arXiv:2601.17138v1 Announce Type: new Abstract: Artificial intelligence (AI) is accelerating progress in modeling T and B cell receptors by enabling predictive and generative frameworks grounded in sequence data and immune context. This chapter surveys recent advances in the use of protein language models, machine learning, and multimodal integration for immune receptor modeling. We highlight emerging […]
Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure Image Enhancement
arXiv:2407.13170v2 Announce Type: replace-cross Abstract: Despite recent strides made by AI in image processing, the issue of mixed exposure, pivotal in many real-world scenarios like surveillance and photography, remains inadequately addressed. Traditional image enhancement techniques and current transformer models are limited with primary focus on either overexposure or underexposure. To bridge this gap, we introduce […]
Domain-Aware Geometric Multimodal Learning for Multi-Domain Protein-Ligand Affinity Prediction
arXiv:2601.17102v1 Announce Type: new Abstract: The accurate prediction of protein-ligand binding affinity is important for drug discovery yet remains challenging for multi-domain proteins, where inter-domain dynamics and flexible linkers govern molecular recognition. Current geometric deep learning methods typically treat proteins as monolithic graphs, failing to capture the distinct geometric and energetic signals at domain interfaces. […]
MetaCLBench: Meta Continual Learning Benchmark on Resource-Constrained Edge Devices
arXiv:2504.00174v2 Announce Type: replace-cross Abstract: Meta-Continual Learning (Meta-CL) enables models to learn new classes from limited labelled samples, making it promising for IoT applications where manual labelling is costly. However, existing studies focus on accuracy while ignoring deployment viability on resource-constrained hardware. Thus, we present MetaCLBench, a benchmark framework that evaluates Meta-CL methods for both […]
Surprisal and Metaphor Novelty Judgments: Moderate Correlations and Divergent Scaling Effects Revealed by Corpus-Based and Synthetic Datasets
arXiv:2601.02015v3 Announce Type: replace-cross Abstract: Novel metaphor comprehension involves complex semantic processes and linguistic creativity, making it an interesting task for studying language models (LMs). This study investigates whether surprisal, a probabilistic measure of predictability in LMs, correlates with annotations of metaphor novelty in different datasets. We analyse the surprisal of metaphoric words in corpus-based […]
Equivariant Flow Matching for Point Cloud Assembly
arXiv:2505.21539v3 Announce Type: replace-cross Abstract: The goal of point cloud assembly is to reconstruct a complete 3D shape by aligning multiple point cloud pieces. This work presents a novel equivariant solver for assembly tasks based on flow matching models. We first theoretically show that the key to learning equivariant distributions via flow matching is to […]
TheoremForge: Scaling up Formal Data Synthesis with Low-Budget Agentic Workflow
arXiv:2601.17332v1 Announce Type: new Abstract: The high cost of agentic workflows in formal mathematics hinders large-scale data synthesis, exacerbating the scarcity of open-source corpora. To address this, we introduce textbfTheoremForge, a cost-effective formal data synthesis pipeline that decomposes the formalization process into five sub-tasks, which are textitstatement formalization, textitproof generation, textitpremise selection, textitproof correction and […]
PEAfowl: Perception-Enhanced Multi-View Vision-Language-Action for Bimanual Manipulation
arXiv:2601.17885v1 Announce Type: cross Abstract: Bimanual manipulation in cluttered scenes requires policies that remain stable under occlusions, viewpoint and scene variations. Existing vision-language-action models often fail to generalize because (i) multi-view features are fused via view-agnostic token concatenation, yielding weak 3D-consistent spatial understanding, and (ii) language is injected as global conditioning, resulting in coarse instruction […]
Addressing LLM Diversity by Infusing Random Concepts
arXiv:2601.18053v1 Announce Type: cross Abstract: Large language models (LLMs) are known to produce outputs with limited diversity. In this work, we study whether infusing random concepts in the prompts can improve the diversity of the generated outputs. To benchmark the approach, we design a systematic evaluation protocol which involves prompting an LLM with questions of […]
High-Fidelity Longitudinal Patient Simulation Using Real-World Data
arXiv:2601.17310v1 Announce Type: new Abstract: Simulation is a powerful tool for exploring uncertainty. Its potential in clinical medicine is transformative and includes personalized treatment planning and virtual clinical trials. However, simulating patient trajectories is challenging because of complex biological and sociocultural influences. Here, we show that real-world clinical records can be leveraged to empirically model […]
FastInsight: Fast and Insightful Retrieval via Fusion Operators for Graph RAG
arXiv:2601.18579v1 Announce Type: cross Abstract: Existing Graph RAG methods aiming for insightful retrieval on corpus graphs typically rely on time-intensive processes that interleave Large Language Model (LLM) reasoning. To enable time-efficient insightful retrieval, we propose FastInsight. We first introduce a graph retrieval taxonomy that categorizes existing methods into three fundamental operations: vector search, graph search, […]
On the Impact of the Utility in Semivalue-based Data Valuation
arXiv:2502.06574v3 Announce Type: replace Abstract: Semivalue-based data valuation uses cooperative-game theory intuitions to assign each data point a value reflecting its contribution to a downstream task. Still, those values depend on the practitioner’s choice of utility, raising the question: How robust is semivalue-based data valuation to changes in the utility? This issue is critical when […]