PANINI-DR: Controlled Deep Research through Generative Semantic Workspaces
cs.CL, cs.AI, cs.IR
Submitted: 2026-10-10
Updated: 2026-10-10
Sources
- Open Deep Search: Democratizing Search with Open-source Reasoning Agents
- HyCE-RAG: Hypergraph Chain-of-Evidence Retrieval-Augmented Generation for Explainable Multi-hop Question Answering
- IterResearch: Rethinking Long-Horizon Agents with Interaction Scaling
- Prove2Me: An Open Collaborative Platform for Scaling Math Formalization
- BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent
- R2VC: Modular Fact-Checking with Retrieval, Verification, and Confidence Calibration
- Memory-Aware and Uncertainty-Guided Retrieval for Multi-Hop Question Answering
- Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
- LLM-as-a-Verifier: A General-Purpose Verification Framework
- CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
- SentGraph: Hierarchical Sentence Graph for Multi-hop Retrieval-Augmented Question Answering
- Let's Verify Step by Step
- Track, Rank, Crack: Epistemic Working Memory Scales Multi-Hop Reasoning in Language Agents
- Omni-Decision: Evidence-Ledger Planning for Omni-Modal Agents
- Self-Refine: Iterative Refinement with Self-Feedback
- MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning
- MemGPT: Towards LLMs as Operating Systems
- Panini: Continual Learning in Token Space via Structured Memory
- Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering
- Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
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