LRM-1B: Towards Large Routing Model | Paper Citation Record

Observation 12765b3c-a70b-4027-b554-92f4ff607306 · outbound

This paper cites Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws.

LRM-1B: Towards Large Routing Model Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 1

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Observation 50a7c496-fa4b-4e43-808f-2fb4fffc505a · outbound

This paper cites Machine learning for combinatorial optimization: a methodological tour d’horizon.

LRM-1B: Towards Large Routing Model Machine learning for combinatorial optimization: a methodological tour d’horizon

Reference 2

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Observation 60e08898-587f-46bb-9f4c-84fae9d81d3b · outbound

This paper cites Routefinder: Towards foundation models for vehicle routing problems.

LRM-1B: Towards Large Routing Model Routefinder: Towards foundation models for vehicle routing problems

Reference 3

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Observation 094944b8-4cdf-4f93-b3df-e7204188007f · outbound

This paper cites Learning generalizable models for vehicle routing problems via knowledge distillation.

LRM-1B: Towards Large Routing Model Learning generalizable models for vehicle routing problems via knowledge distillation

Reference 4

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Observation 4e6c04e0-40ad-4900-ae72-886b80f1eb05 · outbound

This paper cites Towards deeper deep reinforcement learning with spectral normalization.

LRM-1B: Towards Large Routing Model Towards deeper deep reinforcement learning with spectral normalization

Reference 5

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Observation a61da1e9-ce77-4cf4-a061-9b0213a54e49 · outbound

This paper cites Machine learning to solve vehicle routing problems: A survey.

LRM-1B: Towards Large Routing Model Machine learning to solve vehicle routing problems: A survey

Reference 6

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Observation 3fb6a591-8cac-4b47-9d0c-ceafdd9c50b7 · outbound

This paper cites Evolving diverse tsp instances by means of novel and creative mutation operators.

LRM-1B: Towards Large Routing Model Evolving diverse tsp instances by means of novel and creative mutation operators

Reference 7

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This paper cites Language models are few-shot learners.

LRM-1B: Towards Large Routing Model Language models are few-shot learners

Reference 8

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Observation 82f8ede9-c344-4544-9e3d-e7b08b488607 · outbound

This paper cites Combining reinforcement learning and constraint programming for combinatorial optimization.

LRM-1B: Towards Large Routing Model Combining reinforcement learning and constraint programming for combinatorial optimization

Reference 9

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Observation 5e8289b8-a77b-407d-a9d2-37d15661ab4e · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

LRM-1B: Towards Large Routing Model FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 10

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Observation d3c02aa2-d2ab-4976-983d-59f5ae096db5 · outbound

This paper cites The case for 4-bit precision: k-bit inference scaling laws.

LRM-1B: Towards Large Routing Model The case for 4-bit precision: k-bit inference scaling laws

Reference 11

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Observation 2490eabc-9adf-490e-9126-a04667f6c866 · outbound

This paper cites The Llama 3 Herd of Models.

LRM-1B: Towards Large Routing Model The Llama 3 Herd of Models

Reference 12

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Observation baed7630-f33b-4232-8c02-c391425b8961 · outbound

This paper cites Learn to Design the Heuristics for Vehicle Routing Problem.

LRM-1B: Towards Large Routing Model Learn to Design the Heuristics for Vehicle Routing Problem

Reference 13

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Observation 9252f4e0-1b55-4890-88e2-feadb43770c4 · outbound

This paper cites Generalization of neural combinatorial solvers through the lens of adversarial robustness.

LRM-1B: Towards Large Routing Model Generalization of neural combinatorial solvers through the lens of adversarial robustness

Reference 14

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Observation 373b96f3-f5c5-4a59-ba24-9e95a4889d25 · outbound

This paper cites Data and parameter scaling laws for neural machine translation.

LRM-1B: Towards Large Routing Model Data and parameter scaling laws for neural machine translation

Reference 15

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Observation effe07a5-6938-4881-bdae-b0e9d7c6d261 · outbound

This paper cites Scaling Laws for Transfer.

LRM-1B: Towards Large Routing Model Scaling Laws for Transfer

Reference 16

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Observation b5c186fe-a9cc-481e-b1db-f18bd75369b0 · outbound

This paper cites Training Compute-Optimal Large Language Models.

LRM-1B: Towards Large Routing Model Training Compute-Optimal Large Language Models

Reference 17

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Observation 2b0c78f3-aa11-4ebd-a459-c7f7bb0faca0 · outbound

This paper cites Liger Kernel: Efficient Triton Kernels for LLM Training.

