Pith Number

pith:CVVQ5U6Z

pith:2024:CVVQ5U6ZAPZQOEDEYH7QLSO6PQ

not attested not anchored not stored refs resolved

SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents

Fangzhi Xu, Jianbing Zhang, Kanzhi Cheng, Qiushi Sun, Yantao Li, Yougang Chu, Zhiyong Wu

Advancements in GUI grounding directly improve the performance of visual agents that automate tasks from screenshots alone.

arxiv:2401.10935 v2 · 2024-01-17 · cs.HC · cs.AI

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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

✓ Portable graph bundle live · download bundle · merged state

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

advancements in GUI grounding directly correlate with enhanced performance in downstream GUI agent tasks

C2weakest assumption

That the automatically curated GUI grounding data is sufficiently high-quality and representative to enable effective transfer to real agent tasks across environments.

C3one line summary

SeeClick improves visual GUI agents via GUI grounding pre-training on automatically curated data and introduces the ScreenSpot benchmark, with results indicating that stronger grounding boosts downstream task performance.

References

81 extracted · 81 resolved · 24 Pith anchors

[1] Aho and Jeffrey D 1972

[2] Publications Manual , year = "1983", publisher = 1983

[3] Chandra and Dexter C 1981 · doi:10.1145/322234.322243

[4] Scalable training of

[5] Dan Gusfield , title =. 1997 1997

Formal links

2 machine-checked theorem links

Cited by

23 papers in Pith

InquireMobile: Teaching VLM-based Mobile Agent to Request Human Assistance via Reinforcement Fine-Tuning

MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents

InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners

MGA: Memory-Driven GUI Agent for Observation-Centric Interaction

Agentic Reasoning for Large Language Models

Receipt and verification

First computed	2026-05-17T23:38:14.418669Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

156b0ed3d903f3071064c1ff05c9de7c107098706c7beb3b249c632e0ef6faf4

Aliases

arxiv: 2401.10935 · arxiv_version: 2401.10935v2 · doi: 10.48550/arxiv.2401.10935 · pith_short_12: CVVQ5U6ZAPZQ · pith_short_16: CVVQ5U6ZAPZQOEDE · pith_short_8: CVVQ5U6Z

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/CVVQ5U6ZAPZQOEDEYH7QLSO6PQ \
  | 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: 156b0ed3d903f3071064c1ff05c9de7c107098706c7beb3b249c632e0ef6faf4

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c715e54ca2fc5df30d79d57a56510b383f51991a97577d6e6757f967c307f952",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.HC",
    "submitted_at": "2024-01-17T08:10:35Z",
    "title_canon_sha256": "9c130e118c2a1c05f74f4892c3b481834ad6af8d966941736349a41f6527fb8a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2401.10935",
    "kind": "arxiv",
    "version": 2
  }
}