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CompactNav argues that a rank-16, instruction-conditioned latent representation is minimally sufficient for vision-and-language navigation, and that this compression yields new state-of-the-art results on two benchmarks while cutting infere

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2026-08-01 03:21 UTC pith:W47L3YYT

load-bearing objection A competent VLN-CE engineering combination with plausible benchmark gains, but the theoretical minimal-sufficiency proof is circular and the empirical claims lack code, seeds, and error bars; needs major revision. the 5 major comments →

arxiv 2607.23181 v1 pith:W47L3YYT submitted 2026-07-25 cs.CV

Towards Dual-Brain Minimal Sufficient Representation for Vision-Language Navigation

Yihao Wu , Chenyi Xu , Liqi Yan , Chenhuan Cai , Geyong Min , Bin Lin , Fangli Guan , Jianhui Zhang
show 1 more author
Pan Li
This is my paper
classification cs.CV
keywords vision-language navigationcontinuous environmentsminimal sufficient representationinformation bottleneckCP decompositionlow-rank latent world modelinstruction-conditioned attentionembodied navigation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

CompactNav sets out to prove a design principle: for an agent following natural-language instructions through a continuous 3D scene, the right internal world state is not a faithful copy of the environment but the smallest representation that still determines the next action. It claims to realize this by chaining three mechanisms: instruction-first attention that filters observations through the language, a Canonical Polyadic (CP) low-rank factorization that squeezes the world tensor through a rank-16 bottleneck, and a latent world model that predicts future states inside that compressed space. The paper's central theorem asserts that the compressed latent is a minimal sufficient statistic for the action given the instruction and the observation. If aggressive instruction-conditioned compression is correct, then vision-language navigation agents can navigate unseen scenes more accurately and far more cheaply than fidelity-maximizing world models, since most perceptual detail is noise for the task.

Core claim

On the paper's own terms, the discovery is that minimally sufficient world representation, rather than visual fidelity, is the load-bearing ingredient for reliable vision-and-language navigation. CompactNav's Focus-Former uses the low-entropy instruction as a prior to select task-relevant visual features before they enter a multimodal LLM; a Minimalist Constraint Alignment module then CP-factorizes the latent world tensor into rank-16 basis vectors; and a Compression World Model performs action-conditioned transitions in that factorized space. Theorem 1 claims that because the CP rank bounds the mutual information between the raw latent and its compressed form while the policy objective maxi

What carries the argument

The central machinery is CP (Canonical Polyadic) decomposition used as a differentiable information bottleneck: the world tensor (segments, tokens, features) is reconstructed as a sum of R rank-one outer products u_r ⊗ v_r ⊗ w_r, with a shallow MLP generating the factors and R=16. This turns latent-state evolution from O(N·M·D) into O(R(N+M+D)) and gives gradient flow a task-driven pruning mechanism: factors for irrelevant detail shrink toward zero. The Focus-Former's cascaded attention is the supporting mechanism: queries are first anchored on the instruction, then attend to observations, so visual features activate only when they satisfy the language.

Load-bearing premise

Everything rests on the assumption that the rank-16 CP representation retains all action-relevant information from the raw observation—formally that the action is conditionally independent of the raw observation given the compressed representation—which is exactly the sufficiency property the theorem sets out to prove.

What would settle it

On a held-out set of R2R-CE episodes, compare next-action accuracy from the full latent (before CP factorization) versus the rank-16 compressed latent using the same policy head; if the full latent predicts actions better, the minimal-sufficiency claim is falsified. A complementary check: remove one action-relevant geometric channel, such as door or opening locations, and observe whether the compressed latent's ability to predict actions drops; under sufficiency it should not.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • If the minimal-sufficiency claim holds, world models for embodied agents should be built to discard perceptual detail rather than reconstruct it; reconstruction fidelity becomes a secondary convenience, not the objective.
  • The rank-16 bottleneck implies a concrete efficiency budget: state updates scale with the sum of the tensor dimensions, so longer instructions or higher-resolution observations need not inflate planning cost.
  • Instruction-conditioned filtering predicts that joint attention over concatenated language and pixels dilutes task cues; replacing it with cascaded anchored attention should generalize to other language-conditioned policy problems.
  • The paper's error bound says navigation error is governed by CP rank and sample size, so tuning the rank gives an explicit accuracy-efficiency trade-off rather than a blind hyperparameter sweep.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • A reader could test the sufficiency claim directly: compare next-action prediction accuracy from the full latent before CP factorization against the rank-16 compressed latent on held-out episodes; if the full latent predicts significantly better, the minimal-sufficiency guarantee fails even if the benchmark numbers stand.
  • The sharp performance collapse at rank 64 suggests the bottleneck is largely acting as a regularizer; an adaptive-rank variant that grows the rank only when validation success stalls might beat the fixed R=16 configuration.
  • The same instruction-conditioned CP bottleneck is portable to other language-grounded control problems—manipulation from text, instruction-following in driving—where discrete intent must align with continuous dynamics; that transfer is a testable extension the paper does not claim.
  • The paper's ablation shows the posterior-prior matching loss is the most critical component, hinting that long-horizon value comes from topological coherence of predicted states rather than from compression itself; ablating compression while keeping coherent latent dynamics would separate the two effects.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper proposes CompactNav (called BrainNav in the abstract), a VLN-CE framework combining an instruction-conditioned Focus-Former, a CP-decomposed low-rank cross-modal bottleneck (MCA), and a Compression World Model. The authors claim state-of-the-art results on R2R-CE and RxR-CE and provide theorems intended to show that the rank-R CP representation is a minimal sufficient representation of the action variable. The empirical section reports SR/SPL gains of 2.0/1.0 on R2R-CE val-unseen and 0.94/0.78 on RxR-CE val-unseen, along with ablations and inference-time measurements.

Significance. If the empirical results and the theoretical guarantees were both reliable, the paper would make a meaningful contribution to efficient, instruction-conditioned world modeling for VLN-CE. The architecture is clearly described and the ablation studies target the right components. However, the central theoretical claim is not established: the proof of Theorem 1 assumes the sufficiency property it is meant to prove, and the minimality argument does not connect CP-rank sparsity to task-relevant information. The empirical validation also lacks error bars, seeds, and code, and hyperparameters are tuned on the same val-unseen split used for the headline numbers. The significance of the paper therefore cannot be assessed from the manuscript as written.

major comments (5)
  1. [Appendix A.2.1, Theorem 1] The sufficiency proof is circular. After the DPI chain-rule expansion, the proof sets E[DKL(p(A|O,Zhat,I)||p(A|Zhat,I))] = 0. That equality is exactly the conditional independence p(A|O,Zhat,I)=p(A|Zhat,I) defining sufficiency, and no step in the proof derives it from the LLMM loss, the predictive offset loss, the policy loss, or the CP rank bottleneck. The sentence "Upon convergence, equality is achieved" restates the goal rather than proving it. Without an inductive or architectural argument that the rank-16 bottleneck cannot discard action-relevant geometry, Theorem 1 is unsupported.
  2. [Appendix A.2.1, Theorem 1 minimality] The minimality proof is also not valid. The rate-distortion bound R <= I(O;A|I)/log(1/delta) concerns reconstruction error of Zhat, not preservation of task-relevant information about A. The L1-sparsity argument shows coefficient shrinkage, not that discarded dimensions are action-irrelevant. Moreover, R=16 is selected by tuning on the same val-unseen split reported in Tables 1 and 4 (Figure 4), so the claimed "minimal" rank is entangled with evaluation-set tuning rather than established by the information-theoretic argument.
  3. [Appendix A.3.1, Theorem 2] The noise-suppression proof is internally inconsistent. It treats PR as a linear contraction, but the CP projection is not generally a contraction and the step ||PR(Otask)-PR(Otask+epsilon)||_F^2 = ||PR(epsilon)||_F^2 assumes linearity without justification. The mutual-information calculation concludes I(Zhat;epsilon)=0 from I(O;epsilon)-H(epsilon)=0, which requires H(epsilon|O)=0 and I(O;epsilon|Zhat)≈I(O;epsilon), neither of which is proved. The corollary in Appendix A.4 inherits these gaps; the TV-bound step uses Theorem 1 as if it had been established.
  4. [Section 3.4 / Eq. (10)-(11)] The paper states that the IB objective is instantiated as an L1 penalty, and later acknowledges that the CP reconstruction is deterministic and the IB notation is "descriptive." This is not a minor caveat: Eq. (10) writes an IB Lagrangian with I(Z;Zhat) and I(Zhat;A), but Zhat is a deterministic function of Z through the MLP and CP outer product, so the variational upper bound and the L1 surrogate are not derived. The theoretical claims in the appendix rely on this equation as if it were a real information-theoretic constraint, so the gap is load-bearing.
  5. [Section 4 / Tables 1-6] The empirical evaluation does not support the SOTA claim as presented. No results are averaged over seeds, no error bars or confidence intervals are reported, and code is only promised to be released. Hyperparameters lambda1, lambda2, Tp, and especially the CP rank R=16 are tuned on the same val-unseen split used for the headline numbers, so the comparisons are not a clean evaluation of generalization. At minimum, the paper should report mean/std over multiple seeds and use a validation split separate from the reported val-unseen results.
minor comments (6)
  1. [Title/Abstract and Section 1] The terminology is inconsistent: the title refers to "Dual-Brain," the abstract uses "BrainNav," and the main text uses "CompactNav." The "Dual-Brain" concept is never defined. Please harmonize the naming throughout.
  2. [Table 1] Two rows are both labeled NavMorph with different results; presumably one uses ETPNav as backbone and the other HNR, but this is not stated in the table. Please clarify the labeling.
  3. [Section 4.2] The text attributes the "low-rank CP bottleneck" to Focus-Former, but the CP decomposition is part of the MCA module (Section 3.2). Please correct the attribution.
  4. [Appendix A.2.1] The Markov chain is written A -> O -> Zhat, but the proof is conditioned on I throughout. The chain should be A -> (O,I) -> Zhat or the conditioning should be made explicit in the DPI step.
  5. [Eq. (9)] The notation log p(F_t | s_t) is used as a reconstruction term, but F_t is later defined as the CP factor set rather than an observation. Please define the reconstruction distribution explicitly.
  6. [Figure 4] The definition of Success Efficiency as SR/Time*100 should state the units of Time (seconds per episode, presumably) and how it is measured.

