Connect with us

AI Research

Baidu Researchers Propose AI Search Paradigm: A Multi-Agent Framework for Smarter Information Retrieval

Published

6 days ago

on


The Need for Cognitive and Adaptive Search Engines

Modern search systems are evolving rapidly as the demand for context-aware, adaptive information retrieval grows. With the increasing volume and complexity of user queries, particularly those requiring layered reasoning, systems are no longer limited to simple keyword matching or document ranking. Instead, they aim to mimic the cognitive behaviors humans exhibit when gathering and processing information. This transition towards a more sophisticated, collaborative approach marks a fundamental shift in how intelligent systems are designed to respond to users.

Limitations of Traditional and RAG Systems

Despite these advances, current methods still face critical limitations. Retrieval-augmented generation (RAG) systems, while useful for direct question answering, often operate in rigid pipelines. They struggle with tasks that involve conflicting information sources, contextual ambiguity, or multi-step reasoning. For example, a query that compares the ages of historical figures requires understanding, calculating, and comparing information from separate documents—tasks that demand more than simple retrieval and generation. The absence of adaptive planning and robust reasoning mechanisms often leads to shallow or incomplete answers in such cases.

Several tools have been introduced to enhance search performance, including Learning-to-Rank systems and advanced retrieval mechanisms utilizing Large Language Models (LLMs). These frameworks incorporate features like user behavior data, semantic understanding, and heuristic models. However, even advanced RAG methods, including ReAct and RQ-RAG, primarily follow static logic, which limits their ability to effectively reconfigure plans or recover from execution failures. Their dependence on one-shot document retrieval and single-agent execution further restricts their ability to handle complex, context-dependent tasks.

Introduction of the AI Search Paradigm by Baidu

Researchers from Baidu introduced a new approach called the “AI Search Paradigm,” designed to overcome the limitations of static, single-agent models. It comprises a multi-agent framework with four key agents: Master, Planner, Executor, and Writer. Each agent is assigned a specific role within the search process. The Master coordinates the entire workflow based on the complexity of the query. The Planner structures complex tasks into sub-queries. The Executor manages tool usage and task completion. Finally, the Writer synthesizes the outputs into a coherent response. This modular architecture enables flexibility and precise task execution that traditional systems lack.

Use of Directed Acyclic Graphs for Task Planning

The framework introduces a Directed Acyclic Graph (DAG) to organize complex queries into dependent sub-tasks. The Planner chooses relevant tools from the MCP servers to address each sub-task. The Executor then invokes these tools iteratively, adjusting queries and fallback strategies when tools fail or data is insufficient. This dynamic reassignment ensures continuity and completeness. The Writer evaluates the results, filters inconsistencies, and compiles a structured response. For example, in a query asking who is older than Emperor Wu of Han and Julius Caesar, the system retrieves birthdates from different tools, performs the age calculation, and delivers the result—all in a coordinated, multi-agent process.

Qualitative Evaluations and Workflow Configurations

The performance of this new system was evaluated using several case studies and comparative workflows. Unlike traditional RAG systems, which operate in a one-shot retrieval mode, the AI Search Paradigm dynamically replans and reflects on each sub-task. The system supports three team configurations based on complexity: Writer-Only, Executor-Inclusive, and Planner-Enhanced. For the Emperor age comparison query, the Planner decomposed the task into three sub-steps and assigned tools accordingly. The final output stated that Emperor Wu of Han lived for 69 years and Julius Caesar for 56 years, indicating a 13-year difference—an output accurately synthesized across multiple sub-tasks. While the paper focused more on qualitative insights than numeric performance metrics, it demonstrated strong improvements in user satisfaction and robustness across tasks.

Conclusion: Toward Scalable, Multi-Agent Search Intelligence

In conclusion, this research presents a modular, agent-based framework that enables search systems to surpass document retrieval and emulate human-style reasoning. The AI Search Paradigm represents a significant advancement by incorporating real-time planning, dynamic execution, and coherent synthesis. It not only solves current limitations but also offers a foundation for scalable, trustworthy search solutions driven by structured collaboration between intelligent agents.

Check out the Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter.


Nikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.



Source link

Related Topics:
Continue Reading
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

AI Research

2 Artificial Intelligence (AI) Stocks Even Risk-Averse Investors Can Buy Without Hesitation

Published

48 minutes ago

on

July 8, 2025

By


Betting big on the next hot thing can sometimes burn investors. That can be true even when the next hot thing is as exciting and promising as artificial intelligence (AI).

Concerns about being burned might cause some investors to be leery of buying AI stocks. However, this fear could result in them missing out on huge long-term returns. Are there alternatives for investing in AI that aren’t super risky? Absolutely. Here are two AI stocks that even risk-averse investors can buy without hesitation.

Image source: Getty Images.

Two AI titans

If bigger is better, you won’t find many better AI stocks than Amazon (AMZN -0.07%) and Microsoft (MSFT -0.24%). Amazon ranks as the fourth-largest publicly traded company based on market cap, while Microsoft holds the No. 2 spot. And their AI credentials are impeccable.

Amazon Web Services (AWS) is the global leader in cloud services, with a market share of 29%. Microsoft Azure is in second place with a market share of 22%. Both cloud platforms continue to enjoy strong growth, thanks in large part to organizations rushing to build and deploy AI models in the cloud.

Amazon and Microsoft boast partnerships with other top AI companies as well. Both companies have teamed up with Nvidia. Microsoft’s investments in ChatGPT creator OpenAI are paying off handsomely, and Amazon has invested $8 billion in Anthropic, the developer of the powerful Claude large language model (LLM).

