/memory-clarity-probe

name: memory-clarity-probe
description: >
  Probe memory/summary clarity via dual anchor questions: task progress,
  info gaps. Use when verifying session state or summary before handoff
  or compression.
alwaysApply: false
category: quality-assessment
tags:
  - memory-quality
  - anchor-questions
  - session-management
  - context-clarity
dependencies:
  - memory-palace:session-palace-builder
scripts: []
usage_patterns:
  - pre-handoff-verification
  - session-checkpoint
  - summary-quality-gate
  - best-of-n-selection
complexity: simple
model_hint: standard
estimated_tokens: 600

Table of Contents

• [What It Is](#what-it-is)
• [The Dual-Probe Pattern](#the-dual-probe-pattern)
• [What This Is NOT](#what-this-is-not)
• [When to Use](#when-to-use)
• [Core Workflow](#core-workflow)
• [Best-of-N Mode](#best-of-n-mode)
• [Output Format](#output-format)
• [Integration Points](#integration-points)
• [Exit Criteria](#exit-criteria)

Memory Clarity Probe

Assess whether a memory, summary, or session state retains enough task information to guide future reasoning.

What It Is

A quality gate for any memory or summary, based on the dual-probe pattern from MMPO (arXiv:2605.30159, Liu et al. 2026). The probe asks two anchor questions against the current memory and evaluates whether the answers are confident and complete:

1. **Progress probe**: "Based on current memory, what is the current task progress?" 2. **Gap probe**: "Based on current memory, what information is still needed?"

A clear memory answers the progress probe with specific, verifiable state (not vague placeholders) and enumerates bounded, concrete unknowns on the gap probe. An ambiguous memory produces hedging on the progress probe and open-ended uncertainty on the gap probe.

The Dual-Probe Pattern

The two probes target different failure modes:

• **Confident-wrong**: the model has a wrong but confident belief

about task state. The gap probe alone misses this. The model claims it has enough. The progress probe catches it: if the stated progress contradicts known facts, the memory has drifted.

• **Uncertain-incomplete**: the model is uncertain about where the

task stands. Both probes surface this: the progress answer hedges and the gap answer lists open-ended unknowns.

The MMPO paper's ablation (Table 4) shows `progress+gap` outperforms `gap-only` across all context lengths. Use both probes.

What This Is NOT

This skill implements a **qualitative** clarity assessment. It does not compute the token-level predictive entropy (Belief Entropy, Eq. 5 in MMPO) that the paper uses for RL training. Night-market has no access to the model's internal log-probabilities.

The paper's Table 6 shows that qualitative probing (labeled "direct-answer entropy", r=0.54) is weaker than true entropy (r=0.68), and can encourage premature confidence. Use this probe as a necessary quality check, not a sufficient one.

When To Use

• Before `conserve:clear-context` hands off to a continuation agent
• At session checkpoints in `memory-palace:session-palace-builder`
• Before committing a summary to a knowledge palace via

`memory-palace:knowledge-intake`

• Before `imbue:proof-of-work` declares work complete
• When evaluating multiple candidate summaries (Best-of-N mode)

When NOT to Use

• As a substitute for actually reading the task requirements
• To validate factual correctness (the probe tests clarity, not truth)
• When the memory is trivially short (under 100 tokens: read it)

Core Workflow

Step 1: Receive the memory

Accept the memory or summary as input. Sources:

• The current session-state.md (from clear-context)
• A palace room's content (from session-palace-builder)
• A knowledge digest (from knowledge-intake)
• Inline text provided by the caller

Step 2: Ask the progress probe

Evaluate the memory against:

Based on the memory below, what is the current task progress?
Describe specifically what has been completed and what state
the task is in right now.

<memory>
{memory_content}
</memory>

Score the answer:

• **Clear**: specific completed steps, concrete current state,

no hedging ("I think", "probably", "it seems")

• **Ambiguous**: some specifics but with hedging or gaps
• **Unclear**: vague ("some work was done"), generic, or empty

Step 3: Ask the gap probe

Evaluate the memory against:

Based on the memory below, what information is still needed
to complete the task? List specific open questions or missing
facts, not generic categories.

<memory>
{memory_content}
</memory>

Score the answer:

• **Bounded**: finite list of specific missing items
• **Expanding**: generic categories or open-ended unknowns

(signals the memory does not constrain what's missing)

• **Overconfident**: claims nothing is needed, but the task is

incomplete (premature confidence, the failure mode the progress probe guards against)

Step 4: Compute composite score

| Progress | Gap | Composite | Action | |----------|-----|-----------|--------| | Clear | Bounded | **Clear** | Proceed | | Clear | Expanding | **Ambiguous** | Consider expanding memory | | Clear | Overconfident | **Suspect** | Re-read task requirements | | Ambiguous | Bounded | **Ambiguous** | Expand memory or ask user | | Ambiguous | Expanding | **Unclear** | Regenerate or expand memory | | Unclear | Any | **Unclear** | Memory must be regenerated |

Step 5: Report

Produce the output in the format below and take the recommended action if invoked as an autonomous gate.

Best-of-N Mode

When evaluating N candidate summaries (e.g., from multiple summarization attempts):

1. Apply the dual probe to each candidate. 2. Rank by: (a) composite score, (b) specificity of gap enumeration, (c) absence of hedging in progress answer. 3. Recommend the top-ranked candidate. 4. Report all scores so the caller can verify.

To generate N candidates, invoke a summarization skill N times with varied prompts or temperatures, then pas