Circularity Check

3 steps flagged

Theorem 1's sufficiency proof assumes the conditional independence it must establish, and Theorem 2 assumes its own conclusion; the minimal-sufficiency guarantee reduces to the training objective by construction.

specific steps
  1. self definitional [Appendix A.2.1, Theorem 1 proof of sufficiency]
    "=I(A; ˆZ |I) + 0 ... By optimizing reconstruction and policy losses, the model forces ˆZ to capture all information predictive of action A. Upon convergence, equality is achieved: I( ˆZ;A |I) =I(O;A |I)"

    The '0' in the identity is the cross term DKL(p(A|O,ˆZ,I)||p(A|ˆZ,I)). Setting it to zero is exactly the conditional independence A⊥O|ˆZ,I that defines sufficiency in Theorem 1. No step derives this from LLMM, the predictive-offset loss, the policy loss, or the CP rank bottleneck; 'Upon convergence, equality is achieved' only restates the policy-maximization objective. Thus sufficiency is assumed, not proved, and Theorem 2/Corollary inherit the assumption.

  2. self definitional [Appendix A.2.1, Theorem 1 proof of minimality]
    "≤min{rank(Z(1)),rank(Z(2)),rank(Z(3))} ≤⌈ I(O;A |I)/log(1/δ)⌉ (from rate-distortion theory) ... This L1 regularization term enforces sparsity ... ensuring that ˆZ captures only the structurally significant components ... Therefore, ˆZ has the minimal possible dimension R while maintaining sufficiency."

    Minimality is imposed by construction: R is the user-set CP rank (tuned to R=16 in Fig. 4), and the L1 penalty shrinks coefficients. The rate-distortion bound concerns reconstruction fidelity, not I(ˆZ;A|I), and coefficient sparsity does not show that discarded dimensions are action-irrelevant or that no smaller rank preserves the same task information. Concluding 'minimal possible dimension' equates the training regularization with the theorem's conclusion.

  3. other [Appendix A.3.1, Theorem 2 proof]
    "Since ˆZ is a compressed representation of O, and ϵ is part of O, but the design goal of ˆZ is to strip away ϵ. In the limiting case, when ˆZ generalizes perfectly, ϵ is not encoded into ˆZ"

    The statement that the design goal is to strip away ϵ, followed by 'when ˆZ generalizes perfectly, ϵ is not encoded into ˆZ,' is the desired conclusion I(ˆZ;ϵ)≈0 restated as a premise. The preceding gradient-descent argument only shows the projection of noise may be small; it does not establish that the learned CP-filtered representation is noise-free. The proof therefore assumes robustness rather than deriving it.

full rationale

The main circularity is internal to the appendix proofs, not a self-citation chain. Section 3.2 also concedes that 'the IB notation is used to describe the intended capacity-controlled bottleneck' and that the CP reconstruction is deterministic, so the I(Z;ˆZ) and I(ˆZ;A) quantities in Theorem 1 are descriptive rather than quantities whose inequalities are verified. In A.2.1, sufficiency is obtained by inserting DKL(p(A|O,ˆZ,I)||p(A|ˆZ,I))=0, which is precisely the property to be proved; the follow-up sentence 'Upon convergence, equality is achieved' recites the training objective. Minimality similarly reads the chosen rank and L1 shrinkage as task-relevant minimality without a bound connecting them to I(ˆZ;A|I). Theorem 2 carries the same circular structure ('design goal ... strip away ϵ'). The empirical R2R-CE/RxR-CE results are self-contained benchmark comparisons and are not themselves circular; what collapses is the claimed information-theoretic guarantee. A score of 7 reflects that the central 'minimal sufficient representation' derivation reduces by construction, while the architecture and benchmark numbers still have independent empirical content.

Axiom & Free-Parameter Ledger

5 free parameters · 7 axioms · 0 invented entities

The central theoretical claim rests on assumptions that amount to the conclusion: conditional independence of actions from raw observations given the compressed representation, plus convergence to a sufficient representation. Empirical performance depends on the fitted CP rank and loss weights selected on the evaluation split.

