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Paper Citation Record · LEDGER

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis

As of 15 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.19794.

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

pith.paper-citation-record.v1
2607.19794 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T11:45:22.646912Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved36
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 70ca29f9-8ae8-45eb-b399-bbb55905f636 · outbound

This paper cites FinBERT: Financial Sentiment Analysis with Pre-trained Language Models.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis FinBERT: Financial Sentiment Analysis with Pre-trained Language Models

Reference 1

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source=pdf_text observed=2026-08-01T11:45:19.781013Z digest=sha256:47e561b98fac2e2ed3573bb6baf32bb90485a8b2de341d35b96bf91500037968

Observation 01e51a3f-5023-4962-9cec-0c29cfba5e2c · outbound

This paper cites MetaGPT: Meta programming for a multi-agent collaborative framework.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis MetaGPT: Meta programming for a multi-agent collaborative framework

Reference 6

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Observation f2d03173-9a73-4548-abd8-edbafb5441b4 · outbound

This paper cites an unresolved cited work.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Unresolved cited work

Reference 7

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Observation 3fc53cfd-9dd8-4011-8eec-2b04f2402544 · outbound

This paper cites Mistral 7B.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Mistral 7B

Reference 9

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source=pdf_text observed=2026-08-01T11:45:20.884693Z digest=sha256:88b2b05cc20897b263e092a15b734a26cb157971dc552b6d36019a2f00298f5f

Observation 0c47ab58-06cb-4b01-afb2-4e7053e04ed9 · outbound

This paper cites Is BERT really robust? a strong baseline for natural language attack on text classifi- cation and entailment.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Is BERT really robust? a strong baseline for natural language attack on text classifi- cation and entailment

Reference 10

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source=pdf_text observed=2026-08-01T11:45:20.969338Z digest=sha256:4a16fb95d78a8fdaad82f3f33f7c81eed957bce346435f270a0664875ce83d59

Observation 27d36794-4f1e-4647-9643-e930ebc51677 · outbound

This paper cites CAMEL: Commu- nicative agents for “mind” exploration of large language model society.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis CAMEL: Commu- nicative agents for “mind” exploration of large language model society

Reference 14

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source=pdf_text observed=2026-08-01T11:45:21.320505Z digest=sha256:0b4fa614e17188f3f67d1e58649d5d3865b752d71f33bf2d8f64105cf26ab27b

Observation 04593537-942d-4a86-9d2f-58e369ae6958 · outbound

This paper cites Encouraging di- vergent thinking in large language models through multi- agent debate.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Encouraging di- vergent thinking in large language models through multi- agent debate

Reference 15

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source=pdf_text observed=2026-08-01T11:45:21.392831Z digest=sha256:05b0572f944053d43ea7c07061222f24907299a6a43e12514c5c94d86c4c0415

Observation 6f9d17c1-19fa-4f85-b758-bf72681d740f · outbound

This paper cites When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks.The Journal of Finance, 66(1):35–65,.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks.The Journal of Finance, 66(1):35–65,

Reference 16

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source=pdf_text observed=2026-08-01T11:45:21.467919Z digest=sha256:f956110dbe6cdcb4f8b2b42f9529b7371ad51cb8518bf890c372b7a8b74e2a0c

Observation cf78c0fa-d430-48f2-b424-8dc997fd910b · outbound

This paper cites Gales, Mark J.˙ SelfCheckGPT: Zero-resource black-box hallucination detection for generative large lan- guage models.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Gales, Mark J.˙ SelfCheckGPT: Zero-resource black-box hallucination detection for generative large lan- guage models

Reference 18

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source=pdf_text observed=2026-08-01T11:45:21.587230Z digest=sha256:79f2d75349f4c8b6bb9de16ad42a18ec6f048643c9d061338aae793f44072f4e

Observation fa540caf-f98f-49cc-9d6a-91810feee47e · outbound

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

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis RouteLLM: Learning to Route LLMs with Preference Data

Reference 19

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source=pdf_text observed=2026-08-01T11:45:21.661359Z digest=sha256:d1a825dbf20002ebce823b27ed1dfb4b9e0dfcaa6a0f4024e5f598ddc707f793

Observation 87d84281-8ec5-4e45-a960-d341e8765816 · outbound

This paper cites Ignore Previous Prompt: Attack Techniques For Language Models.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Ignore Previous Prompt: Attack Techniques For Language Models

Reference 20

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source=pdf_text observed=2026-08-01T11:45:21.799132Z digest=sha256:ae236cca53f881902dcb836c4f1eef8abf904d7d575f847ce3c711f5124012ac

Observation 149136f6-3b1e-4889-b7d1-dd360b4df49c · outbound

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

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Sentence-BERT: Sentence embeddings using siamese BERT-networks

Reference 21

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source=pdf_text observed=2026-08-01T11:45:21.932811Z digest=sha256:b6d3677acd9d58d6ec5974281f23399925d63d5c9b3e79bae7bebef5e2ac27e8

