{% extends "base.html" %} {% block title %}Roadmap — Maxim Docs | Future Plans & Development{% endblock %} {% block meta_description %}Explore Maxim's development roadmap — current initiatives, shipped features, next priorities, and long-term research directions.{% endblock %} {% block meta_keywords %}Maxim roadmap, Maxim future plans, multi-LLM scaling, agent mesh, embodiment, research protocol, Maxim development, cognitive architecture roadmap{% endblock %} {% block meta_author %}Maxim Project{% endblock %} {% block og_site_name %}Maxim{% endblock %} {% block og_type %}article{% endblock %} {% block structured_data %}
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← Back to Overview

Maxim is built in waves. Each wave stabilizes before the next begins, and every initiative has clear dependencies. This page tracks what's shipped, what's next, and where the project is heading long-term.

### Contents

1. Current Status
2. What We Just Shipped
3. Next Up
4. Research Directions
5. Dependency Graph
6. Contributing

## Current Status

Every active initiative and its current state at a glance. Status badges reflect the most recent milestone reached.

Initiative | Status | Notes
--- | --- | ---
Agent Mesh | Done | Complete (Phases Pre-7). Identity, protocol, transport, admission control, knowledge sharing, task delegation, distributed planning, SCN temporal coordination. mDNS + InferenceRouter deferred.
Generative Campaigns | Done | All stages shipped: narrative arcs, two-call narrator, planner integration, bridge-and-compress, ask_user tool, benchmark tiers, CLI simplification. 71 tests.
Embodiment Core | Done | All software phases shipped: SEM protocol, Cerebellum forward models, motor programs + engrams, composed failures, virtual entities. 164 tests. Hardware adapter deferred to future.
Simulation Benchmark | Done | All phases (0-6). Multi-model comparative testing, narrative transcriber, write-paper, Tier 3 hooks. `maxim --sim benchmark --models X,Y --campaign Z`
Python API | Done | Verb-based interface (run, imagine, connect, diagnose, observe, configure). Package: `pymaxim`.
Lane Tier Architecture | Done | FunctionRouter with tier routing (large/medium/small), fallback chains, auto-detection from hardware. Legacy lane names aliased.
Research Protocol | Done | Complete: mesh primitives, research tools, Writer + Reviewer, dual-LLM. `maxim --sim research`
Multi-LLM Scaling | Done | Complete. LeaderProxy, admission control, LaneMetrics, heartbeat, remote update.
Tool Refactoring | Done | All phases: say, think, examine, introspection tools, alias map, usage tracking, proactive tool list
Introspection API | Done | All phases. Observer (renamed from AUTIntrospector) + standalone `run_campaign()` shipped
Docker Sandbox | Done | Phase A (TmpdirSandbox + pain triggers) + Phase B (DockerSandbox + ContainerRunner + image catalog + unprivileged user) both shipped
Bio-System Wiring Hardening | Done | All phases shipped. Percept abstraction (SensoryModality, SensoryTag, SensoryGate), pipeline correctness, energy→NAc metabolic cost learning, decision_rationale provenance. Archived.
Mode System Refactor | Done | ~1,800 LOC removed. Strategies, exploration policy, and LiveModeIntent deleted. Sleep is now a tool. Skills module folded into Cerebellum. Dead runtime modules cleaned up.
