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#biological-ai News & Analysis

6 articles tagged with #biological-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles
AIBullisharXiv – CS AI · Mar 97/10
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Predictive Coding Networks and Inference Learning: Tutorial and Survey

Researchers present a comprehensive survey of Predictive Coding Networks (PCNs), a neuroscience-inspired AI approach that uses biologically plausible inference learning instead of traditional backpropagation. PCNs can achieve higher computational efficiency with parallelization and offer a more versatile framework for both supervised and unsupervised learning compared to traditional neural networks.

AINeutralarXiv – CS AI · Mar 57/10
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Inference-Time Toxicity Mitigation in Protein Language Models

Researchers developed Logit Diff Amplification (LDA) as an inference-time safety mechanism for protein language models to prevent toxic protein generation. The method reduces predicted toxicity rates while maintaining biological plausibility and structural viability, addressing dual-use safety concerns in AI-driven protein design.

AINeutralarXiv – CS AI · Mar 37/104
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The Information-Theoretic Imperative: Compression and the Epistemic Foundations of Intelligence

Researchers propose the Compression Efficiency Principle (CEP) to explain why artificial neural networks and biological brains develop similar representations despite different substrates. The theory suggests both systems converge on efficient compression strategies that encode stable invariants rather than unstable correlations, providing a unified framework for understanding intelligence across biological and artificial systems.

AIBullisharXiv – CS AI · Mar 176/10
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ES-Merging: Biological MLLM Merging via Embedding Space Signals

Researchers propose ES-Merging, a new framework for combining specialized biological multimodal large language models (MLLMs) by using embedding space signals rather than traditional parameter-based methods. The approach estimates merging coefficients at both layer-wise and element-wise granularities, outperforming existing merging techniques and even task-specific fine-tuned models on cross-modal scientific problems.

AIBullisharXiv – CS AI · Feb 276/104
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Multi-Dimensional Spectral Geometry of Biological Knowledge in Single-Cell Transformer Representations

Researchers decoded the internal representations of scGPT, a single-cell foundation model, revealing it organizes genes into interpretable biological coordinate systems rather than opaque features. The model encodes cellular organization patterns including protein localization, interaction networks, and regulatory relationships across its transformer layers.