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Paper Citation Record · LEDGER

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models

As of 4 August 2026, this Paper Citation Record lists 100 of 300 outbound references and 0 inbound Pith citation observations for arXiv:2606.04177.

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

pith.paper-citation-record.v1
2606.04177 v1

Coverage vector

measured 100 of 300 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T10:01:13.190629Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+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

100 of 300 outbound references displayed

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  • verified fuzzy0
  • unresolved66
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 00615278-e02b-4e68-b6a9-938ddf8d9e11 · outbound

This paper cites an unresolved cited work.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Unresolved cited work

Reference 1

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Observation ec3c91ba-8e38-41f3-97f5-20340f0e8456 · outbound

This paper cites Research on LLM s-Empowered Conversational AI for Sustainable Behaviour Change.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Research on LLM s-Empowered Conversational AI for Sustainable Behaviour Change

Reference 2

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Observation 8b5fae7a-c721-48ec-af1f-f2e3acb53021 · outbound

This paper cites Deep Reinforcement Learning of LLM s​ using RLHF.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Deep Reinforcement Learning of LLM s​ using RLHF

Reference 3

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Observation c21052f6-6a11-43ec-bc99-356656425d4e · outbound

This paper cites Conversational Collaborative Robots.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Conversational Collaborative Robots

Reference 4

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Observation da65d574-51cc-470f-8125-b1846fad4f6a · outbound

This paper cites Dialogue System using Large Language Model-based Dynamic Slot Generation.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Dialogue System using Large Language Model-based Dynamic Slot Generation

Reference 5

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Observation e7a08e8c-c82d-4937-83a2-b1a7bed261a4 · outbound

This paper cites Towards Adaptive Human-Agent Collaboration in Real-Time Environments.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Towards Adaptive Human-Agent Collaboration in Real-Time Environments

Reference 6

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Observation 29a4edfd-0a33-4f6f-9a4c-9295060ac9c2 · outbound

This paper cites Towards Human-Like Dialogue Systems: Integrating Multimodal Emotion Recognition and Non-Verbal Cue Generation.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Towards Human-Like Dialogue Systems: Integrating Multimodal Emotion Recognition and Non-Verbal Cue Generation

Reference 7

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Observation 20f9597a-2049-485b-b363-5c550511c82f · outbound

This paper cites Controlling Dialogue Systems with Graph-Based Structures.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Controlling Dialogue Systems with Graph-Based Structures

Reference 8

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Observation 21c13a3b-887a-4ad9-a5f2-2f37d09c2fc4 · outbound

This paper cites Multimodal Agentic Dialogue Systems for Situated Human-Robot Interaction.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Multimodal Agentic Dialogue Systems for Situated Human-Robot Interaction

Reference 9

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Observation 0d02d9ca-9f6e-44d7-99c3-34d5cc8468a0 · outbound

This paper cites Knowledge Graphs and Representational Models for Dialogue Systems.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Knowledge Graphs and Representational Models for Dialogue Systems

Reference 10

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Observation 02df9e4e-44e9-47c6-8b12-8d62684355e5 · outbound

This paper cites Front Matter.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Front Matter

Reference 11

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Observation 051ed568-0570-4c40-af08-798880000fba · outbound

This paper cites Fine-Tuning Large Language Models for Relation Extraction within a Retrieval-Augmented Generation Framework.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Fine-Tuning Large Language Models for Relation Extraction within a Retrieval-Augmented Generation Framework

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Observation bc0a7e10-46d3-4f81-9c5e-8c9e54480974 · outbound

This paper cites Benchmarking Table Extraction: Multimodal LLM s vs Traditional OCR.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Benchmarking Table Extraction: Multimodal LLM s vs Traditional OCR

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Observation 89243b72-b029-4e17-865e-41e5faf1d0c6 · outbound

This paper cites Injecting Structured Knowledge into LLM s via Graph Neural Networks.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Injecting Structured Knowledge into LLM s via Graph Neural Networks

Reference 14

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Observation 3ff90c1b-9a9e-4dae-97dc-826b2d181088 · outbound

This paper cites Regular-pattern-sensitive CRF s for Distant Label Interactions.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Regular-pattern-sensitive CRF s for Distant Label Interactions

Reference 15

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Observation 6d7c2987-8bc6-4526-bece-37dc9d904c28 · outbound

This paper cites From Syntax to Semantics: Evaluating the Impact of Linguistic Structures on LLM -Based Information Extraction.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models From Syntax to Semantics: Evaluating the Impact of Linguistic Structures on LLM -Based Information Extraction

Reference 16

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Observation 2b4e9497-ea62-4309-9ae5-79ae9b0722c0 · outbound

This paper cites Detecting Referring Expressions in Visually Grounded Dialogue with Autoregressive Language Models.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Detecting Referring Expressions in Visually Grounded Dialogue with Autoregressive Language Models

Reference 17

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Observation 74224b75-a7ff-4f6d-9a0a-2edd6b42f5b0 · outbound

This paper cites Exploring Multilingual Probing in Large Language Models: A Cross-Language Analysis.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Exploring Multilingual Probing in Large Language Models: A Cross-Language Analysis

Reference 18

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Observation ad2d0efe-3adc-4040-9c56-3610481d467a · outbound

This paper cites Self-Contrastive Loop of Thought Method for Text-to- SQL Based on Large Language Model.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Self-Contrastive Loop of Thought Method for Text-to- SQL Based on Large Language Model

Reference 19

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Observation f2c87edb-b379-4e73-91fc-181b3a70742b · outbound

This paper cites Combining Automated and Manual Data for Effective Downstream Fine-Tuning of Transformers for Low-Resource Language Applications.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Combining Automated and Manual Data for Effective Downstream Fine-Tuning of Transformers for Low-Resource Language Applications

Reference 20

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Observation d4e2fa6a-d8c9-4b2b-abab-66ca36fd8268 · outbound

This paper cites Seamlessly Integrating Tree-Based Positional Embeddings into Transformer Models for Source Code Representation.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Seamlessly Integrating Tree-Based Positional Embeddings into Transformer Models for Source Code Representation

Reference 21

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Observation 1574fcc1-a4c5-417d-ae91-8689dc0d153c · outbound

This paper cites Enhancing AMR Parsing with Group Relative Policy Optimization.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Enhancing AMR Parsing with Group Relative Policy Optimization

Reference 22

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Observation 00cc9c26-5776-4baf-b074-3e49733fc6d3 · outbound

This paper cites Structure Modeling Approach for UD Parsing of Historical M odern J apanese.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Structure Modeling Approach for UD Parsing of Historical M odern J apanese

Reference 23

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Observation 42928ff6-9e69-4d5b-a122-d70e28d67d8d · outbound

This paper cites BARTABSA ++: Revisiting BARTABSA with Decoder LLM s.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models BARTABSA ++: Revisiting BARTABSA with Decoder LLM s

Reference 24

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Observation 83568c1d-c916-4554-99aa-0128ec1dee43 · outbound

This paper cites Typed- RAG : Type-Aware Decomposition of Non-Factoid Questions for Retrieval-Augmented Generation.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Typed- RAG : Type-Aware Decomposition of Non-Factoid Questions for Retrieval-Augmented Generation

Reference 25

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Observation 21bd70b1-c86f-4062-a6a7-94d4bd630612 · outbound

This paper cites Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad Prediction.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad Prediction

Reference 26

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Observation 2125fff5-70e7-40be-8783-1f3bfa3c213f · outbound

This paper cites Can LLMs Interpret and Leverage Structured Linguistic Representations? A Case Study with AMRs.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Can LLMs Interpret and Leverage Structured Linguistic Representations? A Case Study with AMRs

Reference 27

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Observation b1c17cbe-5c44-46e9-ae7b-537ac3a3936c · outbound

This paper cites LLM Dependency Parsing with In-Context Rules.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models LLM Dependency Parsing with In-Context Rules

Reference 28

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Observation 0e8b0490-dd61-4c34-b94a-1a1486fa3c25 · outbound

This paper cites Cognitive Mirroring for D oc RE : A Self-Supervised Iterative Reflection Framework with Triplet-Centric Explicit and Implicit Feedback.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Cognitive Mirroring for D oc RE : A Self-Supervised Iterative Reflection Framework with Triplet-Centric Explicit and Implicit Feedback

Reference 29

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Observation 9002833d-e8bf-4e81-a183-01698349cc69 · outbound

This paper cites Cross-Document Event-Keyed Summarization.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Cross-Document Event-Keyed Summarization

Reference 30

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:b7ab5a18ea4e0b0ea23c8946cd0a9469906f4d44ff1a764fd5dbdfe346bf9b9e

Observation fe366d0e-ca6b-42a5-9cf0-80d2e3c133dd · outbound

This paper cites Transfer of Structural Knowledge from Synthetic Languages.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Transfer of Structural Knowledge from Synthetic Languages

Reference 31

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:f446ae2584daff8f9c74bd659aaea04be3411b258741d79440ac58c8a5c05c93

Observation 87257dc5-4e3c-4b46-9338-485d5aa60037 · outbound

This paper cites Language Models are Universal Embedders.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Language Models are Universal Embedders

Reference 32

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Observation 7997bac0-8779-4528-a57f-31df22e6f8ee · outbound

