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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 209
Edit Discovery in Human-AI Interactions
Human-AI interactions are becoming a rich source of data for improving AI systems: while many model responses are already satisfactory, others expose failures that could become valuable supervision…AlphaBet: Reinforcement Learning for LLM Event Forecasting Agents
Forecasting an unresolved event requires finding relevant evidence and deciding how strongly it supports each outcome.Quantum-Classical Neural Networks for Solving Lindblad Master Equations
Variational quantum algorithms have emerged as a promising approach for simulating open quantum systems, yet their expressive power and trainability remain poorly understood.Optimal approximation of smooth functions by ReLU neural networks
We study uniform approximation of the Hölder ball $\mathcal H_1^s([0,1]^D)$ by fully connected ReLU networks.Chance-Constrained Personalized LLM Routing with Online Preference Learning
We study personalized LLM routing, where a router selects among heterogeneous models with different costs and capabilities.Data Provenance Aggregation and Structural Descriptors for Query-Template Identification
Runtime query similarity identification supports several activities, including workload analysis, identifying recurring behaviour, auditing, debugging, and anomaly detection.Normalizing Flow Maps
Scalable approaches to learning neural dynamical systems have placed diffusion, flow matching, and few-step generators in flow maps at the forefront of generative modeling.Spectral Limits of Polynomial Graph Filters on the Long-Range Ising Model Benchmark
The Long-Range Ising Model benchmark (LRIM) guarantees long-range dependence by construction: each label is the node's spin times the spin field convolved with a power-law coupling, so any model's…First Passage Attention
Augmenting softmax attention with forget gates typically relies on summing decay contributions along the key--query path.CoLU: Confidence-Weighted Layerwise Unlearning for Large Language Models
Large language model (LLM) unlearning aims to forget certain information while preserving others, without retraining from scratch.LLM-Attack Suite: A Unified Red-Team Framework for Privacy Evaluation of Split and Distributed LLM Systems
Split language models keep input text on a client but send intermediate representations to a server.OrderFlowDiT: Transport-Regularized Diffusion Transformer for Probabilistic Limit-Order Supply Forecasting
We study the conditional distribution of limit-order volume submitted over the next five minutes, given the current order book and recent order flow.Reading the News Without Hindsight: NoLBERT Topics and the Cross-Section of Stock Returns
Language models that read financial news are usually trained on text from the same years used to test them, so a backtest can reward hindsight.Native Geometry vs. Linear Recoverability: How Alignment Changes Platonic Scaling
Do larger models learn increasingly similar representations of the same world?Refusal Is Not Robustness: Measuring Harmful Reachability in Autoregressive Language Models
LLM safety evaluations via jailbreaking typically index on attack success rate (ASR): whether a generated response violates safeguards.Weakly Supervised Discovery of Motion Styles for Compositional Robot Control
When a robot possesses more degrees of freedom than a task requires, multiple trajectories can achieve the same objective using distinct motion styles.SPADE-KD: Peer-to-Peer Collaborative Learning under Heterogeneous Settings
Peer-to-peer collaborative learning is an emerging distributed deep learning paradigm that eliminates the need for a central aggregator and disseminator, avoiding the single-point-of-failure and…TWIN-MoE: Two-stage Intra-Expert Imputation Network for Incomplete Multimodal Learning
Recent Mixture-of-Experts (MoE) approaches address missing modalities either by routing each missingness pattern to a dedicated expert, which scales poorly as the number of modalities grows, or by…TopicMerge: A Divide and Conquer Approach to Topic Modeling
Recent advancements in topic modeling have split into two main paths: efficient embedding-based methods that offer scalability but are often outperformed on standard clustering metrics, and Large…Why don't Graph Transformers attend to the same token?
In large language models (LLMs), attention concentrates heavily on the first token.