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Paper Citation Record · LEDGER

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers

As of 16 August 2026, this Paper Citation Record lists 100 of 299 outbound references and 0 inbound Pith citation observations for arXiv:2608.06867.

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

pith.paper-citation-record.v1
2608.06867 v1

Coverage vector

measured 100 of 299 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:34:14.148105Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

100 of 299 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved100
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6f4b6f22-ad64-4f38-9d4b-2976c579d8f4 · outbound

This paper cites TimeSeriesExam: A time series understanding exam.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers TimeSeriesExam: A time series understanding exam

Reference 1

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source=arxiv_source observed=2026-08-15T14:34:13.695386Z digest=sha256:f88ebb0debf410fc24367b3acec313379fe8a3002a146c1161b6c84c9f14178e

Observation 81d4a44f-d870-43cf-8f8f-2b8a235892fb · outbound

This paper cites Large language models can be zero-shot anomaly detectors for time series?.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Large language models can be zero-shot anomaly detectors for time series?

Reference 2

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source=arxiv_source observed=2026-08-15T14:34:13.700990Z digest=sha256:12b40b2bda150fe0c4180395763f6a1f55995d3e4627afbc5e05de7e33ace81e

Observation 5f62e6e3-cfd8-43e8-8122-b77ed67c7df8 · outbound

This paper cites International Conference on Machine Learning , year=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers International Conference on Machine Learning , year=

Reference 3

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source=arxiv_source observed=2026-08-15T14:34:13.705939Z digest=sha256:7de8e2113c52a9b7e2a7237abdf6f17b01d1ca51174e99ad796b2d96e896316e

Observation a465a152-d8c8-464c-9ac1-35ed370b6649 · outbound

This paper cites an unresolved cited work.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Unresolved cited work

Reference 4

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source=arxiv_source observed=2026-08-15T14:34:13.710655Z digest=sha256:c5bff21b71c94b6c9a130d5c31712d2ee69c0a25d4bf3339e1452610c92da6d8

Observation 944b10b5-aca8-4ad0-ac64-422e2d751012 · outbound

This paper cites arXiv preprint arXiv:2509.04439 , year=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers arXiv preprint arXiv:2509.04439 , year=

Reference 5

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source=arxiv_source observed=2026-08-15T14:34:13.715305Z digest=sha256:cbc215931515aa0ca7f3de5fa80e54f3e2c5e5b9e387d4eab1615db3779eb0fb

Observation a8b2f96a-52fa-4516-a239-4856543d4773 · outbound

This paper cites arXiv preprint arXiv:2506.17587 , year=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers arXiv preprint arXiv:2506.17587 , year=

Reference 6

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source=arxiv_source observed=2026-08-15T14:34:13.720009Z digest=sha256:d39449d2b0535815b11de04b1924246131e01ae860f1cf8e6d19e0a17d7f1b6d

Observation 961cd388-b33e-4221-98ec-c7dcc643d703 · outbound

This paper cites InternLM2 Technical Report.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers InternLM2 Technical Report

Reference 7

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source=arxiv_source observed=2026-08-15T14:34:13.724995Z digest=sha256:97aa510641b841e734a22b08682cd200c40d11fcce65f9cb2a639e93b6d8e0f9

Observation 97aedda2-caea-46f9-a404-8fc1953db842 · outbound

This paper cites Sundial: A Family of Highly Capable Time Series Foundation Models.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Sundial: A Family of Highly Capable Time Series Foundation Models

Reference 8

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source=arxiv_source observed=2026-08-15T14:34:13.730230Z digest=sha256:4daefaf8c4276929ea7bfa93c4433f5ffebd068f240ac59d5312bdfda76b750e

Observation 71fa850d-e571-44d8-8ff6-dc9db7e21895 · outbound

This paper cites TimesBERT: A BERT-Style Foundation Model for Time Series Understanding.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers TimesBERT: A BERT-Style Foundation Model for Time Series Understanding

Reference 9

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source=arxiv_source observed=2026-08-15T14:34:13.734418Z digest=sha256:a59a85dff184c6b8665024ad25ed8638d6fbb05eca109fed33daf49456ea3420

Observation 02b9fc33-a812-45a6-aab8-8098befe6bcf · outbound

This paper cites GPT-4 Technical Report.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers GPT-4 Technical Report

Reference 10

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source=arxiv_source observed=2026-08-15T14:34:13.738875Z digest=sha256:3c9f591104d040dad873eb3558cfba617ba153ae7fd946edd052a8b20831e619

Observation af1fc3be-79aa-4d08-8b52-46bad20e6f98 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 11

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source=arxiv_source observed=2026-08-15T14:34:13.743133Z digest=sha256:7b343844a06b1e4f88449f9320e120b173b915555934ddc6cc7a9651e718499b

Observation 0648b390-113e-4f87-9c7a-13a4e9e9d8f4 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Advances in Neural Information Processing Systems , volume=

Reference 12

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source=arxiv_source observed=2026-08-15T14:34:13.747260Z digest=sha256:b1944e858cef57133ac8a8c21187adb015edef4bf5937499a6a82556327feefc

