Paper Citation Record · LEDGER

Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents

As of 4 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 1 inbound Pith citation observation for arXiv:2604.06753.

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

pith.paper-citation-record.v1
2604.06753 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:32:37.703624Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T01:03:24.382077Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

13 of 13 outbound references displayed

verified exact10
verified fuzzy2
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9d78e302-10f7-450c-83a7-45f97d8f3bcd · outbound

This paper cites FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance.

Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:56:45.568475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 94bdf6e6-6c67-4ad3-8799-8037e75b6d84 · outbound

This paper cites LLM+P: Empowering Large Language Models with Optimal Planning Proficiency.

Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T18:36:18.754973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 711a35eb-0769-43a4-8015-752aa2e5b7bd · outbound

This paper cites RouteLLM: Learning to Route LLMs with Preference Data.

Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents RouteLLM: Learning to Route LLMs with Preference Data

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:27:40.993552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation d786e55b-2582-459c-96ee-0842414b32a8 · outbound

This paper cites Humanity's Last Exam.

Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents Humanity's Last Exam

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-11T00:25:50.918265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T22:08:32.844211+00:00.

Observation daf75198-97b0-422f-8079-db9f3f963813 · outbound

This paper cites Qwen3 Technical Report.

Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents Qwen3 Technical Report

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-11T00:25:50.892186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 99cffbfd-f1de-4523-82fe-5a4a72f9132f · outbound

This paper cites Large Language Model Routing with Benchmark Datasets.

Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents Large Language Model Routing with Benchmark Datasets

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:25:50.940194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 93f6a832-6885-4366-9d2a-769a21ac5223 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-11T00:25:50.960902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c20d82ca-fa79-4c1a-9467-5ea7d1429a93 · outbound

This paper cites Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models.

Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:43:46.984626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 5d785746-e02a-4274-afe1-2b2244caff6f · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-11T00:25:50.970304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation f0925a76-9055-49d1-981b-0899a721e394 · outbound

This paper cites Toward Efficient Agents: Memory, Tool learning, and Planning.

Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents Toward Efficient Agents: Memory, Tool learning, and Planning

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-07T02:16:02.482997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation feb0c6b9-5f71-4f44-9d6b-6f54109872e1 · outbound

This paper cites $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains.

Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:19:00.987380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 664b6dff-9914-4b05-8cdf-7d80f4369443 · outbound

This paper cites SATISFACTORY.

Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents SATISFACTORY

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:33:16.127920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 1cf98632-5170-4d5f-9d66-8fed0637c237 · outbound

This paper cites India” based on parametric knowledge. Most strikingly, Reflection made 10 tool calls andstillanswered “India.

Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents India” based on parametric knowledge. Most strikingly, Reflection made 10 tool calls andstillanswered “India

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:33:16.125347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Pith citing papers

Observation d94cfa5a-10d9-4a6e-9850-db91ea62abc6 · inbound

Large Language Model for Operations Research Formulation Selection in Multi-Warehouse Inventory Allocation cites this paper.

Large Language Model for Operations Research Formulation Selection in Multi-Warehouse Inventory Allocation Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T01:03:24.382077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.