in this case found to useful, whereas in all other layouts, the agents collect less reward when using just the blue hat agent's own speech.
G.3 How sensitive is MIMIC to hyperparameters?
Figure 9 shows how performance varies with change in the initial step and polling window of the simulation the D3IL benchmark. The performance goes down by delaying the first step of the inner speech update. The performance improves when increasing the update window in the Aligning dataset, but only up to a limit after which the success rate starts going down while the entropy increases. We find that higher polling windows are preferred in Stacking and Sorting environments, while other trends are similar to Aligning.
Figures 10b and 10c shows the hyperparameter sensitivity in Overcooked cramped room environment and we find similar trend as D3II. benchmark increasing the performance with increase in the initial step to some extent. while update/polling windows show drop in performance after a point.
32

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Table 5: Comparison of MIMIC against BC with the DDPM-T architecture the D3IL benchmark.
Environment
Model
Pdvop
to
W
Success rate f
Distance
Entropy
Aligning
BC
0.6645
0.1105
0.4743
MIMIC-S
0.1
50
50
0,8021
0.0664
0.4184
MIMIC-E
0.1
12
50
0.7229
0.0847
0.6148
Aligning-Viste Vision
BC MIMIC-S
0.1833
0.1875
0.0895
0.1
20
0.2229
1818
0.0849
MIMIC-E
0.0
0.2083
0.1849
0.1473
Sorting-Visie Vision
BC MIMIC-S
0.7972
0.3596
0.0
100
200
0.8417
0.3719
MIMIC-E
0.0
50
100
0,8083
0.4494
box / box
box 2 box / box
Stacking
0.8027 0.8129
00.8799
0.2058 0.1503 0.1049 0.1774 0.0737 0.0334
MIMIC-S
0.0
50
0.6074
Table 6: Comparison of MIMIC against BC with DDPM-T the Overcooked environments. '.'
denotes "action Wasserstein" is not feasible or not available. denotes values taken directly from [5]. Note that "state Wasserstein' is infeasible due to large dimension (96) of state features.
Environment
Model
Párop
to W Collective reward
Action Wasserstein
PPO
155 160
H. Hprosy
Cramped
BC
115.8 + 3.86
0.24
room
MIMIC
0.1
10
100
151.8 + 2.45
0.25
MIMIC-MA
0.1
1
148.4 1 2.17
0.25
Cramped
BC
73.6 6.18
room-
MIMIC
0.0
1
108.8 1 4.84
Vision
MIMIC-MA
0.0
1
20
103.6 + 3.69
PPO
H
145 150
Coordination
BC
113.0 + 2.21
0.08
ring
MIMIC
0.1
10
121 = 1.93
0.09
MIMIC-MA
0.1
10
20
128.6 + 1.75
0.03
Asymmetric
PPO
125 130
Hprozy
advan-
BC
215.8 + 3.04
0.14
tages
MIMIC
0.1
10
200
227.6 + 2.69
0.10
MIMIC-MA
0.1
10
50
227.0 + 1.84
0.11
We also evaluate the effect of changing the embedding and VLM the overcooked environment. Figure 10a shows that CLIP-encoded and GPT-4o-scaafolded inner speech is most effective in obtaining the highest collective reward in the Overcooked cramped room. However, we find that even by changing the embedding and VLM, MIMIC still outperforms the BC variant.
G.4 How does MIMIC compare against other strong imitation learning approaches?
While our choice of BC (DDPM-T, [33]) is motivated by its high benchmark performance, we also compare against two additional approaches for comprehensiveness: BESO [36] and BeT [39]. For a fair comparison, we use the BESO's diffusion model architecture as the underlying policy network in MIMIC instead of a DDPM-T architecture. Table shows that MIMIC substantially outperforms these approaches well, further highlighting the advantages of using inner speech.
33

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Success
Distance
Pultin Arndee
Pollin
(a) Initial Step
Update Window
(c) Sorting: Initial Step (d) Sorting: Window
Success box
Entrapy
Entropy
Success box
Entrapy
14.
(e) Stacking: Initial Step
(1) Stacking: Window
Figure 9: Hyperparameter sensitivity MIMIC on the D3IL benchmark.
Nean Collectie
Usy cue
0rr4a Niser
open
Pulling Window
(a) VLM and embedding
(b) Initial Step
(c) Polling Window
Figure 10: Sensitivity on overcooked Cramped-room
G.5
How efficient MIMIC during inference?
Table 7: Runtime (s) for different vision envi-
ronments.
We confirm the findings Appendix reppricalll by showing in Table that MIMIC's simulation runtime matches that of DDPM-T BC vision- based environments. Since MIMIC's CVAE is vision-based, it would be unfair to compare against non-vision policy networks.
Environment
BC
MIMIC
Aligning
40.69
57.16
Sorting
71.50
78.5
Overcooked
93.23
94.72
G.6 How well does MIMIC enable designer-speciffied control generate desired behaviors?
Figure 11 shows examples of different behaviors produced by the MIMIC model conditioned with different descriptions of behaviors. Figure 1 la shows a quick repositioning at the start, but due to mediating inner speech, is not realized later. Figure 11b, on the other hand, shows an attempt to align/match the edges at the start according to the condition. Figure 1lc shows attempt to adjust box position before pushing straight ahead after mediation. We find a right side curve approach in Figure 11d, but due to misalignment, it does lot of rotation at the end. A mediation would have helped here. We find that zig-zag motion is exhibited in both Figures le and 11f while Figure 1lf shows more adjustment the final step described in the input behavior description.
34

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Table Comparison with other imitation learning models. Here, we use BESO as the base diffusion policy network in MIMIC instead of DDPM-T for faimess.
Aligning
Overcooked cramped room
Model
Success rate
Distance
Entropy
Model
Collective Reward
BeT 0.51667
0. 0.19949
0.40475
BeT
47.2 + 4.64 67.8 4.55
BeSO
0.85417
0.04954
0.6141
BESO
MIMIC-S 0.88125
0.04234
0.7215
MIMIC
120.2 + 2.86
MIMIC-E
0.86875
0.04759
0.7706
MIMIC-MA
141.2 + 3.68
quickly reposition and (b) align the edges first adjust the box position (d) I around from
push achieve contact
before making full contact. slightly before making the right side, adjusting for final approach
alignment way.
(c) apply zigzag path to (f) I use a zigzag pattern to (g) use swift motion (h) begin with swift side navigate around an obsta-close the distance, adjust-from the top, maintaining approach, then slow down ele
ing final position carefully steady path for alignment ensure precise alignment
Figure 11: Conditional generation the Aligning dataset. The color gradient (Plasma) shows the simulation time going from 0 (dark purple) the end (bright yellow), going through red and orange. Inner speech updates are marked as yellow circles with corresponding times inside; the first inner speech is equal to the specified condition.
On the other hand, Figure 1 11g shows swift motion moving from the top and fast convergence to the desired box, as mentioned in the input text while Figure 11h begins with a swift approach but then spends lot of time in the final alignment with the desired box mentioned in the text, similar to Figure 1 1f. These results demonstrate significant success achieved by MIMIC towards enabling steerable imitation of desired behaviors.
