Pith Number

pith:XJ3JXN75

pith:2025:XJ3JXN752PRA6MHC4ZS4PXUZLT

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MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation

Bin Xie, Erjin Zhou, Fengrong Liu, Gao Huang, Haoqiang Fan, Hao Shi, Lin Sun, Tiancai Wang, Xiangyu Zhang, Yingfei Liu

MemoryVLA adds a perceptual-cognitive memory bank to vision-language-action models to supply temporal context for long-horizon robotic manipulation.

arxiv:2508.19236 v2 · 2025-08-26 · cs.RO · cs.CV

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

On 12 real-world tasks spanning general skills and long-horizon temporal dependencies, MemoryVLA achieves 84.0% success rate, with long-horizon tasks showing a +26 improvement over state-of-the-art baseline.

C2weakest assumption

That the adaptive retrieval, fusion, and redundancy-merging operations in the Perceptual-Cognitive Memory Bank will reliably supply temporally relevant context without introducing noise or stale entries that degrade action generation.

C3one line summary

MemoryVLA introduces a perceptual-cognitive memory bank and working-memory retrieval mechanism into VLA models, raising success rates on long-horizon robotic tasks by up to 26 points over prior baselines.

References

41 extracted · 41 resolved · 20 Pith anchors

[1] GPT-4 Technical Report · arXiv:2303.08774

[2] Qwen Technical Report · arXiv:2309.16609

[3] RT-1: Robotics Transformer for Real-World Control at Scale · arXiv:2212.06817

[4] RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control · arXiv:2307.15818

[5] AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems · arXiv:2503.06669

Formal links

2 machine-checked theorem links

Cited by

31 papers in Pith

EvoScene-VLA: Evolving Scene Beliefs Inside the Action Decoder for Chunked Robot Control

From Abstraction to Instantiation: Learning Behavioral Representation for Vision-Language-Action Model

Global Prior Meets Local Consistency: Dual-Memory Augmented Vision-Language-Action Model for Efficient Robotic Manipulation

Universal Pose Pretraining for Generalizable Vision-Language-Action Policies

VLANeXt: Recipes for Building Strong VLA Models

Receipt and verification

First computed	2026-05-17T23:38:50.222119Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

ba769bb7fdd3e20f30e2e665c7de995cf7eb435fa991e42cbde370a7f699b1fa

Aliases

arxiv: 2508.19236 · arxiv_version: 2508.19236v2 · doi: 10.48550/arxiv.2508.19236 · pith_short_12: XJ3JXN752PRA · pith_short_16: XJ3JXN752PRA6MHC · pith_short_8: XJ3JXN75

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/XJ3JXN752PRA6MHC4ZS4PXUZLT \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: ba769bb7fdd3e20f30e2e665c7de995cf7eb435fa991e42cbde370a7f699b1fa

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2025-08-26T17:57:16Z",
    "title_canon_sha256": "7fe391cae22206b215ed6d4e34c19b7986d01255dd96a3ec7d4e4ea657e09818"
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