Paper Citation Record · LEDGER

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation

As of 21 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2508.06781.

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

pith.paper-citation-record.v1
2508.06781 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:37:00.047687Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T06:59:48.014736Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T07:00:10.326450Z

Reference resolution

47 of 47 outbound references displayed

verified exact2
verified fuzzy30
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0b04d550-0d09-4304-a50b-bc32f0eaf02e · outbound

This paper cites MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Reference 1

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Source-reported events for the cited work

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Observation b360b076-37b9-427d-bb23-60b463fc6e18 · outbound

This paper cites Results shown for the best-performing configuration per loss type among BGE-trained models.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Results shown for the best-performing configuration per loss type among BGE-trained models

Reference 2

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Observation eef61a84-e2e0-47db-90a0-7cd2ba1892d1 · outbound

This paper cites Your task is to judge how well the passage answers the query. 0 - Irrelevant, 1 - Perfectly relevant, exact answer. Answer with 0, or 1.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Your task is to judge how well the passage answers the query. 0 - Irrelevant, 1 - Perfectly relevant, exact answer. Answer with 0, or 1

Reference 3

Resolution: verified exact; raw_fallback, observed 2026-08-05T22:37:00.229375Z

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Observation 24a426c8-8751-4ad1-bcab-e1a17215aa88 · outbound

This paper cites Zhuyun Dai, Vincent Y Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith Hall, and Ming-Wei Chang.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Zhuyun Dai, Vincent Y Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith Hall, and Ming-Wei Chang

Reference 5

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Observation e79fe366-5c5c-4b18-86ee-48dc1d66999a · outbound

This paper cites Retrieve the most relevant passages to the given query.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Retrieve the most relevant passages to the given query

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:37:00.493021Z

Source-reported events for the cited work

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Observation 833b58ec-fec0-4921-b300-2179274fd34f · outbound

This paper cites Learning deep representations by mutual information estimation and maximization.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Learning deep representations by mutual information estimation and maximization

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:37:00.823409Z

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Observation c691d895-1633-4b32-b330-4cc426c2bbc9 · outbound

This paper cites Dense passage retrieval for open-domain question an- swering.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Dense passage retrieval for open-domain question an- swering

Reference 10

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Observation a0638d20-9f68-4e7a-8be9-48cb0444e5a7 · outbound

This paper cites NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Reference 11

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Observation b67f62d4-1db6-49d4-b8af-0c29a7916b5c · outbound

This paper cites Lightblue reranker distillation dataset.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Lightblue reranker distillation dataset

Reference 13

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Observation cc69a113-e9a4-4c13-aa79-47b676c77eac · outbound

This paper cites Sadhika Malladi, Kaifeng Lyu, Abhishek Panigrahi, and Sanjeev Arora.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Sadhika Malladi, Kaifeng Lyu, Abhishek Panigrahi, and Sanjeev Arora

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:37:00.767841Z

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Observation a31ca482-1692-408a-b221-32dd7f10da5c · outbound

This paper cites Generative Representational Instruction Tuning.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Generative Representational Instruction Tuning

Reference 15

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Observation 36bfb7f9-ed42-4a04-9a5e-9ea896a1ff38 · outbound

This paper cites Mitigating false-negative contexts in multi- document question answering with retrieval marginalization.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Mitigating false-negative contexts in multi- document question answering with retrieval marginalization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:37:00.754249Z

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Observation a8c7de54-cc23-4537-93d0-78fa1ced5144 · outbound

This paper cites Large dual encoders are generalizable retrievers.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Large dual encoders are generalizable retrievers

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:37:00.739730Z

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Observation b83e775d-dbee-4ce3-9520-377191cb6732 · outbound

This paper cites RocketQA: An optimized training approach to dense passage retrieval for open-domain question answering.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation RocketQA: An optimized training approach to dense passage retrieval for open-domain question answering

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:37:00.725987Z

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Observation d88e6bc3-44d5-45f3-a7a0-e41444070a7c · outbound

This paper cites SQuAD: 100,000+ questions for machine comprehension of text.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation SQuAD: 100,000+ questions for machine comprehension of text

