·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Amazon’s Bee launches two new personal AI devices, starting at $751h◆Amazon’s always-listening Bee wearable can now help you buy stuff1h◆Your secrets aren’t safe with this cute robot toy — they’re on Chinese servers ready to be hacked2h◆Figma announces new features to define how agents work on its platform8h◆Narrowing the Collaboration Gap, Probably9h◆Iterate on an LLM, Serve on an SLM9h◆Relational Semantic Grounding for Test-Time Discovery9h◆MARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL9h◆SkillForgeBench: Evaluating Agent Reliability Grounded in Agent Skill Specifications9h◆Harmony Harness: Multi-Objective Self-Evolution of Agent Harnesses9h◆State Representation Shapes Long-Horizon Agent Learning and Transfer: An Empirical Study9h◆CoverSynth: Coverage-Guided Generation of Retrieval Evaluation Sets9h◆SpecForge: Recursive Self-Improvement for Coding Agents via Behavioral Specifications9h◆AuthGateBench: Benchmarking LLMs as Authorization Gate9h◆Boosting the Learning for Ranking Patterns9h◆Stable Token-level Scheduling for Serverless LLM Inference9h◆ECMR: Evidence-Aware Deliberation Control for Multi-Agent LLM Reasoning9h◆Beyond Black Boxes: Visual Grounding for Text Embeddings9h◆Can Agents Forget When Asked? A Benchmark for Memory Unlearning in LLM Agents9h◆Do you "see" what I "see"? A Multi-panel Visual Question Answer Dataset for Large Language Model Chart Analysis9h◆Amazon’s Bee launches two new personal AI devices, starting at $751h◆Amazon’s always-listening Bee wearable can now help you buy stuff1h◆Your secrets aren’t safe with this cute robot toy — they’re on Chinese servers ready to be hacked2h◆Figma announces new features to define how agents work on its platform8h◆Narrowing the Collaboration Gap, Probably9h◆Iterate on an LLM, Serve on an SLM9h◆Relational Semantic Grounding for Test-Time Discovery9h◆MARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL9h◆SkillForgeBench: Evaluating Agent Reliability Grounded in Agent Skill Specifications9h◆Harmony Harness: Multi-Objective Self-Evolution of Agent Harnesses9h◆State Representation Shapes Long-Horizon Agent Learning and Transfer: An Empirical Study9h◆CoverSynth: Coverage-Guided Generation of Retrieval Evaluation Sets9h◆SpecForge: Recursive Self-Improvement for Coding Agents via Behavioral Specifications9h◆AuthGateBench: Benchmarking LLMs as Authorization Gate9h◆Boosting the Learning for Ranking Patterns9h◆Stable Token-level Scheduling for Serverless LLM Inference9h◆ECMR: Evidence-Aware Deliberation Control for Multi-Agent LLM Reasoning9h◆Beyond Black Boxes: Visual Grounding for Text Embeddings9h◆Can Agents Forget When Asked? A Benchmark for Memory Unlearning in LLM Agents9h◆Do you "see" what I "see"? A Multi-panel Visual Question Answer Dataset for Large Language Model Chart Analysis9h◆
News/Green SARC: Predictive Cost and Carbon Governance for Agentic AI Systems
arxiv
PublishedJune 16, 2026 at 4:00 AM

Green SARC: Predictive Cost and Carbon Governance for Agentic AI Systems

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

arXiv:2606.15954v1 Announce Type: cross Abstract: Agentic AI systems act through tools and sub-agents, yet the controls meant to bound their financial and environmental cost still sit on dashboards evaluated beside or after execution. Green SARC applies the SARC governance-by-architecture framework

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

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

Your email is used only to send The Bubble Brief. Privacy policy

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
arxivNarrowing the Collaboration Gap, Probably9harxivIterate on an LLM, Serve on an SLM9harxivRelational Semantic Grounding for Test-Time Discovery9harxivMARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL9h
The Bubble Brief
MON · WED · FRI

Read AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

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

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews