·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Adobe’s ‘natural look’ camera app embraces generative AI2h◆Introducing Cosmos 3 Edge2h◆YouTube clarifies policies around AI slop and upsetting videos2h◆China delivers a one-two punch to America’s AI dominance7h◆Safety and alignment in an era of long-horizon models8h◆AI is more likely than humans to form biases when hiring9h◆ADS-C: Antidistillation Sampling for Classification14h◆Do Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?14h◆Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes14h◆From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems14h◆A Formally Grounded ODRL Evaluator: Implementation and Comparison14h◆Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution14h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents14h◆EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections14h◆Before the Action: Benchmarking LLMs on Prospective Hypothesis Discovery14h◆How Much Human Label Variation Does Formal Semantic Structure Explain?: Group-Level Effects and Item-Level Ceilings in NLI14h◆Learn to Memorize: Scalable Continual Learning in Semiparametric Models with Mixture-of-Neighbors Induction Memory14h◆Echoes Across Vietnam's Highlands, Delta, and Coast: A Multilingual Corpus for Cham, Khmer, and Tay-Nung14h◆Stochastic Reset Pathfinding: Path-Level Regret for Cascading Bandits over Graph Paths14h◆Publicly-Verifiable Certificates for Statistical Algorithms14h◆Adobe’s ‘natural look’ camera app embraces generative AI2h◆Introducing Cosmos 3 Edge2h◆YouTube clarifies policies around AI slop and upsetting videos2h◆China delivers a one-two punch to America’s AI dominance7h◆Safety and alignment in an era of long-horizon models8h◆AI is more likely than humans to form biases when hiring9h◆ADS-C: Antidistillation Sampling for Classification14h◆Do Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?14h◆Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes14h◆From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems14h◆A Formally Grounded ODRL Evaluator: Implementation and Comparison14h◆Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution14h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents14h◆EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections14h◆Before the Action: Benchmarking LLMs on Prospective Hypothesis Discovery14h◆How Much Human Label Variation Does Formal Semantic Structure Explain?: Group-Level Effects and Item-Level Ceilings in NLI14h◆Learn to Memorize: Scalable Continual Learning in Semiparametric Models with Mixture-of-Neighbors Induction Memory14h◆Echoes Across Vietnam's Highlands, Delta, and Coast: A Multilingual Corpus for Cham, Khmer, and Tay-Nung14h◆Stochastic Reset Pathfinding: Path-Level Regret for Cascading Bandits over Graph Paths14h◆Publicly-Verifiable Certificates for Statistical Algorithms14h◆
News/Precomputing the Future-Offset Average in TriAttention
arxiv
PublishedJuly 16, 2026 at 4:00 AM
—neutral

Precomputing the Future-Offset Average in TriAttention

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

arXiv:2607.13051v1 Announce Type: cross Abstract: TriAttention is a recent method for shrinking the KV cache of long-reasoning LLMs: it scores each cached key by how much attention it is likely to receive and evicts the lowest-scoring ones. Because a key does not know how far away its future queries

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
arxivADS-C: Antidistillation Sampling for Classification14harxivDo Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?14harxivBeyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes14harxivFrom Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems14h
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