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Paper Citation Record · LEDGER

Adapting Embedding Models for Agent Capability Retrieval

As of 22 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2607.17347.

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

pith.paper-citation-record.v1
2607.17347 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T18:19:41.222748Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

30 of 30 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved30
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 65222fa6-178a-435b-b45c-dfc676f8540f · outbound

This paper cites MCP-Zero: Active Tool Discovery for Autonomous LLM Agents.

Adapting Embedding Models for Agent Capability Retrieval MCP-Zero: Active Tool Discovery for Autonomous LLM Agents

Reference 1

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no resolver link, observed 2026-08-01T18:19:37.664631Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T18:19:37.664631Z digest=sha256:7633e05376b696f90780711276a7bcc3703ed6a7dbea0e5615562294d78ff15e

Observation 241c025d-ad15-4526-85c8-83fbe3e0bbf7 · outbound

This paper cites RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation.

Adapting Embedding Models for Agent Capability Retrieval RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation

Reference 2

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source=pdf_text observed=2026-08-01T18:19:37.745270Z digest=sha256:bc791e669ac876bc2af3b5d9f8f7f29720dffc8420ba4d1894fbeba0c28cde76

Observation 5600ec56-4e41-4ac5-8633-b6451d751462 · outbound

This paper cites an unresolved cited work.

Adapting Embedding Models for Agent Capability Retrieval Unresolved cited work

Reference 3

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source=pdf_text observed=2026-08-01T18:19:37.880305Z digest=sha256:27eb8bf880cc0c1a75c189c72ee18be97ad11118c11816e2322645d2abc3b630

Observation a3809a90-6dbd-4c46-a8ad-aa7e746c43cc · outbound

This paper cites RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs.

Adapting Embedding Models for Agent Capability Retrieval RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs

Reference 4

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source=pdf_text observed=2026-08-01T18:19:38.026339Z digest=sha256:83b939ebdf7eeed3511829129860dcdcf049d7f96c7108b868eae27e238384bb

Observation de722b0f-004b-4132-9683-780381ebcbc1 · outbound

This paper cites Universal Model Routing for Efficient LLM Inference.

Adapting Embedding Models for Agent Capability Retrieval Universal Model Routing for Efficient LLM Inference

Reference 5

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no resolver link, observed 2026-08-01T18:19:38.139298Z

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source=pdf_text observed=2026-08-01T18:19:38.139298Z digest=sha256:4e410652c778d78e1b1e8431739307baa8536b23aa3dee55ec6bb80d466b87b8

Observation 71e0014d-37f9-4bb2-9a6a-cc8402384719 · outbound

This paper cites an unresolved cited work.

Adapting Embedding Models for Agent Capability Retrieval Unresolved cited work

Reference 6

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no resolver link, observed 2026-08-01T18:19:38.242901Z

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source=pdf_text observed=2026-08-01T18:19:38.242901Z digest=sha256:7036652fa8bddf3075e8f9769162f9b1aa07c45648c78b91cdbe23c16afe91b7

Observation d5c39d4f-2be5-4926-9a8e-60b8b1cebbc3 · outbound

This paper cites Evaluation Report on MCP Servers.

Adapting Embedding Models for Agent Capability Retrieval Evaluation Report on MCP Servers

Reference 7

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source=pdf_text observed=2026-08-01T18:19:38.312333Z digest=sha256:31cf9f47748a4a9ea79a55f270b016c8f43923690f3dd89d0a54c14e90e7580d

Observation 9690e676-819d-4e5e-92a1-442286fc8195 · outbound

This paper cites an unresolved cited work.

Adapting Embedding Models for Agent Capability Retrieval Unresolved cited work

Reference 8

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source=pdf_text observed=2026-08-01T18:19:38.368756Z digest=sha256:ee30e6a0bad74fef0110ccb9649b07fb66725ebcf2f166687067cfc3ad28a66f

Observation 22ad3c37-4a21-4213-a313-c46fa03fb713 · outbound

This paper cites Efficient and Scalable Estimation of Tool Representations in Vector Space.

Adapting Embedding Models for Agent Capability Retrieval Efficient and Scalable Estimation of Tool Representations in Vector Space

Reference 9

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source=pdf_text observed=2026-08-01T18:19:38.452065Z digest=sha256:e806b4604cdfc2de6c0a3f1e728f7043de86a5bf931525bf04b0c02587056600

Observation 8770b271-fbef-4fb9-b9a7-1a685875db6d · outbound

This paper cites 2026.MuleRun: World’s Largest AI Agent Store.