We also extend this analysis to the Sorting dataset as we find how prioritizes the closest red block before any blue in Figure 12a, alternate color sorting in Figure 12b, and behavior of grouping before moving into desired sorted places in Figure 12c.
G.7 What does generated inner speech look like during simulation?
Since CVAE-generated inner speech lies in the latent embedding space, hard to fully interpret and visualize them. We thus employ heuristic technique to analyze the inner speech during simulation by retrieving the top-2 training descriptions CLIPP's embedding space at each update step. We use the cosine similarity find the nearest training description and provide the value in parentheses.
35

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(a) prioritize the closest red block hefore any hlue.
(b) alternate between picking red block and blue block.
(c) sort blocks by color, grouping similar colors together before moving them.
Figure 12: Conditional generation the Sorting dataset. The color gradient (Plasma) shows the simulation time going from O (dark purple) to the end (bright yellow) going through red and orange. Inner speech updates marked yellow circles with corresponding times inside; the first inner speech is equal to the specified condition.
Here, we provide examples for both conditional and unconditional simulations and find how the top captions change along with the similarity score.
G.7.1
Conditional
I rotate the box first before aligning it with the target.
Timestep
Closest description
Similarity
(=49
push the box directly without rotation, aiming for straightforward
0.9183
alignment.
[=99
approach directly, then rotate in place to align perfectly.
0,9614
[= [=149
approach directly, then rotate place to align perfectly.
0.9614
(=249
Starting from slightly rotated angle, need a mid-action adjustment to
0.9616
align.
I begin with swift side approach, then slow down to ensure precise alignment.
Timestep
Closest description
Similarity
(=49
approach the box from the side, rotating slightly to align smoothly with the fixed box.
0.9471
[=99
approach directly, then rotate in place to align perfectly.
0.9614
t= 149
approach directly, then rotate in place to align perfectly.
0.9619
(=249
approach with direct path but make last-second adjustment to align
0.9611
perfectly.
I use swift motion from the top, maintaining a steady path for alignment.
Timestep
Closest description
Similarity
(=49
exxcctee a direct push from behind, minimizing lateral movement.
0.9477
[=99
carefully approach from the left, ensuring alignment from diagonal perspective.
0.9613
t=149
Starting from slightly rotated angle, need mid-action adjustment to align.
0.9613
(=249
approach with direct path but make last-second adjustment to align perfectly.
0.9613
36

---

G.7.2 Unconditional
Aligning
Timestep
Closest description
Similarity
[=0
start with a straight approach from a central position, requiring minimal rotation for alignment.
0.9693
[=50
start with a straight approach from central position, requiring minimal rotation for alignment.
0.9691
t=100
start with straight approach from central position, requiring minimal rotation for alignment.
0.9696
Sorting
Timestep
Closest description
Similarity
[=100
focus on sorting all blocks of one color first before switching to the
0.9607
other.
(=300
focus on sorting all blocks of one color first before switching to the other.
0.9604
Overcooked Cramped room
Timestep
Closest description
Similarity
[=100
quickly grab onions from the pile and place them in the pot, prioritizing
0.9082
speed over precision.
(=200
quickly grab onions from the pile and place them in the pot, prioritizing
0.9096
speed over precision.
[=300
quickly grab onions from the pile and place them in the pot, prioritizig
0.9062
speed over precision.
(=400
quickly grab onions from the pile and place them in the pot, prioritizing speed over precision.
0.9086
Overcooked Coordination ring
Timestep
Closest description
Similarity
(=0
adjust its movement pattern avoid congestion, optimizing its task execution efficiency.
0.9521
t=110
adjust its movement pattern to avoid congestion, optimizing its task 0.9405
execution efficiency.
161
adjust its movement pattern to avoid congestion, optimizing its task 0.9466
execution efficiency. adjust its movement pattern to avoid congestion, optimizing its task execution efficiency. adjust its movement pattern to avoid congestion, optimizing its task execution efficiency.
[=210
0.9407
t=260
0.9395
Overcooked Asymmetric Advantages
Timestep
Closest description
Similarity
t=0
maneuver around obstacles effectively.
0.8391
[=100
balance interactions with other agents and time efficiency.
0.8416
t=150
balance interactions with other agents and time efficiency.
0.8463
t=200
maneuver around obstacles effectively.
0.8443
t=250
balance interactions with other agents and time efficiency.
0.8401
37


Reviews:
[{"review_id": "TNr29vyD88", "reviewer": "Reviewer_sVYU", "summary": "This paper introduces MIMIC (Modeling Inner Motivations for Imitation and Control), a novel framework that endows imitation-learning agents with an inner speech mechanism to guide behavior. The authors propose learning a conditional policy p(a|s,m) instead of the usual p(a|s), where m is an internal language-like representation (‘inner speech’) mediating action selection. Concretely, MIMIC uses a vision-language model (e.g. GPT-4/CLIP) to generate textual descriptions of behaviors as “scaffolding” and trains a conditional VAE to produce inner-speech tokens from observations. A diffusion-based behavior cloning policy (Transformer+DDPM) then samples actions conditioned on both the state and the generated inner speech. At test time, the agent auto-generates its own inner-speech and can also be steered by providing designer-specified text prompts. MIMIC is evaluated on the D3IL benchmark of diverse robotic manipulation tasks and in multi-agent overcooked coordination games.", "questions": "- Clarifications are needed on the analysis of the method, with proper baselines. \n\n- Clarifications are needed on the generalization and failure modes.", "limitations": "N/A", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "***Paper Strengths***\n\n**[S1]** The paper and supplementary are well-written. The idea of using inner speech as an interpretable latent variable in imitation learning is novel and well-motivated. \n\n**[S2]** On the newly-proposed D3IL benchmark , MIMIC outperforms a high-capacity diffusion BC baseline and implicitly also captures multiple modes. The paper reports higher success rates and entropy across diverse tasks (Table 1) and lower Wasserstein distances between generated vs. real trajectory distributions.\n\n\n***Paper Weaknesses***\n\n**[W1]** Limited Baseline Comparisons: The experimental comparison is narrow. The only baseline is a diffusion-based behavior cloning using the same DDPM-T architecture. While diffusion-BC is a strong choice, the paper omits comparisons to other recent multi-modal IL methods (many could be here, e.g., Behavior Transformers, or other language-conditioned IL approaches ). Without these, it’s hard to assess how much of the gain is due to inner speech vs. other factors. The authors claim “state-of-the-art” performance, but cite only the BC variant.