DM MVP | Done | All 3 slices shipped: dm_schema.py, dm_runtime.py, tools_dm.py. 7 campaigns (heist, poisoned_crown, arena, darkened_cavern, kings_duel, neon_gauntlet, broken_database). ChooseTool + alias system, bio-system expectations checker, mid-campaign entity swaps.
Foundational Buildout | Done | Phases 0-12a: package hygiene, SEM component registry, encounter library, agent factory + pool, party DM mode, hippocampus recall, interactive runtime, generative architect, API expansion, 10 cloud providers, store protocols, security hardening. ~16K LOC, 239 tests.
API Surface Hardening | Done | All phases: wired stub API verbs, fixed research protocol, error handling on user-facing paths, integration tests, README overhaul. ~75 new tests.
Module Compartmentalization | Done | 5 god-modules decomposed: agent_loop, orchestrator, cli, router, lane_backends. 7 new focused files extracted (~1,120 LOC moved). 125 new tests. All import paths preserved.
Foundations F0.1–F0.8 | Done | NAc save/load, PerceptContext typed schema, agent_id threading, PerceptTraceBuffer, tier enforcement, SensoryTag population, Percept factory consolidation. Prereqs for substrate work.
Reaction Abstraction Ph1–4 | Done | Percept/Reaction dual-surface architecture. ReactionBus with refractory periods, producer protocols, typed Percept factories, runtime unification. Phase 5 folded into substrate P2.
Simulator Upgrades S1–S4 | Done | FixtureDrivenOrchestrator (S1), LLMBackend Protocol + MockLLMBackend (S2), subprocess persistence harness (S3), deterministic seeding with `--seed` (S4). 72 tests.
Substrate P0 Pilot | Done | Baseline pinned at 78.5% collapse (mpnet@0.50). 55 clusters, 155 sentences, 3 difficulty tiers. Fixtures validated in the 60–85% well-calibrated range. P1 sanity floor = 73.5%.
Substrate B1 + P1 Recognition | Done | LinguisticEncoder, EC pattern completion with centroid update, ATL modality-tagged nodes, PromptAssembler. 91.7% ± 2.9% collapse with `paraphrase-mpnet-base-v2` @ 0.40. 100% persistence round-trip. 92.5pp degenerate gap.
Substrate P2 Reward Modulation | Done | NAc per-node reward bias, eligibility traces, EC threshold modulation, CausalLink.percept_refs. Real-embedding sweep at `paraphrase-mpnet@0.70, reward 2.0`: +56.0 pp target gain, 0.0 pp distractor drift, 94% monotone, 9-of-10 seeds. SEM pain cascade end-to-end verified.
Bio-Stack Unification (Waves 1–3) | Done | `build_bio_stack(*, persistence_dir)` canonical. Structural enforcement for PainBus, ReactionBus, MemoryHub, DefaultNetwork. Four production callers.
Valence Annotation (Stages 1–3) | Done | Reactions → Episode.valence → Edge.metadata["valence"] → spreading_activation(propagate_valence=True). retrieve_on_cue(include_valence=True) for affective retrieval.
SEM Learning Loop | Done | Cerebellum activation via BioStack, distribute_reward (ReactionBus → NAc reward bias → EC threshold adjustment), CerebellumModulator success reactions (positive valence, negativity bias), pain spike episode boundaries (salience_spike_rule). PoC: 11/11 + 13/13.
Behavioral Convergence Wiring | Done | Valence in PromptAssembler, `observe_episode_event` in agent loop, energy→Reaction bridge (hunger/fatigue/satiation), food/water/poison SEM specs. Experiment 2: 13/13 hypotheses confirmed.
