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Paper Citation Record · LEDGER

AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.19073.

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

pith.paper-citation-record.v1
2406.19073 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:32:11.083905Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T08:01:00.193410Z

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b701b11c-6821-4ac6-8a89-c84bdcd219b7 · inbound

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types cites this paper.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.083905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.083905Z digest=sha256:dfd5b86b740683867b941bc4ef38ee46424f9e75ab29b2c7a74eab6ae7ba1aeb

Observation 5538b631-824e-4882-be67-1604f0ec4d8a · inbound

Code Benchmarks Should Prioritize Rigor, Reliability, and Reproducibility cites this paper.

Code Benchmarks Should Prioritize Rigor, Reliability, and Reproducibility AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-10T19:04:25.974940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:04:25.974940Z digest=sha256:46ee0a15dc9a4345dc9b8ea2e1e3b1df935297b5d61b2caedd9bc7438a9ebd06

Observation 1f0393e4-d08d-4aa5-857b-69a5ecc449ce · inbound

Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities cites this paper.

Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries

Reference 102

Resolution
unresolved
no resolver link, observed 2026-08-07T13:12:42.704838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:12:42.704838Z digest=sha256:bc94ec5163078f34865f16dafdcddf00bb26ab14a9ca92df2d1065eb65bebbef

Observation cee28f58-acbd-4009-b67e-c0f9a4eef660 · inbound

Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs cites this paper.

Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries

Reference 243

Resolution
verified exact
arxiv_id, observed 2026-05-11T07:45:59.482223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-05-10T16:58:10.013475Z digest=sha256:50b5d8387d329972969dc7db72586d9428c08a674654c9b33c6a395953077340

Observation 9a923ecc-f5fd-4057-b6b6-37823ed10a5c · inbound

Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning cites this paper.

Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries

Reference 228

Resolution
verified exact
arxiv_id, observed 2026-05-11T08:01:00.195875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-05-10T16:51:19.555272Z digest=sha256:4f2e842bd78dbf99ce63b45d171eecd1f0785bd70833e68f81bdbc7734be1ae1