Who Verifies the Verifier? Co-Evolving Inspectable Graders with Self-Improving Agents

Xing Zhang et al.

#arXiv #cs.AI #cs.CL #cs.LG

Tracing the Thoughts of a Coding Agent Playing ARC-AGI-3: Lessons for Continual Learning

Chen Wu, Josh Passenger, Yin Song

#arXiv #cs.AI

📌 AliO: Output Alignment Matters in Long-Term Time Series Forecasing

Kwangryeol Park, Jaeho Kim, Seulki Lee

#arXiv #cs.AI

GameCommBench: A Unified Benchmark and Type-Aware Evaluation for AI-Generated Game Commentary

Qirui Zheng et al.

#arXiv #cs.AI #cs.CL

Self-Supervised Keyframe Discovery for Horizon-Invariant Behavior Cloning

Prabin Kumar Rath, Omkar Patil, Nakul Gopalan

#arXiv #cs.AI #cs.RO

HRIL: Learning Multimodal Synergy via Higher-Order Tensor Modeling

Qun Dai et al.

#arXiv #cs.AI #cs.LG

Prior or Feedback? What an LLM Uses When Adapting Neural Operators

Julian Chan, Javier Mora Jimenez

#arXiv #cs.AI #cs.LG #physics.comp-ph

Looking Inside LLMs: Small-World Connectivity as a Signature of Reasoning Performance

Zheng Huang et al.

#arXiv #cs.AI

Is Memorization Context-Sensitive? Prefix-Based Extraction Beyond Isolated Prefixes

Ali Satvaty et al.

#arXiv #cs.AI

Who Verifies the Verifier? Co-Evolving Inspectable Graders with Self-Improving Agents

Xing Zhang et al.

#arXiv #cs.AI #cs.CL #cs.LG

Tracing the Thoughts of a Coding Agent Playing ARC-AGI-3: Lessons for Continual Learning

Chen Wu, Josh Passenger, Yin Song

#arXiv #cs.AI

📌 AliO: Output Alignment Matters in Long-Term Time Series Forecasing

Kwangryeol Park, Jaeho Kim, Seulki Lee

#arXiv #cs.AI

GameCommBench: A Unified Benchmark and Type-Aware Evaluation for AI-Generated Game Commentary

Qirui Zheng et al.

#arXiv #cs.AI #cs.CL

Self-Supervised Keyframe Discovery for Horizon-Invariant Behavior Cloning

Prabin Kumar Rath, Omkar Patil, Nakul Gopalan

#arXiv #cs.AI #cs.RO

An arXiv paper argues artificial general intelligence is mathematically impossible, countering claims grounded in universal approximation theorems and rising benchmark scores. The authors claim both the theoretical and…

#AGI #AIResearch #MachineLearning #Arxiv
https://arxiv.org/abs/2610.11424

A new arXiv paper re-estimates METR's AI time horizons using splines and item-response theory on 228 tasks and 26 AIs, finding that the difficulty curve is nearly flat between 2 and 30 minutes. This means a jump from 3 to…

#AI #DevTools #SoftwareEngineering #ArXiv
https://arxiv.org/abs/2610.12466

New arXiv paper ReCast proposes a step representation learning method to improve failure attribution in LLM-based agent systems by transforming hidden states from a frozen LLM into attribution-oriented representations.

#AI #LLMAgents #Research #ArXiv
https://arxiv.org/abs/2610.11334

Learning to Plan by Looking Back: Hindsight Hierarchies for Training Reasoning Models

Lars Simon, Holger Eble, Manuel Radons

#arXiv #cs.AI #cs.LG #cs.LO

Q-Shaped Options for Hierarchical Reinforcement Learning

Clarisse Wibault et al.

#arXiv #cs.AI

EvoAlloc: A Self-Evolving Resource Allocation Agent for Efficient Program Evolution

Yanning Dai et al.

#arXiv #cs.AI