This paper cites D ia DP @ XLLM 25: Advancing C hinese Dialogue Parsing via Unified Pretrained Language Models and Biaffine Dependency Scoring.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models D ia DP @ XLLM 25: Advancing C hinese Dialogue Parsing via Unified Pretrained Language Models and Biaffine Dependency Scoring

Reference 33

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:eaf4537152128440e329ecb5e7878a9d6e456117a4deb04a8b610c392b0536a9

Observation d7ab6768-c20a-49ff-a709-0d754b0628e5 · outbound

This paper cites LLMSR @ XLLM 25: Less is More: Enhancing Structured Multi-Agent Reasoning via Quality-Guided Distillation.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models LLMSR @ XLLM 25: Less is More: Enhancing Structured Multi-Agent Reasoning via Quality-Guided Distillation

Reference 34

Resolution
verified exact
doi, observed 2026-06-28T10:01:52.266346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:86ed61b638b3aa3fe21590ec0e2fdc8d60f3a3d5eb37086b9dfac57783f10b13

Observation 46129829-7697-41a1-95c6-7ae0ac85c4f3 · outbound

This paper cites S peech EE @ XLLM 25: End-to-End Structured Event Extraction from Speech.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models S peech EE @ XLLM 25: End-to-End Structured Event Extraction from Speech

Reference 35

Resolution
verified exact
doi, observed 2026-06-28T10:01:52.117385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:632bd1cdf4d8c6f68a5ee73bc77fa8c5ea4be6079d02951573766128203973c0

Observation 0b05d48e-def4-4ce3-b8a1-35a42398dd9a · outbound

This paper cites Luu, Son and Van Nguyen, Kiet.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Luu, Son and Van Nguyen, Kiet

Reference 36

Resolution
verified exact
doi, observed 2026-06-28T10:01:51.967248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:a511e8e99d9e6d37469829b95a9a7a7781e1ab85fbcf8d577ce583502ff6908b

Observation 48444815-4767-40b3-a223-53a563f9c2b9 · outbound

This paper cites D oc IE @ XLLM 25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models D oc IE @ XLLM 25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations

Reference 37

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doi, observed 2026-06-28T10:01:51.968336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:79333742b1abd187d6382153873aac398a2887a5361300897af96609be651374

Observation fe8ae9be-b91c-4460-90f4-c6ee86b06c86 · outbound

This paper cites LLMSR @ XLLM 25: Integrating Reasoning Prompt Strategies with Structural Prompt Formats for Enhanced Logical Inference.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models LLMSR @ XLLM 25: Integrating Reasoning Prompt Strategies with Structural Prompt Formats for Enhanced Logical Inference

Reference 38

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doi, observed 2026-06-28T10:01:52.118872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:85471a187f22165c0e8315f53dd12fe450db00c5e7deed0e7539e7660f4bd2a9

Observation b57840ac-9524-4ee4-8d16-397c57d8db30 · outbound

This paper cites D oc IE @ XLLM 25: UIEP rompter: A Unified Training-Free Framework for universal document-level information extraction via Structured Prompt.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models D oc IE @ XLLM 25: UIEP rompter: A Unified Training-Free Framework for universal document-level information extraction via Structured Prompt

Reference 39

Resolution
verified exact
doi, observed 2026-06-28T10:01:52.001737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:3d283bd2145b13b898b0376ccb43c04d27b4ff05ed9b7e3c63790037811e59c5

Observation 22ded1c7-5d37-4567-aa8a-7c61777497f5 · outbound

This paper cites LLMSR @ XLLM 25: SWRV : Empowering Self-Verification of Small Language Models through Step-wise Reasoning and Verification.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models LLMSR @ XLLM 25: SWRV : Empowering Self-Verification of Small Language Models through Step-wise Reasoning and Verification

Reference 40

Resolution
verified exact
doi, observed 2026-06-28T10:01:52.241682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:a243fe482e50f7cf73c18dc97e86312682b1f1478285e2cd178294f2d4d02773

Observation 67a4cacc-f598-41bd-86ac-d0a9d4e08cf0 · outbound

This paper cites LLMSR @ XLLM 25: An Empirical Study of LLM for Structural Reasoning.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models LLMSR @ XLLM 25: An Empirical Study of LLM for Structural Reasoning

Reference 41

Resolution
verified exact
doi, observed 2026-06-28T10:01:52.239830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:0d027a3b8fabb5457a11afe69de5350c3ad982c1628ba43906c7eb3bc8d51ace

Observation 468bcd4d-a9c9-4c67-84da-fc6aea3c1bd0 · outbound

This paper cites LLMSR @ XLLM 25: A Language Model-Based Pipeline for Structured Reasoning Data Construction.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models LLMSR @ XLLM 25: A Language Model-Based Pipeline for Structured Reasoning Data Construction