free parameters (5)
  • CP rank R = 16
    Ablation in Figure 4; controls bottleneck capacity and is central to the minimal-sufficiency claim.
  • loss weight lambda_1 (LLMM) = 8e-3
    Heuristically set to balance loss magnitudes; controls sparsity of CP factors.
  • loss weight lambda_2 (LPO) = 1e-3
    Heuristically set; weights the predictive offset loss that calibrates the low-rank reconstruction.
  • future prediction horizon Tp = 2
    Number of future latent states predicted at each timestep; structural prior for planning.
  • beta in IB objective = not reported
    Equation 10 defines beta for balancing minimality/sufficiency, but the implementation uses an L1 surrogate and the beta value is not given.
axioms (7)
  • standard math Data Processing Inequality and standard information-theoretic identities
    Used throughout Appendix A.2 and A.3.
  • domain assumption Zhat is a deterministic function of O, so A->O->Zhat forms a Markov chain
    Needed for Theorem 1 sufficiency proof; plausible by construction but not stated precisely.
  • ad hoc to paper Conditional independence A independent of O given Zhat and I (p(A|O,Zhat,I)=p(A|Zhat,I))
    Assumed in the equality step of Appendix A.2.1; this is exactly the sufficiency property being proved.
  • ad hoc to paper Task-relevant environment dynamics lie on a rank-16 manifold; task-irrelevant noise is high-rank
    Section 3.3 says stochastic perturbations manifest as high-rank residuals; no evidence beyond the rank ablation.
  • ad hoc to paper Training converges to a globally sufficient representation
    Appendix A.2.1: 'Upon convergence, equality is achieved'—assumes the theorem's conclusion.
  • domain assumption Noise epsilon is independent of action A
    Definitional in Theorem 2; reasonable but unverified in practice.
  • domain assumption Pretrained ETPNav initialization transfers and is not responsible for the gains
    Appendix B says the model inherits initialization from [19]; no control experiment isolates this factor.

pith-pipeline@v1.3.0-alltime-deepseek · 17536 in / 14059 out tokens · 122660 ms · 2026-08-01T03:21:56.026027+00:00 · methodology

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read the original abstract

Vision-and-Language Navigation in continuous environments (VLN-CE) requires an agent to ground language in egocentric observations and plan in unseen scenes. Although recent multimodal large models and world-model-based methods have improved navigation, they often preserve excessive task-irrelevant detail, weakening generalization and increasing computational burden. We propose BrainNav, a navigation framework grounded in the Principle of Minimal Sufficiency. BrainNav consists of three components: a Logical Anchor Model that implements instruction-aware selective perception to suppress environmental noise, a Minimalist Constraint Alignment module that serves as a compact cross-modal bottleneck, efficiently synchronizing discrete linguistic intent with continuous latent dynamics while filtering out redundant information, and a Compression World Model that predicts action-conditioned states within a condensed, low-rank latent space. These modules align semantic intent with spatial perception, enhancing the agent's robustness and efficiency in complex tasks. Experiments show that BrainNav improves over prior SOTA by 2.0 % / 1.0 in SR/SPL on R2R-CE val-unseen and 0.94 % / 0.78 on RxR-CE val-unseen. These results indicate that minimally sufficient world representations provide an effective foundation for robust VLN.

Figures

Figures reproduced from arXiv: 2607.23181 by Bin Lin, Chenhuan Cai, Chenyi Xu, Fangli Guan, Geyong Min, Jianhui Zhang, Liqi Yan, Pan Li, Yihao Wu.

Figure 1
Figure 1. Figure 1: Comparison with representative VLN paradigms. (a) VLM-based policies directly map observations and instructions to actions but lack explicit future modeling. (b) World-model-based frameworks incorporate future-state prediction yet remain limited by perceptual redundancy. (c) CompactNav introduces a low-rank cross-modal alignment mechanism through the proposed MCA module to learn minimally sufficient latent… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of CompactNav. Given a language instruction together with RGB-D observa￾tions, CompactNav first employs the (§ 3.1) Logical Anchor Model to align instruction semantics with visual perception and produce an instruction-grounded latent embedding Z. This embedding is then compressed through (§ 3.2) Minimalist Constraint Alignment, where a shallow MLP pre￾dicts CP factors u, v, and w to reconstruct a … view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative comparison of attention heatmaps between Q-Former and Focus-Former. This figure shows that CompactNav achieves more focused and task-relevant attention during naviga￾tion. cially, removing the reconstruction loss (LRe) incurs only a marginal 2% SR penalty, suggesting that while reconstruction provides some auxiliary benefit, the model’s core navigation ability is largely decoupled from high-fre… view at source ↗
Figure 4
Figure 4. Figure 4: Ablation Study on CP Factor Rank. Success Efficiency: SE = SR/Time × 100. Impact of Focus-Former. As shown in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

68 extracted references · 27 linked inside Pith

  1. [1]

    Aligning cyber space with physical world: A comprehensive survey on embodied ai,

    Y . Liu, W. Chen, Y . Bai, X. Liang, G. Li, W. Gao, and L. Lin, “Aligning cyber space with physical world: A comprehensive survey on embodied ai,”IEEE/ASME Transactions on Mechatronics, 2025

    2025

  2. [2]

    A survey on robotics with foundation models: toward embodied ai,

    Z. Xu, K. Wu, J. Wen, J. Li, N. Liu, Z. Che, and J. Tang, “A survey on robotics with foundation models: toward embodied ai,”arXiv preprint arXiv:2402.02385, 2024

    Pith/arXiv arXiv 2024

  3. [3]

    Beyond the nav-graph: Vision and language navigation in continuous environments,

    J. Krantz, E. Wijmans, A. Majundar, D. Batra, and S. Lee, “Beyond the nav-graph: Vision and language navigation in continuous environments,” inEuropean Conference on Computer Vision (ECCV), 2020

