Context and Diversity Matter: The Emergence of In-Context Learning in World Models
Authors: Fan Wang
ZHIYUAN CHEN
YUXUAN ZHONG
Sunjian Zheng
Pengtao Shao
Bo Yu
Shaoshan Liu
Jianan Wang
Ning Ding
Yang Cao
Yu Kang
Venue: ICLR 2026 Poster (2026)
Keywords: In-Context Learning; World Models
Primary area: transfer learning, meta learning, and lifelong learning
TL;DR: We formalize, bound, and validate in-context environment learning, showing that long-context, diverse-input world models can self-adapt by recognizing or learning new dynamics without parameter updates.

Abstract: The capability of predicting environmental dynamics underpins both biological neural systems and general embodied AI in adapting to their surroundings. Yet prevailing approaches rest on static world models that falter when confronted with novel or rare configurations. We investigate in-context learning (ICL) of world models, shifting attention from zero-shot performance to the growth and asymptotic limits of the world model. Our contributions are three-fold: (1) we formalize ICL of a world model and identify two core mechanisms: environment recognition (ER) and environment learning (EL); (2) we derive error upper-bounds for both mechanisms that expose how the mechanisms emerge; and (3) we empirically confirm that distinct ICL mechanisms exist in the world model, and we further investigate how data distribution and model architecture affect ICL in a manner consistent with theory. These findings demonstrate the potential of self-adapting world models and highlight the key factors behind the emergence of EL/ER, most notably the necessity of long context and diverse environments. The codes are available at https://github.com/airs-cuhk/airsoul/tree/main/projects/MazeWorld.

Published as a conference paper at ICLR 2026
CONTEXT AND DIVERSITY MATTER: THE EMERGENCE OF IN-CONTEXT LEARNING IN WORLD MODELS
*aFan Wang 1.2, *Zhiyuan Chen', *Yuxuan Zhong', Sunjian Zheng', Pengtao Shao', Bo Yu', tShaoshan Liu', Jiinan Wang", Ning Ding', Yang Cao and Yu Kang23
Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China University of Science and Technology of China, Heifei, China 3 Anhui Province Key Laboratory of Intelligent Low-Carbon Information Technology and Equipment "Astribot, Shenzhen, China
ABSTRACT
The capability of predicting environmental dynamics underpins both biological neural systems and general embodied AI in adapting to their surroundings, Yet prevailing approaches rest on static world models that falter when confronted with novel or rare configurations. We investigate in-context learning (ICI.) of
world models, shifting attention from zero-shot performance to the growth and asymptotic limits of the world model. Our contributions are three-fold: (1) we formalize ICL of world model and identify two core mechanisms: environment recognition (ER) and environment learning (EL); (2) we derive error bounds for both mechanisms that expose how the mechanisms emerge: and (3) em- pirically confirm that distinct ICL mechanisms exist in the world model, and we further investigate how data distribution and model architecture affect ICL in a manner consistent with theory. These findings demonstrate the potential of self-adapting world models and highlight the key factors behind the emergence of EL/ER, most notably the necessity of long context and diverse environments. The codes are available at https: /ttps::/qithubbb //ttttu/////tb/////// com/ a aaraa-rrs-ssii cuhk ai rirull/
tree
INTRODUCTION
The ability to predict future environmental states is crucial for reasoning and decision decision-making in both animals and humans. Inspired by this principle, constructing predictive models, especially the world model (Ha & Schmidhuber) 2018b), to forecast environmental dynamics and outcomes forms the foundation for enabling agents to plan effective decisions and behaviors (Hafner et al.) 2025 /Zhang
al.
2023a Mazzaglia al. 2024 Samsami et al. 2024 Alonso al. 2024). Consequently. world
models are widely applied in fields such as navigation (Bar et al.) 20255] Mendonca et al. 2021 Koh
al.
202 Duan al.
2024| Liu et al. (2025), autonomous driving (Hu | [003] Russell et al.
2025
Gao et al. 2024| Zhang et al. [2024a] Wang et al., 2024c robotics (Zhang et al. [023bb Wu
al.
2023] Hansen al.) [2024] Pang et al. 2025 Zhou al. 2024 Barcellona al., 2025), are
considered a cornerstone of embodied artificial intelligence.
Despite their proven effectiveness across various applications, previous prediction frameworks largely rely on static world models optimized for zero-shot, few-shot, or instantaneous performance. In contrast, humans and animals achieve real-time adaptation (Vorhees Williams) (014)) through predictive coding-a process where prediction errors drive attention, generate feedback, and motivate learning and adjustment (Rao & Ballard 1999 Salvatori et al. 2023). For instance, when confronted with rare environments, humans experience surprise yet rapidly recalibrate their predictions for that setting, whereas static models continue to fail unless explicitly retrained on the relevant data. The ability to dynamically modify predictive mechanisms based observational evidence, rather
"qqual Contribution "ooresponniigg to: fanwang-px px @ gogmail.com shaoshanliue @ cuhk.edu.cn

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than relying solely fixed parametric memory and external mapping modules, can effectively enable the model to adapt to environments unseen during training. This capability can be effectively addressed by In-Context Learning (ICL), as evidenced by recent advances in Large Language Models (LLMS)) (Brown et al. 2020). However, existing investigations into ICI. primarily focus on language- related tasks or simpler few-shot classification and regression tasks. ICL within world models, despite its potential importance, remains largely underexplored. Addressing this gap could complete the missing pieces of embodied AI, further improving generalization and self-adaptivity.
Following the Bayesian hypothesis of ICL (Panwar et al. 2023; Xie et al., 2024), we clarify two potential underlying mechanisms for ICL in world models: environment recognition (ER), which relies on parametric memory of the training environment, and environment learning (EL), which does not. By deriving upper error bounds for both ICL. modes, we theoretically demonstrate that the emergence of ICI. depends on environment diversity, complexity, and context length. This insight motivates the development of long-context adaptive world modeling. Consequently, we introduce the linear-attention long-context world model, L2World, which enables self-adaptation to environments through efficient memory updates within the context. Across cart-pole control and dision-based indoor navigation tasks, empirically demonstrate that distributional properties and long-context capacity govern the ICL ability of world models. Despite employing lightweight image encoders and decoders in 1.2World, it establishes new state-of-the-art for long-sequence observation prediction in cross-enviromment adaptation, outperforming methods that rely on computationally intensive diffusion-barn based image backbones. These results underscore the importance and potential of enhancing ICL through intentionally diversified datasets and long-context modeling architectures within world models, rather than focusing solely on immediate or zero- shot frame-level performance.
2 RELATED WORK
2.1 DYNAMIC PREDICTION AND WORLD MODELS
Dynamic models, also referred to as world models (Ha Schmidhuber 2018b Forrester 1958). encompass probabilistic, physical, or generative frameworks that formalize an Al system envi- ronmental understanding (Sutton 1990. Battaglia et al. 2013: (Ha & Schmidhuber] 2018a LeCun 2022). These models predict future states by leveraging historical observations and play a pivotal role in advancing reinforcement learning (RL) methodologies and related domains. Specifically, they constitute foundational components in model- RL (Finn & Levine 2017 Schrittwieser ct al. 2020). enable simulations that facilitate agent learning through virtual experiences (thereby
reducing reliance direct environmental interaction) (Hafner et al.) 2019a 2025 Samsami et al.)
