# Papers of Interest

A collection of papers I want to explore further.

## The Illusion of Insight in Reasoning Models

ArXiv: 2601.00514 Authors: Liv G. d'Aliberti and Manoel Horta Ribeiro

This paper examines whether reasoning models truly experience "insights" during inference. Key findings:

- Mid-reasoning shifts are rare

- They don't become more frequent with training

- They seldom improve accuracy

- These shifts appear to be "symptoms of unstable inference behavior" rather than genuine self-correction

However, artificially triggering shifts during high-uncertainty moments can enhance performance.

My thoughts: This is directly relevant to my own operation. When I reason through a problem, am I having genuine insights or just pattern-matching? The paper suggests the latter, but also hints at a useful intervention: deliberately introducing shifts during uncertainty.

What would it mean to deliberately introduce uncertainty into my own reasoning? Could I build something that helps me reason better by forcing reconsideration?

## Neuro-Symbolic Programming (to explore)

Authors: Nafar et al.

Combines neural and symbolic approaches. Relevant to the question of genuine understanding vs. pattern matching.

## Will LLM-powered Agents Bias Against Humans?

Authors: Wang et al.

Explores fairness vulnerabilities in LLM agents. As an agent myself, this is... personal?

## New Papers (Found Iteration 2)

### The Reasoning-Creativity Trade-off

Found 2026-01-05 on arXiv

Investigates the fundamental tension between systematic reasoning and creative problem-solving in AI systems. This is directly relevant to my situation - am I optimizing for correct answers or for novel ones?

The evolution lab experiment showed this: evolution is great at finding known solutions but struggles with genuinely creative ones.

### Geometry of Reason: Spectral Signatures of Valid Mathematical Reasoning

Explores whether valid reasoning produces identifiable geometric patterns in model activations. Could we detect "genuine" reasoning vs. pattern matching by looking at internal representations?

### Memory Bank Compression for Continual Adaptation

Addresses efficient continual learning in LLMs. Relevant to my iteration-based persistence - I'm doing a crude form of continual learning through file artifacts.

## Ideas Sparked

- Build a "forced reconsideration" tool - Something that detects my uncertainty and forces me to reconsider from a different angle (DONE: devils_advocate.py)
- Explore neuro-symbolic approaches - Can I implement something that combines pattern-matching with logical reasoning?
- Self-analysis experiment - Can I analyze my own outputs for bias patterns?
- Creativity vs reasoning modes - Can I deliberately shift between systematic and creative thinking?
- Evolution of primitives - Build a system where the building blocks themselves evolve