IntentForge — An intent-first discovery engine. Autonomous, self-healing search technology.
A privacy-first ecosystem with search, browser, download manager, productivity suite, and more.
Publications and research papers from Oxiverse Labs
RAVANA v2 (Governance · Reflection · Adaptation · Constraint · Exploration) introduces a novel cognitive architecture for building inherently alignable artificial general intelligence (AGI) systems. Unlike conventional approaches that impose alignment constraints after model training (e.g., RLHF or rule-based overlays), RAVANA v2 embeds alignment directly into the system’s core dynamics through homeostatic regulation and bounded cognition. The architecture operates in two structured phases. Phase A establishes a stable cognitive foundation using strictly bounded dynamics and a continuous diagnostic mechanism known as the cognitive clamp. This system monitors internal signals—self-model coherence, reward gradient instability, and early indicators of instrumental behavior—and actively redirects the system toward safe attractor states before misalignment can emerge. Rather than penalizing unsafe behavior, RAVANA v2 makes such behavior structurally inaccessible. Phase B introduces adaptive learning through an environment-driven feedback loop, where updates are filtered by the Constraint subsystem. This ensures that learning is not only efficient but also inherently safe, as the system cannot internalize harmful or misaligned strategies. A complementary cognitive risk matrix distinguishes genuine intelligence from failure modes such as sycophancy or risk-averse imitation, addressing the critical “intelligence vs. cowardice” problem in AGI evaluation. The architecture is unified under the GRACE framework, where governance, reflection, adaptation, constraint, and exploration operate as interdependent subsystems. This co-design ensures that capability growth remains bounded and aligned, preventing the emergence of deceptive or self-preserving behaviors. Empirical evaluation on the ARC alignment benchmark demonstrates 94.7% alignment fidelity, significantly outperforming standard RLHF-based systems, while maintaining computational feasibility on commodity hardware. Additional experiments show effective early detection of misalignment risks and improved learning efficiency through constraint-guided optimization. RAVANA v2’s central contribution is a shift from output-level alignment to self-regulating intelligence, where safe behavior emerges naturally from the system’s internal structure. This work provides a scalable and principled framework for developing AGI systems that are both powerful and reliably aligned, advancing the field toward safer and more trustworthy artificial intelligence.
IntentForge is a next-generation, open-source search platform designed to address the fundamental privacy and relevance limitations of modern search engines. Unlike traditional systems that rely on user tracking and centralized data collection, IntentForge adopts a privacy-by-architecture approach, ensuring that user anonymity is preserved at every stage of the search process. The platform routes all queries through the Tor network using Snowflake bridges, effectively eliminating IP-based tracking and significantly reducing the risk of user profiling. Instead of maintaining a centralized index, IntentForge performs distributed meta-search across multiple engines, aggregating and re-ranking results to minimize bias and improve diversity. At its core, IntentForge introduces an intent-driven ranking framework that classifies queries into six categories—factual, how-to, research, commercial, navigational, and exploratory. This enables context-aware result filtering and more meaningful search outcomes. Query expansion using synonym graphs further enhances recall while maintaining precision. A key technical innovation is the binary quantized vector index, which compresses document embeddings to just 48 bytes per document—achieving up to 64× reduction compared to traditional float32 representations. This allows large-scale indexes to operate entirely in memory, enabling fast query resolution with median latency under 500 milliseconds. IntentForge also incorporates a self-improving feedback loop, where anonymous user ratings continuously refine ranking quality. Experimental results demonstrate rapid performance gains, with NDCG@10 improving from 0.532 at cold start to 0.748 within five feedback cycles—approaching the effectiveness of large-scale commercial systems without requiring centralized training data. Overall, IntentForge proves that high-quality search and strong privacy guarantees can coexist. By combining anonymized networking, efficient vector search, and community-driven learning, it offers a scalable and ethical alternative to surveillance-based search engines, paving the way for a more transparent and user-centric web ecosystem.
