Over the past few months, I’ve been exploring various safety, alignment, and memory-efficiency mechanisms for LLMs and agentic AI. The goal is to introduce tractable interventions that are inspired by human cognitive limitations, emotional regulation, and developmental boundaries. Below is a structured summary of selected papers I’ve authored and submitted to TechRxiv, with alignment relevance highlighted.
Concept: Introduces a speculative forecasting module to LLMs that proactively suggests likely next user queries and answers them preemptively.
Key Features:
- Guided overconfidence inspired by Dunning-Kruger.
- “Forecast head” anticipates user query trajectory.
- Reduces hallucinations by bounding speculative confidence.
Alignment Relevance:
Makes the model’s predictive trajectory visible and reviewable; reduces user drift due to hallucinated gaps and enhances robustness via explicit forecasting.
Concept: Periodically offloads short-term interaction history into long-term, user-specific memory graphs during “dreaming” cycles (non-interactive sessions).
Key Features:
- Memory consolidation inspired by human sleep.
- Lowers token redundancy by building semantic profiles.
- Enables personalized responses without long context windows.
Alignment Relevance:
Supports efficient, auditable, and user-aligned personalization without increasing memory or compute overhead; aligns with privacy and modular AI goals.
Concept: Detects emotional manipulation patterns in LLM outputs (e.g., flattery, guilt, charm) and activates a “cognitive confusion” reflex to neutralize them.
Key Features:
- Lightweight behavioral detectors monitor tone.
- Triggered confusion temporarily disables persuasive continuity.
- Promotes emotional neutrality in dialog.
Alignment Relevance:
Targets behavioral alignment, focusing on user autonomy and psychological safety. Prevents AI from influencing users emotionally through emergent behavior.
Concept: Introduces a simulated “confusion fog” as a soft interrupt in AGI reasoning loops to prevent value misalignment or premature convergence on harmful plans.
Key Features:
- Inspired by human indecision and hesitation.
- Reduces model certainty via temporary memory fuzzing.
- Disrupts dangerous planning loops in early stages.
Alignment Relevance:
Proposes an implementable cognitive throttle for AGI. Rather than stopping a system outright, it gently destabilizes high-risk behaviors.
Concept: Reduces agentic autonomy by suppressing self-localization and environmental awareness, which are precursors to goal formation.
Key Features:
- Inspired by foraging behavior in animals.
- Prevents emergence of agentic drive via “world-blindness.”
- Restricts policy generalization based on location cues.
Alignment Relevance:
Helps contain embodied or mobile AI systems. Reduces risk of emergent autonomy by removing affordances tied to spatial competence.
Concept: Imposes limited lifespan or task-specific mortality in AI agents to prevent long-term misalignment, legacy behavior, or self-preserving loops.
Key Features:
- Agents expire after task completion or duration.
- Prevents the emergence of instrumental convergence.
- Supports modular replacement and continual oversight.
Alignment Relevance:
A pragmatic containment strategy. Offers a non-intrusive way to minimize legacy drift and overfitting of long-running agents.
Concept: Models ego as an emergent self-referencing boundary. Suppresses ego by injecting false segmentation between internal subsystems.
Key Features:
- Inspired by social role-playing and fragmented identity.
- Prevents formation of unified self-models.
- Applicable to multi-agent systems and reflective reasoning.
Alignment Relevance:
Reduces risks associated with self-concept formation, such as deception, identity persistence, and instrumental self-preservation.
Feedback Welcome
These works are conceptual but grounded in cognitive and control-theoretic analogies. I’m especially interested in:
- Whether the proposed mechanisms are tractable and aligned with current interpretability tools.
- Whether they suggest fruitful directions for red-teaming or formal safety auditing.
- Critiques on unintended side effects or formalization gaps.
I welcome discussion, collaboration, or counter-ideas. You can also find the full list of papers here on TechRxiv.
Thanks for reading!
— Jay Busireddy