Adapting Embedding Models for Agent Capability Retrieval 2026.MuleRun: World’s Largest AI Agent Store

Reference 10

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source=pdf_text observed=2026-08-01T18:19:38.549522Z digest=sha256:a70648e6711ba730b3fab8ed1c7d135e4e62ed30781b1843463ede6f468853b8

Observation 8bc32a90-8636-44a2-9305-a7b17d502249 · outbound

This paper cites Tulip Agent -- Enabling LLM-Based Agents to Solve Tasks Using Large Tool Libraries.

Adapting Embedding Models for Agent Capability Retrieval Tulip Agent -- Enabling LLM-Based Agents to Solve Tasks Using Large Tool Libraries

Reference 11

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source=pdf_text observed=2026-08-01T18:19:38.730220Z digest=sha256:442e01479cc24005f243edab55db85a98df6481b4532dbc85ff74812a94f84c7

Observation bf9e772d-fc5b-4bae-9198-4673278d1397 · outbound

This paper cites Gonzalez, M Waleed Kadous, and Ion Stoica.

Adapting Embedding Models for Agent Capability Retrieval Gonzalez, M Waleed Kadous, and Ion Stoica

Reference 12

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source=pdf_text observed=2026-08-01T18:19:38.976042Z digest=sha256:f051920268adddda95c74140592e9b3517875e614ebef2f1a26578d78677fdba

Observation 73d85000-5d49-451e-ae74-dc567a06569c · outbound

This paper cites 2025.ClawHub: Skill Registry and Marketplace for OpenClaw.

Adapting Embedding Models for Agent Capability Retrieval 2025.ClawHub: Skill Registry and Marketplace for OpenClaw

Reference 13

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no resolver link, observed 2026-08-01T18:19:39.146205Z

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source=pdf_text observed=2026-08-01T18:19:39.146205Z digest=sha256:4b8c59abad423cc54edb78ecb794b479d2257840c7d1030c6662eef8822635ee

Observation 1c118da7-93bd-4042-9e8c-41cc2d05fc9b · outbound

This paper cites ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs.

Adapting Embedding Models for Agent Capability Retrieval ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

Reference 14

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no resolver link, observed 2026-08-01T18:19:39.329871Z

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source=pdf_text observed=2026-08-01T18:19:39.329871Z digest=sha256:129ef44205dd8e5fd6df322a935f511583f440e553454277dff41cbe155b4393

Observation a23bfccb-0c01-4142-a9a3-ad539d610c99 · outbound

This paper cites an unresolved cited work.

Adapting Embedding Models for Agent Capability Retrieval Unresolved cited work

Reference 15

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source=pdf_text observed=2026-08-01T18:19:39.494503Z digest=sha256:a09cf1b2c7087d198ce54d10bb405907448da68439581aa3c67f13a3deaba1b5

Observation c3a0f06f-af88-4c9e-871f-bb2c5e785006 · outbound

This paper cites an unresolved cited work.

Adapting Embedding Models for Agent Capability Retrieval Unresolved cited work

Reference 16

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source=pdf_text observed=2026-08-01T18:19:39.629731Z digest=sha256:b40d2f0df559081c60def6e7848a100a29e5ca2be18d5703aa8c78a7fc7ce0ea

Observation 7c3b09d4-6248-424a-a96c-705f50ea16a9 · outbound

This paper cites an unresolved cited work.

Adapting Embedding Models for Agent Capability Retrieval Unresolved cited work

Reference 17

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source=pdf_text observed=2026-08-01T18:19:39.767584Z digest=sha256:b9be7fec57463407509c272e285bda2bdcb38f2e49b093430bc19d3d301d1846

Observation 55d513aa-f7c0-4649-98a3-c37350eda4d5 · outbound

This paper cites an unresolved cited work.

Adapting Embedding Models for Agent Capability Retrieval Unresolved cited work

Reference 18

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no resolver link, observed 2026-08-01T18:19:39.935422Z

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source=pdf_text observed=2026-08-01T18:19:39.935422Z digest=sha256:91b81625de66e2116f4ac29804eb2c2d84633313f36457c24dd1f9820c1db68e

Observation 327bd448-e272-4fa8-8ed0-7089f5a506f1 · outbound

This paper cites Arch-Router: Aligning LLM Routing with Human Preferences.

Adapting Embedding Models for Agent Capability Retrieval Arch-Router: Aligning LLM Routing with Human Preferences

Reference 19

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source=pdf_text observed=2026-08-01T18:19:40.105693Z digest=sha256:2decf59e99699184dc9bd88c3547c1c4a6eaa330dd2faa33b901aead64ff34cc

Observation 7fc4360b-9f53-453b-b71a-2a364611ff66 · outbound

This paper cites SkillX: Automatically Constructing Skill Knowledge Bases for Agents.

