JanusVLN: Decoupling Semantics and Spatiality with Dual Implicit Memory for Vision-Language Navigation
Authors: Shuang Zeng
Dekang Qi
Xinyuan Chang
Feng Xiong
Xie Shichao
Xiaolong Wu
Shiyi Liang
Mu Xu
Xing Wei
Venue: ICLR 2026 Poster (2026)
Keywords: Vision-Language Navigation
Spatial Understanding
Dual Implicit Memory
Primary area: applications to robotics, autonomy, planning

Abstract: Vision-and-Language Navigation (VLN) requires an embodied agent to navigate through unseen environments, guided by natural language instructions and a continuous video stream. Recent advances in VLN have been driven by the powerful semantic understanding of Multimodal Large Language Models (MLLMs). However, these methods typically rely on explicit semantic memory, such as building textual cognitive maps or storing historical visual frames. This type of method suffers from spatial information loss, computational redundancy, and memory bloat, which impede efficient navigation. Inspired by the implicit scene representation in human navigation, analogous to the left brain's semantic understanding and the right brain's spatial cognition, we propose JanusVLN, a novel VLN framework featuring a dual implicit neural memory that models spatial-geometric and visual-semantic memory as separate, compact, and fixed-size neural representations. This framework first extends the MLLM to incorporate 3D prior knowledge from the spatial-geometric encoder, thereby enhancing the spatial reasoning capabilities of models based solely on RGB input. Then, the historical key-value (KV) caches from the spatial-geometric and visual-semantic encoders are constructed into a dual implicit memory. By retaining only the KVs of tokens in the initial and sliding window, redundant computation is avoided, enabling efficient incremental updates. Extensive experiments demonstrate that JanusVLN outperforms over 20 recent methods to achieve SOTA performance. For example, the success rate improves by 10.5-35.5 compared to methods using multiple data types as input and by 3.6-10.8 compared to methods using more RGB training data. This indicates that the proposed dual implicit neural memory, as a novel paradigm, explores promising new directions for future VLN research.

Published as a conference paper at ICLR 2026
JANUSVLN: DECOUPLING SEMANTICS AND SPATIAL- ITY WITH DUAL IMPLICIT MEMORY FOR VISION- LANGUAGE NAVIGATION
Shuang Zengl.2, Dekang Qi Xinyuan Chang", Feng Xiong", Shichao Xie*, Xiaolong Wu', Shiyi Liang Mu Xu², Xing Wei"
Xi' an Jiaotong University AAmap, Alibaba Group (zengshuang, sy.liang202300suu.xx http://ssu.ttu...e.
www.:u..ttuu......... edw. en, weixing@mail il xjtu etu..edu.cm on,
(qidekang. qddk, changxinyuan.com cxy, huanlu.wxi, xumu.xm]@a wwaiabaaabnnnni com, (xf250971, tenan. Genan.xoc/@autonavi.co
ABSTRACT
Iision-and-Languag Navigation (VLN) requires an embodied agent to navigate through unseen environments, guided by natural language instructions and con- tinuous video stream. Recent advances in VLN have been driven by the powerful semantic understanding Multimodal Large Language Models (MLLMs). How- ever, these methods typically rely on explicit semantic memory, such as building textual cognitive maps or storing historical visual frames. This type of method suf- fers from spatial information loss, computational redundancy, and memory bloat, which impede efficient navigation. Inspired by the implicit scene representation in human navigation, analogous to the left brain's semantic understanding and the right brain's 's spatial cognition, we propose JanusVLN, novel VLN frame- work featuring a dual implicit neural memory that models spatial-geometric and visual-semantic memory as separate, compact, and fixed-size neural representa- tions. This framework first extends the MLLM to incorporate 3D prior knowledge from the ppatial-geometiy encoder, thereby enhancing the spatial reasoning capa- bilities of models based solely on RGB input. Then, the historical key-value V) caches from the into a dual implicit memory. By retaining only the KVs of tokens in the initial and sliding window, redundant computation is avoided, enabling efficient incre- mental updates. Extensive experiments demonstrate that Janus outperforms over 20 recent methods to achieve SOTA performance. For example, the success rate improves by 10.5-35.5 compared to methods using multiple data types as in- put and by 3..- compared to methods using more RGB training data. This indicates that the proposed dual implicit neural memory, as a novel paradigm, explores promising new directions for future VIN research. Ours project page: httpss//mii-sjjuugiibbbbi//aa/ou Vwt.i////aa......nnno.o
and visual-semantic encoders are constructed
INTRODUCTION
Vision-and- Laiio-and-Language Navigation (VLN) is a foundational task in embodied AI, requiring an agent to navigate through unseen environments guided by visual inputs and natural language instructions. Recently, capitalizing on the advanced visual perception and semantic understanding capabilities
of Multimodal Large Language Models (MLLMs) Lan et al. (2025a by: Qi et al. (0026); Li et al.
(022b)) a new line of research (Zhang et al. 2025b; Yuan et al.) 2025 Liang et al., 2025 Shan
et al. 2025) has emerged. These approaches leverage vast-scale training data to adapt MLLMs into
VLN models, thereby reshaping the future landscape of VLN research.
To support navigation models in conducting prolonged and effective exploration, these approaches typically only construct an explicit semantic memory. One class of methods (Zhang et al. 2025c
Work done during the internship at Amap, Alibaba Group. Corresponding author.

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Published as a conference paper at ICLR 2026
Nightstond
Instruction Move flowers from table to left nightstand.
Visualization of Dual Implicit Memory
Visualize Visualization of Visual Semantic Tokens
Flowers
Dual Implicit Memory
Related
Sliding Window
Depth Maps
Point Coud
Incremental Update
Initial Window
Visualization of Spatial Geometry Tokens
Figure I: JanusVLN, using RGB-only video, decouples visual semantics and spatial geometry to construct novel, fixed-size dual implicit memory. This memory incrementally updated during navigation, and its spatial geometry component can be further visualized as depth and point cloud.
Zeng et al. 2024: Chen et al. 2025c Liu et al. 2025a) builds a semantic cogmitive map using textual descriptions for object nodes and relational edges in the detection method Wang et al. (2226) Qu et al. 2025b a): 2026). However, purely textual descriptions struggle to precisely convey the spa-
tial relationships and orientation of objects, leading the loss of crucial visual, spatial- geomeerrc, and contextual information. Moreover, repetitive descriptions introduce substantial redundancy and
noise. Another class of methods (Cheng et al. 2025 Xiang al. 2025 Yang et al., 2025c Li et al. 2025a) stores historical observation frames, which necessitates reprocessing the entire history of observations along with the current frame each action prediction step, resulting in significant redundant computation. Finally, in both types of approaches, the explicit semantic memory grows exponentially as navigation time increases. This makes exceedingly difficult for the model to ex- tract critical information from a vast, cluttered, and fragmented memory, thereby leading to severe inefficiency.
More importantly, these methods collectively face fundamental contradiction. Navigation is an inherently 3D physical interaction, yet the visual encoders existing VLA models almost exclu- sively inherit the CLIP paradigm pre-trained on 2D image-text pairs. This approach enables these encoders to excel at capturing high-level semantics while leaving them deficient in understanding 3D geometric structures and spatial information. However, frequently overlooked yet critical in- sight that 2D images are not merely isolated planes of pixels but are projections the 3D physical world, inherently containing wealth of 3D spatial cues such perspective, occlusion. and geomet- ric structures. Whereas human observers can effortlessly perceive depth and comprehend spatial layouts from single static image, existing models neglect this readily available implicit 3D infor- mation in their inputs. This oversight fundamentally constrains their spatial reasoning capabilities complex navigation tasks.
