Paper Citation Record · LEDGER

Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2405.08355.

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

pith.paper-citation-record.v1
2405.08355 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:00:19.632773Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T03:36:29.706174Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2d504428-c82f-45f3-9994-25289e0ccef6 · inbound

Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey cites this paper.

Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark

Reference 135

Resolution: unresolved; no resolver link, observed 2026-08-07T06:00:19.632773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecff0fad-2b2f-4a9f-8827-e3e5dd9e48b1 · inbound

MemTool: Optimizing Short-Term Memory Management for Dynamic Tool Calling in LLM Agent Multi-Turn Conversations cites this paper.

MemTool: Optimizing Short-Term Memory Management for Dynamic Tool Calling in LLM Agent Multi-Turn Conversations Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T12:53:49.428866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b814983d-f1e4-41b1-b7d4-e0377ad15bbe · inbound

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

Toward Efficient Agents: Memory, Tool learning, and Planning Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark

Reference 148

Resolution: unresolved; no resolver link, observed 2026-08-03T09:21:45.717364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01e01045-3b79-42f0-b170-23ae11f296a6 · inbound

Training LLMs for Multi-Step Tool Orchestration with Constrained Data Synthesis and Graduated Rewards cites this paper.

Training LLMs for Multi-Step Tool Orchestration with Constrained Data Synthesis and Graduated Rewards Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-15T00:18:22.727725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a1043c91-fa87-4a6f-bbab-5a8e448e393c · inbound

JTPRO: A Joint Tool-Prompt Reflective Optimization Framework for Language Agents cites this paper.

JTPRO: A Joint Tool-Prompt Reflective Optimization Framework for Language Agents Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T11:40:19.416412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 08cd1f7c-cbc3-4fdd-9af9-1a1a48664f99 · inbound

Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments cites this paper.

Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T03:36:29.707462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.