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Paper Citation Record · LEDGER

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems

As of 8 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 3 inbound Pith citation observations for arXiv:2507.21276.

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

pith.paper-citation-record.v1
2507.21276 v1

Coverage vector

measured 74 of 74 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:03:46.238908Z

measured 77 of 77 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T12:16:10.904456Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T16:04:52.780559Z

Reference resolution

74 of 74 outbound references displayed

  • verified exact5
  • verified fuzzy44
  • unresolved25
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 177d1b42-c342-480a-88ef-819c21259556 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Chain-of-thought prompting elicits reasoning in large language models,

Reference 1

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Observation b12168bc-f0b5-49b5-9af6-8045c652343a · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Tree of thoughts: Deliberate problem solving with large language models,

Reference 2

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no resolver link, observed 2026-08-06T13:03:36.097595Z

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source=pdf_text observed=2026-08-06T13:03:36.097595Z digest=sha256:c139069375257dc7342250eb867f6a475c5795fe17a81f86e230c009d8053612

Observation e11d4212-2a6a-4316-ba6d-82c0daeea771 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Judging llm-as-a-judge with mt-bench and chatbot arena,

Reference 3

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raw_fallback, observed 2026-08-06T13:03:56.743274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 20a54e55-2ca8-44f8-8656-281595118176 · outbound

This paper cites Test- time training with self-supervision for generalization under distribution shifts,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Test- time training with self-supervision for generalization under distribution shifts,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 2baf4354-e480-4f73-a687-1a363a2de7a7 · outbound

This paper cites The Surprising Effectiveness of Test-Time Training for Few-Shot Learning.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems The Surprising Effectiveness of Test-Time Training for Few-Shot Learning

Reference 5

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source=pdf_text observed=2026-08-06T13:03:36.469588Z digest=sha256:de73cee0da54237dbd109308f46a78ef8fa76796511f93ffcbddf814d63f48b5

Observation 43f81271-6de4-4091-b2bc-d12b07540d97 · outbound

This paper cites Machine learning model training over time,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Machine learning model training over time,

Reference 6

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raw_fallback, observed 2026-08-06T13:03:56.365685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 8a004639-5a0c-45bf-9703-cf6f9986f584 · outbound

This paper cites Multi-model Machine Learning Inference Serving with GPU Spatial Partitioning.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Multi-model Machine Learning Inference Serving with GPU Spatial Partitioning

Reference 7

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local_arxiv, observed 2026-08-06T13:03:47.613837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation c0be8ee7-1c90-4dfe-9855-a575f804eebf · outbound

This paper cites Optimized training and inference of hugging face models on azure,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Optimized training and inference of hugging face models on azure,

Reference 8

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:36.806663Z digest=sha256:909611bd0d3a268df32d138ce2edd2c3056f84b60ece66c5a9ebc4c669cfede2

Observation 799ebf13-ca01-4435-8dac-21f908d25fca · outbound

This paper cites Train a model with amazon sagemaker,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Train a model with amazon sagemaker,

Reference 9

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raw_fallback, observed 2026-08-06T13:03:56.165680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:36.944082Z digest=sha256:0353ef9bcc42ca976ac9eec11cdee1f1ba831d3e241dc376e6b1deb665e6de53

Observation 9e3680e2-0995-4e85-996e-bb8f3ac9e61a · outbound

This paper cites Serving heterogeneous machine learning models on Multi-GPU servers with Spatio-Temporal sharing,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Serving heterogeneous machine learning models on Multi-GPU servers with Spatio-Temporal sharing,

Reference 10

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e4b54b5a-1239-442d-9f64-41da400ef0d1 · outbound

This paper cites Efficient memory management for large language model serving with pagedattention,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Efficient memory management for large language model serving with pagedattention,

Reference 11

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source=pdf_text observed=2026-08-06T13:03:37.323683Z digest=sha256:ddf6ca0e5cf1cba86dbe1d8de26ae918298d03848d9282b563055bbdf3288725