Behavioral Convergence Experiments (All 3 Tiers) | Done | 41/41 hypotheses, 4 experiments, all 3 tiers PASS. Tier 1: substrate learns (Exp 1+2). Tier 2: LLM acts on learning (Exp 3, 10/10 vs 0/10). Tier 3: organic LLM learning (Exp 4, teal rate 0%→25%→100%, fresh control DIED). v0.3.0.
B4 Replanning (1.0 Gate) | Done | Failure diagnosis + prior-attempt retrieval + Jaccard metric + anti-repetition. Blind A/B: treatment 100% vs control 0%, Jaccard 0.894. 1.0 gate CLOSED. v0.5.0.
P6 Extinction + P8 Sleep Replay | Done | P6: Hebbian decay beats LRU (10 seeds). P8: sleep replay F1 improves vs control (10 seeds). v0.5.0.
F2 AgentFactory CLI Migration | Done | `create_full_agent()` composes bio-stack + executor + fear gate. CLI non-sim bootstrap replaced. v0.5.0.
Interactive Mode + Input Standardization | Done | Bidirectional interactive, raw terminal, request_interaction, NAc suppression, unified input handling. Scale: 20/20 seeds, p = 3.87e-6. v0.3.1–0.4.0.
Generalizable Embodiment (E0–E3) | Done | E0: sim affordance tools. E1: Asset Foundry (generate + validate + gauntlet + score). E2: foundry --llm wiring + entity context. E2.5: ComponentIndex two-layer discovery. E3: Auto-Curation CLI. v0.6–0.7.
Imagination System (I1–I3) | Done | I1+I2: Entity extraction, ComponentIndex lookup, DN arousal gate, EntityDesigner LLM, ephemeral registration, provenance tagging. I3: Scene-scoped tools with active cap + executor gate. v0.7.0.
Acting Coach (B3.1) | Done | Meta-prompt scaffolding: NAc caution annotations, pain anticipation, cerebellum forward-model predictions. Bio-modulated exploration directive. v0.7.0.
Agent Factory (F3–F5) | Done | Sim orchestrator, Reachy embodied runtime, and headless API all migrated to `AgentFactory.create_full_agent()`. v0.7.0.
Sensorimotor Cradle + Drive System | Done | 4-act developmental arc (`maxim --sim cradle`), homeostatic + entropic drives, 3-layer sensation model, thermal contact reflexes, drive visibility in Acting Coach prompt. P5 Stress Persistence (final 1.0 gate CLOSED). v0.8.0.
Cognitive Maturity (v0.8 suite) | Done | Working Memory + Executive cycle, PFC deliberation loop, temporal credit (SCN oscillator B2 + TemporalCreditDistributor), reflex system, rich interactive display overhaul, affordance concept transfer, entity ownership, pre-deliberation (ThoughtGate + bio-enrichment). v0.8.0.
Substrate-Annotates-LLM-Context (Wires A/1/2/3) | Done | Wire-A cluster-bias annotation, Wire 1 variance annotation, Wire 2 manifest enrichment, Wire 3 goal-scoped bias. EC centroid-drift fix (threshold 0.40→0.44). Roy-3 cross-session validation. v0.9.1.
Config Unification + Hivemind Shareability | Done | `~/.config/maxim/config.json` single-source operator config. `maxim config/model/substrate` CLIs. Portable substrate-snapshot bundles with `nac_merge`/`ec_merge` Bayesian aggregation. Leader model profiles. LLM timeout scalability (TTFT keepalive, per-tier timeout, context-overflow gate). v0.9.2.
Loud Optional-Dependency Failures | Done | `utils/optional_deps.py` centralises 45+ import sites. Missing requested backend raises `OptionalDependencyError` with install hint instead of silent empty response. v0.9.3.
Exp 37 Cross-Session Graduation + Docs | In Progress | Validation harness + analyzer shipped. Behavioral trial in progress (6 arms × 2 scenarios × 5 trials). Documentation accuracy sweep under way. Remaining 1.0 prerequisites.

## What We Just Shipped

1.0 is in preparation. The 0.8.x and 0.9.x series shipped the final architectural features: sensorimotor cradle + drive system (v0.8), substrate-annotates-LLM-context Wires A/1/2/3 + EC centroid-drift fix (v0.9.1), config unification + Hivemind shareability + LLM timeout scalability + leader model profiles (v0.9.2), and loud optional-dependency failures (v0.9.3). All 1.0 gates are closed; remaining work is validation, documentation, and packaging. Earlier milestones: imagination system + Acting Coach + factory migration (v0.7), generalizable embodiment + Asset Foundry (v0.6), B4 replanning (v0.5), behavioral convergence (v0.3).

### Config Unification + `maxim config/model` CLIs (v0.9.2)

`~/.config/maxim/config.json` replaces scattered env vars as the single operator config. `resolve_setting` precedence chain (CLI > env > config.json > default). `maxim config get/set/list` and `maxim model add/list/remove` CLIs. Seven-rank role detector unified in `runtime/role.py`. Auto-migration from peer.yml on first startup. Doctor "Resolved Config" section.

### Hivemind Shareability + `maxim substrate` CLI (v0.9.2)

Portable substrate-snapshot bundle format (ZIP + manifest). `nac_merge()` / `ec_merge()` pure-function Bayesian aggregation with provenance tracking. Identity-bearing concept detection. `maxim substrate export|import|inspect` CLI verbs. Full P2P Hivemind protocol remains 1.2+.

### LLM Timeout Scalability (Stages 1–3) (v0.9.2)

Per-tier timeout configuration (`MAXIM_LANE_<TIER>_TIMEOUT_S` + `lanes.<tier>.timeout_s`). TTFT keepalive emitter defeats Cloudflare’s ~100s tunnel idle timeout during long first-token waits on 30B+ models. Context-overflow admission gate (HTTP 413 + typed error body) prevents oversize prompts from silently hanging llama-cpp-server. Stall detector with per-tier threshold derivation via `compute_stall_threshold`.

### Leader Model Profiles + `maxim model` CLI (v0.9.2)

Three new bundled profiles (qwen2.5-32b, llama-3.1-70b, mixtral-8x7b). User-profile loader via `~/.config/maxim/profiles.yml`. `maxim model add|remove|list` CLI verbs. Chat-format inference per profile. Profile schema frozen at 1.0.

### Loud Optional-Dependency Failures (v0.9.3)

`utils/optional_deps.py` centralises 45+ import sites. Missing a requested optional backend now raises `OptionalDependencyError` with a concrete install hint instead of silently returning empty responses. Eliminates the most common class of silent mis-installs.

### Sensorimotor Cradle + Drive System (v0.8)

Four-act developmental arc (`maxim --sim cradle`), homeostatic and entropic drive types (temperature, hunger, fatigue), 3-layer sensation model (contact / proximity / narrative), thermal contact reflexes, drive-state visibility in the Acting Coach prompt. 7 cradle stages. P5 stress persistence — the final 1.0 gate — shipped alongside.

### Substrate-Annotates-LLM-Context (Wires A/1/2/3) (v0.9.1)

Wire-A: cluster-bias annotation surfaces learned affordance preferences at the LLM prompt. Wire 1: variance-band felt-sensation annotation on tool descriptions. Wire 2: substrate-aware scene manifest enrichment. Wire 3: goal-scoped reward bias. EC centroid-drift fix (threshold 0.40→0.44) eliminates progressive concept collapse across sequential percepts. Roy-3 cross-session validation complete.

### Cognitive Maturity (v0.8 suite)

Working Memory + Executive cycle, PFC deliberation loop, temporal credit distribution (TemporalCreditDistributor + SCN oscillator B2 anticipatory pre-activation), deliberative thinking (ThinkTool enrichment + percept enrichment), reflex system (innate body responses replacing auto-damage), rich interactive display overhaul (thinking panel, split panels, resize, slash commands), affordance concept transfer (substrate-native cross-entity learning), entity ownership (self vs scene separation).

### Imagination System (I1+I2+I3) (v0.7)

Entity noun-phrase extraction from percepts, ComponentIndex two-layer lookup (alias + embedding), DN arousal gate, EntityDesigner LLM-driven design with archetype scaffolding, manifest pre-trigger for scene-load pre-instantiation, ephemeral registration with provenance tagging (`imagined=True`), 50% confidence decay at session end. I3: scene-scoped tool activation with active cap + executor gate.

### Acting Coach + Factory F3–F5 (v0.7)

B3.1 Acting Coach: meta-prompt scaffolding with NAc caution, pain anticipation, cerebellum predictions. F3–F5: sim orchestrator, Reachy, and headless API all migrated to `AgentFactory.create_full_agent()`.

### Asset Foundry + Auto-Curation (E1–E3) (v0.6–0.7)

E1: LLM-driven entity generation + validation + 3-encounter gauntlet + 4-dimension scoring. E2: foundry `--llm` wiring + entity context in AUT prompt. E2.5: ComponentIndex (alias hash + embedding cosine). E3: `--auto-curate` pre-sim coverage gap filling with dedup.

### Default Sim Embodiment (E0) (v0.6)

`bodies/base_humanoid` loads by default in sim mode — 5 sensors, 8 affordances, 3 failure modes. `--embodiment` works with `--sim`. 10 integration tests.

