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Paper Citation Record · LEDGER

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering

As of 21 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2505.12476.

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

pith.paper-citation-record.v1
2505.12476 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:38:09.537766Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

61 of 61 outbound references displayed

  • verified exact2
  • verified fuzzy12
  • unresolved47
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bc4bc04f-7518-4d2d-a5ce-10012ac96f42 · outbound

This paper cites Free- base: a collaboratively created graph database for structuring human knowledge,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Free- base: a collaboratively created graph database for structuring human knowledge,

Reference 1

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:38:09.284813Z digest=sha256:99762c6edfa8420eabaec505ddf0ea8e0bb420483f5b46b9bc5e09d872f5b454

Observation 7de643a5-0e89-46fc-83a9-a2841134133e · outbound

This paper cites Wikidata: a free collaborative knowl- edgebase,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Wikidata: a free collaborative knowl- edgebase,

Reference 2

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source=pdf_text observed=2026-08-15T20:38:09.289767Z digest=sha256:de7508f16845b83b3e1b806883b20ab565b73f91781746895604f3972f5cd2cc

Observation c2b0a96a-883c-4299-bfe8-3a3a9c11c9aa · outbound

This paper cites Dbpedia: A nucleus for a web of open data,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Dbpedia: A nucleus for a web of open data,

Reference 3

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raw_fallback, observed 2026-08-15T20:38:10.302459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:38:09.294296Z digest=sha256:1d238d3848256c27d8508021abe3544b7274535fbfeddc7e6b597e0892548ff7

Observation 1c81ff72-5cbd-42b0-bca7-3a531928b561 · outbound

This paper cites Complex knowledge base question answering: A survey,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Complex knowledge base question answering: A survey,

Reference 4

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raw_fallback, observed 2026-08-15T20:38:10.287423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:38:09.298996Z digest=sha256:5049c94a86874ac9a2ef6aad5a5449fb8b7d966781240a4babff299dda4b73bf

Observation e28b18ce-d4cf-4de9-9af4-69fca232d309 · outbound

This paper cites Hello gpt-4o,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Hello gpt-4o,

Reference 5

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source=pdf_text observed=2026-08-15T20:38:09.303317Z digest=sha256:7eca49a2cfca27ac8b5297ad45911ab7232c45df16fcc017fc8f6e1a5cd6bf7f

Observation e12d8e10-1938-4ede-ac89-f4921af18c85 · outbound

This paper cites Introducing chatgpt,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Introducing chatgpt,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:38:09.307482Z digest=sha256:b012bc6447d0701e80b5e4ab57ee7d2f4ea8905994ca2ce42afe41081867e6d0

Observation 92f1ff04-be1e-4dbf-94da-2626ded6c8ad · outbound

This paper cites Qwen2.5 Technical Report.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Qwen2.5 Technical Report

Reference 7

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source=pdf_text observed=2026-08-15T20:38:09.312225Z digest=sha256:3a0dbe112084f911d452332bc7428bfb83c1d10e70660b47164d636336cdd17d

Observation 51610099-99c4-4793-83c1-44b45e8b8d94 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Chain-of-thought prompting elicits reasoning in large language models,

Reference 8

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source=pdf_text observed=2026-08-15T20:38:09.316895Z digest=sha256:acd176ed5ec00c1f433df71db036e938eade1579614ca57e0537bd7899eb7357

Observation 8cf358b0-2732-486c-b2d1-660894d3f0e0 · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Tree of thoughts: Deliberate problem solving with large language models,

Reference 9

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source=pdf_text observed=2026-08-15T20:38:09.321138Z digest=sha256:08e0513edc521dd4218f628883afa3ba82586daec50c072ca7fa7a460d23e1b7

Observation bd41abe0-910d-4a71-a8b2-bd0e10d00a48 · outbound

This paper cites Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs

Reference 10

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source=pdf_text observed=2026-08-15T20:38:09.325326Z digest=sha256:6d585815616b2fa2addd3cd9eb7a48cde5216242bbe4e1df1fbc50796fc57edc

Observation 30aaa097-b07e-4bfe-9348-8863edf698b5 · outbound

This paper cites Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation

Reference 11

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source=pdf_text observed=2026-08-15T20:38:09.330041Z digest=sha256:65ef2c66daaa6498503a61252393377b7235b717559c7aa543d2e86331b06af1

Observation ebe7bc32-b7d5-481d-b013-d796b2f74931 · outbound

This paper cites Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs

Reference 12

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source=pdf_text observed=2026-08-15T20:38:09.334657Z digest=sha256:a29dc8e7641eb68094eab6a98c0038ad9861f3a26397470dfe27bfc9c8bb3b36

Observation 6506dc7a-2cfa-4c6a-a64d-8c11a16b839d · outbound

This paper cites Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph

Reference 13

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source=pdf_text observed=2026-08-15T20:38:09.339192Z digest=sha256:7ce1926f42962404cf645f4ceab30ddc2b2a0bd1301b91f0fb0830d9382abc13

Observation 0504b1f5-c3a1-4097-83dd-a067ae0055d3 · outbound

This paper cites Graph Retrieval-Augmented Generation: A Survey.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Graph Retrieval-Augmented Generation: A Survey

Reference 14

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source=pdf_text observed=2026-08-15T20:38:09.343570Z digest=sha256:146be10fac77a59a8ea5c41e727153d77acfee5b34eee4bf0adbaa1fc01a9121

Observation 1578edf2-815a-4a2c-8d18-e1c4ec5b1092 · outbound

This paper cites Large-scale semantic parsing without question-answer pairs,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Large-scale semantic parsing without question-answer pairs,

Reference 15

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raw_fallback, observed 2026-08-15T20:38:10.234478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:38:09.347770Z digest=sha256:d4b3cdc8adbc31c310b1174677a1e573ebbdaad3301e0e0fe5c1cde270159206

Observation c7bc306a-1386-4b46-a21b-d898bedfa8c4 · outbound

This paper cites StructGPT: A General Framework for Large Language Model to Reason over Structured Data.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering StructGPT: A General Framework for Large Language Model to Reason over Structured Data

Reference 16

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source=pdf_text observed=2026-08-15T20:38:09.351324Z digest=sha256:267df4242cb1b08c4b0e3a9a64a5039b34c1f81eb09c880ff46cf1ac2f8335a2

Observation 24ef0a97-b881-40a0-afe3-e0d210e45ba6 · outbound

This paper cites RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering

Reference 17

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source=pdf_text observed=2026-08-15T20:38:09.355073Z digest=sha256:22c248d4d78275f6857ab402e20a7ae147a71755bcce5b42d0bb6d1302eddebe

Observation c556faab-e7f4-41fc-9ec4-91e03296359b · outbound

This paper cites ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering

Reference 18

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source=pdf_text observed=2026-08-15T20:38:09.358723Z digest=sha256:6174e45d20b8544ad443b5d401bc4e6ac9536cc1a7b843a56aee2ad62c80c3bf

Observation e94e3189-58b6-421b-ac89-695ded071149 · outbound

This paper cites Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings

Reference 19

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source=pdf_text observed=2026-08-15T20:38:09.362264Z digest=sha256:33e7dd9cb3c8bece4168fc4f718c4b8f0171fb269d191908310c132716524af8

Observation 23d72adb-5474-4c4d-980f-16308b3298bb · outbound

This paper cites Neural-based mixture probabilistic query embedding for answering fol queries on knowledge graphs,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Neural-based mixture probabilistic query embedding for answering fol queries on knowledge graphs,

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:38:09.365971Z digest=sha256:96a9d6dea06c4d0d1699e3eb1effd33fabb10579e43b435183b1019d74b49915

Observation a9f14bca-ac6e-46d1-a768-b77158ad03de · outbound

This paper cites Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering

Reference 21

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source=pdf_text observed=2026-08-15T20:38:09.369367Z digest=sha256:a6bf3a7d4827060598cf912159b024c91a860d7e036ec37f24ac8530ec2c5915

Observation 23b101c7-54fc-4600-81ad-29cf59201221 · outbound

This paper cites Improving multi-hop knowledge base question answering by learning intermediate supervision signals,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Improving multi-hop knowledge base question answering by learning intermediate supervision signals,

Reference 22

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:38:09.372844Z digest=sha256:9effc8f82b68aacb487c492ae0f4931582c9dc75786f7703b3882df79c3edf83

Observation d31b81d0-9132-4dd1-b1fb-18434e98ec10 · outbound

This paper cites PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text

Reference 23

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source=pdf_text observed=2026-08-15T20:38:09.375969Z digest=sha256:52683a9d55571ca4afccd56e71687935849d62251e34db25929f4c3b2cf0d194

Observation ba965757-0c90-4259-acb7-00eca1fc99bb · outbound

This paper cites GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning

Reference 24

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source=pdf_text observed=2026-08-15T20:38:09.380251Z digest=sha256:9c94f2feb85b08d0943a7ecaa9525edfab87fc44818acc71aff4c9408db0e173

Observation b36d8f69-0fba-4292-bab0-9821e2909d06 · outbound

This paper cites Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning

Reference 25

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source=pdf_text observed=2026-08-15T20:38:09.384379Z digest=sha256:1b3b5c051df5c2d162c489c6488caf6cc5c13fc7fe952ea4972cb9cf8bacbb57

Observation 25e0dcee-d183-4d32-a43f-eb9acc0dfa87 · outbound

This paper cites EPERM: An Evidence Path Enhanced Reasoning Model for Knowledge Graph Question and Answering.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering EPERM: An Evidence Path Enhanced Reasoning Model for Knowledge Graph Question and Answering

Reference 26

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local_arxiv, observed 2026-08-15T20:38:09.834893Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:38:09.388553Z digest=sha256:a7c41e906fdf3d72b9a5643b7f00bb9a2396ac0bb050e97b2e0b7f442c564800

Observation 207f6f9a-e1b8-4451-a16f-698d3d4bab4a · outbound

This paper cites UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph

Reference 27

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source=pdf_text observed=2026-08-15T20:38:09.392729Z digest=sha256:cf34d2e2e1a72b07b8b7fc18d0b3c8f2523f09f0abf476c07e3a8b22686494db

Observation 745f3785-afdb-4c77-98cf-ad848f613f93 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 28

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source=pdf_text observed=2026-08-15T20:38:09.397310Z digest=sha256:ee1a3152a915eb43ce1ffbc83137ad3ba685c4d0e807733869ed1f43ef8f5009

Observation 8da52b1f-4059-4aed-abad-3964852b4bcc · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Tree of thoughts: Deliberate problem solving with large language models,

Reference 29

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source=pdf_text observed=2026-08-15T20:38:09.401703Z digest=sha256:364ad145278b891ad4bae67d3ec194a085c1b6af898d77df2f98a8d0699a87cc

Observation 59af5831-274f-4703-91ea-548cd05cb770 · outbound

This paper cites React: Synergizing reasoning and acting in language models,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering React: Synergizing reasoning and acting in language models,

Reference 30

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source=pdf_text observed=2026-08-15T20:38:09.405748Z digest=sha256:e2f2d50e698b54c94b083948462e4df495fbbaddbb59eb24a2d72790ee3df9cb

Observation cae5c279-3d3e-4993-b940-1babbd8b1104 · outbound

This paper cites Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning

Reference 31

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source=pdf_text observed=2026-08-15T20:38:09.409839Z digest=sha256:e072588a0a6f1b3b8f29a01a58461b42a6c7a026fb5830434f64464b7185fdcb

Observation a1730391-b7fd-42df-b2ca-3a978270a9ec · outbound

This paper cites Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers

Reference 32

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source=pdf_text observed=2026-08-15T20:38:09.414318Z digest=sha256:02a0b15f4b3f36895bacd3fa376decf0bd0480dd8c3a9a8516f0b4d283ec3381

Observation 2b8f735d-2799-4145-b54a-06a8b8c257a7 · outbound

This paper cites LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning

Reference 33

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source=pdf_text observed=2026-08-15T20:38:09.418765Z digest=sha256:3bd69c13d2ac48deeb7c865067720ea5bd51383c835bd32a651a425381d008ce

Observation 2b82bf22-3c0e-4e2b-8051-5cfc3f45cac6 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 34

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source=pdf_text observed=2026-08-15T20:38:09.423154Z digest=sha256:c18375d7f0a62258b2d78cee80ba294224754b3de78fd05fcf52ca97eaddbe05

Observation fdadd819-77b9-40d0-b74f-9ec671c5ceb3 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 35

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source=pdf_text observed=2026-08-15T20:38:09.427479Z digest=sha256:45b8b7392a7be6e590cdce88cee4f257af7b7de257bb8ca3cb3d95d5d240cd09

Observation 4819ef61-d895-46a2-aa92-b62eefbc383a · outbound

This paper cites ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search

Reference 36

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source=pdf_text observed=2026-08-15T20:38:09.431662Z digest=sha256:7c059406c098755100455d3b35b4f113b32c9f33189e8d51907d10138cbe6670

Observation 731c961d-734e-490f-8c3c-1b1f2481f54c · outbound

This paper cites Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Reference 37

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source=pdf_text observed=2026-08-15T20:38:09.435909Z digest=sha256:06f68215e478ebea4d9e35384b48513858a91e6966a740074a709c850ab5d6c2

Observation 1360c936-bd92-4abd-88a1-ac842eb9149c · outbound

This paper cites Mastering the game of go with deep neural networks and tree search,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Mastering the game of go with deep neural networks and tree search,

