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Paper Citation Record · LEDGER

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning

As of 18 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:2607.08940.

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

pith.paper-citation-record.v1
2607.08940 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T07:51:15.259452Z

measured 48 of 48 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:34:14.984415Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T14:34:15.131489Z

Reference resolution

47 of 47 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved46
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9236b78a-69af-4b58-a81c-2414b1d9caf3 · outbound

This paper cites Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs

Reference 1

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source=pdf_text observed=2026-08-02T07:51:11.404084Z digest=sha256:6de482acf0bc45c1a3c4bb2fa304f4c5f347463b829285edf4ab9d057e540d45

Observation b8774cf8-e36d-4298-b103-767e4a6cf5e0 · outbound

This paper cites ✓ Qwen3-VL-8B(Text+Vision) Ans: (D)— The data shows a clear progression through multiple trend phases.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning ✓ Qwen3-VL-8B(Text+Vision) Ans: (D)— The data shows a clear progression through multiple trend phases

Reference 3

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source=pdf_text observed=2026-08-02T07:51:15.180148Z digest=sha256:006ba6f937cb6ca340df0216ec7168b2de2926740c2e3efab18051cdad503d12

Observation bf7f08f0-f680-4a02-b9ac-cf8563d664c9 · outbound

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

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 5

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source=pdf_text observed=2026-08-02T07:51:11.920174Z digest=sha256:c9e1e7c94d175ed8b4a54363f83e8bbbc0fedc6423302aae5c78dc7e186f0df6

Observation d94e7079-f9ce-49ff-ad8e-23d8d0ae334d · outbound

This paper cites Mtbench: A multimodal time series benchmark for temporal reasoning and question answering.arXiv preprint arXiv:2503.16858,.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Mtbench: A multimodal time series benchmark for temporal reasoning and question answering.arXiv preprint arXiv:2503.16858,

Reference 6

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source=pdf_text observed=2026-08-02T07:51:12.013364Z digest=sha256:c57690b65d035cec1f92ec04a676e362f8367234865a8435e6b5b7f422397c68

Observation 23909df9-a53f-4ef1-ab81-a0061a003ec1 · outbound

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

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters

Reference 7

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source=pdf_text observed=2026-08-02T07:51:12.170031Z digest=sha256:f5618704bc3f565ffbb28dc215a14e23fd067ebe165af2cebccb6c56c500c609

Observation d8f93125-a4b0-41b5-afd2-83c97395bbed · outbound

This paper cites Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing

Reference 9

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source=pdf_text observed=2026-08-02T07:51:12.437343Z digest=sha256:9883087e59fa3d07192a26a8b63cadc5efd9ad1305e7d48a66544119cef06699

Observation 5e7b9e10-cf53-478e-9925-80902aea1c1b · outbound

This paper cites GraphRouter: A Graph-based Router for LLM Selections.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning GraphRouter: A Graph-based Router for LLM Selections

Reference 10

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source=pdf_text observed=2026-08-02T07:51:12.547698Z digest=sha256:a0c724cb5079ccb7c9656f915aa3dc30d5d0eaf3dc7e18e1e8cfa1a3214ed26a

Observation aaa7683f-f078-425b-b0ea-da31256f1bad · outbound

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

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning GraphPlanner: Graph Memory-Augmented Agentic Routing for Multi-Agent LLMs

Reference 11

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source=pdf_text observed=2026-08-02T07:51:12.700912Z digest=sha256:fe4b67382d35a300211bb838db4bf914dc602ae8b4e5747cfc9f73c0b94f221a

Observation c329a9d8-8bf1-4a1d-a4b4-225a08e85a94 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Fast Graph Representation Learning with PyTorch Geometric

Reference 12

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source=pdf_text observed=2026-08-02T07:51:12.817955Z digest=sha256:d1d807dd7569c368733681a1c8666711adc43bc64d5f66a71392bede159162d6

Observation e51b15f6-da45-4615-aae3-555785b151c7 · outbound

This paper cites TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models

Reference 14

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source=pdf_text observed=2026-08-02T07:51:13.031013Z digest=sha256:f0d79c60deb68dd4be4a1ba218807ed26fd38bd1feb2f2cc4579682c1a0806aa

Observation 0ac8d810-e1af-42a5-9183-5ed9acf488e2 · outbound

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

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning TimeOmni-VL: Unified Models for Time Series Understanding and Generation

Reference 15

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source=pdf_text observed=2026-08-02T07:51:13.110516Z digest=sha256:8ec6e07a163a788faf5c88d0858b496b76dbdbce7d296a6e702ce71e1aea1c0a

