Paper Citation Record · LEDGER

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents

As of 8 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2607.22688.

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

pith.paper-citation-record.v1
2607.22688 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T22:56:53.419369Z

measured 15 of 15 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

verified exact0
verified fuzzy0
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2a08760f-b0dd-4262-85ab-1fd022b6b2de · outbound

This paper cites Machines of loving grace.

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents Machines of loving grace

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T22:56:52.477994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcdc35e3-d1cf-49dd-b4bb-0d5e97a7c45c · outbound

This paper cites DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines.

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T22:56:52.527443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e2ee142-1f70-4a11-8a45-588de54f4322 · outbound

This paper cites Optimizing instructions and demonstrations for multi-stage language model programs.

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents Optimizing instructions and demonstrations for multi-stage language model programs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T22:56:52.613265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25cc8f42-5a30-4fdc-9e2f-29e95cde319b · outbound

This paper cites Optimizing generative ai by backpropagating language model feedback.Nature, 639(8055): 609–616, 2025.

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents Optimizing generative ai by backpropagating language model feedback.Nature, 639(8055): 609–616, 2025

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T22:56:52.665383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92a4d885-cb1c-4065-82f3-1fce5a340fc9 · outbound

This paper cites GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning.

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T22:56:52.716130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3cae87b-b79a-4b62-aebe-3dac247dc208 · outbound

This paper cites Expel: Llm agents are experiential learners.

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents Expel: Llm agents are experiential learners

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T22:56:52.775951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7edac95-9f85-4ce4-a629-cb35a1beb1aa · outbound

This paper cites Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models.

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T22:56:52.826146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daa8aceb-84db-46e8-8dcf-d9e1787885eb · outbound

This paper cites Meta- harness: End-to-end optimization of model harnesses, March 2026.

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents Meta- harness: End-to-end optimization of model harnesses, March 2026

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T22:56:52.919232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a21a1c8-fd05-4bd2-b325-ef7530900341 · outbound

This paper cites Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses.

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T22:56:52.972137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87d28766-d7cf-4bf8-b4e9-b187e8027026 · outbound

This paper cites Harnessing Agentic Evolution.

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents Harnessing Agentic Evolution

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T22:56:53.053745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9dc5f17d-b8cf-4ef3-880f-c0fd098e1e4a · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T22:56:53.135527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0b60c51-e1fd-49c6-bacb-25ecb6680746 · outbound

This paper cites ReTool: Reinforcement Learning for Strategic Tool Use in LLMs.

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents ReTool: Reinforcement Learning for Strategic Tool Use in LLMs

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T22:56:53.185682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f134a709-de0c-4f9d-9fbf-e0f22b86e1b4 · outbound

This paper cites ToRL: Scaling Tool-Integrated RL.

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents ToRL: Scaling Tool-Integrated RL

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T22:56:53.271564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db3dc8b4-3420-4a0e-a93b-0aaf10821690 · outbound

This paper cites Understanding Tool-Integrated Reasoning.

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents Understanding Tool-Integrated Reasoning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T22:56:53.338911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e0d2907-75a8-4316-a6f8-f537e5327cc8 · outbound

This paper cites Automated Design of Agentic Systems.

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents Automated Design of Agentic Systems

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T22:56:53.419369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.