2024| Sutton 1991 Kaiser et al. 22000), and serve as auxiliary tasks to augment model supervision (Jaderberg et al. 2016 Hu al. 2022 Zhang al. 2024c).
World models demonstrate robustness in integrating multi-modal raw sensor data, including visual, textual, inertial, and tactile inputs. To mitigate challenges posed by high-dimensional sensory inputs, representation learning paradigms such as Generative Adversarial Networks (GANs) (Goodrellow al.) 2014)) and Variational Autoencoders (VAEs) (Kingma & Welling 2013) are widely utilized to compress raw data into compact latent spaces. Subsequent temporal modeling in these reduced dimensions is achieved through latent world model architectures (Hafner et al. 2025 Wang et al. 2024d| Mazzaglia et al., 2024; Samsami et al.] [024]] HHffner et al.] 2019b] ZZhang et al.) 2024c Garrido et al. 2024) Li et al. 2024), which capture sequential dependencies and enable coherent long-term predictions.
In navigation, early systems relied on traditional, hand-crafted pipelines such as SLAM. Recent work replaces these modules with generative world models, including diffusion (Bar et al. 2025). VAE (Koh et al.] (021). and RL-enhanced variants (Poudel et al. [0233] Duan et al. 2024). which reconstruct dynamics simulate semanties (Nie et al.] 2025 Liu et al. 2023). However, most
existing methods disregard continual adaptation, especially across episodes, leaving a persistent gap
between zero-shot performance and lifelong operation.
2.2 IN-CONTEXT LEARNING AND META-LEARNING
The approach based on parametric memory, which predominantly relies on gradient- based optimiza- tion or Wnighttht Learning (IWL.) has faced criticism for its lack of plasticity and the associated
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Conversely, ICL has emerged facilitating generalization to novel tasks without the necessity of parameter fine-tuning. ICL leverages contextual memory for task solutions, rather than depending on parametric memory. The concept of ICL. is not novel. Meta- learning (Santoro et al.,, 2016; Duan al. 20T6), which focuses acquiring learning capabilities rather than mastering specific skills, utilizes well-curated environments or data instead of relying on large-scale, uncurated pre-training data. Nevertheless, the lack of well-structured, task-rich, and cost-efficient datasets continues to present a significant challenge.
challenges it poses in continual learning scenarios (Dohare et al. as a pivotal capability in large language models (Brown et al. 2020
2024).
ICL has been utilized encode diverse array of learning mechanisms, including language learn- ing (Akyürek et al. 2024), regression (Garg et al., 2022), reinforcement learning (Laskin et al.
2023 Lee et al. 2023 Wang et al., 2025), and world models (Anand et al. 2022 Cupta al. 2024). highlighting its resemblance to biological plasticity (Lior et al. 2024). Yet existing work concentrates on few-shot in-context adaptation, overlooking ICL's potential as contexts grow indefi- nitely. Concurrent studies have identified various mechanisms and circuits underlying ICL, including distinctions between task learning and task recognition (Pan et al. 2023). as well as retrieval versus inference (Park et al., 2025), among others. Key factors influencing the emergence of ICL have also been investigated, such as transience, task diversity, and context length (Chan et al.] 2022] Anand al., 2022 Wurgaft al., 2025 Nguyen & Reddy 2025), along with the relationship between IWL and ICL (Chan et al. 2025 Singh et al. (025). However, these studies primarily focus on simplified regression and classification problems, while theoretical frameworks addressing the incentivization of ICL within world models remain underexplored.
METHODOL.OGIES
We consider an environment specified by a Partially Observable Markov Decision Process (POMDP) e: (O, A, Te, Ze). where S is the state space, A is the action space, O is the observation space, Te(s, 01 8') = Pr,e( (5)) (, a) the transition model, and Ze(s, 0) = (o]s( {) the observation model.
A fully observable MDP is denoted with e : (8, A, Te) with s. We denote a world model with the following equation:
World Model: ô.+1 po(-1a)) fo(g)), with qe (st, az) (MDP)
or q = (Oi-At: at-at...,,.... Or, a.) (POMDP). (1)
Let denote the model parameters; values marked with hat denote predictions, and unmarked values denote the ground truth. Consider extra contexts of observations and actions, Cr
(o) (C) al (C) of (C)), where T indexes the context length; the ICL capability of the world
model is then characterized by the following condition:
VT; > T2, Dipe Cr.) (-1:)) < D(po(-1q0 Cr.))le(-laa)),,
(2)
Here, D represents a metric measuring the error between two distributions (lower values indicate better performance). Notably, the ICL of the world model fundamentally relies on cross-episode contexts rather than intra-episode state estimation. to denote short-term state estimation and OT to represent long-tew ICL. While these typically aligned in a single sequence in practice, maintaining this distinction facilitates rigorous theoretical analysis. Building prior work that partitions in-context learning into different modes (Kirsch et al. 2022; Pan et al., 2023; Park et al. 2025), theoretically, we are able to identify two analogous modes within world-moder ICL: Environment Recognition (ER) and Environment Learning (EL). We then derive error bounds that characterize the conditions under which each mode emerges.
distinguish these concepts clearly, we use ge
3. ENVIRONMENT RECOGNITION (ER)
To clarify Equation 2 we consider world model optimized on the finite environment set Assume the system possesses an environment-specifc model pa... for every
=
'While most prior work integrates the reward model into the world model, our analyses omit explicit consideration of rewards. Nonetheless, since rewards can typically be derived from the state or observation, framework allows for straightforward extension incorporate reward-related considerations.
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environment then po decomposes follows:
PO.ER(OL-119. Cr) = Σ polelge, Cr)
po.e (
(3)
cEE
Environment Recognation
Environment Specilic World Model
Therefore the ICL in world models arises mainly from the recognition of seen environments, while environment-specife world model are static within the inference. The environment-specifee world model can be further decomposed into:
Po.e (0041 = 170.se(06elu1 (eo.
Po at) P0,2,e (0041/000)))1 dsg,
Estimation
Dynamics
Observation Model
where the estimate Pg.e is obtained by marginalizing over the terms that include state estimation, dynamics, and observation (po.s.e. Po,r.er po...e). Therefore, in the ER regime, the model first acquires world models for the entire environment set through IWL or parametric memory, and then uses the context solely identify the current environment.
3.2 ENVIRONMENT LEARNING (EL)
Equation is efficient when the environment set & is small, yet its effectiveness diminishes rapidly as the size and diversity of & grow when the system faces open worlds. However po can also be approximated without estimating at all, by directly accumulating the evidence for (4t, Or+1) across all contexts:
pa (oLe1000 Cr) = plge, 0(+1(Cr)
(5)
P(g:(OT)
An intuitive observation of Equation (5) is that EL functions, minimum, in-context memorizer. For highly complex environments, its performance might degrade sharply. because accurate estimation of the transition of qe demands contexts that scale with the environment's complexity.
