TokenGate: Adapting LLMs for Fast and Memory-Efficient Prefill with Learned Token Pruning

19 Oct 2026Reported by arXiv24

TokenGate: Adapting LLMs for Fast and Memory-Efficient Prefill with Learned Token Pruning. It centres on Fine-tuning, and also names GitHub. Reported by arXiv. Bharat Hunt files it under AI Models and AI Coding — the section covering a new or updated model, its capabilities, benchmarks or availability.

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Reported by arXiv

First seen 19 oct 2026

arXivTokenGate: Adapting LLMs for Fast and Memory-Efficient Prefill with Learned Token Pruning19 Oct 2026Liana Patel

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Topology-Stratified Materials Discovery with A Flow-Based Generative Model

Topology-Stratified Materials Discovery with A Flow-Based Generative Model. It centres on Benchmarks, and also names Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.

arXiv22 Sept 2026

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Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation

Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation. It centres on Reasoning Models, and also names Fine-tuning and Inference. 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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TokenGate: Adapting LLMs for Fast and Memory-Efficient Prefill with Learned Token Pruning

Companies, models and people in this story

Reported by arXiv

Topology-Stratified Materials Discovery with A Flow-Based Generative Model

Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation

On the Lexical Superstition of Large Language Models for Code Comprehension: Re-evaluation on Code of Low Lexical Quality

OMatG-flash: An All-Atom Flow Map with Reinforce Adjoint Matching for Scalable Materials Discovery

Companies, models and people in this story

Reported by arXiv

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Topology-Stratified Materials Discovery with A Flow-Based Generative Model

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On the Lexical Superstition of Large Language Models for Code Comprehension: Re-evaluation on Code of Low Lexical Quality

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