Motivation in Large Language Models
arXiv:2603.14347v1 Announce Type: new Abstract: Motivation is a central driver of human behavior, shaping decisions, goals, and task performance. As large language models (LLMs) become increasingly aligned with human preferences, we ask whether they exhibit something akin to motivation. We...
Exposing Long-Tail Safety Failures in Large Language Models through Efficient Diverse Response Sampling
arXiv:2603.14355v1 Announce Type: new Abstract: Safety tuning through supervised fine-tuning and reinforcement learning from human feedback has substantially improved the robustness of large language models (LLMs). However, it often suppresses rather than eliminates unsafe behaviors, leaving rare but critical failures...
BiT-MCTS: A Theme-based Bidirectional MCTS Approach to Chinese Fiction Generation
arXiv:2603.14410v1 Announce Type: new Abstract: Generating long-form linear fiction from open-ended themes remains a major challenge for large language models, which frequently fail to guarantee global structure and narrative diversity when using premise-based or linear outlining approaches. We present BiT-MCTS,...
Knowledge, Rules and Their Embeddings: Two Paths towards Neuro-Symbolic JEPA
arXiv:2603.13265v1 Announce Type: new Abstract: Modern self-supervised predictive architectures excel at capturing complex statistical correlations from high-dimensional data but lack mechanisms to internalize verifiable human logic, leaving them susceptible to spurious correlations and shortcut learning. Conversely, traditional rule-based inference systems...
Spatially Aware Deep Learning for Microclimate Prediction from High-Resolution Geospatial Imagery
arXiv:2603.13273v1 Announce Type: new Abstract: Microclimate models are essential for linking climate to ecological processes, yet most physically based frameworks estimate temperature independently for each spatial unit and rely on simplified representations of lateral heat exchange. As a result, the...
RelayCaching: Accelerating LLM Collaboration via Decoding KV Cache Reuse
arXiv:2603.13289v1 Announce Type: new Abstract: The increasing complexity of AI tasks has shifted the paradigm from monolithic models toward multi-agent large language model (LLM) systems. However, these collaborative architectures introduce a critical bottleneck: redundant prefill computation for shared content generated...
FedUAF: Uncertainty-Aware Fusion with Reliability-Guided Aggregation for Multimodal Federated Sentiment Analysis
arXiv:2603.13291v1 Announce Type: new Abstract: Multimodal sentiment analysis in federated learning environments faces significant challenges due to missing modalities, heterogeneous data distributions, and unreliable client updates. Existing federated approaches often struggle to maintain robust performance under these practical conditions. In...
Machine Learning Models to Identify Promising Nested Antiresonance Nodeless Fiber Designs
arXiv:2603.13302v1 Announce Type: new Abstract: Hollow-core fibers offer superior loss and latency characteristics compared to solid-core alternatives, yet the geometric complexity of nested antiresonance nodeless fibers (NANFs) makes traditional optimization computationally prohibitive. We propose a high-efficiency, two-stage machine learning framework...
Task Expansion and Cross Refinement for Open-World Conditional Modeling
arXiv:2603.13308v1 Announce Type: new Abstract: Open-world conditional modeling (OCM), requires a single model to answer arbitrary conditional queries across heterogeneous datasets, where observed variables and targets vary and arise from a vast open-ended task universe. Because any finite collection of...
Preventing Curriculum Collapse in Self-Evolving Reasoning Systems
arXiv:2603.13309v1 Announce Type: new Abstract: Self-evolving reasoning frameworks let LLMs improve their reasoning capabilities by iteratively generating and solving problems without external supervision, using verifiable rewards. Ideally, such systems are expected to explore a diverse problem space and propose new...
Neural Approximation and Its Applications
arXiv:2603.13311v1 Announce Type: new Abstract: Multivariate function approximation is a fundamental problem in machine learning. Classic multivariate function approximations rely on hand-crafted basis functions (e.g., polynomial basis and Fourier basis), which limits their approximation ability and data adaptation ability, resulting...
Linear Predictability of Attention Heads in Large Language Models
arXiv:2603.13314v1 Announce Type: new Abstract: Large language model (LLM) inference is increasingly bottlenecked by the Key-Value (KV) cache, yet the fine-grained structure of attention-head activations remains poorly understood. We show that pretrained Transformers exhibit a pervasive inter-head linear structure: for...
Residual Stream Analysis of Overfitting And Structural Disruptions
arXiv:2603.13318v1 Announce Type: new Abstract: Ensuring that large language models (LLMs) remain both helpful and harmless poses a significant challenge: fine-tuning on repetitive safety datasets, where unsafe prompts are paired with standard refusal templates, often leads to false refusals, in...
Modular Neural Computer
arXiv:2603.13323v1 Announce Type: new Abstract: This paper introduces the Modular Neural Computer (MNC), a memory-augmented neural architecture for exact algorithmic computation on variable-length inputs. The model combines an external associative memory of scalar cells, explicit read and write heads, a...
Lipschitz-Based Robustness Certification Under Floating-Point Execution
arXiv:2603.13334v1 Announce Type: new Abstract: Sensitivity-based robustness certification has emerged as a practical approach for certifying neural network robustness, including in settings that require verifiable guarantees. A key advantage of these methods is that certification is performed by concrete numerical...
