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Paper Citation Record · LEDGER

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

As of 18 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 2026-08-18T06:34:40.430872+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:da1daed7ac2556f302c38c528cf8a425f7fe366da549022421af1e2bbc995e91

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=sha256:1ca8771fb24bf699812ea6d4790645e8a6643f31c08a31b0fe4e66555fa5c277

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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no resolver link, observed 2026-08-15T14:34:13.705939Z

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source=arxiv_source observed=2026-08-15T14:34:13.705939Z digest=sha256:188600de612d329717945305e0efab8ffd04da94ee82512f6bf5fa9a8fc7be8f

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:f27393a6a5c363334c7c28a13ad15e6e38df6f27e66e2ab3bd8ade6c60f62516

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:b15512e94f2d6aa7c9c817c5c8b00493f3fb5fc002ba80f8c316fc3feb316266

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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no resolver link, observed 2026-08-15T14:34:13.720009Z

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source=arxiv_source observed=2026-08-15T14:34:13.720009Z digest=sha256:ff93e5ec03a8b11f48cfad1de496b74105a68ca08cd223cb8053fdbe06457c39

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=sha256:6aaaca4d064d1ba2ff180943300c8a7d3b255f64d39e31ab1d5ad773908f8e61

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=sha256:809e7ebc9aa6b0aacde47917b7d8fd1a0b6890cfa5fd04e471d6bebac28ddd18

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:acd581a405a84d72642d8b64e0aa910d6abcf74c2e503803d611c518b672d70e

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:aaea3188ba772f787ffe66de67e778d21fcde3a5cbf5eba6905002d3a33bf308

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:d30a342c255aacf581fb221aaece7bd94b68d8a42296a4100d44fc710de7a1dd

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=sha256:79874f78598101fe4b0b2976ba39f776926fc329010256c7400f7c6d743c1fbf

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=sha256:1fb362d422c74b61ead64d892c6c6e73caa421c79e734bc79e78c6646589a486

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:d26199a3128b0da8eaccc0c7a00cc65ddfaba7ac3409fee1ade7cfcd8429dd91

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:d082982c4869d0b085efb1798e6378584237a13579b4640184e321239282ce57

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:dce87fedaa1c36e780fd7bd8d9dbe705d630fecb660ed1aeb66eed4e2fd66667

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=sha256:3adeb876f6654c8c788de78bfedd9f0d7efd87f9ac93b537d0da911d17ddd9ec

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=sha256:35c8af648abeaa0463d08c21918d78161fa8bdcac016e2d1ef3df1d2e085cc04

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=sha256:6c2c50f4a5ec486b8ecd464c7ade74e8758595f1e25b35ce55fb47e5b757d0f1

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=sha256:045534090ed2229836cc484799fd6818711c6bf0c5027d611e40db0736471f9c

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=sha256:610f70fd7b56158be4efcf2662b437b66af149b8466094023a0c63099b0469d1

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=sha256:923158b705bec720dcceecc3080b848ace17c38431ab0c1146eb9d521fd71e18

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=sha256:47afcf96d708515c826200373f403c985069c6b6ba35f76b845b3e73a69856bf

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=sha256:58c73a81bd9f6fc42bd04eff69b15d020977b046391f6c6249fe3e7cf8691f04

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=sha256:1456c0ee7995b792ed22079e9a4fd07344ff9b0044e08357d7f4402073a4f0ff

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=sha256:20bf087c1becedad2b44222fd4bf8717a1ed3bed325b07078331bc2ad9ba7f16

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=sha256:0aa75480b07b01086eeb0d6091ec2221f71b240e2c42cd7be5b11c1ca2ed2b1e

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:f830b31d7b48c6da957ccf224b4890ecf248d83e8a5b27f475a206c3118ee40a

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:dc4e05ef9a2de0e4a5c12772a6339d5560c61866d7e69445d2331a8272b7be8a

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:f744560648b20460146ddfed05e1e268aa93d894977fe81f67ab3ef132a3a418

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=sha256:0adef92f4d83d129425db2fbdd66a5a65842eef5a42741d7b57f0513227af86e

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=sha256:5cdc20271e090f202bbcec22295fca7d94e272ae70cb96722013283c20c36335

