AIBearisharXiv – CS AI · 11h ago7/10
🧠Researchers traced how ESM2-8M, a protein language model, predicts that proteins begin with methionine—a near-universal biological rule. The analysis reveals the model doesn't recognize methionine through direct evidence detection, but rather retrieves it via a distributed computational circuit anchored at the sequence start token. Critically, the model fails on sequences where biology diverges from the statistical default, suggesting that model confidence may not reflect genuine biological understanding.
AINeutralarXiv – CS AI · 6d ago7/10
🧠BioArc introduces a neural architecture search framework that systematically discovers optimal model architectures for biological foundation models, moving beyond generic adaptation of NLP and computer vision models. The research identifies design principles and proposes methods to predict architectures for new biological tasks, providing foundational methodology for next-generation biology-focused AI systems.
AIBullisharXiv – CS AI · Mar 97/10
🧠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
🧠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
🧠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.
AINeutralarXiv – CS AI · 2d ago6/10
🧠Researchers introduce Planktonzilla-17M, the largest unified plankton image dataset with 17.4 million images across 602 taxonomic classes from thirteen imaging systems. The work demonstrates that supervised learning with taxonomic lineage outperforms CLIP-style training and reveals limitations in current biological foundation models like BioCLIP for marine imaging applications.
AINeutralarXiv – CS AI · May 286/10
🧠Researchers introduced SpatialBench-Long, a comprehensive benchmark testing AI agents' ability to conduct end-to-end scientific reasoning on complex spatial biology data without prescribed methods. The benchmark spans 24 evaluations across multiple cancer and aging systems using diverse measurement technologies, with current leading models achieving only 11.1% success rate, revealing significant limitations in AI's capacity for autonomous biological discovery.
🏢 OpenAI🧠 GPT-5🧠 Gemini
AIBullisharXiv – CS AI · Mar 176/10
🧠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
🧠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.
AINeutralarXiv – CS AI · Mar 34/103
🧠Researchers developed a spike-driven sensor-motor system that identifies critical limits for neuronal learning. The study found that learning collapses when the number of motor neurons or independent synaptic bundles exceeds certain thresholds, providing insights into biological spike-based control mechanisms.
