Pith. sign in

Paper Citation Record · LEDGER

Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 23 inbound Pith citation observations for arXiv:2503.16734.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2503.16734 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 23 of 23 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:15:47.935226Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3d7a4cc3-5c57-40d2-9106-d55a27dc3b19 · inbound

Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI cites this paper.

Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 167

Resolution
unresolved
no resolver link, observed 2026-08-07T14:15:47.935226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:15:47.935226Z digest=sha256:3f6c2ab0363c4c5f95c62c4f5f5366476613d4185f7c0080ac40fcd78b7b8d22

Observation 66637594-8eb4-441f-872c-840813ae97da · inbound

AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems cites this paper.

AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T14:14:09.416079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:14:09.416079Z digest=sha256:9f055bb41daa8db3461ba02308b0258da3233d36f5d4c07f069249dfff6c351b

Observation 4c82896c-10b4-47a0-918e-0b4aafa55f7a · inbound

RecoWorld: Building Simulated Environments for Agentic Recommender Systems cites this paper.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-04T17:56:38.786978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.786978Z digest=sha256:e853a9b92a382780fec4b0d38e2af54c505dbac2f891f56c5848c3713d37d9df

Observation 335effd0-c58b-45b1-8773-219a1ddc6801 · inbound

TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations cites this paper.

TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-05-10T14:40:32.707859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T14:38:34.377387Z digest=sha256:61d0441e146434a2c5ae88f056b1a569b14e7cabbb80292f7dd8fcdfa6a95ebe

Observation 00def110-0162-4fff-b7bc-e0dcb9d87f94 · inbound

MEG-RAG: Quantifying Multi-modal Evidence Grounding for Evidence Selection in RAG cites this paper.

MEG-RAG: Quantifying Multi-modal Evidence Grounding for Evidence Selection in RAG Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-11T21:56:12.177338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T03:51:49.033280Z digest=sha256:e63372ac9daa1aa7418da3a9fbebccb0d3993b39d35b0db7688b99151310d78b

Observation c7d23993-ee4f-40d3-b1a2-4c9b5740c659 · inbound

Factorized Latent Reasoning for LLM-based Recommendation cites this paper.

Factorized Latent Reasoning for LLM-based Recommendation Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-12T09:21:25.793005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-07T11:19:46.718542Z digest=sha256:f1f3a621b9d6c7ee4dc435718521fc76455434b5d848c5a3ddac77c190b74f12

Observation cfdd95a7-e8fc-45db-a81d-dc5a308b883b · inbound

Purifying Multimodal Retrieval: Fragment-Level Evidence Selection for RAG cites this paper.

Purifying Multimodal Retrieval: Fragment-Level Evidence Selection for RAG Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-12T10:11:28.110259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-07T07:19:44.125479Z digest=sha256:2097abdb30b0be3e3d5076610e39b36e3c7edb7f0d7e51bb2c14ed892adf9455

Observation 32d111d8-e8db-45ec-b401-184e70fbe060 · inbound

Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems cites this paper.

Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-09T06:35:40.953202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T18:16:45.474902Z digest=sha256:8e4ee44adacbc9c39fd3189478a72d965449c8d831183439c264142f7e5aca59

Observation d434c5d9-2684-433e-a6ff-33c5dcb44242 · inbound

Skill-CMIB: Multimodal Agent Skill for Consistent Action via Conditional Multimodal Information Bottleneck cites this paper.

Skill-CMIB: Multimodal Agent Skill for Consistent Action via Conditional Multimodal Information Bottleneck Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 95

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T08:01:28.202507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-12T01:24:59.721212Z digest=sha256:9366a6bcd34ccd9b873902e55740ac1fe57080b49ea7973dcfb88004795d0ebe

Observation 326ec5f8-daa8-46f8-bfaf-296000cdc477 · inbound

OLIVIA: Online Learning via Inference-time Action Adaptation for Decision Making in LLM ReAct Agents cites this paper.

OLIVIA: Online Learning via Inference-time Action Adaptation for Decision Making in LLM ReAct Agents Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 100

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T02:27:07.221788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-13T02:24:42.530103Z digest=sha256:0b01eca206a5efcdec931e4b5a8ac40bbc3cecd7c59adfbe1d885498c851eec8

Observation 7b39863a-f738-4636-b733-5e73b40a17da · inbound

RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems cites this paper.

RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-13T05:07:17.496339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-13T05:04:08.454422Z digest=sha256:edc015c28dc6b50e12e598d764ff3d0a179e72a1157809ee71864729bd38419d

Observation 1719341a-ef87-41cb-8c98-9812995fcc8b · inbound

F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking cites this paper.

F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-14T19:52:52.358216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-14T19:51:45.657658Z digest=sha256:0477edceb448606df9f456fac9f7985819227f5d3a40d4e34d7f2b7c3faf2408

Observation a6826159-a71e-4792-81df-475bba00b3e5 · inbound

SmartWalkCoach: An AI Companion for End-to-End Walking Guidance, Motivation, and Reflection cites this paper.

SmartWalkCoach: An AI Companion for End-to-End Walking Guidance, Motivation, and Reflection Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 40

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T20:45:03.275665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-30T20:37:54.610523Z digest=sha256:51e5293bb40fe16d5cd5f1d481baca469e75538a8bcfa3003566d5a9f7e5266c

Observation f5d34ab0-ae3f-483d-8373-bce7adc51295 · inbound

RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents cites this paper.

RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-20T22:29:09.311662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-20T22:27:16.974169Z digest=sha256:49f9c1f6daa1343ccc45a50d0392d4fee95fe2686b3d7bd3a7b5227b2b036d7e

Observation a135567a-c9ea-4375-b713-7cb03e314083 · inbound

Do Recommendation Algorithms Work When Users Are LLM Agents? A Case Study on Moltbook cites this paper.

Do Recommendation Algorithms Work When Users Are LLM Agents? A Case Study on Moltbook Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-06-30T16:24:57.377156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-30T04:45:49.798987Z digest=sha256:051d3dc08af9ec595ea91535aa644a4a63f24d9f875f27e1183f6af4ae6c1134

Observation 848212e0-8452-4101-af09-cabf25cd5f92 · inbound

Do Recommendation Algorithms Work When Users Are LLM Agents? A Case Study on Moltbook cites this paper.

Do Recommendation Algorithms Work When Users Are LLM Agents? A Case Study on Moltbook Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 28

Resolution
unresolved
no resolver link, observed 2026-07-13T07:14:21.482287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T07:14:21.482287Z digest=sha256:718a84dd5460c539f58c54fa19d500e4f56975f544e0b430e94d723188ed25b6

Observation 7916742b-f54b-48aa-8547-379bbd7f6c3b · inbound

Beyond Item Order: Temporal Gap Tokenization for Generative Recommendation with Semantic IDs cites this paper.

Beyond Item Order: Temporal Gap Tokenization for Generative Recommendation with Semantic IDs Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-07-11T23:02:18.007239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T23:02:18.007239Z digest=sha256:3c4358f4abaddca03171b2747092cf0a4b9229c26e36a8081de56e678fcea484

Observation de24e927-d170-4cd1-8bcb-9848ae75c9f1 · inbound

Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems cites this paper.

Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 75

Resolution
unresolved
no resolver link, observed 2026-07-11T19:14:13.105401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T19:14:13.105401Z digest=sha256:625d32f21304b3a3e2f66926fba16804c060c62801df86d518a8c9e2e957b675

Observation e20767b8-5b8a-4fa4-8312-cce478c7d747 · inbound

Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable cites this paper.

Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-02T05:40:33.703390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T05:40:33.703390Z digest=sha256:54c109a8af623464d5176e65ff704c06bbbb7d027b37b1de39e595fc22d859d7

Observation 336d7cae-21e2-460b-9114-13c3f1ab7b12 · inbound

Three-Body Alignment: Aligning Chess Agent with Human Reasoning through Reranked Rationale cites this paper.

Three-Body Alignment: Aligning Chess Agent with Human Reasoning through Reranked Rationale Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-01T06:10:10.859399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T06:10:10.859399Z digest=sha256:c211753ebb73d3b64b3b44f2b5633540419b7eb6ad4144023489be7371977d45

Observation e5bb53bb-de0b-443c-a726-79d86b5d94fa · inbound

A Position Paper on Recommender Systems in the Era of Autonomous Agents cites this paper.

A Position Paper on Recommender Systems in the Era of Autonomous Agents Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-01T20:37:15.441480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T20:37:15.441480Z digest=sha256:95f07831a7cebe48c54a9d93bee9e510d2d069a7ec21e404d9f28edee2f4812e

Observation 00d221a3-45b2-4756-94b0-8a96ff3bc536 · inbound

A Self-Triggered Agentic Push Recommendation System cites this paper.

A Self-Triggered Agentic Push Recommendation System Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-04T17:56:23.637340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T17:56:23.637340Z digest=sha256:79bed6ba913da5ef09863c29db4a686938cfe52a166014d64b25fc6fd573a845

Observation 9e9a32d8-801a-4106-80f1-a2fcb03e24bd · inbound

Attacking and Defending Multi-Agent Collaborative Filtering Systems Through Connectivity cites this paper.

Attacking and Defending Multi-Agent Collaborative Filtering Systems Through Connectivity Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-05T22:07:42.038221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:07:42.038221Z digest=sha256:e38ab81b4188c31194c43bb48bf3987761263a0bf5bc171b7dd69daebd4ba7a7