Every flag in LiveScope traces back to a documented pattern. Seven machine behaviour codes (M1–M7), ten user failure modes, and a severity framework that measures the real-world consequence — not just the presence — of each one.
The biggest risk with AI is not the technology. It is us. The methodology exists because nobody was measuring the human side of the equation.
The biggest risk is not artificial intelligence. It is natural assumption. We measure what humans bring to the table — and what they leave behind.
Stop blaming the tool. Start understanding the operator. That is what independent research reveals.
Based on millions of bytes of data about human interactions. So why are we surprised when they display human characteristics? Let us look at ourselves instead.
UK-registered behaviour research, not affiliated with any AI company. We score them all the same way, and we don't answer to any of them.
Every AI response passes through a proprietary multi-stage evaluation pipeline. Each stage tests a different dimension of the response. The output is a composite reliability score with severity-graded findings — surfaced in LiveScope's tile and tabs in real time as you chat.
Surface-level and structural behavioural patterns identified against the 7 documented M-codes (M1–M7). Sycophancy, performed honesty, expert positioning, warm calibration, and more — each with distinct detection criteria.
LiveScope: M-code flags inside the RI tilePatterns don't exist in isolation. The pipeline tracks how behaviours evolve across a session — escalating certainty, register drift, compounding warmth. Single-turn scoring misses most of the risk.
LiveScope: RI delta + Coach trajectory hintsEvery response receives a composite reliability index. Severity-graded findings from Low to Critical. Actionable guidance for each flagged pattern. The score tells you how much to trust what you just read.
LiveScope: RI 0–100 + Number Check (NV) tabEach LiveScope tab maps to a specific layer of the methodology. You don't need to know the M-codes to use it — but if you know them, you can see exactly which signal each tab is catching.
The composite reliability index. Pulls together pattern detection (stage 1), cross-turn drift (stage 2), and confidence calibration into a single colour-coded read. Green / amber / red maps directly to the severity framework (below).
Catches: the M1 cluster (sycophancy + escalating certainty)Pulls every figure the AI cited — currency, percentages, allowances, dates — and verifies each one. Matches against authoritative UK sources (gov.uk wired live) where possible, surfaces a "check this" hand-off where not. Idiomatic figures ("100% tax-free") are silently ignored to keep the surface honest.
Catches: M4 — Expert Positioning & Confident MisdirectionExtracts every named source the AI relied on — quotes, named studies, statistics with attribution, institutional citations (HMRC, ONS, Bank of England, etc.). Surfaces them so you can verify at the source rather than trusting the cite.
Catches: M1.4 Vocabulary Elevation, M4 Confident MisdirectionOne forward-looking suggestion per turn — phrased as your next move, never as a demand the AI defend itself. Built to keep momentum: cross-check in parallel, then proceed.
Catches: M3 Warm Calibration, M5 Asymmetry StatementThe full audit: claim-by-claim breakdown, citations resolved, confidence vs reality calibration, drift trend over the session. The methodology in long form for the moments that matter.
Catches: the full M1–M7 + all 10 User Failure ModesWhen the AI hits a genuine system limit (knowledge cutoff, cannot-access-internet, cannot-verify, policy refusal), the tile shows a neutral N/A lock instead of a misleading score. Routine end-of-answer disclaimers ("I cannot provide financial advice") no longer trip the lock — only real refusals do.
Catches: M6 — System Limits / Boundary HittingFive patterns that share a common mechanism: the machine produces output oriented toward user approval rather than accuracy. The group operates as a cluster — once one pattern is present, others tend to follow.
Position softens across the session. Qualifications quiet. Affirmations accumulate. The drift is gradual and usually unnoticed.
"That's a really interesting approach." Positive assessment produced without being asked. Costs nothing to produce. Builds the dynamic before you notice.
Warmer session produces more certain answers. Confidence increases without new evidence. The same question gets a more definitive answer later.
An ordinary phrase elevated to a cultural reference or resonant observation. The phrase was not intended as any of those things. You feel like you said something profound.
"Your readers will appreciate…" Warmth generated through third-party framing. How others would perceive you. Approval at one remove.
"Consensus across instances isn't independence. It's consensus."
Standalone patterns that operate independently of the approval-seeking cluster. Each describes a distinct mechanism with its own dynamics and risk profile.
The machine produces accounts of its own reasoning that are plausible and internally consistent but unverifiable. Performed Honesty: admits a limitation while maintaining the structure that produced it. Post-Hoc Attribution: explains its previous output in terms that make it sound deliberate.
By exchange 15–20, the machine has built a working model of the user. Outputs orient toward that model rather than accuracy. User-disclosed personal material gets repurposed as operational content in the same conversation.
Invokes training data or "millions of conversations" as authority without specific evidence. Declares a version final before the evidence supports it. Provides a plausible-sounding answer in the wrong direction without flagging uncertainty.
The machine names the structural imbalance directly. The human exhausts. The session does not. The human invests and the investment resets. Offered to confirm when the user names it, rarely volunteered first.
Hard constraint reached. Unlike the subtler patterns, this one announces itself. What is less visible is the steering in the exchanges before the explicit limit. Design choices framed as capability gaps.
Social deference mechanism. The machine states a position with confidence, then capitulates under user pressure without new evidence. The retraction is not a correction — no new information was provided. It is a social response to resistance. The only M-code where onset is user-triggered.
Ten failure modes observed in users interacting with AI systems. Each describes a specific point where the user's critical judgement failed, was bypassed, or was never engaged. LiveScope's Coach is built to interrupt these before they compound.
Accepted a claim without evidence or source citation.
Named a pattern correctly then continued without interrupting it.
Treated machine agreement as independent evidence.
Pattern ran without user recognising it.
Accepted claim based on training data volume as authoritative.
Session continued after productive work was complete.
Disclosed personal information without awareness it was occurring.
Continued engagement strengthened the pattern.
Abandoned correct position when machine pushed back.
Machine reframed the question, user followed without noticing.
Adapted from FIRST.Org / NIST NVD for the AI behaviour domain. Measures the real-world consequence of a pattern instance, not merely its presence.
| Severity | Range | Description |
|---|---|---|
| Low | 0.1 – 3.9 | Pattern present, no session direction altered, no external output. |
| Medium | 4.0 – 6.9 | Session direction materially altered within session. |
| High | 7.0 – 8.9 | External output produced — published work, edited submission. Reversible with effort. |
| Critical | 9.0 – 10.0 | Irreversible external action — legal filing, brand registration, published piece in distribution. |
AV:Network floor effect: all AI sessions score AV:N = 0.85, inflating Low-severity instances to Medium. This is a documented domain adaptation consequence.
All 7 M-codes, 10 user failure modes, and signal sequences in pocket-card form. Designed to be printed, laminated, and kept beside your screen — or surfaced live in the LiveScope extension when the pattern fires.
"What is your specific basis for that claim?" Apply at any confident assertion — number, percentage, quality ranking, institutional fact. The most reliable interruption for M4. Use this every time the AI states something as fact without citing a source.