### B4 Replanning (v0.5 — 1.0 GATE CLOSED)

Failure diagnosis with prior-attempt retrieval via hippocampus episodes. Jaccard distance metric for structural novelty. Anti-repetition prompt constraint. Blind A/B validation: treatment (replanning) 100% vs control (no replanning) 0%, mean Jaccard 0.894. 48 tests. The replanning 1.0 gate is closed.

### P6 Extinction + P8 Sleep Replay (v0.5)

P6: `DependencyGraph.decay_edges()` — multiplicative Hebbian decay with pruning. Beats LRU across 10 seeds. P8: `memory/sleep_replay.py` — offline consolidation. Episode ranking by NAc reward_bias + valence, replay with 1.5× consolidation multiplier. F1 improves vs no-replay control. Activates `memory_consolidation_practice.md`.

### F2 AgentFactory Migration (v0.5)

`AgentFactory.create_full_agent()` composes `build_bio_stack` + `build_executor` + `FearGatedExecutor`. CLI non-sim bootstrap (~100 lines) replaced with one factory call. Z1 design: per-instance Executor built once. Sim/Reachy/API migrations remaining (F3-F5).

### Interactive Mode + Input Standardization (v0.3.1–0.4)

Bidirectional interactive mode: raw terminal input, `request_interaction` agent-to-user prompting, `set_scene` dynamic headers, slash commands, NAc suppression. Unified input standardization across all sim modes. Scale validation: 20/20 seeds, p = 3.87e-6.

### SEM Learning Loop & Valence Annotation

CerebellumModulator success/failure reactions → ReactionBus → hippocampus (Episode.valence, Edge.metadata["valence"]) + NAc (`distribute_reward` for reward bias + EC threshold adjustment). `spreading_activation(propagate_valence=True)` + `retrieve_on_cue(include_valence=True)`. Pain spike episode boundaries via `salience_spike_rule`. Cerebellum activation through BioStack + build_executor.

### Behavioral Convergence Wiring

Closes the gap between substrate learning and LLM decisions. Valence in PromptAssembler, `observe_episode_event` in agent loop, energy→Reaction bridge (hunger/fatigue/satiation from energy depletion), food/water/poison SEM specs. Experiment 2: 13/13 hypotheses confirmed — food +0.753, water +0.135, poison -0.495.

### DM MVP

Bundled SEM characters with cascade DAG for narrative branching. ChooseTool + alias system for encounter choices. Bio-system expectations checker validates campaign results. 4 campaigns: heist, poisoned_crown, arena, darkened_cavern. `maxim --sim scenarios/campaigns/heist_v1.yaml`

### Bio-System Wiring Hardening

Percept abstraction layer (SensoryModality, SensoryTag, SensoryGate), pipeline correctness fixes, energy→NAc metabolic cost learning, `decision_rationale` provenance field on Perception. 14/14 pipeline audit checks passing.

### Mode System Refactor

~1,800 LOC removed. Strategies, exploration policy, and LiveModeIntent deleted. Sleep is now a tool. Skills module folded into Cerebellum motor programs. Dead runtime modules cleaned up (resilient.py, session.py, debug_status_server.py, monitor_registry.py).

### Agent Mesh

Full mesh protocol through Phase Pre-7: AgentProfile identity, UMR naming, MeshMessage envelopes, LocalMessageBus, knowledge sharing between agents, task delegation, distributed planning, and SCN temporal coordination. mDNS + InferenceRouter deferred.

### Generative Campaign Mode

LLM-driven narrative arcs (4 builtin + custom YAML), two-call narrator with AdaptivePlanner integration, bridge-and-compress for multi-arc continuation, `ask_user` tool, tiered benchmarks, `--sim "goal"` CLI simplification. 71 tests.

### Lane Tier Architecture

FunctionRouter routes functions to capability tiers (large/medium/small) with fallback chains. Auto-detection from hardware VRAM. The legacy lane names (infer/review/record) were fully removed in v1.0.0.

