arXiv AI
Jul 24
AREX: Towards a Recursively Self-Improving Agent for Deep Research
arXiv:2607. 21461v1 Announce Type: new Abstract: Deep research requires agents to find answers that jointly satisfy multiple constraints.
Not Worth Another Token: Marginal Value Estimation for Efficient Deep Research Agents
arXiv:2608. 08389v1 Announce Type: new Abstract: Long-horizon research agents solve open-ended tasks through iterative retrieval, aggregation, and synthesis, but context grows rapidly while the marginal value of additional evidence often declines.
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arXiv AI
Jun 16
S1-DeepResearch: Beyond Search, Toward Real-World Long-Horizon Research Agents
arXiv:2606. 15367v1 Announce Type: new Abstract: Deep research agents aim to solve complex knowledge-intensive tasks through long-horizon planning, evidence gathering, reasoning, and report generation.
arXiv AI
Jul 7
Toward Efficient Agents: Memory, Tool learning, and Planning
arXiv:2601. 14192v2 Announce Type: replace Abstract: Recent years have witnessed increasing interest in extending large language models into agentic systems.
arXiv AI
Jun 9
SearchSwarm: Towards Delegation Intelligence in Agentic LLMs for Long-Horizon Deep Research
arXiv:2606. 09730v1 Announce Type: new Abstract: Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose context demands can grow without bound, yet model context windows remain inherently finite.
arXiv AI
Aug 7
Personalized Deep Research Query Refinement with Graph-Scaffolded Evidence Grounding
arXiv:2608. 05876v1 Announce Type: new Abstract: User requests serve as research specifications for deep research agents, shaping what evidence to seek and how to synthesize it.
arXiv AI
Jun 19
ScaffoldAgent: Utility-Guided Dynamic Outline Optimization for Open-Ended Deep Research
arXiv:2606. 20122v1 Announce Type: new Abstract: Open-ended deep research (OEDR) requires systems to acquire knowledge through multi-round retrieval and generate coherent long-form reports.
arXiv AI
Jun 15
Hybrid Open-Ended Tri-Evolution Makes Better Deep Researcher
arXiv:2606. 13710v1 Announce Type: new Abstract: Deep research and agent evolution serve as de-facto tasks for AI agents in real-world applications toward artificial general intelligence.
Hugging Face Trending Papers
Aug 6
Personalized Deep Research Query Refinement with Graph-Scaffolded Evidence Grounding
User requests serve as research specifications for deep research agents, shaping what evidence to seek and how to synthesize it. In personalized deep research, these specifications must additionally reflect user goals, constraints, preferences, and evaluation criteria.
arXiv AI
Jun 17
DRFLOW: A Deep Research Benchmark for Personalized Workflow Prediction
arXiv:2606. 18191v1 Announce Type: new Abstract: Deep research (DR) systems are increasingly used for complex information-seeking tasks, but existing works mainly focus on generating reports and summaries.
arXiv Machine Learning
Aug 12
SearchArt: Training Long-Horizon Search Agent with Scalable Synthetic and Verified Task
arXiv:2607. 24850v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have enabled search agents to autonomously tackle complex tasks across extended search and reasoning horizons.
Hugging Face Trending Papers
Jul 23
Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
Production AI agents' failures are less often due to an inability to reason well and more often because they cannot manage what is in their reasoning context: conversation histories, large prompts, large tool definitions, and ballooning tool outputs. Agents drown in their own accumulating history while paying a token cost that grows every turn, producing missing recalls within and across conversations.
Hugging Face Trending Papers
Aug 3
CRISP: Critical Step Perception for Training Efficient Deep Search Agents
Large language models (LLMs) are increasingly extended into deep search agents that solve complex questions through multi-step interaction with external search and browsing tools. However, existing agents often incur substantial computational and interaction costs, generating lengthy trajectories that contain redundant queries, inefficient exploration, and irrelevant observations.