Reference 42

Resolution
verified exact
doi, observed 2026-06-28T10:01:52.134369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:8c94f51af0e24547df39db5b6203593601bae263ae597aea042d3b928e6c49b6

Observation 1e561b04-eaa4-4731-8a8d-e772d97e9c8e · outbound

This paper cites S peech EE @ XLLM 25: Retrieval-Enhanced Few-Shot Prompting for Speech Event Extraction.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models S peech EE @ XLLM 25: Retrieval-Enhanced Few-Shot Prompting for Speech Event Extraction

Reference 43

Resolution
verified exact
doi, observed 2026-06-28T10:01:52.155156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:17b37dc3148ac9ec8841aba35ba1a9b2ed98e42546ed8e463ad2c8979a90c86a

Observation 1559f3d5-ca3d-4250-8605-fcad61d8e259 · outbound

This paper cites an unresolved cited work.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Unresolved cited work

Reference 44

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:9636fb1b5b0699832d3ade579d36d64cf50ac6f69c8dddb60f46f466cbc5cb9f

Observation 5b5593d9-a8e6-4063-b1dc-e5c85fd3883c · outbound

This paper cites An introduction to computational identification and classification of Upam \= a alaṇk \= a ra.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models An introduction to computational identification and classification of Upam \= a alaṇk \= a ra

Reference 45

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unresolved
no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:37ec3de511e793dba1b75741f08945e1bbae2823e2088c0a663aa3398033e4db

Observation 13d14043-552f-48da-8b89-fc5858691503 · outbound

This paper cites Aesthetics of S anskrit Poetry from the Perspective of Computational Linguistics: A Case Study Analysis on \'S ikṣ \= a ṣṭaka.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Aesthetics of S anskrit Poetry from the Perspective of Computational Linguistics: A Case Study Analysis on \'S ikṣ \= a ṣṭaka

Reference 46

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:62eae2a6d53732d672b7da2456f719bd41fd1f862e7017cf0c4730affc5998a1

Observation 21fbe473-1579-4665-b462-f88a9953af69 · outbound

This paper cites Itaretara Dvandva: A challenge for Dependency Tree semantics.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Itaretara Dvandva: A challenge for Dependency Tree semantics

Reference 47

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unresolved
no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:fb16167f3918da9a74c7d2b06ab27d19e71226342d3bae15e35ee52a45e3869f

Observation 53ed941a-2398-4e9a-8e6a-eec12dd5b96e · outbound

This paper cites A Case Study of Handwritten Text Recognition from Pre-Colonial era S anskrit Manuscripts.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models A Case Study of Handwritten Text Recognition from Pre-Colonial era S anskrit Manuscripts

Reference 48

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:b019fd47023225b2689bdf5e2893ef14fe7ff538bad847028153ea24aa554a18

Observation 3db17d93-6fd4-4378-802a-d5af20c4f215 · outbound

This paper cites Towards Accent-Aware V edic S anskrit Optical Character Recognition Based on Transformer Models.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Towards Accent-Aware V edic S anskrit Optical Character Recognition Based on Transformer Models

Reference 49

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:a6a7902fde0d9b89cd5c9f06280b4eab7e05d3a456178dec646f84489e5f9eed

Observation a67b993f-02fb-4f92-bbbd-441f77c0f310 · outbound

This paper cites Vedavani: A Benchmark Corpus for ASR on V edic S anskrit Poetry.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Vedavani: A Benchmark Corpus for ASR on V edic S anskrit Poetry

Reference 50

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:003ed03c2dffbe25c4b6452a8c0e1c888c036de3df16e7c847e4781948bdabf4

Observation 1747ad31-0534-4e97-b9d1-94d446d9f58d · outbound

This paper cites Compound Type Identification in S anskrit.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Compound Type Identification in S anskrit

Reference 51

Resolution
unresolved
no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:59a158cd8e5377418fece762479df10496a80934e8a47e837a90c458e43506a5

Observation 483f3b02-00b4-4844-ab26-ed58fa6975ef · outbound

This paper cites IKML : A Markup Language for Collaborative Semantic Annotation of I ndic Texts.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models IKML : A Markup Language for Collaborative Semantic Annotation of I ndic Texts

Reference 52

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:d046105dc6bdb0e1ddb0e926098be0c6ce88926a82f0b627e77995af25eba1e6

Observation 52a0a214-1b7f-4b5f-b2e4-bb147889b0c5 · outbound

This paper cites Challenges in Processing V edic S anskrit: Towards creating a normalized dataset for the Ṛgveda-saṃhit \= a.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Challenges in Processing V edic S anskrit: Towards creating a normalized dataset for the Ṛgveda-saṃhit \= a

