Paper Citation Record · LEDGER

Workspace Optimization: How to Train Your Agent

As of 1 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 3 inbound Pith citation observations for arXiv:2605.09650.

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

pith.paper-citation-record.v1
2605.09650 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-12T02:27:22.169290Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-01T06:32:01.292127+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T09:39:45.987272Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-22T06:11:08.934061Z

Reference resolution

12 of 12 outbound references displayed

verified exact2
verified fuzzy2
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 987c6e61-000b-4df5-8ce1-4552204bb07e · outbound

This paper cites Arcmemo: Abstract reasoning composition with lifelong llm memory.

Workspace Optimization: How to Train Your Agent Arcmemo: Abstract reasoning composition with lifelong llm memory

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T07:37:00.848871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation d865b5cf-e4c5-432f-a49b-7f3524c2125d · outbound

This paper cites Meta-Harness: End-to-End Optimization of Model Harnesses.

Workspace Optimization: How to Train Your Agent Meta-Harness: End-to-End Optimization of Model Harnesses

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T16:15:58.398964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 7f5b59a8-7aad-4c15-b7b6-3df4cbd0439d · outbound

This paper cites ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory.

Workspace Optimization: How to Train Your Agent ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:42:50.464031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation beec7f6b-ca40-4b15-8e27-370253ec5cef · outbound

This paper cites Generative Agents: Interactive Simulacra of Human Behavior.

Workspace Optimization: How to Train Your Agent Generative Agents: Interactive Simulacra of Human Behavior

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-05-12T07:37:00.716210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 50905093-3897-40ee-ad4b-c493f28b4fd0 · outbound

This paper cites From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought.

Workspace Optimization: How to Train Your Agent From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T07:37:00.591676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 7d196f65-9e07-4758-9f74-8720fe83f0b9 · outbound

This paper cites Recursive Language Models.

Workspace Optimization: How to Train Your Agent Recursive Language Models

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-12T07:37:00.196237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 8ccb78dd-f76c-4420-9827-3ed8d3e572b9 · outbound

This paper cites an unresolved cited work.

Workspace Optimization: How to Train Your Agent Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-05-12T22:41:56.470606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 61488062-b0ab-45e5-bb6a-1815dc2972c5 · outbound

This paper cites an unresolved cited work.

Workspace Optimization: How to Train Your Agent Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-05-12T22:41:56.473662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation cb106ca8-7ed7-48ea-8a12-96209a62dafa · outbound

This paper cites The delta is feedback rather than a hard gate, so the role can keep iterating within the round budget.

Workspace Optimization: How to Train Your Agent The delta is feedback rather than a hard gate, so the role can keep iterating within the round budget

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T22:41:56.478196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 74e10418-ca9a-4ef3-9d49-e5914628d286 · outbound

This paper cites an unresolved cited work.

Workspace Optimization: How to Train Your Agent Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-05-12T22:41:56.481417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 71b3e34f-54e8-41bf-958e-a82c2a4da19a · outbound

This paper cites an unresolved cited work.

Workspace Optimization: How to Train Your Agent Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-05-12T22:41:56.484737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation f26b6939-2893-403f-bc4b-81592443140f · outbound

This paper cites ACTION:entity.

Workspace Optimization: How to Train Your Agent ACTION:entity

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T22:41:56.488605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Pith citing papers

Observation a2c397b7-3b45-47de-8f08-d0b5296dc628 · inbound

Adapting the Interface, Not the Model: Runtime Harness Adaptation for Deterministic LLM Agents cites this paper.

Adapting the Interface, Not the Model: Runtime Harness Adaptation for Deterministic LLM Agents Workspace Optimization: How to Train Your Agent

Reference 43

Resolution: metadata mismatch; local_arxiv, observed 2026-05-22T06:11:08.937182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 8af171e6-247a-4db9-b1fd-3e35b521d615 · inbound

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents cites this paper.

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents Workspace Optimization: How to Train Your Agent

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-01T09:39:45.987272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7afbaea1-5df8-4121-a4b3-36dcc3f62397 · inbound

Tycho: Active Abstraction with Programmatic World Models for ARC-AGI-3 cites this paper.

Tycho: Active Abstraction with Programmatic World Models for ARC-AGI-3 Workspace Optimization: How to Train Your Agent

Reference 85

Resolution: unresolved; no resolver link, observed 2026-07-31T12:16:33.719014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.