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Paper Citation Record · LEDGER

LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 48 inbound Pith citation observations for arXiv:2306.02561.

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

pith.paper-citation-record.v1
2306.02561 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 48 of 48 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:22:28.893446Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:50:11.019242Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9a2cd744-77f5-4e1d-92ce-a88234198074 · inbound

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

RouteLLM: Learning to Route LLMs with Preference Data LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 19

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arxiv_id, observed 2026-05-11T23:27:40.609975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-11T23:27:40.397360Z digest=sha256:9e0b61c14a8a7cc6c357d85bcd84edfeb68ab0f65b60586410db9919ab35cf5f

Observation 906c4d2c-0195-49fb-8b93-d3d0fdb74bd5 · inbound

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

Large Language Monkeys: Scaling Inference Compute with Repeated Sampling LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 34

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arxiv_id, observed 2026-05-12T04:42:23.593082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-12T04:42:23.297389Z digest=sha256:1fe3346e00bb6df3760ca888dbf8eefafa22dc28aa781fe389efa319301b4aa3

Observation 4e442fa7-fe4f-40bf-9439-cd1ae760b253 · inbound

Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs cites this paper.

Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 11

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arxiv_id, observed 2026-05-17T16:18:01.631525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-17T16:18:01.560780Z digest=sha256:cc8a8f83ef2349a863f04d5b73acb86227a8edbbd2f5bfa04c6e4768323d683c

Observation 8caa511e-12f4-4ef1-917a-a22af59ca594 · inbound

LLM Bandit: Cost-Efficient LLM Generation via Preference-Conditioned Dynamic Routing cites this paper.

LLM Bandit: Cost-Efficient LLM Generation via Preference-Conditioned Dynamic Routing LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 16

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no resolver link, observed 2026-08-09T11:22:28.893446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:22:28.893446Z digest=sha256:304a5c33dfdabb91f12ec1786aaefc2bb4f8e6fbecd60d53b74a0d1e534c2906

Observation 181e6b0a-2277-48fa-a44e-5381194b7130 · inbound

COSMosFL: Ensemble of Small Language Models for Fault Localisation cites this paper.

COSMosFL: Ensemble of Small Language Models for Fault Localisation LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 43

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no resolver link, observed 2026-08-09T10:44:43.531286Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-09T10:44:43.531286Z digest=sha256:b02c9878e59e575a3d508a865c5a4820b2f948a2b8f81adf1f07f252a2863e0a

Observation 520b2511-708c-4cc1-92ca-65140a3a2d77 · inbound

KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems cites this paper.

KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 20

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no resolver link, observed 2026-08-08T13:08:22.646485Z

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source=arxiv_source observed=2026-08-08T13:08:22.646485Z digest=sha256:8d2b74b6e00a9c2efb4ac905c0a0311bdeb8f9ffd561e6454765183c7ab86978

Observation cca31f8b-28f7-4787-952c-a5dcb5af890a · inbound

ORI: O Routing Intelligence cites this paper.

ORI: O Routing Intelligence LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 27

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no resolver link, observed 2026-08-07T19:40:07.766237Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T19:40:07.766237Z digest=sha256:a95b6412f462c7d70eae6dd7103bb3614ceb5ddf578b60143a1ad72fd227595b

Observation 46f4965b-a0fd-4222-977b-7393c212c9cf · inbound

Rethinking Predictive Modeling for LLM Routing: When Simple kNN Beats Complex Learned Routers cites this paper.

Rethinking Predictive Modeling for LLM Routing: When Simple kNN Beats Complex Learned Routers LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 15

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arxiv_id, observed 2026-05-22T15:14:57.453781Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-22T15:13:28.927880Z digest=sha256:40d02f5e452f12d8001c1f7458b927365efed22dba9411f8bf827de32d4f86c3

Observation 49450460-5ef7-4d2f-872b-e5b496c3bb6f · inbound

LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead cites this paper.

LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 15

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:11:17.439648Z digest=sha256:8380c432c709e46a49855cfd329dfa86bde1161ee6f07e96579f8afaaf01792f

Observation d6597a11-85a1-48dc-8b7c-a64ae0802d7d · inbound

Bayesian Optimization for Enhanced Language Models: Optimizing Acquisition Functions cites this paper.