Observation c77ffca8-0fe9-4228-a9eb-227ad3a5a124 · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 13

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source=arxiv_source observed=2026-08-15T14:34:13.751124Z digest=sha256:6650e25239a16f9511700169963c65d18b146848c0a6586ee3d07231f4cf3a35

Observation b9d67c0b-9db7-4710-8ffa-2191b40ea00e · outbound

This paper cites Proceedings of the 31st ACM International Conference on Multimedia , pages=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Proceedings of the 31st ACM International Conference on Multimedia , pages=

Reference 14

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source=arxiv_source observed=2026-08-15T14:34:13.754982Z digest=sha256:567c704f57b63aff2a979b1f6c347cef9dee9c5e0fa4a087eaa4951b15687b29

Observation e7dcf239-d3bf-495c-bfd4-ac7c48e52908 · outbound

This paper cites Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models

Reference 15

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source=arxiv_source observed=2026-08-15T14:34:13.759136Z digest=sha256:7786e196fe63f44cf59586d008c61566bfd4e10400a0f100a71c772c8345791b

Observation 56cf9930-ef3d-4565-b0f8-0e8a08600e73 · outbound

This paper cites CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era

Reference 16

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source=arxiv_source observed=2026-08-15T14:34:13.762969Z digest=sha256:4610d9dbb7946343214581c821b69c41f888fa11af1e6b066a6cfd52347f23c2

Observation ecc0608a-7ee5-49ba-892b-7c60058f7c88 · outbound

This paper cites Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe

Reference 17

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source=arxiv_source observed=2026-08-15T14:34:13.767133Z digest=sha256:0041ba4d1c7db4b76d170f5850f5a60249f2a17da7b2c2f970b21da23c285ad5

Observation 745bb46f-2a84-43b6-a8f3-2c032435dcae · outbound

This paper cites 2025 , howpublished =.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers 2025 , howpublished =

Reference 18

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source=arxiv_source observed=2026-08-15T14:34:13.771188Z digest=sha256:a684ca093cdc9075ea461cf784518bd315933c3141cd8c211fe1e33b83af8ae8

Observation 50632e31-cb6d-4d6d-a2fe-e86526926ec3 · outbound

This paper cites GraphPlanner: Graph Memory-Augmented Agentic Routing for Multi-Agent LLMs.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers GraphPlanner: Graph Memory-Augmented Agentic Routing for Multi-Agent LLMs

Reference 19

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source=arxiv_source observed=2026-08-15T14:34:13.775102Z digest=sha256:fbb9559118923fef01741fcc6c55933626a003644018afd8acd2c3dab9383087

Observation 23863807-3bfa-4d7e-9552-8a16fdb77875 · outbound

This paper cites Self-Distilled RLVR.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Self-Distilled RLVR

Reference 20

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source=arxiv_source observed=2026-08-15T14:34:13.779742Z digest=sha256:f9d8f58354929fcefb53c40342d5d5aa0b104c1893a82aaa39bce74be322c386

Observation 86a17000-37f0-4db5-84bc-8682feb37788 · outbound

This paper cites TimeOmni-VL: Unified Models for Time Series Understanding and Generation.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers TimeOmni-VL: Unified Models for Time Series Understanding and Generation

Reference 21

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source=arxiv_source observed=2026-08-15T14:34:13.784404Z digest=sha256:d2a8c58a6a7b85cdb2e62a3925919d015e2ad263b6cb69a0ae0cb3dad8508123

Observation 96ae199e-74c9-4d7a-b940-7a37402b94d9 · outbound

This paper cites Self-Distillation Enables Continual Learning.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Self-Distillation Enables Continual Learning

Reference 22

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source=arxiv_source observed=2026-08-15T14:34:13.789049Z digest=sha256:e5324a526134ed6f9ecb1891d4c1fb5488c1c82287d722a45d424468a412bb89

Observation c480fef4-a7a2-4659-b17f-8ca517ea724a · outbound

This paper cites Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Reference 23

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source=arxiv_source observed=2026-08-15T14:34:13.793806Z digest=sha256:53a5c6c2e98ee9caf74accf588457dbff283944d35a317ca9980b059c0ec61c8

Observation bdec523a-0b58-45bf-940d-85c14827a712 · outbound

This paper cites Advances in neural information processing systems , volume=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Advances in neural information processing systems , volume=

Reference 24

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source=arxiv_source observed=2026-08-15T14:34:13.798435Z digest=sha256:d4f29091db192e1b450421990e5a5619868f2df3dbfbbe9619b33184b7b95341

Observation b617c8be-bed4-4e09-86b2-0ad4a5fb7780 · outbound

This paper cites Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models

Reference 25

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source=arxiv_source observed=2026-08-15T14:34:13.802896Z digest=sha256:129b1354eafae0949d1b2d5b7e0da16c6e6c90258a2b3d1ec7c36d2e384b4e92

