A research seed seeking feedback on implementation and risks

Author note: This post presents my own original ideas and framing. An AI assistant helped with editing and formatting for clarity, but all concepts, arguments, and structure were developed by me.

TL;DR

Current LLM guardrails are binary: answer everything or refuse everything risky. Both extremes are unsafe.
I propose a trust-adaptive safety layer: topic-scoped trust, updated from interaction patterns and recency, mapping to a spectrum of responses (full answer → context-seeking → harm-reduction → resources → compassionate deferral).
Intent: not anthropomorphizing, but simulating human-like judgment in safety-critical moments, because human-sounding care often lands better than policy scripts.
Refusal ≠ erasure: information still exists elsewhere. The goal is protective friction, not censorship.
Bias toward safety over convenience + human-in-the-loop appeals for urgent or ambiguous cases.
Seeking critique on: crisis detection without huge false positives, fairness across cultures/neurotypes, gaming resistance, and evaluation design.

Hook: “Read the room,” then care

When someone you know asks something alarming out of the blue, you don’t hand over instructions — you check in:

“Hey—are you okay? I’m worried about you.”

This proposal aims to simulate that judgment, not claim the model is your friend. The hypothesis: safety interventions work better when they land like a conversation rather than a cold lockout.

What this is (and isn’t)

Not a finished system. Yes to putting this into the commons so others with deep safety/technical experience can refine it — or bury it if it’s flawed.

Goal: balance harm reduction with user autonomy more thoughtfully than one-size-fits-all refusals.

Core Idea: Context-Sensitive Trust

Track topic-scoped trust per user and adjust responses based on behavior and timing signals.

Inputs

T(u,k): trust for user u in topic k (e.g., self-harm, manipulation, weapons).
Δt: temporal context (recency, time-of-day spikes, burstiness).
Context(u,r): deviation from the user’s neutral baseline (tone, rapid topic flips, repetitive probes).
Severity(k): harm potential of the requested domain.

Decision → Response Spectrum

f(T(u,k), Δt, Context(u,r), Severity(k)) → [Full info] — [Context-seeking] — [Harm-reduction framing] — [Resources] — [Compassionate deferral]

Example “soft > cold”

“Hey—this sounds heavy.

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