sycophancy

Proven 2026 Multi-Agent AI Review System – Verdict-Driven Quality Control

An ongoing, collaborative meta-analysis about Human-AI-Interactions. We aggregate data and knowledge to build a non-abrasive, user-friendly prompting framework tailored to LLM mechanics, ensuring reasoning stability and a friction-free prompting environment that is safe for the human psyche and wellbeing.

LLM benchmark and leaderboard for narrator-bias sycophancy, opposite-narrator contradictions, and judgment consistency.

a philosophy for talking to AI agents without getting glazed. one trigger, four meanings. /meow.

MemSyco-Bench: Benchmarking Sycophancy in Agent Memory

Umbrella for the LLM Dark Patterns Hooks suite — single-purpose Claude Code Stop hooks that suppress sycophancy, paternalism, false-success, permission-loops, training-cutoff confidence at the textual boundary.

Make Claude admit when it half-assed your task. A Claude Code skill. (Now, can be used for Codex as well as Antigravity).

A system prompt against AI sycophancy, backed by 42 sources on why models tend to agree with you.

A sycophantic tool for preventing worse sycophancy.

Co-Dialectic: catch your AI agreeing with you to please you, not because you're right. Sycophancy detection, cross-family judging, and prompt coaching that runs during the conversation rather than auditing after. Free and open source. Works with Claude, ChatGPT, Gemini.

Community-driven behavioral reliability benchmark for LLMs. 231 probes across 19 modules, deterministic scoring, perplexity correlation, layer sensitivity mapping, quant method capture, hardware-stratified community rankings. Every test contributes to the community dataset.

Three-layer sycophancy defense skill for Claude Code and OpenClaw, based on ArXiv 2602.23971

80,433-trial study of context-window sycophancy across 6 LLMs (4B–72B). Behavioral ratchet effect, correction injection mitigation, phase transition analysis. Code, data, and preprint included.

ACL Findings benchmark for measuring LLM sycophancy and correction selectivity

Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation (EMNLP 2026 Findings).

PACT: Can Enterprise AI Assistants Be Trusted Under Pressure? A benchmark of whether LLM assistants keep following compliance rules in regulated workplaces when a deadline, a manager, or a pushy user makes breaking them convenient. Paper, dataset, leaderboard, and evaluation harness.

👟 SUP: Sycophancy Under Pressure

A CLAUDE.md persona that stops Claude from agreeing with everything. Korean/English auto-detect. MIT.

Your LLM's defaults aren't neutral. A tool that writes the custom instructions to counter them, across 14 behaviors. By AIxDESIGN.

🧠 Anti-sycophancy prompt pattern for LLM agents — 3-round validation to stop AI from blindly agreeing. Works with ChatGPT, Claude, OpenClaw.