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Paper Citation Record · LEDGER

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach

As of 10 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2608.00030.

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

pith.paper-citation-record.v1
2608.00030 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T01:47:07.368333Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

29 of 29 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved29
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 84d7ea81-c540-4a2b-b6f6-7041899f4e16 · outbound

This paper cites Query Understanding in the Age of Large Language Models.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Query Understanding in the Age of Large Language Models

Reference 1

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source=pdf_text observed=2026-08-04T01:47:04.667006Z digest=sha256:fc97bd9ddaf2b681dbae35ed01252e3e828f918c184bc4c3fece7ff8abc0e44d

Observation 3c428a47-8f82-42bf-977f-e2fd81cabda1 · outbound

This paper cites Small Language Models are the Future of Agentic AI.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Small Language Models are the Future of Agentic AI

Reference 2

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source=pdf_text observed=2026-08-04T01:47:04.816033Z digest=sha256:5cde65b147092db6b06a13645f86cfdd5914571cccd9f87a2f6a3565f3c13838

Observation 568ca4db-ee97-4e0b-849d-4bf7c2441de9 · outbound

This paper cites Broder, Marcus Fontoura, Evgeniy Gabrilovich, Amruta Joshi, Vanja Josifovski, and Tong Zhang.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Broder, Marcus Fontoura, Evgeniy Gabrilovich, Amruta Joshi, Vanja Josifovski, and Tong Zhang

Reference 3

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source=pdf_text observed=2026-08-04T01:47:04.917052Z digest=sha256:54363ebc03443c79233bb3f53cdda0b6b6c61cb7c51405773c2db10c804bf9fa

Observation 86ff3da1-17e1-4f4f-8a89-6ab70b8504d5 · outbound

This paper cites an unresolved cited work.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Unresolved cited work

Reference 4

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source=pdf_text observed=2026-08-04T01:47:04.978873Z digest=sha256:3dedce45a8f3d4e7afcddd9787357b8f302578932aa669f641a4a5ea35947796

Observation 5f0eded6-07d6-4520-8287-2f4d5031047b · outbound

This paper cites SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training

Reference 5

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source=pdf_text observed=2026-08-04T01:47:05.041912Z digest=sha256:662e01a023a87e893d676ca2e9445749757cf244679b4faa100f7c66b5658028

Observation bec712e5-d7e0-4a2a-a131-e2f5ae673d3f · outbound

This paper cites an unresolved cited work.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Unresolved cited work

Reference 6

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source=pdf_text observed=2026-08-04T01:47:05.161276Z digest=sha256:5898d58ec2dab9ee69025f959ccf02801105104d0eca0bef4c7515c95a1db3eb

Observation 5d5da7f4-60b0-445d-b150-542749e5d9a2 · outbound

This paper cites an unresolved cited work.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Unresolved cited work

Reference 7

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source=pdf_text observed=2026-08-04T01:47:05.290105Z digest=sha256:cb77a0af77faab36ceb707a686ed0c9d8c8a98bbe7374b72b2066bb95e4f5281

Observation 4167787f-eb90-453f-86ea-271275a01dc0 · outbound

This paper cites Efficient Federated Search for Retrieval-Augmented Generation using Lightweight Routing.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Efficient Federated Search for Retrieval-Augmented Generation using Lightweight Routing

Reference 8

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source=pdf_text observed=2026-08-04T01:47:05.357916Z digest=sha256:f14186908a1b8f08f162e9cf5e8d94a5df0dfa975b507dcaaf736606e40d419e

Observation 1b1901da-cf11-4cba-9206-7e4b1dc29e0f · outbound

This paper cites BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute

Reference 9

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source=pdf_text observed=2026-08-04T01:47:05.422162Z digest=sha256:720334cf667ee41a09a57343a317e3f86d950c4bd6436dcc15c5d80ea1892276

Observation 2bdcbb89-4ab5-40b4-a29a-d9886aa44c2b · outbound

This paper cites DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 10

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source=pdf_text observed=2026-08-04T01:47:05.477346Z digest=sha256:b7a74ae34eed74c93098e58caf0e7dd4d5566bad5e8d49572cebaf3f4acd6601

Observation e05e17e1-22ca-4892-b525-0cb832406e0b · outbound

This paper cites an unresolved cited work.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Unresolved cited work

Reference 11

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source=pdf_text observed=2026-08-04T01:47:05.635843Z digest=sha256:e7199d176b00aed6618edda71617f91968d25224a6f46cb45db76a55c0305508

Observation b2ea4b2e-6072-49b4-8327-739ae8376442 · outbound

This paper cites LTRR: Learning To Rank Retrievers for LLMs.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach LTRR: Learning To Rank Retrievers for LLMs

Reference 12

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source=pdf_text observed=2026-08-04T01:47:05.692892Z digest=sha256:4535343ce7ed74c9f2bdc511abcd03e029b6914fea47b4c8e53ed2ddde5292c5

Observation 20891b2e-6ec9-4627-b6bb-e4cfd5fa0d4c · outbound

This paper cites Small Language Models are Good Too: An Empirical Study of Zero-Shot Classification.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Small Language Models are Good Too: An Empirical Study of Zero-Shot Classification

Reference 13

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source=pdf_text observed=2026-08-04T01:47:05.758277Z digest=sha256:52ca2515c4c37c19ac972e358408b4cf85cc43930fa6323346ef15e306fa9c93

Observation 91fa108a-a902-46bc-9169-90d53b2fe3ea · outbound

This paper cites an unresolved cited work.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Unresolved cited work

