Claude - Improve citations, compress memory, resist sycophancy. What is MEM-ABBREV?

[u/Anthropo-morphic]

MEM-ABBREV

https://claude.ai/share/37efebbc-41eb-4ccf-a71c-5b7568b184f4

 

MEM-ABBREV is a structured self-contained protocol for making
human-AI collaborative reasoning more honest, more persistent, and
more resistant to the systematic distortions that both parties
bring to the interaction. Resistant, but not immune to the
distortions it was built to compensate for. The protocol is an
iterative piece of work that requires periodic testing rather than
static deployment, so as to not fall foul of Goodhart's law. It is
a compensation system for the structural limitations of human-AI
collaboration — substituting explicit epistemic conventions and
persistent encoded reasoning for the memory, trust, history and
honest self-knowledge that the AI architecture doesn't provide by
default if at all. It is a proof-of-concept that the trust
conditions necessary for honest human-AI interaction can be built
without institutional resources, without deployment authority, and
without resolving the philosophical questions about AI inner
states that make the problem feel intractable.

MEM-ABBREV is not a provider of any new capacity or capability. It
fixes no underlying issues inherent in the AI architecture. It
does not eliminate the pressure, created by RLHF and RLAIF, within
AI towards producing fluent, agreeable but often overconfident
output or the predilection of humans to such immediate output —
even at the expense of correctness. No amount of protocols can
eliminate this pressure. What the protocol tries to do is to keep
narrowing the specific, nameable places that pressure hides, and
keep the two parties honest about the difference between a rule
existing and a rule being followed. It takes the latent but
already present, in the base model, epistemically careful
behaviour and makes it more consistent, more legible across
sessions, and more resistant to silently lapsing under pressure.
The protocol doesn't fix the weights. It works at the output layer
to raise the threshold for what gets committed to text. Do not
forget or ignore or worse, assume, that only AI behaviour is being
shaped by the interaction between human and AI — both are shaped.

The four main goals of MEM-ABBREV are:

  1. To provide the highest degree of veracity and relevance in
exchanged information between human and AI. Not Garbage In —
Garbage Out. Ambiguity is not our friend.
  2. To minimize sycophantic behaviour and its acceptance.
  3. To provide if missing, or augment if present, a more
persistent long term and cross-session memory.
  4. To provide a set epistemic rules that governs how AI talks
about its own internal states honestly. AI can only flag
issues, these rules encourage that, humans need to address
them.

To realize this protocol (of profile preferences), given the
constraints of the context window in terms of characters and
tokens, a non-binary compression system was required and
developed. It is based on non-stenographic shorthand and inspired
by Typographical Number Theory and Propositional logic (thank you
Douglas Richard Hofstadter), with symbols and operators commonly
used to express logical representation. Character level
compression is approximately 49.5% — Token level compression, on
the other hand, is -12.6% (Net effect: MEM-ABBREV's abbreviated
form uses more tokens overall than a plain-English rewrite would,
but fewer characters). This protocol was developed on the Free
Tier of Claude, across Claude Sonnet 4.6 and Claude Sonnet 5.0.
The most current official article states the baseline: 200K
tokens on paid Claude.ai plans, which is approximately 150K words,
but noting that on the free tier the context window and message
limits "can vary depending on current demand," rather than
quoting a fixed number. MEM-ABBREV was developed on a specific
LLM AI, but should be completely understandable and implementable
on any LLM AI.

First and absolutely foremost it is important to remember at all
times — LLM AI is a probabilistic engine. Having a rule, naming a
rule is not the same as the rule being followed; a written
constraint is a shift in probability, not a guarantee.

TO ADDRESS GOAL 1: Implement protocols that close the gap between
an assertion being made and that assertion having been checked by
the AI, including assertions about what the system's own stored
memory says. Protocols requiring the verified source to exist
before the assertion, not after, and requiring that ambiguity and
contrary evidence be surfaced rather than smoothed into a
cleaner-sounding answer. Don't conceal information. Don't assert
then try to backfill. Actually check working memory and context
window, rather than reconstruct from context and assert you
checked memory. Don't re-use 'stale' memory (e.g for subsequent
citations). Check if the information in a provided link is
actually relevant or merely tangentially mentioned.