Bayesian Optimization for Enhanced Language Models: Optimizing Acquisition Functions LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 5

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source=pdf_text observed=2026-08-07T15:02:26.329866Z digest=sha256:c76ea8ffdc26ef0d5ab9b1a40cd928273c7cdb270f6e338468285e528eb46032

Observation 69ef58c9-a619-4f44-aec4-2f4dc4773214 · inbound

Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing cites this paper.

Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 25

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no resolver link, observed 2026-08-07T14:48:08.836617Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:48:08.836617Z digest=sha256:fdd9f9a8dca1491b69c3b8d47ccd307078442e9517fbfa84c3badf53edd8af7c

Observation 3bc6cbd6-ce91-4ebe-b662-afd965667325 · inbound

How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning cites this paper.

How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 8

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no resolver link, observed 2026-08-07T12:32:21.788871Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:32:21.788871Z digest=sha256:e3bc6bf03b903be62b49f73c53252c1b7b109e5aa8fdff04809ccab1216804c7

Observation 83ab8c1a-22ac-4a8f-9eac-3cb3de3b8b80 · inbound

RewardAnything: Generalizable Principle-Following Reward Models cites this paper.

RewardAnything: Generalizable Principle-Following Reward Models LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 74

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no resolver link, observed 2026-08-07T11:04:07.031741Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:04:07.031741Z digest=sha256:157cf2aadce5fd5aa8014ea9e97b601a913ac39501b90f5032511b7d44749ce4

Observation b897ca20-8400-4e57-94b6-ba9e268f6016 · inbound

Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques cites this paper.

Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 81

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no resolver link, observed 2026-08-07T05:56:56.989615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:56:56.989615Z digest=sha256:8f0cd14c842eed2fce4274f14930ee8483e1a251667278db0d176c461fb7e68a

Observation a5d7070e-ea2d-45b0-9f0f-1f8dc7e67019 · inbound

Federated In-Context Learning: Iterative Refinement for Improved Answer Quality cites this paper.

Federated In-Context Learning: Iterative Refinement for Improved Answer Quality LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 18

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no resolver link, observed 2026-08-07T05:42:38.707387Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:42:38.707387Z digest=sha256:c46195ccb1d1542a34a3bafb2c22903175f714e1e557d2eabe6206d5cdf78239

Observation 7793a646-d39c-4b91-93d5-06c8be2ec339 · inbound

Leveraging LLMs to Evaluate Usefulness of Document cites this paper.

Leveraging LLMs to Evaluate Usefulness of Document LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 27

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no resolver link, observed 2026-08-07T05:11:38.299577Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:11:38.299577Z digest=sha256:036f881f3e37f0bf4c0e9ba8aa996a021f142ada4fe7b2c7a34a08ee0a111e51

Observation 6b271064-6b92-40f7-b857-bd7ba9331e1c · inbound

MEMETRON: Metaheuristic Mechanisms for Test-time Response Optimization of Large Language Models cites this paper.

MEMETRON: Metaheuristic Mechanisms for Test-time Response Optimization of Large Language Models LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 20

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no resolver link, observed 2026-08-07T05:10:47.699915Z

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source=pdf_text observed=2026-08-07T05:10:47.699915Z digest=sha256:f0b3b14c36ea5a7ae4bee8aa9bd676560aff0989c2b8479489834d0839e11938

Observation 38c27a63-7aab-4904-91fd-52fa2f6f72cc · inbound

AdaptiveLLM: A Framework for Selecting Optimal Cost-Efficient LLM for Code-Generation Based on CoT Length cites this paper.

AdaptiveLLM: A Framework for Selecting Optimal Cost-Efficient LLM for Code-Generation Based on CoT Length LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 26

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source=pdf_text observed=2026-08-07T04:30:43.639675Z digest=sha256:e0bef07f5abba48f8cca58fedd1199cf08a91f852845e9ad77432244b157fa25

Observation 483fd1da-e1d9-4aa9-9052-affc7e5e6bf2 · inbound

BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute cites this paper.

BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 23

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no resolver link, observed 2026-08-06T22:05:27.625864Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T22:05:27.625864Z digest=sha256:bc8df9a8e26632081e52029cecb43cb3efe183e3f79380b150214f7c50f38af6

Observation 908b419e-cb3d-45e8-b949-634ed9bfb66c · inbound

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs cites this paper.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 27

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no resolver link, observed 2026-08-06T18:12:58.673023Z

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source=pdf_text observed=2026-08-06T18:12:58.673023Z digest=sha256:983a9f5bd3d1db0ed3b6fb8f8d98e1fae01e37821315b628a3aeb00d05224682

Observation 36a2f02c-f215-46ad-bd40-b0da709f528f · inbound

A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement cites this paper.

A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 30

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arxiv_id, observed 2026-05-21T23:30:46.055962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-21T23:26:38.457193Z digest=sha256:bd76cec8cddbebd63e656e077e0ae7bcd3f91c8e22ad67fecc3d7020958cd537

Observation a88aacd8-5d3b-4c5d-b1d6-7c35231fde31 · inbound

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models cites this paper.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 17

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source=arxiv_source observed=2026-08-05T17:15:15.279206Z digest=sha256:352543d6120c57c136711b04d9210fad1fbcb12263aa2a8a2b9f728d1230bee1

Observation 7c622c49-da51-4300-b926-d025b53eb3b7 · inbound

Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units cites this paper.

Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 21

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source=arxiv_source observed=2026-08-05T16:18:45.844235Z digest=sha256:e45a53a41f05c1c12a48d4c6d4221f280dee92b704c18cf1b5052d0068c38567

Observation aae3b31e-57a4-4630-97df-095af7344139 · inbound

DRF: LLM-AGENT Dynamic Reputation Filtering Framework cites this paper.

DRF: LLM-AGENT Dynamic Reputation Filtering Framework LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 6

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source=pdf_text observed=2026-08-05T05:04:52.686656Z digest=sha256:a71b9ce0e35255a9323b169da7364f7e4dadb05a279505f02faa5129be68af88

Observation 9747c1c0-3807-410f-bf75-e47dc570bee1 · inbound

Towards Generalized Routing: Model and Agent Orchestration for Adaptive and Efficient Inference cites this paper.

Towards Generalized Routing: Model and Agent Orchestration for Adaptive and Efficient Inference LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 13

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source=pdf_text observed=2026-08-04T22:03:01.998475Z digest=sha256:16d773f3baa99e99d91508b6fc6dcaebdd80d27de805e1984f086ea1d3b3a572

Observation 7791bab7-543f-4490-9da7-f376ccb0d767 · inbound

StatEval: A Comprehensive Benchmark for Large Language Models in Statistics cites this paper.

StatEval: A Comprehensive Benchmark for Large Language Models in Statistics LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 15

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no resolver link, observed 2026-08-04T10:36:23.672689Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T10:36:23.672689Z digest=sha256:24b095442a0519cf51523724ff37522edf0bb532daaf9ea2062ab12b6deda745

Observation 5529df80-0829-4460-bc9d-24cbda953884 · inbound

Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process cites this paper.

Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 27

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verified exact
arxiv_id, observed 2026-05-16T19:58:22.743646Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-16T19:57:03.999154Z digest=sha256:36615737103e1b5f61b5b75a40f726baf4a9720ea4b744c9d4ba84e9149fdd19

Observation 34049d38-ef2b-4938-bc4e-59cb377bcd12 · inbound

Context Learning for Multi-Agent Discussion cites this paper.

Context Learning for Multi-Agent Discussion LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 10

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arxiv_id, observed 2026-05-16T08:10:45.520815Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-16T08:08:39.182921Z digest=sha256:2469ff08cfdc89162f39fb9362cbc8165ce84c656a546f21bbfc7deeb93233e8

Observation 4c94298c-68e7-4207-a626-7e755e893ae6 · inbound

When Agents Disagree: The Selection Bottleneck in Multi-Agent LLM Pipelines cites this paper.

