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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 177
Improving the Expressive Power of Hybrid Sequence Models via Multi-Hop Retrieval
Hybrid architectures offer a promising approach to efficient language modeling by combining a small number of full-attention layers with a larger number of efficient sequence layers, such as sparse…Persistent Representational Misalignment Induces Vision-Language Model Failures
Vision-language models (VLMs) fail at some tasks that are simple for humans---but why?BatchParScale: Sparse Batch-Mixing for Sequence Models
Recent test-time scaling methods improve model performance by increasing inference-time compute.Emergent Sparsity: How Many Effective Variables Survive Coarse-Graining in AlphaEarth Embeddings?
AlphaEarth encodes each 10 m land pixel as a 64-component vector intended to support diverse global mapping tasks.EasyResearch: Honest Regression, Forecasting and Signal Classification for Researchers Who Do Not Program
EasyClassifier showed that researchers who do not program can obtain honest classification results when the correct evaluation is the default.Federated Learning for Malware Image Classification under Data Heterogeneity
Malware image classification (MIC), in which binary files are converted into visual representations and deep neural networks are trained to identify malware families, has recently emerged as an…EG-GRPO: Evidence-Guided Step-level Credit Assignment for Deep Search Agents
LLM-based search agents demonstrate strong capabilities in long-horizon information seeking and reasoning, typically trained via reinforcement learning.Bridging Concepts: Multi-Agent Reasoning over Scientific Concept Graphs for High-Impact Research Prediction
Within a research community, new topics often emerge when previously unconnected concepts are first studied together.Data-driven forecasting of tropical cloud forest phenology: A dynamical systems and machine learning hybrid approach
Forecasting how species and ecosystems respond to climate change is a critical challenge in ecology, particularly in hyper-diverse tropical biomes.A discrete-continuous-time stochastic framework for attention-deficit/hyperactivity disorder-related symptom modeling and pharmacological intervention via methylphenidate
This paper presents an original mathematical and computational framework for modeling the efficacy of methylphenidate as an optimal control policy acting on the nonlinear dynamics of attention…The Explainability Illusion: Must ViTs Exhibit Patch-Decoding Interpretability?
Transformers are widely assumed to exhibit token-decoding explainability.World Model Interpretability: From Decoding Representations to Understanding Latent Worlds
Emerging Joint-Embedding Predictive Architectures (JEPAs) are moving beyond representation learning towards latent world models that support prediction, planning, and control.Learning When to Abort in Transformer Planning: A Mechanistic Study
Planning does not by itself imply the ability to recognise errors, but recognising errors can improve planning capacity.Where and Why Search-Augmented LLMs Fail When Reasoning over Rapidly Evolving Knowledge
As the world changes, new information is published while older records remain online.Where Groups Split: A Probe Prior for Nonzero Within-Group Variance in RL Post-Training
Group relative policy optimization learns from a prompt only when the rewards in the sampled group disagree.Follow the Mixed Groups: Ridge-Tracking Gaussian Sampling for Zero-Variance Web Rollouts
Group relative policy optimization learns from a question only when the sampled responses disagree.A Distribution-Free Separation Between Learned and Fixed Features
The success of neural networks is largely attributed to their ability to learn useful representations from training data.Formally Verified Autoresearch for Analyzing the Expressivity of Multi-Head Attention
Multi-head attention is a central component of the transformer architecture, yet how its expressivity depends on the number of heads remains poorly understood.Predicted Class Correction: Conformal Thresholds for Classes with No Calibration Data
Class-conditional conformal prediction calibrates a separate score threshold for every class and therefore requires labelled calibration examples of every class.CHSM8: A Chess Transformer that Follows Natural-Language Style Prompts
We present CHSM8, a 210M-parameter chess transformer that plays moves conditioned on a free-text description of a playing style, e.g. "go for the Sicilian Defense", "solid, safe, risk-free chess" or…