\n\n**[W2]** Reliance on External Language Models: MIMIC depends on large pretrained VLMs (GPT-4 variants, CLIP) for both training and steering. This raises practicality concerns. Generating inner-speech captions with GPT-4 and classifying them with CLIP is computationally expensive-how would open-source models compare here? The paper shows the system works with GPT-4o-mini, but the drop in success (due to CLIP failure) indicates brittleness. In real deployments, one may not have such strong LMs. It would be helpful to ablate with smaller or open-source models and to discuss limitations, i.e., to analyze how well MIMIC scales if the language scaffolding is imperfect. Can an experiment with other embedding models instead of CLIP help clarify failures? \n\n**[W3]** Evaluation in Human-AI Partnership is Insufficient: The overcooked experiments provide a reasonable starting point, but there are no examples of coordination with real-humans, only simulated (greedy policy as a human proxy). Notably, in the ‘Asymmetric advantages’ game MIMIC-alone performs worse than BC (196 vs. 216, Table 3 ) - yet this is not discussed? There are also multiple MIMIC models evaluated- are these each tuned for individual tasks? There are multiple MIMIC models introduced, this seems to counteract generalization. \n\n**[W4]** Additional Details on Inner Speech: The paper claims inner speech has structural properties that are exploited, but the experiments do not analyze the learned speech. Are the generated phrases meaningful or consistent? Are certain commands overused? More diagnostics (such as examples of inner-speech outputs, t-SNE of language embeddings) would help. The current evaluation measures only downstream impact, leaving the inner mechanism somewhat opaque. In general, more analysis of failure modes can be beneficial. \n\n**[W5]** Run-time analysis. The authors mention that they generate inner speech in each window size W - but there is no run-time analysis. How long does inference take? \n\n\nWriting issues: \n\nLine 270 “state-of-art”\n\nLine 306 “upto”\n\nLine 285 “We”", "was_revised": true, "final_justification": "The authors addressed the concerns regarding inconsistent results and missing baselines. While there are questions around realism - this is an interesting and well motivated paper", "rebuttal": "We thank the reviewer for their detailed feedback and appreciate their positive comments regarding our novelty, motivation, and strong empirical results. We are grateful for their thoughtful questions, which help us clarify key aspects of our work and strengthen the paper's contribution. Below, we address specific concerns spanning baselines, generalization, and failure modes, providing additional context and evidence where helpful:\n\n> **[1] Limited Baselines:**\n\nWe appreciate the reviewer's observation that the version of Diffusion BC we use is a strong baseline for multimodal imitation learning. This choice is based on published benchmarks and our own evaluations. We now present new results for Behavior Transformers (BeT) on two datasets below, confirming that MIMIC achieves superior performance for both D3IL (Table 1) and Overcooked environments (Table 2). We further compare against BESO [1], a goal-conditioned multimodal IL approach. BESO is a stronger model for behavior cloning and has shown better performance than Diffusion BC on several D3IL tasks. Results on both environments demonstrate that MIMIC significantly outperforms BESO. We will include these and the results of other tasks in the revised paper.\n\n**Table 1: D3IL Results**\n\n| Dataset  | Model| Success rate | Distance | Entropy |\n| -- | -- | -- | -- | -- |\n| Aligning | BeT | 0.51667 | 0.12949 | 0.40475 |\n| | BESO | 0.85417 | 0.04954 | 0.6141 |\n| | MIMIC-S | **0.88125** | **0.04234** | 0.7215 |\n| | MIMIC-E | 0.86875 | 0.04759 | **0.7706** |\n\n**Table 2: Overcooked Results**\n\n| Dataset | Model | Collective Reward |\n| -- | -- | -- |\n| Overcooked Cramped room | BeT | 47.2 $\\pm$ 4.64 | | |\n| | BESO | 67.8 $\\pm$ 4.55 |\n| | MIMIC | **120.2 $\\pm$ 2.86** |\n| | MIMIC-MA | **141.2 $\\pm$ 3.68** |\n\nWhile the reviewer notes comparison with *only* BC variant, BeT and other approaches like BESO are also BC variants. As we have discussed in the paper, BC offers a simple, efficient, and offline approach with remarkable efficacy across domains. Our choice of BC reflects a requirement for offline learning—precluding environment interactions during learning. This is a critical safety constraint that non-BC approaches violate, potentially introducing deployment risks in high-stakes applications.\n\n> **[2] Reliance on External Language Models**\n\nAnalysing how MIMIC scales with various capabilities of language scaffolding is a good suggestion. As we explain below, this is not a limitation of our work. \nFirst, generating inner-speech captions with GPT-4o is inexpensive: we make $N/B$ API calls with average context length of $\\sim 100$ for text and $\\sim B \\cdot (85 + 170n)$ for images (with $n$ tiles of $512\\times512$ px), totaling $\\sim N\\cdot 170 n$ tokens which is about 2 dollars for over 400 trajectories, even with high resolution  $2048\\times 2048$ images. The CLIP model ($\\sim 0.5$ B parameters) is likewise lightweight and can be efficiently used during training to generate the inner speech. We will add this expanded discussion in the paper. \n\n\nNext, we have now also ablated our approach on two fronts:  the VLM and the embedding space. Specifically, we have evaluated Qwen2.5-VL-72B-Instruct as an open-source alternative to GPT-4o, and MPNET (all-mpnet-base-v2) as an alternative to CLIP. In the table below, we report the results on the Aligning dataset. We will include the full suite of results in the paper.\n\n| Model | VLM | Embedding | Success rate | Distance | Entropy |\n| -- | -- | -- | -- | -- | -- |\n| BC | - | - | 0.6645 | 0.1105 | 0.4743 |\n| MIMIC-S | GPT-4o | CLIP | 0.8021 | 0.0664 | 0.4184 |\n| MIMIC-E | GPT-4o | CLIP | 0.7229 | 0.0847 | 0.6148 |\n| MIMIC-S | Qwen | CLIP | 0.7583 | 0.0859 | 0.2027 | \n| MIMIC-E | Qwen | CLIP | 0.7186 | 0.0886 | 0.4546 | \n| MIMIC-S/E | GPT-4o | MPNET | 0.7896 | 0.07108 | 0.5271 |\n\nWe observe that GPT-4o with CLIP embeddings outperforms other variations, but also find that both ablations still outperform the base diffusion BC variant. This shows MIMIC's efficacy, even in the absence of a very powerful LM for inner speech. Furthermore, the lower performance of MPNET highlights the value of  vision-language embedding over sentence-only embedding. \n\n> **[3] Run Time Analysis:** \n\nBased on the reviewer’s comment, we performed a complete complexity analysis, which includes inference time analysis, and report below. We will add this expanded discussion to the paper:\n\nLet $T_{CVAE}$ and $T_{diff}$ denote one forward pass through the CVAE and diffusion models, respectively. Over a simulation horizon $H$ with window size $W$, we perform $H$ diffusion passes and $H/W$ CVAE passes, yielding a total complexity of $O(H T_{diff} + H/W T_{CVAE})$). Since both are vision‐conditioned with similar runtimes and $H > H/W$, the diffusion term dominates—so MIMIC adds no inference overhead. Empirically, we confirm below that MIMIC’s simulation runtime matches that of Diffusion BC across representative vision-based environments.\n\n| Environment | Runtime for BC (s) | Runtime for MIMIC (s) |\n| -- | -- | -- |\n| Aligning | 40.69 | 57.16 |\n| Sorting |71.5 | 78.5 |\n| Overcooked | 93.23 | 94.72 |\n\n> **[4] Performance on overcooked and clarification about multiple models**\n\nWe acknowledge the reviewer's observation regarding our reliance on simulated human proxies rather than empirical human studies. This methodological choice follows current precedents in human-AI collaboration research, where computational validation precedes human subject experimentation. Given the theoretical complexity and technical innovations introduced—specifically the operationalization of Vygotskian cognitive mediation within imitation learning architectures—we adopted a staged research approach. We prioritize establishing computational viability and theoretical coherence before moving to human subject experimentation. We agree that empirical validation with human participants is a useful next phase in this research.