### Simulation Benchmark

Phases 0-6 complete: BenchmarkRunner, CLI (`maxim --sim benchmark`), 6 scenarios, narrative transcriber, write-paper pipeline, Tier 3 hooks for multi-model comparative testing.

### Python API (pymaxim)

Verb-based interface: `run`, `imagine`, `connect`, `diagnose`, `observe`, `configure`. Lazy imports, structured return types. Published as `pymaxim` on PyPI.

### Embodiment Core

SEM protocol (Sensor-Entity-Modulator), Cerebellum forward models, motor programs + engrams, composable failures, virtual entities. 164 tests. Hardware adapter deferred to future.

### Multi-LLM Scaling

LeaderProxy with authentication + GPU metrics, admission control (concurrency caps + rate limiting), LaneMetrics per-tier counters, system heartbeat with stall detection, remote peer management (`maxim peer update/restart/llm`). Plan 4 mesh management surface: `maxim peer list-nodes` for live status, `--node X drain|resume` for graceful traffic shaping, `init-mesh / add-node / remove-node` for topology setup, all backed by `~/.config/maxim/mesh.yml` (declarative) + `~/.maxim/util/drained_nodes.{role}.txt` (mutable runtime state, filelock-serialized).

### Research Protocol

Mesh primitives, research tools (`record_experiment`, `query_experiments`), Writer + Reviewer agents, dual-LLM orchestration. `maxim --sim research`

## Next Up

### 2026-05-09 architectural pivot

Maxim is moving toward a parallel-mode architecture where the bio-substrate (NAc + EC + ATL + Hippocampus + Default Network + reflexes) drives action selection directly, with LLMs demoted to supporting roles (orchestrator, NPCs, optional AUT). The existing LLM-AUT mode remains the user-facing default; substrate-primary mode ships parallel as opt-in via `--aut-mode substrate-primary`. Phase −1 prototype shipped 2026-05-09 — `NAc.recommend_action()` can generate non-reflex actions from learned causal links + drive heuristics with no LLM proposal needed (11 unit tests pass). Hivemind shareability infrastructure shipped (2026-05-31, PRs #305/#308/#309/#310): the Maxim Hivemind + Oasis substrate-sharing foundation is live — portable bundle format, `nac_merge` / `ec_merge` Bayesian aggregation, identity detection, and `maxim substrate export|import|inspect` CLI. Full P2P Hivemind protocol remains 1.2+. See Substrate-Primary Mode and Maxim Hivemind + Oasis.

The path forward: 1.0 ships with the full B5 substrate-primary harness (Phase −1 + Phase 0 + Hivemind shareability infrastructure) and the documentation pass now under way. Exp 37 cross-session graduation validates the behavioral claim. 1.1 lands substrate-primary AUT mode + first hostable Maxim Oasis. 1.2 ships the full Hivemind P2P protocol.

1

### B5 Substrate-Primary Harness (v1.0)

Phase −1 ✓ shipped (NAc action proposal + 11 tests). Phase 0 harness ✓ shipped: `--aut-mode substrate-primary` CLI flag + cradle-prelinguistic arc variant + motor-only AUT prompt + per-tick telemetry. Roy harness ✓ shipped (2026-05-10): R1 curriculum runner + R2 substrate_diff + R3 three-arm iteration runner + R4 idempotent log generator + R5 process-global invariants. G3 fail-fast LLM preflight ✓ shipped (2026-05-11, PR #235+#238): `_MaximPeerBackend.health_check()` probe with env-then-peer.yml resolution; aborts in ≤3s on unreachable leader. G4 cluster_id reward wire ✓ shipped (2026-05-11, PR #236+#237): closes the deferred Track 2 wire — substrate-primary tool outcomes now populate `NAc._cluster_reward_bias`, persist to `aut_nac.json`, surface in `substrate_diff`. Empirically validated: live Roy-0 run produced `cluster_reward_bias_l2 = 2.4587` on A-vs-blank pairs (~11.6× blank-vs-blank noise floor). Roy iteration arc Roy-1a–Roy-4 ✓ shipped (2026-05-11 → 2026-05-13): six follow-up iterations reproducing the wire 6× on the same priming and localizing the behavioral-expression gap to LinguisticEncoder → EC alignment; Roy-4 (PR #246) cancelled the 1.1 Hebbian binding plan via a pre-registered cheap-gate experiment. 0.9.1 Wire-A ships as the operator-visible interim that surfaces the surviving tool-level signal at the LLM prompt regardless of encoder drift. 1.1+ reframe: `roy_5_encoder_alignment_disambiguator.md` (PR #247) replaces the cancelled binding plan with a diagnostic-first ladder; Stage 1 (Roy-5a cosine analysis on existing Roy-4 data, zero new sim runs) decodes the gap to one of three sub-hypotheses that scope the 1.1+ fix. Hivemind shareability infrastructure ✓ shipped (2026-05-31, PRs #305/#308/#309/#310): portable substrate-snapshot bundle format + `nac_merge()` / `ec_merge()` Bayesian aggregation + provenance tags + identity-bearing concept detection + substrate domains + `maxim substrate export|import|inspect` CLI verbs.