These two AI titans are also benefiting from AI in their internal operations. Amazon is using AI to recommend products to customers on its e-commerce platform, for example, while Microsoft has rolled out OpenAI’s GPT-4 throughout its product lineup.

Why risk-averse investors should like Amazon and Microsoft

Risk-averse investors know what they’re getting with Amazon and Microsoft. Both companies are AI leaders, but they’re also much more.

Amazon and Microsoft offer tremendous financial stability. Amazon generated revenue of nearly $638 billion last year, with profits totaling over $59 billion. Microsoft’s revenue topped $245 billion, with earnings of more than $88 billion.

Each of the companies has a boatload of cash — $94.6 billion for Amazon and $79.6 billion for Microsoft.

We’ve already seen that Amazon and Microsoft dominate the cloud services market. These two companies are also leaders in other areas. Amazon reigns as the 800-pound gorilla of e-commerce with a market share of 37.6%. Microsoft’s Windows commands a 70% market share among desktop operating systems. The company’s Office 365 suite ranks No. 2 in the productivity software market.

Both companies continue to deliver solid growth. Amazon’s revenue increased 9% year over year in its latest quarter, with earnings soaring 64%. Microsoft’s revenue jumped 13% year over year, with profits up 18%.

More importantly, both Amazon and Microsoft have strong growth prospects. Each company is poised to benefit from the ongoing AI tailwind and the shift from on-premises IT to the cloud. Amazon’s e-commerce platform and Microsoft’s software products also have solid growth potential.

Not risk-free

I don’t want to leave the impression that Amazon and Microsoft don’t have any risks, though. There’s no such thing as a risk-free stock.

Both Amazon and Microsoft face significant competition despite their current market dominance, and growth could be derailed by regulators in the U.S. and in Europe. Both stocks also trade at high valuations: Amazon’s forward price-to-earnings ratio is 34.6, while Microsoft’s forward earnings multiple is 33.2. These valuations make them more exposed if they experience a significant business disruption.

However, longtime investors know that the best stocks often command premium valuations. Amazon and Microsoft are two of the best stocks, with lifetime gains of around 227,800% and 123,200%, respectively.

Although Amazon and Microsoft face some risks, I think the pros of both stocks far outweigh the cons. If you’re a risk-averse investor who wants to profit from the AI boom, I can’t think of two better picks.

John Mackey, former CEO of Whole Foods Market, an Amazon subsidiary, is a member of The Motley Fool’s board of directors. Keith Speights has positions in Amazon and Microsoft. The Motley Fool has positions in and recommends Amazon, Microsoft, and Nvidia. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy.



Source link

Continue Reading

AI Research

Should You Forget Palantir and Buy These 2 Artificial Intelligence (AI) Stocks Instead? – The Globe and Mail

Published

54 minutes ago

on

July 8, 2025

By



Should You Forget Palantir and Buy These 2 Artificial Intelligence (AI) Stocks Instead?  The Globe and Mail



Source link

Continue Reading

AI Research

Cognigy Leads in Opus Research’s 2025 Conversational AI Intelliview

Published

1 hour ago

on

July 8, 2025

By


Distinguished for Innovation, Enterprise Readiness, and Visionary Approach to Agentic AI

Cognigy, a global leader in AI-powered customer service solutions, has been recognized as the leader in the newly released 2025 Conversational AI Intelliview from Opus Research. The report, titled “Decision-Maker’s Guide to Self-Service & Enterprise Intelligent Assistants,” shows Cognigy as the leading platform across critical evaluation areas including product capability, enterprise fit, GenAI maturity, and deployment performance.

This recognition underscores Cognigy’s commitment to empowering enterprises with production-ready, scalable AI solutions that go far beyond chatbot basics. The report cites Cognigy’s strengths in visual AI agent orchestration, tool and function calling, AI Ops and observability, and a deep commitment to enterprise-grade control—all delivered through a platform built to scale real-time customer interactions across voice and digital channels.

“Cognigy exemplifies the next stage of conversational AI maturity,” said Ian Jacobs, VP & Lead Analyst at Opus Research. “Their agentic approach—combining real-time reasoning, orchestration, and observability—demonstrates how GenAI can move beyond experimentation into meaningful, measurable transformation in the contact center.”

Cognigy was one of the few vendors identified in the report as a “True Believer” in the evolution of GenAI-driven self-service, with tools designed to simplify deployment while giving enterprises full control. The platform’s AI Agent Manager enables businesses to create, configure, and continuously improve intelligent agents—defining persona, memory scope, and access to tools and knowledge—all through a flexible, low-code interface. Cognigy uniquely blends deterministic logic with generative capabilities, ensuring both speed and reliability in automation.

“This recognition from Opus Research is more than a milestone—it’s validation that our strategy is working,” said Alan Ranger, Vice President at Cognigy. “We’re delivering real-world, enterprise-grade automation that’s transforming contact centers. From financial services to healthcare to global retail, our customers are scaling faster, resolving issues in real time, and delivering truly modern service experiences.”

With global Fortune 500 customers and partnerships across the CCaaS and AI ecosystem, Cognigy continues to lead the way in delivering enterprise-ready AI that combines usability, speed, and impact. This latest industry acknowledgment further solidifies its position as the go-to platform for intelligent self-service.

To download a copy of the report, visit https://www.cognigy.com/opus-research-2025-conversational-ai-intelliview.



Source link

Continue Reading

Trending