·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
FRAC-MAS: A Safe and Explainable Multi-Agent System for Fracture Diagnosis7h◆Evaluating the Hidden Costs of Personalization in Large Language Models7h◆MineCEraft: Evaluating Language Models as Construction Engineers in the World of Minecraft7h◆From Location Phrases to Geographic Entities: Task-Adapted Retrieval for People Search7h◆From Analytics to Tumor Boards: An Evidence-Linked Multi-Agent Workflow for Oncology Feature Extraction7h◆Facts Without Rules: Boundary Metadata Collapse in Multi-Agent LLM Handoffs7h◆Learning to Follow In-Context Watermark Instructions via Self-Distillation7h◆EmoLASP: Emotion Recognition with Language Models and Answer Set Programming7h◆Let Prompts Bridge Defense Knowledge: Transferable Graph Purification via Vulnerability-Aware GPL7h◆Nested Convex-Body Chasing for Online Optimization with Evolving Feasible Sets7h◆HANIA: Planner-Guided Multimodal Graph Evidence Selection for Grounded Question Answering7h◆EviAnchor: Mitigating Hallucinations in Large Vision-Language Models via Regional Visual Evidence Compensation7h◆SafeAtlas-VL: Beyond Binary Multimodal Safety with Large-Scale Data and Guard Models7h◆Beyond Correctness: Validity-Oriented Evaluation of Biomedical LLM Judges7h◆APIFlow-Bench: Measuring Whether Agents Survive Long, Dependent API Workflows7h◆More Perspectives, Stronger Signals: Multi-Perspective Enhancement and Progressive Fusion for Multimodal Entity Representation Learning7h◆JudgePanel: A Compact Judge with Panel Deliberation via Adaptive Multi-Reward Reinforcement Learning7h◆An Explainable Coherence Score for Detecting Temporal Inconsistencies in Political News7h◆How Identity and Opinion Shape Political Sycophancy in LLMs7h◆Benevolent Bias in Multi-Turn Human-Agent Dialogue7h◆FRAC-MAS: A Safe and Explainable Multi-Agent System for Fracture Diagnosis7h◆Evaluating the Hidden Costs of Personalization in Large Language Models7h◆MineCEraft: Evaluating Language Models as Construction Engineers in the World of Minecraft7h◆From Location Phrases to Geographic Entities: Task-Adapted Retrieval for People Search7h◆From Analytics to Tumor Boards: An Evidence-Linked Multi-Agent Workflow for Oncology Feature Extraction7h◆Facts Without Rules: Boundary Metadata Collapse in Multi-Agent LLM Handoffs7h◆Learning to Follow In-Context Watermark Instructions via Self-Distillation7h◆EmoLASP: Emotion Recognition with Language Models and Answer Set Programming7h◆Let Prompts Bridge Defense Knowledge: Transferable Graph Purification via Vulnerability-Aware GPL7h◆Nested Convex-Body Chasing for Online Optimization with Evolving Feasible Sets7h◆HANIA: Planner-Guided Multimodal Graph Evidence Selection for Grounded Question Answering7h◆EviAnchor: Mitigating Hallucinations in Large Vision-Language Models via Regional Visual Evidence Compensation7h◆SafeAtlas-VL: Beyond Binary Multimodal Safety with Large-Scale Data and Guard Models7h◆Beyond Correctness: Validity-Oriented Evaluation of Biomedical LLM Judges7h◆APIFlow-Bench: Measuring Whether Agents Survive Long, Dependent API Workflows7h◆More Perspectives, Stronger Signals: Multi-Perspective Enhancement and Progressive Fusion for Multimodal Entity Representation Learning7h◆JudgePanel: A Compact Judge with Panel Deliberation via Adaptive Multi-Reward Reinforcement Learning7h◆An Explainable Coherence Score for Detecting Temporal Inconsistencies in Political News7h◆How Identity and Opinion Shape Political Sycophancy in LLMs7h◆Benevolent Bias in Multi-Turn Human-Agent Dialogue7h◆
News/AI-Driven Synthesis for High-Tech System Design: Automating Innovation
arxiv
PublishedJune 29, 2026 at 4:00 AM

AI-Driven Synthesis for High-Tech System Design: Automating Innovation

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

arXiv:2606.28126v1 Announce Type: new Abstract: This article addresses the combinatorial complexity inherent in modern high-tech system design by presenting automation-in-design (AiD) as a transformative paradigm. We propose computational design synthesis (CDS), a framework utilising deep learning a

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
arxivFRAC-MAS: A Safe and Explainable Multi-Agent System for Fracture Diagnosis7harxivEvaluating the Hidden Costs of Personalization in Large Language Models7harxivMineCEraft: Evaluating Language Models as Construction Engineers in the World of Minecraft7harxivFrom Location Phrases to Geographic Entities: Task-Adapted Retrieval for People Search7h
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