Inspired by the human brain's hemispheric specialization for navigation, where the left hemisphere handles semantic understanding and the right manages 3D spatial cognition form implicit repre- sentations (Gazzanigal 1967: ZZng et al. 2025b) |aan et al. 2025b) Li et al., 2025e), we propose fundamental shift from a single, explicit memory to dual, implicit neural memory.
this end,
introduce SanusVLN dual implicit memory framework for VLN that features both spatial- geometric and visual- -emal--emantic memory in Figure We model these two types of memory respec- tively as fixed-size, compact neural memory, whose size does not grow with the trajectory length. This design is analogous to the human brain's ability to perform efficient memorization within finite capacity.
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To construct this dual implicit memory, we extend the MLLM into novel VLN model by incorpo- rating a feed-forward 3D visual geometry foundation model, which provides 3D spatial geometric structural information solely from RGB video input, obviating the need for any explicit 3D data. Un- like the visual encoders of general MI.MM,, which are predominantly trained on 2D image-text data, this spatial geometry model is typically trained on pixel-3D point cloud pairs, thereby embedding strong 3D perception priors. We establish implicit
and visual-semantic memory by caching historical key-value (KV) from a 3D spatial geometry encoder and MLLM's is semantic vi- sual encoder, respectively. These dual implicit memory are dynamically and incrementally updated through the initial and sliding window, enabling the progressive integration of historical informa- tion for each new frame without recomputing past frames. Extensive experiments demonstrate that JLN significantly enhances spatial comprehension while lowering inference overhead, achiev- ing SOTA performance on VIN-CE benchmarks. It establishes a new paradigm for VLN research, propelling a shift from being 2D semantics dominated to 3D spatial-semante synergy. This marks pivotal direction toward building the next generation of spatially-awar embodied agents.
In summary, our contributions are as follows:
We introduce a novel dual implicit memory paradigm for VLN. Inspired by human cogni- tive science, this framework simultaneously captures visual semantics and spatial geometry to overcome the inherent limitations of existing navigation LLM.
We unlock the potential of spatial geometric foundation models in streaming VLN. By im- plementing dual-window and attention fusion mechanisms in VGGT, efficiently update and integrate historical information incrementally.
Comprehensive experiments on the VLN-CE benchmark demonstrate that JL. achieves SOTA results without requiring auxiliary 3D data. This validates the efficacy of Janus VLN and establishes a new memory paradigm for the field of VLN.
2
RELATED WORK
2.1
VISION-LANGUAGE NAVIGATION WITH MULTIPLE VISUAL INPUTS
Vision-Language Navigation Krantz et al. 2020: Hao et al. 2024 Wu et al. 2025b), the task of guiding embodied agent to target location in unseen environments by following instructions, has recently garnered sigmificant attention. Early research (Anderson et al., 2018 Wu et al. 2024 Zheng et al. 2025bb Zhang al. 2025e) predominantly focused on discrete environments, where agent
navigates by teleporting between predefined nodes. However, these approaches (Hong et al.
2022
Wu & Zhang 2025 Du al. 2024) often exhibit poor performance when deployed real-va -world
robots operating in continuous 3D spaces. In contrast, more recent studies (Krantz et al 2020 Wang
ct al. 2025c Zhang et al. 2024a 2023) have concentrated on continuous environments, enabling agents to navigate freely to any co collision-free location within simulators. To foster a better spatial understanding and enhance navigational capabilities, some recent works (Wang Lee 2025| Xie
et al. 2025b] Liu et al.] [2023) Wang et al., (0255) have also begun to investigate monocular RGB-D vision. However, the reliance on additional, expensive hardware for this approach, which is often unavailable in many practical settings, restricts its real-world applicability. In this paper, we propose BanusVLN, a method that enhances spatial understanding using only RGB visual input, climinating the need for any supplementary 3D data.
2.2 MULTTI-MODAL. LARGE LANGUAGE MODELS FOR RGB ONLY NAVIGATION
The recent, rapid advancement of Multi-moda Large Language Models Bai et al.
2025 Zeng
jtt al.) 2025c. Lin al., 2025 Ren et al.) 2025 Yang et al., 2025d) has injected new momentum the field of Visual Language Navigation. Some approaches (Zhang et al. 2024b) Zhao et al.) 2023
Yang et al. 2025a Fan et al. 2025a Wei et al. 2025a) have begun to leverage RGB-only video models to build monocular VLN systems, aiming for enhanced generalization and practical value.
However, the agents in these studies (Zhang et al. 20256 Xie et al.] 2025a Cai et al. 2025| Chen et al. 2025b] Qian et al. 2025) typically construct only explicit semantic memory and rely solely on a single, front RGB camera, which poses significant challenges to spatial understanding and often requires extensive auxiliary data to improve performance. In this paper, we introduce Janus
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VLN framework featuring a dual implicit memory system that encompasses both Opatial-geomette memory and sesual-semantic memory.
2.3 SPATIAL REASONING VIA VISION-LANGUAGE MODELS
Increasing research (Chen et al. 2024a Zeng al. 2025a Zheng al. 2025b Zhang et al. 2025a
Sun & Li 2025) efforts have recently aimed to advance the spatial reasoning abilities of Vision-
Language Models (VLMs). Previous studies (Chen al.] 2024b Liu et al. 2025b He 2025 Ma
et al. 2025 Wei et al. 2025c) have predominantly centered on incorporating 3D data (c.g., point
clouds, depth maps) into VLMs to infuse them with explicit spatial information. However, such
methods [i et al. (025d); Hu et al.
2025): : Li et al. (2025g): [unn et al. (2019): |Wei et al. (2026)
often rely on expensive auxiliary hardware, limiting their viability practical applications. While some recent approaches (Wu et al. 2025a Zheng et al. 2025a Jiang et al. 2025 Li et 2023 Tan et al. 2025> leverage spatial encoders to derive spatial in information directly from videos, they require the entire sequence to be re-processed upon the arrival of each frame, leading to significant computational redundancy. JanusVLN extracts -eomell features directly from video in an online, streaming fashion. This eliminates repetitive calculations and markedly lowers the inference cost.
METHOD
3.1 PRELIMINARY
Navigation task definition. The task of Vision-and-Langee Navigation (VLN) in continuous environeent is defined as follows. At the timestep f, an embodied agent provided with a natural language instruction I of 7 words and ego-centric RGB video Or .Fo where each frame I₁ IR x The agent's s goal is to predict a low-level action a++1 E A for the subsequent step. The action space is defined A Each low-level action corresponds to fine tne-grained physical change: a small rotation (30)) a for- ward step (25 cm) or stop, which allows for flexible maneuverability in continuous spaces. Upon executing the action 4,-1, the agent receives new observation 2++1- This process iterates until the agent executes the Stop action at the target location as specified by the instruction.
[Move_Forwarde Turn.Left, Turn_Right, Stop).
Visual geometry grounded transformer (VGGT). Building upon traditional 3D reconstruction.
recent learning-based end-to-end methods (Wang al. 2025a Yang et al., 2025b Liu et al. 2025c
Li & Sun 2025) employ neural networks encode scene priors, directly predicting 3D structures from multi-vien images. VGGT (Wang et al., (225a),, which is based on a transformer feed-forward architecture, comprises three key components: an encoder for extracting single-image feature,
fusion decoder for cross-frame interaction to generate geometric tokens G, E R & |x| |xC where p is the patch size, and a spsk-spe prediction head for 3D attributes. The reconstruction pipeline can be formulated as:
(G.) Decoder(Encoder((zz)]]]
(Pr, Head(G.),
(1)
where a Multi-Layer Perceptron (MLP) head predicts a point map P. E x and per-pixel confidence map C. HR from these geometric tokens. As our focus on feature extraction, which embeds 3D geometry prior information, rather than directly outputting 3D attributes, we leverage the encoder and the fusion decoder as our 3D visual geometry encoder.
