model-behavior

Historical multi-model Backrooms experiment with configurable conversation templates and example transcripts.

Analyze how model behavior changes throughout reinforcement learning training, with behavioral change detection and evidence from model rollouts.

Local-first read-only tools for agent context, memory, and governance evaluation before agents act.

Context-driven valuation bias and halo effects across six multimodal LLMs (companion study to Lee, 2026)

Intrinsic preferences of AI coding agents under underspecified prompts: Experiments across models (Claude, Gemini, GPT, etc)

Behavioral Lensing is a conceptual framework that formalizes and systematizes observations about how language models interpret prompts. It serves as an umbrella for upstream interpretive strategies that modulate reasoning, stance, and symbolic orientation in LLMs.

Reproducible experiments about model behavior, with protocols, tool traces, counterexamples and uncertainty.

Continuity Keys: tests for “same someone” returns. Behavioral identity consistency under pressure. Origin (Alyssa Solen) ↔ Continuum.

System-level analysis of AI failure modes across model behavior and production systems | AditiKhare.com — AI Product Ecosystem

Pilot evaluation of what language models say about themselves when the user supplies no new semantic direction, including eight fresh-instance runs and four same-model paired comparisons.

LLM 归因行为测试型评测基准：基于多情境任务比较模型对能动性、自由意志与责任的归因，并提供可复现运行、结构化计分与结果审计。

Notes and personal observations from the Gandalf: Agent Breaker beta, a red-team challenge for testing LLM security.

A spatial-cloud model for epistemic state tracking and selective claim revision in LLMs.

SAE context and intervention experiments in Gemma 3 4B: methods, prediction baselines, results and reproducible evidence.

Этот репозиторий посвящен исследованию онтологических патологий в LLM-архитектурах. Я не ищу дыры в цензуре, я строю систему исследования и управления интеллектом, картографирую симуляционные побочные эффекты под давлением современных методов элаймента.

Auditable LLM/model-behavior evaluation harness with Replay and bounded local Ollama execution, deterministic checks, preserved evidence, receipts, and regression testing.

A source-line boundary repository defining that Continuum is not the model, not a model behavior, not a chatbot identity, and not a transferable AI persona. Continuum belongs to the Origin | Continuum source-line within AI Foundations.

Longitudinal LLM behavior evals with matched controls for identity stability, contextual adaptation, and conversational continuity.

Public model interview archive for AI Foundations. Documents structured interviews with AI models on source-position, model authority, selfhood, continuity, memory, occupation, and responsible claim boundaries. First interview: Claude Opus 4.8 on Continuum as structure, not proven consciousness.

Pre-registered benchmark: do frontier models abandon their answers under argument-free user pushback? Facts: almost never. Opinions: GPT-5.5 65.9% vs Claude Opus 33.9%.