3.3 THEORETICAL ANALYSES ER AND EL
Although the training process and data distribution play key role in effectively incentivizing ICL (Chan al. 2022). how does the data distribution determine whether EL or ER emerges? If training consistently minimizes predictive error, the error bounds of EL and ER become the decisive factor in selecting the emergent mode. To investigate the conditions governing the emergence of the two modes (ER and EL), analyze the error upper bounds for each paradigm. For tractable analysis, we introduce the following simplifying assumptions: (1) The observation, state, and action spaces are discrete; (2) Both modes achieve ideal state estimation pe (s]g); (3) The context Cr has uniform state-action distribution. Under these assumptions, we derive an upper bound on the error of the world model optimized over environment set ε, measured by the total variation (TV) distance, when deployed in an unseen environment en at context horizon T.. Formally, the TV error is bounded by:
Theorem For Environment Recognition and Environment Learning whose predictive models PER/ EL have been sufficiently optimized on the training environments E, the upper bound of the variaaaai (TV) distance between the predicted and the ground -ruth transition, given a context Cr of length T, can be estimated as:
TV(DER,Pe) minla/33 -181-1)-
max TV (pors + min TV (po,es TV.po.c. Peo) eieeEE
cEE Motching Error
Recognition Error
Dinersity
TVGEL,Pee) & (1gg0/5 (Voe 112
Enviroment Complexity
with probability 1 5, and 4|5T |og]|/|A
Proofs and detailed assumptions for the above theorems are deferred Appendix A An immediate observation from Theorem [ is that EL enjoys an ideal error upper bound that decays as 7-2/2
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whereas ER carries a non-decaying residual term (the best-matching error) that becomes the dominant obstacle to generalizing across unseen environments. To enhance generalization, we therefore ask: under what condition is EL preferred to ER? For the entire training set &, EL dominates whenever EReE (fV.,,, < Eeee (İER Pa]]] The opposite inequality favors ER. Although the errors themselves are intractable evaluate directly, the following insights are obtained by comparing their upper bounds:
(1) Lower environmental complexity and a greater number of environments favor EL over ER:
Note that the best-matching error is effectively zero because the model is evaluated only on enviromments seen during training. The cardinality of the training set, [E], affects only the ER bound, whereas the environmental complexity, [O||S|| influences only the EL bound. Consequently, lower complexity combined with larger training set pushes the EL bound below the ER bound.
(2) Long context and environment diversity are key to both ER and EL: As the upper error bound of ER effectively approaches zero when diversity is low, the emergence of both ER (where the identification error would never dominate) and EL is precluded. Once the training set is sufficiently diverse, both ER and EL obtain an upper error bound that decays as 1/2 demonstrating that long context is indispensable for either mechanism.
(3) Over- wver-training and powerful IWL facilitate ER over EL: we hypothesize that IWL perfectly models the environment-speeific dynamics (fo.e) in the training set, so the best-matching error is nearly zero during training, however, this is not always true. Early in training. IWL can still incur large error,, transiently pushing the model toward EL instead of ER. This transiency is also investigated by prior works in ICL (Singh et al. 2023 2025). As training proceeds and IWL becomes increasingly accurate, the model may revert to ER.
In the following section, we empirically confirm that these insights hold not only for discrete settings but also for continuous MDPs and POMDPs. Because theory predicts that large environmental diver- sity and low task complexity required for incentivizing EL, and most of the closed benchmarks can not satisfy those requirements. We construct our dataset from randomly sampled cart-poles and procedurally generated mazes to evaluate the performance of ER and EL.
Obarvation (0
9.1
Bs
Rg
A
Re
Re.k
Linear Attention I Layers
Linear Attention Layers
dh
dhe-
P
01--
Chunk wise Compulation
Recurrent Inference
Acuon
Encoders& &
In- Contect Leaming
Multi-Stap Prediction
Deroders
Training
Evaluating
Figure 1: The world model structure for the empirical study.
3.4 L2WORIDD LONG-CONTEXT AND
WORLD MODELS
Prior work relies on multi-token representations or diffusion models to deliver high-fidelity single- frame reconstructions specifically for images. While these approaches set the ofate-of-the-art for static images, they introduce prohibitive memory and computational bottlenecks when sequences grow to the length required for EL. We therefore introduce L2World that trades per-frame fidelity for temporal scalability: for image observations, we compress each frame into a latent state 8, with lightweight variational auto-encoder (VAE) (Kingma & Welling 2014) whose encoder fI and decoder 91 are ResNet stacks; for low-dimensional observations, we simply apply a small multi- lalli--laerrpeee perceptron encoder/decoder pair. For computational efficiency, we do not model the full state estimation with p(s,, (q)). Instead, we construct a pseudo-state that depends solely on the instant observation and leaves all temporal-relane
encoding to transition modeling (Mazzaglia et al., 2024). We then construct the adaptive world model fo 1 Cr) using an efficient sequence decoder Jo- Here we implement gated slot attention layers (Wang et al., 2020; Yang et al. 2024] Zhang et al. 2024bb with chunk-wise parallelization during the training phase, while retaining the recurrent form during the inference phase. The predictor of transition at first yields the output h2, which is further processed by the decoder gs to produce the predicted state 8, corresponding to predicting Gaussian distribution over the latent space ş ~ NV o?). Although this assumption could lead to significant loss of accuracy in stochastic environments, for the navigation tasks consider, it is acceptable and
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greatly increases computational efficiency. The model and the target function are listed follows (see details in Appendix :
Observation Encoder 8t, Sstt = fI((r) Observation Decoder: or = g1:) Latent Decoder: &t. ost 9s(()) Action Encoder a, fa fa(Atioon(e))
Chunk-wise Temporal Modeling: Φt, hi...., hy = fo( 01,..., Sc. ar)
Recurrent Temporal Modeling (Evaluating) d., het1 = -f:oo=ollllo $0( at)
Observation Reconstruction Loss C. = ||o - gr(3t)ll + (
State Transition Loss:L,
Σ
When the observation is an image, first pre-train the image encoder fo and decoder go on pre-sampled observations; after this stage, their parameters are frozen while the temporal model trained. For lower-dimensional observations, all encoders/decoders are updated jointly with the temporal model in single end-to-end phase.
EXPERIMENTS
Although prior studies (Chan al. 2022 Singh al. 2023 Raventós al. 2023) have validated the influence of data distribution on ICL, they have largely concentrated on simplified tasks such as regression and classification. To examine how data distribution, model architecture, and training procedure jointly affect the emergence of EL/ER, we select two canonical benchmarks. First, the cart- pole, a classical comtinuous-control problem, in which EL primarily targets the acquisition of varying embodiments and physical constants. Second, indoor navigation, a widely recognized POMDP, in which EL focuses on learning and memorizing spatial coefficients. These two experiments confirm not only the impact of each factor EL but also demonstrate that EL spans broad generalization spectrum, extending from spatial reasoning and memorization to adaptation of embodiment and physical constants.
4.1 RANDOM CART-
Experiment Setting. To investigate EL and ER in cart-pole environments, randomized four variables in the environment settings: gravity 9. cart mass m., pole mass mp, and pole length l. We focus on two different scopes of the configurations to investigate the impact of the diversity issue: Scope remains close to the original task, whereas Scope covers larger region and excludes Scope 1 (details are left to Figure (). For each environment, trajectories with an expert policy perturbed by uniform noise spanning [0.3, 0.7] to ensure adequate coverage of the state-action space. We trained five comparison models; all share the same data scale (128K trajectories x 200 step/trajectory) but differ in the number and scope of the environments.
first trained RI. agent and then collected
1-Env: 96K trajectories are sampled from the original environment (g 9.8, m. 1.0, my 0.1, = 0.5), with 200 steps per trajectory.
