·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Dave Eggers told OpenAI staff that ChatGPT was ‘silencing an entire generation’55m◆Kimi: Threat or menace?2h◆The apps, gadgets, and tools every reader needs9h◆Neil Rimer thinks the AI money is coming back out17h◆Democratizing Agent Deployment Safety: A Structural Monitoring Approach17h◆Harnessing LLMs for Reliable Academic Supervision: A Comparative Study17h◆Accelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap17h◆TIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation17h◆The Steering Budget: Examples beat Knobs17h◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs17h◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination17h◆HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization17h◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models17h◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation17h◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System17h◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets17h◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility17h◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation17h◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks17h◆OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios17h◆Dave Eggers told OpenAI staff that ChatGPT was ‘silencing an entire generation’55m◆Kimi: Threat or menace?2h◆The apps, gadgets, and tools every reader needs9h◆Neil Rimer thinks the AI money is coming back out17h◆Democratizing Agent Deployment Safety: A Structural Monitoring Approach17h◆Harnessing LLMs for Reliable Academic Supervision: A Comparative Study17h◆Accelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap17h◆TIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation17h◆The Steering Budget: Examples beat Knobs17h◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs17h◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination17h◆HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization17h◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models17h◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation17h◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System17h◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets17h◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility17h◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation17h◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks17h◆OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios17h◆
News/TernaryLM: Memory-Efficient Language Modeling via Native 1.5-Bit Quantization with Adaptive Layer-wise Scaling
arxiv
PublishedMarch 30, 2026 at 4:00 AM

TernaryLM: Memory-Efficient Language Modeling via Native 1.5-Bit Quantization with Adaptive Layer-wise Scaling

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2602.07374v2 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve remarkable performance but demand substantial computational resources, limiting deployment on edge devices and resource-constrained environments. We present TernaryLM, a 132M-parameter transformer trained

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivDemocratizing Agent Deployment Safety: A Structural Monitoring Approach17harxivHarnessing LLMs for Reliable Academic Supervision: A Comparative Study17harxivAccelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap17harxivTIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation17h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews