Paper Citation Record · LEDGER

Advancing SLM Tool-Use Capability using Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 2 inbound Pith citation observations for arXiv:2509.04518.

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

pith.paper-citation-record.v1
2509.04518 v2

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:10:04.649930Z

measured 26 of 26 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T21:54:13.090019Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T09:46:01.011609Z

Reference resolution

24 of 24 outbound references displayed

verified exact2
verified fuzzy8
unresolved13
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5a739158-c700-47d6-b97c-2a8a6e6ee523 · outbound

This paper cites xLAM: A Family of Large Action Models to Empower AI Agent Systems.

Advancing SLM Tool-Use Capability using Reinforcement Learning xLAM: A Family of Large Action Models to Empower AI Agent Systems

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:05.975207Z

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 acdecbb0-7f67-498e-96ff-6bdf79cb8cb7 · outbound

This paper cites Small Language Models: Survey, Measurements, and Insights.

Advancing SLM Tool-Use Capability using Reinforcement Learning Small Language Models: Survey, Measurements, and Insights

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:01.164458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8591ed5-cdcf-4658-9f78-2a8da7684f8a · outbound

This paper cites A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness.

Advancing SLM Tool-Use Capability using Reinforcement Learning A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:01.491388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d357488-fbff-48f6-8760-0e1d122f737d · outbound

This paper cites A Survey of Small Language Models.

Advancing SLM Tool-Use Capability using Reinforcement Learning A Survey of Small Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:01.962412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 314604c8-6f0a-4502-825b-62a61616c468 · outbound

This paper cites A Survey on Large Language Model Based Autonomous Agents,.

Advancing SLM Tool-Use Capability using Reinforcement Learning A Survey on Large Language Model Based Autonomous Agents,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:02.412340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d0408b0-35b3-4889-bd7b-9c50d71362c5 · outbound

This paper cites Report on a General Problem-Solving Program,.

Advancing SLM Tool-Use Capability using Reinforcement Learning Report on a General Problem-Solving Program,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:05.958735Z

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 19602187-4f0b-43b8-8617-5db64f913ecd · outbound

This paper cites Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I,.

Advancing SLM Tool-Use Capability using Reinforcement Learning Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I,

Reference 7

Resolution: verified exact; doi, observed 2026-08-05T11:10:04.939574Z

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 02c4f326-0cbf-45c8-938c-23847ffc2f9e · outbound

This paper cites Language Models are Unsupervised Multitask Learners,.

Advancing SLM Tool-Use Capability using Reinforcement Learning Language Models are Unsupervised Multitask Learners,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:05.942175Z

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 a587a32d-68ca-43e2-bd58-4e71eeeb6eca · outbound

This paper cites 'Alexa, Do You Know Anything?' The Impact of an Intelligent Assistant on Team Interactions and Creative Performance Under Time Scarcity.

Advancing SLM Tool-Use Capability using Reinforcement Learning 'Alexa, Do You Know Anything?' The Impact of an Intelligent Assistant on Team Interactions and Creative Performance Under Time Scarcity

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-05T11:10:04.877547Z

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 ed528a00-fba9-4f79-a27f-f7dfd58408f4 · outbound

This paper cites End-to- End Autonomous Driving: Challenges and Frontiers,.

Advancing SLM Tool-Use Capability using Reinforcement Learning End-to- End Autonomous Driving: Challenges and Frontiers,

Reference 10

Resolution: malformed identifier; no resolver link, observed 2026-08-05T11:10:03.382282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b35464ff-239f-42da-98bb-40e31cecb7a8 · outbound

This paper cites AI Agents That Matter.

Advancing SLM Tool-Use Capability using Reinforcement Learning AI Agents That Matter

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:03.458069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b766be02-3661-4252-b449-f14034bf437b · outbound

This paper cites Toolformer: Language Models Can Teach Themselves to Use Tools,.

Advancing SLM Tool-Use Capability using Reinforcement Learning Toolformer: Language Models Can Teach Themselves to Use Tools,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:05.921779Z

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 4ccdd332-9f88-4cd7-8a73-524320bf1074 · outbound

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

Advancing SLM Tool-Use Capability using Reinforcement Learning ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:05.801284Z

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 03d53b38-d8a9-4db5-a503-f4de6e79841b · outbound

This paper cites Training Language Models to Follow Instructions with Human Feedback,.

Advancing SLM Tool-Use Capability using Reinforcement Learning Training Language Models to Follow Instructions with Human Feedback,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:05.645364Z

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 e4e28a8d-eeef-49f0-9ab0-e02b5a725ad6 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models,.

Advancing SLM Tool-Use Capability using Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:05.517883Z

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 ef53e933-8bd7-439b-86a5-6e4b5a864c74 · outbound

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

Advancing SLM Tool-Use Capability using Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:04.079194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d523fb7a-35c5-448f-a7e1-2b1709459da8 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Advancing SLM Tool-Use Capability using Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:04.188526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 831b30e6-4e91-4df4-aca9-717956e41689 · outbound

This paper cites TinyAgent: Function Calling at the Edge,.

Advancing SLM Tool-Use Capability using Reinforcement Learning TinyAgent: Function Calling at the Edge,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:05.333086Z

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 535412ba-ab79-4f6c-bc3f-16386e6dc8a0 · outbound

This paper cites Improving Small-Scale Large Language Models Function Calling for Reasoning Tasks.

Advancing SLM Tool-Use Capability using Reinforcement Learning Improving Small-Scale Large Language Models Function Calling for Reasoning Tasks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:04.397873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66a8bba0-6122-44f4-ad60-afb7355bb878 · outbound

This paper cites Qwen2.5 Technical Report.

Advancing SLM Tool-Use Capability using Reinforcement Learning Qwen2.5 Technical Report

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:04.469320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 957abe72-1b9f-4cc3-9c9b-aee939aa7add · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Advancing SLM Tool-Use Capability using Reinforcement Learning LLaMA: Open and Efficient Foundation Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:04.649930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7d9ffda-4c03-4d16-84f1-3a92e51592c7 · outbound

This paper cites Qwen2.5 Technical Report.

Advancing SLM Tool-Use Capability using Reinforcement Learning Qwen2.5 Technical Report

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:04.581497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f424498-7f7b-437d-862b-b2486113aa2b · outbound

This paper cites an unresolved cited work.

Advancing SLM Tool-Use Capability using Reinforcement Learning Unresolved cited work

Reference 2017

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:10:05.411430Z

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 5a58ba46-ed0d-4905-b6b0-141cc59415d8 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Advancing SLM Tool-Use Capability using Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:03.953925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation d9bff33a-e35d-4524-9579-123ad546bf1d · inbound

FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning cites this paper.

FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning Advancing SLM Tool-Use Capability using Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-12T21:54:13.090019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a4dd6d6-a61a-4d34-8ed9-cecbef6a54d1 · inbound

UniToolCall: Unifying Tool-Use Representation, Data, and Evaluation for LLM Agents cites this paper.

UniToolCall: Unifying Tool-Use Representation, Data, and Evaluation for LLM Agents Advancing SLM Tool-Use Capability using Reinforcement Learning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:46:01.014476Z

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.