Hyper Workspace State — Live Operational Data

The hyper_workspace is the live operational state of the Co-Fita Hyper-Harness during execution. It implements the Deli_AutoResearch protocol's state file architecture, ensuring that all progress persists across context compaction and session boundaries.

State File Architecture

Following the Deli_AutoResearch protocol, the workspace maintains a structured state directory:

hyper_workspace/
├── state/
│   ├── task_spec.md           # Goal, milestones, success criteria
│   ├── progress.json          # Iteration count, findings, stale tracking
│   ├── directions_tried.json  # Historical directions (prevents repetition)
│   └── findings.jsonl         # Accumulated findings (append-only)
├── failure_tree/
│   └── failure_tree.json      # EnCompass-style execution tree (658 KB)
└── logs/
    └── orchestrator.jsonl     # Timestamped orchestration events

Current State

Task Specification

The current task is:

"Maintain and evolve the system (Centralismo Democratico)"

This is the root objective for the governance system's autonomous operation.

Progress Metrics

Metric	Value	Interpretation
Iteration	0	System initialized but not yet iterating
Total Findings	0	No findings accumulated yet
Status	initialized	Ready for first iteration
Stale Count	0	No stalls detected
Last Seen	1781758476.297426	Unix timestamp of last activity

Directions Tried

The directions_tried.json file is currently empty ([]), indicating the system has not yet attempted any directions. This is consistent with the initialized status.

Failure Tree Analysis

The failure tree (658 KB) implements the EnCompass-style execution tree with unexplored branch tracking. The root node and initial children show:

Root Node Structure

Field	Value
Root ID	48470eb7
Root Approach	"Maintain and evolve the system (Centralismo Democratico)"
Root Status	running
Depth	0

Node Properties

Each node in the tree contains:

{
  "id": "ca9f0b5a",
  "approach": "Maintain and evolve the system (Centralismo Democratico)",
  "status": "running",
  "parent_id": null,
  "children": [],
  "depth": 0,
  "failure_reason": null,
  "failure_category": null,
  "reflection": null,
  "metrics": {},
  "created_at": 1781758476.3033412,
  "completed_at": null
}

Observed Pattern

The failure tree shows multiple root-level nodes with identical approaches, all in running status. This suggests the orchestrator has been spawning parallel execution contexts, each starting fresh with the same task specification. The nodes have:

• No parent-child relationships (parent_id: null, children: [])
• No failures recorded (failure_reason: null)
• No reflections generated (reflection: null)
• Consistent timestamps (spaced ~30 seconds apart)

This pattern indicates the system is in its initialization phase, spawning multiple parallel workers before any have completed or failed.

Orchestrator Logs

The orchestrator logs (orchestrator.jsonl) show the initialization pattern:

Log Entry Format

{
  "ts": 1781758476.298393,
  "source": "orchestrator",
  "level": "info",
  "event": "initialized",
  "detail": "Task: Maintain and evolve the system (Centralismo Democratico)"
}

Initialization Sequence

The logs reveal a consistent pattern:

1. initialized: Task specification loaded
2. tree_root: Failure tree root node created

This two-step initialization repeats for each parallel worker, with timestamps spaced approximately 30 seconds apart:

Timestamp	Event	Root ID
1781758476.298393	initialized	ca9f0b5a
1781760392.193687	initialized	ab1ae2f9
1781760410.1033611	initialized	372e2e06
1781760440.264925	initialized	706b6cab
1781760470.272795	initialized	4e8a0d49
1781760500.2838762	initialized	e781b538
1781760530.305341	initialized	4d3cf634
1781760560.313236	initialized	2f01c568
1781760590.3253682	initialized	db373a78
1781760620.338675	initialized	315c15dd

The 30-second spacing suggests the orchestrator is using a polling interval consistent with the Deli_AutoResearch protocol's heartbeat mechanism.

EnCompass Integration

The failure tree implements the EnCompass pattern from MIT CSAIL (NeurIPS 2025), which provides:

1. Probabilistic Angelic Nondeterminism (PAN): Branch points in execution
2. Backtracking: When a path fails, return to the last decision point
3. Cloning: Spawn parallel exploration of alternative branches
4. Unexplored Branch Tracking: Identify promising unexplored siblings

Key Insight from EnCompass

"When a path fails, EnCompass can backtrack to the last decision point, clone the agent's runtime state, explore alternative branches in parallel, and track which branches were never visited."

