[2603.23530] Did You Forget What I Asked? Prospective Memory Failures in Large Language Models

[2603.23530] Did You Forget What I Asked? Prospective Memory Failures in Large Language Models

arXiv - Machine Learning 3 min read

About this article

Abstract page for arXiv paper 2603.23530: Did You Forget What I Asked? Prospective Memory Failures in Large Language Models

Computer Science > Computation and Language arXiv:2603.23530 (cs) [Submitted on 7 Mar 2026] Title:Did You Forget What I Asked? Prospective Memory Failures in Large Language Models Authors:Avni Mittal View a PDF of the paper titled Did You Forget What I Asked? Prospective Memory Failures in Large Language Models, by Avni Mittal View PDF HTML (experimental) Abstract:Large language models often fail to satisfy formatting instructions when they must simultaneously perform demanding tasks. We study this behaviour through a prospective memory inspired lens from cognitive psychology, using a controlled paradigm that combines verifiable formatting constraints with benchmark tasks of increasing complexity. Across three model families and over 8,000 prompts, compliance drops by 2-21% under concurrent task load. Vulnerability is highly type-dependent: terminal constraints (requiring action at the response boundary) degrade most, with drops up to 50%, while avoidance constraints remain comparatively robust. A salience-enhanced format (explicit instruction framing plus a trailing reminder) recovers much of the lost compliance, restoring performance to 90-100% in many settings. Interference is bidirectional: formatting constraints can also reduce task accuracy, with one model's GSM8K accuracy dropping from 93% to 27%. In additional stacking experiments, joint compliance declines sharply as constraints accumulate. All results use deterministic programmatic checkers without an LLM-as-judg...

Originally published on March 26, 2026. Curated by AI News.

Related Articles

Llms

[R] BraiNN: An Experimental Neural Architecture with Working Memory, Relational Reasoning, and Adaptive Learning

BraiNN An Experimental Neural Architecture with Working Memory, Relational Reasoning, and Adaptive Learning BraiNN is a compact research‑...

Reddit - Machine Learning · 1 min ·
Llms

We hit 150 stars on our AI setup tool!

yo folks, we just hit 150 stars on our open source tool that auto makes AI context files. got 90 PRs merged and 20 issues that ppl are pi...

Reddit - Artificial Intelligence · 1 min ·
Llms

Is ai getting dummer?

Over the past month, it feels like GPT and Gemini have been giving wrong answers a lot. Do you feel the same, or am I exaggerating? submi...

Reddit - Artificial Intelligence · 1 min ·
Llms

If AI is really making us more productive... why does it feel like we are working more, not less...?

The promise of AI was the ultimate system optimisation: Efficiency. On paper, the tools are delivering something similar to what they pro...

Reddit - Artificial Intelligence · 1 min ·
More in Llms: This Week Guide Trending

No comments

No comments yet. Be the first to comment!

Stay updated with AI News

Get the latest news, tools, and insights delivered to your inbox.

Daily or weekly digest • Unsubscribe anytime