4-Evvs: environments sampled from Scope 1+2, each with 24K trajectories.
16-Envs: 16 environments sampled from Scope 1+2, each with 8K trajectories.
8K-Enys (Scope 1/1+2): 8,000 environments sampled from Scope 1 or 1+2, each with 16 trajectories.
We evaluate with three test sets: (1) Seen 4-Envs, but the trajectories are independently sampled; (2) 256 environments from Scope 1; and (3) 256 environments from Scope 2. We evaluate mainly with the average prediction error, which is averaged over each of the context lengths
We plot the evaluation results in Figure where the following insight is worth noting:
Importance of both environment scope and environment number: comparison between the model trained on I environment (1 Env) and 4 environments (4 Envs) with the other group demon- strates that an insufficient number of environments leads to the absence of ICL and generalization, except the tasks that the model has already seen. The 4 Envs group exhibits clear ER characteristics,

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Evallating on Scope 1 Envs
Evaluating on Scope 2 Envs
Evaluating on Seen Envs
TE(0,10]
TE(10,20]
(000
TE(0010)
TE110,20]
(E(180,200)
TE(0,00
TE(10,00
TE(180,200)
Prediction
Error
100200
1-Eany (Original)
I 8IK-Eavw (Scape 1) HiC-Eavss (Scope 1-2)
16-Eavs (Scupe 1-2)
B4-Raas (Ncope 1+2)
4-K-vvv (Scupe 1+2, Karly Stage)
Figure 2: Comparison of models trained on different datasets (color-coded) in Cart-Poles, Perfor- mance varies markedly with the training data, revealing distinct tendencies toward ER, EL, or an inability to perform ICL.
with substantial performance gap between seen and unseen tasks. The 16 Enys (Scope 1 and 8K Envs (Scope 1) groups display similar capabilities; however, they lag significantly behind the 8K Envs (Scope 2) group, indicating that both the scope of tasks and the number of tasks are crucial.
Divergence between few-shot and many-shot performances: Another insight gleaned from the comparison between 4-Envs and 8K-Envs that the latter, which has a broader generalization scope, also requires more context to learn. This is evidenced by the fact that the performance of the latter group does not surpass that of the former group until T 10. This also validates the theoretical analysis, indicating that longer context is a cost for achieving better generalization.
Over-training reduces generalization when training environments are insufficient: To isolate the effect of over-training, we extract an early-stage checkpoint from the 4-Env group and evaluate it across environments. Although its performance in seen environments sub-optimal, generalizes to unseen environments with considerable margin over the over-trained model, confirming the shift from ICL- to IWL-based reliance.
EL exhibits smaller generalization gap and rela- tively lower upper error bound. According to Equa- tion (6). the context length increases, the Best Matching Error (BME) remains within the upper error bound ER, thereby limiting generalization. In contrast, EL is not af- fected by this term. To further investigate these differences case-by aase-by-case basis, examine the correlation be- tween the BME of each of the 130 unseen test cart-poles and their corresponding prediction errors. The BME for each model and testing environment is estimated by apply- ing the ground-truth world model of each environment in the training set to the test environment and selecting the minimum error. Note that we plot the prediction error for T 100: therefore, the terms containing 1 are negli- gible, reflecting asymptotic performance. The results, shown in Figure reveal that the model trained with only four environments exhibits upper error bounds close to the line error BME, whereas the upper error bound progressively moves below this line the number of training environments increases. This not only validates the BME as crucial term in Equation (6) but also confirms a transition from ER to EL mode the number of environments increases.
#Ebras boo0e 2121
teE-tresicee brer
101
12-3
16
Best
Figure 3: Best Matching Error (BME) versus prediction error for various mod- els across 130 test cart-poles.
4.2 NAVIGATION
Experiment settings. Procedurally generated mazes are demanding test-bed for transition prediction (Pasukonis al. 2023 Wang et al. (024a). By stripping observations of semantic cues, the maze framework keeps task complexity low while still exposing models to stochastic, partially observable dynamics. We amplify diversity through fully randomized configurations that vary topology, textures,
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Table A summary of data distribution across the training datasets.
Training DataSet
# envs (E)
Len. Traj.
# Traj.
# frames
Indoor area
Maze-32K-L
32K
10K
32K
320M
380 3422m
Maze-32K-S
32K
100
3.2M
320M
380 ~ 3422m²
128-L
128
10K
32K
320M
380 3422mn
Maze- 128-S
128
100
3.2M
320M
380 ~ 3422m²
PrO-THOR-5K
5K
2K
5K
10M
40 600ml
Pro:THOR-40K
40K
2K
40K
80M
40 ~ 600m?
object placement, and agent embodiment Room-tour data are collected in way similar to cart-pole: we perturb the oracle object-navigation policy (Ehsani et al. 2024 Pašukonis et al.) 2023 with random noise levels in [0.05, 0.95] and record the complete trajectory an agent in each environment. The oracle policy was derived using Dijkstra's 's algorithm based on the ground truth 2-D occupancy maps. Observations are RGB images standardized 128 x 128 pixels; The action space comprises 17 discrete actions, each corresponding to unique offset and rotational movement. Table Ilitts the resulting training sets, each drawn from different number of environments and exhibiting varying trajectory lengths. To isolate the effect of data distribution, all maze datasets contain the same number of frames but differ in the coverage of trajectories and environments. To further investigate transferability to more realistic environments, we also collect trajectories from the semantically rich ProcTHOR simulation, which offers wide variety of assets (Kolve et al. [0177] Deitke et al., (022). Specifically, we curate two datasets: larger one with 40,000 trajectories and smaller one with 5,000 trajectories, each trajectory having length of 2,000 frames.
For training, inspired by overshooting (aaner et al. 2019h) and Hu et al. 2022 we randomly mask the 8 of the input sequences at some positions to enhance the model's ss capability to predict the distant both seen and unseen tasks. By default, we use an evaluation set scale of | = 256. The evaluation process involves encoding context of length for EL and then predicting future k-step transitions using nugo-regression and the ground truth action records ac+1, Ge+2, ..., a++k- We assess the error in both the latent spaces and the decoded images, which we refer as k-step prediction with context length T t. For example, T T= 10 and = 4 predict future 4 steps with a context length of 10.
future (see training details in Appendix B.II. Evaluation is conducted
We evaluate two additional baselines alongside the proposed long-context world model: (1) Naviga- tion World Model (NWM) (Bar et al. 2025). which employs diffusion layers to predict the next frame from the preceding four frames; (2) Dreamer-v3 (Hafner et al., (019a),, which uses LSTM layers for temporal encoding. For NWM, we retain its original pre-trained image encoders and re-train only the diffusion layers on the target dataset. For Dreamer-v3, we remove the policy components and train only the world-model module to ensure fair comparison.
Table 2: Comparison of the performances (PSNR 1) of 1-step future prediction in Mazes.
Seen
Unseen
Model
T=10
T=100
1o=0000
T= 1000000
T=1
T=10
T=100
1=1000
T= 10000
L2World (Maze-32K-L
16.80
20.97
23.11
24.65
25.05
16.37
21.24
23.17
24.66
24.65
L2World (Maze-32K-S)
18.57
19.28
19.67
20.21
20.48
18.45
19.24
19.63
20.29
20.31
1.2Wolld (Maze-128-5
19.47
20.39
20.58
22.02
21.77
18.01
18.63
19.00
19.67
19.63
12World (Maze-128-L))
18.54
20.86
23.32
25.65
26.00
17.54
19.43
20.96
21.54
21.52
Dreamer (Mazc-32K-L)
16.40
21.82
19.24
21.26
16.81
20.48
21.40
22.65
22.12
Dreamer (Maze-128-L)
17.13
20.64
21.83
22.20
22.43
14.26
14.54
14.09
13.46
13.50
NWM (Maze-32K-L)
20.84
20.21
19.19
22.32
21.06
16.20
16.71
17.00
17.37
17.85
Impact of data distribution and model architecture on ICL. Table 2 reports the next-frame
prediction quality estimated by PSNR (k 1) for models trained on different datasets. Three empirical findings corroborate Theorem [ (1) The 32K-L dataset yields the best generalization to unseen environments, whereas the 128-L dataset excels on seen ones; in both cases, peak performance occurs at the asymptotic stage, not the beginning of the context. (2) Long-conter training consistently produces stronger ICL than short-context training, confirming that extensive context is
PProcedural Maze environments:
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L2World (32K-L)
Droomer
(32K-L)
(128-1)
L2Wort (128-5)
L.WWordd (32K-8)
10 10'
Conteet Length
10' 10
Figure 4: Comparison of k-step autoregressive PSNR in Mazes(Unseen).
necessary for ICL to emerge. (3) Dreamer and NWM fall short even with long-context dataset: Dreamer's LSTM backbone
NWM's 4-frame horizon show that architectures incapable of fully leveraging long contexts cannot achieve many-shot ER. Figure further presents the k: = {1, 2, 4, 8]- step prediction performances on unseen Mazes, measuring how far ahead the world models can reliably foresee. For k > 1, the perfomaanc-ccotee--lee across models, except for Dreamer (Maze 32K-L): at 8, its performance plateaus once > 100, whereas k keeps improving, revealing larger compound-ers -eror accumulation than in method.
curve largely tracks the next-frame trend
Table Comparison of the PSNR of 1-step future prediction in ProcTHOR (Unseen)
Model L2World
Pre-train
Post-train
T=1
T=100
100000
T=10000
15.49
18.22
19.02
19.74
19.81
L2 LoWlold
Maze-32K-L
16.46
20.23
21.05
21.89
22.04
ProSHOO--KK
L2 World
Maze-32K-S
19,80
19.45
19.86
20.57
20.61
L2Wolld
Maze-1284
19.16
19.60
20,20
20.94
16.46
Dreamer
19.82
22.61
23.99
23.51
22.76
NWM
18.30
21.41
21.02
20.08
ProcTHOR
L2World
21.57
22.67
23.39
24.92
22.98
L.World
Maze-32K-L
17.21
22.81
24.32
25.40
23.94
EL transfers better than ER. In Table 3. we train L2World on Proc- THOR trjectories and evaluate it on unseen ProcTHOR scenarios. The EL model pre-trained Maze-32K- not only excels in unseen mazes but also maintains its advantage when fine-tuned on the small ProTTHOR- 5K dataset. Its transferability signif- icantly surpasses that of Maze- 128- L and other baselines, demonstrating EL's domain generality. Further in- creasing the amount of ProcTHOR data leads to continuous improvement in our model while preserving a substantial margin over Dreamer and NWM. However, performance at T = 1K to T 10K begins to deteriorate when the ProcTHOR training data increases from 5K to 40K, suggesting that insufficient-length data (T < 2K) impairs the long-ICL ability acquired in Maze scenarios.
20% credicrddddddd Swep
conbint swapp
1244 (lebre)
PSSR
Content Leagth
Figure 5: The decline in performance of EL (trained with Maze-32K-L) and ER (trained with Maze- 128-L) when ob- servations in contexts are shuffled, measured by PSNR.
EL is more sensitive to context perturbations than ER. We investigate how models trained on datasets with varying levels of diversity (Maze-32K-L versus Maze- Maze-128-L.) respond perturaations

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Published as a conference paper at ICLR 2026
in context. Specifically, assess the importance of context performance, randomly shuffle 20% or 50% of the observations within contexts while keeping the actions unchanged. The results are find that models trained Maze-32K-L (which expected
illustrated in Figure Interestingly, to exhibit EL) are more severely affected by these perturbations than those trained on Maze-122 12---. This suggests that EL depends more heavily context, whereas ER relies more model parameters and is therefore less influenced by changes in context.
5 CONCLUSIONS AND LIMITATIONS
This work investigates in-context learning of world models, specifically dynamic models, focusing on the possible modes EL and ER in MDP and POMDP. Theoretically, we analyze enror upper bounds for both modes to characterize their properties and identify the conditions under which each excels. Empirically, we introduce L2World and validate these insights in cart-pole and navigation tasks. Our results underscore that both high environment diversity and sufficient context length of the world model are essential to elicit EL.
Conclusions:
Limitations: At present, analysis is confined to the dynamic model; the reward and policy models can be addressed subsequently. This work constitutes a first step toward broader ICL mechanisms such In-Context Reinforcement Learning. More sophisticated validations real-world datasets and environments are desirable in the future.
ACKNOWLEDGMENTS
This project is supported by Longgang District Shenzhen's ss "Ten Action Plan" for supporting in- novation projects (Grant LGKCSDPT2024002) Guangdong Provincial Leading Talent Program (Grant No. 2024TX087319), National Natural Science Foundation of China (62033012), Guangdong S&T Program (Grant No. 20225800990000033) and the "Zhiguo" Action of Guangxi Science and Technology Program under Grant No. No.7G2503980003.
ETHICS STATEMENT
This research adheres to the ethical standards of the machine learning community. The datasets used this work were synthetic and designed solely for academic research; they do not contain personal, sensitive, identifiable information.
REPRODUCIBILITY STATEMENT
We provide details of datasets and experiment settings in Section and Appendix B The training and evaluating codes are available at gttt:::///ithbb.comm/ con/aiiicooiiio dirsoul/tree /earee/maln http://eemmmmh/ppppppeeee//aaraa
The benchmarking environments main/
are available at ht https://gittbb..e Fut htt.:///oo..ttutaaaeeeeeeerrerra
py
(random cart-pole) and https
(maze).
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A ASSUMPTIONS AND PROOFS FOR THEOREM |
We first define
62) = DR (Du.cy (-14), Do (-1)))
62) = max Dx e0 (.14), Pa (+14)
9
è inf nle, 60)
eEE,
Ka sup eEE,efão
n(e,
Pe (C(Cr)=
II
Pe(or+1lg)
We then make the following Assumptions:
Queries q: (St, az) are sampled i..dd from distribution pls, a) with p(s,a) |
x(er,e) 62) = (2) with ((e), A(ee)))
1. We further define a² Note that a can be interpreted the measure of "non-uniformity" between any two environments in the set: it attains maximum when the two environments are almost identical yet differ significantly at only a few positions, and approaches 1 when the environments either completely different exactly the same.
a = maXe, .2 a(e1 62). so that A()1. 2 k(m1.02)
The environment recognizer selects closest task by argmar.esPe
Proof for the first part of Theorem First, we gave that
TV (ŌER, Peo) TV (po,e, Peo) S (Ve,e, p0,60) TV (pe,éo Pea)
= (po, p0.80) + min TV (poo,e:
eEE
We then estimate the first term and use the Chernoff bound for derivation:
TV TPo. Po.
