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Bridging Search and CRM: Productionizing AI Product Research Agents for Customer

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1 SIGNALFIRST DETECTED 21 August 2026
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12 Sept
Agentic Share-of-Search: A Multi-Agent AI System for Competitive Decision-Making in LLM-Mediated E-Commerce

arXiv:2609.11190v1 Announce Type: new Abstract: AI shopping assistants increasingly redirect consumer discovery, creating an urgent need for tools that support seller-side

REPORT · WHAT · ArXiv CS.AIOPEN STORY
13 Apr
AlphaLab: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs

arXiv:2604.08590v1 Announce Type: cross Abstract: We present AlphaLab, an autonomous research harness that leverages frontier LLM agentic capabilities to automate the full

REPORT · WHAT · ArXiv CS.AIOPEN STORY
11 Jul
Context Graphs for Proactive Enterprise Agents

arXiv:2607.07721v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) and agentic frameworks have advanced enterprise AI considerably, yet agents remain

REPORT · WHAT · ArXiv CS.AIOPEN STORY
31 Mar
AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design

arXiv:2603.27195v1 Announce Type: new Abstract: Designing microstructures that satisfy coupled cross-physics objectives is a fundamental challenge in material science. This

REPORT · WHERE · ArXiv CS.AIOPEN STORY
16 Oct
Kolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows

arXiv:2610.21109v1 Announce Type: new Abstract: When humans collaborate with AI agents on long-horizon tasks, what happens to the agents' contributions? For solving long-horizon

REPORT · WHEN · ArXiv CS.AIOPEN STORY
16 May
GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration

arXiv:2605.13848v1 Announce Type: new Abstract: Agentic LLM frameworks that rely on prompted orchestration, where the model itself determines workflow transitions, often suffer

REPORT · WHERE · ArXiv CS.AIOPEN STORY
26 May
Right-Sizing Communication and Recommendation Set Size in AI-Assisted Search

arXiv:2605.23944v1 Announce Type: new Abstract: We model the interaction between a user and an AI driven recommendation system. The user initiates the process by conveying

REPORT · WHAT · ArXiv CS.AIOPEN STORY
18 Sept
Characterizing Web Search by Conversational LLM Agents: From Search Decisions and Strategies to Results and Responses

arXiv:2609.19244v1 Announce Type: new Abstract: Conversational LLM agents increasingly rely on Web search, yet the end-to-end lifecycle of agentic search remains poorly

REPORT · WHAT · ArXiv CS.AIOPEN STORY
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