Proof pending. Core topic summary fields are still materializing.
Theoretical AI is advancing our understanding of intelligence and learning mechanisms in artificial systems. Recent research has proposed quantitative definitions of intelligence that emphasize generalization over memorization, while also exploring self-improvement techniques for language models. These frameworks provide insights into how AI systems can optimize their performance without external feedback, revealing the underlying principles that govern their capabilities. The implications of these findings extend to various applications, including enhancing model robustness and interpretability. As builders engage with these theoretical advancements, they can leverage them to create more efficient and capable AI systems, ultimately driving innovation in technology and applications across diverse fields.
We propose an operational, quantitative definition of intelligence for arbitrary physical systems. The intelligence density of a system is the ratio of the logarithm of its independent outputs to its ...
Neural networks represent more features than they have dimensions via superposition, forcing features to share representational space. Current methods decompose activations into sparse linear features...
Can language models improve their accuracy without external supervision? Methods such as debate, bootstrap, and internal coherence maximization achieve this surprising feat, even matching golden finet...
The encounter between human reasoning and generative artificial intelligence (GenAI) cannot be adequately described by inherited metaphors of tool use, augmentation, or collaborative partnership. This...
Self-Rewarding Language Models (SRLMs) achieve notable success in iteratively improving alignment without external feedback. Yet, despite their striking empirical progress, the core mechanisms driving...
Recently, flow-based generative models have shown superior efficiency compared to diffusion models. In this paper, we study rectified flow models, which constrain transport trajectories to be linear f...
For each axiom of KM belief update we provide a corresponding axiom in a modal logic containing three modal operators: a unimodal belief operator $B$, a bimodal conditional operator $>$ and the unimod...
Dense Associative Memory (DAM) generalizes Hopfield networks through higher-order interactions and achieves storage capacity that scales as $O(N^{n-1})$ under suitable pattern separation conditions. E...
We develop a unified framework for analyzing cross-modal compatibility in learned representations. The core object is a modality-independent neighborhood site on sample indices, equipped with a cellul...
We show that the error-gated Hebbian rule for PCA (EGHR-PCA), a three-factor learning rule equivalent to Oja's subspace rule under Gaussian inputs, can be systematically derived from Oja's subspace ru...
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Canonical route: /topics
Agent Handoff
Canonical ID theoretical-ai | Route /topic/theoretical-ai
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/theoretical-aiMCP example
{
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"cluster": "Theoretical AI"
}
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}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.