Well Begun is Half Done: Low-resource Preference Alignment by Weak-to-Strong Decoding
arXiv:2506.07434v1 Announce Type: cross Abstract: Large Language Models (LLMs) require alignment with human preferences to avoid generating offensive, false, or…
Fast Geometric Embedding for Node Influence Maximization
arXiv:2506.07435v1 Announce Type: cross Abstract: Computing classical centrality measures such as betweenness and closeness is computationally expensive on large-scale graphs.…
Distillation Robustifies Unlearning
arXiv:2506.06278v2 Announce Type: replace-cross Abstract: Current LLM unlearning methods are not robust: they can be reverted easily with a few…
Understanding Financial Reasoning in AI: A Multimodal Benchmark and Error Learning Approach
arXiv:2506.06282v1 Announce Type: new Abstract: Effective financial reasoning demands not only textual understanding but also the ability to interpret complex…
ECoRAG: Evidentiality-guided Compression for Long Context RAG
arXiv:2506.05167v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown remarkable performance in Open-Domain Question Answering (ODQA) by leveraging…
Optimization-Free Universal Watermark Forgery with Regenerative Diffusion Models
arXiv:2506.06018v1 Announce Type: cross Abstract: Watermarking becomes one of the pivotal solutions to trace and verify the origin of synthetic…
When to Trust Context: Self-Reflective Debates for Context Reliability
arXiv:2506.06020v1 Announce Type: cross Abstract: Large language models frequently encounter conflicts between their parametric knowledge and contextual input, often resulting…
Exploring Diffusion Transformer Designs via Grafting
arXiv:2506.05340v2 Announce Type: replace-cross Abstract: Designing model architectures requires decisions such as selecting operators (e.g., attention, convolution) and configurations (e.g.,…
A Path to Loving
arXiv:2506.05352v1 Announce Type: new Abstract: This work lays the foundations for a rigorous ontological characterization of love, addressing its philosophical…