The
Parable
Meet Palo: Attachable AI memory engines by Mpalo. Experience humanlike, transparent, and ethically designed memory for your LLMs, chatbots, and robotics applications. Discover our Lite, Palo Bloom, and DEEP models, each designed for specific needs.
The Palo Model Family
Palo by Mpalo offers a suite of AI memory engines tailored to diverse needs, from lightweight applications to demanding enterprise solutions. Each model is built on our core principles of privacy, transparency, and human-centric design. The "Palo Large" model is currently postponed as we focus on refining and enhancing our core Lite, Palo Bloom, and DEEP offerings.
Palo Mini
Fast and efficient, Palo Mini provides essential episodic memory for applications needing quick, contextual recall with low latency. Ideal for simple chatbots, command-line interfaces, or where minimal resource usage is critical. Supports basic multi-modal data handling (text, simple image references).
Learn more »Palo Bloom
A balanced and versatile model, Palo offers enhanced episodic and semantic memory capabilities. Optimized for mobile applications, edge devices, and personal assistants demanding a good mix of performance, recall depth, and resource efficiency. Supports broader multi-modal inputs.
Learn more »Palo DEEP
Our most advanced offering, Palo DEEP, is engineered for complex tasks requiring profound reasoning, rich contextual understanding, and robust long-term memory. It integrates advanced features like Memory Mapping and is influenced by Memory-Traversal concepts for comprehensive semantic network building and nuanced recall. Ideal for enterprise-grade applications, sophisticated research, and advanced robotics requiring multi-modal data fusion and complex pattern recognition.
Learn more »
Precise Episodic Memory with Palo
Palo offers improved recall of specific past interactions and events. This focuses on strengthening the timeline of experiences, allowing for more detailed and accurate references to previous points in a dialogue or process.
Innovative Foundations: Memory-Traversal & Mapping
Palo leverages novel approaches to AI memory, inspired by cognitive science and driven by empirical research. While the specific internal architecture remains proprietary as we introduce it to the market, key concepts include robust episodic recall and dynamic semantic networks. Certain Palo configurations, particularly our DEEP model, incorporate advanced methods:
- Memory-Traversal: A core concept influencing Palo's architecture, particularly prominent in DEEP. Memory-Traversal enables the system to navigate and link sequences of related past interactions or knowledge points, forming coherent event chains or logical pathways. This contributes to more robust contextual understanding, improved reasoning over time, and relevant recall, especially critical for maintaining coherence in long dialogues or complex process execution. While initially envisioned as a central pillar for the postponed Palo Large, its principles enhance the capabilities of Palo DEEP.
- Memory Mapping: A key feature of Palo DEEP, Memory Mapping allows the model to create and utilize rich, interconnected knowledge structures. It's not just about storing isolated facts, but about understanding relationships, hierarchies, and causal links between pieces of information. This enables Palo DEEP to access, synthesize, and reason over a broader and more integrated range of past interactions and learned knowledge, identifying subtle patterns and connections. Essential for deep analytical tasks, complex problem-solving, and building comprehensive domain understanding.
Our focus is always on creating memory that feels natural, useful, and trustworthy.
See Our BenchmarksAdvanced Reasoning through Memory-Traversal
Certain configurations of Palo incorporate techniques like Memory-Traversal. This allows for more complex reasoning by utilizing sequences of recalled information, contributing to deeper contextual understanding and enhanced problem-solving processes.
Expanded Recall with Memory Mapping
More advanced Palo capabilities include Memory Mapping. This feature enables the system to access and synthesize information from a broader range of past interactions, helping to identify subtle patterns and connections within the data.
Proactive Contextual Synthesis in Palo
Future-oriented developments for Palo focus on proactively synthesizing relevant memories. The goal is to anticipate user needs and offer contextually relevant insights, aiming for a more integrated integration of recall and application.