Observation 3b9b2ad3-bb39-4cbb-a98d-1e1f0f425b63 · outbound

This paper cites Llumnix: Dynamic scheduling for large language model serving,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Llumnix: Dynamic scheduling for large language model serving,

Reference 12

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:37.553106Z digest=sha256:44f3874475b62066f1c8ae6eba9b75f03ff268891c849ea56c21cef6f95ac277

Observation 2e54d2b0-a006-4aea-b32f-5bf99d7d6851 · outbound

This paper cites {DistServe}: Disaggregating prefill and decoding for goodput-optimized large language model serving,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems {DistServe}: Disaggregating prefill and decoding for goodput-optimized large language model serving,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:37.722838Z digest=sha256:df7bd6e842eb631142a4edc1da3d06a7b75ed5b4c00db67571ced6e5c04f7b15

Observation 5e4f8bd2-b03a-41c1-a841-cbf4ab8559ec · outbound

This paper cites Taming {Throughput-Latency} tradeoff in {LLM} inference with {Sarathi-Serve},.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Taming {Throughput-Latency} tradeoff in {LLM} inference with {Sarathi-Serve},

Reference 14

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source=pdf_text observed=2026-08-06T13:03:37.858647Z digest=sha256:0cf5d49f423e8aed69e9cf1a2295bbafffba6432e227b5e340af30c1a2b0a27a

Observation 2618bdb3-1e07-4b1c-ac46-8ba37440dc27 · outbound

This paper cites {dLoRA}: Dynamically orchestrating requests and adapters for {LoRA}{LLM} serving,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems {dLoRA}: Dynamically orchestrating requests and adapters for {LoRA}{LLM} serving,

Reference 15

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:38.004123Z digest=sha256:821034f2b838662a272359307789c0e97c9287e6004af0e6f70686549a8686c8

Observation 2aa118aa-acff-4367-9dfb-9143caec705a · outbound

This paper cites {ServerlessLLM}:{Low-Latency} serverless inference for large language models,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems {ServerlessLLM}:{Low-Latency} serverless inference for large language models,

Reference 16

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:38.092134Z digest=sha256:98accd394142320006e6c5409f018c8069576979eef2915a13f13bb24553cd70

Observation c20a0451-bb56-44c3-8e7d-a83ed5e9d262 · outbound

This paper cites Orca: A distributed serving system for Transformer-Based generative models,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Orca: A distributed serving system for Transformer-Based generative models,

Reference 17

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source=pdf_text observed=2026-08-06T13:03:38.187808Z digest=sha256:af8341b18cbf4ed64392e85dbaf59d53f15748c5ca62c9685ddcd9007dd68e3f

Observation 5b4ceab4-6b39-48e1-8a9a-62e743bea3fd · outbound

This paper cites AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks

Reference 18

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local_arxiv, observed 2026-08-06T13:03:47.160939Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:38.361567Z digest=sha256:0c72beab61650337e92e61eaaec718133c578da0a4f7332ef70cc7586247199a

Observation 6701e1d6-dde6-41e0-8ce1-724e10dbb90d · outbound

This paper cites Pipedream: generalized pipeline parallelism for dnn training,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Pipedream: generalized pipeline parallelism for dnn training,

Reference 19

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source=pdf_text observed=2026-08-06T13:03:38.551490Z digest=sha256:d3632a7be922f5a1a8ebb725b868170e41815e61266ed152a89987fca69d1681

Observation ef37bd81-b7b8-4d1d-b645-7e295918b987 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 20

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source=pdf_text observed=2026-08-06T13:03:38.747768Z digest=sha256:0bed65fe83a94b39921cf73b4bebf0b0def95acb05d6bae17a58fbd8f572a715