When Agents Disagree: The Selection Bottleneck in Multi-Agent LLM Pipelines LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 10

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source=pdf_text observed=2026-08-02T17:52:54.761095Z digest=sha256:23d2690a36a08ebadfa215b02daefea44cecfca90671db002904c14e953f62e0

Observation d8504df3-7680-4447-b66e-8e1d4ba037ec · inbound

FREE-Switch: Frequency-based Dynamic LoRA Switch for Style Transfer cites this paper.

FREE-Switch: Frequency-based Dynamic LoRA Switch for Style Transfer LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 15

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verified exact
arxiv_id, observed 2026-05-11T09:36:07.060071Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T15:55:16.158145Z digest=sha256:1d11f5e61dde1060e2b24ec0015c1c649d93b858742a6b2a5252bee7fa8ff4d3

Observation c0b067d5-8037-41d9-9a46-d834587c9638 · inbound

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) cites this paper.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 13

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verified exact
arxiv_id, observed 2026-05-11T08:40:58.908052Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T16:34:11.393056Z digest=sha256:a783d2df5a718cd104208753c61acc085266c47932952eef6ad8bb372004aed3

Observation 6d191fb7-be81-4ebc-a3f4-4b1be9cc4839 · inbound

Privacy-Preserving LLMs Routing cites this paper.

Privacy-Preserving LLMs Routing LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 8

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verified exact
arxiv_id, observed 2026-05-10T08:32:51.612098Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T08:32:43.015370Z digest=sha256:6082524745fafdd36d171ef3491a1ab354e855bcb503d348e95fd7768116ab75

Observation 814a8f01-eb21-4054-8780-a411504833ac · inbound

CADMAS-CTX: Contextual Capability Calibration for Multi-Agent Delegation cites this paper.

CADMAS-CTX: Contextual Capability Calibration for Multi-Agent Delegation LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 14

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arxiv_id, observed 2026-05-10T05:36:02.123951Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T05:31:27.700576Z digest=sha256:8a0928115ff364c6dd76d87629a4f949cc9a55239d7340f798c06cdfa282fddb

Observation 74e8d109-16ab-402f-8277-8a449cbbda41 · inbound

Response Time Enhances Alignment with Heterogeneous Preferences cites this paper.

Response Time Enhances Alignment with Heterogeneous Preferences LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 91

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metadata mismatch
arxiv_id, observed 2026-05-11T04:46:00.166854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-11T01:04:26.288913Z digest=sha256:bddc20aebb9120d7501082594f0ae9f2bc10c25af7c3282b75f173fbd1b6a140

Observation 4e4b216f-ee15-44a4-9083-a507888d1e62 · inbound

TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination cites this paper.

TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 42

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T18:02:42.355523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-19T18:01:06.649723Z digest=sha256:0aa6265c0cf6f479bed0eb6bea95335712d13d0ecfb67ae22174a1e4ea1dabba

Observation 7e315b3f-ef97-41f6-936b-b235ca51369c · inbound

LRanker: LLM Ranker for Massive Candidates cites this paper.

LRanker: LLM Ranker for Massive Candidates LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-06-29T10:33:18.835695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-29T10:30:39.472561Z digest=sha256:c1e566480925021f3883059995d6aaa2fd3d652e4b19b25e6c52df6f370b9bf9

Observation 1aaf0e39-0fb3-47c8-9a66-7389d7fc0356 · inbound

Online Pandora's Box for Contextual LLM Cascading cites this paper.

Online Pandora's Box for Contextual LLM Cascading LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 70

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T17:37:14.307640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-27T21:59:43.429931Z digest=sha256:6742fb80f3a60152e3c1cc72617fdb9c2c17ac4fa1d39aa3a27987cf90571f4a

Observation c35e507b-9b72-4fa9-9768-bb6a55d13dea · inbound

RouteJudge: An Open Platform for Reproducible and Preference-Aware LLM Routing cites this paper.

RouteJudge: An Open Platform for Reproducible and Preference-Aware LLM Routing LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-07-04T00:19:13.486216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-26T21:17:29.543901Z digest=sha256:c04dc477db216c788787d0406427dab23edd89a8b9a57fd0ade6f0d3a08e959e

Observation cfa73881-c34d-4d1d-a5f2-845c3898e66e · inbound

A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation cites this paper.