Reference 20

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Source-reported events for the cited work

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Observation 3bee9089-e839-4fe3-a589-17fe0d94e969 · outbound

This paper cites Sentence-BERT: Sentence embeddings using Siamese BERT-networks.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Sentence-BERT: Sentence embeddings using Siamese BERT-networks

Reference 21

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Source-reported events for the cited work

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Observation 90a3ffb5-3ef5-4966-896a-0df54de06265 · outbound

This paper cites RocketQAv2: A joint training method for dense passage retrieval and passage re-ranking.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation RocketQAv2: A joint training method for dense passage retrieval and passage re-ranking

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:37:00.685210Z

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Observation 94862c12-02a0-4871-be1a-65a978100191 · outbound

This paper cites The probabilistic relevance framework: Bm25 and beyond.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation The probabilistic relevance framework: Bm25 and beyond

Reference 23

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Observation 5f85ecb9-216f-47b1-b2d1-ad2463eb894a · outbound

This paper cites doi: 10.1007/978-981-15-5554-1.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation doi: 10.1007/978-981-15-5554-1

Reference 24

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Observation db2b4b02-d941-4c45-a140-b37af8ef5e53 · outbound

This paper cites ColBERTv2: Effective and efficient retrieval via lightweight late interaction.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation ColBERTv2: Effective and efficient retrieval via lightweight late interaction

Reference 25

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Observation dcbcbedd-2591-45cb-9cc1-22ced9d2bdc5 · outbound

This paper cites Repetition Improves Language Model Embeddings.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Repetition Improves Language Model Embeddings

Reference 26

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Observation 76f73f82-8458-4db0-9264-1237de72191b · outbound

This paper cites Smith, Luke Zettlemoyer, and Tao Yu.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Smith, Luke Zettlemoyer, and Tao Yu

Reference 27

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Observation f402b592-5f67-44fe-bf4f-a8782c0a7d84 · outbound

This paper cites Is ChatGPT good at search? investigating large language models as re-ranking agents.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Is ChatGPT good at search? investigating large language models as re-ranking agents

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:37:00.631366Z

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Observation beaead08-da13-4a72-a356-6a7b1452f399 · outbound

This paper cites FEVER: a large-scale dataset for fact extraction and VERification.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation FEVER: a large-scale dataset for fact extraction and VERification

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:37:00.617211Z

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Observation 01c46671-498b-4a9e-a658-b34933d2c04b · outbound

This paper cites Text Embeddings by Weakly-Supervised Contrastive Pre-training.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 30

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Observation 4421920f-5bb6-4003-b3f8-d42696ee7531 · outbound

This paper cites Improving Text Embeddings with Large Language Models.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Improving Text Embeddings with Large Language Models

Reference 31

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BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Unresolved cited work

Reference 32

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Observation 8abbbe1b-d919-4866-9382-b583cc083167 · outbound

This paper cites Contrastive learning of sentence embeddings from scratch.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Contrastive learning of sentence embeddings from scratch

Reference 33

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Observation deba85a8-b4d8-4439-8bed-2fb2dde3dea7 · outbound

This paper cites MIRACL: A Multilingual Retrieval Dataset Covering 18 Diverse Languages.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation MIRACL: A Multilingual Retrieval Dataset Covering 18 Diverse Languages

Reference 34

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Observation 8684a056-a74f-412d-89d5-6b1fe01ccf98 · outbound

This paper cites Classical retrieval methods like TF-IDF and BM25 (Robertson & Zaragoza,.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Classical retrieval methods like TF-IDF and BM25 (Robertson & Zaragoza,

Reference 35

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Observation 1f5417aa-64a3-4e71-8abc-b6758fa131a0 · outbound

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BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Unresolved cited work

Reference 37

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Observation 8386efb4-f5e8-4d26-ba06-651c940c8fd8 · outbound

This paper cites 1” through “5.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation 1” through “5

Reference 38

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Observation bc30ac33-5aa4-4439-a097-ff837dbc4064 · outbound

