Zhemal Khamidun
Hamidun News editorial desk led by Editor-in-Chief Zhemal Khamidun (CPO of AlpinaGPT). Daily curation across 50+ global AI labs, research feeds, and industry reports in 6 languages.

CLUE Builds an Event Ontology Without Experts in Three Steps: Embeddings, Clusters, Names
The CLUE framework combines verbal and semantic views of an event, clusters events with a contrastive loss, and names the types itself. The code is already open on…

AutoIndexer Teaches a Language Model to Edit Its Own Output with Three Types of Edits
AutoIndexer simulates a chain of edits: the model moves a cursor, inserts, replaces and deletes tokens in already generated text, and does not lose quality on…

OGPO Teaches VLA Models to Get Back on Track After a Failure, Without Online Fine-Tuning
OGPO shifts VLA post-training from copying actions to reaching key task states. The method was tested on LIBERO, MetaWorld and the new LIBERO-ReAct benchmark.

New Method Aligns Encoders With 100–500 Pairs Instead of Millions

LLM Pipeline Turns Datasheets into Knowledge Graphs with 98.8% Accuracy

EvoSyn: Multi-Agent Data Synthesis Lifts Task Success by 13.3 Points

EliteTable Makes LLMs Evolve Controller Code: Gains in 6 of 6 Tasks

MCTS Loses to Random Restarts by 2.88% in DAG Scheduling at Equal Budget
The authors gave MCTS and a simple list scheduler with restarts 2048 schedules each on DAG tasks. The search tree came out 2.88% worse than random restarts.

arXiv paper finds five flaws in XAI faithfulness metrics
An arXiv position paper says a decade of XAI has not raised trust in models, and faithfulness metrics have five systemic flaws. The authors propose four research…

Bain Capital's Bridge Data Centres Seeks to Raise Its Loan to $6.6 Billion

Firmus Grid IPO Failure Wipes More Than $699 Million Off Maas Group's Market Value

Dongshan Precision Prepares $2–3 Billion Hong Kong Listing

Zebra3D speeds up 3D reconstruction by up to 7.1× on 1024 frames without training from scratch

Linear Head on Qwen3-VL Beats GPT-4o at Scoring Answers Without Human Labels
A vision-language reward model with no human labels: a few thousand parameters on a frozen Qwen3-VL 32B beat GPT-4o on VL-RewardBench by 6.6 points.

GEPA Lifts a Deep-Research Agent's Reward from 0.702 to 0.781 on Unseen Topics
GEPA prompt evolution raised the citation reward on held-out topics by 0.079. Nearly half of the winning prompt is an answer key, but memorization is not what…