Observation 8b6b2fb9-b281-44eb-a99d-c0dc21899489 · outbound

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

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Reasoning with Language Model is Planning with World Model

Reference 16

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source=pdf_text observed=2026-08-02T07:51:13.172119Z digest=sha256:89a65dce210d2194dccb272019e66f968879bc23c77f8bf9332f8d412692bc2c

Observation 9ea61da3-5cc7-4352-a16b-f2ee2571349d · outbound

This paper cites Arcmemo: Abstract reasoning composition with lifelong llm memory.arXiv preprint arXiv:2509.04439,.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Arcmemo: Abstract reasoning composition with lifelong llm memory.arXiv preprint arXiv:2509.04439,

Reference 17

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source=pdf_text observed=2026-08-02T07:51:13.224527Z digest=sha256:6d5777762f079a742f337a1649dfc4819b3dd7e6e708c77ed283a9659ecf6f9c

Observation f34caf6a-99e1-40ed-9320-702c458f8809 · outbound

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

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Reference 18

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source=pdf_text observed=2026-08-02T07:51:13.275479Z digest=sha256:01707ea88669e2994b98d9ba9b3496f8897710a58d3bff6e78039126e22940a1

Observation 9e51d9d2-dacd-4f6e-abda-bffefa71dfa9 · outbound

This paper cites RouterBench: A Benchmark for Multi-LLM Routing System.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning RouterBench: A Benchmark for Multi-LLM Routing System

Reference 19

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source=pdf_text observed=2026-08-02T07:51:13.336001Z digest=sha256:dccef5960f19ca600a925dd74de9f18a4ed0555f5d15170f15b49afd98abba83

Observation a840040a-2bc8-4978-850a-16856819d3a7 · outbound

This paper cites Heterogeneous graph trans- former.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Heterogeneous graph trans- former

Reference 20

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source=pdf_text observed=2026-08-02T07:51:13.390327Z digest=sha256:85e6a578f7e914b7a650a27159dff657963ad10dde7626242c3ec27b14f54e45

Observation 03d67b81-d5a5-4de0-89a0-f929bd5d5b9b · outbound

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

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 22

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Observation 382de849-6e48-4b74-97ee-89ec4a29320b · outbound

This paper cites Reasoning with Sampling: Your Base Model is Smarter Than You Think.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Reasoning with Sampling: Your Base Model is Smarter Than You Think

Reference 23

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source=pdf_text observed=2026-08-02T07:51:13.573397Z digest=sha256:f836e06e8d71b821edce880b477e41978edb45a5e3d646c96905a8907457f403

Observation d066b844-2144-4baf-9dca-fe7cf398bfe0 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Adam: A Method for Stochastic Optimization

Reference 24

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Observation 9ddccd15-0f5c-4b25-a6fb-129adaeb6c13 · outbound

This paper cites Qinghua Liu, Sam Heshmati, Zheda Mai, Zubin Abraham, John Paparrizos, and Liu Ren.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Qinghua Liu, Sam Heshmati, Zheda Mai, Zubin Abraham, John Paparrizos, and Liu Ren

Reference 27

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source=pdf_text observed=2026-08-02T07:51:13.943504Z digest=sha256:f51a60487b0e19c4c0ea2b4100bed7ab7ea81fa0397d5a2b9cca50074fe27cfc

Observation 56f75502-927e-4305-ba18-efcf6c45e5f4 · outbound

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

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Language Models Still Struggle to Zero-shot Reason about Time Series

Reference 29

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source=pdf_text observed=2026-08-02T07:51:14.067963Z digest=sha256:cb63ac3b57ff3ff2c030625cea4d4bd9f15ce7925575e09c4c7ef357675f293e

Observation 8bd44b6a-a62a-4be7-bf4b-2bf8d207ca47 · outbound

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

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models

Reference 30

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source=pdf_text observed=2026-08-02T07:51:14.115517Z digest=sha256:5edaa2422742b428b303d9d9e53f404ebeb0a26ad56950d0bf4fdff410b44105

Observation 0177097a-7f18-4c92-8602-ca128615150c · outbound

This paper cites RouteLLM: Learning to Route LLMs with Preference Data.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning RouteLLM: Learning to Route LLMs with Preference Data

Reference 31

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Observation 6e60d083-c47b-4ed0-8504-3850ea0bcf77 · outbound