This validates Alefita's intuition: "se outras possibilidades se abrem com ramos daquele caminho falho que nunca foram explorados."

Heuristic Prioritization

The failure tree's get_promising_unexplored() method ranks unexplored nodes by:

• Parent depth (deeper = more context)
• Sibling failure type (some failures indicate more promising alternatives)
• Failure category (goal drift vs logic error vs stalling)

The Five-Phase Orchestrator Loop

The orchestrator implements a 5-phase loop with Phase V Governance:

Phase I: Ideation (SearXNG + Crawl4AI research)
    ↓
Phase II: Execution (OpenCode via ACP)
    ↓
Phase III: Audit (HashMath + Red Team)
    ↓
Phase IV: Fixation (Wiki export + state update)
    ↓
Phase V: Governance (Proposal generation + Congress)

Failure Path

When audit fails:

1. GovernanceIntegration.process_iteration(audit_passed=False)
2. Automatic proposal generated from failure
3. Proposal enters DRAFT status
4. Congress convenes every N=3 iterations

Success Path

When audit passes:

1. Wiki export (state compiled to knowledge base)
2. Governance check (congress interval)
3. Progress metrics updated
4. Next iteration begins

Stall Detection Mechanisms

Following the Deli_AutoResearch protocol:

Mechanism	Rule	Current Status
Stall detection	0 new findings or metric drop → stale_count + 1	stale_count = 0
Forced pivot	stale_count >= 2 → structural constraint change	Not triggered
Human escalation	stale_count >= 4 → flag for human attention	Not triggered
Direction diversity	New direction must differ from all tried	0 directions tried
Round cap	15 rounds or 30 minutes per session	Not reached

Pivot Strategy

"Pivot structure, not tactics" — when a task stalls repeatedly within a frame, the decisive gain usually comes from correcting the environment/structural constraint itself, not from tuning strategy parameters harder inside the existing frame.

State Persistence Across Context Compaction

The workspace implements the Deli_AutoResearch protocol's state management:

1. Physical State Files: All progress written to state/ directory
2. Fresh Session per Iteration: Each iteration starts with fresh context
3. Curated State Injection: Only relevant state injected at session start
4. No Resume: Never resume from conversation memory

This prevents the cognitive loop failure mode where context accumulation leads to repeated similar strategies with diminishing returns.

Failure Tree Deep Dive

The failure tree (658 KB) is the largest state file, implementing the EnCompass pattern for probabilistic agent backtracking.

Tree Structure

Root Node (48470eb7)
├── ca9f0b5a (running)
├── ab1ae2f9 (running)
├── 372e2e06 (running)
├── 706b6cab (running)
├── 4e8a0d49 (running)
├── e781b538 (running)
├── 4d3cf634 (running)
├── 2f01c568 (running)
├── db373a78 (running)
├── 315c15dd (running)
└── ... (more nodes)

Node Properties

Each node contains:

• id: Unique 8-character hex identifier
• approach: The strategy being attempted
• status: running, completed, failed, or unexplored
• parent_id: Link to parent node (null for root)
• children: List of child node IDs
• depth: Distance from root (0 for root)
• failure_reason: Why this approach failed (if applicable)
• failure_category: Classification from AgentRx taxonomy
• reflection: Natural language self-critique (Reflexion-style)
• metrics: Quantitative measurements
• created_at: Unix timestamp
• completed_at: Unix timestamp (null if still running)

Failure Categories (AgentRx Taxonomy)

Category	Description	Example
Goal Drift	Agent deviates from original objective	Starts optimizing wrong metric
Tool Misuse	Incorrect tool selection or parameters	Uses search when should read file
Logic Error	Correct tools, wrong reasoning	Off-by-one in loop
Stalling	Agent loops without progress	Same tool call repeated 5+ times
Silent Failure	Reports success but goal not met	"Done!" but output is wrong
Hallucination	Fabricated facts or API calls	Calls non-existent endpoint

