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Paper Citation Record · LEDGER

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models

As of 11 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2506.09084.

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

pith.paper-citation-record.v1
2506.09084 v2

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:14:53.939726Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:29:06.054516Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T21:29:10.970289Z

Reference resolution

51 of 51 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved49
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1c620f20-6546-4467-b8d3-7d6e3ad201fb · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.650368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.689412Z digest=sha256:140f676213cab2f46608fe6e972ca19c049d9485aee228977f2a086a405aa8a0

Observation da7a9f02-6810-42ef-92ee-8bcddfb42a04 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.636339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.694653Z digest=sha256:e2bec87f275a1b1b594e3d99a0ebb4b05d093a0e7d0b80b92d9e9d140cfbc038

Observation e7084212-6408-4b5b-9c5c-37e6391a829f · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.699486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.699486Z digest=sha256:febe2655bef54fac9475a15d3003ce99bd92c641c01161b4c4172d951e19d789

Observation 451028c1-0eaa-4c87-b4d8-028dc2e06e45 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.705268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.705268Z digest=sha256:1792e9abd35f8c0dea1b79e5547df0f4d563011ec90ce765fb7a29388ca8a6a3

Observation 845a7333-e81d-4c85-b3c3-030d04f21db6 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.711001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.711001Z digest=sha256:91b3b03fe5f3d7faf12d8452522193e64d866ea50418365db716cc6b7b781487

Observation 1017f90b-af28-4f0e-9641-31e06c3eab80 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.593436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.715611Z digest=sha256:6f7ecec0cf006d81b669babfbe622323b4426654cf25306fe908e59bc81a45e1

Observation 425c7742-3eb8-47e2-b14d-5554ee5ee6ae · outbound

This paper cites M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.725271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.725271Z digest=sha256:3ef39b5b126eb05e56f2c342dc57acfeb45b0ca9a45e16f1c62c07fa366c7809

Observation 680abd0a-6883-4bfe-a153-78357a2b025e · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.730243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.730243Z digest=sha256:1865ad8ec02dd42058209572017dfd17efa78bef34626be3de97a1bf40811f52

Observation 921da315-344c-49b5-9326-b047c5b66b96 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 9

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.563897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.734738Z digest=sha256:9f15b6d9e6042fe5a43ce6a21f3e623fd4e77d44dbe90d68c75e41a6bc7a8957

Observation e28f4d4d-0af6-49f8-a80d-1d991d38c453 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.548752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.739709Z digest=sha256:804d335516ecd69074aa2fa22e91cc3cd68893f1d9d6273e90ccc5a8095335e4

Observation 264598d0-df33-4734-ba81-49371344cd8b · outbound

This paper cites Leveraging Large Language Models in Conversational Recommender Systems.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Leveraging Large Language Models in Conversational Recommender Systems

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.744862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.744862Z digest=sha256:cb510640eebcfbcba34b5aa52e63934b3ffe7dbe08f649244da97eef923b1208

Observation 75b78ff8-f863-41c2-8dba-d5fd19925ab7 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.750172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.750172Z digest=sha256:62d3969f211879c0f01d5a6bcc011dd594a9b3c9b0d15d778abc0cf346f1cf23

Observation 6fdd30d6-ed98-4529-a695-564fbda6d3c8 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.524252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.754799Z digest=sha256:fd3a78664a9558c2704a00e90989df83559bed3e832ba4bdfde5033c1811ff97

Observation f3bda4e8-e3f8-40cb-9147-4f3741fbfd59 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.759895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.759895Z digest=sha256:f862d4ec039cf64d8473030efd31e22c5e41c178869a60f81d7ad73d3f6059b7

Observation 7f3bfd67-d442-4fcc-afb7-263c483429f3 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.768955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.768955Z digest=sha256:9e336d4ebdf655c771791e4e0283feed44d581843bd2c28ab6a1c32799f946ac

Observation 1f28dc21-6299-44af-ba69-4deb1b855e3b · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.772970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.772970Z digest=sha256:b246b501d92099f443068d930234974ffe4d5ba057220c45c994405ed0209fce

Observation 87e05273-b990-44e9-a334-ea6de3d8d8c3 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.777799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.777799Z digest=sha256:9c02a07f8eee061dff3d944a270acf64b88896cf436a7f408c52299a539cc72b