    2020

  4. [4]

    Bridging the gap between learning in discrete and continuous en- vironments for vision-and-language navigation,

    Y . Hong, Z. Wang, Q. Wu, and S. Gould, “Bridging the gap between learning in discrete and continuous en- vironments for vision-and-language navigation,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 15 439–15 449

    2022

  5. [5]

    Matterport3d: Learning from rgb-d data in indoor environments,

    A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Nießner, M. Savva, S. Song, A. Zeng, and Y . Zhang, “Matterport3d: Learning from rgb-d data in indoor environments,” 2017. [Online]. Available: https://arxiv.org/abs/1709.06158

    Pith/arXiv arXiv 2017

  6. [6]

    Towards long-horizon vision-language navigation: Platform, benchmark and method,

    X. Song, W. Chen, Y . Liu, W. Chen, G. Li, and L. Lin, “Towards long-horizon vision-language navigation: Platform, benchmark and method,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025, pp. 12 078–12 088

    2025

  7. [7]

    Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,

    J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” inInternational conference on machine learning. PMLR, 2023, pp. 19 730–19 742

    2023

  8. [8]

    Perceiver io: A general architecture for structured inputs & outputs,

    A. Jaegle, S. Borgeaud, J.-B. Alayrac, C. Doersch, C. Ionescu, D. Ding, S. Koppula, D. Zoran, A. Brock, E. Shelhameret al., “Perceiver io: A general architecture for structured inputs & outputs,”arXiv preprint arXiv:2107.14795, 2021

    Pith/arXiv arXiv 2021

  9. [9]

    Flamingo: a visual language model for few-shot learning,

    J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y . Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynoldset al., “Flamingo: a visual language model for few-shot learning,”Advances in neural information processing systems, vol. 35, pp. 23 716–23 736, 2022

    2022

  10. [10]

    Cross from left to right brain: Adaptive text dreamer for vision-and-language navigation,

    P. Zhang, Y . Su, P. Wu, D. An, L. Zhang, Z. Wang, D. Wang, Y . Ding, B. Zhao, and X. Li, “Cross from left to right brain: Adaptive text dreamer for vision-and-language navigation,”arXiv preprint arXiv:2505.20897, 2025

    Pith/arXiv arXiv 2025

  11. [11]

    Span-nav: Generalized spatial awareness for versatile vision-language navigation,

    J. Liu, T. Xu, J. Chen, L. Yue, J. Zhang, Z. Wang, M. Li, Q. Zhao, A. Li, Q. Suet al., “Span-nav: Generalized spatial awareness for versatile vision-language navigation,”arXiv preprint arXiv:2603.09163, 2026

    arXiv 2026

  12. [12]

    Structured observation language for efficient and generalizable vision-language navigation,

    D. Peng, F. Ma, and J. Ma, “Structured observation language for efficient and generalizable vision-language navigation,”arXiv preprint arXiv:2603.27577, 2026. 10

    Pith/arXiv arXiv 2026

  13. [13]

    Navforesee: A unified vision-language world model for hierarchical planning and dual-horizon navigation prediction,

    F. Liu, S. Xie, M. Luo, Z. Chu, J. Hu, X. Wu, and M. Xu, “Navforesee: A unified vision-language world model for hierarchical planning and dual-horizon navigation prediction,”arXiv preprint arXiv:2512.01550, 2025

    arXiv 2025

  14. [14]

    Mapdream: Task-driven map learning for vision-language navigation,

    G. Lian, S. Wang, Y . Wang, Y . Wang, M. Chen, K. Wang, B. Zhang, Z. Su, D. Li, and Z. Fan, “Mapdream: Task-driven map learning for vision-language navigation,”arXiv preprint arXiv:2602.00222, 2026

    arXiv 2026

  15. [15]

    Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments,

    P. Anderson, Q. Wu, D. Teney, J. Bruce, M. Johnson, N. Sünderhauf, I. Reid, S. Gould, and A. Van Den Hengel, “Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 3674–3683

    2018

  16. [16]

    Room-across-room: Multilingual vision-and- language navigation with dense spatiotemporal grounding,

    A. Ku, P. Anderson, R. Patel, E. Ie, and J. Baldridge, “Room-across-room: Multilingual vision-and- language navigation with dense spatiotemporal grounding,” inProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020, pp. 4392–4412

    2020

  17. [17]

    Cosmo: Combination of selective memorization for low-cost vision-and-language navigation,

    S. Zhang, Y . Qiao, Q. Wang, Z. Yan, Q. Wu, Z. Wei, and J. Liu, “Cosmo: Combination of selective memorization for low-cost vision-and-language navigation,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2025, pp. 5511–5522

    2025

  18. [18]

    3d gaussian map with open-set semantic grouping for vision-language navigation,

    J. Gao, R. Liu, and W. Wang, “3d gaussian map with open-set semantic grouping for vision-language navigation,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2025, pp. 9252–9262

    2025

  19. [19]

    Etpnav: Evolving topological planning for vision-language navigation in continuous environments,