\n\nMIMIC and MIMIC-MA are not separate models, but variants of the same architecture using either self-only inner speech (MIMIC) or both agents’ inner speech (MIMIC-MA). As discussed in lines 255-259, this addition is inspired by the mirror neuron theory of social cognition, which is made possible through our framework. We still find that in almost all cases (except Asymmetric advantages), both variants still outperform the baseline while showing that modeling another agent’s speech is often important in multi-agent coordination tasks. This effectively supports the generalization of our approach to a dynamic multi-agent environment. \n\n*Performance drop of MIMIC on Asymmetric Advantages:* This is a good catch as we suspect an error in this result. First, note that the MIMIC-MA model outperforms diffusion-BC baseline. Hence it was surprising to see its performance drop when using only its own inner speech. Our initial investigation suggests a color‐flip bug—where the ego agent was inadvertently using the other agent’s speech instead of its own (not our intended setup). We’re rerunning the experiments and will update the results as soon as they’re available.\n\n> **[5] Additional Details on Inner Speech**\n\nWe thank the reviewer for identifying this important consideration. As shown in Section 2, Figure 2, our t-SNE visualization of language embeddings on the Aligning dataset demonstrates the behavior diversity captured by inner speech; we’ll include these plots for all datasets in the Appendix.\n\nWe further provide a new analysis of inner speech during steerable behavior simulation via conditional generation, by retrieving the nearest training descriptions in CLIP’s embedding space at each polling step. Below are two examples of different conditions (from the Aligning dataset (with $W=50$ and $t_0=49$), with cosine similarities to the actual inner speech in parentheses. We will include such examples from all datasets in the final version of the paper.\n\n1. **Condition:** I need to begin with a swift side approach, then slow down to ensure precise alignment.\n\n**Inner Speech:**\n- I am approaching the box from the side, rotating slightly to align smoothly with the fixed box. (0.9471)\n- I am approaching directly, then rotate in place to align perfectly. (0.9619)\n- I approach with a direct path but make a last-second adjustment to align perfectly. (0.9611)\n\n2. **Condition:** I need to rotate the box first before aligning it with the target.\n\n**Inner Speech:**\n- I push the box directly without rotation, aiming for a straightforward alignment. (0.9183)\n- I am approaching  directly, then rotate in place to align perfectly. (0.9614)\n- I am starting from a slightly rotated angle, needing a mid-action adjustment to align. (0.9616)\n\nThis shows that MIMIC mediates an agent’s actions by generating an appropriate inner speech based on its behavior during simulation. The high similarity with a true description in the CLIP space further shows that the generated inner speech is indeed meaningful.\n\nBelow we also discuss a couple of challenges to provide insights on potential failure modes:\n\n- The fixed polling window W is a critical hyperparameter whose misalignment with task-specific behavioral rhythms can induce failure cascades. Excessive polling generates behavioral instability through premature re-planning (overcorrection), while insufficient polling permits accumulation of distributional drift (undercorrection), particularly problematic in tasks requiring precise temporal coordination. \n\n- The CVAE's stochastic generation may produce inner speech vectors that progressively diverge from behaviorally-meaningful representations. While our polling mechanism provides periodic recalibration (which is a sweet byproduct of our approach as it helps mitigate the brittleness of Behavior Cloning), rapid environmental transitions could outpace the agent's cognitive updating, resulting in misaligned actions.\n\nWe will add discussions and examples in regard to these points to the final version of the paper."}, {"review_id": "3I1vKZbYLS", "reviewer": "Reviewer_5D71", "summary": "The paper introduces the MIMIC framework, an innovative approach to imitation learning that models inner speech as a mediator between perception and action, enabling behavioral diversity and designer control. The framework leverages vision-language models to bootstrap inner speech generation, allowing for scalable and flexible behavior imitation without the need for extensive human linguistic annotations. Despite its promising results, the framework faces challenges related to computational efficiency, interpretability of the latent space, and its performance in multi-agent environments. The paper makes a strong contribution to the field of cognitive imitation learning, with potential for further refinement and expansion.", "questions": "1.\tHow does the inner speech generation mechanism perform when the environment contains significant noise, or when visual observations are imperfect or ambiguous? Can the framework still generate coherent behaviors under these conditions?\n\n2.\tHow does the MIMIC framework adapt to multi-agent environments, particularly when agents must coordinate with each other without explicit communication? Are there extensions or modifications that allow the model to handle complex, multi-agent coordination tasks?\n\n3.\tCan you provide a more formal explanation of the latent space used in the CVAE model? How do you ensure that the latent representations correspond to meaningful and interpretable actions, and how do you deal with the non-linearities in behavior generation?", "limitations": "1.\tThe MIMIC framework's dependence on CVAEs and diffusion models introduces high computational costs, particularly in multi-step and large state space environments. The authors should explore ways to optimize or approximate the generative process for more efficient real-time application.\n\n2.\tThe framework relies on vision-language models (VLMs) that may inherit biases from the training data. Since these models influence the inner speech generation process, any bias in the VLM could lead to unintended ethical consequences. The paper would benefit from a discussion on bias mitigation strategies and the ethical implications of using inner speech models for behavior generation.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**:\n\n- The paper introduces the MIMIC framework, which models inner speech as a mediational process between perception and action selection in imitation learning. This approach offers a cognitive model that contrasts with traditional behaviorist frameworks, where actions are learned directly from input-output state mappings. By incorporating inner speech as a deliberative layer, the framework enables behavioral diversity and contextual adaptability, which are crucial for learning in complex, dynamic environments. This model opens up new avenues for understanding and replicating human-like variability in behavior.\n\n- The use of conditional variational autoencoders (CVAEs) to generate inner speech as a latent variable process is a solid computational foundation. This probabilistic approach introduces diversity and flexibility in behavioral generation, allowing the model to handle multiple possible actions for identical stimuli. It also enables steerable imitation, where the generated behavior can be influenced by high-level linguistic instructions, providing a semantic control mechanism without the need for direct human annotations for each task.\n\n- The integration of vision-language models (VLMs) to bootstrap the inner speech generator from visual observations is an elegant solution to overcome the lack of extensive linguistic supervision in traditional imitation learning. This approach allows for automatic generation of linguistic descriptions, which are then mapped to the inner speech process for task execution. This step significantly reduces reliance on manually labeled training data, making the framework more scalable and applicable in real-world settings.