Observation 84c0efd4-eca2-404f-89c6-155c378a4d3b · outbound

This paper cites Making monolingual sentence embeddings multilingual using knowledge distillation.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Making monolingual sentence embeddings multilingual using knowledge distillation

Reference 22

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source=pdf_text observed=2026-08-01T11:45:22.009174Z digest=sha256:21e812cbf01f35c69f8b0757894abf0cabbed0a8c4b73cea5ba4567d31178f28

Observation 758fc6e8-0a90-4c61-a94f-d33a8229bb13 · outbound

This paper cites Active learning literature sur- vey.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Active learning literature sur- vey

Reference 23

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source=pdf_text observed=2026-08-01T11:45:22.142164Z digest=sha256:ebf5b69f7a942bf422ffb5f19845995686031fd70255a5f3c0454513a533e9f1

Observation b2185f6a-6193-4e56-8097-3c6db81b66ac · outbound

This paper cites Qwen2.5 Technical Report.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Qwen2.5 Technical Report

Reference 25

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source=pdf_text observed=2026-08-01T11:45:22.319840Z digest=sha256:b28124df0952b4963686cb1d270218c67e61e5232ccfbbd8979a990ebe687091

Observation 24dd6e5b-073f-4729-ab43-f2ab3c4cf95a · outbound

This paper cites https://huggingface.co/datasets/zeroshot/ twitter-financial-news-sentiment,.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis https://huggingface.co/datasets/zeroshot/ twitter-financial-news-sentiment,

Reference 26

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source=pdf_text observed=2026-08-01T11:45:22.349747Z digest=sha256:70683cff22bb504774ac1db857033ec8c50e5e098763862419019edadeb557bf

Observation a5206f43-21e1-489b-85ea-074ca8a1fb7c · outbound

This paper cites Mixture of cheap and expensive models for cost-effective inference.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Mixture of cheap and expensive models for cost-effective inference

Reference 27

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source=pdf_text observed=2026-08-01T11:45:22.378311Z digest=sha256:a2d6acca4ff4cd0fdec34ec3ce612ba889a5b34bd4dd91590a8db83e02a29f79

Observation a7eee2d7-adec-4f52-ae3d-707a676347ee · outbound

This paper cites Self- consistency improves chain of thought reasoning in language models.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Self- consistency improves chain of thought reasoning in language models

Reference 28

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source=pdf_text observed=2026-08-01T11:45:22.406037Z digest=sha256:1e30d62b96156ad91d6b37e5cf8bee85c31df3d90120fba4cbd24ac8a87e788a

Observation 37cc9a4f-829a-44e6-b42f-059b4f8712c9 · outbound

This paper cites HuggingFace’s transformers: State-of-the-art natural language process- ing.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis HuggingFace’s transformers: State-of-the-art natural language process- ing

Reference 29

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source=pdf_text observed=2026-08-01T11:45:22.430992Z digest=sha256:f207917b5d57646ba11d9f34fc7fc955e7e5f8428b833ab13d7a0c26d79ad6f9

Observation 5bf25539-f83a-46c4-be87-9897c77a0250 · outbound

This paper cites BloombergGPT: A Large Language Model for Finance.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis BloombergGPT: A Large Language Model for Finance

Reference 30

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source=pdf_text observed=2026-08-01T11:45:22.444652Z digest=sha256:cd22dc702f342fc20752dfce701d6557773fbedefcb701c38bd6dc6ee8b3e4b7

Observation 76b7f7bb-38bb-4136-bc4f-52ce7408c206 · outbound

This paper cites AutoGen: En- abling next-gen LLM applications via multi-agent conver- sation framework.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis AutoGen: En- abling next-gen LLM applications via multi-agent conver- sation framework

Reference 31

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source=pdf_text observed=2026-08-01T11:45:22.473156Z digest=sha256:76a27f83453c8dadfd879573c46490e46aff52c9599c4a83519543e67c935d03

Observation 322ed4f2-4daf-4d64-9959-dabfbccc9175 · outbound

This paper cites PIXIU: A large language model, instruction data and evaluation bench- mark for finance.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis PIXIU: A large language model, instruction data and evaluation bench- mark for finance

Reference 32

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source=pdf_text observed=2026-08-01T11:45:22.513375Z digest=sha256:ac714bf904a8bb8c49c931b1667df8bbff754a0197e69255041acf4a0db0bd89

Observation fac9ac24-bb7c-4e46-a810-121e10ca9c05 · outbound

This paper cites FinBERT-tone- Chinese: Domain-specific BERT for chinese finan- cial sentiment.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis FinBERT-tone- Chinese: Domain-specific BERT for chinese finan- cial sentiment

Reference 33

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source=pdf_text observed=2026-08-01T11:45:22.552745Z digest=sha256:caf1c08c1c2710a6980820492738c6f7714044505ad8cb95a7df00a8cc0c8945

Observation 9d211644-a29c-44fc-87b2-d899f829e179 · outbound

This paper cites FinBERT: A Pretrained Language Model for Financial Communications.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis FinBERT: A Pretrained Language Model for Financial Communications