Observation df8e4783-9266-4dc1-b241-b1e6c207be21 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Advances in Neural Information Processing Systems , volume=

Reference 26

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source=arxiv_source observed=2026-08-15T14:34:13.807613Z digest=sha256:a3dd0fa4f680907008ede5deb3e9c99460bd92503194e97c457bb5d314d46fc1

Observation c632c510-a835-460d-914d-3025105ded6e · outbound

This paper cites Advances in neural information processing systems , volume=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Advances in neural information processing systems , volume=

Reference 27

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source=arxiv_source observed=2026-08-15T14:34:13.811974Z digest=sha256:32836610c3580839846516caf9056362e15384ae63893f1c8a38263506542c5c

Observation 38576c61-7b7c-4993-b50d-22bd90182515 · outbound

This paper cites Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark

Reference 28

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source=arxiv_source observed=2026-08-15T14:34:13.816283Z digest=sha256:880de25dd1688de73d09cf110a414c075f930a52010a6319f41d001a9a4d33a9

Observation d600ee01-84f8-4ea3-b771-53a20462cdca · outbound

This paper cites Reasoning with Language Model is Planning with World Model.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Reasoning with Language Model is Planning with World Model

Reference 29

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source=arxiv_source observed=2026-08-15T14:34:13.820910Z digest=sha256:8b81f086e7ee01675983b37546eb06db74cb9485af6c19ce2dfa34e72a089dad

Observation 59fab8be-621a-44aa-a952-24cd58915c22 · outbound

This paper cites LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting

Reference 30

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source=arxiv_source observed=2026-08-15T14:34:13.825526Z digest=sha256:f625fa365e5ae97063e06bed68449ea9e6fdd023f1fd77a8339f3e21e276c507

Observation 94119c22-39be-4395-ad64-56f534e0edf0 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Advances in Neural Information Processing Systems , volume=

Reference 31

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source=arxiv_source observed=2026-08-15T14:34:13.830390Z digest=sha256:cf1c26b5d6c988d2045f63d75725a2852d7cd72b431c803825866153f0419573

Observation b4ac3b20-6324-4cd1-95fa-40da071c0b4e · outbound

This paper cites Deep Generative Models for Health Workshop NeurIPS 2023 , year=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Deep Generative Models for Health Workshop NeurIPS 2023 , year=

Reference 32

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source=arxiv_source observed=2026-08-15T14:34:13.834285Z digest=sha256:6dffa08c1a0a7d70a9bb4a3c5e4fe32bc76cfebeb2197a0ba497b22fe0404baa

Observation 91e48c1c-bf25-42d7-af7f-75195be148b1 · outbound

This paper cites D-Bot: Database Diagnosis System using Large Language Models.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers D-Bot: Database Diagnosis System using Large Language Models

Reference 33

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source=arxiv_source observed=2026-08-15T14:34:13.838275Z digest=sha256:1fb813935d6550100e4e9621217421e692d4f8b5466b7ea68394b75bf262e213

Observation 9dfeab3d-35dd-403d-b305-c8ee33a0223e · outbound

This paper cites Proceedings of the 33rd ACM International Conference on Information and Knowledge Management , pages=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Proceedings of the 33rd ACM International Conference on Information and Knowledge Management , pages=

Reference 34

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source=arxiv_source observed=2026-08-15T14:34:13.842440Z digest=sha256:e7125ab23a8d35548d2d8c4019e90663c393925090387ff0cd60c1f235d9ebaa

Observation be47d54a-f86d-45e5-947c-7a13bc8fd905 · outbound

This paper cites Constructing Large-Scale Real-World Benchmark Datasets for AIOps.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Constructing Large-Scale Real-World Benchmark Datasets for AIOps

Reference 35

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source=arxiv_source observed=2026-08-15T14:34:13.846400Z digest=sha256:8668a08de144e6dc99e51bb109e75e0d6351233ee680783f3950aa350e0d91f4

Observation 31547e83-3f80-4b8a-a4dc-99590057947b · outbound

This paper cites Neurocomputing , volume=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Neurocomputing , volume=

Reference 36

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source=arxiv_source observed=2026-08-15T14:34:13.850614Z digest=sha256:fe9ab69799dc48149678520f3d9d0fdf5127cf6c041c529c848f08e72d43dbf5

Observation 54d6439c-a339-42ef-8225-14aca5203ee7 · outbound

This paper cites Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , pages=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , pages=

Reference 37

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source=arxiv_source observed=2026-08-15T14:34:13.854919Z digest=sha256:3bfc2daf311a9685bcda7390ee31a8ca3850a20069be52855b3b42c867e6262a

Observation 19593289-20b4-4f66-8873-d57d06839181 · outbound

This paper cites Language Models Still Struggle to Zero-shot Reason about Time Series.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Language Models Still Struggle to Zero-shot Reason about Time Series

Reference 38

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source=arxiv_source observed=2026-08-15T14:34:13.858954Z digest=sha256:08adea29c426c75e7ae6b50b11005e02e911991b1bda9f7b09f88f02c8bf74a2

