Sentient

Open Deep Search: Closing the Gap Between Proprietary and Open-Source Search AI - Sentient Open Deep Search: Closing the Gap in Search AI 🔍 Open-Source Excellence An open-source agentic framework that matches and surpasses proprietary AI search solutions 🎯 Key Achievements Open Deep Search (ODS) is an open-source agentic framework that wraps around an LLM of choice, closing the gap between proprietary and open-source search AI 75.3% accuracy on FRAMES benchmark, outperforming OpenAI's GPT-4o Search Preview (65.6%) by nearly 10 percentage points 88.3% accuracy on SimpleQA , nearly matching GPT-4o Search Preview's 90.0% Fully open-source ,...

Context Manipulation Attacks: Why Web Agents Need Secure Memory - Sentient Context Manipulation Attacks: Why Web Agents Need Secure Memory 🛡️ Critical Security Research Exposing vulnerabilities in stateless web agents and the urgent need for secure memory architectures 🎯 Key Findings Stateless web agents rely on external memory systems that can be corrupted, creating a critical new attack surface Plan injection attacks insert malicious steps into an agent's task plan, achieving up to 3× higher success than prompt attacks Semantic alignment drives attack efficacy—contextually aligned attacks are significantly more successful Standard prompt ...

Fingerprinting: Enabling Open-Source Monetization on the Model Layer - Sentient Fingerprinting: Enabling Open-Source Monetization on the Model Layer 🔐 Verifiable AI Ownership Embedding digital signatures into models for provable ownership, control, and alignment in open-source AI 🎯 Key Takeaways Loyal AI = Ownership + Control + Alignment; ensures AI models remain true to creators and community values Fingerprinting embeds unique digital signatures into models, allowing verifiable proof of ownership and control Fingerprints consist of subtle, undetectable key-response pairs deeply integrated during fine-tuning, resistant to tampering Smart contr...

ROMA: The Backbone for Open-Source Meta-Agents - Sentient ROMA: The Backbone for Open-Source Meta-Agents Recursive Open Meta-Agent An open-source framework for building high-performance multi-agent systems that solve complex problems Introducing ROMA Meet ROMA (Recursive Open Meta-Agent): a groundbreaking open-source meta-agent framework designed for building high-performance multi-agent systems. ROMA orchestrates simpler agents and tools to solve complex, multi-step problems that challenge traditional AI systems. At its core, ROMA provides a powerful structure for multi-agent systems: a hierarchical, recursive task tree where parent nodes break complex goal...