    D. An, H. Wang, W. Wang, Z. Wang, Y . Huang, K. He, and L. Wang, “Etpnav: Evolving topological planning for vision-language navigation in continuous environments,”IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

    2024

  20. [20]

    Dreamwalker: Mental planning for continuous vision- language navigation,

    H. Wang, W. Liang, L. Van Gool, and W. Wang, “Dreamwalker: Mental planning for continuous vision- language navigation,” inProceedings of the IEEE/CVF international conference on computer vision, 2023, pp. 10 873–10 883

    2023

  21. [21]

    Lookahead exploration with neural radiance representation for continuous vision-language navigation,

    Z. Wang, X. Li, J. Yang, Y . Liu, J. Hu, M. Jiang, and S. Jiang, “Lookahead exploration with neural radiance representation for continuous vision-language navigation,” 2024, pp. 13 753–13 762

    2024

  22. [22]

    Navgpt: Explicit reasoning in vision-and-language navigation with large language models,

    G. Zhou, Y . Hong, and Q. Wu, “Navgpt: Explicit reasoning in vision-and-language navigation with large language models,” inProceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 7, 2024, pp. 7641–7649

    2024

  23. [23]

    Navid: Video-based vlm plans the next step for vision-and-language navigation,

    J. Zhang, K. Wang, R. Xu, G. Zhou, Y . Hong, X. Fang, Q. Wu, Z. Zhang, and H. Wang, “Navid: Video-based vlm plans the next step for vision-and-language navigation,” 2024. [Online]. Available: https://arxiv.org/abs/2402.15852

    Pith/arXiv arXiv 2024

  24. [24]

    Uni-navid: A video-based vision-language-action model for unifying embodied navigation tasks,

    J. Zhang, K. Wang, S. Wang, M. Li, H. Liu, S. Wei, Z. Wang, Z. Zhang, and H. Wang, “Uni-navid: A video-based vision-language-action model for unifying embodied navigation tasks,” 2025. [Online]. Available: https://arxiv.org/abs/2412.06224

    Pith/arXiv arXiv 2025

  25. [25]

    Vln-r1: Vision-language navigation via reinforcement fine-tuning,

    Z. Qi, Z. Zhang, Y . Yu, J. Wang, and H. Zhao, “Vln-r1: Vision-language navigation via reinforcement fine-tuning,” 2025. [Online]. Available: https://arxiv.org/abs/2506.17221

    Pith/arXiv arXiv 2025

  26. [26]

    Dream to control: Learning behaviors by latent imagination,

    D. Hafner, T. Lillicrap, J. Ba, and M. Norouzi, “Dream to control: Learning behaviors by latent imagination,” arXiv preprint arXiv:1912.01603, 2019

    Pith/arXiv arXiv 1912

  27. [27]

    Learning latent dynamics for planning from pixels,

    D. Hafner, T. Lillicrap, I. Fischer, R. Villegas, D. Ha, H. Lee, and J. Davidson, “Learning latent dynamics for planning from pixels,” inInternational conference on machine learning. PMLR, 2019, pp. 2555–2565

    2019

  28. [28]

    Pathdreamer: A world model for indoor navigation,

    J. Y . Koh, H. Lee, Y . Yang, J. Baldridge, and P. Anderson, “Pathdreamer: A world model for indoor navigation,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 14 738–14 748

    2021

  29. [29]

    Panogen: Text-conditioned panoramic environment generation for vision-and- language navigation,

    J. Li and M. Bansal, “Panogen: Text-conditioned panoramic environment generation for vision-and- language navigation,”Advances in neural information processing systems, vol. 36, pp. 21 878–21 894, 2023

    2023

  30. [30]

    Do visual imaginations improve vision-and-language navigation agents?

    A. Perincherry, J. Krantz, and S. Lee, “Do visual imaginations improve vision-and-language navigation agents?” inProceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 3846–3855

    2025

  31. [31]

    Navmorph: A self-evolving world model for vision-and-language navigation in continuous environments,

    X. Yao, J. Gao, and C. Xu, “Navmorph: A self-evolving world model for vision-and-language navigation in continuous environments,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2025, pp. 5536–5546. 11

    2025

  32. [32]

    Dreamnav: A trajectory-based imaginative framework for zero-shot vision-and-language navigation,

    Y . Wang, Y . Fang, T. Wang, Y . Feng, Y . Tan, S. Zhang, P. Liu, Y . Ji, and R. Xu, “Dreamnav: A trajectory-based imaginative framework for zero-shot vision-and-language navigation,” 2025. [Online]. Available: https://arxiv.org/abs/2509.11197

    Pith/arXiv arXiv 2025

  33. [33]

    Navigation world models,

    A. Bar, G. Zhou, D. Tran, T. Darrell, and Y . LeCun, “Navigation world models,” inProceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 15 791–15 801

    2025

  34. [34]

    The expression of a tensor or a polyadic as a sum of products,

    F. L. Hitchcock, “The expression of a tensor or a polyadic as a sum of products,”Journal of Mathematics and Physics, vol. 6, no. 1-4, pp. 164–189, 1927

    1927

  35. [35]

    Some mathematical notes on three-mode factor analysis,

    L. R. Tucker, “Some mathematical notes on three-mode factor analysis,”Psychometrika, vol. 31, no. 3, pp. 279–311, 1966

    1966

  36. [36]

    Disentangling factors of variation in deep representations using adversarial training,

    M. Mathieu, J. Zhao, P. Sprechmann, A. Ramesh, and Y . LeCun, “Disentangling factors of variation in deep representations using adversarial training,” 2016. [Online]. Available: https://arxiv.org/abs/1611.03383

    Pith/arXiv arXiv 2016

  37. [37]

    Hamiltonian latent operators for content and motion disentanglement in image sequences,

    A. Khan and A. J. Storkey, “Hamiltonian latent operators for content and motion disentanglement in image sequences,”Advances in Neural Information Processing Systems, vol. 35, pp. 7250–7263, 2022

    2022

  38. [38]

    Iso-dream: Isolating and leveraging noncontrollable visual dynamics in world models,

    M. Pan, X. Zhu, Y . Wang, and X. Yang, “Iso-dream: Isolating and leveraging noncontrollable visual dynamics in world models,”Advances in neural information processing systems, vol. 35, pp. 23 178– 23 191, 2022

    2022

  39. [39]

    Adaworld: Learning adaptable world models with latent actions,

    S. Gao, S. Zhou, Y . Du, J. Zhang, and C. Gan, “Adaworld: Learning adaptable world models with latent actions,”arXiv preprint arXiv:2503.18938, 2025

    Pith/arXiv arXiv 2025

  40. [40]

    Factored latent action world models,

    Z. Wang, C. Shi, J. Hu, K. Rohling, R. Martín-Martín, A. Zhang, and P. Stone, “Factored latent action world models,” 2026. [Online]. Available: https://arxiv.org/abs/2602.16229

    Pith/arXiv arXiv 2026

  41. [41]

    Compressing neural networks using the variational information bottleneck,

    B. Dai, C. Zhu, B. Guo, and D. Wipf, “Compressing neural networks using the variational information bottleneck,” inInternational conference on machine learning. PMLR, 2018, pp. 1135–1144

    2018

  42. [42]

    Learning sparse latent representations with the deep copula information bottleneck,

    A. Wieczorek, M. Wieser, D. Murezzan, and V . Roth, “Learning sparse latent representations with the deep copula information bottleneck,” 2018. [Online]. Available: https://arxiv.org/abs/1804.06216

    Pith/arXiv arXiv 2018

  43. [43]

    Attention-based guided structured sparsity of deep neural networks,

    A. Torfi, R. A. Shirvani, S. Soleymani, and N. M. Nasrabadi, “Attention-based guided structured sparsity of deep neural networks,” 2018. [Online]. Available: https://arxiv.org/abs/1802.09902

    Pith/arXiv arXiv 2018

  44. [44]

    Learning latent dynamic robust representations for world models,

    R. Sun, H. Zang, X. Li, and R. Islam, “Learning latent dynamic robust representations for world models,”

  45. [45]

    Rethinking latent redundancy in behavior cloning: An information bottleneck approach for robot manipulation,

    S. Bai, W. Zhou, P. Ding, W. Zhao, D. Wang, and B. Chen, “Rethinking latent redundancy in behavior cloning: An information bottleneck approach for robot manipulation,” 2025. [Online]. Available: https://arxiv.org/abs/2502.02853

    Pith/arXiv arXiv 2025

  46. [46]

    Enhanced structured lasso pruning with class-wise information,

    X. Liu, M. Li, X. Li, L. Qu, G. Wang, Z. Peng, Y . Song, Z. Liu, L. Jiang, and J. Li, “Enhanced structured lasso pruning with class-wise information,” 2026. [Online]. Available: https://arxiv.org/abs/2502.09125

    arXiv 2026

  47. [47]

    Object-centric learning with slot attention,

    F. Locatello, D. Weissenborn, T. Unterthiner, A. Mahendran, G. Heigold, J. Uszkoreit, A. Dosovitskiy, and T. Kipf, “Object-centric learning with slot attention,”Advances in neural information processing systems, vol. 33, pp. 11 525–11 538, 2020

    2020

  48. [48]

    Qwen2.5 technical report,

    A. Yang, B. Yang, B. Zhang, B. Hui, B. Zheng, B. Yu, C. Li, D. Liu, F. Huang, H. Wei, and et al, “Qwen2.5 technical report,” 2025. [Online]. Available: https://arxiv.org/abs/2412.15115

    Pith/arXiv arXiv 2025

  49. [49]

    Tensor decompositions and applications,

    T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,”SIAM review, vol. 51, no. 3, pp. 455–500, 2009

    2009

  50. [50]

    Tensorizing neural networks,

    A. Novikov, D. Podoprikhin, A. Osokin, and D. P. Vetrov, “Tensorizing neural networks,”Advances in neural information processing systems, vol. 28, 2015

    2015

  51. [51]

    Deep variational information bottleneck,

    A. A. Alemi, I. Fischer, J. V . Dillon, and K. Murphy, “Deep variational information bottleneck,”arXiv preprint arXiv:1612.00410, 2016