Adapting Embedding Models for Agent Capability Retrieval SkillX: Automatically Constructing Skill Knowledge Bases for Agents

Reference 20

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source=pdf_text observed=2026-08-01T18:19:40.242616Z digest=sha256:7c8d7cc6dd5ef160db89b5a80d1a757a85d02df62035ecadf36e23e881317083

Observation d8018539-f436-489d-b49c-bde5cdf31924 · outbound

This paper cites Voyager: An Open-Ended Embodied Agent with Large Language Models.

Adapting Embedding Models for Agent Capability Retrieval Voyager: An Open-Ended Embodied Agent with Large Language Models

Reference 21

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source=pdf_text observed=2026-08-01T18:19:40.298283Z digest=sha256:d1e3bc886873462a9e5b08f12ebb488caa8f206d3f8e8f92fe7b984ff94a394d

Observation 366fdabc-653f-4b5f-96af-5f61fc57c410 · outbound

This paper cites an unresolved cited work.

Adapting Embedding Models for Agent Capability Retrieval Unresolved cited work

Reference 22

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source=pdf_text observed=2026-08-01T18:19:40.356111Z digest=sha256:8a2b3fa83aa472a06876c150a6a09d2ff52ff3818e6c83d473cf778c64220bc5

Observation 969159fe-8aea-475f-82ab-db9f1ff90228 · outbound

This paper cites an unresolved cited work.

Adapting Embedding Models for Agent Capability Retrieval Unresolved cited work

Reference 23

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no resolver link, observed 2026-08-01T18:19:40.444397Z

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source=pdf_text observed=2026-08-01T18:19:40.444397Z digest=sha256:b0fc4629d2115d3e5848580425a031d0c7549e1c2d6d14886eef92395179bd1a

Observation 9f7ea600-ebe8-4854-acd7-45c62687a4ad · outbound

This paper cites an unresolved cited work.

Adapting Embedding Models for Agent Capability Retrieval Unresolved cited work

Reference 24

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source=pdf_text observed=2026-08-01T18:19:40.518950Z digest=sha256:493ff5a69c0a5716ff47f7c4ac2b899a34331485ec9868ca70bab6b877e56bbc

Observation a89025a5-9134-4c26-b466-e9cf67bddb56 · outbound

This paper cites Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward.

Adapting Embedding Models for Agent Capability Retrieval Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward

Reference 25

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source=pdf_text observed=2026-08-01T18:19:40.626271Z digest=sha256:4da57cd18a5206359714e85d5a933b827ee12080bedbb1013a37d37dc7f6de62

Observation 90f130c9-c4d1-437c-9555-7b6c065dd0b4 · outbound

This paper cites an unresolved cited work.

Adapting Embedding Models for Agent Capability Retrieval Unresolved cited work

Reference 26

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source=pdf_text observed=2026-08-01T18:19:40.730883Z digest=sha256:b088de59d65c85ab2be31a2e5e70f9fb1c1b1cb2d2a459244d99e5538c2fa14d

Observation b13ef693-36bd-45f9-a5c1-dc0108d0a257 · outbound

This paper cites an unresolved cited work.

Adapting Embedding Models for Agent Capability Retrieval Unresolved cited work

Reference 27

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source=pdf_text observed=2026-08-01T18:19:40.828748Z digest=sha256:386dc1820524e4cb5928130a20c7a054d6ba2c46237e37bfc5761ab89566ccc3

Observation 4d8a15be-85f9-4ee4-ba67-e52db3fc4a05 · outbound

This paper cites 2026.Equipping Agents for the Real World with Agent Skills.

Adapting Embedding Models for Agent Capability Retrieval 2026.Equipping Agents for the Real World with Agent Skills

Reference 28

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source=pdf_text observed=2026-08-01T18:19:40.921973Z digest=sha256:6efbba35166f82912e3bbf65d77ec4abf0fc64a965ff32fa5c4e1f269059adc8

Observation fc0232e0-12c5-4292-a2b8-bca70a8940fa · outbound

This paper cites an unresolved cited work.

Adapting Embedding Models for Agent Capability Retrieval Unresolved cited work

Reference 29

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source=pdf_text observed=2026-08-01T18:19:41.029723Z digest=sha256:bfac767e1646fa53276271e7e4143020d2fc1c66c33cd080115ec06e6b101a0e

Observation 14c6c336-a551-4ea0-8319-b9b1235b7bc1 · outbound

This paper cites an unresolved cited work.

Adapting Embedding Models for Agent Capability Retrieval Unresolved cited work

Reference 30

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source=pdf_text observed=2026-08-01T18:19:41.222748Z digest=sha256:746d0509900f261fbcf99e80a0661512bc588d90604845312002ebb1dfab52d9

Pith citing papers

No inbound Pith citation observations are available.