A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 80

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T19:50:11.020770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-25T20:58:53.119386Z digest=sha256:f85fe46747de0d7c2c601d108a370ab49a3587310ba8c225e755580fbaef38fb

Observation 62b80367-163c-4fcd-bba9-360fedd7756d · inbound

A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation cites this paper.

A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 80

Resolution
unresolved
no resolver link, observed 2026-08-02T10:16:44.905476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T10:16:44.905476Z digest=sha256:d28e0d75465d4f5539e79c63fe85ac79bf1cdb2fee7575e404419a5623c2dfed

Observation e46e67cd-140b-4bef-8264-3858b6cb8fa1 · inbound

When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents Across 67 Frontier Models cites this paper.

When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents Across 67 Frontier Models LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-07-04T14:19:53.623152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-26T04:20:02.722108Z digest=sha256:1fc45e186b931b94be2afb5426d0abfdd829a9471783cafa8b40f519f5d5bd27

Observation 292be8c5-a6da-402a-8463-7f6c9041b839 · inbound

SWE-Router: Routing in Multi-turn Agentic Software Engineering Tasks cites this paper.

SWE-Router: Routing in Multi-turn Agentic Software Engineering Tasks LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 56

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T18:37:16.509464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-02T18:19:43.146102Z digest=sha256:afa692ae8bb69601b9839f993e1675293ce2794a375b97eae5393f6bd68648aa

Observation a2c8d4ee-c42e-4dd3-a3ac-3723e7ed33ac · inbound

Multi-Turn On-Policy Distillation with Prefix Replay cites this paper.

Multi-Turn On-Policy Distillation with Prefix Replay LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 277

Resolution
unresolved
no resolver link, observed 2026-07-11T13:53:36.775836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T13:53:36.775836Z digest=sha256:2703466d66d469ab86d41e0c1861055a8c9fcab4402465062927dd31501785ed

Observation aabd426a-27ae-4397-b967-f579a7284acf · inbound

Multi-Turn On-Policy Distillation with Prefix Replay cites this paper.

Multi-Turn On-Policy Distillation with Prefix Replay LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 278

Resolution
unresolved
no resolver link, observed 2026-08-02T08:41:04.832285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T08:41:04.832285Z digest=sha256:363de6f38a35a6fa4a44beb20d2e2971c71b9764b68c64c6e07c309b7382e6e5

Observation e8787761-f6cc-4b33-866b-02215d92ab96 · inbound

Are Diversity Metrics Measuring Diversity? A Capability-Controlled Audit of Majority-Vote Gain in LLM Ensembles cites this paper.

Are Diversity Metrics Measuring Diversity? A Capability-Controlled Audit of Majority-Vote Gain in LLM Ensembles LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-01T09:29:44.637481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T09:29:44.637481Z digest=sha256:476c0d3b42062c646d6e9ff836abba2ccbb0b4498850ab85df2f872079c3166f

Observation 0adee1f2-c634-4bcc-936b-efa9db5f12d7 · inbound

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization cites this paper.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-01T05:09:54.163495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T05:09:54.163495Z digest=sha256:6e798402dc215f2d6135376548503b89b0d166f02e3330b1d5ab6c2f5265bdbb

Observation 42958fc3-d9e3-4267-a06b-abbfc2a90ab8 · inbound

Opti-Q: A Constraint-Based Optimization Framework for Multi-LLM Question Planning cites this paper.

Opti-Q: A Constraint-Based Optimization Framework for Multi-LLM Question Planning LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-02T11:01:29.440785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T11:01:29.440785Z digest=sha256:0e34b204fc5d937c3091e35ac1ad5a5064f5110f4594d8e8d508072384e40865

Observation 7957c398-a41b-47a6-8971-22d0b63af353 · inbound

Training-Free versus Training-Based Intent Classification in LLMs: Accuracy, Robustness, and Failure Modes cites this paper.

Training-Free versus Training-Based Intent Classification in LLMs: Accuracy, Robustness, and Failure Modes LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 99

Resolution
unresolved
no resolver link, observed 2026-08-04T07:49:39.975819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T07:49:39.975819Z digest=sha256:adee2731b63fd067043b2b1edb2efdf03a11cf5d865fb72795d4bef8120c0b9a