Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-08T17:47:51.021875Z
Paper Citation Record · LEDGER
Evaluating Prompting and Execution-Based Methods for Deterministic Computation in LLMs
As of 15 August 2026, this Paper Citation Record lists 7 of 7 outbound references and 0 inbound Pith citation observations for arXiv:2605.03227.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
7 of 7 outbound references displayed
- verified exact0
- verified fuzzy7
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 359c0b72-8343-4408-9436-0bec20bdf1c4 · outbound
This paper cites Wei et al., Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, NeurIPS.
Evaluating Prompting and Execution-Based Methods for Deterministic Computation in LLMs Wei et al., Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, NeurIPS
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T06:41:51.787388Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2122fb39-2a39-4c2b-995e-bde241505a20 · outbound
This paper cites Wang et al., Self-Consistency Improves Chain of Thought Reasoning in Language Models, ICLR.
Evaluating Prompting and Execution-Based Methods for Deterministic Computation in LLMs Wang et al., Self-Consistency Improves Chain of Thought Reasoning in Language Models, ICLR
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T06:41:51.760955Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 49fb42e6-0ae1-495f-83e2-a5c126cf79f6 · outbound
This paper cites Zhou et al., Least-to-Most Prompting Enables Complex Reasoning in Large Language Models, ICLR.
Evaluating Prompting and Execution-Based Methods for Deterministic Computation in LLMs Zhou et al., Least-to-Most Prompting Enables Complex Reasoning in Large Language Models, ICLR
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T06:41:51.777569Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 1a4a1873-2f2a-4a77-8820-d295ca0a6efb · outbound
This paper cites Chen et al., Program of Thoughts Prompting: Disentangling Computation from Reasoning, arXiv.
Evaluating Prompting and Execution-Based Methods for Deterministic Computation in LLMs Chen et al., Program of Thoughts Prompting: Disentangling Computation from Reasoning, arXiv
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T06:41:51.764543Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2d468ea9-5b8b-4191-b873-1cde09d37749 · outbound
This paper cites Wang et al., CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation, EMNLP.
Evaluating Prompting and Execution-Based Methods for Deterministic Computation in LLMs Wang et al., CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation, EMNLP
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T06:41:51.773934Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 4f02896f-80ed-494d-b026-9dc3a3949bc7 · outbound
This paper cites Brown et al., Language Models are Few-Shot Learners, NeurIPS.
Evaluating Prompting and Execution-Based Methods for Deterministic Computation in LLMs Brown et al., Language Models are Few-Shot Learners, NeurIPS
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T06:41:51.783574Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation afb6ec33-49be-4746-9377-9299d3d78219 · outbound
This paper cites Schick et al., Toolformer: Language Models Can Teach Themselves to Use Tools, NeurIPS.
Evaluating Prompting and Execution-Based Methods for Deterministic Computation in LLMs Schick et al., Toolformer: Language Models Can Teach Themselves to Use Tools, NeurIPS
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T06:41:51.780333Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Pith citing papers
No inbound Pith citation observations are available.