Observation 0a7a3cb3-3862-4ccf-bfd2-7bdf2fefd259 · outbound

This paper cites Varuna: scalable, low-cost training of massive deep learning models,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Varuna: scalable, low-cost training of massive deep learning models,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:38.982766Z digest=sha256:1f12f82f20fb14cd5d3a3b2e2c7523e8fb814400e60de627c858d23cc802142e

Observation e73fc8bc-5437-4f6a-8469-f286dd7a341e · outbound

This paper cites {EnvPipe}: Performance-preserving {DNN} training framework for saving energy,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems {EnvPipe}: Performance-preserving {DNN} training framework for saving energy,

Reference 22

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:39.085310Z digest=sha256:d95553d0157267964deed708561b5d30e9dfb5a4f457b9f630346c6fc052a071

Observation d33eb98e-cb67-4e37-be45-faa7d410c8d0 · outbound

This paper cites {AlpaServe}: Statistical multiplexing with model parallelism for deep learning serving,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems {AlpaServe}: Statistical multiplexing with model parallelism for deep learning serving,

Reference 23

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 41ebbb8a-601c-4fea-a59d-d2e11e5e4f3d · outbound

This paper cites Merak: An efficient distributed dnn training framework with automated 3d parallelism for giant foundation models,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Merak: An efficient distributed dnn training framework with automated 3d parallelism for giant foundation models,

Reference 24

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation c7de026b-bd1c-4557-8642-b5c46516a0f3 · outbound

This paper cites Gpipe: Efficient training of giant neu- ral networks using pipeline parallelism,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Gpipe: Efficient training of giant neu- ral networks using pipeline parallelism,

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:39.527881Z digest=sha256:ce027fa0ecf6ef872f940a003f3913e213e2535a70cc1985c172bd6bb66ca366

Observation 38659fbb-96cb-41c6-9abb-510b9a159129 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 26

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Observation 6ef29b9d-8232-45e2-b465-319bc18765b2 · outbound

This paper cites Lmsys-chat-1m: A large-scale real-world llm conversation dataset,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Lmsys-chat-1m: A large-scale real-world llm conversation dataset,

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation aa35d284-238d-417a-8c01-fc588cd9d932 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Judging llm-as-a-judge with mt-bench and chatbot arena,

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:03:39.957147Z digest=sha256:69d7678d674074e60d89838fc1c20cd077f9bc21d9200ec9f1a46590b066e54e

Observation c86bfd86-13c9-4906-a291-c64f5cb3152e · outbound

This paper cites Fairness in serving large language models,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Fairness in serving large language models,

Reference 29

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raw_fallback, observed 2026-08-06T13:03:53.185402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:40.145040Z digest=sha256:db486bb3fc0215c625a35848bdcf0a4ec792b927a73e292d2ff2b5186f440cf6

Observation 22c0a8d6-5c64-411a-8d6a-39b0c21083ab · outbound

This paper cites Large language models empowered autonomous edge ai for connected intelligence,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Large language models empowered autonomous edge ai for connected intelligence,

Reference 30

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raw_fallback, observed 2026-08-06T13:03:52.868593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:40.276469Z digest=sha256:38085535d23e1c3df2fe4881370944e3b6dd1a49ddfeaa7446f12bc2eb8002fa

Observation 65bd21a4-870d-40b3-8119-3b9bd4af99b5 · outbound

This paper cites Deep reinforcement learning from human preferences,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Deep reinforcement learning from human preferences,

Reference 31

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:03:40.340778Z digest=sha256:800309c0f5227279a4a2de98994d4105e8d1c18ddf7d7086c7bcb30cff27471d

Observation 7bdf36ea-d166-4920-ab69-7a41e602e358 · outbound

This paper cites Dr Genre: Reinforcement Learning from Decoupled LLM Feedback for Generic Text Rewriting.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Dr Genre: Reinforcement Learning from Decoupled LLM Feedback for Generic Text Rewriting

Reference 32

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:03:40.516658Z digest=sha256:cbff89f36f32c4392540676317f776060ab8f7091f4de1f9b2e9b8bc2b6e5805

