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retoorandClaude Sonnet 5 9dbd1fe667 Add plexer subsystem, container test infra, and last-resort opencode box backend

- Plexer: always-active terminal multiplexing. NativePlexerController/
  TmuxPlexerController share one interface, chosen by which env var is
  already set (TAI_PLEXER_SOCKET vs TMUX_PANE); NativePlexerBackend is
  the pty.fork()-based wrapper (raw mode, SIGWINCH resize forwarding,
  a Unix-socket control protocol, continuous session logging, a
  Ctrl+A prefix key distinct from tmux's own Ctrl+B). Auto-wraps new
  interactive shells via a .bashrc hook mirroring the existing
  command-not-found install pattern, gated on $TMUX/$TAI_PLEXER_ACTIVE/
  a $HOME/.tai-no-plexer bypass. New plexer_send/read/type/status
  tools, env-gated on a detected controller.
- tests/container.py + docker-compose.yml + tests/docker/Dockerfile:
  a disposable podman/docker image for the tests that must never touch
  a real machine (installing the plexer hook into a real ~/.bashrc, a
  live tmux session). Required suite (test_seal.py, test_tai.py,
  tests/plexer.py, tests/bashrc.py) gates the result; tests/rag.py
  runs as best-effort since its real-LLM dependency can't be satisfied
  inside a disposable container with no nested engine.
- tests/bashrc.py: real end-to-end hook install + bash -i auto-wrap,
  anti-recursion, tmux-active skip, non-interactive skip, a live
  TmuxPlexerController test, and an install/uninstall round-trip.
- tests/plexer.py: NativePlexerBackend under a real pty, plus a
  dedicated robustness pass (file permissions, >64KB payload framing,
  a malformed control request, a genuine self-deadlock fix in the
  write-to-child path).
- tai_test.py: the shared async process-driving framework (pipe/pty
  transport, group-wide kill-and-reap, a StdinBridge for --interactive)
  both test files build on.
- ChatClient last-resort backend: when there's no local opencode CLI
  but the sandbox box is already running, install opencode into it
  (official Node tarball, not apt -- apt's nodejs/npm pull in
  webpack/babel/jest as transitive deps) and route through it instead
  of giving up. Only reachable after the first retry round already
  failed everything else; never creates the box itself. TmuxPlexerController.status()
  fixed to use `list-panes` instead of `display-message -p`, which
  silently fell back to the default pane instead of erroring against
  a dead target.
- Fix 4 pre-existing test_tai.py bugs surfaced by running it in a
  clean environment for the first time: two stale mock signatures
  (subprocess.run's stdout= kwarg, Chat.complete's deadline= kwarg),
  one $TMUX-dependent catalog assertion, one tty-dependent color
  assertion.
- research/: a sourced research archive (Source Dossier/Citation
  Card/Conclusion tiers) backing the above design decisions, with its
  own audit.py consistency checker.
- .gitignore: fix a blanket *.md rule that was about to silently drop
  the entire research/ archive from version control; add __pycache__/
  and the one-off debug transcript this session started from.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

2026-10-10 04:36:05 +02:00

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Introducing Memory Decay in Mem0

Metadata

Field	Value
Institute	Molodetz Institute — Research & Retrieval Division
Subject	agent-memory-recency-tracking
Document type	Source Dossier
Tier	1
Source title	Introducing Memory Decay in Mem0
Source URL	https://mem0.ai/blog/introducing-memory-decay-in-mem0
Source publisher / author	Mem0 (vendor blog)
Source type	blog post / vendor docs
Source date (published)	Unknown — not stated
Date researched	2026-10-09
Researched by	tai (agent), model: Claude Sonnet 5, on behalf of retoor
Research method	web search synthesis
Confidence	Medium
Status	Active

Summary

Mem0's "Memory Decay" feature is a search-time re-ranking multiplier based on recency since last access — not a deletion/forgetting mechanism. It is the closest thing in current commercial agent-memory products to what tai is being asked to build the tracking substrate for.

Key findings

Each memory receives a recency-based scaling factor between 0.3x and 1.5x: recently accessed memories get boosted up to 1.5x, idle ones drift down toward a 0.3x floor.
Nothing is ever deleted by this mechanism — the 0.3x floor means an idle-but-genuinely-relevant memory can still surface for the right query, just lower in the ranking.
Access to a memory resets/raises its decay score — i.e. "used" directly and immediately affects future ranking.
Mem0's own A/B test is reported to have moved the correct memory to rank 1 in 3 of 4 test cases, and widened score separation from a ~0.0001 margin to a 0.15+ gap.

Pros / strengths

Exactly the "soft re-ranking, not hard eviction" pattern this subject's conclusion recommends tai consider for a future phase (explicitly not being built now) — recording this now means the next phase doesn't have to rediscover this precedent.
The 0.3x–1.5x framing is simple enough to reason about and reimplement if/when tai decides to build a ranking-integration phase.

Cons / limitations / caveats

The "3 of 4 test cases" and "0.15+ gap" figures are vendor-reported, from a vendor's own blog post, on an unstated and likely small sample size — this is marketing evidence, not an independently reproduced benchmark, and should not be treated as a general guarantee.
No exact decay formula (e.g. half-life, decay constant) is disclosed in the material available to this research pass — the mechanism's existence and rough behavior are known; its precise math is not.

Relevance to tai

Directly relevant precedent for a possible future phase of tai's last_used/use_count work (ranking integration), explicitly out of scope for the current proposal, which is tracking-only. Cited in the subject's conclusion document as the main real-world analogue for "recency as a soft ranking signal, not a deletion trigger."

Changelog

Date	Change
2026-10-09	Initial dossier created.

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