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Machine Learning
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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 232
PRISM: Calibrating Black-Box Judges with Counterfactual Probes
Large language models are increasingly used as judges to reduce reliance on costly human evaluation, yet their scores can systematically disagree with human judgments.Beyond RMSE and Granger Gain: Multi-Horizon Forecast-Error Entropy for Nonlinear Predictive Coupling
We investigate whether the past of an auxiliary time series provides predictive information about a target beyond that contained in the target's own history.LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs
Equivariant machine learning interatomic potentials (MLIPs) promise atomistic simulations with accuracy approaching that of quantum-mechanical calculations.Overcoming Catastrophic Forgetting: Continual Learning for Radar Waveform Classification
Automated radar waveform recognition classifies the waveform and modulation type of a received signal.Multi-Stream Residual Computation in Diffusion Language Models
Diffusion language models reconstruct masked tokens in parallel, offering an alternative to autoregressive generation.Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs
Prompt-based adaptation offers several ways to specialize a frozen language model, ranging from natural-language prompt optimization to learned continuous prompts.Agaram 2.5B: The First Tamil-First Language Model Trained from Scratch with Tool Calling. A Playbook for Under-Represented Languages
We trained Agaram 2.5B, a 2.5-billion-parameter Tamil-English language model, from scratch on about 50 billion tokens (58% Tamil, the rest English, math, code and parallel text) on one node of eight…Principled Design of Expected Improvement for Continuous Simulation Optimization
Expected improvement based on stochastic kriging (SK) surrogate is widely used in continuous simulation optimization, but unknown heteroscedastic noise makes three basic design choices…FastEEP: Fast Intra-Instance Elastic Scaling for Expert-Parallel MoE Serving
Large-scale expert parallelism (EP) has become the standard deployment configuration for Mixture-of-Experts (MoE) models in production.Learning Perturbation-Specific Representations through Conditional Energy Prior for Cellular Response Prediction
AI-based cellular perturbation prediction is essential to accelerate drug discovery and therapeutic identification.Kairos: Transactional Security Enforcement at the System Boundary for LLM Agents
Large language model (LLM) agents are becoming an integral part of the software development lifecycle, and the capabilities granted to them allow an agent to act with the user's authority on any…Teacher-Guided Tool Learning for Small Language Models via Bidirectional KL Constraints
External tools can extend the capabilities of small language models (SLMs), enabling their deployment in resource constrained environments.Readout Scaling Shapes Sparse Feature Recovery
Readout scaling changes what a sparse autoencoder (SAE) learns, but the benefit of removing it depends on the data.Graph Metanetworks for Transformer Based Language Models
Weight-space learning treats the weights of neural networks as data, processed by other models called metanetworks.Simplifying and Improving On-Policy Distillation for Few-Step Diffusion
Few-step distillation reduces the sampling cost of diffusion and flow models, but existing approaches often require auxiliary score models or specialized architectures.ProTok: Unified Protein Tokenization via Modular Multi-Codebook Quantization
Protein sequence, structure, and functional annotations each capture complementary aspects of protein biology, yet existing tokenizers encode each modality independently, limiting the expressiveness…Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements
Quantum computing promises new capabilities beyond classical computation.ReTerm: Recovery Training for Self-Improving Terminal Agents
Terminal agents have shown strong abilities in complex coding tasks.SoK: Rethinking Membership Inference in Tabular Machine Learning
Membership Inference Attacks (MIAs) are currently the dominant approach to evaluating privacy in machine learning applications.Recalibrating Many Questions with Few Labels: Minimax Rates and Identification Limits
Many related prediction problems, which we call questions, come with few labels each.