Observation 7cf8dc94-db69-4e77-bc92-f0ca869d00f8 · outbound

This paper cites Safe rlhf: Safe reinforcement learning from human feedback,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Safe rlhf: Safe reinforcement learning from human feedback,

Reference 33

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raw_fallback, observed 2026-08-06T13:03:52.686854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:40.765503Z digest=sha256:017418ef5cf4e11619350964a596e953cd2c740c00906ad60a5b1a7fb2da252f

Observation 1931c4cc-f0f8-4aa2-89b4-6b883ce5c810 · outbound

This paper cites Safety alignment in nlp tasks: Weakly aligned summarization as an in-context attack,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Safety alignment in nlp tasks: Weakly aligned summarization as an in-context attack,

Reference 34

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raw_fallback, observed 2026-08-06T13:03:52.558977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation dc8ea10d-f524-4420-82fe-bb19d7fb0bd6 · outbound

This paper cites Beyond data and model parallelism for deep neural networks.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Beyond data and model parallelism for deep neural networks

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:41.145421Z digest=sha256:dcbc8a996944b3b9e393ea024a3ebaa277b958afeb9915661236de72d03407de

Observation 62402bcf-6d8f-4e78-b840-45221ac38601 · outbound

This paper cites How many servers are needed to run chatgpt?.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems How many servers are needed to run chatgpt?

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-06T13:03:52.065583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:41.342039Z digest=sha256:7918f04dbdab719ec1ae620c3beac3d88592994ad8bd78f650af40332ed7208e

Observation a7a1e361-6c28-44d8-86d4-baa6275d5343 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 37

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no resolver link, observed 2026-08-06T13:03:41.456851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:03:41.456851Z digest=sha256:0c88d92e63fd622eca23094bbf9f72edc36dcf60a97e637f56d0a72188384bac

Observation fec44e91-1914-424d-b7c5-2bb5f2af80f8 · outbound

This paper cites Understanding dataset difficulty with V-usable information,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Understanding dataset difficulty with V-usable information,

Reference 38

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verified fuzzy
raw_fallback, observed 2026-08-06T13:03:51.769600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:41.603740Z digest=sha256:c326a438376043da2649915d441df4b1f642efeaf6e2e7e9aa1900153ba54341

Observation db6d5645-20d7-4db9-9fd5-33cdf4bbf17b · outbound

This paper cites Attention is all you need,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Attention is all you need,

Reference 39

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source=pdf_text observed=2026-08-06T13:03:41.731363Z digest=sha256:f0328d67349fd2cb74733226a889db4c675d5d86e12959e489026df3458f6141

Observation b5d17bd8-30fb-410b-b304-ab4c4ae30408 · outbound

This paper cites Transparent {GPU} sharing in container clouds for deep learning workloads,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Transparent {GPU} sharing in container clouds for deep learning workloads,

Reference 40

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verified fuzzy
raw_fallback, observed 2026-08-06T13:03:51.490567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:41.881055Z digest=sha256:125a5430367bb0b709cba47f4f4eebb1c0c04f1803b2eb9e8d5629e781f1f80b

Observation 52e1ffe4-71a8-4491-94e0-265ca36e1d37 · outbound

This paper cites Efficiently scaling transformer inference,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Efficiently scaling transformer inference,

Reference 41

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no resolver link, observed 2026-08-06T13:03:42.047920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:03:42.047920Z digest=sha256:c2b44959f91f010c102ec50d56f9a2acae7a432b6af252f4e7e060bd066d05df

Observation fed08b25-c7f6-4efd-a7af-aabb6d642953 · outbound

This paper cites Methods and infrastructure in the era of accelerator-centric architectures,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Methods and infrastructure in the era of accelerator-centric architectures,