Reference 34

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source=pdf_text observed=2026-08-01T11:45:22.570508Z digest=sha256:c0857d5f60c51659006b4663d2726e9d4495706b7d8306647e2b243e5d816c10

Observation fbe51f29-764b-4bd7-8d89-57c1722d5bbc · outbound

This paper cites FinGPT: Open-source financial large language models.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis FinGPT: Open-source financial large language models

Reference 35

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source=pdf_text observed=2026-08-01T11:45:22.608791Z digest=sha256:6f32ca0919f1f6478ba2c0eb15df11e90680fa2a187ac0efa99f36a0069617f2

Observation fe1f52d2-da68-421e-8e70-1080c1168523 · outbound

This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 36

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source=pdf_text observed=2026-08-01T11:45:22.646912Z digest=sha256:eb079bbaab28c6d3ad06f30ca832de30d75dc4fdd0434536823bf37602a6495d

Observation de173938-26a7-4d79-ba08-1aff4cdf4cfc · outbound

This paper cites Large Language Model Routing with Benchmark Datasets.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Large Language Model Routing with Benchmark Datasets

Reference 2009

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source=pdf_text observed=2026-08-01T11:45:22.255870Z digest=sha256:3d7ab5d34a7415b7dad90a10799c6b6db522102a9338cc5dca5e8d0651431625

Observation 53e9f6fd-117d-440b-a24e-3020364efc41 · outbound

This paper cites Good debt or bad debt: Detecting semantic orientations in economic texts.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Good debt or bad debt: Detecting semantic orientations in economic texts

Reference 2011

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source=pdf_text observed=2026-08-01T11:45:21.541941Z digest=sha256:42641468aec1dbca790d9e88f431f0519fc17f6a7fbcd3e9f559e4d394d7fadb

Observation 9f7406e5-1b26-484f-ab92-8f89a06f18cc · outbound

This paper cites Survey of hallucination in natural language generation.ACM Computing Surveys, 55(12):1–38,.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Survey of hallucination in natural language generation.ACM Computing Surveys, 55(12):1–38,

Reference 2014

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source=pdf_text observed=2026-08-01T11:45:20.686351Z digest=sha256:3ea34fd005d7c5b39df90c18bf2ec19ba523434e42296140aeb8fbc61ff733e7

Observation f10b9fdc-4c41-4f26-9d81-b7b9f8565c0a · outbound

This paper cites A Survey of Large Language Models in Finance (FinLLMs).

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis A Survey of Large Language Models in Finance (FinLLMs)

Reference 2017

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source=pdf_text observed=2026-08-01T11:45:21.240451Z digest=sha256:c46404c351f289e9dda24cdfda5f4c7e14f5d5b77cb52dfd8d95b2a4d8d5c0ce

Observation f1d25316-1c12-4387-b586-fa0c8a1b072f · outbound

This paper cites FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance

Reference 2019

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source=pdf_text observed=2026-08-01T11:45:19.960123Z digest=sha256:451e8350ed25ab6f4d92d719c27aa8378b62d236c04a64bfa393d1c4b6f76f5a

Observation 78781b94-db38-4a6f-a671-e3777eff2d5e · outbound

This paper cites Billion-scale similarity search with GPUs.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Billion-scale similarity search with GPUs

Reference 2020

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source=pdf_text observed=2026-08-01T11:45:21.037062Z digest=sha256:ac7bc22404a4d09b06633972258661819bf260e51c7053297afeca228216d735

Observation b65ff8b5-626c-4dd1-a187-3d7156b33f19 · outbound

This paper cites Simple and scal- able predictive uncertainty estimation using deep ensem- bles.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Simple and scal- able predictive uncertainty estimation using deep ensem- bles

Reference 2021

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source=pdf_text observed=2026-08-01T11:45:21.163737Z digest=sha256:6af3d9131afb95be67846986170b099844da690ec7d53ddf53eb9a6b56291a2c

Observation 30688598-213e-4ead-94e7-2e0f6da8d20f · outbound

This paper cites Tenenbaum, and Igor Mordatch.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis Tenenbaum, and Igor Mordatch

Reference 2022

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source=pdf_text observed=2026-08-01T11:45:20.113856Z digest=sha256:17207324f22b1525fd956bb98a4ffe4b13748392f90875d78eb4c021097cadc7

Observation ee79a955-1a92-475a-a1a2-2323aa0f0306 · outbound

This paper cites LLM.int8(): 8-bit matrix multiplication for transformers at scale.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis LLM.int8(): 8-bit matrix multiplication for transformers at scale

Reference 2023

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source=pdf_text observed=2026-08-01T11:45:20.035157Z digest=sha256:cbbdc2f3c4e313ce3561b6e8f20210b40cd8bdb7fe131517e061c0b06a666c18

Observation 832b219b-92cf-4c85-9544-900e57e7cfde · outbound

This paper cites https://huggingface.co/datasets/ChanceFocus/ flare-fpb,.

TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis https://huggingface.co/datasets/ChanceFocus/ flare-fpb,

Reference 2024

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no resolver link, observed 2026-08-01T11:45:20.247349Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.