Proactive Interference (PI) — Cog4LLM
Minimal read, optional deep‑dive. Interference‑limited retrieval in LLMs.
COLM 2026 Main AAAI 2026 LaMAS Workshop — Oral ICML 2025 LCFM Workshop Ongoing
The work was accepted to the ICML 2025 Long Context Foundation Models Workshop, selected for an oral presentation at the AAAI 2026 LaMAS Workshop, and accepted to COLM 2026 Main.
What does our test reveal?
Classic retrieval tests (Needle‑in‑a‑Haystack; MRCR) add many similar needles. We show the “haystack” isn’t necessary: by isolating and controlling the number of similar needles, we directly measure how interference limits retrieval. Retrieval declines log‑linearly with interference across major transformers.
In short: a core working‑memory bottleneck from interference, not just context length; a clean way to compare models’ PI susceptibility.
Selected venues
- COLM 2026 Main
- ICML 2025 Long Context Foundation Models Workshop
- AAAI 2026 LaMAS Workshop — Oral
- AAAI 2026 TrustAgent Workshop — Poster
- AAAI 2026 Workshop on New Frontiers in Information Retrieval
Abstract
Information retrieval in LLMs intertwines with generation. We adapt proactive interference (PI) from cognitive science: sequentially stream semantically related key‑value updates and query the last value. Accuracy declines log‑linearly toward zero as interference accumulates; prompt‑based mitigation is limited. Findings suggest a working‑memory bottleneck beyond context access.
Cognitive science foundation
PI is a foundational paradigm for human working memory. Repeating cues updated with new values create outdated associations that must be suppressed to report only the latest value. Humans are resilient; LLMs show interference‑driven limits. Porting this paradigm to LLMs makes interference measurable and comparable.
Why cognitive angle
After earning a physics degree, I spent two years self‑studying philosophy of science (admitted but not enrolled at University of Bristol) and took a two‑year gap at UChicago Divinity School (Eastern philosophy of religion). These experiences motivated the PI‑for‑LLMs project.