This paper cites These experiments were carried out on two diverse datasets, LightBlue (multilingual, in- batch only) and BGE-M3 (Chen et al.,.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation These experiments were carried out on two diverse datasets, LightBlue (multilingual, in- batch only) and BGE-M3 (Chen et al.,

Reference 44

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Observation d20feb5c-6ff3-42a6-b8eb-fcf8655d1fa1 · outbound

This paper cites For fair comparison, we run hyperparameter search for all methods to tune for learning rates and logit scales, as well as hyperparameters specific to each training loss.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation For fair comparison, we run hyperparameter search for all methods to tune for learning rates and logit scales, as well as hyperparameters specific to each training loss

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:37:00.432754Z

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Observation a43bde40-d3ae-4475-88e6-a41081dd560e · outbound

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BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Unresolved cited work

Reference 100

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Observation 439f52fd-60ab-4763-93fe-5b6c11ef41f3 · outbound

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BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Unresolved cited work

Reference 256

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Observation 6be7d0d4-6688-4841-910e-c899e758d70e · outbound

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BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Unresolved cited work

Reference 2005

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Observation 1bf855ea-1c48-4349-83db-b54676ff31d0 · outbound

This paper cites Neural dense retrieval methods address this by embedding texts into dense semantic vector spaces using pre-trained language models (Karpukhin et al., 2020; Xiong et al., 2021).

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Neural dense retrieval methods address this by embedding texts into dense semantic vector spaces using pre-trained language models (Karpukhin et al., 2020; Xiong et al., 2021)

Reference 2009

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Observation 1bf97bd4-159a-48db-a55a-ebaa22922d84 · outbound

This paper cites We apply no weight decay.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation We apply no weight decay

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:37:00.506668Z

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Observation 981f691a-cd2b-4384-99b3-32be92be1f31 · outbound

This paper cites NV-Retriever: Improving text embedding models with effective hard-negative mining.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 2017

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Observation d5b2bb0b-a9d4-493f-965c-4defdf5ed287 · outbound

This paper cites LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2018

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Observation 2e77627b-4235-4c5b-a8f9-812b083b2c10 · outbound

This paper cites SimCSE: Simple contrastive learning of sentence embeddings.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation SimCSE: Simple contrastive learning of sentence embeddings

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:37:00.836993Z

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Observation 61adbe76-3621-4c4a-ad29-9e8f1d7c3be7 · outbound

This paper cites PairDistill: Pairwise relevance distillation for dense retrieval.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation PairDistill: Pairwise relevance distillation for dense retrieval

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:37:00.809835Z

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Observation 106eccc2-1e4d-4b27-93c4-57d9e6da6713 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Representation Learning with Contrastive Predictive Coding

Reference 2022

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Observation 4f6af325-5e7f-4fa2-ab16-4516f24b759b · outbound

This paper cites Rahmani, Daniel Campos, Jimmy Lin, Ellen M.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Rahmani, Daniel Campos, Jimmy Lin, Ellen M

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:37:00.864342Z

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Observation d504c6d6-fed1-4d00-8de9-a9aaf02d859b · outbound

This paper cites Improving con- trastive learning of sentence embeddings from AI feedback.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Improving con- trastive learning of sentence embeddings from AI feedback

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:37:00.878270Z

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Observation 58c5d901-bc32-4696-9cd4-eedd22665f6d · outbound

This paper cites Towards General Text Embeddings with Multi-stage Contrastive Learning.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation Towards General Text Embeddings with Multi-stage Contrastive Learning

Reference 2025

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Pith citing papers

Observation d536d0bf-e225-430e-8bfd-7b8c8c960e8a · inbound

MemReranker: Reasoning-Aware Reranking for Agent Memory Retrieval cites this paper.

MemReranker: Reasoning-Aware Reranking for Agent Memory Retrieval BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:56:09.521192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ffb9e471-ac35-4f0c-8eca-2747c0a4dec8 · inbound

MemReranker: Reasoning-Aware Reranking for Agent Memory Retrieval cites this paper.

MemReranker: Reasoning-Aware Reranking for Agent Memory Retrieval BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-15T07:00:10.330136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.