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Paper Citation Record · LEDGER

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering

As of 7 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2507.03018.

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

pith.paper-citation-record.v1
2507.03018 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:48:15.795768Z

measured 19 of 19 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 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

19 of 19 outbound references displayed

  • verified exact0
  • verified fuzzy5
  • unresolved14
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d9655f6b-a721-461a-b922-e5feb34f39a9 · outbound

This paper cites The Llama 3 Herd of Models.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering The Llama 3 Herd of Models

Reference 1

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no resolver link, observed 2026-08-06T20:48:14.439919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:48:14.439919Z digest=sha256:4481335e15214556db45b336420dfd7df7527918383be3d14a7e5499228e448f

Observation 48ba2f14-1ff3-431c-bf7e-27661b8a3de8 · outbound

This paper cites Qwen2.5 technical report, 2025.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering Qwen2.5 technical report, 2025

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:48:14.506241Z digest=sha256:774621bfb05cb9aa7391b1ad28b0dfe3505e8f77e7bbcdbc073dce7466dfa49a

Observation eaaa8038-770b-4763-bf7e-28247a1cf1c1 · outbound

This paper cites Qwen3 Technical Report.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering Qwen3 Technical Report

Reference 3

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no resolver link, observed 2026-08-06T20:48:14.565267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:48:14.565267Z digest=sha256:86dab80d1c0cd3821fb09677b6933d5a6d2c4ce83483bd3970d5fa99660fcdb3

Observation 71dbe344-1917-4f2c-9978-99864768be26 · outbound

This paper cites Proximal Policy Optimization Algorithms.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering Proximal Policy Optimization Algorithms

Reference 4

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no resolver link, observed 2026-08-06T20:48:14.611393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:48:14.611393Z digest=sha256:d4f09dc4b08ceedcb9d246802bdf2ffe65d3aa7febbdfd3291ec99b807ab77ec

Observation 931333d2-c2b1-45a6-ae19-4998721a98ab · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering Direct preference optimization: Your language model is secretly a reward model

Reference 5

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no resolver link, observed 2026-08-06T20:48:14.672057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:48:14.672057Z digest=sha256:3e6e02af9940b1568be5b5bad9494c2d4d594d0141299f2dd21b6da4f058d9b7

Observation f67a256f-1452-4d1e-8f3f-a9a1dc7bd5ef · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 6

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unresolved
no resolver link, observed 2026-08-06T20:48:14.735755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:48:14.735755Z digest=sha256:4e8b2136524496654e79aadca2aae9c94af45e23df669644a5bc5f9ea36f662d

Observation 73db049e-2451-4bf5-abaf-ef61e2eb777d · outbound

This paper cites Nousresearch/hermes-function-calling.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering Nousresearch/hermes-function-calling

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:48:17.303862Z

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=2026-08-06T20:48:14.787217Z digest=sha256:abfb7eedd281e977ed29e3acca2fbda53a4603ce295a918a2d34b0aa2b909327

Observation f8b2d1cd-4f00-4064-bb69-0a0ac1f66f11 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 8

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unresolved
no resolver link, observed 2026-08-06T20:48:14.839589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:48:14.839589Z digest=sha256:9ebc78f1db812ea61344aea73bc837b315cc96213fddf6c47e22b3efda11eae8

Observation 1dadb1ff-70df-4e1c-b881-087864c2daf0 · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 9

Resolution
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no resolver link, observed 2026-08-06T20:48:14.961604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:48:14.961604Z digest=sha256:d93b2d717be54f78fa94947f4b23f6919bc0d56bca8f294e22d7e945920b0fc4

Observation a8f70119-f45e-4c48-a784-99fa5982f4bd · outbound

This paper cites Open-WikiTable: Dataset for Open Domain Question Answering with Complex Reasoning over Table.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering Open-WikiTable: Dataset for Open Domain Question Answering with Complex Reasoning over Table