Reference 38

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:38:09.440221Z digest=sha256:c30f56bced54dced939186ba8c0f6915fdce8d1e567163f3df08c02f0e44a86b

Observation 242111a6-9822-4e14-9569-1680e4d777c6 · outbound

This paper cites Mastering atari, go, chess and shogi by planning with a learned model,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Mastering atari, go, chess and shogi by planning with a learned model,

Reference 39

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source=pdf_text observed=2026-08-15T20:38:09.444399Z digest=sha256:a89441c90f6990bb8f655a075f4a70bb286b0729a46d1ae696ba715febe173cd

Observation c6961e7d-ff75-4aec-ba63-73e4201ddc55 · outbound

This paper cites Bandit based monte-carlo planning,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Bandit based monte-carlo planning,

Reference 40

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:38:09.449275Z digest=sha256:3b329553847aaf21d79fb1101a92c676a9d40ec3b175b7771eb524677fda6100

Observation 07d17cd6-4be4-48ed-b510-9e902f38660b · outbound

This paper cites Learning entity and relation embeddings for knowledge graph completion,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Learning entity and relation embeddings for knowledge graph completion,

Reference 41

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source=pdf_text observed=2026-08-15T20:38:09.453478Z digest=sha256:929c49e20ac3665af217549e4d5805dd9adc314e3bdd15df71c0778f0e490f17

Observation d54d5f3f-620c-4e34-a122-eca8e5230eae · outbound

This paper cites Kgdm: A diffusion model to capture multiple relation semantics for knowledge graph embedding,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Kgdm: A diffusion model to capture multiple relation semantics for knowledge graph embedding,

Reference 42

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T20:38:09.457581Z digest=sha256:591de8ae646171e78befa2015b89d494342353a8a1690cc4113c1b3a0321bc21

Observation f41acd9d-1547-4748-a04f-6908bf165273 · outbound

This paper cites Fact embedding through diffusion model for knowledge graph completion,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Fact embedding through diffusion model for knowledge graph completion,

Reference 43

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source=pdf_text observed=2026-08-15T20:38:09.461798Z digest=sha256:41c0b999745a3837fbf731e981569a6db35e1bbaccf16b1eb11c81e10d774ac7

Observation 57d1d9a6-d0cf-4418-87b7-93c5e3726dd9 · outbound

This paper cites Let's Verify Step by Step.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Let's Verify Step by Step

Reference 44

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source=pdf_text observed=2026-08-15T20:38:09.466093Z digest=sha256:dbca1e6792515f5cc839cb4a3906ae86fc2c25b3f052d38e7df278324b2a462a

Observation 2f160bc8-802a-443e-b3c8-91d9f23f8804 · outbound

This paper cites The Web as a Knowledge-base for Answering Complex Questions.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering The Web as a Knowledge-base for Answering Complex Questions

Reference 45

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:38:09.470143Z digest=sha256:1ee22e679b4ba0deb7387f9e5c493fa7e42fcefcf33cf43c5488b66610bbcc1d

Observation 7aab34be-8caa-4fb3-a9bb-7f77803b9df8 · outbound

This paper cites The value of semantic parse labeling for knowledge base question answer- ing,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering The value of semantic parse labeling for knowledge base question answer- ing,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:38:10.119842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:38:09.474278Z digest=sha256:50dde4dc7d2ddbd479b7a1c587eaf9f843ea0ea0a3d94c8b262fe480f65ae422

Observation bc5e1400-7490-444f-8c84-cd44389aed6d · outbound

This paper cites Beyond iid: three levels of generalization for question answering on knowledge bases,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Beyond iid: three levels of generalization for question answering on knowledge bases,

Reference 47

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:38:09.478382Z digest=sha256:23fe4e02ae46db8f2c9bbb388fc3547c168e8bcee0b881366a8b5ad3c3929a14

Observation 993cc4dc-1609-4766-b3a6-c6f90de98689 · outbound

This paper cites Semantic parsing on freebase from question-answer pairs,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Semantic parsing on freebase from question-answer pairs,

Reference 48

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source=pdf_text observed=2026-08-15T20:38:09.481921Z digest=sha256:446ac7441efb8a536103050cbd8cd15f0b43561691581207e2b76a4f0e638464

Observation ac1fe745-d733-4d2a-8d73-ca758a54ca1b · outbound

This paper cites Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources

Reference 49

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source=pdf_text observed=2026-08-15T20:38:09.485537Z digest=sha256:d434993a2f1dbd1cb3a7e19f75e91793e00bf44b22122342ea89cd37125e3772

