2.9 KiB
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