Deep Research Agents

Related topics:

• A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications
  This survey examines the rapidly evolving field of Deep Research systems—AI-powered applications that automate complex research workflows through the integration of large language models, advanced inf…
• AI-Researcher: Autonomous Scientific Innovation
  The powerful reasoning capabilities of Large Language Models (LLMs) in mathematics and coding, combined with their ability to automate complex tasks through agentic frameworks, present unprecedented o…
• Agent Laboratory: Using LLM Agents as Research Assistants
  This framework accepts a human-provided research idea and progresses through three stages—literature review, experimentation, and report writing to produce comprehensive research outputs, including a …
• AgentRxiv: Towards Collaborative Autonomous Research
  To address these challenges, we introduce AgentRxiv—a framework that lets LLM agent laboratories upload and retrieve reports from a shared preprint server in order to collaborate, share insights, and …
• Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research
  Agentic Reasoning, a framework1 that enhances large language model (LLM) reasoning by integrating external tool-using agents. Unlike conventional LLM-based reasoning approaches, which rely solely on i…
• Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward
  Large language models (LLMs) exhibit remarkable problem-solving abilities, but struggle with complex tasks due to static internal knowledge. Retrieval-Augmented Generation (RAG) enhances access to ext…
• Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey
  Building autonomous machines that can explore open-ended environments, discover possible interactions and build repertoires of skills is a general objective of artificial intelligence. Developmental a…
• Beyond "Not Novel Enough": Enriching Scholarly Critique with LLM-Assisted Feedback
  Novelty assessment is a central yet understudied aspect of peer review, particularly in highvolume fields like NLP where reviewer capacity is increasingly strained. We present a structured approach fo…
• Beyond GPT-5: Making LLMs Cheaper and Better via Performance-Efficiency Optimized Routing
  Balancing performance and efficiency is a central challenge in large language model (LLM) advancement. GPT-5 addresses this with test-time routing, dynamically assigning queries to either an efficient…
• Beyond Ten Turns: Unlocking Long-Horizon Agentic Search with Large-Scale Asynchronous RL
  Recent advancements in LLM-based agents have demonstrated remarkable capabilities in handling complex, knowledge-intensive tasks by integrating external tools. Among diverse choices of tools, search t…
• Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers
  Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autono…
• Characterizing Deep Research: A Benchmark and Formal Definition
  Information tasks such as writing surveys or analytical reports require complex search and reasoning, and have recently been grouped under the umbrella of deep research — a term also adopted by recent…
• Deep Research: A Systematic Survey
  Abstract: Large language models (LLMs) have rapidly evolved from text generators into powerful problem solvers. Yet, many open tasks demand critical thinking, multi-source, and verifiable outputs, whi…
• Deep Researcher with Test-Time Diffusion
  Deep research agents, powered by Large Language Models (LLMs), are rapidly advancing; yet, their performance often plateaus when generating complex, long-form research reports using generic test-time …
• Deep Think with Confidence
  Large Language Models (LLMs) have shown great potential in reasoning tasks through test-time scaling methods like self-consistency with majority voting. However, this approach often leads to diminishi…
• DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL
  First, data-wise, most existing QA datasets usually feature relatively simple questions that do not reflect true “hard-to-find” cases. For example, questions in HotpotQA [Yang et al., 2018] can often …
• DeepNet: Scaling Transformers to 1,000 Layers
  In this paper, we propose a simple yet effective method to stabilize extremely deep Transformers. Specifically, we introduce a new normalization function (DEEPNORM) to modify the residual connection i…
• DeepRAG: Thinking to Retrieval Step by Step for Large Language Models
  In this paper, we propose DeepRAG, a framework that models retrieval-augmented reasoning as a Markov Decision Process (MDP), enabling strategic and adaptive retrieval. By iteratively decomposing queri…
• DeepResearchGym: A Free, Transparent, and Reproducible Evaluation Sandbox for Deep Research
  Deep research systems represent an emerging class of agentic information retrieval methods that generate comprehensive and well-supported reports to complex queries. However, most existing frameworks …
• DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments
  Large Language Models (LLMs) equipped with web search capabilities have demonstrated impressive potential for deep research tasks. However, current approaches predominantly rely on either manually eng…
• Evolving Deeper LLM Thinking
  We explore an evolutionary search strategy for scaling inference time compute in Large Language Models. The proposed approach, Mind Evolution, uses a language model to generate, recombine and refine c…
• From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
  However, traditional keyword-based search engines are increasingly inadequate for handling complex, multi-step information needs. Our position is that Large Language Models (LLMs), endowed with reason…
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
  Sparse MoE models activate a subset of model parameters per input token by learning to dynamically route tokens to a subset of parameters (experts); this allows them to decouple total model capacity f…
• HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches
  Recently, large reasoning models have demonstrated strong mathematical and coding abilities, and deep search leverages their reasoning capabilities in challenging information retrieval tasks. Existing…
• How Far Are We from Genuinely Useful Deep Research Agents?