Observation 04e1acc7-d15c-4bb4-a7d9-3a3d5db090b2 · outbound

This paper cites Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering

Reference 50

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source=pdf_text observed=2026-08-15T20:38:09.489255Z digest=sha256:8a756509f84a200b0bb14c30801d5263662f801bc1ec2998703a728e192c05d2

Observation 957b71f7-2aca-44db-95fc-c76fafb6553c · outbound

This paper cites Build the future of ai with meta llama 3,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Build the future of ai with meta llama 3,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:38:10.086454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:38:09.492928Z digest=sha256:723b493c34dbded2d0f505b561a62d18434f0dbb11f04d84c11ea56641c82abe

Observation d6cfdc5a-4637-4cbe-8d63-901931de5896 · outbound

This paper cites Hello gpt-4,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Hello gpt-4,

Reference 52

Resolution
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raw_fallback, observed 2026-08-15T20:38:10.072223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:38:09.497099Z digest=sha256:6f6ed6a0bcb5491e161efea19f55131228614551c8465e31b668dfb38fad8a8d

Observation 96978912-f96c-4323-a1d1-cc42509eadc5 · outbound

This paper cites Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

Reference 53

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source=pdf_text observed=2026-08-15T20:38:09.501185Z digest=sha256:de41d3dff0da547554b4f1e41f7e1f8b845d4dfc0c8bb885f54cce00cdc10e5e

Observation af97d024-992d-4965-8f04-3a21d1aed636 · outbound

This paper cites Enhancing complex question answering over knowledge graphs through evidence pattern retrieval,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Enhancing complex question answering over knowledge graphs through evidence pattern retrieval,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:38:10.060003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:38:09.505988Z digest=sha256:e5784f37c7b440a4ddccacff86eeee1d1a2c94cd783dc6ef51e01c24860ffaf1

Observation 817a7012-363c-4bb1-9b1d-36f1c9f07d90 · outbound

This paper cites DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases

Reference 55

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:38:09.510404Z digest=sha256:a8fb9b3f6f780c6b65dadc6cc661e9f1171ba0b8ccbb138a503e64ab8767e983

Observation 6e297df2-9934-49f6-8580-aad09046ca1c · outbound

This paper cites TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases

Reference 56

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source=pdf_text observed=2026-08-15T20:38:09.516111Z digest=sha256:d44e1c544e62a6f61e38fda335189927b819484712769d4d3380795713bb30dc

Observation 31f02d0c-22c2-4724-9d27-8d0fb12d8c6f · outbound

This paper cites FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering

Reference 57

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verified exact
local_arxiv, observed 2026-08-15T20:38:09.607195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:38:09.520369Z digest=sha256:5be81f81f00b8982bfb83f918b953a6c4f120a7896e3a8f8260c6c91eea37e09

Observation 7dd953de-d05b-4591-b3ff-6b98d4ecd31d · outbound

This paper cites Flexkbqa: A flexible llm-powered framework for few-shot knowledge base question answering,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Flexkbqa: A flexible llm-powered framework for few-shot knowledge base question answering,

Reference 58

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raw_fallback, observed 2026-08-15T20:38:10.047455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:38:09.524680Z digest=sha256:490de5d3fe00e189298ada528c06f8577476afb9520e8e5ab75de05520b9a38f

Observation c6684d36-d9cb-463d-bb41-c9593b9b886f · outbound

This paper cites Distribution shifts are bottlenecks: Extensive evalua- tion for grounding language models to knowledge bases,.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Distribution shifts are bottlenecks: Extensive evalua- tion for grounding language models to knowledge bases,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:38:10.034197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:38:09.528803Z digest=sha256:d4c3b818102441ec7aba1539d0961242344ef7471379c90f1de5815e93c7b107

Observation e3faf6c8-a44c-4334-aab2-bc9e761b9257 · outbound

This paper cites Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models

Reference 60

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:38:09.533398Z digest=sha256:84808128bab0721a79101915dda15533df1f8e2cc7561853c9f64a4bfc171c2a

Observation 8a9ee70f-77fb-4cdc-9c6f-f28cb3005fd8 · outbound

This paper cites Few-shot In-context Learning for Knowledge Base Question Answering.

Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering Few-shot In-context Learning for Knowledge Base Question Answering

Reference 61

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source=pdf_text observed=2026-08-15T20:38:09.537766Z digest=sha256:da77ee02a684284aced62da486c26543815522e508721db73335d5316ad16e0c

Pith citing papers

No inbound Pith citation observations are available.