Open access / permanent DOIs / CC BY 4.0
I build production AI systems, and I write about the parts that break. Every paper here is deposited on Zenodo under a permanent DOI, free to read, free to reuse with attribution. No paywall, no publisher, no waiting.
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…
Formal verification of neural networks has matured into a real research area. Tools such as alpha-beta-CROWN, ERAN, and Marabou now reliably verify L-infinity robustness properties of ReLU networks at the ResNet scale, with the annual…
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…
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…
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…
The detection of AI-generated text under adversarial transformation — particularly paraphrasing — is widely characterised as an unsolved problem. The reality is more structured. Two technically distinct approaches, post-hoc detection and…
Modern reasoning systems are dominated by autoregressive models trained with reinforcement learning on reasoning trajectories — the o1, R1, and Claude 3.7 family of "thinking" models. Energy-based models (EBMs) offer a theoretically…
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…
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…
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…
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…
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…
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…
As security concerns continue to escalate globally, there is an increasing demand for highly responsive, intelligent surveillance systems capable of proactively managing and mitigating threats. This paper explores an innovative approach to…
The emergence of Large Language Models (LLMs) has revolutionized our understanding of artificial intelligence's capabilities in emotional processing. These models demonstrate remarkable proficiency in generating emotionally appropriate…
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Every paper carries its own DOI and a ready-made BibTeX entry on its page. The full record set lives on Zenodo and on ORCID.
