4 citations · 6 across the 6 of their papers we have counts for
6 papers
When Can One Neuron Fix Repetition Loops in LLMs?
Aristotelis Lazaridis, Aman Sharma, Dylan Bates +3
The Gemma 4 instruction-tuned models share a reproducible failure: on long factual enumeration prompts, such as TV episodes, the 88 IAU constellations, or the 151 original Pokemon,…
EDGE-OPD: Internalizing Privileged Context with Evidence Guided On-Policy Distillation
Aristotelis Lazaridis, Dylan Bates, Aman Sharma +3
On-Policy Distillation (OPD) has gained wide attraction as an LLM post-training paradigm due to its effectiveness in improving capabilities without introducing model distribution d…
Measuring and Eliminating Refusals in Military Large Language Models
Jack FitzGerald, Dylan Bates, Aristotelis Lazaridis +17
Military Large Language Models (LLMs) must provide accurate information to the warfighter in time-critical and dangerous situations. However, today's LLMs are imbued with safety be…
EdgeRunner 20B: Military Task Parity with GPT-5 while Running on the Edge
Jack FitzGerald, Aristotelis Lazaridis, Dylan Bates +17
We present EdgeRunner 20B, a fine-tuned version of gpt-oss-20b optimized for military tasks. EdgeRunner 20B was trained on 1.6M high-quality records curated from military documenta…
Lucy-SKG: Learning to Play Rocket League Efficiently Using Deep Reinforcement Learning
Vasileios Moschopoulos, Pantelis Kyriakidis, Aristotelis Lazaridis +1
A successful tactic that is followed by the scientific community for advancing AI is to treat games as problems, which has been proven to lead to various breakthroughs. We adapt th…
REIN-2: Giving Birth to Prepared Reinforcement Learning Agents Using Reinforcement Learning Agents
Aristotelis Lazaridis, Ioannis Vlahavas
Deep Reinforcement Learning (Deep RL) has been in the spotlight for the past few years, due to its remarkable abilities to solve problems which were considered to be practically un…