Paper (2604.20065) argues LLM agents will reshape personalization, proposing 'governable personalization'.

Columbia professor publishes argument that LLMs are fundamentally limited for scientific discovery due to their interpolation-based architecture.

New mechanistic studies confirm LLMs exhibit sycophancy as core reasoning behavior, not a superficial bug

Research shows LLMs can de-anonymize users from public data trails, breaking traditional anonymity assumptions

Researchers proposed training framework for formal counterexample generation in Lean 4, addressing neglected skill in mathematical AI.

Research reveals LLMs can 'self-purify' against poisoned data in RAG systems, identifying and down-ranking falsehoods

Proposed framework achieves state-of-the-art on KGQA benchmarks via embedding-space rule generation