Core ML Research · Jun 9 Daily Digest
Hybrid Model Architectures
- 🔥 HARMONY: Large-scale architecture search for efficient hybrid transformer-Mamba-MoE language models via automated...

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Latest papers, benchmarks, and announcements on ML theory, algorithms, model architectures, optimization, training
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OmniGameArena introduces a unified real-time benchmark across 12 UE5 games (Solo, PvP, Coop) to evaluate diverse VLM agents on equal footing.
Two distinct strategies are advancing efficient LLM architectures:
Three distinct memory designs tackle core bottlenecks in video world models for action prediction.
On-policy distillation occupies its own parameter-space regime: fewer affected weights, stronger avoidance of principal directions than SFT, and...
Trajectory extrapolation error measures deviation from linear paths in LLM hidden states and predicts human self-paced reading times independently of surprisal. The effect holds across GPT-2 variants on Natural Stories and garden-path sentences.
A textbook manuscript delivers a unified mathematical framework for deep representation learning, showing how systems transform high-dimensional data into compact representations that function as empirical memory or world models.
A new benchmark across 26 endpoints and 165k compound records challenges the scale-centric view of AI in drug discovery.
Revolut replaced fragmented task-specific models with Pragma, a single foundation model trained on raw banking events to predict patterns like fraud...
Two recent papers expose unexpected internal mechanisms shaping how neural networks represent and learn data.
A new SIA framework lets a Feedback-Agent simultaneously rewrite task scaffolds and fine-tune model weights, breaking the isolation between...
Two June papers advance MLLMs beyond offline clips toward dynamic human-view and streaming 3D understanding.
Extending TextGrad to multi-objective settings reveals gradient dilution (task-focus drops 59%, from 9.0 to 3.7) when the gradient LLM handles joint...
Databricks' Instructed-Retriever-1 replaces sequential reasoning loops with parallel query/filter generation plus multi-pivot reranking, boosting...
Together AI has pushed LLM context lengths to 5 million tokens by introducing training techniques that tackle transformer bottlenecks in quadratic computation and linear memory usage. This yields improved efficiency for long-context models.
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