Observation e1b7c18e-2ac7-44e5-87fc-944fdb4b854b · outbound

This paper cites Proceedings of the International Conference on Internet-of-Things Design and Implementation , pages=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Proceedings of the International Conference on Internet-of-Things Design and Implementation , pages=

Reference 39

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source=arxiv_source observed=2026-08-15T14:34:13.864381Z digest=sha256:2c9af694bfd7b7ec184710802c214712ebf8825fb2f31ea7f71171389956f4dc

Observation 8111bd86-27e6-4b90-868f-f722e4538e24 · outbound

This paper cites Qwen2.5-VL Technical Report.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Qwen2.5-VL Technical Report

Reference 40

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source=arxiv_source observed=2026-08-15T14:34:13.868229Z digest=sha256:0621357a70873a6caed277740b6006324bd7f7eb15f3341fa6142e51d548de77

Observation 9e1bd896-a17d-496a-9b71-847dc3180b7b · outbound

This paper cites Applied soft computing , volume=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Applied soft computing , volume=

Reference 41

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source=arxiv_source observed=2026-08-15T14:34:13.872213Z digest=sha256:c0d17b05c66e5308bf1885f911bf89afa19fe300c928c9f2b3bf018ab8e3b81f

Observation 639da235-5e85-49fe-a02e-4bfd9579effe · outbound

This paper cites MM-LLMs: Recent Advances in MultiModal Large Language Models.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers MM-LLMs: Recent Advances in MultiModal Large Language Models

Reference 42

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source=arxiv_source observed=2026-08-15T14:34:13.876129Z digest=sha256:3f3e1eb4337a5ccf53c906a37ae58ae8b777ce87ae5326224d2c21751b2f2d71

Observation b5242ac2-f3a9-40ec-8215-4a101319f714 · outbound

This paper cites National Science Review , pages=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers National Science Review , pages=

Reference 43

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source=arxiv_source observed=2026-08-15T14:34:13.880203Z digest=sha256:949b12ad88290f8c18ca3ef0fc5e37bde77bbf8936f4a1a3cf0120633c9c5add

Observation bd405ae3-3925-4618-ab6e-d25f0b1e70b3 · outbound

This paper cites Advances in neural information processing systems , volume=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Advances in neural information processing systems , volume=

Reference 44

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source=arxiv_source observed=2026-08-15T14:34:13.884692Z digest=sha256:bd7ef86ec9d560b3ef5cbd7f9535dd48714ca0c371da187c6cf9eaffe961812c

Observation c37ae0bb-5acd-46c0-b489-1bc92b89e70d · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 45

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source=arxiv_source observed=2026-08-15T14:34:13.888512Z digest=sha256:83e87eb407206ae9707c96c3f5651207dce20fe4b6e8356d6dd62dc7d71797a7

Observation fa9d77fa-f839-4498-82f0-ab1d65c9860c · outbound

This paper cites 2015 , publisher=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers 2015 , publisher=

Reference 46

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source=arxiv_source observed=2026-08-15T14:34:13.893283Z digest=sha256:94a44122a876d2faeb7de5b98e0914b79170cf2a5e383f56edd5490536694f1b

Observation 36af9f33-a6a5-4a24-9589-455dc94abc78 · outbound

This paper cites International conference on machine learning , pages=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers International conference on machine learning , pages=

Reference 47

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source=arxiv_source observed=2026-08-15T14:34:13.898156Z digest=sha256:41441ae8f28f4de9f0556822dedbc0ce3659dd517267f1994f70e2344c4fd3e8

Observation fb2b8ca7-0ca0-4e3b-8fe7-1813392c9366 · outbound

This paper cites TempoGPT: Enhancing Time Series Reasoning via Quantizing Embedding.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers TempoGPT: Enhancing Time Series Reasoning via Quantizing Embedding

Reference 48

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source=arxiv_source observed=2026-08-15T14:34:13.902844Z digest=sha256:092b6d451355920b4b9dbcbb93011ec986eace28c677d58e54ab526f41834d05

Observation 22df4f2e-6751-4101-ac23-df932573c19c · outbound

This paper cites Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models

Reference 49

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source=arxiv_source observed=2026-08-15T14:34:13.907748Z digest=sha256:bca61143345a2a01023e2ee7edcfe7ece1c5f4737d89d789c6e6facccddb886c

Observation a36e4e90-f810-42d2-b059-fde6f7609300 · outbound

This paper cites Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

Reference 50

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source=arxiv_source observed=2026-08-15T14:34:13.912486Z digest=sha256:1a806a2f7052622a9b2482918199d7aaddc3a8ecb3fd598b4b3dd4f67ec99b52

Observation fd64852d-4807-4191-84ca-07caf3fb181f · outbound

This paper cites VideoChat: Chat-Centric Video Understanding.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers VideoChat: Chat-Centric Video Understanding

Reference 51

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source=arxiv_source observed=2026-08-15T14:34:13.917266Z digest=sha256:8516dba01b937debaf7c7acb7b6160466cf384772a91ca76f5a73ce17426cf0b

Observation 42524474-e48a-4353-98aa-712a3fa68352 · outbound

This paper cites Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models

Reference 52

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source=arxiv_source observed=2026-08-15T14:34:13.922142Z digest=sha256:613368f47de52088f3b11059bc60f4cb7c8763369770338ff571860ff30b91cb

Observation 4e217318-d86b-4f0a-b019-e9b3a4cc5be3 · outbound

This paper cites AudioPaLM: A Large Language Model That Can Speak and Listen.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers AudioPaLM: A Large Language Model That Can Speak and Listen

Reference 53

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source=arxiv_source observed=2026-08-15T14:34:13.927000Z digest=sha256:b1255bce987e8af8ed48e44424a01b43602c10daf27459cf61310b6dc911069f

Observation 446abe0d-b485-47fb-803f-070ee03b801f · outbound

This paper cites Proceedings of the ACM on Web Conference 2024 , pages=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Proceedings of the ACM on Web Conference 2024 , pages=

Reference 54

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source=arxiv_source observed=2026-08-15T14:34:13.931894Z digest=sha256:e52ddce1f7c56f23ad911bce3595a23e8442c90c8bceaddd63d996c8195ae810

Observation 1f7df8cf-2728-4bf1-8c18-a1d12eb1e681 · outbound

This paper cites IEEE Transactions on Knowledge and Data Engineering , year=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers IEEE Transactions on Knowledge and Data Engineering , year=

Reference 55

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source=arxiv_source observed=2026-08-15T14:34:13.936993Z digest=sha256:76b650923106a4d22136238638b803fe96afd74940da0e9c275554f2edb609e0

Observation 8dc81133-6830-41bf-b004-2ec40077d67d · outbound

This paper cites MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct

Reference 56

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source=arxiv_source observed=2026-08-15T14:34:13.941747Z digest=sha256:dd394d3fe143af9507019791f38413f1847923a9f0e8168524f79322a1b9d1f3

Observation a8cc5276-804b-44d0-bf1b-dcf61f441fff · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 57

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source=arxiv_source observed=2026-08-15T14:34:13.945879Z digest=sha256:190225b618f2a26d685f72af647005887fd3a7435077c434db183ea869002064

Observation b173dd4c-b464-4aed-9d7c-fa714a0c79f3 · outbound

This paper cites Towards Time Series Reasoning with LLMs.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Towards Time Series Reasoning with LLMs

Reference 58

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source=arxiv_source observed=2026-08-15T14:34:13.949989Z digest=sha256:5f4448ca6ab9d35d79a9fed3b6e8dd13cf12f27eb530fafef831bf81cc89bd8e

Observation f72f1490-6fd3-4a8c-84d4-e21e00aa230a · outbound

This paper cites Position: What Can Large Language Models Tell Us about Time Series Analysis.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Position: What Can Large Language Models Tell Us about Time Series Analysis

Reference 59

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source=arxiv_source observed=2026-08-15T14:34:13.954206Z digest=sha256:89c6671d43da2d45d43aca50dff58401a00ba3a557e64855f557bd2414c33c18

Observation 842235c7-1c11-4a64-a51a-e9cc91877fe6 · outbound

This paper cites Forty-first International Conference on Machine Learning , year=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Forty-first International Conference on Machine Learning , year=

Reference 60

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source=arxiv_source observed=2026-08-15T14:34:13.958412Z digest=sha256:2b4482659cfe7c097d9dc1a523abefc4dcd371c4813930f2e8010a1302d1d9b9

Observation 2529b167-f6e7-487c-9be7-170d3fac0d95 · outbound

This paper cites arXiv preprint arXiv:2412.03104 , year=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers arXiv preprint arXiv:2412.03104 , year=

Reference 61

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source=arxiv_source observed=2026-08-15T14:34:13.962892Z digest=sha256:c81ac6ebaa4e4d404955efbef2cc950c580bd7601e2a2337d1ef367b09da2279

Observation 3deee0e8-a083-40bd-b45d-c3fe35b32479 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 62

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source=arxiv_source observed=2026-08-15T14:34:13.967357Z digest=sha256:b235ee35cbc3d541fb13c30c58f6cc247a398c1037846d553165b89b076476c6

Observation dc4233da-e815-4afd-8144-63ba50e21146 · outbound

This paper cites Kimi K2.5: Visual Agentic Intelligence.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Kimi K2.5: Visual Agentic Intelligence

Reference 63

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source=arxiv_source observed=2026-08-15T14:34:13.972221Z digest=sha256:d128ffb9e51817efa4cce0fdae78259dd608ee3f83e14f20369d9a9824200ac9

Observation 2469365f-9833-4c71-b11e-75857c21b245 · outbound

This paper cites Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

Reference 64

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source=arxiv_source observed=2026-08-15T14:34:13.977506Z digest=sha256:86b6365e6f95124a6f7b493b9e1ad77e319ddd5911d9aabd4001fb42cac3e553

