arXiv Machine Learning
Aug 4
Semantic Alignment of AI Models: Concept Collapse, Checkpoint Dynamics, and Cross-Lingual Transfer
arXiv:2608. 01585v1 Announce Type: cross Abstract: Language model benchmarking is a difficult task.
Persistent Convolution: A Topological Framework for AI Alignment Testing and Semantic Space Characterization
arXiv:2607. 29008v1 Announce Type: cross Abstract: Modern opaque AI models prize performance over interpretability, which makes testing difficult.
Related stories
arXiv AI
Jun 4
A Unified Geometric Space for Topological Alignment Between Transformer-Based Models and Human Brain Networks
arXiv:2510. 24342v2 Announce Type: replace Abstract: Prior brain-AI alignment studies are typically constrained by specific inputs and tasks, limiting their ability to capture organizational properties across models with different modalities.
arXiv AI
Jun 9
The Topological Dual of a Dataset: A Logic-to-Topology Encoding for AlphaGeometry-Style Data
arXiv:2604. 18050v2 Announce Type: replace Abstract: AlphaGeometry represents a milestone in neuro-symbolic reasoning, yet its architecture faces a log-linear scaling bottleneck within its symbolic deduction engine that limits its efficiency as problem complexity increases.
arXiv AI
Jun 26
From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP
arXiv:2606. 26535v1 Announce Type: cross Abstract: Current VLM evaluations often conflate language priors with genuine spatial reasoning.
Hugging Face Trending Papers
Jun 25
From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP
Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we introduce CRISP, a novel structural-diagnostic evaluation paradigm that assesses visual spatial intelligence through consistency, the alignment between implicit perception and explicit reasoning.
arXiv AI
Aug 11
Explaining, Verifying, and Aligning Semantic Hierarchies in Vision-Language Model Embeddings
arXiv:2603. 26798v2 Announce Type: replace-cross Abstract: Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image-text embedding space, yet the semantic organization of this space is rarely inspected.
OpenAI Blog
Aug 24, 2022
Our approach to alignment research
We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems.
arXiv AI
Jun 24
Mind the Heads: Topological Representation Alignment for Multimodal LLMs
arXiv:2606. 23885v1 Announce Type: cross Abstract: Representation alignment has emerged as an effective approach to improve Multimodal Large Language Models (MLLMs) by regularizing their internal representations toward those of an external vision encoder.
Hugging Face Trending Papers
Jul 28
Rashomon Alignment
We propose Rashomon Alignment (RA), a new measure to assess functional similarity between two models. Existing functional similarity measures are distributional, quantifying differences between outputs of models applied to real-world data.
OpenAI Blog
Aug 27, 2025
Collective alignment: public input on our Model Spec
OpenAI surveyed over 1,000 people worldwide on how AI should behave and compared their views to our Model Spec. Learn how collective alignment is shaping AI defaults to better reflect diverse human values and perspectives.
arXiv AI
Jul 29
Rashomon Alignment
arXiv:2607. 25680v1 Announce Type: cross Abstract: We propose Rashomon Alignment (RA), a new measure to assess functional similarity between two models.
arXiv AI
Jun 3
Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models
arXiv:2605. 18160v2 Announce Type: replace-cross Abstract: In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information.