This paper cites Time2Lang: Bridging Time-Series Foundation Models and Large Language Models for Health Sensing Beyond Prompting.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Time2Lang: Bridging Time-Series Foundation Models and Large Language Models for Health Sensing Beyond Prompting

Reference 32

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Observation 83fbe965-1f97-443c-be53-ccb8d86110cd · outbound

This paper cites Large Language Model Routing with Benchmark Datasets.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Large Language Model Routing with Benchmark Datasets

Reference 34

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Observation add633f4-cf08-4ded-8ae0-b783d117cfad · outbound

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

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Kimi K2.5: Visual Agentic Intelligence

Reference 35

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source=pdf_text observed=2026-08-02T07:51:14.445961Z digest=sha256:8c27d620e56097c8be099779c3d746393f5f408e3e7174f9af89f9ebbf1f8ae4

Observation 91e0ff98-08cd-4529-87cf-384f8bcd9229 · outbound

This paper cites Chatts: Aligning time series with llms via synthetic data for enhanced understanding and reasoning.arXiv preprint arXiv:2412.03104,.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Chatts: Aligning time series with llms via synthetic data for enhanced understanding and reasoning.arXiv preprint arXiv:2412.03104,

Reference 36

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Observation 615964aa-d62b-465a-b227-d4656a9769b0 · outbound

This paper cites Qwen3 Technical Report.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Qwen3 Technical Report

Reference 37

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source=pdf_text observed=2026-08-02T07:51:14.579552Z digest=sha256:32660d3fb057d7e91e19268c4a6a2ed87b4dc042c3d5763ed99e3ff1ed9c7776

Observation e948ad28-7085-42d4-9a90-a50f049638f8 · outbound

This paper cites Self-Distilled RLVR.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Self-Distilled RLVR

Reference 38

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source=pdf_text observed=2026-08-02T07:51:14.629934Z digest=sha256:28e47a99cd0bea44e4704b4e0b7e071486cc173527bc02cd03c22881297f282c

Observation 772c211b-e20b-46ea-bea7-b47d568d47e4 · outbound

This paper cites Ts-reasoner: Aligning time series foundation models with llm reasoning.arXiv preprint arXiv:2510.03519, 2025a.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Ts-reasoner: Aligning time series foundation models with llm reasoning.arXiv preprint arXiv:2510.03519, 2025a

Reference 40

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source=pdf_text observed=2026-08-02T07:51:14.736822Z digest=sha256:96c5e40d59e7d860bab50931b5e4f83db8b3e0321de168f59646094926e07ba3

Observation f00a7f7a-cd43-4b67-9ff7-14a765116739 · outbound

This paper cites Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 41

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Observation 0692c5d8-152e-4a8e-892d-2fb2bf959292 · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 42

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Observation 17466f7e-26fb-4f5e-9f7e-d8db267ef154 · outbound

This paper cites an unresolved cited work.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Unresolved cited work

Reference 43

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Observation 2fe29b0f-efd7-4739-a06d-be27d5174e71 · outbound

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TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Unresolved cited work

Reference 44

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Observation b0d0f3ea-14f1-46cf-84e1-8b4d3283bc00 · outbound

This paper cites Given a query withNchannels, we plot each channel as a separate subplot, with a shared x-axis (time/index) and individual y-axes.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Given a query withNchannels, we plot each channel as a separate subplot, with a shared x-axis (time/index) and individual y-axes

Reference 45

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source=pdf_text observed=2026-08-02T07:51:15.090321Z digest=sha256:8ed90d076ab0973f1d904c035e507730dff14c8d1aab92aa9ceb006bfd59711c

Observation 7e8fa8fa-dc29-42c7-9192-a47bc85574c8 · outbound

This paper cites In contrast, all three VLMs correctly identify the trend ordering from the visual plot, where the curvature of each regime is di- rectly observable.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning In contrast, all three VLMs correctly identify the trend ordering from the visual plot, where the curvature of each regime is di- rectly observable

Reference 47

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source=pdf_text observed=2026-08-02T07:51:15.259452Z digest=sha256:3fc2cb1283f8f736c6fd67ca560d5494afde8d59e1fa351150932cbe1a3235e0

Observation 81ddd3e4-a26e-4aef-a1eb-a845e58486d0 · outbound

This paper cites Self-Distillation Enables Continual Learning.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Self-Distillation Enables Continual Learning

Reference 1990

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source=pdf_text observed=2026-08-02T07:51:14.333017Z digest=sha256:bb4998f941e0ac832c679485d4e37d1944b4567a23a4237e872ccda8dda39810

