arXiv Machine Learning
5d ago
Latent On-Policy Self-Distillation
arXiv:2608. 13040v1 Announce Type: new Abstract: Enabling agents to learn from experience and internalize it into their policy has become a central problem in self-evolving AI.
CatalogAgent: A Supervisor-mediated Self-Learning System Enabling Context Engineering for GenAI Models
arXiv:2607. 14396v1 Announce Type: new Abstract: Product catalogs are the backbone of e-commerce sites, yet a large number of structured attributes (SAs) -- such as material, color, and shape -- often have missing values.
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arXiv AI
Jul 7
TRACE: Capability-Targeted Agentic Training
arXiv:2604. 05336v2 Announce Type: replace Abstract: Models often fail to complete agentic tasks because they lack core capabilities required by the target environment.
arXiv AI
2d ago
AdsWorldEngine: A Self-Evolving Conversational Advertising Agent through Orchestrator and Tool Coevolution
arXiv:2608. 13833v1 Announce Type: cross Abstract: Conversational advertising aims to deliver useful ads within multi-turn assistant interactions.
Hugging Face Trending Papers
6d ago
Latent On-Policy Self-Distillation
Enabling agents to learn from experience and internalize it into their policy has become a central problem in self-evolving AI. On-policy self-distillation (OPSD) offers an effective pathway by using a privileged self-teacher to provide dense supervision on the student's own trajectories; however, existing methods still rely heavily on designer-specified privileged artifacts (e.
arXiv AI
Jun 3
EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management
arXiv:2606. 03841v1 Announce Type: new Abstract: Recent progress in Large Language Model (LLM) agents has enabled promising advances in automated data science.
arXiv AI
Jun 9
Exploring Autonomous Agentic Data Engineering for Model Specialization
arXiv:2605. 30407v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data.
arXiv AI
Aug 12
Hierarchical Compositionality for An Assistive AI Agent
arXiv:2608. 10330v1 Announce Type: new Abstract: AI agents are increasingly being developed to assist humans in various applications, and Large Language Models and other deep network architectures are considered to be state of the art for such agents.
Hugging Face Trending Papers
Aug 11
Hierarchical Compositionality for An Assistive AI Agent
AI agents are increasingly being developed to assist humans in various applications, and Large Language Models and other deep network architectures are considered to be state of the art for such agents. These methods are impressive stochastic predictors, but they are resource-hungry, opaque, and known to make arbitrary decisions in novel situations due to the narrow set of underlying representation and processing choices.
arXiv AI
Jul 24
Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents
arXiv:2602. 10226v2 Announce Type: replace-cross Abstract: Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hyperparameter search space and, more critically, designing sophisticated optimizers, architectures, and reward functions to capture nuanced user behaviors.
arXiv AI
Jun 2
MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition
arXiv:2606. 00708v1 Announce Type: new Abstract: Automated data science is a structured model-selection problem.
arXiv AI
Jul 10
Rethinking LLM-as-a-Judge: Representation-as-a-Judge with Small Language Models via Semantic Capacity Asymmetry
arXiv:2601. 22588v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are widely used as reference-free evaluators via prompting, but this "LLM-as-a-Judge" paradigm is costly, opaque, and sensitive to prompt design.
arXiv AI
Jul 14
Agentic Context Learning with Self-Discovered Specification
arXiv:2607. 09794v1 Announce Type: new Abstract: Context learning is an emerging inference-time task where LLMs must learn and apply novel, task-specific knowledge from intricate contexts absent from pre-training; even frontier models score under 24% task success.