Reference 10

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no resolver link, observed 2026-08-06T20:48:15.045521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:48:15.045521Z digest=sha256:79ccbbdd0ffda5af3320b52676d6aebe2f1744d84059c882508928068b5e3267

Observation fadd12b9-712c-404f-9f73-edc7edfdb1b7 · outbound

This paper cites TAPAS: Weakly Supervised Table Parsing via Pre-training.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering TAPAS: Weakly Supervised Table Parsing via Pre-training

Reference 11

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no resolver link, observed 2026-08-06T20:48:15.166374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:48:15.166374Z digest=sha256:34b560590f70e18dd3798d2106ac61f3eb5466ea450fdb507d5ede7820a80938

Observation 50de6d48-024b-4a36-bc2c-675945442484 · outbound

This paper cites TAPEX: Table Pre-training via Learning a Neural SQL Executor.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering TAPEX: Table Pre-training via Learning a Neural SQL Executor

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T20:48:15.279540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:48:15.279540Z digest=sha256:1a1d5f5a542d79ef4b71d6ecd38b445af2814e2d6f5f26d3eb6a3213383b241c

Observation 71fb8287-afa3-40ed-9ce3-a0fb06077aa1 · outbound

This paper cites Toolformer: Language models can teach themselves to use tools.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering Toolformer: Language models can teach themselves to use tools

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:48:16.945370Z

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=2026-08-06T20:48:15.370495Z digest=sha256:9306bfa787f82748a2703bfbed4fc9161980deaba62f868a80559317a1c1b128

Observation 34387c3c-32ce-457b-a532-3231b8bb10a2 · outbound

This paper cites The probabilistic relevance framework: Bm25 and beyond.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering The probabilistic relevance framework: Bm25 and beyond

Reference 14

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unresolved
no resolver link, observed 2026-08-06T20:48:15.454246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:48:15.454246Z digest=sha256:62aa371b299ba73b26837ed01c4021345102900e6bd57c252c0eaedc0507045c

Observation 256ac739-9d68-46e1-bac1-e6e02b0c4297 · outbound

This paper cites Dense passage retrieval for open-domain question answering.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering Dense passage retrieval for open-domain question answering

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T20:48:15.480811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:48:15.480811Z digest=sha256:8634454c7721288a6c168dae74af9a04360ef6cceb87325c347c3626194cda57

Observation 748e790b-1903-42b6-b4b0-0803b734cb77 · outbound

This paper cites {SparTA}:{Deep-Learning} model sparsity via {Tensor-with-Sparsity-Attribute}.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering {SparTA}:{Deep-Learning} model sparsity via {Tensor-with-Sparsity-Attribute}

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:48:16.633459Z

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=2026-08-06T20:48:15.533821Z digest=sha256:ddbd3d32945d6628f4cb26d65820c217087d0e49111f528dec0b81db85fc4655

Observation 88da2eec-0652-48d4-94fc-69f44d5a0fdf · outbound

This paper cites Training language models to follow instructions with human feedback.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering Training language models to follow instructions with human feedback

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:48:16.318880Z

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=2026-08-06T20:48:15.610921Z digest=sha256:01607e30404fc850927e7a6e42892ef808c8fda8d1bfc7ae23c2242832471c34

Observation d8b70dcc-1e13-4512-8cdd-c9ef4e083c3f · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:48:15.677456Z digest=sha256:107f1138de5b8a384fc6b63f2f165f01d1f40db5a93d0329bd3110f365ddc9cf

Observation af8b5d05-6eaa-4d6b-a38f-8202b3ce594e · outbound

This paper cites Asynchronous methods for deep reinforcement learning.

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering Asynchronous methods for deep reinforcement learning

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:48:16.117784Z

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=2026-08-06T20:48:15.795768Z digest=sha256:056a03e42820fea275471aa09fb7d2017d0527e0e88adc8ce417a95ad4aafde1

Pith citing papers

No inbound Pith citation observations are available.