    Pith/arXiv arXiv 2016

  52. [52]

    The information bottleneck method,

    N. Tishby, F. C. Pereira, and W. Bialek, “The information bottleneck method,”arXiv preprint physics/0004057, 2000

    Pith/arXiv arXiv 2000

  53. [53]

    Sim-2-sim transfer for vision-and-language navigation in continuous environments,

    J. Krantz and S. Lee, “Sim-2-sim transfer for vision-and-language navigation in continuous environments,” 2022, pp. 588–603. 12

    2022

  54. [54]

    Bridging the gap between learning in discrete and continuous environments for vision-and-language navigation,

    Y . Hong, Z. Wang, Q. Wu, and S. Gould, “Bridging the gap between learning in discrete and continuous environments for vision-and-language navigation,” 2022, pp. 15 418–15 428

    2022

  55. [55]

    Dreamwalker: Mental planning for continuous vision- language navigation,

    H. Wang, W. Liang, L. V . Gool, and W. Wang, “Dreamwalker: Mental planning for continuous vision- language navigation,” 2023, pp. 10 873–10 883

    2023

  56. [56]

    Gridmm: Grid memory map for vision-and-language navigation,

    Z. Wang, X. Li, J. Yang, Y . Liu, and S. Jiang, “Gridmm: Grid memory map for vision-and-language navigation,” 2023, pp. 15 625–15 636

    2023

  57. [57]

    Bevbert: Topo-metric map pre-training for language-guided navigation,

    D. An, Y . Qi, Y . Li, Y . Huang, L. Wang, T. Tan, and J. Shao, “Bevbert: Topo-metric map pre-training for language-guided navigation,”arXiv preprint arXiv:2212.04385, 2022

    Pith/arXiv arXiv 2022

  58. [58]

    Fast-slow test-time adaptation for online vision-and-language navigation,

    J. Gao, X. Yao, and C. Xu, “Fast-slow test-time adaptation for online vision-and-language navigation,” in ICML, 2024, pp. 14 902–14 919

    2024

  59. [59]

    Language-aligned waypoint (law) supervi- sion for vision-and-language navigation in continuous environments,

    S. Raychaudhuri, S. Wani, S. Patel, U. Jain, and A. X. Chang, “Language-aligned waypoint (law) supervi- sion for vision-and-language navigation in continuous environments,” inEMNLP, 2021, pp. 4018–4028

    2021

  60. [60]

    Affordances-oriented planning using foundation models for continuous vision-language navigation,

    J. Chen, B. Lin, X. Liu, X. Liang, and K.-Y . K. Wong, “Affordances-oriented planning using foundation models for continuous vision-language navigation,”arXiv preprint arXiv:2407.05890, 2024

    Pith/arXiv arXiv 2024

  61. [61]

    1st place solutions for rxr-habitat vision-and-language navigation competition,

    D. An, Z. Wang, Y . Li, Y . Wang, Y . Hong, Y . Huang, L. Wang, and J. Shao, “1st place solutions for rxr-habitat vision-and-language navigation competition,”arXiv preprint arXiv:2206.11610, 2022

    Pith/arXiv arXiv 2022

  62. [62]

    General evaluation for instruction conditioned navigation using dynamic time warping,

    G. Ilharco, V . Jain, A. Ku, E. Ie, and J. Baldridge, “General evaluation for instruction conditioned navigation using dynamic time warping,”arXiv preprint arXiv:1907.05446, 2019

    Pith/arXiv arXiv 1907

  63. [63]

    An image is worth 16x16 words: Transformers for image recognition at scale,

    A. Dosovitskiy, “An image is worth 16x16 words: Transformers for image recognition at scale,”arXiv preprint arXiv:2010.11929, 2020

    Pith/arXiv arXiv 2010

  64. [64]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” inCVPR, 2016, pp. 770–778

    2016

  65. [65]

    Cross- modal map learning for vision and language navigation,

    G. Georgakis, K. Schmeckpeper, K. Wanchoo, S. Dan, E. Miltsakaki, D. Roth, and K. Daniilidis, “Cross- modal map learning for vision and language navigation,” 2022, pp. 15 439–15 449

    2022

  66. [66]

    Lxmert: Learning cross-modality encoder representations from transformers,

    H. Tan and M. Bansal, “Lxmert: Learning cross-modality encoder representations from transformers,” arXiv preprint arXiv:1908.07490, 2019

    Pith/arXiv arXiv 1908

  67. [67]

    Roberta: A robustly optimized bert pretraining approach,

    Y . Liu, “Roberta: A robustly optimized bert pretraining approach,”arXiv preprint arXiv:1907.11692, vol. 364, 2019. A Proofs of theorems A.1 Notations Definition.Let (O, I,A)denote the triplet of observation O, instruction I, and navigation action A in an continuous navigation environment. Let ˆZ denote the latent representation learned by CompactNav via ...

    Pith/arXiv arXiv 1907

  68. [2024]

    Available: https://arxiv.org/abs/2405.06263

    [Online]. Available: https://arxiv.org/abs/2405.06263

    Pith/arXiv arXiv