Pith Number

pith:ERDK2YZA

pith:2026:ERDK2YZATZGPT4XDIYVR5CLI4D

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DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments

Aleksis Pirinen, Anindya Sarkar, Nathan Jacobs, Srikumar Sastry, Yevgeniy Vorobeychik

A diffusion model that reconstructs full geospatial maps from partial aerial glimpses enables a target-conditioned reinforcement learning planner to search for multiple object types at once.

arxiv:2605.15519 v1 · 2026-05-15 · cs.CV · cs.AI

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Claims

C1strongest claim

DiffVAS leverages a diffusion model to reconstruct the entire geospatial area from sequentially observed partial glimpses, which enables a target-conditioned reinforcement learning-based planning module to effectively reason and guide subsequent search steps.

C2weakest assumption

The diffusion model produces reconstructions of unobserved regions that are sufficiently accurate and useful for the downstream RL planner to improve search performance over baselines in partially observable settings.

C3one line summary

DiffVAS combines diffusion-based reconstruction of unobserved geospatial regions with target-conditioned RL planning to enable multi-object visual active search in partially observable environments.

References

36 extracted · 36 resolved · 3 Pith anchors

[1] Luca Bartolomei, Lucas Teixeira, and Margarita Chli. 2020. Perception-aware path planning for uavs using semantic segmentation. In2020 IEEE/RSJ International Conference on Intelligent Robots and Syste 2020

[2] Elizabeth Bondi, Debadeepta Dey, Ashish Kapoor, Jim Piavis, Shital Shah, Fei Fang, Bistra Dilkina, Robert Hannaford, Arvind Iyer, Lucas Joppa, et al

[3] InProceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies

[4] Tung Dang, Christos Papachristos, and Kostas Alexis. 2018. Autonomous exploration and simultaneous object search using aerial robots. In2018 IEEE Aerospace Conference. IEEE, 1–7 2018

[5] Fei Fang, Thanh Nguyen, Rob Pickles, Wai Lam, Gopalasamy Clements, Bo An, Amandeep Singh, Milind Tambe, and Andrew Lemieux. 2016. Deploying paws: Field optimization of the protection assistant for wil 2016

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Receipt and verification

First computed	2026-05-20T00:01:02.933310Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2446ad63209e4cf9f2e3462b1e8968e0ec89d37e5bd912e562c7e458dd0a4a4d

Aliases

arxiv: 2605.15519 · arxiv_version: 2605.15519v1 · doi: 10.48550/arxiv.2605.15519 · pith_short_12: ERDK2YZATZGP · pith_short_16: ERDK2YZATZGPT4XD · pith_short_8: ERDK2YZA

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ERDK2YZATZGPT4XDIYVR5CLI4D \
  | 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: 2446ad63209e4cf9f2e3462b1e8968e0ec89d37e5bd912e562c7e458dd0a4a4d

Canonical record JSON

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    "abstract_canon_sha256": "db0329acb72c5ad1eb33d780d915e3208f368927781a9105547b35aa7d42d22d",
    "cross_cats_sorted": [
      "cs.AI"
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    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-15T01:30:07Z",
    "title_canon_sha256": "144f4e7cf25d1c7dfd9b825a81d215c5a9cb6e0dc7ba38b9789c06acf95f7830"
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  "source": {
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    "kind": "arxiv",
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}