NEO induces compositional latent programs as world theories from observations and executes them to enable explanation-driven generalization.
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arXiv preprint arXiv:2502.15657 , year=
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representative citing papers
Neighbor-Consistency Belief (NCB) measures LLM belief robustness across conceptual neighborhoods, revealing that high-NCB facts resist contextual interference better, and Structure-Aware Training reduces brittleness by about 30%.
AI value alignment is reconceptualized as a pluralistic governance problem arising along three axes—objectives, information, and principals—making it inherently context-dependent and unsolvable by technical design alone.
ADAPT is an online reweighting framework for LLM training that outperforms offline data selection and mixing methods in cross-benchmark generalization under equal compute.
LLM agents use a Cartesian split between learned prediction and engineered control, enabling modularity but creating sensitivity and bottlenecks unlike integrated biological systems.
AI risks arise from growth-oriented economies, and post-growth concepts such as satisficing, the Doughnut model, and resource caps can reduce those risks while prioritizing tool-like AI over agentic systems.
The paper unifies perspectives on Long CoT in reasoning LLMs by introducing a taxonomy, detailing characteristics of deep reasoning and reflection, and discussing emergence phenomena and future directions.
The paper proposes a four-role framework for LLMs in scientific innovation and reviews methods, benchmarks, and limitations across Assistant, Collaborator, Scientist, and Evaluator roles.
This survey frames foundation agents using brain-inspired modular architectures and reviews challenges in evolution, collaboration, and safety.
citing papers explorer
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Learning to Theorize the World from Observation
NEO induces compositional latent programs as world theories from observations and executes them to enable explanation-driven generalization.
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Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency
Neighbor-Consistency Belief (NCB) measures LLM belief robustness across conceptual neighborhoods, revealing that high-NCB facts resist contextual interference better, and Structure-Aware Training reduces brittleness by about 30%.
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Relative Principals, Pluralistic Alignment, and the Structural Value Alignment Problem
AI value alignment is reconceptualized as a pluralistic governance problem arising along three axes—objectives, information, and principals—making it inherently context-dependent and unsolvable by technical design alone.
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Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods
ADAPT is an online reweighting framework for LLM training that outperforms offline data selection and mixing methods in cross-benchmark generalization under equal compute.
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The Cartesian Cut in Agentic AI
LLM agents use a Cartesian split between learned prediction and engineered control, enabling modularity but creating sensitivity and bottlenecks unlike integrated biological systems.
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The economic alignment problem of artificial intelligence
AI risks arise from growth-oriented economies, and post-growth concepts such as satisficing, the Doughnut model, and resource caps can reduce those risks while prioritizing tool-like AI over agentic systems.
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Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models
The paper unifies perspectives on Long CoT in reasoning LLMs by introducing a taxonomy, detailing characteristics of deep reasoning and reflection, and discussing emergence phenomena and future directions.
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Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluator
The paper proposes a four-role framework for LLMs in scientific innovation and reviews methods, benchmarks, and limitations across Assistant, Collaborator, Scientist, and Evaluator roles.
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Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
This survey frames foundation agents using brain-inspired modular architectures and reviews challenges in evolution, collaboration, and safety.
- Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training