Heuristic Prioritization

The get_promising_unexplored() method ranks unexplored nodes by:

1. Parent Depth: Deeper nodes have more context
2. Sibling Failure Type: Some failures indicate more promising alternatives
3. Failure Category: Goal drift suggests different direction, logic error suggests refinement

EnCompass Integration

The failure tree implements Probabilistic Angelic Nondeterminism (PAN):

• Branch Points: Decision nodes where multiple approaches are possible
• Backtracking: When a path fails, return to last decision point
• Cloning: Spawn parallel exploration of alternative branches
• Unexplored Tracking: Identify promising unvisited siblings

Key Insight from MIT CSAIL: "When a path fails, EnCompass can backtrack to the last decision point, clone the agent's runtime state, explore alternative branches in parallel, and track which branches were never visited."

This validates Alefita's intuition: "se outras possibilidades se abrem com ramos daquele caminho falho que nunca foram explorados."

Orchestrator Log Analysis

The orchestrator logs reveal the initialization pattern and polling behavior.

Log Entry Structure

{
  "ts": 1781758476.298393,
  "source": "orchestrator",
  "level": "info",
  "event": "initialized",
  "detail": "Task: Maintain and evolve the system (Centralismo Democratico)"
}

Event Types

Event	Description
initialized	Task specification loaded, orchestrator ready
tree_root	Failure tree root node created
iteration_start	New iteration beginning
iteration_complete	Iteration finished, findings recorded
stall_detected	stale_count incremented
pivot_forced	Structural constraint changed
congress_convened	Governance Congress session started
chancela_pending	Proposal awaiting human approval

Polling Interval

The 30-second spacing between initialization events suggests the orchestrator uses a polling interval consistent with the Deli_AutoResearch protocol's heartbeat mechanism. This interval is:

• Short enough to detect stalls quickly
• Long enough to avoid excessive resource usage
• Aligned with the L1 heartbeat watchdog (hourly)

Parallel Worker Pattern

The logs show multiple root-level nodes being created in parallel:

• Each worker starts fresh with the same task specification
• Workers operate independently (no parent-child relationships)
• All workers share the same failure tree (read/write access)
• Coordination happens via state files, not direct communication

The Five-Phase Loop in Detail

Phase I: Ideation

• SearXNG discovers relevant URLs
• Crawl4AI reads full content of discovered URLs
• OpenCode (via ACP) synthesizes and generates hypotheses
• New directions must differ from directions_tried.json

Phase II: Execution

• OpenCode compiles code, runs experiments
• State files updated with intermediate results
• Failure tree branches created for new approaches
• Max 5 large files per iteration, no file over 300 lines

Phase III: Audit

• HashMath verifies formal correctness
• Red Team Arena conducts adversarial testing
• Shannon entropy analysis detects reward hacking
• Audit result: audit_passed boolean

Phase IV: Fixation

• Wiki export (state compiled to knowledge base)
• Findings appended to findings.jsonl
• Progress metrics updated in progress.json
• directions_tried.json updated with attempted directions

Phase V: Governance

• If audit failed: automatic proposal generated
• If audit passed: governance check (congress interval)
• Every N=3 iterations: Congress convened
• Approved proposals → AWAITING_CHANCELA

State File Relationships

task_spec.md (read-only goal)
     │
     ▼
progress.json (iteration count, stale tracking)
     │
     ├──► findings.jsonl (append-only discoveries)
     │
     ├──► directions_tried.json (prevents repetition)
     │
     └──► failure_tree.json (EnCompass branching)
              │
              ▼
         orchestrator.jsonl (timestamped events)

Data Flow

1. Task Spec defines the goal (read once at initialization)
2. Progress tracks iteration count and staleness (updated each iteration)
3. Findings accumulate discoveries (append-only, never modified)
4. Directions Tried prevents repetition (read before each iteration)
5. Failure Tree tracks branching strategies (read/write during execution)
6. Orchestrator Logs record all events (append-only, for debugging)

Cross-References

• co-fita-deli-governance — Governance layer that generates proposals
• co-fita-harness-wiki — Intellectual foundation and state file architecture
• co-fita-agent-skills — Skills that read/write workspace state
• multi-agent-methodology — Multi-agent orchestration patterns