A Definition of Good Explanations and the Challenges Explaining LLM Outputs
arXiv:2606.14838v1 Announce Type: new Abstract: How to define a good explanation is a long-standing philosophical debate which has found recent…
When and How Severely: Scenario-Specific Safety Envelopes for Driving VLAs
arXiv:2606.14238v2 Announce Type: replace-cross Abstract: Safety certification of Vision-Language-Action (VLA) driving planners under ISO 21448 (SOTIF) rests on an Operational…
AuAu: A Benchmark for Auditing Authoritarian Alignment in Large Language Models
arXiv:2606.16127v1 Announce Type: cross Abstract: The worldwide surge of authoritarianism, combined with the increasing central role in users’ everyday lives,…
Scaling Adaptive Depth with Norm-Agnostic Residual Networks
arXiv:2606.16112v1 Announce Type: cross Abstract: Residual architectures are ubiquitous in deep learning, but they suffer from a subtle structural limitation:…
Implicit Reasoning for Large Language Model-based Generative Recommendation
arXiv:2606.14142v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access…
A Deep Reinforcement Learning (DRL)-Based Transformer Method for Solving the Open Shop Scheduling Problem
arXiv:2606.13682v1 Announce Type: new Abstract: The open shop scheduling problem (OSSP) arises in many industrial and service settings but remains…
Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversations
arXiv:2606.13464v2 Announce Type: replace-cross Abstract: Automatic speech recognition (ASR) correction has traditionally focused on isolated utterances or short local contexts.…
Fodor and Pylyshyn’s Systematicity Challenge Still Stands
arXiv:2606.14512v1 Announce Type: cross Abstract: The recent successes of neural networks producing human-like language have caused significant stir in cognitive…
A Fixed-Point Neural Operator for Size- and Functional-Transferable Hamiltonian Prediction
arXiv:2606.14498v1 Announce Type: cross Abstract: Predicting the Kohn-Sham Hamiltonian with machine learning can accelerate density functional theory while retaining access…
TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization
arXiv:2606.13054v2 Announce Type: replace-cross Abstract: Large language models (LLMs) exhibit exceptional general language processing capabilities, but their memory and compute…