Zhemal Khamidun
Head of AI at Alpina Digital, CPO of AlpinaGPT (~30-person engineering & AI team). Founder of Vibe Academy & Hamidun Consulting, author of the AI channel «Готовим ИИшницу» (@jemal_hamidun).

How to Explain Continual Learning in Neural Networks: Attention Rollout and Grad-CAM on Time Series
Researchers used attention rollout and Grad-CAM to explain how neural networks adapt on nonstationary time series, using groundwater monitoring data as a test case.

Air Quality Arena: A Benchmark for Forecasting Air Pollution with Foundation Models
Researchers have released Air Quality Arena, an open dataset of 14,000+ series covering 6 pollutants in 7 countries, plus a benchmark of 11 time-series foundation…

CruiseBench: A New Benchmark for Evaluating Neural Networks on Aircraft Engine Lifetime Prediction
Researchers have introduced CruiseBench, an N-CMAPSS-based benchmark that isolates the cruise phase of flight to fairly compare neural networks predicting aircraft…

Sentence Splitter: How a T5 Model Finds the Hidden Factual Structure of Sentences

VizRAG: Visualizing Hypergraphs to Improve RAG Retrieval for Multimodal Models

TriAgent: A Committee of Small Models Instead of an Expensive LLM Saves $9.3M a Year

Rubric4Setwise: Evaluating Whole Document Sets for RAG Instead of Ranking by Relevance

Neural Network Pinpoints a Speaker's Origin from Their Arabic Dialect to Within 481 km
A new neural network predicts where an Arabic speaker comes from as latitude and longitude, with a median error of 481.2 km, without sorting speakers into countries.

HIPE-2026: How to Extract Person-Place Relations from Historical Newspapers Without Neural Networks
At HIPE-2026, team DS@GT HIPE showed that person-place relations in historical newspapers can be extracted without large language models, using a system under 847…

MultiMDM: A Diffusion Language Model That Generates Text in a Few Steps

NLI Hypergraph vs. LLM Judge: A New Way to Audit AI Reasoning Without a Reference Answer

Adaptive Capitulation in LLMs: How Chatbots Give In to Vulnerable Users (Study)

Small Qwen Language Models Ignore Instructions but Look Accurate on Benchmarks

Spectral-LSH: Up to 16× Prompt Compression Without Fine-Tuning to Speed Up LLM Inference
The new Spectral-LSH method compresses long prompts 8–16× with no model fine-tuning: it hashes similar tokens into groups in attention's spectral space and…

AI Tutor "Scaffolding Collapse": New Method Keeps Socratic Dialogue Intact on Qwen3-8B
Scaffold-Preserving Representation Alignment targets "scaffolding collapse" in AI tutors: on Qwen3-8B, the collapse rate fell to 32% and dialogues hold for more…