Reference 53

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:53c9a8e442abcea7d8a08d3a2208247ce1ddde136858d4c9fa2f9fa038209413

Observation 9e684e57-b933-43d9-957c-5a454aef6f1a · outbound

This paper cites P \= a ṇḍitya: Visualizing S anskrit Intellectual Networks.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models P \= a ṇḍitya: Visualizing S anskrit Intellectual Networks

Reference 54

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:9ce36ab7148949577d3dfe319ea7864f86a51c9d56af80a9353485b1bc9301b4

Observation d6facd40-9a83-4117-84b2-c5e686d9efa9 · outbound

This paper cites Anveshana: A New Benchmark Dataset for Cross-Lingual Information Retrieval on E nglish Queries and S anskrit Documents.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Anveshana: A New Benchmark Dataset for Cross-Lingual Information Retrieval on E nglish Queries and S anskrit Documents

Reference 55

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:7e9c3902af5a29e5b9efeecdad4b85ce1b523f58475423aad9bdad960d9f104d

Observation 51679ec3-c28a-477e-9173-dd6d7f2331f8 · outbound

This paper cites Concordance of S anskrit Synonyms.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Concordance of S anskrit Synonyms

Reference 56

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:d6fe69663a010caa836965976025e781719184f77d2c17ac5651df78d3d3adde

Observation 755bad2a-1e96-4fae-acc8-87211bbc415d · outbound

This paper cites an unresolved cited work.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Unresolved cited work

Reference 57

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:11a961bdc765c8f871f8db7688754112c893f44383d040b5c0774824bf4a8c42

Observation 57c33dc6-033d-4fe5-9a03-bbfdc05c9730 · outbound

This paper cites Chain-of- M eta W riting: Linguistic and Textual Analysis of How Small Language Models Write Young Students Texts.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Chain-of- M eta W riting: Linguistic and Textual Analysis of How Small Language Models Write Young Students Texts

Reference 58

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:b3961d7bcfe84520fb28c0bdd63c9a478147e44fa909618d94d9cbf44bcc84c8

Observation 3c4ad51f-f725-4f89-b759-3ca1d82fd2dc · outbound

This paper cites Semantic Masking in a Needle-in-a-haystack Test for Evaluating Large Language Model Long-Text Capabilities.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Semantic Masking in a Needle-in-a-haystack Test for Evaluating Large Language Model Long-Text Capabilities

Reference 59

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:71d0f6df5af27fa2fff73088006300ea1c8f5174fea09ce4cf9d1001976b0231

Observation 44b4c9d8-a189-4263-98de-7613de883625 · outbound

This paper cites Reading Between the Lines: A dataset and a study on why some texts are tougher than others.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Reading Between the Lines: A dataset and a study on why some texts are tougher than others

Reference 60

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unresolved
no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:e5216ddaa7259ce71b436cf0ad24a59419103fc272bd9c69d9609ffcb4a88a01

Observation 66143ba5-96b3-4be1-bc6d-1754245a7eaf · outbound

This paper cites P ara R ev : Building a dataset for Scientific Paragraph Revision annotated with revision instruction.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models P ara R ev : Building a dataset for Scientific Paragraph Revision annotated with revision instruction

Reference 61

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:39c752c00c79b91287f7e02025740c6ecd0f572c465b5e1f70a2924bf516de7d

Observation c6dbf609-ebfa-4c5b-aa5c-ed47ebda05bf · outbound

This paper cites Towards an operative definition of creative writing: a preliminary assessment of creativeness in AI and human texts.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Towards an operative definition of creative writing: a preliminary assessment of creativeness in AI and human texts

Reference 62

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:d4e4343c2f97f67aff2cf3f712a181d02fd9f171f037df1b77fd14ea873b4edb

Observation f0948a9e-70d4-425c-a73f-f228d439def2 · outbound

This paper cites Decoding Semantic Representations in the Brain Under Language Stimuli with Large Language Models.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Decoding Semantic Representations in the Brain Under Language Stimuli with Large Language Models

Reference 63

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:3a879755d6e4c435a86f3ea47566dae339e0c3b43283b73d6dc15865d030c41c

Observation 2d482fd8-f9de-4fc1-afa5-a6a9dbd02699 · outbound

This paper cites an unresolved cited work.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Unresolved cited work

Reference 64

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verified exact
doi, observed 2026-06-28T10:01:52.013959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:fe9205771445df48e65fabcb36d2bfe1bb8cbbbb434e6deaaf36ad866641d0ec

Observation b971c92c-1fe5-4f8d-bfeb-cc5ce25aba4c · outbound

This paper cites an unresolved cited work.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Unresolved cited work

Reference 65

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unresolved
no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:d747d9856ed0e2e809308cac3f33ada43a505d5fe570623db14d096b3b518513

