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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 142
Harness Scaling for Generalizable Agentic Reinforcement Learning
Agent harnesses have become a central component of modern agentic systems, shaping how models use tools, manage context, and coordinate long-horizon interaction.OmniSimulator: Aligning Small Language Models for Authentic Heterogeneous Behavior Modeling
Large language models (LLMs) have shown great potential as general-purpose user simulators for interactive systems.Asymmetry Tunneling Beyond Adaptive Equivariance Relaxation
Equivariant neural networks encode prescribed symmetries as powerful inductive biases, yet real-world data may exhibit symmetry-breaking structure that these priors cannot fully capture.Density-Level Backward Error for Low-NFE Generative Flows
Low-NFE generative ODE sampling is constrained not only by solver order, but also by how a fixed budget of network evaluations is distributed along the flow.Generative Modeling via Nonlinear Schrödinger Bridges and Branching Diffusions
We propose a new generative modeling framework grounded in forward-backward partial differential equations (PDEs).Rethinking Agentic Model Orchestration for Time Series Forecasting
Time series foundation models (TSFMs) perform unevenly across forecasting tasks.Asynchronous, Generation-Free ABC: Streaming AMIS Reweighting for Heterogeneous Simulator Workloads on HPC
Approximate Bayesian computation (ABC) enables Bayesian inference for models with intractable likelihoods by estimating parameter posterior distributions from simulated data.Shortcut-Resistant CAM Distillation for Long-Tailed Recognition
Real-world datasets often follow a long-tailed distribution, making generalization to tail classes difficult.A unified approach for estimation of unnormalized models via Stein operators
In this paper, we develop a unified framework connecting proper scoring rules and Stein's method for the estimation of unnormalized models.BRIDGE: An Acceptance Gate to Block False Improvements in Model Updates, with a Retrospective Clinical Evaluation
Models are updated repeatedly, and an update that looks better on validation data but not on new data is a false improvement that can misguide clinical decisions.Mitigating Query Shortcuts in Chain-of-Thought with Context-Aware Decoding
While Chain-of-Thought (CoT) prompting improves the reasoning performance of large language models (LLMs), recent studies have shown that final answers do not always depend on the generated…Supervision-Adaptive Curriculum and Guarded Anti-Curriculum Learning for Dense Prediction
Curriculum learning orders samples by difficulty, but difficulty alone does not establish supervision reliability for dense prediction.pplx-embed-v2-late Technical Report
In this report, we introduce pplx-embed-v2-late, a pair of 0.6B and 9B multilingual and multimodal late-interaction models.Geometric knowledge spaces, Attention pairing and Architectures
In this paper, we propose a geometric approach to deriving attention mechanisms and exploring potential new transformer architectures.Are Joint-Embedding Predictive Objectives Effective for Longitudinal EHRs?
Masked language modelling (MLM) is the dominant pretraining objective for transformer encoders applied to longitudinal electronic health records (EHRs).Residual-Based Causal Discovery via Pretrained In-Context Quantile Regression
Residual-based causal discovery assumes that each variable is generated from its parents and an independent noise source.Phase Transitions in Learning Driven by Extremal Data
We study an extremal regression problem, an example of multiple instance learning (MIL), where the predictors are given as multisets, called bags.Block Diffusion for Longitudinal EHR: Accuracy, Efficiency, and Representation Trade-offs
Autoregressive (AR) models have proved effective for zero-shot prediction from longitudinal electronic health records, but impose strictly sequential generation on trajectories containing temporally…Stochastic Pruning of the DPO Computation Graph: 1.4x Faster Training and Lower Peak Memory
Direct preference optimization (DPO) incurs training costs in output scoring, sequence execution, and backpropagation, so reducing supervision alone does not necessarily save computation.Hyperspherical Simplex Representations for Backward-Compatible Retrieval
Training modern AI models has become increasingly expensive, and updating deployed models can alter their internal feature representations, affecting applications built upon them.