3.2 DUAL IMPLICIT MEMORY
The limitations of traditional explicit semantic memory, including memory inflation, computational redundancy, and the loss of spatial information, coupled with the original VGGT's requirement to reprocess the entire sequence for each new frame, impede the real-time performance and effective- ness of streaming navigation. To address these challenges, we introduce the VGGT as a spatial geometry encoder and propose a novel dual implicit memory paradigm for VLN research in Fig- ure 2 This paradigm models spatial geometry and visual semantics fixed-size, compact neural representations by respectively leveraging the history initial and sliding window KV cache of the dual encoders. The spatial memory within the spatial geometry encoder is modeled as follows:
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Instruction: Tum right and walk towards the door...
Action:
Large Language Model
Attention Fusion
Attention Fusion
Sliding Window
Initial Window
Initial Window
Sliding Window
Dual Implicit Memory
2D Visual Semantic Tokens
3D Spatial Geometry Tokens
2D Visual Semantic Encoder
3D) Spatial Geometry Encoder
Stream
Physical World
Vidco
Record
The framework of Jan Given an RGB-only video stream and navigation instruc- tions, JanusVLy utilizes a dual--ncoder to separately extract visual-semantic and spatial-geometric features. It concurrently caches historical veluve from initial and recent sliding window into dual implicit memory to facilitate feature reuse and prevent redundant computation. Finally, these two complementary features are fused and fed into LLM to predict the next action.
Figure 2:
Implicit neural representation. In contrast to previous methods that store nigh-dimensional. un- processed, and explicit historical frames, we innovatively caches historical KV M that have been deeply processed by neural networks. These KV, derived from the output of attention modules such transformers, constitute high-level semantic abstractions and structured representations of the past environment. This implicit memory is not merely compact, efficient storage entity, but a con- densed knowledge representation refined by the neural networks. It enables the agent retrieve and reason over information with minimal computational cost.
Hybrid incremental update. For the implicit neural representation, employ a hybrid cache update strategy instead of caching all historical KV. This approach mitigates the significant memory overhead and performance degradation that arise from extended navigation sequences. The strategy partitions the memory into two components. The first is a sliding window queue Matiding with capacity of n, which stores the KV caches of the most recent n frames a First-In, First-Out (FIFO) manner. This mechanism ensures the model focuses on the most immediate and relevant contextual information, which is critical for real-time veeiiion-makkig.... When this queue reaches its capacity, the oldest frame's cache is evicted to accommodate the current frame, enabling dynamic incremental updates. The second component permanently retains the KV cache Minitial from the initial few frames. The model exhibits sustained high attention weights towards these initial frames, which function as "Attention Sinks" (Xiao et al.) 2024, Yang et al.) 2026 Lu et al., 2025a Li et al.) (025f). These sinks provide critical global anchors for the entire navigation task and effectively restore performance. By integrating these two mechanisms, we construct a dynamically updated, fixed-size implicit memory that preserves an acute perception of the recent environment while maintaining a long-term memory of global task information.
For each incoming new frame, we compute cross-attention between its image tokens and the implicit memory to directly retrieve historical information, thereby obviating the need for redundant feature extraction from past frames.
G =
(Minstial, Matiding!).
3. VGGT's inference time grows exponentially with each new frame due to its need to reprocess the entire sequence, resulting in out-of-memory error 48G GPU with only 48 frames. In contrast, our approach avoids reprocessing historical frames, causing its inference time to increase only marginally and thereby demonstrating excellent efficiency.
As shown in Figure
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For both the semantic encoder and the LLM, namely Qwen2.5-VL, we employ standard KV Cache during the inference stage for acceleration, retaining only the key-value pairs within the initial and sliding windows. Furthermore, these implicit memories and tokens can be visualized to inspect the spatial and semantic information they encapsulate.
3.3 JANUSVIN ARCHITECTURE
Building upon the dual implicit memory paradigm, we propose JLN in Figure 2 enhances the spatial understanding capa- bilities without requiring costly 3D data (e.g., depth).
Time (ms) 8,000,
2,500
Oura
2,000
Decoupling visual perception: semantics and spatiality. To equip embodied agents with the dual capabilities of semantic un- derstanding ("what it is") and spatial awareness ("where it is and how it's related"), JanusVLN is proposed as a dual-encoder ar- chitecture that decouples semantic and spatial information from visual inputs. For 2D semantic encoder, we adopt the original vi- sual encoder from Qwen2.5-VL to interactively encode the input frame Te with the semantic memory into a semantic tokens:
1,500
1,000
500
0
16
Frames
Figure 3: Inference time com- parison for the current frame of varying sequence lengths.
S. E SE
S Encodersem
(3)
Additionally, Qwen2.5-VL Bai al. 2025) groups spatially adjacent 2x2 patches into a single
image token to reduce computational cost, yielding St E TR I* For 31) spatial-geometric encoder, we employ the pre-trained encoder and fusion decoder from VGGT (Wang al.) 2025a) model to interactively encode the input frame with spatial memory into spetial-goometric token Gr.
Spatial-aware feature fusion. Upon acquiring the semantic features Sy and spatial geometric features G,, we first employ the spatial merging strategy from Qwen2.5-VL. (Bai al. (025) This
strategy concatenates spatially adjacent 2x2 feature blocks within G, to form G² Rlap therehy aligning its shape with that of Sy Subsequently, utilize lightweight two-layer MILP projection layer to fuse the semantic and spatial geometric information:
2 x
J
= + x MLP(G)),
(4)
where À represents the weight for the spatial geometric features, and F, denotes the final, spatially- geometrically enhanced visual features. Subsequently, the final visual features, along with the text embedding of instruction I, are fed into the backbone of the MLLM to generate the next action.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Simulation environments and metrics. Following established methods (Zang et al. 2025b Li
al. 2025c Li 2025 Chu et al. 2025), we conducted experiments on two of the most recognized
VIN-CE Krantz et al. 2020) benchmark datasets: R2R-CE (Anderson et al. 2018) and RxR- CE (uu al. These datasets comprise trajectories collected from Matterport3D (Chang et al.)
2017) scenes using the Habitat simulator (Savva et al.) 2019). Consistent with prior work (Cheng ct al. 2025 Dai et al. 2025 Yin et al.) 2025; Lu et al. 2024), we report performance on the unseen splits using standard VLN metrics, including Navigation Error (NE), Oracle Success Rate (OS), Path Length (SPL), and normalized Dynamic Time Warping (nDTW). Among these. SR and SPL widely regarded the primary metrics, reflecting task completion and path efficiency, respectively Wei et al. (2025b) Lu et al. (202ce b).
Success Rate (SR), Success-weighht
Real-world evaluation setup. In real-world experiments, we use the Unitree Go2 as the robotic platform, equipped with an Insta360 X5 camera capture front RGB. JanusVLN runs remote server with an A10 GPU to continuously process RGB and instructions, returning the inference re- sults to the robot for action execution. We focus navigation tasks requiring spatial understanding.

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Published as a conference paper at ICLR 2026
Instruction: Tur lelt and enter the room on your left. You will see two yellow stools. Stop next to the one that is farthest from you.
