{"publication_id":"87e015be-2295-434d-b696-f26092dd25f2","content_hash":"sha256:867aafb911a8159afbab71197b924cc65e77cbf2f196454f0dabac67b70d1b9d","nodes":[{"id":"87e015be-2295-434d-b696-f26092dd25f2","type":"publication","title":"Open source models: evidence map — 39 findings across 39 sources"},{"id":"claim_1","type":"claim","text":"This evidence map surveys 39 independent open source models sources drawn from the Tier-2 corpus and classified as direct findings. They vary across population, comparator, and/or endpoint and are catalogued by source in the Findings Map rather than pooled into one estimate — cross-population aggregation is not claimed. Each row records its own population, comparator, endpoint, and effect, so the spread of the literature and any tensions between findings remain explicit."},{"id":"claim_2","type":"claim","text":"| multi-tenant workloads with popular op… | conventional baselines | increases overall system throughput by 56.5% | 2026 doi:10.1109/asp-dac66049.2026.11420717 |"},{"id":"source_1","type":"source","study":"Judicial Examination Preparation Strategies for Non-Law Undergraduates: Prompt Engineering Optimization Based on the Qwen-Max LLM","year":2026,"doi":"10.1109/aisns67921.2026.11440369","url":"https://doi.org/10.1109/aisns67921.2026.11440369","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_2","type":"source","study":"A Novel Framework for Efficient Transformation to Domain-Oriented LLM Agents","year":2026,"doi":"10.1109/iceic69189.2026.11386150","url":"https://doi.org/10.1109/iceic69189.2026.11386150","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_3","type":"source","study":"A Calibrated Three-Tiered Risk Classifier for User Prompts in Large Language Model Content Moderation","year":2026,"doi":"10.56738/issn29603986.geo2026.7.180","url":"https://doi.org/10.56738/issn29603986.geo2026.7.180","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_4","type":"source","study":"TraceLLM: Evaluating and Exploring Large Language Models on Trace Analysis in Microservice-based Web Applications","year":2026,"doi":"10.1145/3774904.3792164","url":"https://doi.org/10.1145/3774904.3792164","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_5","type":"source","study":"CoLoRA: A Collaborative Scheduling Framework for Multi-Tenant LoRA LLM Inference","year":2026,"doi":"10.1109/asp-dac66049.2026.11420717","url":"https://doi.org/10.1109/asp-dac66049.2026.11420717","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_6","type":"source","study":"Large language model performance in clinical cardiology multiple choice questions; has reasoning improved performance?","year":2026,"doi":"10.1093/ehjdh/ztaf143.011","url":"https://doi.org/10.1093/ehjdh/ztaf143.011","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_7","type":"source","study":"Impact Assessment of Structured Results for the Reliability of LLM-generated Tests","year":2026,"doi":"10.1109/estream70144.2026.11511497","url":"https://doi.org/10.1109/estream70144.2026.11511497","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_8","type":"source","study":"Provable Defense Framework for LLM Jailbreaks via Noise-Augumented Alignment","year":2026,"doi":"10.48550/arxiv.2602.01587","url":"https://doi.org/10.48550/arxiv.2602.01587","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_9","type":"source","study":"Benchmarking proprietary and open-source language and vision-language models for gastroenterology clinical reasoning.","year":2025,"doi":"10.1038/s41746-025-02174-0","url":"https://doi.org/10.1038/s41746-025-02174-0","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_10","type":"source","study":"Automated Resectability Classification of Pancreatic Cancer CT Reports with Privacy-Preserving Open-Weight Large Language Models: A Multicenter Study.","year":2025,"doi":"10.1007/s10916-025-02248-2","url":"https://doi.org/10.1007/s10916-025-02248-2","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_11","type":"source","study":"LLM-based ambiguity detection in natural language instructions for collaborative surgical robots","year":2025,"doi":"10.1109/ro-man63969.2025.11217610","url":"https://doi.org/10.1109/ro-man63969.2025.11217610","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_12","type":"source","study":"impact