Observation d88540e7-424c-47c3-8013-70545b611bbd · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.787319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.787319Z digest=sha256:9dda20dd7487a730b94d18e00142fc5dbbfb2ecd53126d01b8f0bb56ce2c7d9d

Observation 88ccd9a6-38a9-4c5e-9f56-c8b5bb7acdd0 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.791878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.791878Z digest=sha256:3592e055bba2abb2bfeb7e7db2d7a5eb5ef852e1af27965303a3cd7ee2ac223d

Observation f92d5c02-792c-4f55-b0a3-1c9ba68fc5e6 · outbound

This paper cites Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.796277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.796277Z digest=sha256:b84e1da623ae709410922edf52f719445e4664b242ce62b16842a5cdbb303517

Observation 4726e0b6-1503-4c90-81fc-1907d6f0beb1 · outbound

This paper cites PBNR: Prompt-based News Recommender System.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models PBNR: Prompt-based News Recommender System

Reference 21

Resolution
verified exact
local_arxiv, observed 2026-08-07T05:14:54.163340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.801312Z digest=sha256:436360713c659e3151962979e5bd90646aa805a36cae2237f53f0bde76911eff

Observation 4a6d14d7-8deb-4f03-8a0c-2580364b4354 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.811925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.811925Z digest=sha256:fbebcbfb3dc2ef64a9f50fbae6725772421eb0347d1571e9c4dde8d6b96171f4

Observation 8c5e5ca8-e5ac-4b9e-9cda-6055b51bb065 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 23

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.431005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.816473Z digest=sha256:26d40ec472aabcd45147d574f589da9e9530a5dba2a0d3bf51d03ea71306713a

Observation 5e410608-4ac8-4f91-9354-bd6efa3c0448 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 24

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.416630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.820592Z digest=sha256:d981f2a7626a7c5ea2c6cd4280d377a708f76f15d006b1808eee8f3789aa1be5

Observation 509a68ad-7c94-4f74-8f76-755d41e84b25 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 25

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.401637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.825162Z digest=sha256:308c764b4a7fa52a18d278325e4580fc9c37ea4a31f9bb048e769ae6a7ca6d9a

Observation dc3de1d4-52fd-4044-ab10-6e9e03dc2d60 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 26

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.386138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.829628Z digest=sha256:72d0f9a8eb718bff79b4ba176186d89b77c86e18b4bb845fd40e8708d8cef997

Observation 4ec8a601-f1be-4d03-83a7-ab8ef212e01a · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.833972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.833972Z digest=sha256:5889afcf9b4638e127f791d4ff04febc809f7188cbbeb90f4fb69f37f5e103e8

Observation f4ee7dc5-c831-4ecf-bf45-904417d7fc0f · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.839609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.839609Z digest=sha256:ab28d02430bda0c0a4d029f1ef793ef790743a4c7ca0cbaf9c3ed5a0b6c7303e

Observation 50c1e07e-f0a7-47a3-8cfb-ac3fc6413973 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.849978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.849978Z digest=sha256:4c45e24f2accb0714d484fedd1054e9d593da7f5d6117cafc7b30d7d3b8f2aa7

Observation e47ea2d8-edc4-4dcd-a44e-94021d6163e4 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 30

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.332254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.860054Z digest=sha256:28013d8195a6b7ebd1a65864ccb4cd6239b1e97b1b40ff1912459c57547e80fd

Observation d5acc270-963c-4e46-aed9-40d4bf30708f · outbound

This paper cites LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.864785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.864785Z digest=sha256:fc113db14f6fde749f71eff84a2fa354dca46d02ddeaf77c7ab3d1932d1468af

Observation 480ff5e9-e259-4bf5-8983-d94b28945888 · outbound

This paper cites RecMind: Large Language Model Powered Agent For Recommendation.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models RecMind: Large Language Model Powered Agent For Recommendation

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.869440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.869440Z digest=sha256:f6730abf48b0e818a58aff51b2033f2ccbfadc4b50858eb5cfe6a84b9623b630

Observation a854ff20-7293-4fbb-bb7d-bb9c5d7cb40f · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 33

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.314061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.874562Z digest=sha256:fcc36d03b262b059d7a2e41ad082d8d005af2cdadfffeb68443dd0f890a7a845