\n\n- The experiments conducted on D3IL and Overcooked environments show that MIMIC significantly outperforms traditional behavior cloning (BC) in terms of imitation fidelity and behavioral diversity. The results demonstrate the model's capability to produce contextually relevant behavior in challenging tasks such as sorting, stacking, and cooperative cooking, highlighting the framework’s potential in multi-agent and human-AI collaborative scenarios.\n\n**Weakness**:\n\n- While the framework introduces a powerful model for behavioral diversity, its computational demands are a concern. The use of CVAEs and diffusion models requires significant training time and computational resources, especially in complex environments with large state spaces. Additionally, the stochastic sampling process introduces extra computational overhead, making it less feasible for real-time applications. The paper does not sufficiently address the scalability of the framework in larger, more complex tasks or the trade-offs between performance and resource consumption.\n\n- The success of the framework heavily depends on the quality of vision-language models (VLMs) used to generate linguistic annotations. While the authors argue that improvements in VLMs will automatically enhance the framework, the reliance on these models can be seen as a limitation, especially when dealing with noisy or imperfect visual data. The authors could explore alternatives or hybrid approaches to reduce the dependency on VLMs for high-quality annotations.\n\n- The paper provides an extensive experimental analysis but lacks a formal theoretical analysis of the framework’s performance. Specifically, there is no exploration of the convergence properties or the optimality guarantees of the proposed approach. A more formal theoretical foundation would make the model more robust, particularly for generalization to new environments and for applications in safety-critical domains.\n\n- While the use of latent variables in CVAEs allows for flexible and stochastic behavior generation, the paper acknowledges that the latent space's connection to semantic content is opaque. This limits the interpretability of the model, making it difficult to understand how specific linguistic inputs affect the resulting behaviors. Developing disentangled latent representations or providing more clarity on how to map the latent space to real-world actions would enhance the transparency and control over the generated behaviors.", "was_revised": true, "final_justification": "Most of my concerns have been solved. I appreciate the detailed explanation and additional results provided.", "rebuttal": "Thank you for your review and supportive comments about scalability, novelty in cognitive grounding, and concrete experiments.  We address your specific concerns below, but all the weaknesses and questions you raise reflect the limitations we already discussed in the Appendix. We do not view these as weaknesses of the work; rather, they are intended to provide suggestions for interesting future research avenues. We think the efficacy and novelty of our framework are already well demonstrated.\n\n> **[1] Concerns about computational demands and insufficient discussion on scalability in larger, more complex tasks**\n\nMIMIC builds on widely used diffusion models and CVAEs—established baselines with well‐characterized computational profiles fully supported by modern research infrastructure—and critically, without adding any extra complexity. Indeed, practitioners routinely deploy similar architectures in production settings where they need behavioral realism. To alleviate the reviewer’s concerns, we’ve included a complete runtime and inference‐time analysis  below and will add this expanded discussion to the paper:\n\nLet $T_{CVAE}$ and $T_{diff}$ denote one forward pass through the CVAE and diffusion models, respectively. Over a simulation horizon $H$ with window size $W$, we perform $H$ diffusion passes and $H/W$ CVAE passes, yielding a total complexity of $O(H T_{diff} + H/W T_{CVAE})$). Since both are vision‐conditioned with similar runtimes and $H > H/W$, the diffusion term dominates—so MIMIC adds no inference overhead. Empirically, we confirm below that MIMIC’s simulation runtime matches that of Diffusion BC across representative vision-based environments.\n\n| Environment | Runtime for BC (s) | Runtime for MIMIC (s) |\n| -- | -- | -- |\n| Aligning | 40.69 | 57.16 |\n| Sorting |71.5 | 78.5 |\n| Overcooked | 93.23 | 94.72 |\n\nFurther, the environments used in our paper are representatively large scale and complex. D3IL is a strong imitation learning benchmark (ICLR 2024) and provides a comprehensive suite of tasks that are currently challenging for AI agents. Similarly, Overcooked is a classical and highly researched environment that presents complex coordination tasks. \n\n> **[2] Reliance on VLMs, noisy or imperfect visual data and ethical considerations**\n\nWe are unclear on the source of noise or imperfection that the reviewer is concerned about. We identify two potential sources of noise and address each of them. \n\n*(i) Behavior demonstrations:* This includes imperfections such as noise in the demonstrated behavior or suboptimal trajectories with respect to the task. We handle this, and it is crucial to focus on such demonstrations as they represent the richness in human behaviors. As we discuss in the paper (line 136), human behavior is noisy, diverse and non-Markovian in nature and hence, we indeed focus on performing well when provided with these kinds of demonstrations. Both the datasets we use comprise only human demonstrations, with noise and suboptimal behavior. \n\n*(ii) Visual observations:* This includes imperfections such as blurry images or an obscured view.  We believe that improvement in VLMs will continue to help address these kinds of scenarios. At the same time, we do not know of suitable datasets, which would prevent an empirical study.  And to specifically address the reviewer's comment, the other alternative to relying on VLMs is to use a human-annotated dataset. But this would be strictly more expensive and still prone to noise.  We are curious to know if the reviewer has any suggestions on the hybrid approach they mention.\n\nBroadening out the discussion, one approach to address the concerns about reliance on VLMs (and we would also include the CLIP model here) is to compare against other models. Our ablation in Figure 3(d) examines the effect of VLM choice on description quality. We compare o4-mini, and 4o for inner speech, showing GPT-4o’s marginal lead (0.72 success rate), with performance differences reflecting CLIP embedding space alignment. This implies description quality influences but doesn’t dominate performance—demonstrating robustness to VLM variation. We also introduce new ablations on VLM and embedding space: evaluating Qwen2.5-VL-72B-Instruct as an open-source alternative to GPT-4o, and MPNET (all-mpnet-base-v2) as an alternative to CLIP. The table below reports these Aligning-dataset results and we will include the full suite of results in the paper. \n\n| Model | VLM | Embedding | Success rate | Distance | Entropy |\n| -- | -- | -- | -- | -- | -- |\n| BC | - | - | 0.6645 | 0.1105 | 0.4743 |\n| MIMIC-S | GPT-4o | CLIP | 0.8021 | 0.0664 | 0.4184 |\n| MIMIC-E | GPT-4o | CLIP | 0.7229 | 0.0847 | 0.6148 |\n| MIMIC-S | Qwen | CLIP | 0.7583 | 0.0859 | 0.2027 | \n| MIMIC-E | Qwen | CLIP | 0.7186 | 0.0886 | 0.4546 | \n| MIMIC-S/E | GPT-4o | MPNET | 0.7896 | 0.07108 | 0.5271 |\n\nWe observe that GPT-4o with CLIP embeddings outperforms other variations, but also find that both ablations still outperform the base diffusion BC variant. This shows that MIMIC, even in the absence of a very powerful LM for inner speech, can enhance the performance over the diffusion BC variant. Furthermore, the lower performance of MPNET highlights the value of vision-language embedding over sentence-only embedding.