Instruction: Go forward and enter the room right. Stop beside the green potted plant, not in front of
Instruction: Go straight to the intersection and turn lefft. Continue strii ghtt until you reach the green potted plant, and tum left again. Procood forward and stop stool that orange cabinet, not the first you
Figure 4: Qualitative results of Jan on real-workes
Implementation details. We constructed JanusVLN based on Qwen2.5-VL. VL 7B (Bai et al. 2025) and VGGT Wang al. (2025a). The model trained for one epoch, during which we exclusively fine-tune the LLM and the projection layer with learning rates of 2e-5 and le-5, respectively, while keeping the semantic and spatial encoders frozen. We set the initial and sliding window size to 8 and 48 frames. The weight for the spatial geometric features À is set to 0.2. For extra data, following StreamVLN (Wei et al.| (025b). we incorporated an additional 155 K trajectories from subset of the VLN (Wang et al. 2003cc) comprising approximately 9207 K image-action pairs. Furthermore, we employed the DAgger (Ross et al. 2011) algorithm to collect 14 K trajectories (approximately 1485 K image-action pairs) from the standard R2R-CE and RxR-CE datasets.
4.2
MAIN RESULTS
Results on VLN-CE benchmark. As presented in Table Table we evaluate our Jan on the two most prominent VIN-CE benchmarks: R2R-CE and RxR-CE. Compared to methods utilizing multiple input types like panoramic views and odometry, Ja. achieves 10.5-35.5 improvement in SR using only single RGB input, demonstrating the effectiveness of our approach. Furthermore, JLN outperforms SOTA methods that use additional 3D depth data, such as g3D- LF and Na by 12.6-16.7. indicating its ability effectively enhance spatial understanding with only RGB video streams. Against methods employing explicit textual cognitive maps (e.g., MapNav) or historical frames (e.g.. NaVILA, Stream StreamVLN), JanusVLY achieves improvements of 20.8, 10.8, and 3.6, respectively, while using less auxiliary data, highlighting the superiority of its dual implicit memory novel paradigm. Furthermore, Stream VLN* by 10.8-15 in SR when using a comparable amount of data. Notably, even without any additional data, VanusVLN' still outperforms the aforementioned methods that rely on partial extra data by margin of 3.7-18.8 in SPL. On the RxR-CE dataset, Janus VLN improves the SR metric by 3.3-30.7 over previous methods, demonstrating its superior generalizability. In summary, Janus consistently surpasses various prior methods across all settings, exhibiting strong generalization ca- pabilities. This suggests that the dual implicit memory, as novel memory paradigm, can effectively replace conventional textual cognitive maps and historical frames.
method surpasses NaVILA* * and
Real-world qualitative results. We selected several navigation tasks that demand spatial under- standing in Figure including depth perception (the farthest yellow stool), 3D orientation and rela- tive positioning (beside the green potted plant rather than in front of it), and spatial association (the stool beside the orange cabinet). By leveraging the spatial-geometric memory within dual implicit memory, Janus VLN effectively enhances its spatial reasoning, enabling the successful completion of these challenging tasks. For more visualizations, please refer the supplementary materials.

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Published as a conference paper at ICLR 2026
Table Comparison with SOTA methods on VLN-CE R2R Val-Unseen split. External data includes any sources beyond the standard R2R/RxR -RR datasets (e.g.. EnvDrop, DAgger, general VQA, etc.). Stream VLN* uses EnvDrop as external data. NaVILA* excludes suman-following data. All results are from their respective papers. A training sample is an action or QA pair. Pano, Odo, Depth, and S.RGB respectively represent panoramic view, odometry, depth, and single RGB.
Observation
R2R Val-Unseen
Training
Method
Pano. Odo. Depth S.RGB NEJ Ost SRT SPL
External Data
HPN+DN [ICC21]]
Krantz et al. 2021
6.31 40.0 36.0 34.0
CMA [CVPR22] Hong et al. 2022
6.20 52.0 41.0
36.0
Sim2Sim [FCCV22 Krantz & Lee
2022
6.07 52.0 43.0
36.0
VEN [CVPR22] Hong al. 2022
5.74 53.0 44.0
39.0
Ego2-Map pCCv23]
(Hon et al. 20233
V
5,54 56.0 47.0
41.0
DreamWalke Walker HCCv23] Wang
2023a
V
5.53 59.0 49.0
44.0
GridMM [CCVV23) (Wang al 20235b
5.11 61.0 49.0
Reborn (ICCV23) Wang ct al.
2023b)
5.40 57.0 50.0
46.0
InstructNav [CoRL24] (Long et al. 2024
6.89
31.0
24.0
COSMO [CCV25) (Zhang et al. 2025d
56.0
47.0
40.0
AO-Planner [AAA (Chen et al. 2025a
5.55 59.0
47.0
33.0
LAW [EMNLP21] Raychaudhuri
02021
6.83 44.0 35.0
31.0
MapNav [ACL25]
Zhang et al.
2025e
4.93 53.0
37.2
g3D-LLF [CVPR25] Wang & Loc
2025
5.70 59.5 47.2
34.6
Seq2Seq [FCCV200
Krantz ct al.
202()
7.77 37.0 25.0
22.0
NaVid-4D [ICRA25]
[Lu al.
2225b)
5.99 55.7 43.8
37.1
NavMorph [CCCV25]
(Xuan Yao & Xu
2025
S.75 56.9
47.9
33.2
VaVid [RSS24] Zhang ct al.
2024b)
5.47 49.1
37.4
35.9
953K
Sim2Rcal [CoRL22] (Wang ct 2024)
5.95 55.8 44.9
30.4
OK
Stream [5rXiv25 Wes
b025b
6.05 53.8
41.6
10033К
Uni-NaVid (RSS25] (Zhang el al.
2025b
5.58 53.3 47.0
42.7
3577K
NaVILA* [RSS25] CCeng al. 2025
V
5.37 57.6 49.7
45.5
12574K
Janus VL (Ours)
5.17 58.0 52.8
49.2
OK
Na NaVILA [RSS25] (Cheng et al. 2025
5.22 62.5 54.0
13132K
Stream VLN [arXiv25] We1 et al.
(205bb)
4.98 64.2 56.9
51.9
26330
Janus (Ours)
4.78 65.2 60.5 56.8
10692K
Table 2: Comparison with SOTA methods on VLN-CE RxR Val-Unseen split.
Observation
RxR Val-Unseen
Training
Method
Pano. Odo. Depth S.RGB NE, SR1 SPL.1 nDTW1 External Data
CMA [CVPR22] Hong 2022
8.76 26.5 22.1
47.0
VLNÇ BERT [CVP22] Hong
2022)
8.98 27.0 22.6
46.7
Reborn [ICCV23] Wang et al.
2023b
5.98 48.6
42.0
63.3
AO-Planner [AAAI25] (Chen
2025a
7.06 43.3 30.5
LAW [EMNLP21] Raychaudhur cf 2021
10.90 8.0 8.0
38.0
Seq2Seq [BCCV20] Krantz ct al.
2020
12.10 13,9 11.9
30.8
NavMorph [ICCV25] Xuan Yao & Xu
2025
8.85 30.8
22.8
44.2
Sim2Real [CoRL24) Wang et al
2024
8.79 36.7 25.5
18.1
0K
Uni-NaVid [RSS25] Zhang al.
2025b
6.24 48.7 40.9
3577
NaVILA (RSS25] (Cheng ct
49.3
44.0
13132A
Janus V (Ours)
6.46 51.4
44.3
0K
Stream VL. [arXiv25]
Wei ct al.
2025b
6.22 52.9
46.0
61.9
26330K
Janus VLN (Ours)
6.06 56.2
47.5
62.1
10692K
4.3 ABLATION STUDY
In this section, unless otherwise stated, we use no additional data and conduct ablation studies on the R2R-CE benchmark. For more ablation studies, please refer the supplementary material.