Reference 42

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verified fuzzy
raw_fallback, observed 2026-08-06T13:03:51.195820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:42.293530Z digest=sha256:2c62761ff452c9a09901b0e558cb0cc5fa788d040950c810b96d693ad6936c5d

Observation 4fe6ae8a-8781-4d0c-bc7a-b3f930bda0bc · outbound

This paper cites Rt-lm: Uncertainty-aware resource management for real-time inference of language models,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Rt-lm: Uncertainty-aware resource management for real-time inference of language models,

Reference 43

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verified fuzzy
raw_fallback, observed 2026-08-06T13:03:50.924196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:42.367020Z digest=sha256:2a8c0d05dc9b20568e498167bfbd4289cf2a5eaede9414c3de45c5715a1589e6

Observation 9db6ea39-66d1-4a1e-bbd7-00c40156c28b · outbound

This paper cites Mixtraining: A Better Trade-Off Between Compute and Performance.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Mixtraining: A Better Trade-Off Between Compute and Performance

Reference 44

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no resolver link, observed 2026-08-06T13:03:42.479866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:03:42.479866Z digest=sha256:b0860e0297295380ede2e8741c87294a54d96f2f5b73164eb15bb493692a084c

Observation e2705dc9-e105-41ea-864d-908d1d0bc6ed · outbound

This paper cites Sglang: Efficient execution of structured language model programs,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Sglang: Efficient execution of structured language model programs,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:03:50.710272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:42.609614Z digest=sha256:e9f7e431bbc35692a0deb803a46b9e57d0dd88d589c62b11d7b337b4ac5a8e19

Observation 65f29f82-f46e-4f28-8d50-1f3465d682cf · outbound

This paper cites {Check-N-Run}: A checkpointing system for training deep learning recommendation mod- els,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems {Check-N-Run}: A checkpointing system for training deep learning recommendation mod- els,

Reference 46

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raw_fallback, observed 2026-08-06T13:03:50.520601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:42.727557Z digest=sha256:827c0bbdff19905232beffa1a9304e5b8bc2d57d5cfd4d70e8d3173d6e0f663d

Observation ce2f717d-c000-498c-92dd-4ea744903f5b · outbound

This paper cites Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale,

Reference 47

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:03:42.847099Z digest=sha256:e9733bb065f951a6573e1efa686d313c6a6583f7d45acd35574a621e90eceab9

Observation 2d5b3269-9b38-4ceb-adf1-f853eff0e6a5 · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Flashattention: Fast and memory-efficient exact attention with io-awareness,

Reference 48

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no resolver link, observed 2026-08-06T13:03:42.963397Z

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Observation d45ce188-3d1f-4609-842d-02025234a453 · outbound

This paper cites Dialogpt: Large-scale generative pre- training for conversational response generation,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Dialogpt: Large-scale generative pre- training for conversational response generation,

Reference 49

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no resolver link, observed 2026-08-06T13:03:43.087636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:03:43.087636Z digest=sha256:dab21e263d777ad6bc32be18a4e5c7105e0c75cc629541deae8d6d57baa9c69f

Observation 0353e34e-c94c-40d1-b031-e51b4ae872e7 · outbound

This paper cites Utilitiy accrual scheduling with real-time java,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Utilitiy accrual scheduling with real-time java,

Reference 50

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raw_fallback, observed 2026-08-06T13:03:50.358006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:43.216500Z digest=sha256:488d10df0735deb362a196e0ae97551887ed4bf839798391bdb51890e998029f

Observation 2a755fdd-66e5-48a1-92db-d9ef7b64993c · outbound

This paper cites Horovod: fast and easy distributed deep learning in TensorFlow.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Horovod: fast and easy distributed deep learning in TensorFlow

Reference 51

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no resolver link, observed 2026-08-06T13:03:43.354587Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:03:43.354587Z digest=sha256:1fd642ae971189d2234d5555d3002546764433798b994c615d2c9d9fa53d0123