LRM-1B: Towards Large Routing Model Liger Kernel: Efficient Triton Kernels for LLM Training

Reference 18

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Observation 0b556422-2c38-4d61-bbf1-1315036ed7d2 · outbound

This paper cites Learning to solve routing problems via distributionally robust optimization.

LRM-1B: Towards Large Routing Model Learning to solve routing problems via distributionally robust optimization

Reference 20

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Observation e185c1a4-3882-47a2-9da5-3bb56221f846 · outbound

This paper cites Scaling Laws for Neural Language Models.

LRM-1B: Towards Large Routing Model Scaling Laws for Neural Language Models

Reference 21

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Observation 3ad60c5d-7334-4b9c-a959-8b2644bccfc2 · outbound

This paper cites Learning collaborative policies to solve np-hard routing problems.

LRM-1B: Towards Large Routing Model Learning collaborative policies to solve np-hard routing problems

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:53.521500Z

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Observation f544f3bc-34f3-4096-ac99-c43f0a13a6a1 · outbound

This paper cites Sym-nco: Leveraging symmetricity for neural combinatorial optimization.

LRM-1B: Towards Large Routing Model Sym-nco: Leveraging symmetricity for neural combinatorial optimization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:53.339492Z

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Observation 50f238c7-24f0-42c2-8e99-b02187ee00f0 · outbound

This paper cites Attention, learn to solve routing problems! In International Conference on Learning Representations, 2019.

LRM-1B: Towards Large Routing Model Attention, learn to solve routing problems! In International Conference on Learning Representations, 2019

Reference 24

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Observation 41234771-a171-483f-b593-37daeb041ea2 · outbound

This paper cites Deep policy dynamic programming for vehicle routing problems.

LRM-1B: Towards Large Routing Model Deep policy dynamic programming for vehicle routing problems

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:53.169030Z

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Observation 896b51bb-cf59-40a2-ac90-ec1d324084a8 · outbound

This paper cites Scaling Laws for Precision.

LRM-1B: Towards Large Routing Model Scaling Laws for Precision

Reference 26

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Observation 0807e4b7-810b-4f5b-a95d-57ea27a51505 · outbound

This paper cites Pomo: Policy optimization with multiple optima for reinforcement learning.

LRM-1B: Towards Large Routing Model Pomo: Policy optimization with multiple optima for reinforcement learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:53.017307Z

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Observation 6b08b9fe-6c85-4f06-819c-f4968e478821 · outbound

This paper cites An inverse scaling law for clip training.

LRM-1B: Towards Large Routing Model An inverse scaling law for clip training

Reference 28

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Observation 2faa44fd-c46b-4a19-99f4-9a6f33b1cf3b · outbound

This paper cites Cross-problem learning for solving vehicle routing problems.

LRM-1B: Towards Large Routing Model Cross-problem learning for solving vehicle routing problems

Reference 29

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Observation 983617a7-aa77-4d88-a8c7-48e0570a85d6 · outbound

This paper cites Why spectral normalization stabilizes gans: Analysis and improvements.

LRM-1B: Towards Large Routing Model Why spectral normalization stabilizes gans: Analysis and improvements

Reference 30

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Observation 5132edd0-00a2-437b-b404-3ce110e5cfde · outbound

This paper cites Multi- task learning for routing problem with cross-problem zero-shot generalization.

LRM-1B: Towards Large Routing Model Multi- task learning for routing problem with cross-problem zero-shot generalization

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:52.276330Z

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Observation 8acc19b4-d2ea-448f-96b6-a9b210643578 · outbound

This paper cites Neural combinatorial optimization with heavy decoder: Toward large scale generalization.

LRM-1B: Towards Large Routing Model Neural combinatorial optimization with heavy decoder: Toward large scale generalization

Reference 32

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Observation 5fb4c41e-17d5-49d8-8596-8cc638d54446 · outbound

This paper cites Spectral Normalization for Generative Adversarial Networks.

LRM-1B: Towards Large Routing Model Spectral Normalization for Generative Adversarial Networks

Reference 33

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Observation 7f4f27cc-0345-476a-bacc-71ad54f9964d · outbound

This paper cites A deep reinforcement learning algorithm using dynamic attention model for vehicle routing problems.

LRM-1B: Towards Large Routing Model A deep reinforcement learning algorithm using dynamic attention model for vehicle routing problems

Reference 34

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Observation d6a30581-04cc-4090-966b-098af62fd1e9 · outbound

This paper cites Learning transferable visual models from natural language supervision.