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Ablation of the dual implicit memory. The ablation study for dual implicit memory is presented in Table Removing the spatial memory led a substantial drop in the SPL score from 49.2 to 40.9. This finding demonstrates that the understanding. Furthermore, removing the semantic memory results in a 13.8% decrease in the SR, underscoring the necessity of the semantic memory. Finally, the simultaneous removal of both memory modules leads to a near-collapse in model performance. In summary, these experiments highlight the complementary and indispensable nature of our proposed dual implicit memory.
memory effectively enhances the agent's spatial
Table 3: The ablation experiments of each component of the proposed Janus VLN.
Method
OS1
SR
SPL
CanusVLN w/o Spatial Implicit Memory
5.17
58.0
52.8
49.2
6.58
54.3
47.0
40.9
w/o Semantic Implicit Memory
6.75
53.1
45.5
40.0
w/o Dual Implicit Memory
7.85
36.9
24.8
16.8
Ablation of 3D geometric priors. We provide ablation study in Table to investigate the effect of introducing additional encoders. When the spatial geometric encoder VGGT Janus replaced by other visual encoders (e.g., DINOv2 (Oquab et al. (023), and SigLIP 2 (Tschannen et al. (2255), the performance did not significantly improve. The reason is that these alternative encoders are generally pre-trained 2D image-fext pairs. While this makes them proficient in capturing high-level semantics, this information is largely redundant with that from the original visual encoder of Qwen2.5-VL, and consequently, offers no significant improvement. Conversely, VGGT, being pre-trained on pixel-to-3D point cloud pairs, contributes complementary information. Moreover, a randomly initialized VGGT, devoid of pre-trained 3D spatial-geon geommerie priors, showed no notable gains. This demonstrates that the advantage of Janus VLN lies in its enhanced spatial comprehension, rather than simply increasing model parameters.
Table 4: Comparison between additional, different semantic encoders and spatial encoder.
Encoder
NEG
Ost
SR1
SPL
JanusVLN w/o extra encoder
6.58
54.3
47.0
40.9
VLN w/ extra DINOv2
6.44
55.4
47.5
41.5
TanusVLy w/ extra SigLIP 2
6.38
55.2
47.9
41.9
Janus VLN w/ extra VGGTIrandom init]
6.61
54.7
47.2
40.8
TanusVLY w/ extra VGGT
5.17
58.0
52.8
49.2
Table 5:
Inference time and performance comparison for the current frame of varying sequence lengths between cached memory and VGGT for the online setting.
Memory Size
Inference Time
NE]
Ost
SR1
SPL
VGGT (8)
268 ms
5.99
56.2
50.2
45,0
VGGT (32)
1549 ms
5.66
56.8
51.2
47.6
Cached Memory (8)
82 ms
5.91
56.0
50.5
45.7
Cached Memory (32)
149 ms
5.52
57.1
51.7
48.3
Cached Memory (48)
195 ms
5.17
58.0
52.8
49.2
Cached Memory (64)
244 ms
5.27
57.5
52.3
49.4
Cached Memory inttia''s (48)
171 ms
5.66
56.8
51.0
47.5
Ablation memory size. We present the ablation studies memory size in Table 5 First, as shown in the first row, with a memory of 8 frames, the original VGGT model without caching necessitates re-computation of the entire sequence for each new frame's 's feature extraction. This sults an inference overhead of 268 ms. Furthermore, as the memory size increases, the inference overhead of VGGT grows exponentially, rendering it impractical for real-world applications. In

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Published as a conference paper at ICLR 2026
contrast, Janus dynamically caches historical KV, eliminating the need for re-computation. This approach significantly reduces inference overhead by 69%-90% while also yielding a slight performance improvement, thereby demonstrating the effectiveness of the implicit neural memory. As the memory size increases, JanusVINN's performance progressively improves, saturating at 48 frames. This suggests that compact, fixed-size implicit memory is sufficiently effective. Finally, when we omit the preservation of the initial window's 's KV, a slight performance degradation is ob- served, indicating that the first few frames of memory do indeed capture significant model attention.
CONCLUSION
This paper introduces JanusVLN, novel VLN framework and the first to feature a dual implicit neural memory. Inspired by the implicit scene representation in human navigation, which inte- grates left-brain semantic understanding with right-brain spatial cogmition, JanusVLN constructs two complementary. fixed-size, compact neural memory. This approach overcomes the bottlenecks of traditional methods memory inflation, computational redundancy, and the absence of spatial perception. By synergistically integrating MILM with a feed-forward 3D spatial geometry foun- dation model, LanusVLN achieves perception of spatial geometric structures solcly from RGB video, obviating the need for auxiliary 3D data. The dual implicit memory are derived from the histori- cal KV caches of spatial geometry encoder and a semantic visual encoder, respectively. They updated with high efficiency through incremental process that retains only initial and sliding window of KVs, thus avoiding re-computation. Extensive experiments demonstrate the superior- ity of lanusVLN, steering VLN research from 2D semantiss-domnnat toward 3D spatial-semantic synergy, a critical direction for developing next-generation spatial embodied agents.
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ACKNOWLEDGMENTS
This work was support by the National Natural Science Foundation of China No. 62572385, the Fundamental Research Funds for the Central Universities No. xxj032023020, and CAAI-CANN Open Fund, developed on Openl Community.
ETHICAL STATEMENT
We anticipate that JLN technology will advance the application of embodied AI in benefi- cial domains, such as providing navigational assistance for the visually impaired, improving task efficiency in domestic service robots, and performing search and rescue operations in disaster sce- narios. We also recognize that any advanced autonomous navigation technology presents a potential for misuse in negative applications like unauthorized surveillance or military operations, challenge known as the dual-use problem. The fundamental motivation this research is to foster scientific progress and social welfare. We condemn any use of this technology for unethical malicious purposes and call upon the academic community to jointly establish and abide by guidelines for the responsible development and application of Al.
REPEATABILITY
To ensure the reproducibility of our research, the implementation details of Janus provided in Section 4.1 To foster academic exchange and technical transparency, we will publicly release source code, model configurations, and fine-tuned model weights in accordance with relevant licenses. This will enable other researchers to replicate our findings and build upon our work.
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A THE USE OF LARGE LANGUAGE MODELS (LLMS)
In this paper, the application of Large Language Models (LLMM)) was strictly limited to enhancing writing quality. Upon the completion of the manuscript, we employed Gemini 2.5 Pro (Comanici (et al. 2 to refine the text and identify grammatical or stylistic errors. The model was guided by the following prompt: "You are top-tier academic expert specializing in refining academic papers. Please polish this text, identify any writing errors, and ensure the original meaning is preserved without altering its substantive content."
B MODEL STRUCTURE DETAILS
Global Attention
Cross-Attention
K/V D0000
Q
Post
Current Key&Value Value
Current Query
Key
Value
(Initial Window
FIFO Sliding Window
Frame Attention
Deemmatin-AAtentionn X
Current Tokens
Image Encoder
3D Spatial Geometry Encoder
Frame
Figure 5: Details of the implicit memory of the spatial geometric encoder.
In the original VGGT, frame attention and global cross-frame attention are executed alternately. In Figure|3Jurr spatial encoder, in contrast, fuses information through interaction with cache during the global attention process. Specifically, the tokens of the current frame first pass through frame attention to establish a local context. Then, during global attention, these current-frane frame tokens generate the Query. The final Key and Value constructed by concatenating the historical KV cache with the newly generated KV from the current frame, which are then used to compute the attention. This alternating execution of frame attention and global attention is repeated.
