Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-09T14:07:43.507761Z
Paper Citation Record · LEDGER
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling
As of 6 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2605.01566.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-09T03:36:57.168246Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-09T03:45:55.534976Z
Reference resolution
18 of 18 outbound references displayed
- verified exact5
- verified fuzzy3
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch9
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b1d3d7c6-86b6-46d1-9156-6c8fed48d02f · outbound
This paper cites Stay Focused: Problem Drift in Multi-Agent Debate.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling Stay Focused: Problem Drift in Multi-Agent Debate
Reference 1
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T17:06:03.954920Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 327d15b7-e6a5-469b-b23c-5fb5bac26362 · outbound
This paper cites Training Verifiers to Solve Math Word Problems.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling Training Verifiers to Solve Math Word Problems
Reference 2
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-11T17:06:03.958989Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 94f9400c-5db8-4f85-842a-4162020c0d57 · outbound
This paper cites Improving Factuality and Reasoning in Language Models through Multiagent Debate.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling Improving Factuality and Reasoning in Language Models through Multiagent Debate
Reference 3
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-12T03:01:45.301651Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 3d6190e3-2adc-4199-8762-fdb03b7bf5ef · outbound
This paper cites Large Language Models Cannot Self-Correct Reasoning Yet.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling Large Language Models Cannot Self-Correct Reasoning Yet
Reference 4
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-12T05:48:28.335998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 11b868db-ae8d-4e5e-a1c4-d667044d409c · outbound
This paper cites InProceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024), pages 105–119, Bangkok, Thailand.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling InProceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024), pages 105–119, Bangkok, Thailand
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T02:26:30.158161Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation cd757e76-605d-481c-a94f-b81911b71bce · outbound
This paper cites Mind the Gap Between Spatial Reasoning and Acting! Step-by-Step Evaluation of Agents With Spatial-Gym.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling Mind the Gap Between Spatial Reasoning and Acting! Step-by-Step Evaluation of Agents With Spatial-Gym
Reference 6
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-11T17:06:03.926913Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 325bfc76-8678-4d41-946d-5c2757f585bf · outbound
This paper cites Scaling Laws for Neural Language Models.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling Scaling Laws for Neural Language Models
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T17:06:03.962458Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 39c0b790-40d8-401f-9589-4db5ccafcdf4 · outbound
This paper cites Self-Refine: Iterative Refinement with Self-Feedback.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling Self-Refine: Iterative Refinement with Self-Feedback
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T17:06:03.965474Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation eb943f7a-b03a-46bd-9a42-0283e59703eb · outbound
This paper cites The Llama 3 Herd of Models.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling The Llama 3 Herd of Models
Reference 9
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T17:06:03.932948Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 5039904a-1f1f-4379-809d-79398ce67b24 · outbound
This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Reference 10
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-11T17:06:03.950400Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 7552b230-5f20-47e3-8b64-b7e88137f3b4 · outbound
This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T17:01:10.451911Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b205710e-600d-4d3e-b26c-2923fc9e67b3 · outbound
This paper cites Mixture-of-Agents Enhances Large Language Model Capabilities.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling Mixture-of-Agents Enhances Large Language Model Capabilities
Reference 12
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-16T19:29:34.516338Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 42617628-578f-44d7-9742-9f36916819c3 · outbound
This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling Self-Consistency Improves Chain of Thought Reasoning in Language Models
Reference 13
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-11T17:01:10.436307Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation d8f3b0b5-94f1-4391-9da1-2a8cdff683a3 · outbound
This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Reference 14
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-11T17:01:10.429053Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 334b9a48-f8cd-4efd-8a9f-efb92cf0e7ad · outbound
This paper cites Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models
Reference 15
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-18T06:38:37.536547Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 69bdb243-3903-40f2-b014-874be9a2d89a · outbound
This paper cites FLOPs are calculated with the formulas from Table 4 of Appendix B.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling FLOPs are calculated with the formulas from Table 4 of Appendix B
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T02:26:30.149968Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 37cc218c-9373-4643-a7c3-e62d04852c65 · outbound
This paper cites FLOPs are estimated by assuming that each parameter is used for one multiplication and one addition per token.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling FLOPs are estimated by assuming that each parameter is used for one multiplication and one addition per token
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T02:26:30.146256Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 8ca8adbe-9cf7-43b5-8ded-9f3a74a0f64d · outbound
This paper cites an unresolved cited work.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling Unresolved cited work
Reference 18
- Resolution
- unresolved
- raw_fallback, observed 2026-05-26T02:26:30.154013Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Pith citing papers
Observation 532cbc44-7530-4413-97c4-723818559cab · inbound
Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops cites this paper.
Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling
Reference 71
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T03:45:55.536724Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.