Paper Citation Record · LEDGER

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior?

As of 17 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2511.12576.

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

pith.paper-citation-record.v1
2511.12576 v2

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T22:04:10.949676Z

measured 24 of 24 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 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

24 of 24 outbound references displayed

verified exact0
verified fuzzy0
unresolved24
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b504e302-65d0-446a-8273-bacaaae984a3 · outbound

This paper cites Attention is all you need,.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Attention is all you need,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:09.210493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46f03d8b-04c4-462e-aa55-ab5049e5b5fa · outbound

This paper cites Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:09.257591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f028b499-9b78-486b-a66d-a41341c15e9f · outbound

This paper cites Scaling down to scale up: A cost-benefit analysis of replacing openai’s llm with open source slms in production,.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Scaling down to scale up: A cost-benefit analysis of replacing openai’s llm with open source slms in production,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:09.310852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b79b7a7c-839d-4e63-9420-331ab921e673 · outbound

This paper cites A comprehensive survey of small language models in the era of large language models: Techniques, enhancements, applications, collaboration with llms, and trustworthiness,.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? A comprehensive survey of small language models in the era of large language models: Techniques, enhancements, applications, collaboration with llms, and trustworthiness,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:09.371247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e186a3f0-addb-410a-9273-6e7c13e6d2a1 · outbound

This paper cites A survey of llm- based agents: Theories, technologies, applications and suggestions,.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? A survey of llm- based agents: Theories, technologies, applications and suggestions,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:09.483986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e50ee058-6563-4c60-b578-4275f4029063 · outbound

This paper cites Nld-llm: A systematic framework for evaluating small language transformer models on natural language description,.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Nld-llm: A systematic framework for evaluating small language transformer models on natural language description,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:09.655998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72644b1f-1fd1-47f1-88ff-8fb15f3776f8 · outbound

This paper cites Xgen- q: An explainable domain-adaptive llm framework with retrieval- augmented generation for software security,.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Xgen- q: An explainable domain-adaptive llm framework with retrieval- augmented generation for software security,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:09.824609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fc61fa6-e8f6-4bff-81e0-2cf196ce0be0 · outbound

This paper cites Sban: A framework & multi-dimensional dataset for large language model pre-training and software code mining,.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Sban: A framework & multi-dimensional dataset for large language model pre-training and software code mining,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:09.874743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9087399d-dd4c-4bdf-946d-bbf2ac2d6306 · outbound

This paper cites Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:09.943222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b872ca4-08cd-4337-bc4d-ca6da0554859 · outbound

This paper cites Feasibility Study for Supporting Static Malware Analysis Using LLM.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Feasibility Study for Supporting Static Malware Analysis Using LLM

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:10.003274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38589da4-6659-474a-b8f3-a3e23ad229e2 · outbound

This paper cites Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:10.062111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e776316-00a4-4781-9eaa-343db6a43e2b · outbound

This paper cites Deep security challenge: Malware detection with llms,.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Deep security challenge: Malware detection with llms,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:10.161464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d6b80a4-b020-43bf-9b4b-e7ec4615d864 · outbound

This paper cites Llmalmorph: Leveraging llms to generate malware vari- ants,.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Llmalmorph: Leveraging llms to generate malware vari- ants,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:10.227406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0391efb8-db6d-4427-a61b-e3ac4ccf8523 · outbound

This paper cites Mitigating distribution shifts in graph-based an- droid malware classification with llm embeddings,.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Mitigating distribution shifts in graph-based an- droid malware classification with llm embeddings,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:10.395684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e89a38a-0222-4981-888b-19714fe0f242 · outbound

This paper cites Small Language Models can Outperform Humans in Short Creative Writing: A Study Comparing SLMs with Humans and LLMs.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Small Language Models can Outperform Humans in Short Creative Writing: A Study Comparing SLMs with Humans and LLMs

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:10.444455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83fab79a-6288-4644-a021-1ad6fc2f0a4c · outbound

This paper cites DeepSeek-V3 Technical Report.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? DeepSeek-V3 Technical Report

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:10.499242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2245ba5c-9be9-4832-983d-fcde7624f0d8 · outbound

This paper cites Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:10.561277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9620e0c1-04ca-4609-bcb2-c58b226e8d8e · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? LLaMA: Open and Efficient Foundation Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:10.644023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ffd9b82-99b6-4cc3-a55f-c0d77623a855 · outbound

This paper cites Qwen2.5-Coder Technical Report.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Qwen2.5-Coder Technical Report

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:10.695610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06c5af90-9250-4283-8a6b-e048f4d38ee8 · outbound

This paper cites Mistral 7b,.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Mistral 7b,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:10.762811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c737ed8-e484-4128-8fb4-197443fa4615 · outbound

This paper cites Fewfine: Few-shot malware traffic classification via transfer learning based on fine-tuning strategy,.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Fewfine: Few-shot malware traffic classification via transfer learning based on fine-tuning strategy,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:10.800411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation add55a23-279e-46c9-9288-2b8581e3ce88 · outbound

This paper cites Exploring the application of transfer learning in malware detection by fine-tuning pre- trained models on binary classification to new datasets on multi-class classification,.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Exploring the application of transfer learning in malware detection by fine-tuning pre- trained models on binary classification to new datasets on multi-class classification,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:10.862650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ff29061-dcaa-4cea-815e-d6e5e73e396a · outbound

This paper cites Llm- maldetect: A large language model-based method for android malware detection,.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Llm- maldetect: A large language model-based method for android malware detection,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:10.886533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f4fd8f8-3a38-4687-bbf6-f592c0088c93 · outbound

This paper cites Prompt chaining-assisted malware detection: A hybrid approach utilizing fine-tuned llms and domain knowledge-enriched cybersecurity knowledge graphs,.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Prompt chaining-assisted malware detection: A hybrid approach utilizing fine-tuned llms and domain knowledge-enriched cybersecurity knowledge graphs,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:10.949676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.