Paper Citation Record · LEDGER

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

As of 9 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 2026-08-09T06:31:02.800959+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
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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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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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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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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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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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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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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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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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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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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:03:55.748090Z

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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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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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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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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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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-reported events for the cited work

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

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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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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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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

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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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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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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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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:03:50.358006Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

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

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:03:50.246190Z

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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

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

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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

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

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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

Resolution: 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 2026-08-09T06:31:02.800959+00:00.

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

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

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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

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

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:03:49.458564Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

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

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

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

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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

Resolution: 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 2026-08-09T06:31:02.800959+00:00.

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

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

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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

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

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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

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

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

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

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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

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

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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

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

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

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

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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

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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

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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 2026-08-09T06:31:02.800959+00:00.

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

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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 2026-08-09T06:31:02.800959+00:00.

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 2026-08-09T06:31:02.800959+00:00.

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 2026-08-09T06:31:02.800959+00:00.

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

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