LRM-1B: Towards Large Routing Model Learning transferable visual models from natural language supervision

Reference 35

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Observation ee85b70f-96ab-4274-af2f-f04100867211 · outbound

This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

LRM-1B: Towards Large Routing Model Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 36

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Observation 8bea5e76-6dd2-4aa3-856b-ab375ea9af5f · outbound

This paper cites GLU Variants Improve Transformer.

LRM-1B: Towards Large Routing Model GLU Variants Improve Transformer

Reference 37

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Observation 126e2c8e-ae27-4e5a-ae17-edeed347ecf1 · outbound

This paper cites Adafactor: Adaptive learning rates with sublinear memory cost.

LRM-1B: Towards Large Routing Model Adafactor: Adaptive learning rates with sublinear memory cost

Reference 38

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Observation f6101f01-bbca-4ad0-be9a-9aed6a6dcc56 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

LRM-1B: Towards Large Routing Model Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:51.474855Z

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Observation 7f869424-b45f-4215-9189-0b62a2bc001e · outbound

This paper cites Learning heuristic selection using a time delay neural network for open vehicle routing.

LRM-1B: Towards Large Routing Model Learning heuristic selection using a time delay neural network for open vehicle routing

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:51.325134Z

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Observation 7306cc45-c1d5-425f-8c30-1019325a7ee1 · outbound

This paper cites Attention is all you need.

LRM-1B: Towards Large Routing Model Attention is all you need

Reference 41

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Observation 442a1ce5-78d6-4c73-b752-f53dad14f77c · outbound

This paper cites Hybrid genetic search for the cvrp: Open-source implementation and swap* neighborhood.

LRM-1B: Towards Large Routing Model Hybrid genetic search for the cvrp: Open-source implementation and swap* neighborhood

Reference 42

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Observation 02f62262-5b18-434c-8cb1-083e7c0da726 · outbound

This paper cites Efficient Training of Multi-task Neural Solver for Combinatorial Optimization.

LRM-1B: Towards Large Routing Model Efficient Training of Multi-task Neural Solver for Combinatorial Optimization

Reference 43

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Observation 0e2b1f76-883f-42b8-9ddc-6cd74f785bf8 · outbound

This paper cites A game-theoretic approach for improving generalization ability of tsp solvers.

LRM-1B: Towards Large Routing Model A game-theoretic approach for improving generalization ability of tsp solvers

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:51.153387Z

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Observation f9cbff85-31a5-4ed0-aa74-7b14af90c2a2 · outbound

This paper cites Simple statistical gradient-following algorithms for connectionist reinforce- ment learning.

LRM-1B: Towards Large Routing Model Simple statistical gradient-following algorithms for connectionist reinforce- ment learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:50.950846Z

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Observation 13839bab-7c08-4fea-a651-a64f5ff798c1 · outbound

This paper cites Pyvrp: A high-performance vrp solver package.

LRM-1B: Towards Large Routing Model Pyvrp: A high-performance vrp solver package

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:50.772288Z

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Observation e786fd79-49d5-48c0-a365-fa1fd0512848 · outbound

This paper cites Multi-decoder attention model with embedding glimpse for solving vehicle routing problems.

LRM-1B: Towards Large Routing Model Multi-decoder attention model with embedding glimpse for solving vehicle routing problems

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:50.583710Z

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Observation 1b6b29c3-be17-4919-be9c-7618a47e7ad4 · outbound

This paper cites Generative adversarial training for neural combinatorial optimization models, 2022.

LRM-1B: Towards Large Routing Model Generative adversarial training for neural combinatorial optimization models, 2022

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:50.380292Z

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Observation f24aa1a8-ed14-4a36-942b-1e0cd82580e8 · outbound

This paper cites Spectral Norm Regularization for Improving the Generalizability of Deep Learning.

LRM-1B: Towards Large Routing Model Spectral Norm Regularization for Improving the Generalizability of Deep Learning

Reference 49

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Observation 98ec0f67-8510-4fa9-9ba2-463a8116c304 · outbound

This paper cites Stabilizing transformer training by preventing attention entropy collapse.

LRM-1B: Towards Large Routing Model Stabilizing transformer training by preventing attention entropy collapse

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:50.230886Z

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Observation b9a2f358-599e-45b6-a845-1c600dcaa499 · outbound

This paper cites Scaling vision transform- ers.

LRM-1B: Towards Large Routing Model Scaling vision transform- ers

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:50.007407Z

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Observation be1ab63c-82c2-4e75-b7b1-8a3a5005cdac · outbound

This paper cites Root mean square layer normalization.

