A matched benchmark shows GUI computer-use agents at 59.1% full pass rate versus 48.2% for original-skill CLI agents, rising to 69.3% with verifier-guided augmentation, indicating modality-specific execution bottlenecks.
Hidden Persuasion: Detecting Manipulative Narratives on Social Media During the 2022 R ussian Invasion of U kraine
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6verdicts
UNVERDICTED 6representative citing papers
A two-stage hybrid search pipeline paired with a synthetic-data fine-tuned and compressed Ukrainian language model delivers competitive local question answering under strict compute limits.
LLM-generated ML pipelines show higher bias (87.7% sensitive attributes) than conditional statements (59.2%), indicating that simple if-statement tests underestimate bias risk in practical code generation.
Finetuning Qwen3-32B with data augmentation and self-training achieves competitive 8th-place ranking on SemEval-2026 conspiracy detection.
Finetuning LLMs with QLoRA and multilingual data augmentation for polarization detection, type, and manifestation in SemEval-2026 Task 9.
Fine-tuning LLMs by adapting the mdok approach produces competitive results on binary detection, source attribution, and hybrid/adversarial code identification in SemEval-2026 Task 13.
citing papers explorer
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GUI vs. CLI: Execution Bottlenecks in Screen-Only and Skill-Mediated Computer-Use Agents
A matched benchmark shows GUI computer-use agents at 59.1% full pass rate versus 48.2% for original-skill CLI agents, rising to 69.3% with verifier-guided augmentation, indicating modality-specific execution bottlenecks.
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An End-to-End Ukrainian RAG for Local Deployment. Optimized Hybrid Search and Lightweight Generation
A two-stage hybrid search pipeline paired with a synthetic-data fine-tuned and compressed Ukrainian language model delivers competitive local question answering under strict compute limits.
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From If-Statements to ML Pipelines: Revisiting Bias in Code-Generation
LLM-generated ML pipelines show higher bias (87.7% sensitive attributes) than conditional statements (59.2%), indicating that simple if-statement tests underestimate bias risk in practical code generation.
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mdok-style at SemEval-2026 Task 10: Finetuning LLMs for Conspiracy Detection
Finetuning Qwen3-32B with data augmentation and self-training achieves competitive 8th-place ranking on SemEval-2026 conspiracy detection.
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mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection
Finetuning LLMs with QLoRA and multilingual data augmentation for polarization detection, type, and manifestation in SemEval-2026 Task 9.
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mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code
Fine-tuning LLMs by adapting the mdok approach produces competitive results on binary detection, source attribution, and hybrid/adversarial code identification in SemEval-2026 Task 13.