Journal article
In-context learning (ICL) — the ability of a transformer language model to acquire a new input-output mapping from a handful of demonstrations in its prompt, with no weight updates — remains the most striking and least understood capability of large language models. Mechanistic interpretability…
Journal article
Multilingual hallucination in large language models has begun to receive systematic attention. Recent work has measured aggregate hallucination rates across 19 to 30 languages (FACTSCORE-multilingual, MFAVA, Poly-FEVER), introduced multilingual benchmarks (HalluVerseM3, BHRAM-IL for some…
Journal article
Causal representation learning (CRL) — the recovery of latent causal variables and their causal graph from high-dimensional observations — has produced a substantial body of theoretical results in the past five years. CITRIS, CauCA, CRID, mechanismsparsity methods, and the recent grouping-based…
Journal article
Reinforcement learning from human feedback (RLHF) is widely understood to incur an alignment tax: aligning a language model with human preferences can degrade capabilities the base model possessed. This phenomenon is well documented in single-round comparisons. What is not well documented, despite…
Journal article
State-space models did not merely "open a door" beyond attention; they walked through it. Mamba, Mamba-2, Samba, Jamba, Granite 4, Hymba, and a growing family of hybrid attention-SSM architectures are now in production language models, demonstrating that linear-time alternatives to attention can…
Journal article
Spiking neural networks (SNNs) on neuromorphic hardware are routinely proposed as the path to energy-efficient on-device inference for language models. The framing usually treats this as a single technical question. It is not. There are three distinct technical layers — SNN algorithms for language…
Journal article
There are over 300 sign languages in active use worldwide, serving an estimated 70 million Deaf signers. Despite this, the overwhelming majority of AI research on sign languages targets American Sign Language (ASL), with smaller but growing efforts on German, British, Chinese, and Indian Sign…
Journal article
When multiple large-language-model agents negotiate, communicate, or compete, do they spontaneously develop covert signalling — channels of communication that human observers cannot decode? Recent work has established that such behaviour is possible: LLMs can be trained or pressured into…
Journal article
Abstract This paper introduces a novel theoretical framework for understanding semantic processing in Large Language Models through the lens of sheaf theory and quantum contextuality. We propose that the semantic structure of LLM-generated text can be rigorously modeled as a sheaf over the…
Journal article
In the nascent field of quantum consciousness computing, we present groundbreaking research that fundamentally transforms our understanding of both human consciousness and artificial intelligence. This paper introduces the Quantum Mirror Framework (QMF), a revolutionary approach that creates a…
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