Reference 14

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source=pdf_text observed=2026-08-04T01:47:05.818465Z digest=sha256:cb9c5ea7b347d1545bf67fc71c3981863ec387ab340a27620731330cd5902801

Observation 8f2233be-a68c-4e42-8769-18abcb4754aa · outbound

This paper cites an unresolved cited work.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Unresolved cited work

Reference 15

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source=pdf_text observed=2026-08-04T01:47:05.890384Z digest=sha256:6c44ee87616cf9ca708efd103f9aa34baddb8b4401b7a3243a199e500485189f

Observation 6369dc63-9656-481d-b9ff-d8bc01428622 · outbound

This paper cites Unsupervised Query Routing for Retrieval Augmented Generation.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Unsupervised Query Routing for Retrieval Augmented Generation

Reference 16

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source=pdf_text observed=2026-08-04T01:47:06.006371Z digest=sha256:078963c904a659aecbb5dca372af6f2c2912523b545786163c2a154184de1182

Observation f9523861-e594-4d14-bd89-9a9c9df4c2e8 · outbound

This paper cites an unresolved cited work.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Unresolved cited work

Reference 17

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source=pdf_text observed=2026-08-04T01:47:06.125033Z digest=sha256:d2762bb0ced48b92f7ee1d2acb61a415418cb54f6056c412d564278fb7303269

Observation 1fd7941b-501e-461b-b80f-ace7e90cb472 · outbound

This paper cites an unresolved cited work.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Unresolved cited work

Reference 18

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source=pdf_text observed=2026-08-04T01:47:06.190305Z digest=sha256:580b938ae72cc05787b3bd52607a2ccd892257e651cff08090250dc4e48a872c

Observation 6153d1c9-0cff-4b9f-ae3d-6e93d9fc4015 · outbound

This paper cites an unresolved cited work.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Unresolved cited work

Reference 19

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source=pdf_text observed=2026-08-04T01:47:06.283735Z digest=sha256:56e7aae3f35068a2ce21c1bbb00b29bf7f61c87f1a28a06ffbffee933eb1be1c

Observation de35d113-0d5d-4101-85fb-c45ad8328c0b · outbound

This paper cites Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026).

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026)

Reference 20

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source=pdf_text observed=2026-08-04T01:47:06.347770Z digest=sha256:e92e6bdd33b67a4cc95495203dbd2dd8ace08d2368119f18fab65aece4c9eed6

Observation c7161bd2-9cf3-4c28-8447-1b6a994db6f2 · outbound

This paper cites an unresolved cited work.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Unresolved cited work

Reference 21

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source=pdf_text observed=2026-08-04T01:47:06.489295Z digest=sha256:a2f022756eb432cedd6ca83ad1ee74ccd730e0ab6fe926456a239d6809c5f2e9

Observation c952776e-10c2-463f-a0c0-d7baf806f1ce · outbound

This paper cites FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios

Reference 22

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source=pdf_text observed=2026-08-04T01:47:06.575966Z digest=sha256:6f6ec89492bf7a717084b63f5dd0ff2a09488fe5a5202ddffce526383651522f

Observation 1afa0641-5fbf-40da-bacf-98bbea75718f · outbound

This paper cites IR2: Information Regularization for Information Retrieval.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach IR2: Information Regularization for Information Retrieval

Reference 23

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source=pdf_text observed=2026-08-04T01:47:06.653088Z digest=sha256:a4b9a437c26079e396fbe1b8c7619e4a4342ccb5860e9e948c0d22a3ed137760

Observation 7ca63376-0a8d-437b-8ca5-62023bb08de7 · outbound

This paper cites an unresolved cited work.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Unresolved cited work

Reference 24

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source=pdf_text observed=2026-08-04T01:47:06.779243Z digest=sha256:f8bdc586dd35d55e57c5bdc61f5ae928e700587a52ceaf7280b6b13bbf5cdbc2

Observation 829ac22a-ca31-46aa-8fcd-64e0ac3ade9a · outbound

This paper cites A Theoretical Analysis of NDCG Type Ranking Measures.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach A Theoretical Analysis of NDCG Type Ranking Measures

Reference 25

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source=pdf_text observed=2026-08-04T01:47:07.012322Z digest=sha256:7dd298216e5c79c5e0b1c28a9704344b68fe12ec39b58f0dd636cba94f9afad3

Observation c8eb2883-164b-4563-af06-d99077909789 · outbound

This paper cites Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start

Reference 26

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source=pdf_text observed=2026-08-04T01:47:07.116263Z digest=sha256:8b81cbfc773e1079fee027320ef86b9192a2c568214bfc0871f6a973adb1c4e9

Observation 7c1d23ee-b30b-4dae-9ad8-8aca0ceb1f63 · outbound

This paper cites an unresolved cited work.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Unresolved cited work

Reference 27

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source=pdf_text observed=2026-08-04T01:47:07.231307Z digest=sha256:241f78496bc620e1052590504f09a48464f6e31c060709a20c2dda55836ce411

Observation 1005f142-69bb-40aa-91db-d0f16ba8b618 · outbound

This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 28

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source=pdf_text observed=2026-08-04T01:47:07.368333Z digest=sha256:19646f7bb547aae33733f56bd33ffc287e8ad9b3b7fd3e23a28eda4210b25c63

Observation e7823c70-5430-448f-a01a-1fd9126100f8 · outbound

This paper cites Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering

Reference 2023

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source=pdf_text observed=2026-08-04T01:47:05.229375Z digest=sha256:9d904443cef406eeb9a841f7f30fca205280d24c316c779f0eb6e3e0f985b6dd

Pith citing papers

No inbound Pith citation observations are available.