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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 168
WhatIf-Bench: Benchmarking Intervention-Aware Time Series Forecasting
Recent time series foundation models (TSFMs) can consume covariates in-context, extending zero-shot forecasting to problems driven by external factors.Concerns in Benchmarking Jailbreaks: An Empirical Study of Attack Success Rates
Jailbreak methods are commonly compared using Attack Success Rate (ASR), but these comparisons depend on how responses are generated and judged.Bombadil: A Foundation Model for Plant Electrophysiology
Living plants, like animals, use electrical signals to carry information about their physiological state across their body, from roots to branches.ITU-DESC: a missingness-aware machine-learning dementia risk score in the UK Biobank cohort
Existing dementia risk scores rely on fixed, pre-selected features and linear models, or ignore bias from informative missingness in cohort data.A Probabilistic, Rotation-Preserving Kernel Operator Surrogate for Continuum-Robot Design Spaces
We develop an uncertainty-aware, operator-learning-based surrogate for tendon-driven continuum robots (TDCRs) that generalizes across robot design and actuation while predicting the complete…Degree Bounds for Separating Polynomial and Neural Invariants for Point Clouds
Neural networks that respect the symmetries of point clouds commonly compute high-order representations of the rotation group to improve their expressivity.Global Time-Aware Conditioning (GTC): A Mechanism for Time-Aware Pediatric Sleep Representation Learning
Sleep physiology evolves throughout the night, with changes in sleep cycles, respiratory stability, and cardiovascular dynamics.SleepPairNet: Learning Multimodal Pediatric Sleep Representations with Random-Pair Cross-Attention
Analyzing physiological sleep signals is important for assessing sleep quality and detecting sleep-related disorders.VaDar: Implicit Goal Conditioning via Value Disaggregation
Long-horizon tasks are important for modern AI and robotics, yet pose a challenge for reinforcement learning.When the harness decides success: state that cannot be re-observed
Agent harnesses decide what a language model sees across turns: they drop old turns, summarize them, or replay the model's own earlier reasoning.The Effectiveness and Durability of Pretraining Data Filtering in Protein Language Models
Pretraining filtering is one of the most common safeguards for open-weight language models.The Development of Sycophancy in Language Models: Tracing Vulnerabilities from Pretraining to Post-Training
Language models can change a committed answer to accommodate a user's opinion or pushback without new evidence.Nested Shared-Operator Recursion on ARC-AGI-1: An Empirical Study of HRM, TRM and URM
Recursive reasoning models refine persistent internal states by repeatedly applying learned operators.PACT: Plan-Augmented Coordination via Trace-Graphs for Dynamic Multi-Agent Combinatorial Optimization
Multi-agent approaches have been investigated recently as a means of improving the scalability of reinforcement learning for neural combinatorial optimization.Which Model for Which Role? Cost-Efficient Pareto Recommendation for LLM Agentic Workflows
LLM agentic workflows increasingly employ different models for specialized roles.Intra-Rubric Reward Divergence: Mitigating Rubric Hacking through Latent Dimension Recovery and Balancing
Rubric hacking limits both performance and training robustness in rubric-based reinforcement learning.Parallel Language Models via Variational Decoding
Autoregressive language models emit one token per forward pass.GeoSplit: Selective Edge--Server Map Acquisition for Cross-User Travel-Mode Recognition
Networked mobility sensing couples local trajectory inference with remote geospatial context, making communication decisions part of the prediction system.Doubly Robust Learning of Heterogeneous Survival Treatment Effects under Time-Varying Treatments
Estimating heterogeneous survival treatment effects (HSTEs) from time-to-event data under time-varying treatments is important in fields such as medicine (e.g., to decide which sequence of…Deep Pontryagin Learning for Continuous-Time Stochastic Control with Jumps
We develop a deep Pontryagin learning framework for high-dimensional continuous-time stochastic control problems that naturally accommodates jump-diffusions.