Paper Citation Record · LEDGER

Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2311.04205.

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

pith.paper-citation-record.v1
2311.04205 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:00:31.257267Z

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

12
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 e1859421-59fc-4ca2-9546-a4b19fcb86b8 · inbound

A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications cites this paper.

A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-12T21:52:10.392091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 70bb02cc-9368-4585-8dc5-fdea72995c78 · inbound

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems cites this paper.

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 190

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:42:10.855084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bb08272e-e5e1-47fa-9ccc-fec03fabd272 · inbound

MenTeR: A fully-automated Multi-agenT workflow for end-to-end RF/Analog Circuits Netlist Design cites this paper.

MenTeR: A fully-automated Multi-agenT workflow for end-to-end RF/Analog Circuits Netlist Design Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:31.257267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5191659f-e14a-4324-83af-c7a7530a7e91 · inbound

ORPP: Self-Optimizing Role-playing Prompts to Enhance Language Model Capabilities cites this paper.

ORPP: Self-Optimizing Role-playing Prompts to Enhance Language Model Capabilities Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T11:27:26.029242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5166dc91-ecfa-4a99-88db-d20dbaab96d8 · inbound

Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks cites this paper.

Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:40.677055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0846945b-a566-4fbe-8bb3-1473da2080ba · inbound

Taxonomy of migration scenarios for Qiskit refactoring using LLMs cites this paper.

Taxonomy of migration scenarios for Qiskit refactoring using LLMs Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T05:44:15.576703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 720ed0e9-ce08-4b09-94b6-01ddb696ef27 · inbound

Identifying Helpful Context for LLM-based Vulnerability Repair: A Preliminary Study cites this paper.

Identifying Helpful Context for LLM-based Vulnerability Repair: A Preliminary Study Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:55.498756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0f35775-3720-4b7f-b4f7-37a4560a1d08 · inbound

Thought Graph Traversal for Test-time Scaling in Chest X-ray VLLMs cites this paper.

Thought Graph Traversal for Test-time Scaling in Chest X-ray VLLMs Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:17:13.908252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e8694535-1e80-4547-8b12-b8791f89d7ed · inbound

Automatic Qiskit Code Refactoring Using Large Language Models cites this paper.

Automatic Qiskit Code Refactoring Using Large Language Models Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T00:22:49.409497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24e3f5a0-b293-443b-acfa-b7ac81996448 · inbound

A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1 cites this paper.

A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1 Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 1589

Resolution: unresolved; no resolver link, observed 2026-08-06T18:19:59.516955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1a2721c-5df4-4e42-a31c-01d126593d62 · inbound

Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation cites this paper.

Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T16:27:23.078614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61c2836c-e3e3-47ee-8d72-e2d23d27def9 · inbound

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting cites this paper.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T17:39:20.828070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8259be97-7aa6-4e26-b87f-483c48fe1f0c · inbound

PromptGuard: An Orchestrated Prompting Framework for Principled Synthetic Text Generation for Vulnerable Populations using LLMs with Enhanced Safety, Fairness, and Controllability cites this paper.

PromptGuard: An Orchestrated Prompting Framework for Principled Synthetic Text Generation for Vulnerable Populations using LLMs with Enhanced Safety, Fairness, and Controllability Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T20:04:29.405226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acd00132-8fa9-4d01-9f5a-7ba1fc400e9d · inbound

How Tokenization Limits Phonological Knowledge Representation in Language Models and How to Improve Them cites this paper.

How Tokenization Limits Phonological Knowledge Representation in Language Models and How to Improve Them Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:51:46.135334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 46cc06f5-95f9-4dc6-8e96-466e1ed64992 · inbound

Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering cites this paper.

Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 57

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T06:21:26.946791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 29435c52-2fea-4d43-a442-51e0243bd16c · inbound

TCARD: Nearly Balanced Two-Level Designs with Treatment Cardinality Constraints with an Application to LLM Prompt Engineering cites this paper.

TCARD: Nearly Balanced Two-Level Designs with Treatment Cardinality Constraints with an Application to LLM Prompt Engineering Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 53

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T03:19:29.011394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bf224a25-4e63-41a5-8d4e-a72c0f40e6e4 · inbound

Make LLM Learn to Synthesize from Streaming Experiences through Feedback cites this paper.

Make LLM Learn to Synthesize from Streaming Experiences through Feedback Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-29T07:43:13.590014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1a79eb00-fada-472b-a1b6-677f1a2a7c09 · inbound

Qiskit Code Migration with LLMs cites this paper.

Qiskit Code Migration with LLMs Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-06-26T16:29:35.802442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fb6d7b7c-11fb-48bf-ae17-6f642973d55a · inbound

Do Encoders Suffice? A Systematic Comparison of Encoder and Decoder Safety Judges for LLM Adversarial Evaluation cites this paper.

Do Encoders Suffice? A Systematic Comparison of Encoder and Decoder Safety Judges for LLM Adversarial Evaluation Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:00:07.958235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c025cfb8-fa2b-46a5-b79c-f58800c0a3ea · inbound

Diagnosing and Repairing Factual Errors in RAG under Budget Constraints cites this paper.

Diagnosing and Repairing Factual Errors in RAG under Budget Constraints Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:24:21.629422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.