Observation ff1db69a-9c26-4c75-9990-096370ff24d4 · outbound

This paper cites A unified architecture for accelerating distributed {DNN} training in heteroge- neous {GPU/CPU} clusters,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems A unified architecture for accelerating distributed {DNN} training in heteroge- neous {GPU/CPU} clusters,

Reference 52

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raw_fallback, observed 2026-08-06T13:03:50.246190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:43.484566Z digest=sha256:27b178d3998afe30d87ae97d043ed743d605be72b671aa2830078a4b05aa96fc

Observation b9a55956-b488-4879-85f8-f075b95f685a · outbound

This paper cites Large scale distributed deep networks,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Large scale distributed deep networks,

Reference 53

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no resolver link, observed 2026-08-06T13:03:43.601662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation abe4b508-1579-461f-8352-0f86adc20c30 · outbound

This paper cites Chimera: efficiently training large-scale neural net- works with bidirectional pipelines,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Chimera: efficiently training large-scale neural net- works with bidirectional pipelines,

Reference 54

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verified fuzzy
raw_fallback, observed 2026-08-06T13:03:50.100215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:43.773979Z digest=sha256:0e045e238d417e516837c48785636ca134fc26ceaf4afddd32a92b91ae9763df

Observation cffc585c-f4f4-42f2-a272-5cdaa0601a1c · outbound

This paper cites Pipefisher: Efficient training of large language models using pipelining and fisher information matrices,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Pipefisher: Efficient training of large language models using pipelining and fisher information matrices,

Reference 55

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verified fuzzy
raw_fallback, observed 2026-08-06T13:03:49.970281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:43.975036Z digest=sha256:e3e2882121001433b4939e6c1b5b6d7bd7a9ebe96df84af6bc4c8780ea9b4c76

Observation bbaefcef-2f47-4072-b644-ab0b34c23c9a · outbound

This paper cites Torch- serve: Serve, optimize and scale pytorch models in production,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Torch- serve: Serve, optimize and scale pytorch models in production,

Reference 56

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verified fuzzy
raw_fallback, observed 2026-08-06T13:03:49.850981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:44.056961Z digest=sha256:5a87ff094f7507328efe7f416c7c01b31162c47dc42d66d868af069e368bb4e6

Observation d3721f57-b094-4cb0-ac8c-66431528be16 · outbound

This paper cites Triton inference server: An optimized cloud and edge inferencing solution,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Triton inference server: An optimized cloud and edge inferencing solution,

Reference 57

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verified fuzzy
raw_fallback, observed 2026-08-06T13:03:49.719470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:44.143282Z digest=sha256:a8356fabdb2809b5120ffbac26c298534afd703f33fdfbe70a92508d85870773

Observation b4349fc9-e916-40fa-8e0c-1978f15d864f · outbound

This paper cites White-box multi-objective adversarial attack on dialogue generation,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems White-box multi-objective adversarial attack on dialogue generation,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:03:49.575798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:44.313731Z digest=sha256:096680787e779d4ccf77a692a6a3880cc1cd62bc0696cffbc3198b2950787a26

Observation 15958372-4cf4-4e87-a8e6-b5dcdefd9298 · outbound

This paper cites Dycl: Dynamic neural network compilation via program rewriting and graph optimization,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Dycl: Dynamic neural network compilation via program rewriting and graph optimization,

Reference 59

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raw_fallback, observed 2026-08-06T13:03:49.458564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:44.449347Z digest=sha256:8fd5a219a47108c7f25ee92994aede239379312ad51667b83c990c735e63cb02

Observation 0b89d23d-7aa1-4cf9-93a3-8d471fb102e3 · outbound

This paper cites Learning to Reverse DNNs from AI Programs Automatically.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Learning to Reverse DNNs from AI Programs Automatically

Reference 60

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verified exact
local_arxiv, observed 2026-08-06T13:03:46.789593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:44.537815Z digest=sha256:0b57a11a0a32d37aec19ad08284e5c8b04c783def0a9ffdc88a0a5982603d87c