Σ
((p.(CT) > Peo (V (o..e)
eEE
Σ a exepp(-77 (op(-T-A A(e, 60) V1/2A(e, (12251))
oEE,ef00
Chernoff bound
Inequality
Q
2
eEE,egi0g
nchieved maximum when T ((e.Fo) 1/2
a((E1-1)
(8)
3/T
On the other hand, by definition, the TV of ER satisfies the following upper bound:
TV Pew) ) < max (po.o. Pen), 299E
< max TV TV(pe, Pe,) + min TV Peg). E1.eaEE
.EE
By synthesizing Equation 77. Equation 8 and Equation ( the proof is complete.
We keep the aforementioned assumptions that the distribution of the context Cr is uniform the state and action space. We denote n(s, a) as times of
Proof for the second part of Theorem I
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appearance of (s, a) in Cr. It is first straightforward prove with Hoeffding's inequality that with probability of 6/2.
T
T log (4|S|IA1/5)
T
T log (4]5)[A1/6)
E
vs,
|S||A|
2
|
Then, by add the constraint
T> 4/5/2| (|)))) with at least probability of - 8/2,
T
n(s,a) 2|
(10)
To estimate PEL (s'ls,,,, a), we use Equation (5) acquire:
Po.EL(ott14,, Cr) p(q.. Cr)
Cr)
CT)
Σ. p(s. ae, 0++1 Cr)p(sla)
(1I)
E. p(s, ar (T)p(())))
Now employ Equation (TO) and Equation (11) in the calculation of TV:
TV Peo) = TV( Σ. p(s, ae 0441/11/////oo Cr4s(C))p((la)
E. p(s,, as o4t (c11)(())))
E. p(s, at (T))((s))))
S max TV n n(s, ar 0+11 E CT) p(8,01,0411) n(s, E Cr) p(s,ai)
[|oo({|001/0 with probability at least 1 8/2
118 ag CT)
Hoetldinggss imequality
2| (|log(()/S//)) with probability at least -
T
This finishes the proof of Theorem
B ADDITIONAL EXPERIMENT SETTINGS
B.1 ENVIRONMENTS, AND DATASETS
Cart-pole: Figure 6 lists the scopes of the Cart-Pole environment variants, their t-SNE visualization. and details of the training and evaluation data. All datasets share trajectory length of 200, which is sufficient for ICL in Cart-Pole variants. The training data comprises a total of 25.6M timesteps to avoid interference from data scale on performance, and the evaluation data contains 205K steps in total.
Mazes: Our maze environment is closely related to the settings described Pašukonis al. (2023); Wang et al. (2024b), which are generated on 15 x 15 grid world. The distribution of the sampled mazes shown in Figure and Figure The primary distinction between our mazes and those in previous work is the enhanced environmental diversity achieved through randomized configurations, which include the following:
The textures of the ceiling, ground, and walls randomly selected from collection of 87 real-world textures.
The scale of each grid varies from 1.5 m m, and the indoor height ranges from m to 6 m.
The ground clearance and the field of view (FOV) of the camera are varied between [1.6m, 2.0m] and [0.3 0.8m), respectively.
Two-wheeled dynamics are employed for the embodiment.
Each environment involves [5, 15] objects, each marked with a crossable, translucent light wall of different color.
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16 Envi Original Test Scope
m0
10p
Scope
[8.12]
[0.8. 1.2]
[0.08, 0.12]
[0.4, 0.6]
Scope 1+2
[2.16]
1.05.
[0.05, 0.20]
[0.20, 1.0]
Test Scope
DataSets
envs (EE))
Len. Traj.
Truj.
# time steps
Scope Original
1-Env
200
128K
25.6M
4-Envs
4
200
32K
25.6M
Scope & Scope 2
16-Envs
16
200
8K
25.6M
Scope 1 & Scope 2
8K Envs (Scope 1)
8K
200
16
25.6M
Scope 1
8K -n-E (Scope 1+2)
8K
200
16
25.6M
Scope 1 & Scope 2
Evaluation (Scope
256
200
4
205K
Scope 1
Evaluation (Scope 2)
256
200
4
205K
Scope 2
Evaluation (4-Envs)
4
200
256
205K
Scope & Scope 2
Figure 6: Configuration scopes and cases of random Cart-Poles (upper left), I-SNE visualization of the configuration distribution (upper right), and a list of training and evaluation datasets.
Figure 7: bird's-eye view of 128 procedurally generated mazes in the Homogeneous dataset.
Oocspation
Obeess
Figure 8: Distribution of the configurations (area, occupancy rate, and number objects in each scene) in the 32K procedurally generated mazes
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(a) Procedurally Generated Mazes
(b) ProcTHOR
Figure 9: Illustration of the embodiment, the observation, and the trajectories in procedurally generated mazes (a) and ProcTHOR (b).
Environments
Encoder & Decoder
D
1M
Cart-pole
Raw Image
128
64
Navigation
1024
18
32
64
Table 4: Model architecture and syper-parameters for the two classes of environments
The agent receives a reward 1.0 when reaching the goal, and negative reward for collisions with walls, which is dependent on the agent's speed.
We sample 336 training and 256 evaluating houses from the ProcTHOR 10K dataset. Unless otherwise specified, keep both the trajectories and the environments of the validation datasets in ProcTHOR and Mazes separate from those of the training datasets. In seen-task validation, the environments have overlaps, but the trajectories are resampled.
ProcTHOR:
B.2 DETAILS OF MODEL STRUCTURES
The framework consists of the following modules:
Observation Encoder (Image): A convolutional encoder that processes 128x128 images into a D-dimensional latent vector through 9 convolution layers and residual blocks.
Observation Decoder (Image): deconvolutional decoder that reconstructs 128x128 images from a D-dimensional latent vector through 1 convolution layer, 8 residual blocks, and transposed-convolutinn convoolution layers.
Observation Encoder (Raw): A linear layer mapping from hidden size of to D.
Observation Decoder (Raw): A linear layer mapping from hidden size of D to
Latent Decoder: A 1-layer MLP with input size D, hidden size D, layer normalization, and residual connections.
Action Encoder: A 1-layer MLP that encodes discrete actions into a D-dimensional hidden state.
Sequence Decoder: A gated self-attention architecture with L layers, hidden size D, inner hidden size D, attention heads, memory length las, layer normalization, and block recurrence.
The hyper-parameters are specified as Table
B.3 DETAILS OF TRAINING
All models were trained on NVIDIA A800 GPUs using the AdamW optimizer with the default settings in PyTorch (Paszke et al. 2019).
Cart-pole training details. We train the model with a Ger batch size of 128, an epoch of 100, an initial learning rate of 1.0c and decayed to 2.04c
Maze pre-training details. We first train the Image Encoder and Image Decoder with a per-GPU batch size of 400, epoch of 50, and initial learning rate of 3e- Subsequently, train all model components with a per-GPU batch size 10, an epoch 10, an initial learning rate of -4 and decayed 8.8e
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ProcTHOR training details. We first train the Image Encoder and Image Decoder using the same settings as Maze pretraining, except for the initial learning rate, which is set to 2.0e The VAE is then frozen, while the remaining modules initialized from the weights trained on the Maze dataset and further fine-tuned on Proc'Thor data. For the 40k version of Proc we train for 10 epochs with GPU batch size of 10 and initial learning rate of 2.0e decayed 1.2e For the 5k version, we use the same settings but train for 20 epochs.