Observation 62a66c06-70b8-4a07-9c93-2482c2dc399c · outbound

This paper cites A Comprehensive Taxonomy of Bias Mitigation Methods for Hate Speech Detection.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models A Comprehensive Taxonomy of Bias Mitigation Methods for Hate Speech Detection

Reference 66

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:b87263f453e14961bc6a1eed55c1d6c9662ed753f041cdad189df9e90f6d5d82

Observation 2d054f1a-c4f0-4a91-9603-a5e53fdd6ba6 · outbound

This paper cites Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation

Reference 67

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:c40080fa9b13c595a498e7838b2bb44ff4be9c6a3b2edb206e7ca8fb626c8a60

Observation 7cf073b0-6153-47da-8b74-a92d5a290f71 · outbound

This paper cites From civility to parity: Marxist-feminist ethics for context-aware algorithmic content moderation.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models From civility to parity: Marxist-feminist ethics for context-aware algorithmic content moderation

Reference 68

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no resolver link, observed 2026-06-28T10:01:13.190629Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:080e481cdbbb398caceb32c2e18d581867a56e49d93aa97d3867d98dfc914942

Observation 05a18f56-b472-428c-9890-4c5e9eb45764 · outbound

This paper cites A Novel Dataset for Classifying G erman Hate Speech Comments with Criminal Relevance.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models A Novel Dataset for Classifying G erman Hate Speech Comments with Criminal Relevance

Reference 69

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:b661500e63716bf67a66d68275cb8a010691bac90a4b062aa9728fa2f7f04142

Observation 1442ccc6-4906-492f-ae03-71898ccdb4e8 · outbound

This paper cites Learning from Disagreement: Entropy-Guided Few-Shot Selection for Toxic Language Detection.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Learning from Disagreement: Entropy-Guided Few-Shot Selection for Toxic Language Detection

Reference 70

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:053b9ac1598d61c76d1600b0154b194202c638038f1e0d52f6347226c1a6c94c

Observation 1c9cf87d-f1c4-4849-b68d-8ea9c42aa7b2 · outbound

This paper cites Debiasing Static Embeddings for Hate Speech Detection.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Debiasing Static Embeddings for Hate Speech Detection

Reference 71

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:5e38381dda9b06dd80636d8de8c1bbf3a0d448034417844e17dad6dc0ed3f967

Observation d15d480a-1f86-4bf1-9a03-1eb02545d41a · outbound

This paper cites Web(er) of Hate: A Survey on How Hate Speech Is Typed.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Web(er) of Hate: A Survey on How Hate Speech Is Typed

Reference 72

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:4271f9f841c58023ecb32df33a32ebdf1279a7ecb9487460d2f78d99617ebcda

Observation 913cfd2a-e8bd-4e13-a578-f0d779757e35 · outbound

This paper cites Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLM s for Countering Hate Speech.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLM s for Countering Hate Speech

Reference 73

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:5a493b93b7153c048a1a3117c20ff2040319403b08f1cf8c27144c308b8bfb7e

Observation f6ae23c1-82ef-4fb6-815d-94e16e738146 · outbound

This paper cites HODIAT : A Dataset for Detecting Homotransphobic Hate Speech in I talian with Aggressiveness and Target Annotation.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models HODIAT : A Dataset for Detecting Homotransphobic Hate Speech in I talian with Aggressiveness and Target Annotation

Reference 74

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:28dbf293b8c4aa0fd2e50742c77a733b4a7a6473ddcd5719070f35be8ac3e26d

Observation 4a5d5acd-2f76-42ae-bb94-ac2d7854fb21 · outbound

This paper cites Beyond the Binary: Analysing Transphobic Hate and Harassment Online.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Beyond the Binary: Analysing Transphobic Hate and Harassment Online

Reference 75

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:90a0a9384d9ff6651024042769eb17109b1e5c0c3b7d0e394a2ad6e64de22751

Observation c3e01677-a697-4755-bcfa-256b882e7d33 · outbound

This paper cites Evading Toxicity Detection with ASCII -art: A Benchmark of Spatial Attacks on Moderation Systems.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Evading Toxicity Detection with ASCII -art: A Benchmark of Spatial Attacks on Moderation Systems

Reference 76

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:f736e81bd2d12cf11b14b0415d4a9997cbcff0340b8b489e50092e5f73b77a96

Observation abd759e3-4738-4446-84e6-da9f8dd340c1 · outbound

This paper cites Debunking with Dialogue? Exploring AI -Generated Counterspeech to Challenge Conspiracy Theories.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Debunking with Dialogue? Exploring AI -Generated Counterspeech to Challenge Conspiracy Theories

Reference 77

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:b886ca75d7ecd65dc1992620ed13035983bf5cab6e5ff81804b4b386e079df72