Qwen2.5-VL. VL employs the standard KV Cache mechanism typical of LLMs. Visual embeddings derived from frame via the semantic encoder generate Queries within the language model. These Queries then compute attention against the Keys and Values of all historical tokens combined with the Keys and Values generated by the tokens the current frame
C MORE ABLATION STUDIES
Real world quantitative results. In our real-world experiments, we employed a Unitree Go2 robotic platform equipped with an Insta360 X5 camera to capture forward-facing RGB images. The Janus model operates on remote server with an A10 GPU, continuously processing RGB images and instructions, and sending the inference results back to the robot for execution. For quantitative real-world evaluation, we used 25 instructions, each repeated three times, covering both general and spatial understanding tasks. trial is considered successful if the robot stops within meter of the target. As shown in Figure 6 VanusVLN outperforms its variant without spatial memory across all scenarios. Notably, it achieves a 23.6% improvement on navigation tasks that require spatial understanding, which demonstrates the effectiveness of JanusVLN
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BR(N)
JamusVVNNlw/ Sparislit)) JanuVVNN
70
00
40
Tons
Nornal Naeigation
Sponial Underttanding
Figure 6: Quantitative experiments the real world.
Table 6: Comparison on recent HM3D-OVON Yokoyama al.
2024b val unseen.
Method
SR
SPL1
VLFM [ICRA24]
Yokoyama et al. 2024a
35.2
19.6
DAgRL+OD [IROS24] Yokoyama et al.
2024b)
37.1
19.8
Uni-Navid (RSS25]
(Zhang et al. 2025b
39.5
19.8
MTU3D ccCv251
(Zhu et al. 2025
40.8
12.1
JL.
44.9
31.7
Results on recent HM3D-OVON. As shown in Table we also test on the more diverse, updated HM3D-OVON (Yokoyama et al. 2024bb benchmark. Our approach JLn surpasses SOTA methods by boosting the Success Rate (SR) from 40.8% to 44.9%, which showcases its strong generalization capabilities.
Ablation of fusion strategies. Table 7 presents the results for different feature fusion strategies. We varied the weight of spatial features from 0.5 to 0.1 and observed that the performance peaked 0.2. We also utilize fusion strategy of Concat and Cross-Attention, where Cross-Attention de- spite exhibiting competitive performance, remains marginally inferior to the simple and lightweight addition method. The exploration of more sophisticated strategies is left for future work.
Table 7: Ablation experiments on the fusion strategies of spatial features and semantic features.
Fusion Strategy
NE.
OSt
SR1
SPL.
À = 0.5
5.61
55.5
50.4
46.9
À 0.2
5.17
58.0
52.8
49.2
A 0.1
5.69
55.8
50.2
46.6
Concat
5.78
55.2
49.4
45.7
CrossAttn
5.24
58.2
52.
48.6
Data Ablation. Table presents the ablation studies on the use of supplementary data. Notably, even without any additional data, JanusVLY outperforms prior methods that utilized partial supple- mentary datasets, demonstrating its robust intrinsic navigation capabilities. Following SLNe observe that incorporating data from VLN and DAgger individually both yield performance improvements. Furthermore, following Stream the concurrent use of both data sources leads to further enhancement, showcasing the model's excellent data efficiency. The integration of even
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60
UM. More HOVLA BrumiMMarnnA
UNiKte NUNTA BwwnnOXaoooth
All Tesko
Spatial Tosko
Figure 7: Performance on spatial understanding tasks.
+ Arerro D4I Gemmeian SA1
15%
Figure 8: Performance on various instruction lengths/complextty.
larger-scale external datasets, akin to the approaches of StreamVLX and NaVILA, reserved for future work construct more powerful navigation agents.
Table 8: Ablation study of different training data compositions.
Data Compositions
NE
OS1
SR1
SPL1
VanusVLN w/o Extra Data
5.17
58.0
52.8
49.2
JanusVLA w/ SCaleVLN
5.08
62.8
55.5
50.9
JanusVL. w/ DAgger
5.02
63.4
56.4
51.7
JanusVLs w/ ScaleVLN & DAgger
4.78
65.2
60.5
56.6
D STATISTICAL ANALYSIS
Success and strengths analysis. In Figure 7 We measured the success rate on instructions requir- ing spatial understanding (i.e., those containing terms like 'farthest," 'nearest," 'larger,' 'smaller; "rightmost," 'leftmost," 'first,' 'second," 'front,' 'back,' etc.). We find that the superiority of Jan over prior methods is more pronounced scenarios requiring spatial understanding than its average gain across all tasks, demonstrating its strong spatial awareness.
Performance by instruction length. We analyzed the trends in SR and SPL for both Stream VLN and JanusVI. VLN as instruction length increases in Figure Both models achieve high SR and SPI.
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Now in a room, facing lowards the snooker table, turn slightly left and walk straight till the wooden wall, from there turn right and walk straight near the chair, go neur the wooden table which in front of the sofa, walk few forward, tum right and go straight till the railing, from railing go towards the dinning table, tum right and walk straight near the sofa sets and stand beside the chair which has floral cushion, this would be your final destination.
Error type: Inadoquate self-correction get back on the right track.
You're starting dining room, facing window with curtains. Turn to your left until the table is your left Rand side and can see hallway going down the right. You will also see white flowers down that hallway. Take step loward the white flowers. You'll bur stools in front of the counter that the flowers stool that's closest you. Then take left into the kitchen, passing the flowers your right. You'll then walk between the two islands your right. There's farm sink on the left island and then there's farm sink farther down the right island. Go ahead and walk straight through these toward the microwave. At the microwave, take a left and walk past the in front of you. refrigerator your right. Then take right past the refrigerator and walk across the hall into the white refrigerators Take another step and you'll
Take step the bur
There bright windows and brown table in that on your right and then your left, the für end of the room, you'll wooden door that has rounded top. It's open. Take step that wooden door. Once you're front of that door, to your right wooden bookshelf and your left bathroom. When you're here, you done.
Error type: Stopped too early and failed to reach the 3-meter runge.
Figure 9: Visualization and presentation of the types of failure cases.
relatively simple instructions (1-150 words). However, their performance declines moderately complex instructions (150-400 words), indicating a need to enhance the models' ability to decom- pose and comprehend complex directives. For the most complex instructions (400-550 words), Stream VLN's performance continues to degrade, eventually reaching zero. In contrast, Janus VLN's performance improves, benefiting from its dual implicit memory paradigm. This is likely because these lengthy instructions provide highly detailed, step-by-step guidance that the model can effec- tively follow.
Failure case analysis. Our statistical analysis reveals two predominant types of failure cases for Jansu VLN in Figure 9 First, when the agent deviates from the optimal trajectory, it attempts to correct its course but often fails to recover, leading to compounding errors and eventual failure. Al- though we collected a limited amount of non-optimal trajectory data via DAgger, it is insufficient to enable robust error correction. Second, JanusVLN appears to employ an overly aggressive stopping policy, sometimes halting prematurely upon sighting the destination and thus failing enter the suc- cess radius. This may be because the spatial information from its VGGT encoder lacks rorl-world scale, resulting in inaccurate distance estimation.
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E MORE QUALITATIVE RESULTS
Visualization analysis of spatial geometric tokens. We demonstrate how spatial geometry to- kens aid navigation by visualizing them as depth maps and point clouds in Figure [0 In the first example, the depth map derived from the tokens captures precise depth information, enabling more accurate localization of the farthest chair. the second, the point cloud constructed from the tokens clearly reveals the chair behind the sink counter. In the third example, both visualizations distinctly represent the size of the door. Finally, in the fourth example, visualizations reveal that the tokens focus on the rightmost house, as reflected in both its depth map and point cloud. In conclusion, the spatial information captured by these tokens is crucial for spatial understanding.