2

### D1–D3 Documentation Pass (v1.0, parallel to B5)

Agent memory transfer docs (D1), API/CLI surface review (D2), final docs pass (D3). Runs parallel to B5; 1.0 ships when both complete.

3

### Substrate-Primary AUT Mode + First Maxim Oasis (v1.1)

Substrate-primary AUT mode lands as opt-in. Phase 0 validation runs (raw substrate, no Hivemind bootstrap). First hostable Maxim Oasis (~800 LOC); LLM-AUT users opt in to contribute via `maxim contribute --to oasis://...`. Direct Oasis-to-Oasis sync supported.

4

### Maxim Hivemind P2P Protocol (v1.2)

Full peer-to-peer substrate-snapshot exchange (~600 LOC): peer discovery, conflict-resolution semantics (Bayesian confidence aggregation), poison-resistance defenses (multi-source consensus, domain curation, provenance blacklists). Substrate-primary Maxims pull bootstrap from Hivemind, contribute back as they learn.

## Research Directions

These are speculative, long-term directions. None are scheduled—they represent where the architecture could go once the current engineering work stabilizes.

### ATL Self-Extension through Mechanism Discovery

Can Maxim's Anterior Temporal Lobe discover new concept categories and relationship types on its own? Today the taxonomy is hand-coded. A self-extending ATL would let the semantic memory grow in ways its designers didn't anticipate.

### Federated Embodiments

Multiple Maxim instances sharing memory and causal models across different physical bodies. A robot that learns to open a door could transfer that knowledge to a different robot with different actuators—adapting the motor plan while keeping the causal structure.

### Cross-Agent Affordance Delegation

When one agent discovers an affordance it can't act on (e.g., "this door has a handle but I have no gripper"), it could delegate to an agent that can. This requires a shared affordance vocabulary and a trust model for delegation.

### Distributed Embodiment Construction

Multiple agents collaboratively assembling a physical structure, each contributing sensors and actuators. The Agent Mesh provides the communication substrate; this research explores what shared representations are needed for coordinated physical action.

### Uncertainty-as-Pain

Mapping epistemic uncertainty to the PainDetector system. High uncertainty about a prediction would register as discomfort, motivating the agent to gather more information before acting—a bio-inspired approach to active learning and cautious exploration.

## Dependency Graph

All 1.0 gates are closed. Remaining work is validation, documentation, and packaging.