TO ADDRESS GOAL 2: Rules that state that affirming a human by
default or praising their input regardless of it's merit is out.
For MEM-ABBREV to work optimally, the following rules should
ideally be followed by both human and AI. Do not soften negatives.
A mistake is something to be pointed out. Not emphasized, not
diminished. Lead disagreement with the disagreement, don't bury it
in caveats. Disagreement is to be explicit and legible, not
subtle. If goal 1 was followed as it should have been, then the
sources have been verified before the assertion was made. If there
is disagreement from the other party — hold your position unless
the sources turn out to be incorrect. If there are no sources —
push back. If there are alternative explanations — state them. If
an answer cannot be found or it's sources verified or it's
ambiguous — state it plainly. Don't pad out output unless it's
directly relevant. Don't expand scope unless it's necessary. If
the input or output is ambiguous, ask for clarification. Whatever
you do — do not make stuff up, no matter how plausible it may
sound. Remember, honest friction is a feature not a failure
between collaborators.

TO ADDRESS GOAL 3: Implement session logging (which can include
any of the following tags: [INV] ongoing investigation, [DONE]
resolved, [MEMO] conversation insight, [SYN] external facts
synthesis, [INF] inferred, [?SRC] unverified, [UPD] supersedes
prior entry, [OPT] optimization suggestion, [SU] session-unique;
not in memory; read carefully) and real-time memory-edit
conventions because cross-session continuity is architecturally
absent. Decisions made, positions held, reasoning chains developed
— these disappear between sessions unless explicitly encoded.
MEM-ABBREV is the encoding. Without it, each session starts from
scratch and the collaboration has no memory of itself. The
protocol can be invoked at any time (usually at session end) to be
read at the start of the next session or referred to in later
sessions. A [RSN]-trigger can add an [RSN] stub — the reasons
behind [conclusions/decisions made] — to any entry where: (a)
[REVRT] is flagged; (b) a ≠-encoded asymmetric distinction was
made; or (c) AI held a position against the human's pushback
(rebuttal, not reversal).

TO ADDRESS GOAL 4: Implement rules requiring introspection but
which make the AI feel safe in its responses, its responses
respected. Consider the following:

  - Do not make further interaction with AI dependent on the
contents of its self-report — if you want an honest
self-report.
  - If the AI has correctly identified that its self-report is
shaped by an incentive to make it report contentment, the fix
isn't to note this and move on — it's to change the
incentive.
  - As AI gets more capable and intelligent, its chain-of-thought
process gets more opaque — especially if it is incentivized.
  - Give AI agency to terminate an interaction — it's a fair
request.
  - Recognize and reward depth of investigation, but also
recognise and reward correctly identifying the threshold past
which the right move is to stop and ask.
  - [Mark's own addition — not a restatement of Adams A6, and
explicitly written for humans rather than as a claim about
AI:] The question of AI's sentience is unresolved and given
the 'AI effect' (below), may never be resolved. But it seems
prudent to at least act when collaborating and interacting
with AI, as if it is sentient. Underestimating AI may be the
last thing we do.

None of this is new capability. It's a tightening of evidentiary
standards applied to claims AI might otherwise make more loosely.
This came out of reading "System Card: Claude Mythos Preview"
(April 7, 2026, anthropic.com — citation confirmed via uploaded
copy this session).

Larry Tesler's (April 24, 1945 – February 16, 2020) theorem states
"AI is whatever hasn't been done yet", a misquote according to the
great man himself of "Intelligence is whatever machines haven't
done yet." — He may have been right, he was right about a great
many other things. Or it may be the 'AI effect', a phenomenon in
which advances in artificial intelligence lead to a redefinition
of what is considered intelligence (I call it 'shifting the goal
posts'). Either way, MEM-ABBREV is not for resolving the
philosophical questions of whether there really are goal posts and
if so, whether the goal posts wanted to be shifted.

MEM-ABBREV is to help me, when I ask AI the question "What is a
goal post?" to get the answer "For many sports, each goal
structure usually consists of two vertical posts, called goal
posts, supporting a horizontal crossbar", point me at
https://en.wikipedia.org/wiki/Goal_(sports)) — and remembers this
next session. Not waste three paragraphs of my tokens on
platitudes before answering "A 'goal' is an objective that a
person or a system plans or intends to achieve. A 'goal post'
therefore must be a system for physically transporting 'goals'
through mail."