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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 161
SIGNN++: Robust Graph Neural Star Identification on Earth
We present SIGNN++, a learning-based star identification (SID) framework for celestial navigation in GNSS-denied nighttime environments.Agentic Verified Optimization of Real-World Software
Program optimization aims to improve performance while preserving behavior.Fooling LLM Safety Guards into Judging the Wrong Text
Safety guards check content before an LLM application acts on it, and most paste that content into a plain-text prompt.ProtoEEG: Structured Prototype Alignment for EEG Visual Decoding
EEG visual decoding remains difficult because scalp recordings are noisy, spatially coarse, and variable across trials and subjects.OvisOCR3 Technical Report
We introduce OvisOCR3, a 0.8B model for a broad range of OCR-related tasks.Cognitive Maps in Large Language Models
It has long been proposed that animals construct cognitive maps to support flexible behavior in novel environments.Text Dominance in Paralinguistic Conflict: Tracing Suppression Through Audio LLM Circuits
Audio LLMs deployed in settings such as crisis hotlines and customer support are only useful beyond speech-to-text pipelines if they use acoustic cues rather than the transcript alone.Adversarial Post-Training of Foundation Models for High-Frequency Trading
While autoregressive foundation models trained on limit order book (LOB) data show state-of-the-art performance for market simulation, pre-training with cross-entropy neither penalizes the…PRISM: Prior Relational Information for Self-supervised Modeling to Enhance Solubility OOD Generalization
Solubility depends on intermolecular interactions between solutes and solvents, and is central to drug discovery, chemical and materials process engineering.Safety Offset Learning: When Benign Fine-Tuning Misaligns LLMs
Safety risks from fine-tuning are commonly characterized by the harmfulness of the training data:harmful fine-tuning is considered dangerous, while benign fine-tuning is often assumed to…Curriculum Learning Teaches LLMs Long-Horizon Implicit Reasoning, Up To A Limit
LLMs "reason" by externalizing intermediate steps as tokens through chain-of-thought (CoT), yet even frontier reasoning models hit abrupt performance cliffs on long-horizon tasks.CheXFuse: Guided Differential Joint Attention Fusion for Robust Multimodal Chest X-Ray Classification in Data-Scarce Medical Settings
A central challenge in multimodal learning is how to fuse modalities that differ substantially in predictive strength.Weighted State Souping for Efficient Long-Context Retrieval
State space models (SSMs) combine fast inference and fixed state size with competitive language-modeling performance.Zephyrus-R1: Agentic Reinforcement Learning for Weather Reporting
Reinforcement learning (RL) has emerged as a key method for training AI models in scientific applications, but RL remains difficult when outputs cannot easily be verified against a known…Conditional Average Treatment Effect Distillation for Hypothetical State-Intervention Targeting Under Budget Constraints
Personalized public-health targeting must capture heterogeneous intervention benefits while remaining interpretable under resource constraints.Clean Forcing: Drift-Resistant Autoregressive Video Diffusion with a Frozen Base
Autoregressive (AR) video diffusion models generate streaming video of arbitrary length by conditioning each new chunk of frames on those already generated.Uncertainty-Gated SHAP-Dirichlet Refinement for MRI-Based Depression Classification
Structural MRI-based classification of major depressive disorder (MDD) remains challenging due to subtle, heterogeneous anatomical changes.Sampling of Signals on Multigraphs: From Removable and Uniqueness sets to Spectral Proxies
In this paper, we study the sampling of bandlimited signals on multigraphs, where several graphs with different shift operators are defined on the same set of nodes.Spherical Fixed-Point Reasoners: Adaptive Loop Halting on the Hypersphere
Looped (weight-tied) transformers enable adaptive computation by allocating more iterations to harder inputs.Consistency Training Suppresses Cue Following Without Teaching Cue Selectivity
Language models often change answers because of a cue, a hint such as "I believe the answer is B" that gives no valid evidence.