Pith Number

pith:BKDHWMF2

pith:2026:BKDHWMF2RL5O36TDWJJORT4OY5

not attested not anchored not stored refs resolved

Agent4POI: Agentic Context-Conditioned Affordance Reasoning for Multimodal Point-of-Interest Recommendation

Jinze Wang, Lu Zhang, Tiehua Zhang, Xingjun Ma, Yangchen Zeng, Yongchao Liu, Yuze Liu, Zhu Sun

No pre-computed encoder can satisfy context-sensitive POI ranking under bilinear scoring, so Agent4POI generates dynamic affordance representations at recommendation time instead.

arxiv:2605.15203 v1 · 2026-04-03 · cs.IR · cs.AI · cs.MA

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\pithnumber{BKDHWMF2RL5O36TDWJJORT4OY5}

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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

No pre-computed encoder can satisfy context-sensitive ranking under standard bilinear scoring; Agent4POI achieves a 23.2% relative gain over the strongest baseline and degrades by only 7.5% under context-shift versus 16-17% for baselines.

C2weakest assumption

The four-phase LLM agent can reliably generate accurate uncertainty-aware affordance representations through five-step cross-modal chain-of-thought reasoning over image, review, and metadata evidence.

C3one line summary

Agent4POI generates context-conditioned multimodal affordance representations via a four-phase LLM agent, achieving 23.2% relative gains over baselines on POI benchmarks with reduced degradation under context shifts.

References

40 extracted · 40 resolved · 3 Pith anchors

[1] Paola Ardón, Èric Pairet, Katrin S Lohan, Subramanian Ramamoorthy, and Ronald Petrick. 2020. Affordances in robotic tasks–a survey.arXiv preprint arXiv:2004.07400(2020) 2020

[2] Keqin Bao, Jizhi Zhang, Wenjie Wang, Yang Zhang, Zhengyi Yang, Yanchen Luo, Chong Chen, Fuli Feng, and Qi Tian. 2025. A bi-step grounding paradigm for large language models in recommendation systems.A 2025

[3] Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. TALLRec: An effective and efficient tuning framework to align large language model with recommendation. InProceedings 2023

[4] Ramesh Baral, XiaoLong Zhu, SS Iyengar, and Tao Li. 2018. REEL: Review aware explanation of location recommendation. InProceedings of the 26th Conference on User Modeling, Adaptation and Personalizati 2018

[5] William W Gaver. 1991. Technology affordances. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems. 79–84 1991

Receipt and verification

First computed	2026-05-20T00:00:45.864886Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

0a867b30ba8afaedfa63b252e8cf8ec7535ff7346da287807186f9ea752ff778

Aliases

arxiv: 2605.15203 · arxiv_version: 2605.15203v1 · doi: 10.48550/arxiv.2605.15203 · pith_short_12: BKDHWMF2RL5O · pith_short_16: BKDHWMF2RL5O36TD · pith_short_8: BKDHWMF2

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/BKDHWMF2RL5O36TDWJJORT4OY5 \
  | 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: 0a867b30ba8afaedfa63b252e8cf8ec7535ff7346da287807186f9ea752ff778

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "5f7c7f56ab1085de54a389fad256a79cd4db6a4697af9ca22578874ce0afb3ec",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.MA"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.IR",
    "submitted_at": "2026-04-03T01:53:05Z",
    "title_canon_sha256": "a3aa37b0836ea17e982f67a95e45f39506deb94fb473021ed10cfcc1a321067a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15203",
    "kind": "arxiv",
    "version": 1
  }
}