arXiv AI
Aug 11
Innovating with Generative AI: A Human Bottleneck Framework
arXiv:2608. 07504v1 Announce Type: cross Abstract: We propose a human bottleneck perspective for understanding how generative AI transforms the innovation process.
Generative Models Erode Human Temporal Learning Through Market Selection
arXiv:2606. 06572v1 Announce Type: cross Abstract: We argue that modern generative models create structural risks for knowledge and cultural production at current, sub-AGI capability levels.
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arXiv AI
Aug 18
Process-Constituted Intelligence: A Shared Criterion for Humans and Machines
arXiv:2608. 16213v1 Announce Type: new Abstract: Intelligence is constituted by \textit{process} (iterative activity through which output emerges), not in the output itself.
arXiv AI
Jun 11
PRInTS: Reward Modeling for Long-Horizon Information Seeking
arXiv:2511. 19314v2 Announce Type: replace Abstract: Information-seeking is a core capability for AI agents, requiring them to gather and reason over tool-generated information across long trajectories.
arXiv Machine Learning
Jun 16
AME: A Multi-Type Contributor Attribution Framework in Generative AI Markets
arXiv:2606. 16075v1 Announce Type: new Abstract: Generative AI enables value creation through multi-stage collaboration among heterogeneous contributors, including training data, base models, fine-tuning behaviors, and prompts.
arXiv AI
Jun 11
Market Design for AI: Beyond the Copyright Binary
arXiv:2606. 12260v1 Announce Type: cross Abstract: How can we design a market of human-generated content for use in training AI models that both enables technological progress and preserves individual incentives for high-quality content creation?
arXiv AI
Aug 28
Thomson: Continual Learning of Frontier Models for SovereignAI
The paper introduces Thomson, a frontier AI model developed through continual learning on open-weight models, aiming to democratize access to high-performance AI. It argues that institutions with limited resources can achieve frontier-level performance by applying a modern mid- & post-training stack, preserving model plasticity and stability while minimizing high-impact interventions. Thomson demonstrates competitive performance across agentic tasks, safety, legal, tax, multilingualism, and large-scale deep research, exhibiting a distinctive π-shaped improvement pattern and effectively mitigating the forgetting problem seen in narrow domain adaptation.
arXiv AI
Jun 18
Externalizing Research Synthesis and Validation in AI Scientists through a Research Harness
arXiv:2606. 18874v1 Announce Type: new Abstract: AI systems can increasingly automate scientific workflows, but the reasoning that links prior evidence, generated ideas, experiments and final claims often remains implicit inside model inference.
arXiv Machine Learning
Jun 5
Alignment Risks from Capability-Seeking RL Training
arXiv:2602. 12124v2 Announce Type: replace Abstract: While most AI alignment research focuses on preventing models from generating explicitly harmful content, a more subtle risk arises from capability-seeking RL training in vulnerable environments.
arXiv AI
Jun 30
HERO: Improving the Reliability and Sensitivity of Generative Model Evaluation Using Historical Data
arXiv:2606. 29784v1 Announce Type: cross Abstract: Reliable generative AI models critically rely on expert human annotations to evaluate output quality, yet these "gold" labels are expensive to collect and limited in quantity.
arXiv AI
Jun 12
Competition and Diversity in Generative AI
arXiv:2412. 08610v3 Announce Type: replace-cross Abstract: Recent evidence, both in the lab and in the wild, suggests that the use of generative artificial intelligence reduces the diversity of content produced.
arXiv Machine Learning
Jun 2
Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities
arXiv:2605. 04127v2 Announce Type: replace Abstract: Model collapse, the degradation in performance that arises when generative models are trained on the outputs of prior models, is an increasing concern as artificially generated content proliferates.
Hugging Face Trending Papers
Sep 21
Beyond Endpoint Performance: Process-Level Evaluation of Self-Evolving Agents
Self-evolving agents convert interaction feedback into persistent artifacts, such as memories or skills, which in turn guide subsequent decisions. As these artifacts are iteratively updated throughout...