\n\nWhile we flagged the potential bias issues in existing VLMs and their ethical implications, the research on mitigation strategies for such biases is out of the scope and an entirely different research field. We believe that any response we provide in terms of mitigation strategies would be speculative and counterproductive to the focus of this work.\n\n> **[3] Formal theoretical analysis, optimality guarantees and generalization**\n\nThe theoretical foundations of our framework derive from established convergence properties of CVAEs (Kingma & Welling, 2014) and diffusion models (Ho et al., 2020), which provide well-characterized optimization landscapes and asymptotic guarantees. Pursuing additional formal analysis was not the goal of our work and would misalign with our core contribution: operationalizing human cognitive processes that, by their very nature, eschew optimality in favor of generality and behavioral diversity.\n\nThe robustness we claim emerges not from convergence proofs but from grounding in Vygotskian cognitive theory (Section 3.1), which explains why human decision-making achieves remarkable generalization. Human cognition's strength lies in generating satisfactory solutions through linguistically mediated deliberation. Our empirical validation across diverse environments (D3IL, Overcooked) demonstrates that incorporating these cognitive principles yields superior generalization compared to optimization-driven approaches, suggesting that foundations from cognitive science provide complementary properties for real-world deployment.\n\n> **[4] Latent representations, transparency and interpretability**\n\nWe highlighted the opaqueness limitation to motivate further work on interpretability—a difficult, open problem—while noting that our cognitive grounding, coupled with linguistic control, takes a good step toward building interpretable behavior generators.\n\nWe appreciate the reviewer’s clarification about the correspondence of latent representations to the actions. As shown in Section 2, Figure 2, our t-SNE visualization of language embeddings on the Aligning dataset demonstrates the behavior diversity captured by inner speech; we’ll include these plots for all datasets in the Appendix.\n\nWe further provide a new analysis of inner speech during steerable behavior simulation via conditional generation, by retrieving the nearest training descriptions in CLIP’s embedding space at each polling step. Below are two examples of different conditions (from the Aligning dataset (with $W=50$ and $t_0=49$), with cosine similarities to the actual inner speech in parentheses. We will include such examples from all datasets in the final version of the paper.\n\n1. **Condition:** I need to begin with a swift side approach, then slow down to ensure precise alignment.\n\n**Inner Speech:**\n- I am approaching the box from the side, rotating slightly to align smoothly with the fixed box. (0.9471)\n- I am approaching directly, then rotate in place to align perfectly. (0.9619)\n- I approach with a direct path but make a last-second adjustment to align perfectly. (0.9611)\n\n2. **Condition:** I need to rotate the box first before aligning it with the target.\n\n**Inner Speech:**\n- I push the box directly without rotation, aiming for a straightforward alignment. (0.9183)\n- I am approaching  directly, then rotate in place to align perfectly. (0.9614)\n- I am starting from a slightly rotated angle, needing a mid-action adjustment to align. (0.9616)\n\nThis shows that MIMIC mediates an agent’s actions by generating an appropriate inner speech based on its behavior during simulation. The high similarity with a true description in the CLIP space further shows that the generated inner speech is indeed meaningful.\n\n> **[5] Multi-agent environments and complex coordination tasks**\n\nThere may be some misarticulation of the reviewer’s concerns here. The reviewer already acknowledged the value of this work in multi-agent environments and human-AI coordination in the “strengths” section and the concerns refer to the same capabilities. To clarify, Overcooked is a classical benchmark of complex coordination without explicit communication; in our experiments, MIMIC outperforms all baselines across its tasks, demonstrating adaptability and efficacy in behavior generation for multi-agent environments. If the reviewer alludes to our limitations section, we reiterate that those points are meant as a forward-looking agenda—such as scaling to more agents or mixed-motive dynamics—which would require substantial additional research, leading to additional publications and should not be viewed as weaknesses of this paper."}, {"review_id": "0MFEjvTVOa", "reviewer": "Reviewer_iCej", "summary": "The paper introduces MIMIC (Modeling Inner Motivations for Imitation and Control), a type of imitation framework motivated by inner speech theory from Vygotsky. The novel lies in the transformation of the visual understanding to an intermediate \"inner speech\" using a vision language model, and further uses it to drive the behaviour cloning policy to select actions. This results in controllability using natural language. The conditional variational autoencoder (CVAE) is conditioned on both the inner speech and the previous observation. Followed by a diffusion-based behaviour cloning policy based on current observation and generated \"inner speech\". The model was evaluated on the D3IL benchmark and the Overcooked human-AI coordination environment.", "questions": "-", "limitations": "Yes", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "### Strengths\n* Thorough grounding of a cognitive theory with neural network-based modelling implementation.\n* Can be ported to multi-agent settings.\n* Controllability using natural language enables zero-shot behaviour modulation because of \"inner speech\".\n\n### Weakness\n* The paper would benefit from a comparison with other BC methods, currently it is mainly only compared with a BC version of the architecture, without the inner speech\n* Since \"inner speech\" representations are quite responsible for driving the CVAE and diffusion process, ablation on those representations might also show the advantage or disadvantage of using CLIP for them vs other models like T5 or ModernBERT.", "was_revised": true, "final_justification": "The authors adressed my questions.", "rebuttal": "We thank the reviewer for their time and engagement with our work. We are excited to see the positive evaluation of our work and the reviewer’s recognition of our theoretical and empirical contributions. We address the reviewer's specific concerns below, providing clarification and additional evidence.\n\n> **[1] Comparison with other BC methods**\n\nWe have conducted new experiments on two additional behavior cloning approaches to compare their performance against MIMIC: (i) Behavior Transformers (BeT), which uses an action-based clustering and transformer architecture to perform multi-modal imitation learning, and (ii) BESO, a goal-conditioned multimodal IL approach. BESO is a stronger model for behavior cloning and has shown better performance on several tasks in the D3IL benchmark compared to Diffusion BC. Table 1 (D3IL comparison) and Table 2 (Overcooked comparison) demonstrate the superior performance of MIMIC across baselines on each of the datasets.