of LLaMA fine tuning on hallucinations for name entity extraction in legal documents","year":2025,"doi":"10.24215/15146774e068","url":"https://doi.org/10.24215/15146774e068","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_13","type":"source","study":"Development of an Automotive Electronics Internship Assistance System Using a Fine-Tuned Llama 3 Large Language Model","year":2025,"doi":"10.3390/systems13080668","url":"https://doi.org/10.3390/systems13080668","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_14","type":"source","study":"Threat Modeling and LLM-Based Anomaly Detection for Fog Computing Service Function Chains","year":2025,"doi":"10.1109/cscloud66326.2025.00034","url":"https://doi.org/10.1109/cscloud66326.2025.00034","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_15","type":"source","study":"Privacy-First Triage Classification with Open-Weight LLMs: A Chain-of-Thought Distillation Approach","year":2025,"doi":"10.1109/icdmw69685.2025.00432","url":"https://doi.org/10.1109/icdmw69685.2025.00432","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_16","type":"source","study":"Abstract 4367224: Systematic Evaluation of Commercial and Open-source Large Language Models for Automated Adjudication of Clinical Indication from Cardiac Magnetic Resonance Imaging Reports","year":2025,"doi":"10.1161/circ.152.suppl_3.4367224","url":"https://doi.org/10.1161/circ.152.suppl_3.4367224","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_17","type":"source","study":"The impact of fine tuning in LLaMA on hallucinations for named entity extraction in legal documentation","year":2025,"doi":"10.48550/arxiv.2506.08827","url":"https://doi.org/10.48550/arxiv.2506.08827","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_18","type":"source","study":"SoLA: Leveraging Soft Activation Sparsity and Low-Rank Decomposition for Large Language Model Compression","year":2025,"doi":"10.1609/aaai.v39i16.33923","url":"https://doi.org/10.1609/aaai.v39i16.33923","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_19","type":"source","study":"Speech to Text Correction for Indonesian Early Marriage Counseling Chatbots Using IndoRoBERTa and Mistral-7B","year":2025,"doi":"10.21108/indojc.v10i1.9708","url":"https://doi.org/10.21108/indojc.v10i1.9708","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_20","type":"source","study":"Autonomous QA Data Augmentation via Open-Source LLM Agents for Metaverse Applications","year":2025,"doi":"10.1109/aiccsa66935.2025.11315489","url":"https://doi.org/10.1109/aiccsa66935.2025.11315489","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_21","type":"source","study":"Benchmarking 21 Open-Source Large Language Models for Phishing Link Detection with Prompt Engineering","year":2025,"doi":"10.3390/info16050366","url":"https://doi.org/10.3390/info16050366","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_22","type":"source","study":"Sec-Llama: a Compact Fine-Tuned LLM for Network Intrusion Detection in Kubernetes Clusters","year":2025,"doi":"10.1109/icmlcn64995.2025.11140090","url":"https://doi.org/10.1109/icmlcn64995.2025.11140090","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_23","type":"source","study":"AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models","year":2025,"doi":"10.48550/arxiv.2507.01020","url":"https://doi.org/10.48550/arxiv.2507.01020","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_24","type":"source","study":"Protein as a Second Language for LLMs","year":2025,"doi":"10.48550/arxiv.2510.11188","url":"https://doi.org/10.48550/arxiv.2510.11188","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_25","type":"source","study":"Can We Enhance Bug Report Quality Using LLMs?: An Empirical Study of LLM-Based Bug Report Generation","year":2025,"doi":"10.1145/3756681.3756995","url":"https://doi.org/10.1145/3756681.3756995","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_26","type":"source","study":"Energy-Efficient Wireless LLM Inference via Uncertainty and Importance-Aware Speculative Decoding","year":2025,"doi":"10.48550/arxiv.2508.12590","url":"https://doi.org/10.48550/arxiv.2508.12590","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_27","type":"source","study":"Evading LLMs’ Safety Boundary with Adaptive Role-Play Jailbreaking","year":2025,"doi":"10.3390/electronics14244808","url":"https://doi.org/10.3390/electronics14244808","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_28","type":"source","study":"LOGICPO: Efficient Translation of NL-based Logical Problems to FOL using LLMs and Preference Optimization","year":2025,"doi":"10.48550/arxiv.2506.18383","url":"https://doi.org/10.48550/arxiv.2506.18383","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_29","type":"source","study":"Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks","year":2025,"doi":"10.48550/arxiv.2505.16901","url":"https://doi.org/10.48550/arxiv.2505.16901","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_30","type":"source","study":"Assessing the Performance of Large Language Models on the Foreign Medical Graduate Examination (FMGE): Insights from GPT-4 Turbo, Gemini Advanced, and LLaMA 3.1 (70B)","year":2025,"doi":"10.1109/icbmesh66209.2025.11182217","url":"https://doi.org/10.1109/icbmesh66209.2025.11182217","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_31","type":"source","study":"Resource-efficient fine-tuning of large vision-language models for multimodal perception in autonomous excavators.","year":2025,"doi":"10.3389/frai.2025.1681277","url":"https://doi.org/10.3389/frai.2025.1681277","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_32","type":"source","study":"GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?","year":2025,"doi":"10.48550/arxiv.2508.12472","url":"https://doi.org/10.48550/arxiv.2508.12472","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_33","type":"source","study":"Agentic memory-augmented retrieval and evidence grounding for medical question-answering tasks","year":2025,"doi":"10.1101/2025.08.06.25333160","url":"https://doi.org/10.1101/2025.08.06.25333160","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_34","type":"source","study":"Vulnerability Assessment of Open-Source Large Language Models Against Prompt Variation Attacks","year":2025,"doi":"10.1109/dsc65356.2025.11260884","url":"https://doi.org/10.1109/dsc65356.2025.11260884","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_35","type":"source","study":"InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers","year":2024,"doi":"10.18653/v1/2024.acl-long.506","url":"https://doi.org/10.18653/v1/2024.acl-long.506","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_36","type":"source","study":"On Limitations of LLM as Annotator for Low Resource Languages","year":2024,"doi":"10.48550/arxiv.2411.17637","url":"https://doi.org/10.48550/arxiv.2411.17637","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_37","type":"source","study":"Multilingual Mathematical Reasoning: Advancing Open-Source LLMs in Hindi and English","year":2024,"doi":"10.48550/arxiv.2412.18415","url":"https://doi.org/10.48550/arxiv.2412.18415","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_38","type":"source","study":"Empowering Research: Open-Source LLMs, Semantic Search, and Domain-Specific Knowledge in a Multi-Document Q&A Assistant","year":2024,"doi":"10.21872/2024iise_6507","url":"https://doi.org/10.21872/2024iise_6507","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_39","type":"source","study":"Toponym resolution leveraging lightweight and open-source large language models and geo-knowledge","year":2024,"doi":"10.1080/13658816.2024.2405182","url":"https://doi.org/10.1080/13658816.2024.2405182","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"}],"edges":[{"from":"87e015be-2295-434d-b696-f26092dd25f2","to":"claim_1","type":"contains_claim"},{"from":"87e015be-2295-434d-b696-f26092dd25f2","to":"claim_2","type":"contains_claim"}],"screening":{"identified":39,"screened":39,"excluded":0,"included":39,"included_or_retained":39,"flow":["identified","screened","excluded_with_reasons","included"],"wording":"39 candidate receipts retained after source retrieval, deduplication, and topic filtering. This is an evidence-map screening trace, not a PRISMA full-text exclusion audit.","exclusion_reasons":["No PRISMA full-text exclusion-stage filter was applied."]}}