Shivik Labs, a deep-tech research and engineering company focused on fundamental artificial intelligence, announced the release of its latest research paper introducing TRIDENT, a new inference framework aimed at enabling autonomous self-improvement in large-scale language models. The framework aims to overcome what researchers describe as the “static intelligence” limitations of current AI systems.
According to the company, most of today's large-scale language models rely on scaling data, parameters, or fine-tuning cycles to improve performance, but their inference processes remain largely unchanged. TRIDENT introduces a different approach by treating inference as a structured search problem rather than a single linear sequence of outputs. This allows the model to explore, evaluate, and refine multiple inference paths before arriving at a solution.
Shivik Labs reported that it has open sourced the TRIDENT framework with a model built on Qwen3-4B. Using this setting, the model's performance on the GPQA benchmark increased from 28.28 percent to 42.42 percent, an improvement of 14.14 percentage points without any additional fine-tuning or new training data. Similar improvements were observed in benchmarks such as GSM8K, MMLU, Winogrande, and ARC-C, demonstrating enhanced reasoning and problem-solving abilities.
The TRIDENT architecture combines three key elements. Tree-based inference structures that explore multiple logical paths simultaneously, graph neural networks that evaluate and score intermediate inference states, and self-generated learning loops that allow models to audit and refine their own inferences without human-created explanations. Combining these components allows the model to identify errors, discard weak inference paths, and autonomously strengthen successful strategies.
Shivik Labs said the framework has already been tested within its construction execution and control platform, validating its applicability in the real world. Founder Abhisek Khandelwal said India's size and operational complexity provide an ideal environment to build resilient inference systems that can address real-world challenges.
The company plans to develop its own inference-focused AI model, with a 2B parameter version targeted for release in early 2026. Shivik Labs has also launched a collaborative pilot program for organizations addressing inference-intensive problems, while also making its research and model output available to the public to foster broader adoption and innovation.
