Talk
The recent introduction of Recursive Language Models (RLMs) represents a paradigm shift from linear
token processing to inference-time computing. By offloading context to a REPL environment and
enabling symbolic recursion, RLMs have demonstrated the ability to handle effectively infinite
context windows.
However, the original RLM paper focuses almost exclusively on capability. It does not address the
reliability of the recursive mechanism itself.
Current literature on recursive scalable oversight suggests high failure rates in hierarchical
supervision, and spiral of hallucination effects in long-horizon agents. We propose to rigorously
test whether RLMs are susceptible to these failure modes when subjected to adversarial or
high-complexity inputs.
The questions will be specifically on:
1. The Decay Hypothesis.
2. The Halting Problem.
3. State Persistence Vulnerability
About the Speaker
AI researcher and Staff Machine Learning Engineer building end-to-end AI systems across healthcare,
finance, safety, and education. Led development of large-scale LLM pipelines, evaluation frameworks,
and time-series forecasting tools, driving client adoption and helping scale SuperAnnotate from
pre-seed through Series B. Co-authored an AAAI-accepted paper on LLM safety and contributed
national-level economic analytics for Armenia’s Ministry of Economy. Combines deep technical
expertise with cross-functional leadership, partnering with product, engineering, and research teams
to translate advanced research into scalable, production-ready AI solutions that deliver measurable
business impact, improve decision-making, and create durable long-term value for organizations and
users worldwide.
Big fan of pubs