AI & Machine Learning · Computer Science & AI
Machine Learning
The newest papers in Machine Learning that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 158
A Reverse-Gold Theorem: Positive-Data Identification by Substrate-Bounded Contrastive Learners
Gold's theorem shows that no learner can identify an arbitrary language in a superfinite class from positive presentations alone.Understanding the Inner Workings of a State-Space Foundation Model for Limit Order Books
Autoregressive foundation models offer state-of-the-art capabilities to simulate the market by generating the message flow of limit order books (LOBs).GPU-Accelerated Construction of the Gabriel Graph: Parallelization Strategies and Applications to Large-Margin Classification and Graph-Based Clustering
The Gabriel graph is a proximity graph widely used in computational geometry and geometric machine learning, underlying large-margin classifiers and, as we show in this work, density-aware…Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?
Frontier coding agents are increasingly framed as end-to-end automated researchers, and ML for science offers a demanding test of that promise: improving a scientific model often requires domain…SkillEval: Learning Interpretable Ability Profiles of LLMs via Cognitive Diagnosis Models
Current evaluation of LLMs on benchmarks typically aggregates performance over individual examples into a single scalar score.ReAPO: Retry-Aware Policy Optimization for Efficient Exploration in LLM Reasoning
RLVR improves language-model reasoning, but commonly relies on independent stochastic rollouts for exploration, which can repeatedly sample similar failed strategies.Motion Equivariant Convolutional LSTM: When Motion Becomes a Gauge Symmetry
Objects move, cameras pan, and weather drifts with the wind: much of what changes in sequential data is motion.Building a Nested LADDER for LLM Unstructured Post-Training Pruning
Non-uniform allocation can improve post-training pruning of large language models, yet quality can still degrade sharply at high sparsity.Second Thoughts: Exploring and Planning through Comparative Imagination in World Models
World models let agents compare imagined outcomes before acting, but an agent must still decide which alternatives to explore and when a proposed plan should replace its learned policy.Extreme Low-State Quantized Transformer for Electricity-Load Forecasting
Aggressive low-state weight quantization can substantially reduce neural-network representational flexibility, making predictive performance difficult to preserve when only a few discrete weight…Measure First, Then Allocate: Calibrated Rollout Budgets for MCP Agents in Reinforcement Learning
Training tool-using language models requires costly interaction, yet identical rewards within a rollout group provide no group-relative learning signal.Is Anyone Enforcing the Cartel? What Reinforcement Learning Teaches LLM Agents in Markets
Evaluations of large language model (LLM) agents report that pricing agents collude at others' expense.ArcFlow: Training Flow Policies for Multiple Solver Budgets
Flow-matching policies adjust inference cost through the number of solver steps, but reinforcement learning at a single fixed solver budget can hide degradation at cheaper deployment budgets and…CALLIOPE: Private Insights into AI Use, Without Embeddings
We introduce CALLIOPE, a top-down framework for discovering topic hierarchies in large corpora of unstructured data (e.g., LLM conversations), while supporting differential privacy (DP).An Explicit Feedback Contract for Iterative ML-Agent Harnesses
Iterative ML agents develop models by editing code, running experiments, reading a validation score, and choosing what to try next.Depth Separation for Neural Networks under Gaussian Measure
We revisit the problem of depth separation for neural networks, as studied by Eldan and Shamir, Daniely, and others.Towards Realistic Models of Change: A Causal Perspective on Decision-Making and Dynamical Systems
While statistical learning has made remarkable progress in recent decades, driven by large datasets and data-driven protocols that have transformed scientific practice, it still faces a key…Combining Long-Term Memory and LLMs Comes at a High Cost: From Prompt Injection to Persistent Compromise
The widespread adoption of generative artificial intelligence in recent years has brought a parallel rise in prompt injection and jailbreak attacks.A Mechanistic Study of Automated Failure Attribution in LLM-based Agentic Systems
Agentic systems have demonstrated strong capabilities on complex tasks by enabling multiple interacting large language model (LLM)-based agents to collaborate and execute subtasks in parallel.Brittle by Design? Multi-Scenario Reinforcement Learning for Robust Mission Coordination in Systems of Systems
As systems engineering (SE) shifts from monolithic systems to Systems of Systems, Mission Engineering (ME) has emerged as a new line of thinking in SE.