MS-GLA: Multi-Scale Gated Linear Attention for Addressing Representational Bottlenecks via Multi-Temporal Resolution

28 Sept 2026Reported by arXiv24

MS-GLA: Multi-Scale Gated Linear Attention for Addressing Representational Bottlenecks via Multi-Temporal Resolution. It centres on Perplexity, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.

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Reported by arXiv

First seen 28 sept 2026

arXivMS-GLA: Multi-Scale Gated Linear Attention for Addressing Representational Bottlenecks via Multi-Temporal Resolution28 Sept 2026Prasoon Dev

AI Models24

DecisionPivot: A Diagnostic Benchmark for Decision Models in LLM Systems

DecisionPivot: A Diagnostic Benchmark for Decision Models in LLM Systems. It centres on Benchmarks, and also names Perplexity. Reported by arXiv. Bharat Hunt files it under AI Models and AI Research — the section covering a new or updated model, its capabilities, benchmarks or availability.

arXiv20 Oct 2026

AI Regulation23

Differentially Private Mixing of Public Datasets Improves Private Learning

Differentially Private Mixing of Public Datasets Improves Private Learning. It centres on Fine-tuning, and also names Perplexity. Reported by arXiv. Bharat Hunt files it under AI Regulation — the section covering law, policy, courts, standards and government positions on AI.

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MS-GLA: Multi-Scale Gated Linear Attention for Addressing Representational Bottlenecks via Multi-Temporal Resolution

Companies, models and people in this story

Reported by arXiv

DecisionPivot: A Diagnostic Benchmark for Decision Models in LLM Systems

Differentially Private Mixing of Public Datasets Improves Private Learning

Output-aware Residual Stream Pruning for Large Language Models

Telescopic Language Models

Companies, models and people in this story

Reported by arXiv

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DecisionPivot: A Diagnostic Benchmark for Decision Models in LLM Systems

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