LRM-1B: Towards Large Routing Model Root mean square layer normalization

Reference 52

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Observation 8b8b5bde-b092-49aa-ad97-875c61c9b1a5 · outbound

This paper cites Examining scaling and transfer of language model architectures for machine translation.

LRM-1B: Towards Large Routing Model Examining scaling and transfer of language model architectures for machine translation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:49.848687Z

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Observation df0147e5-1e09-4d36-9402-03911a0d4f4c · outbound

This paper cites Learning to solve travelling salesman problem with hardness-adaptive curriculum.

LRM-1B: Towards Large Routing Model Learning to solve travelling salesman problem with hardness-adaptive curriculum

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:49.709047Z

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Observation e0db2b90-c7ec-4ca7-9653-57d13d31d3e8 · outbound

This paper cites A hybrid of deep reinforcement learning and local search for the vehicle routing problems.

LRM-1B: Towards Large Routing Model A hybrid of deep reinforcement learning and local search for the vehicle routing problems

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:49.534974Z

Source-reported events for the cited work

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Observation e253fd75-e374-4065-9e26-b9512182c690 · outbound

This paper cites Towards omni-generalizable neural methods for vehicle routing problems.

LRM-1B: Towards Large Routing Model Towards omni-generalizable neural methods for vehicle routing problems

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:49.390413Z

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Observation 71621e68-700f-4421-a7cb-3e0e010fb1b7 · outbound

This paper cites Mvmoe: Multi-task vehicle routing solver with mixture-of-experts.

LRM-1B: Towards Large Routing Model Mvmoe: Multi-task vehicle routing solver with mixture-of-experts

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:49.280090Z

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Observation 5d7f90f2-5337-4374-80f4-05b2e3148da1 · outbound

This paper cites Scaling law for document neural machine translation.

LRM-1B: Towards Large Routing Model Scaling law for document neural machine translation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:49.189321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1ee35cc2-5c3a-4b00-b8e0-cf42b370f5eb · outbound

This paper cites A customer must be served only once, and if the last visited node was the depot, the next move cannot immediately return to the depot (this prevents trivial loops).

LRM-1B: Towards Large Routing Model A customer must be served only once, and if the last visited node was the depot, the next move cannot immediately return to the depot (this prevents trivial loops)

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:49.087681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e4ba39dc-bd2e-498f-8b53-9b97186ea7f1 · outbound

This paper cites In problems without an open-route option, every tour segment must eventually return to the depot within both its time-window and distance limits.

LRM-1B: Towards Large Routing Model In problems without an open-route option, every tour segment must eventually return to the depot within both its time-window and distance limits

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:48.989388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f4b26fdc-c843-4418-9845-0c8ebcbd8c80 · outbound

This paper cites Whenever time windows apply, a customer cannot be chosen if the earliest possible arrival (plus service) would fall after its window closes.

LRM-1B: Towards Large Routing Model Whenever time windows apply, a customer cannot be chosen if the earliest possible arrival (plus service) would fall after its window closes

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:48.885332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 26b73753-d222-432e-a743-0b6346fb26e6 · outbound

This paper cites When backhaul visits are required, they are deferred until all linehaul services are completed.

LRM-1B: Towards Large Routing Model When backhaul visits are required, they are deferred until all linehaul services are completed

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:48.778661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 072add1d-7ba6-43a3-8b23-4268b421fdf7 · outbound

This paper cites A node is only feasible if its demand can be loaded on the vehicle without exceeding the remaining capacity (for pickups) or the available backhaul capacity (for drop-offs).

LRM-1B: Towards Large Routing Model A node is only feasible if its demand can be loaded on the vehicle without exceeding the remaining capacity (for pickups) or the available backhaul capacity (for drop-offs)

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:48.694401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 54dc88a8-cf16-4e52-b750-70c5146c9461 · outbound

This paper cites Every node coordinate is drawn from the uniform distribution ⃗ xi ∼ U (0, 1)2.

LRM-1B: Towards Large Routing Model Every node coordinate is drawn from the uniform distribution ⃗ xi ∼ U (0, 1)2

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:20:48.576175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d8616544-2a37-49f8-96bd-856b28204b74 · outbound

This paper cites This distribution is parameterized by the number of clusters m and a scale factor c.

LRM-1B: Towards Large Routing Model This distribution is parameterized by the number of clusters m and a scale factor c

Reference 65

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T20:20:48.466528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers