NEW YORK, Dec. 19, 2025 /PRNewswire/ -- CamScanner today released its 2025 Annual Summary, marking a year of accelerated product innovation, strengthened AI capabilities, and continued global expansion. With more than 300 million users worldwide, the platform has further solidified its position as one of the most trusted tools for scanning and intelligent document management.
In 2025, CamScanner introduced several key enhancements that redefined efficiency in digital workflows. Building on its strong scanning foundation, the platform launched Auto Page-Turn Capture, a feature that recognizes page flips and captures images automatically. This significantly improves the speed and convenience of digitizing full books and multi-page documents. CamScanner also unveiled the Watermark Camera, which embeds real-time timestamps and location data into photos to ensure authenticity and traceability—an essential function for legal documentation, fieldwork, compliance reporting, and other professional scenarios requiring verifiable visual records.
Another major milestone was the upgrade of CS AI. The enhanced system now delivers more accurate image optimization while improving the clarity, structure, and tone of scanned text. Its refined Q&A and study-assist capabilities enable faster information extraction and smarter learning support, offering users a more seamless and intelligent end-to-end document experience.
CamScanner's brand influence continued to grow globally. The app was featured as Apple Store's "App of the Day" across more than 20 countries and regions, including major markets in Europe and Southeast Asia, and ranked No. 1 in the Productivity category on the App Store in 84 countries and regions. It also received strong international recognition, being named "Best Document Scanning App of 2025" by TechRadar and "Best Mobile Scanning and OCR App" by Zapier, reinforcing its leadership across the global productivity landscape.
Throughout the year, CamScanner supported a wide range of professional and everyday scenarios. Lawyers used the Watermark Camera and precision scanning to securely digitize case files. Students and teachers built searchable digital study libraries, aided by CS AI's instant problem-solving capabilities. Sales professionals streamlined client documentation and contract processing, while researchers restored historical materials with enhanced image clarity. In workplaces, employees relied on CamScanner to accelerate routine document submission, format conversion, and cross-device collaboration.
With continuous advancements and an expanding global community, CamScanner remains committed to delivering smarter, more reliable solutions that empower users across industries and drive the evolution of digital productivity.
** The press release content is from PR Newswire. Bastille Post is not involved in its creation. **
CamScanner Releases 2025 Annual Summary Highlighting Breakthrough Innovation and a Global User Base Exceeding 300 Million
SAN MATEO, Calif., Dec. 19, 2025 /PRNewswire/ -- AI infrastructure company EverMind today released results from its unified, production-grade evaluation framework designed to assess real-world memory performance. Under this standardized protocol, the company's flagship engine, EverMemOS, delivered best-in-class outcomes across the LoCoMo and LongMemEval benchmarks, cementing its position as a leading memory engine for next-generation AI agents.
An Open Standardized Framework for Real-World Memory Evaluation
The evaluation framework was developed to address a critical bottleneck in the AI industry: the absence of consistent, transparent methods to measure memory quality. Today's agents rely on a fragmented landscape of memory tools, often evaluated using disparate datasets and metrics, making cross-system comparison virtually impossible. EverMind's framework establishes a controlled testing environment where systems are benchmarked under identical conditions, ensuring fair, reproducible, and actionable analysis. Within this rigorous structure, EverMemOS achieved the highest scores, establishing new performance benchmarks for long-horizon interactions.
Architectural Advances Behind EverMemOS
Four core technical innovations drive the system's success:
- Categorical Memory Extraction: Sorts memories into distinct taxonomies—such as situational context, semantics, and user profiling—to decouple information while preserving semantic integrity.
- MemCell Atomic Storage: Embeds each memory unit with rich metadata (timestamps, source, tags, and relational links), functioning analogously to biological memory engrams.
- Event Boundaries: Replaces rigid token-based slicing with thematic continuity, defining "events" across conversations to create human-interpretable memory segments.
- Multi-Level Recall: Employs a dual-system approach—fast retrieval for simple queries and multi-hop reasoning for complex tasks—mirroring the collaboration between the prefrontal cortex and hippocampus in the human brain.
Setting New Standards in Long-Horizon AI Memory
The impact of these innovations is quantified in the results. EverMemOS achieved a score of 92.3% on LoCoMo, with a remarkable cross-evaluation reproducibility rate of 92.32%.
Notably, EverMemOS is currently the only memory system to outperform large models utilizing full-context inputs—all while operating with drastically fewer tokens. This outcome challenges the prevailing assumption that "more context is always better." The evaluation demonstrates that excessive context often introduces noise and dilutes attention ("lost-in-the-middle" phenomenon).
EverMemOS embodies a paradigm shift: high-quality memory requires not only precise remembering but also precise forgetting. By acting as an intelligent attention filter, the system reduces cognitive load, directing the model's focus solely to critical information. This reframes memory from a passive archive into an active mechanism that guides reasoning, shapes identity, and enables continuity.
The Future of Intelligent Infrastructure
The implications extend beyond benchmark scores. As long-term memory becomes foundational to AI, it is emerging alongside Model Parameters and Tool Use as the third pillar of modern intelligence infrastructure. Future agents will evolve from isolated chat sessions into coherent, continuously learning entities capable of maintaining context and building long-term relationships.
EverMind's release of this evaluation framework marks an inflection point for the field. As AI progresses toward deeper autonomy, robust long-term memory will define the next chapter of intelligent systems.
Detailed Resources:
About EverMind
EverMind is redefining the future of AI by solving one of its most fundamental limitations: long-term memory. Its flagship platform, EverMemOS, introduces a breakthrough architecture for scalable and customizable memory systems, enabling AI to operate with extended context, maintain behavioral consistency, and improve through continuous interaction.
To learn more about EverMind and EverMemOS, please visit:
Website: https://evermind.ai/
GitHub: https://github.com/EverMind-AI/EverMemOS
X: https://x.com/EverMindAI
Reddit: https://www.reddit.com/r/EverMindAI/
** The press release content is from PR Newswire. Bastille Post is not involved in its creation. **
EverMemOS Redefines Efficiency in AI Memory, Surpassing LLM Full-Context Perfomances with Far Fewer Tokens in Open Evaluation
EverMemOS Redefines Efficiency in AI Memory, Surpassing LLM Full-Context Perfomances with Far Fewer Tokens in Open Evaluation