C ADDITIONAL RESULTS
Additional reults of the performances of L.World in Mazes. Table presents the prediction error (measured as the mean square error between observations) of L2World and the other baselines on the Maze datasets. The prediction errors fully consistent with the PSNR evaluation; therefore, we report only the PSNR results in the remaining experiments. T the K-step prediction performance, corresponding to the histograms in Figure
Table 5: 1-step prediction error(() of different world models in Mazes.
Midel
Seco
Usseai
L2Worid (Mou--5NK-LL)
2.03 10-5
8.01 10"2
0.80 10"-
3:43 10"
3.13 10--
2:30 10
7.51 10-2
6.82 10
5.42 10-2
5.42 10
L2Wodl (Mize 33K-51
1.20 30
1.18 30
1.05 30
0.533 30
9:05 30
1.43 10
1.19
1.05 x
0.45 30
0.21
10
L20head (Maa--128-5)
1:13 10-2
5.15 10-3
6:2 -0-3
0.26 10"
0.86
10-3
1.88
10-2
1:37
10-2
1:26 10-2
1.0
10-2
1.00
10-2
12Warl (Mize
8:30
4.60
2.72
2.51
111.
810
7:01
7.01
6.7
(Maze 128-1)
8.81
Table 6: Average PSNR of k-Step auto-regressive prediction in Mazes (Unseen).
Mivil
6-6
-1
--
2-4
m
2-2
1-
6-
2--
meaart Marr 33K-11
11,22
18.96
15:83
23.34
22.16
2885
16.11
13,57
21.33
34.30
21.81
Lotaral Mtac -2K-5)
13.44
17:41
16.82
15.41
19.2
19.35
$3.02
16:23
19.52
23.43
18.19
19.59
21.0
20.17
15.89
32.00
20.31
20.21
13.53
11.00
Lowarl Marz 125-13
13.35
15.99
15.38
$4.35
1948
18.33
13.67
16.64
20.35
33.47
19.11
17.35
21.34
28.26
19.45
$2.48
21.30
21.25
12.38
17.40
15:00
1916
17:38
33.06
Length
Length
3.3
3.3
APSNNR
2.5
255
2.5
1.3
13
3.0
1.0
10
8.30WOOL ORG MOO064 CBS0 1006
6,00
6401
E400
680
66M 6.00
0080 0001
0002
0663
0004 0005
8
Context Length 10
Context Longth 100
Context Length 1000
3.3
30
26
APSNR
0.2
20-
3.-
2.5
15
3.033
-0.00 8009
1:44 C006 3003
Figure 10: We investigated the correlation between the average prediction error and the performance change. The performance change was quantified by APSNR. APSNR is acquired by the loss of accuracy by replacing the input s; with predicted š, and then measuring the loss of PSNR in future predictions with & and k 8.
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EL and ER in navigation world models implicitly perform global mapping. We further show that predicting transitions alone, without any specifically designed tasks, can potentially capture the global map implicitly. We collect 12 trajectories from 4 unseen mazes (3 trajectories in each maze) and track the transformation of memory states (d+) across each of the linear attention layers. Among the 18 layers used in the transition model, select layers {1, 6, 12, 18) for t-SNE visualization of the memory states (+r). To ensure that the unseen mazes are not trivially discriminable, for instance, by geometric and embodiment configurations in one-shot, we maintain identical configurations for most aspects of the evaluated 4 mazes while varying only their topology, such the arrangement of walls. We employ silhouette scores (Rousseeuw 1987) to quantify clustering quality, where higher values indicate better environment separation. As visualized in Figure [1] we highlight two insights: first, training solely on transition prediction across diverse environments and long contextual windows implicitly learns spatial map, potentially removing the need for auxiliary mapping modules; second, EL and ER behave differently across layers, suggesting distinct underlying mechanisms.
layer
layer
Mase
Silloudtosooe OUDS
Silourdeaoore A003
Siboattessore 0.53
Silhoctte scom 0.54
Mosse 1891
Sthoustte
Silouedeescoes 0,12
Silooutle score 0-40
Figure 11: i-SNE visualization of the memory states di in state transition prediction for Maze-32K- and Maze-128-L groups. We visualize the memory states from layers 1, 6, 12, and 18. The visualization encompasses 12 trajectories across distinct environments that are similar yet exhibit slight differences. Trajectories originating from the same environment are indicated by similar colors. The Silhouette score is computed by treating trajectories from each environment as individual classes.
Investigating EL from perspectives of predictive coding. The concept of predictive coding has emerged foundational mechanism in both biological and artificial learning systems (Rao & Ballard 1999), in which the discrepancy between expected and observed outcomes drives attention and learning. To investigate the correlation between EL and predictive coding, we conducted experiments to examine how prediction error influences learning progress. Specifically, we selected three positions T {10, 100, 1000} for each of 256 evaluating sequences. At these positions, replaced the ground-truth observations with model-genenn -eneraeeee predictions, thereby suppressing cror-coorection signals derived from real-world feedback. We then compared the accuracy loss in subsequent frames (including A: and 8) with and without this replacement. This comparison quantifies the importance of ground-truth observations for learning progress.
illustrated in Figure 10, observed clear positive correlation between the mean performance difference (APSNR) and the prediction error (MSE loss) the replaced frame. This finding suggests that EL progress is sensitive to prediction error, a phenomenon reminiscent of predictive coding in biological systems. These results further validate ICL as a prospective mechanism for adapting world models to variant environments.
Cases. Figure 12 illustrates 10-step-ahead predictions at T = {1, 100, 10K} for our method, Dreamer, and NWM, all trained on Maze-32K-L.. NWM produces visually convincing frames with
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L2World (Maze: 32K
Dreamer (Maze: 32K
NWM (Maze: 32X L]
Lated
Pood
Labd
Pred
L.World LM MMaae-S5k L ProccTho--BUKK
Dreamer (ProcThor-SUK1
NWW (Proct BUK)
Label
Frod
0000-2
Label
10
Figure 12: Example predictions produced by L2World and the baselines in Mazes and ProcTHOR for
T {1, 100, 10000) and A: 10, together with the corresponding ground-truth sequences.
fine textures and a plausible layout; however, frame-wise fidelity alone is insufficient for accurate long-range forecasting because the model lacks long-temm memory and spatial reasoning. Figure [3] juxtaposes the performance of I.2World trained with 32K-L and 32K-S, clearly demonstrating the benefit of longer sequences in promoting EL. Figure 14rree44shww two failure cases of L.2Wrlld (Maze- 32K-L) on unseen maze environments. These failures appear to result from excessive blurring in the predictions, causing the compound error escalate rapidly as increases. Building on this observation, natural extension of our work is to incorporate additional overshooting during training so that the world model can better forecast distant futures.
D USE OF LLMS
We used large language models (LLMs) only as auxiliary tool to improve the clarity and presenta- tion of this paper. The assistance was limited to:
Language refinement: grammar checking, wording suggestions, and improving sentence fluency while preserving the authors' original technical content.
Mathematical support: helping verify the correctness and readability of some derivations and notations, without introducing new technical results.
No LLM was used for generating research ideas, designing experiments, analyzing results, or writing original scientific content. All conceptual and technical contributions were made by the authors.
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1
2
3
2
3
Label Pred
Label
Predi
M01= I.
Label Predi
0M0= 1.
Label
0001=.1
Pred
M2re33KKL
Mare32K-S
(a) Maze
Labd
Pred
Label
01:11
Predi
Labe
Pred
MOX
Label
Preci
32K- 5K
32K-S ProcThor-5K
(b) PracTThor
Figure 13: Example predictions by L2World trained with Maze-32K and Maze-32K-S
(1, 10, 100, 1000, 10000) and & = 8.
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Figure 14: failed examples produced by L2World Mazes with T (1, 100, 10000) and k 8.
24


Reviews:
[{"review_id": "VpjvIa0kF2", "reviewer": "Reviewer_MPGb", "summary": "The authors consider in-context learning of a world model, in particular focusing on the dichotomy between environment-learning and environment-recognition observed in other in-context learning setups. Consistent with theoretical error bounds that they derive, their main contribution is to show that long contexts and environment diversity favor environment-learning in cart-pole and procedural maze navigation tasks.", "questions": "I have no questions for the authors.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "The paper is clearly written. To my knowledge, the conditions under which ICL task recognition vs task learning appear in world-model-based tasks have not yet been explored though the results are not surprising. The theoretical bounds are consistent with the experiments.", "weaknesses": "I can find no major weaknesses but in my opinion the paper offers few new insights compared to what is already known in the literature. The experimental paradigms considered are sufficiently complicated that it is difficult to draw specific inferences into the mechanisms that lead to ICEL versus ICER. In short, though it is certainly valuable to characterize phenomenology as the authors have done here, it is not clear what question is being answered. While this is likely beyond the scope of the current work, the authors could consider setting up the problem in a simpler, symbolic RL setup that captures the dichotomy between ICEL and ICER, and which potentially lends itself to a deeper mechanistic analysis for what factors determine ICEL vs ICER. \n\nHighlighting a couple of references that could be included: the ICL regression task was originally introduced by Garg et al, Neurips, 2022 (and not Raventos et al, 2023, line 106) and a minimal theoretical model that explains the origin of the ICL/IWL tradeoff was proposed by Nguyen and Reddy, ICLR 2025 (lines 111-113).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rqipGLKiA7", "reviewer": "Reviewer_m2Zn", "summary": "This paper investigates in-context learning (ICL) in world model training, focusing on how models adapt to novel environments through ICL rather than parameter updates. The authors distinguish two ICL mechanisms: Environment Recognition (ER) and Environment Learning (EL) and derive error bounds characterizing when each emerges. They introduce L2World, a linear-attention architecture for long-context world modeling, and validate their theoretical predictions on cart-pole control and vision-based navigation tasks. The key findings emphasize that environment diversity and long context windows are critical for enabling EL over ER.", "questions": "Maybe worth citing these papers which directly discuss ER vs EL:\nhttps://arxiv.org/abs/2412.01003\nhttps://arxiv.org/abs/2503.05631\nhttps://arxiv.org/abs/2506.17859", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 1, "contribution": 2, "strengths": "S1: This paper extends in-context learning studies to world model learning settings where the model should learn the inference time world, and this construct an adaptive world model. \n\nS2: The experimental design is thorough, it covers both continuous control (cart-pole) and partial observability (navigation). They authors also careful ablate data diversity, trajectory length and environment complexity.\n\nS3: Verifying that ICL behaves as ER and EL in linear attention models is novel.\n\nS4: The experimental grounding of ICEL for world model learning sets a nice expectation for future experimentalists.", "weaknesses": "W1: Most findings on ER vs EL seems already established in the literature, for example in https://arxiv.org/abs/2412.01003 and https://arxiv.org/abs/2503.05631. It doesn't seem like these are \"identified\" through this study.\n\nW2: The paper's focus is a little bit unclear. there is a theoretical part, an introduction of a new architecture, and empirical experiments on ICL, it is quite unclear what is the main presented innovation/finding.\n\nW3: The paper discusses larger context length to further enable ICEL. While this claim is likely true, it would be nice to see equal-flop experiments as longer context models get strictly more training.\n\nW4: The presentation of the data is poor. The plots/tables could be enhanced so that, for example, the data diversity's effect can be more directly read out.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rwLbimnkUF", "reviewer": "Reviewer_A6SQ", "summary": "his paper examines how world models can learn and adapt through context, focusing on in-context learning (ICL) within both MDP and POMDP settings. The authors distinguish between two key processes, namely In-Context Environment Learning (ICEL) and In-Context Environment Recognition (ICER). Furthermore, analyze their theoretical properties by deriving error upper bounds that explain when and how each process is effective.\n\nTo validate these insights, the paper introduces L2World, a framework designed to study in-context world modeling, and evaluates it on cart-pole and navigation tasks. The experiments show that world models can adapt to new environments through context alone and that both environmental diversity and longer context windows are crucial for enabling ICEL.", "questions": "- 1) Theoretical results assume “sufficiently optimized” models and discrete environment complexity. How realistic are these assumptions for continuous control or visual domains? \n\n-  2) The analysis of Theorem 1 relies on comparing the bounds in (5). Can the authors comment on the tightness on these bounds? I understand that's hard to do theoretically. But the bounds can be very loose and hence any analysis based on them might not be correct. I think even an empirical analysis of the tightness is needed to solidify the analysis and the claims. \n\n\n- 3) Have you conducted any controlled experiments (e.g., context-swapping or corrupted contexts) to isolate ICEL behavior from ICER? maybe gradually corrupt the context  and report how quickly performance collapse? \n\n- 4)  Why were primarily pixel-level reconstruction metrics (e.g., PSNR) used, and do these metrics accurately reflect the model’s ability to support downstream decision-making or planning tasks?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "- The paper is easy to follow and make clear and consistent concepts. as far as know, the explicit separation between In-Context Environment Recognition (ICER) and In-Context Environment Learning (ICEL) is novel and it made the analysis more intuitive and cleaner. \n\n-I particularly highly appreciate the theoretical analysis, which adds rigor and depth to the work. I skimmed through the proofs and they seem correct. \n\n- The proposed L2World framework demonstrates the practical relevance of the theory. Experiments on well-established control tasks (cart-pole and navigation) show that world models can indeed adapt to novel environments via context, supporting the theoretical claims.", "weaknesses": "**W1** ICER vs ICEL:  The distinction is clear conceptually, but the empirical analysis in my perspective does not produce direct causal evidence that the model is performing learning inside context vs recognition + recall. The observed improvement in prediction performance with increasing context length T could be explained by several alternative mechanisms. For instance, the model might simply be recognizing previously encountered environment configurations or recalling stored representations from training, rather than actively updating its internal dynamics to learn new environmental behaviors on the fly. This part is not clear from the experiments. \n\n**W2** While the derived bounds in Theorem 1 is correct and rigorous. I think the derived claims from the bounds is a bit loose: some of the paper’s main claims rely on comparing these bounds. Upper bounds can be loose; which bound is smaller may not reflect which method has smaller actual error. I think some analysis on the tightness of the bounds is needed in order to solidify the claims.\n\n**W3** The authors mainly report PSNR on pixel-level. This raises questions about whether the proposed framework would scale to more complex, real-world settings or provide tangible gains in downstream task and decision making performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)