·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
What to watch for after Jensen Huang’s Japan visit6h◆Can an Apple lawsuit derail OpenAI’s hardware plans?8h◆I hate that I don’t hate this song made with Suno9h◆‘Odyssey’ director Christopher Nolan calls AI an obvious ‘Trojan horse’12h◆Nonprofit Current AI is racing to build the World Wide Web of AI, free for all13h◆Dave Eggers told OpenAI staff that ChatGPT was ‘silencing an entire generation’1d◆Kimi: Threat or menace?1d◆The apps, gadgets, and tools every reader needs1d◆Neil Rimer thinks the AI money is coming back out1d◆The Steering Budget: Examples beat Knobs1d◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs1d◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination1d◆HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization1d◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models1d◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation1d◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System1d◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets1d◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility1d◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation1d◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks1d◆What to watch for after Jensen Huang’s Japan visit6h◆Can an Apple lawsuit derail OpenAI’s hardware plans?8h◆I hate that I don’t hate this song made with Suno9h◆‘Odyssey’ director Christopher Nolan calls AI an obvious ‘Trojan horse’12h◆Nonprofit Current AI is racing to build the World Wide Web of AI, free for all13h◆Dave Eggers told OpenAI staff that ChatGPT was ‘silencing an entire generation’1d◆Kimi: Threat or menace?1d◆The apps, gadgets, and tools every reader needs1d◆Neil Rimer thinks the AI money is coming back out1d◆The Steering Budget: Examples beat Knobs1d◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs1d◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination1d◆HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization1d◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models1d◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation1d◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System1d◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets1d◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility1d◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation1d◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks1d◆
News/CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs
arxiv
PublishedJune 27, 2026 at 4:00 AM
—neutral

CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs

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

arXiv:2606.26650v1 Announce Type: cross Abstract: In this paper, we present CAT-Q, Cost-efficient and Accurate Ternary Quantization, for compressing and accelerating LLMs. Unlike existing state-of-the-art ternary quantization methods that rely on data-intensive and costly quantization-aware training

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
arxivThe Steering Budget: Examples beat Knobs1darxivPolestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs1darxivRxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination1darxivHABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization1d
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