Observation 77e4e8de-38bf-43ee-b925-d9caa3e9db32 · outbound

This paper cites Efficient large-scale language model training on gpu clusters using megatron-lm,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Efficient large-scale language model training on gpu clusters using megatron-lm,

Reference 61

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no resolver link, observed 2026-08-06T13:03:44.686906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:03:44.686906Z digest=sha256:211383a253d8f869d60a9545be748272a33a61ae7d6384fc87193251126a7aab

Observation 853ef611-457a-4715-9eca-e4fc69e9440e · outbound

This paper cites Integrated optimization of large language models: Synergizing data utilization and compression techniques,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Integrated optimization of large language models: Synergizing data utilization and compression techniques,

Reference 62

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verified fuzzy
raw_fallback, observed 2026-08-06T13:03:49.288771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:44.832888Z digest=sha256:f498233883e761fcafcc0ad5572eecdc968576038295ebd22686ac7a016817ff

Observation 876f09a7-d912-4c49-aebb-f76a48e4f186 · outbound

This paper cites Fast Distributed Inference Serving for Large Language Models.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Fast Distributed Inference Serving for Large Language Models

Reference 63

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no resolver link, observed 2026-08-06T13:03:45.031929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:03:45.031929Z digest=sha256:526042cb04459b8e898b6f121c2a503afd588d86cc476a1712a6bdb12777b280

Observation 443bb644-52a2-45b2-91d6-18148ab830fd · outbound

This paper cites Splitwise: Efficient generative llm inference using phase splitting,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Splitwise: Efficient generative llm inference using phase splitting,

Reference 64

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no resolver link, observed 2026-08-06T13:03:45.197515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:03:45.197515Z digest=sha256:7190f8b218ac839752342456f35f1f020037fb99dc6441f4b9c3246f73e9aa98

Observation c5c254a4-c74a-4d8d-9d94-206c535ad5d0 · outbound

This paper cites D´ej`avu: KV-cache streaming for fast, fault-tolerant generative LLM serving,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems D´ej`avu: KV-cache streaming for fast, fault-tolerant generative LLM serving,

Reference 65

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verified fuzzy
raw_fallback, observed 2026-08-06T13:03:49.075642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:45.280433Z digest=sha256:eebe127e35539497bc4470ad1322433fd2a7bd56ca355e2d28d72cbfd2f852b6

Observation 029d8a30-6b03-45b2-8e6e-24de8dc0c5de · outbound

This paper cites Estimating Predictive Uncertainty Under Program Data Distribution Shift.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Estimating Predictive Uncertainty Under Program Data Distribution Shift

Reference 66

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no resolver link, observed 2026-08-06T13:03:45.389549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:03:45.389549Z digest=sha256:5a9b80c7adf7d29ae20464f952d16530951da7f4f0b6284948581713586f2084

Observation c7234d1d-9817-47f3-89e6-27b0ff694747 · outbound

This paper cites Uncertainty Awareness of Large Language Models Under Code Distribution Shifts: A Benchmark Study.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Uncertainty Awareness of Large Language Models Under Code Distribution Shifts: A Benchmark Study

Reference 67

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verified exact
local_arxiv, observed 2026-08-06T13:03:46.522459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:45.485554Z digest=sha256:e91cdb6a63fa5895582aa63bd019a3b5fcf7fe8aa3db559a0427231eca141549

Observation dfe2d45a-e540-44d6-9af9-e7d48c1d79c6 · outbound

This paper cites Distilling the knowledge in a neural network,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Distilling the knowledge in a neural network,

Reference 68

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verified fuzzy
raw_fallback, observed 2026-08-06T13:03:48.946769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:45.597334Z digest=sha256:e4d716e49066b315f04879d23aaedc0d5a47752836e112b284b78744ddc0d2ff

