Paper Citation Record · LEDGER

How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2404.03302.

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

pith.paper-citation-record.v1
2404.03302 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:40:41.738492Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T13:37:06.862301Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 897680a3-9165-4a76-8e82-e94ca4b7bf60 · inbound

E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning cites this paper.

E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-23T20:38:25.053129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 79de8dc0-30d8-43e5-bdbf-06944177f6c1 · inbound

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap cites this paper.

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 112

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:08:20.962013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 44d6087c-4704-4872-ae85-19e9c76d3469 · inbound

HERA: Improving Long Document Summarization using Large Language Models with Context Packaging and Reordering cites this paper.

HERA: Improving Long Document Summarization using Large Language Models with Context Packaging and Reordering How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T19:01:34.593637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d46943d-af3b-4f33-9102-823458bb95e4 · inbound

SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence cites this paper.

SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T23:49:14.744913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 986cb3f8-6e2f-4c75-90a3-324997d86798 · inbound

Improving TCM Question Answering through Tree-Organized Self-Reflective Retrieval with LLMs cites this paper.

Improving TCM Question Answering through Tree-Organized Self-Reflective Retrieval with LLMs How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T22:32:36.084524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc3e44b6-751d-4939-b65b-8c573741c2f7 · inbound

Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey cites this paper.

Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-16T11:40:41.738492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dda14209-2d8e-4fd6-95ec-8e99cf2a9d5d · inbound

A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking cites this paper.

A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-16T01:03:21.524223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e543befb-a2f4-45e9-8f11-3090ee2b5c66 · inbound

The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware) cites this paper.

The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware) How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 104

Resolution: unresolved; no resolver link, observed 2026-08-15T21:10:20.201670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a8fecb3-04c9-402f-9167-6744811763c4 · inbound

SlimRAG: Retrieval without Graphs via Entity-Aware Context Selection cites this paper.

SlimRAG: Retrieval without Graphs via Entity-Aware Context Selection How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T00:42:12.736368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83fd632b-b446-4043-8171-84f3ad0f5460 · inbound

Small Encoders Can Rival Large Decoders in Detecting Groundedness cites this paper.

Small Encoders Can Rival Large Decoders in Detecting Groundedness How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:29.766657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ef1e70a-da9c-4c67-ad6a-7a547c473e80 · inbound

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation cites this paper.

Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T22:31:36.629045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75b79031-f6b4-44fe-8569-8d07270cd9a8 · inbound

Exploring a Gamified Personality Assessment Method through Interaction with LLM Agents Embodying Different Personalities cites this paper.

Exploring a Gamified Personality Assessment Method through Interaction with LLM Agents Embodying Different Personalities How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 137

Resolution: verified exact; arxiv_id, observed 2026-05-19T06:37:07.573404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1707ca5b-1697-43d1-bca7-f5c089abecd4 · inbound

Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality cites this paper.

Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 194

Resolution: unresolved; no resolver link, observed 2026-08-05T15:38:55.187836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05b81656-04fd-445d-836d-07e94e38f17a · inbound

Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering cites this paper.

Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:06:49.854366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 925cec1f-0405-4c37-b74f-5751df9c3de4 · inbound

Robust Audio-Text Retrieval via Cross-Modal Attention and Hybrid Loss cites this paper.

Robust Audio-Text Retrieval via Cross-Modal Attention and Hybrid Loss How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:36:11.054273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 36d8422d-fbc1-4156-90da-e13423f6ed20 · inbound

Seir\^enes: Adversarial Self-Play with Evolving Distractions for LLM Reasoning cites this paper.

Seir\^enes: Adversarial Self-Play with Evolving Distractions for LLM Reasoning How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:22:01.628890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6c900aeb-fc8e-43ab-b7b3-1eeffbfc8ac2 · inbound

CLORE: Content-Level Optimization for Reasoning Efficiency cites this paper.

CLORE: Content-Level Optimization for Reasoning Efficiency How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-22T05:51:08.168132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 71e62ab3-2049-49bb-a6a2-3d208b7c42b8 · inbound

CQC-RAG: Robust Retrieval-Augmented Generation via Cross-Query Consistency cites this paper.

CQC-RAG: Robust Retrieval-Augmented Generation via Cross-Query Consistency How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-03T16:28:39.229811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f8579ae9-8182-4182-aa09-99ea53896cb6 · inbound

Do LLM Attribution Metrics Transfer? Auditing Retrieval-Augmented Generation Evaluation Across Datasets and Constructs cites this paper.

Do LLM Attribution Metrics Transfer? Auditing Retrieval-Augmented Generation Evaluation Across Datasets and Constructs How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T11:09:47.214682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e1eff53c-f1ac-48ab-8bbb-a51ca3376d88 · inbound

APIVOT: Adaptive Planning with Interleaved Vision-Language Thoughts cites this paper.

APIVOT: Adaptive Planning with Interleaved Vision-Language Thoughts How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-07-10T13:37:06.867149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.