  Deep Research Agents (DRAs) aim to automatically produce analyst-level reports through iterative information retrieval and synthesis. However, most existing DRAs were validated on question-answering b…
• MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement
  Agents based on large language models (LLMs) for machine learning engineering (MLE) can automatically implement ML models via code generation. However, existing approaches to build such agents often r…
• MasRouter: Learning to Route LLMs for Multi-Agent Systems
  Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often incur significant costs and face challenges in dynam…
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications
  We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies (e.g., supervised fine-tuning, reinforcement learning) and test-time mechanisms (e.g., prompt engin…
• MindSearch: Mimicking Human Minds Elicits Deep AI Searcher
  Information seeking and integration is a complex cognitive task that consumes enormous time and effort. Search engines reshape the way of seeking information but often fail to align with complex human…
• Mixture of Thoughts: Learning to Aggregate What Experts Think, Not Just What They Say
  Open-source Large Language Models (LLMs) increasingly specialize by domain (e.g., math, code, general reasoning), motivating systems that leverage complementary strengths across models. Prior multi-LL…
• PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts
  We introduce PRELUDE, a benchmark for evaluating long-context understanding through the task of determining whether a character’s prequel story is consistent with the canonical narrative of the origin…
• RAG-Gym: Systematic Optimization of Language Agents for Retrieval-Augmented Generation
  Retrieval-augmented generation (RAG) has shown great promise for knowledge-intensive tasks and recently advanced with agentic RAG, where language agents engage in multi-round interactions with externa…
• RL + Transformer = A General-Purpose Problem Solver
  What if artificial intelligence could not only solve problems for which it was trained but also learn to teach itself to solve new problems (i.e., metalearn)? In this study, we demonstrate that a pre-…
• RL Squeezes, SFT Expands: A Comparative Study of Reasoning LLMs
  Large language models (LLMs) are typically trained by reinforcement learning (RL) with verifiable rewards (RLVR) and supervised fine-tuning (SFT) on reasoning traces to improve their reasoning abiliti…
• ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
  We propose ReSearch, a novel framework that trains LLMs to Reason with Search via reinforcement learning without using any supervised data on reasoning steps. Our approach treats search operations as …
• ReasonFlux-PRM: Trajectory-Aware PRMs for Long Chain-of-Thought Reasoning in LLMs
  Process Reward Models (PRMs) have recently emerged as a powerful framework for supervising intermediate reasoning steps in large language models (LLMs). Previous PRMs are primarily trained on model fi…
• Reasoning Language Models: A Blueprint
  such as OpenAI’s o1 and o3, DeepSeek-V3, and Alibaba’s QwQ, have redefined AI’s problem-solving capabilities by extending large language models (LLMs) with advanced reasoning mechanisms. Yet, their hi…
• Retrieval-augmented reasoning with lean language models
  This technical report details a novel approach to combining reasoning and retrieval augmented generation (RAG) within a single, lean language model architecture. While existing RAG systems typically r…
• Search Arena: Analyzing Search-Augmented LLMs
  Search-augmented language models combine web search with Large Language Models (LLMs) to improve response groundedness and freshness. However, analyzing these systems remains challenging: existing dat…
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
  Despite their remarkable capabilities, large language models (LLMs) often produce responses containing factual inaccuracies due to their sole reliance on the parametric knowledge they encapsulate. Ret…
• Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs
  This survey synthesizes both strands under a unified reasoning-retrieval perspective. We first map how advanced reasoning optimizes each stage of RAG (Reasoning- Enhanced RAG). Then, we show how retri…
• Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models
  We first distinguish Long CoT from Short CoT and introduce a novel taxonomy to categorize current reasoning paradigms. (2) Next, we explore the key characteristics of Long CoT: deep reasoning, extensi…
• TreeRL: LLM Reinforcement Learning with On-Policy Tree Search
  Compared to conventional independent chain sampling strategies with outcome supervision, tree search enables better exploration of the reasoning space and provides dense, on-policy process rewards dur…
• UR2: Unify RAG and Reasoning through Reinforcement Learning
  Large Language Models (LLMs) have shown remarkable capabilities through two complementary paradigms: Retrieval-Augmented Generation (RAG), which enhances knowledge grounding, and Reinforcement Learnin…
• Universe of Thoughts: Enabling Creative Reasoning with Large Language Models
  Reasoning based on Large Language Models (LLMs) has garnered increasing attention due to outstanding performance of these models in mathematical and complex logical tasks. Beginning with the Chain-of-…
• Virtuous Machines: Towards Artificial General Science
  Artificial intelligence systems are transforming scientific discovery by accelerating specific research tasks, from protein structure prediction to materials design, yet remain confined to narrow doma…
• What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversity
  AI research agents offer the promise to accelerate scientific progress by automating the design, implementation, and training of machine learning models. However, the field is still in its infancy, an…
• Why Do Multi-agent LLM Systems Fail?
  [[Routers]] Despite growing enthusiasm for Multi-Agent LLM Systems (MAS), their performance gains across popular benchmarks often remain minimal compared to single-agent frameworks. This gap highlig…