More qualitative results. This section presents further qualitative analysis of JanusVLN in both real-world and simulated environments. For real-world settings in Figure [1] we selected navigation tasks that involve simple and complex instructions, diverse sites, and spatial understanding. where Janus VI.N demonstrates excellent generalization. For simulated environments Figure T2 and T3 we chose complex trajectories and long instructions from the unseen validation sets of R2R-CE and RxR-CE. Leveraging its dual implicit memory, Janus VLN effectively follows these instructions to complete challenging navigation tasks.
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restaurant, turn right, keep walking forward. Stop next
Go forward, turn left, and then stop beside the chair that behind the sink counter.
right, you doors. smaller one...
Exit the beginning room and enter the area. Turn right and enter the farthest right room...
Figure 10: The analysis on the effectiveness spatial gemetric tokens.
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Published as a conference paper at ICLR 2026
Instruction: Turn left, then walk straight down the hallway and by the green plant.
Instruction: Go forward. You will
closest you and stop.
Instruction: Go straight down the hallway, take right at the second intersection, and stop at the door.
Instruction: Turn around, enter the the right, go past the chair, and stop the table.
Instruction: Go straight ahend. After you pass two doors and display board, make a left turn, Then, continue along the hallway until you've gone past single door, and stop there.
Instruction: Keep walking straight. Once you pass two escalators, make a left turn. Continue down the hallway, go past green potted plant, and stop at the first door you see on your right.
Figure 11: Qualitative results of VLN real-world.
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Go around the table turn right and exit the kitchen. Turn left and two steps of the stairs.
Turn around and exit the double doors on the left. Walk out past the stairs and turn left, Finter the room straight
ahead end. Walk into the room and then turn into bathroom door on right stop.
Walk through the bedroom and exit through the door left of the red bed. Head into the next room. Walk forward into next room. Stop at the next door exiting the room.
Go of the closet into the bedroom and turn left. Leave through the door and stand the of the stair case.
Figure 12: Qualitative results of VLN on R2R-CE.
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You are stunding hallway, facing an open, bright area. Go struight down the hullway to un opening on your left, where you'll Il see a black picture with gold lettering. Take a left. Now you face room with two windows at the end. Walk a few feet past the first opening, and at the second opening (hefore the windows), tum right. Continue straight into bedroom. Once inside, go to the of bed, walking between the wall with three pictures the bed your right. Walk the way stand with two orange plates. Stand front that stund, which "Do Not Enter" sign. You at your destination.
Move forward from the place you are standing, s slightly turn to your left and move forward, enter into the area which is right of the staircase, you can an opened door front you, move towards opened door, turn right and enter into the room, turn left, stand in front the black board, that would be your end point.
Now you are facing towards the white open door, turn slight left and move towards the bed and take left turn in front you can see the open door, move towards the door and exit the door, now you are in- front of the white railing. take slight left turn and move forward, you can see the another bed room with an open door, enter into the door take right turn and move towards bed, in-front you can see window and white stand, take "sorry" right turn and move forward and enter into bathroom, in-frent you can see white towel hanger, that your final destination.
You'll face pillar and dining Slightly left, find small wooden table (with plant and remotes) and couches. Walk along the table towards the kitchen entrunce, then with flowers, and slight step left. You'l re now in the kitchen. In the kitchen, your left wooden cupboards and sink, white flower plant in front, and four glass doors leading outside. Step towards the glass doors, then tum left. You'll see hallway with four chairs lined up, and fifth black chair behind them. Step towards that black chair. The glass doors will be on your right, the four chuirs on your left, and the fifth chuir in front. You are done.
step towards the weoden cupbourd
Figure 13: Qualitative results of VLN on RxR-CE.
26


Reviews:
[{"review_id": "sAV1qItypU", "reviewer": "Reviewer_KxJm", "summary": "The paper proposes a novel approach for Vision-and-Language Navigation (VLN) by introducing a framework that effectively decouples semantic and spatial-ity information using a Dual Implicit Memory mechanism. The paper presents a logically sound architecture that aims to overcome the limitations of explicit semantic memory in VLN agents. The empirical results demonstrate that the proposed method significantly advances the state-of-the-art performance across several established VLN benchmarks.", "questions": "1. Given the goal of generalizable navigation, the lack of quantitative real-world validation is a significant drawback. Could the authors provide quantitative results for the real-world scenarios presented? Additionally, please elaborate on the practical and operational details of the real-world experiments, such as sensor configuration, map representations used (if any), and any specific data preprocessing required for deployment.\n\n2. Regarding the dependency on the VGGT model (Weakness 2), please offer a more extensive explanation and stronger comparative evidence to clarify the unique contribution of the decoupling mechanism and the Dual Implicit Memory structure. Specifically, how does this memory module provide a benefit that a simpler, standard memory module would not, when both are paired with the exact same VGGT backbone?\n\n3. To improve the analytical depth (Weakness 3), please strengthen the experimental validity analysis. This should include providing more detailed examples and, ideally, a systematic statistical analysis of the agent's behavior (e.g., analysis of error types, performance on various instruction lengths/complexity, or specific semantic grounding success rates) to better guide and inspire related future research.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-structured and clearly written. The core ideas, particularly the decoupling mechanism and the design of the dual implicit memory, are logically presented, making the methodology straightforward to understand.\n\n2. The empirical results are highly compelling. The proposed method achieves excellent performance and reports SOTA results on the evaluated, well-known VLN benchmarks. This suggests a notable improvement in the agent's navigation and generalization capabilities.", "weaknesses": "1. The primary performance evaluation heavily relies on relatively older and more indoor/limited benchmarks. While the paper includes some results on real-world scenarios, these are unfortunately only qualitative results, completely lacking quantitative metrics. The submission would be significantly stronger if it included quantitative validation on real-world data and performance on more recent, challenging, and diverse VLN benchmarks from the past 3 years.\n\n2. A critical concern is that the proposed method appears to be highly dependent on the capability of the underlying VGGT model. This dependency raises questions about the independent contribution of the novel framework components versus the inherent power of the advanced backbone itself. This requires clearer justification and comparative analysis.\n\n3. The experimental analysis of the overall framework's effectiveness and validity is not deep enough. The ablation studies provided are basic. There is a lack of detailed analysis on: (a) the specific scenarios where the proposed method successfully outperforms others, and (b) a systematic investigation of the current failure modes of the agent. A comprehensive analysis of successful vs. failing cases is crucial for the community to understand the method's limits and aid future improvements.", "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": "dwIlCnIXTW", "reviewer": "Reviewer_y4hH", "summary": "This paper introduces a novel semantic-spatial dual-memory mechanism for visual language navigation tasks. The semantic and spatial memories are stored as KV caches extracted from VLM visual encoder and VGGT representation, respectively. It demonstrates superior performance compared to SOTA baselines in R2R/RxR-CE datasets, with lower computational overhead. Overall the proposed method is novel and effective. The experimental setup and research methodology are sound. My main concern is the lack of open-sourced implementation of the method, which impedes full verification of the results and reproducibility.", "questions": "1. NaVILA and StreamVLN are the two strongest baselines shown in Table 1 and 2. However, they are both trained with different datasets. Comparison to these two approaches trained on the same 10.6k extra data used by JanusVLN will rule out the impact of training data. Could you run such experiments?\n\n2. Any plan to open-source the proposed method?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. This work addresses a critical task that remains highly challenging for current embodied systems. Furthermore, the selection of baselines is comprehensive, incorporating all recent state-of-the-art advancements.\n\n2. The proposed semantic-spatial dual-memory mechanism is both novel and highly effective, demonstrating superior performance over all baselines on both the R2R and RxR-CE datasets.\n\n3. The proposed method has been successfully deployed on a real robot platform. Qualitative results are provided.\n\n4. The ablation studies are thorough and complete, evaluating the impact of critical design choices, including geometric prior selection and memory size.", "weaknesses": "1. The lack of open source code for the proposed method impedes full verification of its reproducibility.", "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": "yVcbyMEIFE", "reviewer": "Reviewer_UnTs", "summary": "This paper introduces a new framework for VLN designed to address the inefficiency of methods that rely on explicit memory. \n\nThe authors propose a dual implicit neural memory inspired by the division of labor in the human brain for semantic and spatial processing. This framework decouples visual semantics from spatial geometry, representing each as a compact, fixed-size memory. Technically, this is achieved by caching the key-value (KV) pairs from two separate encoders: a semantic visual encoder from a Multimodal Large Language Model (MLLM) and a 3D visual geometry encoder (VGGT) that infers spatial priors from RGB input alone. \n\nThis implicit memory is updated efficiently using an initial window + sliding window strategy, which avoids recomputing features for the entire history at each step. The framework reaches SOTA.", "questions": "1.My main conceptual question is about the necessity of decoupling. The paper shows that both semantic and spatial information are important. However, could the authors provide a more rigorous technical justification for why these information streams must be processed by separate encoders and fused late, rather than being learned jointly within a single, deeper architecture? Is there any evidence to suggest that a standard Transformer struggles to learn the complex interplay between semantics and spatial geometry, making this explicit separation necessary? I think a clear answer (either theoretical or experimental) to this would significantly strengthen the paper's conceptual contribution, perhaps even more so than the SOTA results.\n\n2.Following on from the fusion mechanism, I'd be very interested to know the authors' thoughts on why the simple addition fusion outperformed Cross-Attention. Do you have any hypotheses that go beyond what's presented? Could it be that the two feature streams are already well-aligned by the powerful encoders, making complex interactions unnecessary or even noisy?\n\n3.I'm curious about the generalizability of the spatial priors. VGGT is pre-trained on pixel-3D point cloud pairs. I wonder how robust the system would be if deployed in environments with architectural or visual styles that differ significantly from VGGT's pre-training data. Have you considered this potential domain gap?\n\nThese main points are based on the improvement and insights that can benefit the community. If I do have any misunderstanding welcome to point me out.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 2, "strengths": "(1) I think the way JanusVLN incorporates 3D geometric priors from a model like VGGT, using only RGB video as input, is a major strength. It enhances the agent's spatial awareness without requiring specialized hardware like depth sensors. The community is waiting a better engineering solution that applies VGGT to VLN tasks.\n\n(2) The method achieves SOTA results on challenging benchmarks, including those methods that use additional data types or more training data.\n\n(3) The authors have done an excellent job of dissecting their model and validating each component. The ablations on the dual memory, 3D priors, and memory size are all well-designed and provide strong support for the final architecture.", "weaknesses": "My main concern is about the paper's claim on its title: Decoupling Semantics and Spatiality.  Of course the paper indeed successfully argues that both semantic and spatial information are important (as shown by the ablation in Table 3 where removing either hurts performance) . However, it fails to provide a compelling argument for **why** these two types of information must be or had better be decoupled into separate processing streams. \n\nThe paper repeatedly draws an analogy to the human brain's hemispheric specialization. While this is an elegant and intuitive starting point, it cannot substitute for a rigorous technical argument. The paper just provides an experimental result which is certainly great. But for the VLN community nowadays, SOTA is indeed good but it is not that improtance or convincing to this specific question. To the best of my knowledge, there is few evidence or analysis to suggest that a standard, unified Transformer architecture would struggle to learn and represent both semantic and spatial features concurrently, or that their deeper fusion would be detrimental. This makes the central claim that **decoupling is necessary or vital** feel more like an assertion than a proven technical need.", "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": "pl2QSRnBHz", "reviewer": "Reviewer_we6n", "summary": "The paper introduces JanusVLN, a novel framework for Vision-and-Language Navigation (VLN) that proposes a dual implicit memory system—one for visual semantics and another for spatial geometry. The authors decouple semantic and spatial processing using two separate encoders: a semantic encoder (ViT of Qwen2.5VL) and a pretrained geometric encoder (VGGT). The model use cached key-value (KV) memory from both encoders, updated incrementally via an initial and sliding window strategy, to avoid recomputation and reduce inference overhead. JanusVLN achieves state-of-the-art performance on VLN-CE benchmarks using RGB input alone, surpassing previous methods in both Success Rate (SR) and Success-weighted Path Length (SPL).", "questions": "1. Detailed mechnisim of the approach: Please refer to Weakness 2 for concerns regarding the architectural clarity and the caching mechanism. A more thorough explanation of the model components and their interactions is necessary to fully assess the technical soundness of the proposed method.\n2. Can the authors provide visualizations or interpretability analysis of geometric tokens to demonstrate their impact on spatial reasoning? It would be beneficial to include qualitative examples or attention maps that illustrate how these tokens contribute to decision-making during navigation, especially in complex spatial scenarios.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- **Writing Clarity and Problem Framing**: The paper is generally well-written, with a clear logical flow and a well-motivated problem statement. The limitations of prior work are articulated effectively.\n- **Enhanced Spatial Reasoning via Pretrained 3D Geometry Model**: The use of VGGT for feature extraction, trained on pixel-to-point cloud pairs, enables the agent to infer 3D spatial structure from RGB-only input. The approach enhance the capabilitiy of the model to understand 3D environments, and produces improved peformance (shown in ) compared with naive vision encoder of tranditional MLLMs.\n- **Efficient Inference**: The KV caching strategy reduces redundant computation, which is quite crucial to enable real-time deployment on physical robots.\n- **Strong Empirical Results**: JanusVLN achieves SOTA performance across multiple VLN-CE benchmarks, even outperforming methods that use depth, panoramic views, or odometry.\n- **Good Ablation Studies**: The authors provide detailed experiments isolating the impact of spatial memory, semantic memory, and fusion strategies, as well hyper-parameter configurations.", "weaknesses": "1.  **KV Cache Contribution Overstated**: While the paper presents KV caching as a core innovation, this technique has been widely used in LLM inference and has been applied in previous VLN approach (e.g., StreamVLN, which also uses KV cache and sliding window for acceleration). The novelty lies more in its integration than in the mechanism itself.\n2.  **Architectural Clarity**: Several components lack sufficient explanation: \n\n    - The **attention fusion module** is under-described, and Figure 2 does not clearly illustrate its structure.\n    - It is unclear which modules’ KVs are cached—semantic encoder, spatial encoder, or fusion layers?  Specific concerns include:\n        - There are no autoregressive attention between frames in the Qwen2.5-VL’s ViT processing process , so caching may not yield meaningful savings.\n        - VGGT uses non-causal attention, which typically does not support KV caching. If VGGT is frozen during training, how is its computation reduced during inference? \n        - If KV caching is applied only to the fusion module, and that module is lightweight, the overall computational savings may be marginal—especially considering that VGGT is the dominant cost.\n\n3.  **The analysis on the effectiveness of gemetric tokens are insufficient**: The paper does not provide visualization or interpretability analysis of the geometric tokens. This limits understanding of how spatial priors contribute to reasoning.\n\n4.  **Presentation Gaps**: Some figures and descriptions (e.g., memory update mechanism, fusion strategy) could be more precise and better annotated.", "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)