Observation deac0adb-2e0d-4a30-95c4-03913131ce63 · outbound

This paper cites M isinfo T ele G raph: Network-driven Misinformation Detection for G erman Telegram Messages.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models M isinfo T ele G raph: Network-driven Misinformation Detection for G erman Telegram Messages

Reference 78

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:9a83f37b9fb5c03f3c38406e6479204756bff856930de7c61e9e3f8c3661833d

Observation 8c348845-c98d-476d-9a92-4b8d77fcceff · outbound

This paper cites Catching Stray Balls: Football, fandom, and the impact on digital discourse.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Catching Stray Balls: Football, fandom, and the impact on digital discourse

Reference 79

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:f61298e88a43b35aaa5f73e55d4b8ee0bad2844bc1f090ea2aeb9c9cc5bca90a

Observation a9a103ce-8292-4689-ad72-2e346ed0ca84 · outbound

This paper cites e , Justina and Rimkien \.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models e , Justina and Rimkien \

Reference 80

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:9c21583a262455bc955618cf3bfbe0c23a9100ac045caedf6076c3e8460602ca

Observation cfa416f6-5e55-4f42-bca7-3ea3e95ee2f3 · outbound

This paper cites RAG and Recall: Multilingual Hate Speech Detection with Semantic Memory.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models RAG and Recall: Multilingual Hate Speech Detection with Semantic Memory

Reference 81

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:94d0d26c4ebf6caa35c04ead988161767846d47ee961deae2069bb95591f09ca

Observation 8a054876-3da5-4f88-986f-922695070680 · outbound

This paper cites Implicit Hate Target Span Detection in Zero- and Few-Shot Settings with Selective Sub-Billion Parameter Models.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Implicit Hate Target Span Detection in Zero- and Few-Shot Settings with Selective Sub-Billion Parameter Models

Reference 82

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:38b296e9615ba6eb0fda5d527ee5a75ab42a6e4999c00a6594daf7d449f58380

Observation 1b63691f-2f54-4322-bac7-132d8edcb780 · outbound

This paper cites Hate Speech in Times of Crises: a Cross-Disciplinary Analysis of Online Xenophobia in G reece.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Hate Speech in Times of Crises: a Cross-Disciplinary Analysis of Online Xenophobia in G reece

Reference 83

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:5465260b1d5c3f74132b7a6797a183f69f70985903b140568e1cca2ce5e70c5e

Observation 6201c235-599b-4c26-aa50-c783a636b8b1 · outbound

This paper cites Hostility Detection in UK Politics: A Dataset on Online Abuse Targeting MP s.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Hostility Detection in UK Politics: A Dataset on Online Abuse Targeting MP s

Reference 84

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:75c7f2cf6c0c8d6550364490112807712504e4e0540d2138389857e204e96e4a

Observation 0234de3a-d32e-44ef-b3dd-2c2841991964 · outbound

This paper cites Detoxify- IT : An I talian Parallel Dataset for Text Detoxification.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Detoxify- IT : An I talian Parallel Dataset for Text Detoxification

Reference 85

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:e219c80342bd8337aa0f7b0b98a5931c8ac0e26c0c94fb8240a444288e0bc071

Observation c222b285-8c07-42cc-a83c-7008c3b69c32 · outbound

This paper cites Pathways to Radicalisation: On Research for Online Radicalisation in Natural Language Processing and Machine Learning.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Pathways to Radicalisation: On Research for Online Radicalisation in Natural Language Processing and Machine Learning

Reference 86

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:cb98494ff97afaed56c5c5e70a736a7af26f1093ea9faeebd9b02e65e34892fb

Observation 290173d4-ed70-4e29-a3e8-0e0654610a4c · outbound

This paper cites Social Hatred: Efficient Multimodal Detection of Hatemongers.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Social Hatred: Efficient Multimodal Detection of Hatemongers

Reference 87

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:d2784153f55c4cd89e9a54f8e418a26cc92b5701a5e1edc39bc18c0630dd1c4a

Observation da5e7d5a-6ab1-49f9-b2a6-61295239ab4f · outbound

This paper cites Blue-haired, misandriche, rabiata: Tracing the Connotation of `Feminist(s)' Across Time, Languages and Domains.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Blue-haired, misandriche, rabiata: Tracing the Connotation of `Feminist(s)' Across Time, Languages and Domains

Reference 88

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:408b1fc7f2ba3d2e929dd4ba9dea92ee61392d38891973f559de8111b40dc44d

Observation c2183470-906a-4f93-8b7b-68ec0a736cef · outbound

This paper cites Towards Fairness Assessment of D utch Hate Speech Detection.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Towards Fairness Assessment of D utch Hate Speech Detection