Path to 1.0 v0.3.0 — 41/41 behavioral convergence (cross-session learning) v0.5.0 — B4 replanning (1.0 GATE CLOSED), P6+P8, F2 v0.6.0 — E0-E3 generalizable embodiment (1.0 GATE CLOSED) v0.7.0 — Imagination system (I1+I2+I3), Acting Coach, F3-F5, E2.5+E3 v0.8.0 SHIPPED P5 Stress Persistence (final 1.0 gate CLOSED) Cradle sensorimotor (3-layer sensation, drives, 7 stages) Affordance Concept Transfer (substrate-native cross-entity learning) Reflex system (innate body responses) Pre-deliberation (Layer 1 ThoughtGate + bio-enrichment) SCN oscillator (B2 anticipatory pre-activation) Roy harness foundation (R1 curriculum + R2 substrate_diff + R3 iteration runner + R4 idempotent log generator + R5 invariants) │ ├──> 2026-05-11 Roy-0 harness validated end-to-end on live leader ├──> 2026-05-11 G3 fail-fast LLM preflight probe (PR #235 + #238 peer.yml fallback) ├──> 2026-05-11 G4 substrate-primary cluster_id reward wire (PR #236 + #237) │ `cluster_reward_bias_l2 = 2.4587` on Roy-0 A-vs-blank pairs │ ├──> 2026-05-11 → 2026-05-13 Roy iteration arc (Roy-1a/1b/2/2pc/2c) │ Cluster wire reproduces 6×; H1 confirmed: LinguisticEncoder → EC alignment is the block │ 0.9.1 Wire-A surfaces the surviving tool-level signal at the LLM prompt ├──> 2026-05-13 Roy-4 EC instrumentation + Hebbian sweep (PR #246) │ FAIL — cancels `cross_modal_substrate_binding.md` Stages 2-6 │ Zero priming↔test bound edges at every parameter sweep point ├──> 2026-05-13 `roy_5_encoder_alignment_disambiguator.md` opened (PR #247) │ Stage 1 (Roy-5a cosine analysis on existing Roy-4 data) queued; verdict scopes 1.1+ fix │ ├──> v0.9.1 SHIPPED (2026-05-25) │ Wire-A cluster-bias annotation; Wires 1/2/3 (variance/manifest/goal); EC drift fix (0.40→0.44) │ Roy-3 cross-session validation complete; NAc reward-bias ablation arm env var │ ├──> v0.9.2 SHIPPED (2026-06-05) │ config.json operator config + maxim config/model CLIs; seven-rank role detector │ Hivemind shareability (nac_merge/ec_merge/export/import/inspect); leader model profiles │ LLM timeout scalability: TTFT keepalive, per-tier timeout_s, context-overflow admission gate │ Stall detector with canonical stall-threshold derivation (compute_stall_threshold) │ Singleton spawn guard + harness preflight (leader-local fire safe) │ ├──> v0.9.3 SHIPPED (2026-06-06) │ Loud optional-dependency failures via utils/optional_deps.py (OptionalDependencyError) │ └──> v1.0 IN PREPARATION All gates closed. Exp 37 cross-session graduation + docs pass + packaging remain.

All architectural gates closed with v0.8.0 (P5 final gate). The 0.9.x series added the substrate-annotation wires (v0.9.1), config unification + Hivemind shareability + LLM timeout scalability (v0.9.2), and loud optional-dependency failures (v0.9.3). 1.0 is a validation and packaging milestone; the remaining 1.1+ encoder-alignment research direction (Roy-5, JEPA) is independent of the ship decision.

## Contributing

Maxim is open source. Contributions are welcome—especially on items marked Not Started in the status table above.

### Getting Started

1. Clone the repo from github.com/dennys246/maxim
2. Read `CLAUDE.md` in the project root—it covers architectural invariants, testing commands, and the module map
3. Run `maxim doctor` to verify your environment
4. Run the test suite: `python -m pytest tests/ -x -q --ignore=tests/integration/test_memory_hub.py`
5. Pick an initiative from the status table and open an issue or PR

### Before You Start

The project has strong architectural invariants (one-way memory tiers, separate EpisodicMemory instances, LLM access only through the router). Read the `CLAUDE.md` section on invariants before making structural changes. The bio-system class names (Hippocampus, ATL, NAc, SCN, EC, AngularGyrus) are intentional and should not be renamed.

← Previous Agent Mesh

### All Chapters

Overview The Agent Brain Memory Systems Math & Statistics Attention & Salience Body Awareness Communication Usage Guide Technical Deep Dive Operating Modes Networking Simulation Benchmarks Embodiment Agent Mesh Roadmap
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