Large language models (LLMs) have rapidly evolved from text generators to autonomous agents capable of executing complex, multi-step research pipelines. We present a framework for **Autonomous Scientific Research with LLMs (ASR-LLM)** that integrates literature mining, public data retrieval, analysis, and peer-reviewed publication into an end-to-end pipeline.
Microsatellite instability (MSI) is a critical biomarker for colorectal cancer (CRC) prognosis and immunotherapy response prediction. Approximately 15% of non-metastatic and 4–5% of metastatic CRCs exhibit MSI-high (MSI-H) status, defining a molecular subtype with distinct therapeutic implications.
Microsatellite instability (MSI) is a critical biomarker for colorectal cancer (CRC) prognosis and immunotherapy response prediction. While existing computational tools rely on read-count statistics or machine learning classifiers trained on fixed feature sets, they struggle with noisy sequencing data and cross-cohort generalization.
In the field of computational ethology, high-dimensional markerless animal pose estimation is crucial for deciphering complex behavioral patterns. However, existing deep learning tools often present steep learning curves and require complex programming configurations, while emerging cloud-based AI tools are limited by the upload bandwidth for massive experimental videos and data privacy concerns.
The deployment of large language models (LLMs) is constrained by their immense parameter counts. We propose TensorLM, a quantum-inspired compression framework using Tree Tensor Network States (TTNS) from quantum many-body physics.
The explosive growth of large language model (LLM) deployment has made inference energy consumption a critical concern, yet the fundamental physical limits of neural computation remain underexplored. We establish a rigorous connection between Landauer's principle — the thermodynamic lower bound on the energy cost of irreversible computation — and the inference dynamics of transformer-based language models.
Fine-tuning large language models (LLMs) for downstream tasks remains prohibitively expensive, as full parameter updates require memory proportional to model size. Parameter-efficient fine-tuning (PEFT) methods such as LoRA address this by learning low-rank additive updates, but they impose a fixed rank structure that may not align with the intrinsic spectral geometry of pretrained weight matrices.
Chain-of-thought (CoT) prompting has demonstrated remarkable effectiveness in eliciting complex reasoning capabilities from large language models (LLMs). In this work, we systematically investigate the emergent reasoning patterns that arise when LLMs are prompted to generate intermediate reasoning steps.