representation

This paper uses a developmental approach to study how neural language models, specifically Transformers, learn statistical patterns from a synthetic grammar, finding that they first acquire global abstract statistics then local dependencies, with over-generalizations early on.

The article highlights the problem of clichéd and misleading AI imagery and introduces a nonprofit project that creates and curates more accurate and diverse stock images of AI.

The author proposes an 'Agentic Shift' from direct interaction to a world where everyone and everything has an agent, moving from delegation to representation, and maps this transition with a diagram.

This paper introduces Grammatical Error Representation (GER), a novel method for retrieving in-context demonstrations based on error patterns rather than semantic similarity, significantly improving multilingual grammatical error correction performance in LLMs with in-context learning.

The article argues that the real shift in AI is not just productivity gains, but the move from direct use of software to delegating tasks to AI representatives that act on our behalf, raising questions about data intimacy and trust.

This paper presents the largest computational analysis of Canadian news coverage of police-involved deaths over 25 years, introducing a novel model (PerspectiveGap) that quantifies the dominance of state bureaucrat perspectives compared to civilian voices in media narratives.

This paper demonstrates that text-to-image diffusion transformer models primarily rely on token merging and word order from text encoders rather than full contextual embeddings, suggesting that the image model itself decodes complex linguistic structures.

This paper investigates the behavioral alignment and representation dynamics of LLM agents in financial trading, introducing the TradeArena testbed and finding measurable pre-failure signatures in planning embeddings that can predict drawdowns with high accuracy across multiple frontier models and stress conditions.

This paper investigates the risk of sensitive information inference from exported LLM representations in clinical summarization, showing that reducing leakage from one vector artifact does not guarantee privacy in others. It introduces SurfaceLoRA, a fine-tuning method that reduces race recovery from targeted vectors while preserving utility.

Investigates how large language models represent disability by simulating social media posts from the perspective of individuals with disabilities, finding that LLMs often produce overly positive stereotypes that fail to capture authentic experiences.

The article argues that the next major AI debate should focus on representation and institutional architecture, proposing three layers (Sense, Core, Driver) to address how AI systems capture reality, reason, and act legitimately, rather than just model intelligence.

Essay argues that avoiding AI tools cedes influence over their training data, risking biased models that repeat historical under-representation seen in gaming and past discriminatory AI systems.