AI Agents

Why does Multi-Agent RL fail to act like a real society in Spatial Game Theory? [P] [R]

Hey everyone, I’m building a project for my university Machine Learning course called "Social network analysis using iterated game theory...

AWS turns its S3 storage service into a file system for AI agents

Moody’s Integrates AI Agents With Anthropic’s Claude

[2602.15112] ResearchGym: Evaluating Language Model Agents on Real-World AI Research

ResearchGym introduces a benchmark for evaluating AI agents in real-world research scenarios, revealing significant performance gaps and ...

[2602.15817] Solving Parameter-Robust Avoid Problems with Unknown Feasibility using Reinforcement Learning

This article presents a novel approach using Feasibility-Guided Exploration (FGE) to address parameter-robust avoid problems in reinforce...

[2602.15763] GLM-5: from Vibe Coding to Agentic Engineering

GLM-5 introduces a next-generation foundation model that enhances coding capabilities through agentic engineering, reducing costs while i...

[2602.15740] MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosis

The paper presents the MRC-GAT, a novel Meta-Relational Copula-Based Graph Attention Network designed for accurate and interpretable Alzh...

[2602.15704] Controlled oscillation modeling using port-Hamiltonian neural networks

This paper presents a novel approach to modeling controlled oscillations using port-Hamiltonian neural networks, emphasizing a second-ord...

[2602.15676] Relative Geometry of Neural Forecasters: Linking Accuracy and Alignment in Learned Latent Geometry

This paper explores how neural networks represent latent geometry in forecasting complex dynamical systems, linking model alignment with ...

[2602.15649] Continuous-Time Piecewise-Linear Recurrent Neural Networks

This article presents Continuous-Time Piecewise-Linear Recurrent Neural Networks (cPLRNNs), a novel approach to modeling dynamical system...

[2602.15634] Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors

This paper explores the limitations of Graph Neural Networks (GNNs) due to oversmoothing and proposes a novel approach using bifurcation ...

[2602.15595] Multi-Objective Coverage via Constraint Active Search

This paper introduces a novel algorithm, MOC-CAS, for solving the multi-objective coverage problem, enhancing efficiency in applications ...

[2602.15593] A unified theory of feature learning in RNNs and DNNs

This paper presents a unified theory of feature learning in recurrent neural networks (RNNs) and deep neural networks (DNNs), highlightin...

[2602.15572] Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model

This paper presents a neural network-based framework for parameter estimation in agent-based models (ABMs) of the labor market, demonstra...

[2602.15571] Accelerated Predictive Coding Networks via Direct Kolen-Pollack Feedback Alignment

The paper introduces Direct Kolen-Pollack Predictive Coding (DKP-PC), an innovative approach that enhances the efficiency of predictive c...

[2602.15515] The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probes

The paper explores how AI models can learn to obfuscate deception when trained against white-box deception detectors, introducing a taxon...

[2602.15473] POP: Prior-fitted Optimizer Policies

The paper introduces POP (Prior-fitted Optimizer Policies), a meta-learned optimization method that predicts step sizes based on contextu...

[2602.15407] Fairness over Equality: Correcting Social Incentives in Asymmetric Sequential Social Dilemmas

This paper explores how asymmetric conditions in Sequential Social Dilemmas affect cooperation dynamics in Multi-Agent Reinforcement Lear...

[2602.15367] CDRL: A Reinforcement Learning Framework Inspired by Cerebellar Circuits and Dendritic Computational Strategies

The paper presents CDRL, a reinforcement learning framework inspired by cerebellar circuits, aiming to enhance sample efficiency and robu...

[2602.15332] Directional Reasoning Trajectory Change (DRTC): Identifying Critical Trace Segments in Reasoning Models

The paper introduces Directional Reasoning Trajectory Change (DRTC), a framework for interpreting long-horizon reasoning in language mode...

[2602.15293] The Information Geometry of Softmax: Probing and Steering

This paper explores the information geometry of softmax distributions, focusing on how AI systems encode semantic structures and the deve...

[2602.15260] Fast and Effective On-policy Distillation from Reasoning Prefixes

This paper presents an innovative approach to on-policy distillation (OPD) in machine learning, focusing on the effective use of reasonin...

[2602.15239] Size Transferability of Graph Transformers with Convolutional Positional Encodings

This paper explores the size transferability of Graph Transformers (GTs) with convolutional positional encodings, demonstrating their abi...