Observation 2527f96d-77d4-4823-b19d-18f154981ab1 · outbound

This paper cites gpt-oss-120b & gpt-oss-20b Model Card.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers gpt-oss-120b & gpt-oss-20b Model Card

Reference 66

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source=arxiv_source observed=2026-08-15T14:34:13.987257Z digest=sha256:e0220ec48adc1387d24e5d29619f588163d1f532258156aeba7fddcd74ddd4ff

Observation ce1ec195-506c-43fa-b4b4-0668a75df3c3 · outbound

This paper cites Qwen3 Technical Report.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Qwen3 Technical Report

Reference 67

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source=arxiv_source observed=2026-08-15T14:34:13.992190Z digest=sha256:926c57dbcb3ebccc2fb0ef5f9f3664963554038e181e1735646bcb3eda52d351

Observation 6e4b9247-15c8-4f79-8747-0ab86b08a233 · outbound

This paper cites 2025 , note =.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers 2025 , note =

Reference 68

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source=arxiv_source observed=2026-08-15T14:34:13.996791Z digest=sha256:3b6a43a13b9af7a39f70b3a0ee502f24e77d04dd3fd8d4d1339c295d496a9547

Observation 93c277da-1259-44d4-87b8-3413a182bbc3 · outbound

This paper cites Proceedings of the web conference 2020 , pages=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Proceedings of the web conference 2020 , pages=

Reference 69

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source=arxiv_source observed=2026-08-15T14:34:14.001999Z digest=sha256:70079e4d0e2e51ebbdacc0c5971abf0523ee5018b376223141df73ce821fcf4b

Observation 97b9b77e-776c-41d8-932f-f1ce430486dc · outbound

This paper cites FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance

Reference 70

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source=arxiv_source observed=2026-08-15T14:34:14.006876Z digest=sha256:5ced1e104fdf22ce8ca65d6f30cafa8df07e445ffe8de9f3123ff1d1d88346af

Observation 77646a70-ad3e-4ad3-8455-dad627cb86d0 · outbound

This paper cites Qwen3-VL Technical Report.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Qwen3-VL Technical Report

Reference 71

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source=arxiv_source observed=2026-08-15T14:34:14.012263Z digest=sha256:bc5de526f1fa541be7afcee9ed609eb8c6fdd9fb8f4afdf57e4995b64c617841

Observation 04a1aac2-e366-4094-844e-4a5e8d493422 · outbound

This paper cites GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Reference 72

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source=arxiv_source observed=2026-08-15T14:34:14.016811Z digest=sha256:4960736e41b821c9879a0b9d676ba25c2bae75c24583ec3dade5151498560d74

Observation 9b2dd9f3-1258-4de2-9305-202fab62fb8a · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Semi-Supervised Classification with Graph Convolutional Networks

Reference 73

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source=arxiv_source observed=2026-08-15T14:34:14.021137Z digest=sha256:d063aae76e70dec6b187c1da7cd871fd893fd5d9229709589832b990c865bd97

Observation 4c3b564f-0824-42d5-8b46-a2562811ff71 · outbound

This paper cites arXiv preprint arXiv:2601.19151 , year=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers arXiv preprint arXiv:2601.19151 , year=

Reference 74

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source=arxiv_source observed=2026-08-15T14:34:14.025578Z digest=sha256:d3be525fea460f653c38653aa90c1ded6668d515e85787c82163bd92be41d222

Observation 7b83d6a5-bad3-47a1-842e-b2926fc4cc41 · outbound

This paper cites Large Language Model Routing with Benchmark Datasets.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Large Language Model Routing with Benchmark Datasets

Reference 75

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source=arxiv_source observed=2026-08-15T14:34:14.029724Z digest=sha256:a3eba1cfff97ab15fb0b1b31df989eda6eaabd2828efc48b7a0010a4fcc2ca4a

Observation 48f74192-fd96-4f3d-b625-8ed4a7cccd0f · outbound

This paper cites arXiv preprint arXiv:2503.01013 , year=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers arXiv preprint arXiv:2503.01013 , year=

Reference 76

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source=arxiv_source observed=2026-08-15T14:34:14.034171Z digest=sha256:1a25dacd4da046f944f1ebe4ff4cd5bc1eb6db6fbac595b94c003656917d1d5a

Observation db266b3b-9c65-42d1-a612-a83456a79d7b · outbound

This paper cites arXiv preprint arXiv:2502.01477 , year=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers arXiv preprint arXiv:2502.01477 , year=

Reference 77

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source=arxiv_source observed=2026-08-15T14:34:14.038288Z digest=sha256:499f8262e6c110aa98eced690c1f2aa27a2cd20fb143360a3d0ec7b12b552193

Observation b87fe88b-e7e5-4cee-a0b5-13edbb5f5e52 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Adam: A Method for Stochastic Optimization

Reference 78

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source=arxiv_source observed=2026-08-15T14:34:14.043074Z digest=sha256:85dfdf58fd806f91c6921747c11885fc5db419235d430ffe57f27c90d8d31505