Observation f721397d-d94f-4df2-8a41-48c197a3e724 · outbound

This paper cites An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

Reference 69

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unresolved
no resolver link, observed 2026-08-06T13:03:45.705445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:03:45.705445Z digest=sha256:6f3ed1bd5e5e0b0e86210d09d67d3b034857526f0baa8cbda8896079c9e9c51a

Observation f64b7f9c-f1e4-4bf2-830b-72d0877e159d · outbound

This paper cites Uncertainty-aware bootstrap learning for joint extraction on distantly-supervised data,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Uncertainty-aware bootstrap learning for joint extraction on distantly-supervised data,

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:03:48.712845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:45.810863Z digest=sha256:4a7f0fb59eb226b720d2326e10a4be849e0502fa7f3e88794ae0a2df743fd894

Observation 549a8957-4cbb-4f35-9c03-4eb5f81cff81 · outbound

This paper cites Distantly- supervised joint extraction with noise-robust learning,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Distantly- supervised joint extraction with noise-robust learning,

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:03:48.437621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:45.909109Z digest=sha256:c354dd30f6b7194a21a53dcdae17d1da85ad1f9a4e3c7d91da681b415a71f13d

Observation a180dc1d-6dfd-4ad4-b007-6b7ecdf4460c · outbound

This paper cites Ekya: Continuous learning of video analytics models on edge compute servers,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Ekya: Continuous learning of video analytics models on edge compute servers,

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:03:48.265882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:45.995394Z digest=sha256:95f3bad342117b2a1b459e3835c7189d011e30a1eb09113038b0760acbca3fec

Observation 87266bc5-16d4-462b-b0c4-f14522eac508 · outbound

This paper cites Adainf: Data drift adaptive scheduling for accurate and slo-guaranteed multiple-model inference serving at edge servers,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Adainf: Data drift adaptive scheduling for accurate and slo-guaranteed multiple-model inference serving at edge servers,

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:03:48.003626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:46.106072Z digest=sha256:1b5111de50e9994286aff76389f7e92487ebf134e6ddf5005bea923fb4e56f19

Observation d33fcda8-3861-46ff-9d9c-0282282f654f · outbound

This paper cites Lyra: Elastic scheduling for deep learning clusters,.

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems Lyra: Elastic scheduling for deep learning clusters,

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:03:47.824164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:03:46.238908Z digest=sha256:05e126b5ee0dfcce9281b3a65915a07dbbe31520d7979a5902c0c4ad8b68adde

Pith citing papers

Observation e1558f60-c1c4-49c3-bcd2-4291f8735d08 · inbound

PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning cites this paper.

PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:26:38.661743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-25T05:26:22.623367Z digest=sha256:2cc6bd8f30d0d374d05995b46ea75128fdd4090c9ed1251ea41e67ac0592ac10

Observation fceeda55-34cf-45e2-ae9b-b7a5aad81518 · inbound

RED: Adaptive Real-Time DAG Scheduling for Robotic Inference under Environmental Dynamics cites this paper.

RED: Adaptive Real-Time DAG Scheduling for Robotic Inference under Environmental Dynamics LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems

Reference 65

Resolution
verified exact
arxiv_id, observed 2026-06-30T16:04:52.782024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-30T16:01:48.746926Z digest=sha256:e2e46f4e9e76e9323223c1d37279c66adb67261d30e3bbd49b343f42f12063fe

Observation 1fc58a18-4a8f-404e-bc48-da02ced20a4c · inbound

Not Every Sync Is Safe: Calibrated DiLoCo Scheduling for Shared AI Infrastructure cites this paper.

Not Every Sync Is Safe: Calibrated DiLoCo Scheduling for Shared AI Infrastructure LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems

Reference 15

Resolution
unresolved
no resolver link, observed 2026-07-12T12:16:10.904456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T12:16:10.904456Z digest=sha256:0d12f79a21e325ae31e90e578011fe63e1d95945c5b9c5bc61653f27e664133b