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=sha256:8e45603c829f41f6eb2b2cf82aff7889af748b907932a8c41c0e6a6220d4b6e0

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:cce772a5bc7d18d417bbc58f5fbc0cc286fd81ed89c54b094c64757e8d60800d

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:ca1b91f7429a309bb858c9d75083cdd63e52969b76f3708c7295ef0e340d86dd

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:db1b1350ba94b428fa0ac2a3ef6c254d3f1f9ae85c169d62c462033a27a6374c

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=sha256:6394924f7955a8c978514e558c738169021d4d55874462f746ebd59f5dca292a

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=sha256:68561d546dbe7088b0ddd3fead663e8caec4024d44cd04b73d7af4e8f8040cdc

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:c0627f568cb9ddd741e4b4c76429b1cd517b20db2ef1a817a0ba9b1a87ffa040

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=sha256:1b1f08f871257668d090cafe262b1dd5f986efb3b5bfafbd96de9ee6ad389130

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:d1864206f856276af595bcc8ea16163d0ed9db14359fd1dcb33aec13bf8032d2

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=sha256:1623c5a90de4f296392e238be9f13baeb131c3a4f02fdde57139122d3e6bab09

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:af25c53b32e7fa36c21434fd8898ce372a27c1dfe70627b85c953a6f2c05532b

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:de29c47d788a83e8cca5a4e61431ad08ae3d07a82922bb2d651bab5d9a0a5ad6

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=sha256:3357932d5d899c2fa3305fc6b64b4018c68ecaa457be23fc13f4816ce97d004d

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:b513095fa20230a11cccf86630fa45d6048320925d706f8a05790d637e1a040f

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=sha256:7e31d5c4ebf5d39f0abfd0b8aa0c6d36fcc3bfecf3f6678c5768e6f1fc52ed7d

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=sha256:10abd23527cba9bfe7a344327cbd8c801bc42fc5a1116ceac4112e8e637ab9d1

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=sha256:2a67b7d6bc295e3addc2b0596e2337a9552916ce9e57e4a25f6655823065c0e9

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:a16ad746b60d257dc0bcc42f8cead84a3fc86e85d8b695f1fde70e950e5e51c4

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:f26f6cd3b22fa21381bd33fa27c7772cafc8d470f63362390df931a18a5b52e7

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=sha256:95fff40b4b849094781f50ce99259df7e9738fc24c4250813fb755228075a7f1

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:c0811910c6b0193da4d92e39c55c883ab526cc344da6b9fe538f97cf572255b1

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:fe835131eeaf9daee2967e82fd98dfddc8767a829b9666f480f92bd2d9214122

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=sha256:944d1de79e79668bccdfdd579c3d851a24f8e4107f194fdc996f15d459b0fe96

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=sha256:3bb8490bf0ae7e484ba9dfbf92d985c6b84ee4d38f474b694130252e452c996d

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:b83e9272706f76095800c1cdbbf45fecb2feb309a0dbd548d48c07dfc4a61061

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=sha256:14e36de0445c3156e980a4072b4a618f1bc7a9bf5b83fe631b77c7fcda4c01b1

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:ab961882b85123c48e9d3fab0e7b1968c7a94ba072d540ee64e96a1d80a292fa

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=sha256:782849c9853d463887e66828e7725d383e20d12c0f6cc23b509555a7ed9bebdf

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:d6f6c232e6a5b964b3ede11bef0705a44f8568e0c34ce53b8c261749654e69a3

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:de6b7019af7f60f634b6e962f84c4cdaae1069e1c505b87c3bee086a2fc3dfde

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:b9a0a184cb9d01509edb988821f8a8757eb705ce8b528d84fc5908b33d0ddb54

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:e089d78b360a38427c8b0d642c49b7560bf1062bdb8b78d1d357e012db2d92f2

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=sha256:86f4a255a8f75defe82e61c50919bfd3e7b86a43f1f819863bb299f92016523c

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=sha256:73bb8eaf790b9a87c6a4a3638ccc24eb230ace44f5ba6981f43c4fb3359aed2f

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=sha256:289b62f27481dda712d2f2b4724aee87100c82529708358e75c822ad08390c40

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=sha256:7fb0dd9b3e4a07399623d4f82dd263b0ab4db5aa7fd6d74e82b2a53ef88bdea6

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:f0ebabe4da790f969c79c992679bcdb582d308e06072a598c02374264606cf3f

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:a2321911ed227a0dffbc7ca3c65d9af722e15d69cb1adbbc6826fb9f85f86fe4

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:dfbacead224ed26f6c3fbe74db869b7953e6035ab671c8524768689ba70868f3

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:b3b83a614c3baa65f52dffbb1252f2eb88e2fa56e594f093d09e862d6b4a7a71

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=sha256:4f48be8a4527add53376496c62626eff65708df2f1356cd2fa72ac9e69584595

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:f478a67cae592d4817d7bbfffea1034d57583e8a7cab9b15372a1e7c1ac7a8c9

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T14:34:14.034171Z digest=sha256:9904a2c38f592bd103eb9570d7677c2d932da0ae87d17fc85f6d0e53ccad9629

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:a68cba73a86dcd43b943d2fc639567d5f2bf6e12f2e42d996bb4625a7984494a

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=sha256:4ab4fd6509a09b4ec7dcf6f0454c2b7d65ff0de5e9ca74dff159d2960a871ff8

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:cd2769d25c0ee4c614e6adbd5005b7740de8fa29fffb68b9ada2aabcda4ca228

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=sha256:0a479bcdf77158678f4728f4d5ee5b5cd1868b3d77d13436a0c488aff3f5a78c

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=sha256:72824b79e3f80376d9de32fda016ca7c98da435a695bca84e29890cbbebaec54

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=sha256:8a247e29649a12de646d877fc98d68e99a60140c1244468be8ee9c1b9ee971e5

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=sha256:5233eb0d1b0e5bdc09d974a321b55feb6c28e66b1f36297dde28a714500982b3

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=sha256:8dea90782b3702bde50e9ef8ceef213f03316ea329977d4118ddbe37d8d63e6c

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=sha256:4d5c519348399cbdda9a4781aa5825a8bbd3d5edb61de1fb6f9a8bc81698e93a

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=sha256:3e39a00b2066f73984a1026e7a3f29de1dd6ba5024471330b72e520b5e491915

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:ee51ec9aac3ead8628b8d171989d262f82ef35f2feb6804e06a95e6a7a4ff600

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=sha256:20cae7ca71b0c951c84ec4e680404147f8a8df91a282d0bb2bc2ee88f3a516c2

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=sha256:2c37751dffa157e70e620a00a2395de4fcdc250c92ecd522093d59324a141dd8

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=sha256:83e2e93b19a1cc637193435483e99d3c9238de09c40126a7eb1bda24dedbfec0

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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source=arxiv_source observed=2026-08-15T14:34:14.100933Z digest=sha256:fd2ca1e93d0d427592c677a6040f99f0a4e52c0d2e7f819bba8f7b03b7c66009

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:d40ee8457a02f2cfb44d0785dffebcc258d82d94aac35d86209aca60a9e5be28

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=sha256:048088650acede7a313ae6bbe24550c23ca9e52ca43771384cca918d2229c700

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=sha256:3af051c1b4d5d80b2b2036c0e90eeed694cd0b4c200b465e970b0654a28245dd

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=sha256:958989b151905befcdbc62ad0ee3391edcce17983ffd82771cf0e523189e30a3

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=sha256:7e872dbfb2ab06b1bd6f4944a66c65c4c332f23fe4dd6c7e633046ff583b32e5

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=sha256:6629c5ca13300be70bc201d96b1448cafc33281470d62c4e3ec66fa2162eccb6

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:eb591e2dbd874fdf7d8a069a87eaf1b72b388f40f1c701d5cb9d0774b2ff8f37

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=sha256:3977b20f23f740ce0d6ed4416fdad00efee59d99d242c28e4e1f386d25fb4622

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=sha256:6993d497e9ea5bf98c92fb3c009a7b27f17491086d9e9bb1348d6fb61dd67568

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:bc69fa73d30874c0669026d15ce4ef889b8b8615de0e70ff3104cb342d71c7ec

Pith citing papers

No inbound Pith citation observations are available.