Observation aa57c608-f9bd-4b07-ae7f-b1974bd60cef · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.879530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.879530Z digest=sha256:20cedc72404af50f744adeda3432aeb59bbff90c4b6aff2aec2feff6ceabb21a

Observation fc99c1ea-9aee-470b-8d7a-de9b68633f0a · outbound

This paper cites Aligning Large Language Models via Fine-grained Supervision.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Aligning Large Language Models via Fine-grained Supervision

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.884160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.884160Z digest=sha256:24e35069b13ebb6e7b36f1d10e66dca19557296f1d04591be4c4be965efbe6e6

Observation 0bcb839f-441e-43e9-8410-effe79021641 · outbound

This paper cites Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.890011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.890011Z digest=sha256:89682e4bf816e0477b345e0ba9f1f7f8253a32e34ef1d2ce9cfbc0149fe8b50a

Observation a7bced7a-0b29-49b7-a975-30ee6a5c31a0 · outbound

This paper cites Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations

Reference 37

Resolution
verified exact
local_arxiv, observed 2026-08-07T05:14:54.067221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 10c749f9-321a-44d5-9331-94b4450fe5a3 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 38

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation b5d0c6fc-5c92-442f-9eff-f048b2fb185d · outbound

This paper cites LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking

Reference 39

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 72fc2e69-0f65-48ee-8047-bd62e8c59ec2 · outbound

This paper cites Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations

Reference 40

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5b22be86-88ba-40e9-b5ce-694c1de912c6 · outbound

This paper cites Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach

Reference 41

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f1ad0320-2e6c-4dd1-a19d-fbb02341109c · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.275018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation cf5aeb96-f3f0-4777-b5cc-a763905f49a8 · outbound

This paper cites Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Reference 43

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 66132f65-7f21-4201-91dd-3c047c34ecdf · outbound

This paper cites A Survey of Large Language Models.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models A Survey of Large Language Models

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.930076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3175445d-906a-40a5-8e0e-0a036bde2627 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.934987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.934987Z digest=sha256:271ec7f9c579583d15d217aea516c55a2b8b716cf063ca5dd672da921456c287

Observation 268ec62c-f0e0-4174-be98-63b3699c5c45 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 46

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.250810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.939726Z digest=sha256:da0d6a95038d4915cdd54e1edf233ecc7b9847fe5d533a4502702d2601ba6158

Observation 797d0562-979f-479e-8da0-fdc84616b94f · outbound

This paper cites Session-based Recommendations with Recurrent Neural Networks.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Session-based Recommendations with Recurrent Neural Networks

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.782554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.782554Z digest=sha256:78c33be3d1ca6a7d74ea86b5a2d630d2ea533c9777f2652cd96a1ace9558e7b3

Observation 2e7fb0d1-01a8-440a-ab39-ffbad17c1733 · outbound

This paper cites Proximal Policy Optimization Algorithms.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Proximal Policy Optimization Algorithms

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.844915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.844915Z digest=sha256:96044e48115afc16d6c616bef70a2eb7e483d3746ad8f689d37f2ff682d186b9

Observation 3ceaaa2e-9528-4644-ba7a-fbab7a0c5891 · outbound

This paper cites InProceedings of the 28th ACM international conference on information and knowledge management.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models InProceedings of the 28th ACM international conference on information and knowledge management

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:53.854847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:53.854847Z digest=sha256:02fea5ec4b46affaaa352a4d5d0f90d4879f7aff870e87bd990a36d96161a673

Observation 0cc4a72f-11d0-4f7c-b39c-7a927b48b238 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 2023

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.499858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.764640Z digest=sha256:e6632e244737c605d5ce56fa74220998adfefd8236c801abe0a8be0a4962731c

Observation ef2417d0-c98c-43d4-85ec-b1b2e57150b1 · outbound

This paper cites an unresolved cited work.

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Unresolved cited work

Reference 2024

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:14:54.578786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T05:14:53.720229Z digest=sha256:e44375176297a0c33dbd61564971d6ba25d3b14c0452bf3bbf89be6202c4625e

Pith citing papers

Observation 4d898b2d-8dbf-44e1-b734-2cdfbaa5aece · inbound

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives cites this paper.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models

Reference 107

Resolution
verified exact
local_arxiv, observed 2026-08-06T21:29:11.064226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T21:29:06.054516Z digest=sha256:a1481be33e77ad11305307c047364f250e0b59de13e419c4036abaa8fe8c056a