Reference 89

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:2c503d06534b80e827db03fe6b367c52a7db68852564e6d9109303ad5d617c15

Observation d444a901-a977-4afb-95cd-99740711f42b · outbound

This paper cites Between Hetero-Fatalism and Dark Femininity: Discussions of Relationships, Sex, and Men in the Femosphere.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Between Hetero-Fatalism and Dark Femininity: Discussions of Relationships, Sex, and Men in the Femosphere

Reference 90

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:3a8fe58660ed7f9dcff89f89a4b887afd4213e676bbb0875b946df0283a79a93

Observation a550a9ff-e478-4a0e-a209-1ff3bb5ed14d · outbound

This paper cites Can LLM s Rank the Harmfulness of Smaller LLM s? We are Not There Yet.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Can LLM s Rank the Harmfulness of Smaller LLM s? We are Not There Yet

Reference 91

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:fd234f1a8a056df1b4441e5137c95cfaed3eacc49ff0ba9c2438a5c751c78650

Observation a0eafb5d-57fb-4e7a-b73c-8ebcacc6a8dd · outbound

This paper cites Are You Trying to Convince Me or Are You Trying to Deceive Me? Using Argumentation Types to Identify Deceptive News.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Are You Trying to Convince Me or Are You Trying to Deceive Me? Using Argumentation Types to Identify Deceptive News

Reference 92

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:59fe6d3b2f1e154abd97887aecdc5f4bd6d33aa03cccf352a49fc7b4aaa7116d

Observation ba59196b-1619-454d-863e-848ddbca33e4 · outbound

This paper cites QG uard:Question-based Zero-shot Guard for Multi-modal LLM Safety.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models QG uard:Question-based Zero-shot Guard for Multi-modal LLM Safety

Reference 93

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:da0098fc7459d860ba0a86a8a1efbd5efa5161ab2ece57f700c2768fc30212cc

Observation f16aabe4-d05b-4854-8bc0-6d523692f2e0 · outbound

This paper cites Who leads? Who follows? Temporal dynamics of political dogwhistles in S wedish online communities.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Who leads? Who follows? Temporal dynamics of political dogwhistles in S wedish online communities

Reference 94

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:a3919984726d385512bf91f04a68b69c15754380861e2cbc0cf4961ac3fd69ee

Observation 32377788-3614-4c83-b5e0-68a8bbedab9d · outbound

This paper cites Detecting Child Objectification on Social Media: Challenges in Language Modeling.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Detecting Child Objectification on Social Media: Challenges in Language Modeling

Reference 95

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:8b2580908aea367f4330f17d8f26defe4298e47411a76e4856749b840c900618

Observation ced49e3a-1319-4821-bc71-a697fcbc8cc1 · outbound

This paper cites Can Prompting LLM s Unlock Hate Speech Detection across Languages? A Zero-shot and Few-shot Study.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Can Prompting LLM s Unlock Hate Speech Detection across Languages? A Zero-shot and Few-shot Study

Reference 96

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:398368badcebf22bcc59b4aca60c0c445f531b572e864d18e4d8f08a082dd6ff

Observation 76c16514-e9cc-40ba-af26-601579dd15d7 · outbound

This paper cites Multilingual Analysis of Narrative Properties in Conspiracist vs Mainstream Telegram Channels.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Multilingual Analysis of Narrative Properties in Conspiracist vs Mainstream Telegram Channels

Reference 97

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:684172ab8947f8a5f29dd626b1d20e738eaf3409676b472edfd7d6f494fb863a

Observation 633cb370-0104-4bd5-9d58-74d1124116d0 · outbound

This paper cites Hate Explained: Evaluating NER -Enriched Text in Human and Machine Moderation of Hate Speech.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Hate Explained: Evaluating NER -Enriched Text in Human and Machine Moderation of Hate Speech

Reference 98

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:e635c948d51e27cc4b81380439b313134fdda385eb7b2ac72b9a4ccb0c65aa2b

Observation bdc755e7-d48b-433b-a340-82c777f3d669 · outbound

This paper cites Personas with Attitudes: Controlling LLM s for Diverse Data Annotation.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Personas with Attitudes: Controlling LLM s for Diverse Data Annotation

Reference 99

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:4fe04e350f17660278823ad58a29f781480852b0ff49ae01e054fcd98b4a0b17

Observation 1db42c90-d7b4-4ea9-80c8-72ae67ddb672 · outbound

This paper cites Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt.

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt

Reference 100

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source=arxiv_source observed=2026-06-28T10:01:13.190629Z digest=sha256:9f0c300e7498364ccc1b39a3ee9c17a9a5c461d46fb6aa96810f3db6a8cadac9

Pith citing papers

No inbound Pith citation observations are available.