\n\n**Table 1: D3IL Results**\n\n| Dataset  | Model| Success rate | Distance | Entropy |\n| -- | -- | -- | -- | -- |\n| Aligning | BeT | 0.51667 | 0.12949 | 0.40475 |\n| | BESO | 0.85417 | 0.04954 | 0.6141 |\n| | MIMIC-S | **0.88125** | **0.04234** | 0.7215 |\n| | MIMIC-E | 0.86875 | 0.04759 | **0.7706** |\n\n**Table 2: Overcooked Results**\n\n| Dataset | Model | Collective Reward |\n| -- | -- | -- |\n| Overcooked Cramped room | BeT | 47.2 $\\pm$ 4.64 | | |\n| | BESO | 67.8 $\\pm$ 4.55 |\n| | MIMIC | **120.2 $\\pm$ 2.86** |\n| | MIMIC-MA | **141.2 $\\pm$ 3.68** |\n\nWe will add the above results and those on other tasks in the final version of the paper.\n\n> **[2] Ablation on inner speech representations and VLM models**\n \nWe now also ablate our method on two fronts:  the VLM and the embedding space. Specifically, we evaluate Qwen2.5-VL-72B-Instruct as an open-source alternative to GPT-4o, and MPNET (all-mpnet-base-v2) as an alternative to CLIP (our choice of mp-net stems from its performance on MTEB benchmark where it is ranked 102 compared to ModernBert’s rank 202 and T5’s rank 177). In the table below, we report the results on the Aligning, and we will include the results on other tasks in the paper.\n\n| Model | VLM | Embedding | Success rate | Distance | Entropy |\n| -- | -- | -- | -- | -- | -- |\n| BC | - | - | 0.6645 | 0.1105 | 0.4743 |\n| MIMIC-S | GPT-4o | CLIP | 0.8021 | 0.0664 | 0.4184 |\n| MIMIC-E | GPT-4o | CLIP | 0.7229 | 0.0847 | 0.6148 |\n| MIMIC-S | Qwen | CLIP | 0.7583 | 0.0859 | 0.2027 | \n| MIMIC-E | Qwen | CLIP | 0.7186 | 0.0886 | 0.4546 | \n| MIMIC-S/E | GPT-4o | MPNET | 0.7896 | 0.07108 | 0.5271 |\n\nWe observe that GPT-4o with CLIP embeddings outperforms other variations, but also find that both ablations still outperform the base Diffusion BC variant. This shows that MIMIC, even in the absence of a strong LM for inner speech, can enhance performance over the Diffusion BC variant. Furthermore, the lower performance of MPNET highlights the value of  vision-language embedding over sentence-only embedding.\n\n> **Paper formatting concerns**\n\nThank you for catching them. We will update line 49 to say human $\\mathcal{H}$ and decision $a_t$. \n\nFigure 2 shows the t-SNE visualization of the latent space of inner speech, where different clusters exemplify the diversity in demonstrations captured through the inner speech. The color bar shows the cluster number that each data point belongs to, and the ellipsoid shows the variance around the cluster mean. We will make these details clearer in the final version."}, {"review_id": "Kh6SQ6VVcM", "reviewer": "Reviewer_5Wb5", "summary": "This paper proposes a method called Modeling Inner Motivations for Imitation and Control, in which the core idea is to generate an inner speech representation to guide action selection. The inner speech is produced by a language model trained using data generated by a vision-language foundation model (e.g., GPT-4). This generated speech is then used to condition a behavior cloning (BC) policy for downstream control tasks.", "questions": "- Clarify all plots and axes in the figures; ensure ablation settings are well-documented.\n- Tone down or more rigorously support the cognitive claims; consider discussing them separately from the technical contributions.\n- Add a strong goal-conditioned BC baseline for comparison to highlight the specific contribution of the inner speech signal.", "limitations": "see above for technical limitations", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n- The idea of conditioning policies on inner speech could be beneficial for interpretability and generalization.\n\n\nWeaknesses\n- The paper has major presentation issues that hinder my understanding of the proposed method and evaluation result. It is difficult to follow the exact structure of the pipeline and how components (e.g., vision-language models, inner speech generation, BC policy) are connected.\n- The cognitive grounding of “inner speech” feels overstated and is not well-supported by the technical content. The paper could benefit from a clearer distinction between cognitive inspiration and actual biological plausibility.\n\n\n- It is also really diffcutlt to understand the results. For example in Figure 3 the plot is missing labels, what is the y-axis for a and b? What is the different color bar in c and d? What are the ablations?  \n\n- The evaluation lacks comparison with a standard goal-conditioned behavior cloning baseline, which is important given the motivation to capture diverse behaviors and intent. Without this, it is hard to attribute performance gains to the inner speech mechanism itself.", "was_revised": true, "final_justification": "I have read the rebuttal, and I think they addressed most of my concerns and I'm willing to raise my score to weak accept", "rebuttal": "We thank the reviewer for their engagement with our work and their feedback.\n\nFirst, we clarify a technical misunderstanding highlighted in the reviewer’s  summary: inner speech generation employs a Conditional Variational Autoencoder (CVAE) rather than a language model. The CVAE is trained using representations derived from vision-language models, enabling stochastic behavioral mediation through learned latent spaces.\n\n> **[1] Presentation issues**\n\nWe have worked to give a clear exposition in the paper. While other reviewers commended the writing of our exposition and awarded us high scores on clarity, we appreciate that this reviewer found certain sections unclear. To address this, we provide here a systematic summary, with explicit references to figures, equations, and empirical results, to clarify concepts and methods:\n\nThe paper introduces MIMIC, operationalizing Vygotsky's cognitive mediation theory within imitation learning (Section 1, lines 54-65). The core proposition (Proposition 1, lines 61-65) establishes that **agents should learn $\\pi_{\\theta}(a|s,m)$ rather than $\\pi_{\\theta}(a|s)$**, where m represents linguistically-encoded inner speech mediating between perception and action. This paradigm shift from behaviorist state-action mapping to the cognitive framework of state→inner speech→action (Section 3.1, lines 143-183) addresses fundamental limitations in capturing human behavioral diversity while enabling linguistic controllability.\n\nThe MIMIC architecture (Figure 1, page 5) integrates three components in a unified pipeline. During training, a **pre-trained VLM generates behavioral descriptions from demonstration GIFs** using the prompt specified in Section 3.2.2 (lines 221-225), which are then encoded via CLIP embeddings (visualized in Figure 2's t-SNE plot). A **CVAE learns to generate CLIP-encoded inner speech representations** by minimizing Equation 4 (line 238), conditioned on W-length observation windows. The **diffusion policy** (DDPM-T architecture) generates the actions, incorporating the inner speech through classifier-free guidance (Equation 3, line 197). During simulation (Algorithm 2, page 20), the agent generates inner speech every W timesteps starting at $t_0$, with these **polling parameters critically balancing behavioral stability against adaptability** (Figures 3a-b show sensitivity analysis for $W\\in$ {1,10,20,50,100} and $t_0 \\in$ {0,1,W/2,W-1}).