arXiv:2603.17048v1 Announce Type: new Abstract: Modern neural networks achieve strong performance but remain difficult to interpret in high-dimensional visual domains. Counterfactual explanations (CFEs) provide a principled approach to interpreting black-box predictions by identifying minimal input changes that alter model outputs.…
arXiv:2509.22621v3 Announce Type: replace Abstract: Supervised Fine-Tuning (SFT) is used to specialize model behavior by training weights to produce intended target responses for queries. In contrast, In-Context Learning (ICL) adapts models during inference with instructions or demonstrations in the prompt.…
arXiv:2603.17052v1 Announce Type: new Abstract: Vector quantization is a technique in machine learning that discretizes continuous representations into a set of discrete vectors. It is widely employed in tokenizing data representations for large language models, diffusion models, and other generative…
arXiv:2601.09233v2 Announce Type: replace Abstract: The prevailing post-training paradigm for Large Reasoning Models (LRMs) – Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) – suffers from an intrinsic optimization mismatch: the rigid supervision inherent in SFT induces distributional collapse, thereby…
arXiv:2603.17074v1 Announce Type: new Abstract: We present PRISM, a comprehensive empirical study of mid-training design choices for large language models. Through controlled experiments across seven base models spanning four families (Granite, LLaMA, Mistral, Nemotron-H), two architecture types (dense Transformer and…
arXiv:2603.15797v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated exceptional logical reasoning capabilities but frequently struggle with the continuous spatiotemporal dynamics governed by Partial Differential Equations (PDEs), often resulting in non-physical hallucinations. Existing approaches typically resort to costly,…
arXiv:2603.17075v1 Announce Type: new Abstract: Motivated by auto-proof generation and Valiant’s VP vs. VNP conjecture, we study the problem of discovering efficient arithmetic circuits to compute polynomials, using addition and multiplication gates. We formulate this problem as a single-player game,…
arXiv:2505.20321v5 Announce Type: replace-cross Abstract: Biomedical researchers increasingly rely on large-scale structured databases for complex analytical tasks. However, current text-to-SQL systems often struggle to map qualitative scientific questions into executable SQL, particularly when implicit domain reasoning is required. We introduce…
arXiv:2603.17109v1 Announce Type: new Abstract: Decoding brain activity into natural language is a major challenge in AI with important applications in assistive communication, neurotechnology, and human-computer interaction. Most existing Brain-Computer Interface (BCI) approaches rely on memory-intensive fine-tuning of Large Language…
arXiv:2601.10566v4 Announce Type: replace-cross Abstract: Selective knowledge erasure from LLMs is critical for GDPR compliance and model safety, yet current unlearning methods conflate behavioral suppression with true knowledge removal, allowing latent capabilities to persist beneath surface-level refusals. In this work,…
