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Found 16 papers, 13 papers with code
LocAgent: Graph-Guided LLM Agents for Code Localization
By parsing codebases into directed heterogeneous graphs, LocAgent creates a lightweight representation that captures code structures (files, classes, functions) and their dependencies (imports, invocations, inheritance), enabling LLM agents to effectively search and locate relevant entities through powerful multi-hop reasoning.
MedAgentsBench: Benchmarking Thinking Models and Agent Frameworks for Complex Medical Reasoning
Large Language Models (LLMs) have shown impressive performance on existing medical question-answering benchmarks.
Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification
In this paper, we follow the guidance of the central dogma to redesign both the data and model pipeline and offer a comprehensive framework, Life-Code, that spans different biological functions.
Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation
Despite the advancements of large language models (LLMs) in general generation tasks, their potential in facilitating the generation of synthetic clinical trials is under-explored.
Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox
Based on this benchmark, we conduct extensive experiments with two well-known LLMs (English and Chinese) and four quantization algorithms to investigate this topic in-depth, yielding several counter-intuitive and valuable findings, e. g., models quantized using a calibration set with the same distribution as the test data are not necessarily optimal.
A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generation
Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models.
SemiReward: A General Reward Model for Semi-supervised Learning
The main challenge is how to distinguish high-quality pseudo labels against the confirmation bias.
InstructBio: A Large-scale Semi-supervised Learning Paradigm for Biochemical Problems
In the field of artificial intelligence for science, it is consistently an essential challenge to face a limited amount of labeled data for real-world problems.
Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
It takes advantage of MatchExplainer to fix the most informative portion of the graph and merely operates graph augmentations on the rest less informative part.
Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks
Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area.
Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN
We then propose an Architecture-Agnostic Masked Image Modeling framework (A$^2$MIM), which is compatible with both Transformers and CNNs in a unified way.
Discovering and Explaining the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions
Graph neural networks (GNNs) mainly rely on the message-passing paradigm to propagate node features and build interactions, and different graph learning tasks require different ranges of node interactions.
DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations
To waive this requirement, we propose a novel model called DiffMD by directly estimating the gradient of the log density of molecular conformations.
Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport
Training data are usually limited or heterogeneous in many chemical and biological applications.
Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs
Then HMGs are constructed with both atom-level and motif-level nodes.
InsertGNN: Can Graph Neural Networks Outperform Humans in TOEFL Sentence Insertion Problem?
Sentence insertion is an interesting NLP problem but received insufficient attention.