Observation 33793d2f-40c3-439b-a4da-f89f1394bd8b · outbound

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

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Semi-Supervised Classification with Graph Convolutional Networks

Reference 2014

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no resolver link, observed 2026-08-02T07:51:13.771917Z

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source=pdf_text observed=2026-08-02T07:51:13.771917Z digest=sha256:e6bddc917474f265c6fb3da5d7d8bb47c39677d9047afef6561a08b4359bd6fb

Observation 55c6cffe-e696-441e-8f33-d8e1e0dc1bbc · outbound

This paper cites Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement

Reference 2016

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no resolver link, observed 2026-08-02T07:51:13.857375Z

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source=pdf_text observed=2026-08-02T07:51:13.857375Z digest=sha256:8ecbbc809f80195e13be7ff9a692630adb6ef24e63f7d9912902d928ebfa568a

Observation 6af9c81d-b149-435e-977c-19245db82119 · outbound

This paper cites The Llama 3 Herd of Models.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning The Llama 3 Herd of Models

Reference 2019

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source=pdf_text observed=2026-08-02T07:51:12.974025Z digest=sha256:7e45d07cb57eb3646409ede33979ea5ac139294d2912ebf9e991cc23efaf4709

Observation 2c47c604-b89d-461b-ab31-ecc2592a7064 · outbound

This paper cites Timexl: Explainable multi-modal time series prediction with llm-in- the-loop.arXiv preprint arXiv:2503.01013,.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Timexl: Explainable multi-modal time series prediction with llm-in- the-loop.arXiv preprint arXiv:2503.01013,

Reference 2020

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no resolver link, observed 2026-08-02T07:51:13.445232Z

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source=pdf_text observed=2026-08-02T07:51:13.445232Z digest=sha256:f707d9302d01ef6c45e3ac493e26e4a31d1c8bab6b56c8c5d09ed07f9a43610a

Observation aee0e35d-c416-4889-bee9-984dd4de2fb0 · outbound

This paper cites Time Series Analysis for Education: Methods, Applications, and Future Directions.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Time Series Analysis for Education: Methods, Applications, and Future Directions

Reference 2021

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source=pdf_text observed=2026-08-02T07:51:14.002876Z digest=sha256:1b3c76917f701faff5070ef4bc22f8fcfeae7380ab59630e6d872f70a07ca5d6

Observation 10ff0623-4e5b-4213-8f30-0c0d18132869 · outbound

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

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Reference 2022

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source=pdf_text observed=2026-08-02T07:51:14.686446Z digest=sha256:b8f5911a91b62c6a192e66aa21ae791608fc30e13fd717b9fc3d8656142496fa

Observation 38d8f79e-e8f2-492b-8647-fd294ba3e52b · outbound

This paper cites Qwen3-VL Technical Report.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Qwen3-VL Technical Report

Reference 2023

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source=pdf_text observed=2026-08-02T07:51:11.550407Z digest=sha256:cfe2286c6bd67623babb2202f0d1d8e5709ad7f404c5bc4251c92e95c4e58f33

Observation 16d03c42-6903-4f86-abba-d6780f0b0e98 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 2024

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source=pdf_text observed=2026-08-02T07:51:11.857581Z digest=sha256:51d182afb3f1d237ff3258fe88e4cdbe6ad9933ffd71827ceeab95449a980f1b

Observation c865e19c-c78c-4253-81f7-b53482cb0993 · outbound

This paper cites Graph of thoughts: Solving elaborate problems with large language models.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Graph of thoughts: Solving elaborate problems with large language models

Reference 2025

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source=pdf_text observed=2026-08-02T07:51:11.722284Z digest=sha256:543e279eeb6f09ccbf215d86c4851cb27af38c33801225355591b17518962626

Observation b4a218ac-051f-442a-ad70-85b44b4e1470 · outbound

This paper cites Under- standing different design choices in training large time series models.arXiv preprint arXiv:2406.14045,.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Under- standing different design choices in training large time series models.arXiv preprint arXiv:2406.14045,

Reference 2026

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source=pdf_text observed=2026-08-02T07:51:12.324778Z digest=sha256:cb42c51cd01b7b5123db5c16518addcb261854ba0be458d993a3a864e9b49c6c

Pith citing papers

Observation 6bb4c2ac-6f26-4673-925f-54846524fc96 · inbound

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers cites this paper.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning

Reference 293

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local_arxiv, observed 2026-08-15T14:34:15.138056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T14:34:14.984415Z digest=sha256:e0773f6c31b75a023a5d835c80878aba253a666ec2a7c0b0b99c92966abec871