\n\nEmpirical validation demonstrates **20-30% success rate improvements** across D3IL benchmarks (Table 1, page 7) with **behavioral entropy increases up to 40%**. Wasserstein distance metrics (Tables 2 and 6) confirm high-fidelity imitation. In human-AI coordination tasks (Overcooked (Table 3, page 8)), **agents trained with MIMIC achieve 25% higher collective rewards** when paired with human proxies compared to diffusion behavior cloning. Figure 3's ablation studies reveal: (c) MIMIC outperforms random/k-means inner speech alternatives, validating linguistic grounding; (d) GPT-4o marginally outperforms o4-mini/4o-mini VLMs. **Conditional generation experiments** (Table 4, Figure 7) demonstrates zero-shot behavioral steering through designer-specified language. \n\nIn regard to **Figure 3**, we acknowledge the missing details, especially in the caption. We will improve the figure to include additional details, which we also outline below:\n\n*(a) Initial Step Sensitivity ($t_0$ parameter):*  This subplot examines the impact of delaying the first inner speech generation. The x-axis represents $t_0$ values {0, 1, 25, 50}, where $t_0=0$ indicates immediate inner speech generation upon trajectory initiation. Three metrics are evaluated: **Success Rate** (blue, 0-1 scale), measuring task completion frequency; **Entropy** (green, measured in bits), quantifying behavioral diversity through categorical distribution analysis; and **Distance** (red, L2 norm), indicating final position error from target alignment. The declining performance with increasing $t_0$ (success rate drops from ~0.8 to ~0.65) demonstrates that early cognitive mediation critically enhances both task performance and behavioral fidelity.\n\n*(b) Update Window Analysis (W parameter):* This investigation reveals optimal polling frequencies for inner speech regeneration. The x-axis displays $W\\in$ {1, 10, 20, 50} representing timesteps between updates. The non-monotonic relationship observed—peak success at $W=20$ with $0.74$ success rate—indicates a fundamental trade-off: frequent updates $(W=1)$ induce behavioral instability through excessive re-planning, while sparse updates $(W=50)$ permit accumulation of distributional drift. Entropy increases monotonically with $W$, suggesting that less frequent cognitive intervention enables greater behavioral exploration.\n\n*(c) Ablation Study on Inner Speech Representations:* This comparative analysis validates our linguistic grounding hypothesis. Four conditions are evaluated: \"None\" (baseline BC without inner speech), \"Random\" (stochastic vector representations), \"K-means\" (clustered behavioral prototypes), and \"Inner Speech\" (MIMIC). The substantial performance gap—MIMIC achieving 0.72 success rate versus 0.66 for baseline—demonstrates that linguistically-structured representations provide superior behavioral mediation compared to arbitrary or cluster-based alternatives.\n\n*(d) Vision-Language Model Comparison:* This ablation examines the impact of the selection of a VLM on behavioral description quality. Two GPT models (o4-mini, 4o) are used to generate inner speech during training, with performance differences reflecting CLIP embedding space alignment. GPT-4o's marginal superiority (0.72 success rate) over alternatives suggests that description quality influences but does not dominate performance, validating our framework's robustness to VLM variations.\nAt this point, we welcome the reviewer to raise any outstanding concerns or clarification questions, and we will be happy to address them.\n\nAt this point, we welcome the reviewer to raise any outstanding concerns or clarification questions regarding the presentation, and we will be happy to address them. \n\n> **[2] Cognitive grounding and technical support**\n\nWe acknowledge a crucial epistemological distinction raised by the reviewer that warrants clarification. Our invocation of Vygotskian cognitive theory serves as a **conceptual scaffold** rather than a claim of biological fidelity. We explicitly position our work within the tradition of cognitively-inspired AI (Section 2.1, Line 115) —where psychological theories inform computational architectures without asserting neurobiological correspondence.\n\nThe technical operationalization substantiates three core properties of inner speech identified in the cognitive literature: (1) **semantic condensation** through CVAE's latent compression, (2) **stochastic generation** operationalized via diffusion policy and (3) **temporal persistence** via periodic polling mechanisms. These architectural choices translate abstract cognitive principles into concrete computational mechanisms.\n\nProductive AI research often draws inspiration from cognitive science without requiring neural fidelity. Just as convolutional networks leverage principles of visual processing without mimicking V1 neurons, our framework seeks to extract functional principles from inner speech theory. To address the reviewer’s concerns, we will revise our exposition to more precisely delineate between: (a) cognitive theories providing conceptual motivation, (b) computational mechanisms inspired by but not replicating these theories, and (c) empirical validation of behavioral outcomes. \n\nWe emphasize that our computational approach is firmly grounded in cognitive theory and performs direct modeling of “inner speech as behavior guides,” building on Sokolov's neurophysiological model and Vygotsky's mediational theory. The theoretical discussion in Section 3.1 captures the mediational aspect and can be enhanced to include the theoretical specificity required for a complete Sokolovian instantiation. We will augment Section 3.1 to include further enhancements to the equations to clearly map out the cognitive tools we use to the components of our architecture discussed in Section 3.2.\n\n> **[3] Strong Goal Conditioned Baseline**\n\nWe thank the reviewer for this suggestion, which will help to further highlight the contributions. We have conducted new experiments on two additional behavior cloning approaches to compare their performance against MIMIC: (i) Behavior Transformers (BeT), which uses an action-based clustering and transformer architecture to perform multi-modal imitation learning, and (ii) BESO, a strong goal-conditioned multimodal IL approach. BESO is a state-of-the-art approach for behavior cloning that has shown better performance on several tasks in the D3IL benchmark compared to Diffusion BC. Table 1 (D3IL comparison) and Table 2 (Overcooked comparison) demonstrate superior performance of MIMIC across baselines on both datasets.\n\n**Table 1: D3IL Results**\n\n| Dataset  | Model| Success rate | Distance | Entropy |\n| -- | -- | -- | -- | -- |\n| Aligning | BeT | 0.51667 | 0.12949 | 0.40475 |\n| | BESO | 0.85417 | 0.04954 | 0.6141 |\n| | MIMIC-S | **0.88125** | **0.04234** | 0.7215 |\n| | MIMIC-E | 0.86875 | 0.04759 | **0.7706** |\n\n**Table 2: Overcooked Results**\n\n| Dataset | Model | Collective Reward |\n| -- | -- | -- |\n| Overcooked Cramped room | BeT | 47.2 $\\pm$ 4.64 | | |\n| | BESO | 67.8 $\\pm$ 4.55 |\n| | MIMIC | **120.2 $\\pm$ 2.86** |\n| | MIMIC-MA | **141.2 $\\pm$ 3.68** |\n\nWe will add the above results and those on other tasks in the final version of the paper. \n\nWe believe that we have comprehensively addressed all the concerns raised by the reviewer and would welcome any further discussion on any remaining points that could lead the reviewer to reconsider their evaluation."}]

Author rebuttal:

Meta-review: This paper introduces MIMIC, a novel framework for imitation learning inspired by the human cognitive process of inner speech. The core idea is to use language as an internal representation of behavioral intent. The method trains a conditional autoencoder to generate "inner speech" from observations, which then conditions a diffusion-based policy to select actions. 

The paper initially received mixed reviews. However, after a constructive rebuttal and discussion period, the authors successfully addressed the main concerns raised. Consequently, all reviewers have now reached a consensus and are in unanimous agreement to accept the paper.

The AC agrees with the reviewers' final recommendation. This is an interesting and well-executed work. The motivation drawn from cognitive science is particularly appreciated, providing a strong and compelling foundation for the technical approach. The authors are encouraged to revise the final manuscript carefully, incorporating the feedback from the reviewers and the valuable points raised during the discussion phase.
Decision: Accept (spotlight)