Paper Citation Record · LEDGER

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration

As of 7 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2507.23167.

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

pith.paper-citation-record.v1
2507.23167 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:03:16.662413Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

15 of 15 outbound references displayed

verified exact0
verified fuzzy0
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3fa1c2ca-8a20-4113-95d4-24180130eeca · outbound

This paper cites GPT-4 Technical Report.

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:16.466028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42cad935-733f-447c-b499-5832851180d8 · outbound

This paper cites Discovering Latent Knowledge in Language Models Without Supervision.

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration Discovering Latent Knowledge in Language Models Without Supervision

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:16.510374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8168e7e-c108-4633-9bb6-299ca33865d6 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:16.523860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4d52df3-eb53-4d65-9183-38b5fd412504 · outbound

This paper cites Mistral 7B.

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration Mistral 7B

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:16.548580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af928b3c-b0dd-4b99-862c-7185b5d7a57a · outbound

This paper cites ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language.

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:16.584987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dddec3e-6f43-44d2-af30-e6ed09234a96 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration Gemini: A Family of Highly Capable Multimodal Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:16.593889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51be322d-8886-40ec-adcb-06901f72511e · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:16.603594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85a1d04b-36e5-40f6-809e-a3ac0438fc44 · outbound

This paper cites SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference.

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:16.630904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7cc06c9-b362-44a1-933d-68c1bb6707aa · outbound

This paper cites LLM Multi-Agent Systems: Challenges and Open Problems.

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration LLM Multi-Agent Systems: Challenges and Open Problems

Reference 1785

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:16.536967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fd7b625-58c7-445f-b2c3-e8b552696261 · outbound

This paper cites Representation Engineering: A Top-Down Approach to AI Transparency.

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration Representation Engineering: A Top-Down Approach to AI Transparency

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:16.662413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a0d6a05-8c6f-46e0-88ce-3de5f879dc47 · outbound

This paper cites Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought.

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:16.576057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 215dcf7e-8c09-489b-966e-2989a42aca0a · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:16.614323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9650a06f-009a-4587-aba0-11be16cdfc33 · outbound

This paper cites Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection.

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:16.561254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51f48a51-116f-42f5-90af-5c003ae5946e · outbound

This paper cites EnsemW2S: Enhancing Weak-to-Strong Generalization with Large Language Model Ensembles.

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration EnsemW2S: Enhancing Weak-to-Strong Generalization with Large Language Model Ensembles

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:16.481258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0badb2cd-573a-482c-ab91-654e07525471 · outbound

This paper cites MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms.

LENS: Learning Ensemble Confidence from Neural States for Multi-LLM Answer Integration MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:16.496453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.