AdaBox: Adaptive Density-Based Box Clustering with Parameter Generalization
arXiv:2603.13339v1 Announce Type: new Abstract: Density-based clustering algorithms like DBSCAN and HDBSCAN are foundational tools for discovering arbitrarily shaped clusters, yet their practical utility is undermined by acute hyperparameter sensitivity -- parameters tuned on one dataset frequently fail to transfer...
Thermal Robustness of Retrieval in Dense Associative Memories: LSE vs LSR Kernels
arXiv:2603.13350v1 Announce Type: new Abstract: Understanding whether retrieval in dense associative memories survives thermal noise is essential for bridging zero-temperature capacity proofs with the finite-temperature conditions of practical inference and biological computation. We use Monte Carlo simulations to map the...
Nvidia’s DLSS 5 uses generative AI to boost photorealism in video games, with ambitions beyond gaming
Nvidia’s new DLSS 5 uses generative AI and structured graphics data to make video games more realistic. CEO Jensen Huang says the approach could eventually spread to other industries.
AI Model Modulation with Logits Redistribution
arXiv:2603.12755v1 Announce Type: new Abstract: Large-scale models are typically adapted to meet the diverse requirements of model owners and users. However, maintaining multiple specialized versions of the model is inefficient. In response, we propose AIM, a novel model modulation paradigm...
Semantic Invariance in Agentic AI
arXiv:2603.13173v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly serve as autonomous reasoning agents in decision support, scientific problem-solving, and multi-agent coordination systems. However, deploying LLM agents in consequential applications requires assurance that their reasoning remains stable under semantically...
ODRL Policy Comparison Through Normalisation
arXiv:2603.12926v1 Announce Type: new Abstract: The ODRL language has become the standard for representing policies and regulations for digital rights. However its complexity is a barrier to its usage, which has caused many related theoretical and practical works to focus...
Operationalising Cyber Risk Management Using AI: Connecting Cyber Incidents to MITRE ATT&CK Techniques, Security Controls, and Metrics
arXiv:2603.12455v1 Announce Type: cross Abstract: The escalating frequency of cyber-attacks poses significant challenges for organisations, particularly small enterprises constrained by limited in-house expertise, insufficient knowledge, and financial resources. This research presents a novel framework that leverages Natural Language Processing to...
TRACE: Temporal Rule-Anchored Chain-of-Evidence on Knowledge Graphs for Interpretable Stock Movement Prediction
arXiv:2603.12500v1 Announce Type: cross Abstract: We present a Temporal Rule-Anchored Chain-of-Evidence (TRACE) on knowledge graphs for interpretable stock movement prediction that unifies symbolic relational priors, dynamic graph exploration, and LLM-guided decision making in a single end-to-end pipeline. The approach performs...
Marked Pedagogies: Examining Linguistic Biases in Personalized Automated Writing Feedback
arXiv:2603.12471v1 Announce Type: new Abstract: Effective personalized feedback is critical to students' literacy development. Though LLM-powered tools now promise to automate such feedback at scale, LLMs are not language-neutral: they privilege standard academic English and reproduce social stereotypes, raising concerns...
LMEB: Long-horizon Memory Embedding Benchmark
arXiv:2603.12572v1 Announce Type: new Abstract: Memory embeddings are crucial for memory-augmented systems, such as OpenClaw, but their evaluation is underexplored in current text embedding benchmarks, which narrowly focus on traditional passage retrieval and fail to assess models' ability to handle...
MetaKE: Meta-learning Aligned Knowledge Editing via Bi-level Optimization
arXiv:2603.12677v1 Announce Type: new Abstract: Knowledge editing (KE) aims to precisely rectify specific knowledge in Large Language Models (LLMs) without disrupting general capabilities. State-of-the-art methods suffer from an open-loop control mismatch. We identify a critical "Semantic-Execution Disconnect": the semantic target...
SteerRM: Debiasing Reward Models via Sparse Autoencoders
arXiv:2603.12795v1 Announce Type: new Abstract: Reward models (RMs) are critical components of alignment pipelines, yet they exhibit biases toward superficial stylistic cues, preferring better-presented responses over semantically superior ones. Existing debiasing methods typically require retraining or architectural modifications, while direct...
Long-form RewardBench: Evaluating Reward Models for Long-form Generation
arXiv:2603.12963v1 Announce Type: new Abstract: The widespread adoption of reinforcement learning-based alignment highlights the growing importance of reward models. Various benchmarks have been built to evaluate reward models in various domains and scenarios. However, a significant gap remains in assessing...
Spatial PDE-aware Selective State-space with Nested Memory for Mobile Traffic Grid Forecasting
arXiv:2603.12353v1 Announce Type: new Abstract: Traffic forecasting in cellular networks is a challenging spatiotemporal prediction problem due to strong temporal dependencies, spatial heterogeneity across cells, and the need for scalability to large network deployments. Traditional cell-specific models incur prohibitive training...
Bases of Steerable Kernels for Equivariant CNNs: From 2D Rotations to the Lorentz Group
arXiv:2603.12459v1 Announce Type: new Abstract: We present an alternative way of solving the steerable kernel constraint that appears in the design of steerable equivariant convolutional neural networks. We find explicit real and complex bases which are ready to use, for...