LLMForge is a NAS framework with Infinite-Head Attention, a Forge-Former surrogate, and Forge-DSE engine that discovers hardware-specific architectures for edge language models, yielding variants with improved accuracy, energy, or latency on different substrates.
MELTing Point: Mobile evaluation of language transformers
3 Pith papers cite this work, alongside 21 external citations. Polarity classification is still indexing.
3
Pith papers citing it
21
external citations · external index
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Empirical case study on a flagship Android device profiles energy, latency, and quality trade-offs across eight LLMs, revealing a quantization energy paradox and identifying mid-sized models as practical sweet spots.
AgentStop uses execution signals to early-terminate failing local LLM agent trajectories, cutting energy use 15-20% with minimal utility loss.
citing papers explorer
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LLMForge: Multi-Backend Hardware-Aware Neural Architecture Search with Infinite-Head Attention for Edge Language Models
LLMForge is a NAS framework with Infinite-Head Attention, a Forge-Former surrogate, and Forge-DSE engine that discovers hardware-specific architectures for edge language models, yielding variants with improved accuracy, energy, or latency on different substrates.
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Sustainability Is Not Linear: Quantifying Performance, Energy, and Privacy Trade-offs in On-Device Intelligence
Empirical case study on a flagship Android device profiles energy, latency, and quality trade-offs across eight LLMs, revealing a quantization energy paradox and identifying mid-sized models as practical sweet spots.
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AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices
AgentStop uses execution signals to early-terminate failing local LLM agent trajectories, cutting energy use 15-20% with minimal utility loss.