·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Shapley Context Pruning: A Cooperative Game Perspective for Context Reranking and Pruning3h◆A Survey on the Verification of Reinforcement Learning Policies3h◆Symbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers3h◆SelKV: Selective KV Cache Merging with Per-Token Merge-or-Drop and Attention Compensation3h◆RAIL Guard: Closing the Evaluation-to-Remediation Gap in Responsible AI for LLM Agents3h◆LaCache: Exact Caching and Precision-Adaptive Inference for Diffusion Large Language Models3h◆When to Plan: Learning to Select Between Reactive Control and Deliberative Planning3h◆SEER: Supervised Learning to Control Energetic Reasoning3h◆FST.ai 2.5: Explainable and Uncertainty-Aware AI for Olympic and Para-Taekwondo Decision Support, Athlete Digital Twins, and Federation-Scale Analytics3h◆Just A Rather Very Intelligent Spoken Agent3h◆A Research Prototype for Closed-Loop Generative Design of Customized Foot Orthoses via Semantic-Physics Alignment3h◆TopoTuner: Topological Finetuning of Large Language Models3h◆Diversity-Oriented Fine-Tuning for Uncertainty-Based Hallucination Detection3h◆DS@GT ARC at eRisk 2026: Hybrid Multi-Agent LLM System with Structured Algorithmic Guidance for Conversational Depression Screening3h◆Tractable Query Answering under Epistemic Confidentiality Policies in DL Ontologies (extended version)3h◆RECON: Benchmarking Agent Memory for Compositional Reasoning over Long Contexts3h◆Supporting Autonomous Process Execution within a Multi-Perspective Constraint Frame via Numeric Planning3h◆RELIC: Revealed Principles for Learning Interpretable Composable Skills in Multi-Agent Planning3h◆FUSAR-R1: A Large-Scale Reasoning Model for Intelligent Interpretation of SAR Images3h◆From Overload to Insights: How AI Agents Can Support Scientists in Analyzing Complex Data3h◆Shapley Context Pruning: A Cooperative Game Perspective for Context Reranking and Pruning3h◆A Survey on the Verification of Reinforcement Learning Policies3h◆Symbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers3h◆SelKV: Selective KV Cache Merging with Per-Token Merge-or-Drop and Attention Compensation3h◆RAIL Guard: Closing the Evaluation-to-Remediation Gap in Responsible AI for LLM Agents3h◆LaCache: Exact Caching and Precision-Adaptive Inference for Diffusion Large Language Models3h◆When to Plan: Learning to Select Between Reactive Control and Deliberative Planning3h◆SEER: Supervised Learning to Control Energetic Reasoning3h◆FST.ai 2.5: Explainable and Uncertainty-Aware AI for Olympic and Para-Taekwondo Decision Support, Athlete Digital Twins, and Federation-Scale Analytics3h◆Just A Rather Very Intelligent Spoken Agent3h◆A Research Prototype for Closed-Loop Generative Design of Customized Foot Orthoses via Semantic-Physics Alignment3h◆TopoTuner: Topological Finetuning of Large Language Models3h◆Diversity-Oriented Fine-Tuning for Uncertainty-Based Hallucination Detection3h◆DS@GT ARC at eRisk 2026: Hybrid Multi-Agent LLM System with Structured Algorithmic Guidance for Conversational Depression Screening3h◆Tractable Query Answering under Epistemic Confidentiality Policies in DL Ontologies (extended version)3h◆RECON: Benchmarking Agent Memory for Compositional Reasoning over Long Contexts3h◆Supporting Autonomous Process Execution within a Multi-Perspective Constraint Frame via Numeric Planning3h◆RELIC: Revealed Principles for Learning Interpretable Composable Skills in Multi-Agent Planning3h◆FUSAR-R1: A Large-Scale Reasoning Model for Intelligent Interpretation of SAR Images3h◆From Overload to Insights: How AI Agents Can Support Scientists in Analyzing Complex Data3h◆
News/Symbiosis-Inspired Knowledge Distillation for Incremental Object Detection
arxiv
PublishedJuly 16, 2026 at 4:00 AM
—neutral

Symbiosis-Inspired Knowledge Distillation for Incremental Object Detection

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

arXiv:2607.13452v1 Announce Type: cross Abstract: Incremental object detection (IOD) aims to extend detectors to new categories while retaining previously acquired knowledge. Existing methods often adopt a class incremental learning perspective, separating feature spaces to sharpen decision boundari

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
arxivShapley Context Pruning: A Cooperative Game Perspective for Context Reranking and Pruning3harxivA Survey on the Verification of Reinforcement Learning Policies3harxivSymbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers3harxivSelKV: Selective KV Cache Merging with Per-Token Merge-or-Drop and Attention Compensation3h
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