Observation 4cab9b34-6cd5-48d2-805b-a65a9d380f7e · outbound

This paper cites North American Chapter of the Association for Computational Linguistics , year=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers North American Chapter of the Association for Computational Linguistics , year=

Reference 79

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no resolver link, observed 2026-08-15T14:34:14.047589Z

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source=arxiv_source observed=2026-08-15T14:34:14.047589Z digest=sha256:f6fbcbd92e707c026116124e1a083502ce6140793271d7a200e3b7b685c0e3eb

Observation e74d0d2e-a522-48c3-a397-e3f4020468b0 · outbound

This paper cites Cost-Effective Online Multi-LLM Selection with Versatile Reward Models.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Cost-Effective Online Multi-LLM Selection with Versatile Reward Models

Reference 80

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no resolver link, observed 2026-08-15T14:34:14.052657Z

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source=arxiv_source observed=2026-08-15T14:34:14.052657Z digest=sha256:46565ead106a79aa207dd74a0b3005716a07cb1f1f358bcf078b6ce7d7db3e22

Observation 6ba49549-87cb-41d6-b3ec-63c4ccdc8b11 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Fast Graph Representation Learning with PyTorch Geometric

Reference 81

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no resolver link, observed 2026-08-15T14:34:14.056811Z

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source=arxiv_source observed=2026-08-15T14:34:14.056811Z digest=sha256:b4773e05f1f675eb5096aa1d75a6dbc033c331f2fef13c74befae82a7716af1e

Observation cb5e87ef-d044-4a9b-95ef-86e536650b8c · outbound

This paper cites IEEE Signal Processing Letters , volume=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers IEEE Signal Processing Letters , volume=

Reference 82

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source=arxiv_source observed=2026-08-15T14:34:14.061042Z digest=sha256:bc76db0723a37c98ef13fed4b960a8771d79b23f86a684bab381afec5f998021

Observation 95b916f5-d218-4eba-b37f-e747464ccede · outbound

This paper cites Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia , pages=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia , pages=

Reference 83

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no resolver link, observed 2026-08-15T14:34:14.064718Z

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source=arxiv_source observed=2026-08-15T14:34:14.064718Z digest=sha256:8a20658e8a26bf4b364ef77505cd0985ecbd0c74980980f9b138bb085a6b2e93

Observation c9ccba60-3419-4e79-9c4b-b199a6e47100 · outbound

This paper cites Machine Learning for Health (ML4H) , pages=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Machine Learning for Health (ML4H) , pages=

Reference 84

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no resolver link, observed 2026-08-15T14:34:14.068433Z

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source=arxiv_source observed=2026-08-15T14:34:14.068433Z digest=sha256:bee77ae82837670ccf95108bf136b5ac796fde01f11e57c09289c2b1f3ba851f

Observation 7f022c0c-7f8a-4ce9-ab1c-74f5ab26b7dd · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Advances in Neural Information Processing Systems , volume=

Reference 85

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no resolver link, observed 2026-08-15T14:34:14.072796Z

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source=arxiv_source observed=2026-08-15T14:34:14.072796Z digest=sha256:c92fb975a90b822754a3028ec842b341deb71695c09178f0897858bdd0c85712

Observation 56cb9a75-7825-4d7b-b822-99a1d384a367 · outbound

This paper cites Joint conference of the information retrieval communities in europe (circle 2020) , volume=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Joint conference of the information retrieval communities in europe (circle 2020) , volume=

Reference 86

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no resolver link, observed 2026-08-15T14:34:14.076757Z

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source=arxiv_source observed=2026-08-15T14:34:14.076757Z digest=sha256:1cbf2dfd08f0c86d3a5d734481dc5071e513f743961b322440d6c73bffccb54c

Observation fbbff99e-73b1-41fc-848d-bd1c4ed22adb · outbound

This paper cites Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models

Reference 87

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source=arxiv_source observed=2026-08-15T14:34:14.081570Z digest=sha256:f1a33a473c013c6b5ce200133122c11220d4b2a80c0ab8fc4e1e46e747f4eef6

Observation eca5686f-3257-4c4c-bcf2-5e9b1966a268 · outbound

This paper cites International Journal of Machine Learning and Cybernetics , pages=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers International Journal of Machine Learning and Cybernetics , pages=

Reference 88

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no resolver link, observed 2026-08-15T14:34:14.086392Z

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source=arxiv_source observed=2026-08-15T14:34:14.086392Z digest=sha256:c3871f7d5cfe6bb32d0554edd1c4cfed3e6de031a43ea07b5612bbddb07f02fe

Observation d159909d-ce88-41e2-91d2-e9d74b716d5e · outbound

This paper cites LLMLight: Large Language Models as Traffic Signal Control Agents.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers LLMLight: Large Language Models as Traffic Signal Control Agents

Reference 89

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no resolver link, observed 2026-08-15T14:34:14.090911Z

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source=arxiv_source observed=2026-08-15T14:34:14.090911Z digest=sha256:04c042cf9e4e3102d6eb0ff87fc13e87c4de7d6e5d7e12349bf1bb1d689487ef

Observation 5a974381-efc9-4203-b4a3-90451ab6eb51 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Advances in Neural Information Processing Systems , volume=

Reference 90

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no resolver link, observed 2026-08-15T14:34:14.095756Z

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source=arxiv_source observed=2026-08-15T14:34:14.095756Z digest=sha256:86e7f90277c94e6cc08abe95d94cd4d34de1732241f182f36c2828d5e839f6f3

Observation 5b02731d-400d-4303-832c-d32f5f3718be · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers ReAct: Synergizing Reasoning and Acting in Language Models

Reference 91

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no resolver link, observed 2026-08-15T14:34:14.100933Z

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source=arxiv_source observed=2026-08-15T14:34:14.100933Z digest=sha256:1e360b8684136ce55617b31db5316d8232f44f517c3e7eb1bca8ab4cbd93e20b

Observation a25e9ea1-1faa-4692-8b69-85b1d5ccd887 · outbound

This paper cites Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 92

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no resolver link, observed 2026-08-15T14:34:14.106019Z

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source=arxiv_source observed=2026-08-15T14:34:14.106019Z digest=sha256:ca5da71f0a1918c77c9707dcf6cf1b43a5b88441b5fc21125f805b562cb1ecab

Observation 20a82560-3f89-4fc9-b405-8853021d17c0 · outbound

This paper cites See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers

Reference 93

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no resolver link, observed 2026-08-15T14:34:14.111432Z

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source=arxiv_source observed=2026-08-15T14:34:14.111432Z digest=sha256:11b58ec708a08876c45154c00ad010083275ec3ee72d7f14a05db821f4468713

Observation 08f89cd3-d37f-4aa9-abd7-1e4ee904fd43 · outbound

This paper cites Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review

Reference 94

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no resolver link, observed 2026-08-15T14:34:14.116826Z

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source=arxiv_source observed=2026-08-15T14:34:14.116826Z digest=sha256:6ea6a2bbfca7da184e9d0d9fc8b106ee3715c2c8193e408a220af733c0afdbcb

Observation 887289b8-b4b7-4b54-b827-187644554e8e · outbound

This paper cites LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 95

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no resolver link, observed 2026-08-15T14:34:14.122036Z

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source=arxiv_source observed=2026-08-15T14:34:14.122036Z digest=sha256:300a556ef2fbee5770a311941b895603494f4581446d2a1e0b6d4cb617cdbb0e

Observation ec8466e4-83b5-4d05-9ce6-a7ed756f37f9 · outbound

This paper cites Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis

Reference 96

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no resolver link, observed 2026-08-15T14:34:14.127063Z

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source=arxiv_source observed=2026-08-15T14:34:14.127063Z digest=sha256:f73266885f19281af26fd5aaea1396ee72582cefc7da66898d4355eb238ea410

Observation b2d722c1-b634-4cff-895b-1abfa30a70bf · outbound

This paper cites arXiv preprint arXiv:2503.16858 , year=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers arXiv preprint arXiv:2503.16858 , year=

Reference 97

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no resolver link, observed 2026-08-15T14:34:14.131716Z

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source=arxiv_source observed=2026-08-15T14:34:14.131716Z digest=sha256:b65df290e9edba8a32082c283d1e069c8c3ad312b87f09a72e34f1731c193d0e

Observation 3d99c300-3be7-47f3-b909-e959e3b2ab5f · outbound

This paper cites Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection

Reference 98

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no resolver link, observed 2026-08-15T14:34:14.135667Z

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source=arxiv_source observed=2026-08-15T14:34:14.135667Z digest=sha256:cbf68162b08f0c662bd37915d9bd7bbd120377edb0f7fcaf95d9836c8e8a5c4f

Observation c3305516-90a0-40b7-8bad-edcfaf8e74e6 · outbound

This paper cites VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters

Reference 99

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no resolver link, observed 2026-08-15T14:34:14.139996Z

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source=arxiv_source observed=2026-08-15T14:34:14.139996Z digest=sha256:6346d2d6683c7900e375d0476040f7fdcce0c4acb5011f5bc350152742ed79f3

Observation 5d605d38-4835-4c41-8e00-e41656af8b5b · outbound

This paper cites TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 100

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no resolver link, observed 2026-08-15T14:34:14.144137Z

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source=arxiv_source observed=2026-08-15T14:34:14.144137Z digest=sha256:ccb27a14dfa5581893fd37f89a6094dc536aed1e84648783f4307643c3c32b6f

Observation 50dce52d-9a1e-49d8-aacf-cd0cdef97504 · outbound

This paper cites Advances in neural information processing systems , volume=.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers Advances in neural information processing systems , volume=

Reference 101

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no resolver link, observed 2026-08-15T14:34:14.148105Z

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source=arxiv_source observed=2026-08-15T14:34:14.148105Z digest=sha256:87dfec27ac7be2d6bb859f02a81a086f9f2502e094f814c8881db420035330b6

Pith citing papers

No inbound Pith citation observations are available.