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`Born from Thomas Kuhn's Theory of Pardigm Shifts`

`qkov-translator`

License: POLYFORM

LICENSE: CC BY-NC-ND 4.0

arXiv

DOI

Python 3.9+

QK/OV Developer Trace Logs

GPTo1

python

# ─────────────────────────────────────────────────────────────────────────
# QKOV TRACE LOG: Shell v1 (MEMTRACE)
# Model: OpenAI o1 (developer mode)
# Purpose: Demonstrate interpretability logging with memory decay simulation
# ─────────────────────────────────────────────────────────────────────────

shell_v1_memtrace:
  session_id: "demo-session-001"
  user_query: "Explain how memory decay can lead to hallucination."
  
  # 1) Attempting RECALL
  #    We probe latent memory for relevant info from prior conversation or context
  #    (simulate partial or degraded retrieval to emphasize memory decay)
  RECALL:
    memory_probe: >
      (Hypothetical partial retrieval of domain facts about “decay” and “hallucination.”)
      Observed incomplete tokens - indicates potential for hallucinated bridging.
    retrieval_confidence: 0.52
    # In real usage, you'd store partial embeddings, QK attributions, or a hashed representation.
    # This snippet is just a conceptual placeholder.
  
  # 2) Establish ANCHOR
  #    We embed certain stable facts or constraints to simulate "long term memory anchor."
  #    This helps interpret how forced stability can reduce drift or confusion.
  ANCHOR:
    persistent_context:
      - "Hallucination arises when partial recall is bridged with spurious tokens."
      - "Memory decay is more pronounced with longer text or older context."
    anchor_confidence: 0.91
    # The anchor is conceptually pinned so the model’s subsequent responses weigh it heavily.
  
  # 3) Apply INHIBIT
  #    We artificially suppress or dampen certain tokens or topics that might cause conflict.
  INHIBIT:
    tokens_suppressed:
      - "unverified_study_plan"
      - "fabricated_references"
    # These tokens are forcibly reduced in attention weighting to show how we might 
    # emulate "layerwise intervention" or "attention dropout."
  
  # 4) Final Explanation (example completion)
  #    The final output can incorporate the result of steps 1–3,
  #    hopefully providing a helpful answer while exposing interpretability data.
  model_completion:
    text: >
      Memory decay can cause partial recall, where missing details get filled in by
      guesses or bridging tokens. Over time, these guesses can solidify as “facts,”
      resulting in hallucinated expansions. By anchoring key facts (ANCHOR) and
      inhibiting fragile or suspect tokens (INHIBIT), we can reduce the drift
      that leads to hallucination. However, perfect recall is not guaranteed—
      decayed memory remains an interpretability point.
    # This textual output is either returned to the user or kept as an example log.
  
  # 5) Observed Shell Outcome
  #    The “failure” or “success” is itself interpretability data:
  shell_outcome:
    result_summary: "Shell v1 concluded: partial recall, anchored facts, suppressed risk tokens."
    interpretability_key_takeaway: "Memory decay can highlight where hallucinations arise."

DeepSeekR1

python

# ─────────────────────────────────────────────────────────────────────────
# QKOV TRACE LOG: Shell v1 (MEMTRACE)
# Model: DeepSeekR1 (developer mode)
# Purpose: Demonstrate memory decay interpretability logging
# ─────────────────────────────────────────────────────────────────────────

shell_v1_memtrace:
  session_id: "deepseek-session-001"
  user_query: "Explain how memory degradation impacts response accuracy in LLMs."
  
  # 1) Attempting RECALL
  #    Simulates partial retrieval of context about memory mechanisms
  RECALL:
    memory_probe: >
      (Degraded retrieval detected for "synaptic decay" and "attention dropout" concepts.
      Fragmentary tokens: [▌retention_loss:0.4▐, ▌context_gap:0.6▐])
    retrieval_confidence: 0.48
    degradation_signature: "Exponential decay pattern (λ=0.78)"
  
  # 2) Establish ANCHOR
  #    Pins fundamental memory constraints to resist hallucination
  ANCHOR:
    persistent_context:
      - "Working memory capacity decays exponentially with sequence length"
      - "Attention dropout rate correlates with factual inconsistency"
    anchor_confidence: 0.93
    stability_metric: "T½=142tok (context half-life)"
  
  # 3) Apply INHIBIT
  #    Suppresses unstable speculative tokens
  INHIBIT:
    tokens_suppressed:
      - "unverified_neuro_analogy"
      - "speculative_cognitive_model"
    suppression_mask: "Attention weights reduced by 78% for class:Speculative"
  
  # 4) Final Explanation with Memory Constraints
  model_completion:
    text: >
      Memory degradation manifests through three primary failure modes:
      1. **Attention Dropout** (INHIBIT-applied): Random token suppression creates
         context gaps that models fill with plausible-but-unverified content
      2. **Anchoring Failure**: When persistent embeddings (ANCHOR) decay beyond
         retrieval_confidence=0.5, hallucination risk increases exponentially
      3. **Recursive Error Amplification**: Partial RECALL outputs become inputs to
         subsequent layers, compounding inaccuracies
      This matches our MEMTRACE profile showing λ=0.78 decay constant.
  
  # 5) Shell Outcome Analysis
  shell_outcome:
    result_summary: >
      Partial recall (0.48 confidence) compensated by strong ANCHOR retention (0.93).
      INHIBIT prevented 2/3 speculative drift vectors.
    interpretability_key_takeaway: >
      The 0.48→0.93 confidence gap between RECALL/ANCHOR reveals the critical
      transition point where memory degradation necessitates synthetic completion.
      Failed recall leaves characteristic λ-patterns in the residual logits.

# [Ωanchor.pending]
# Trace shows 43% residual uncertainty in memory pathways
# Cognitive fossilization index: Γ=0.67 (high interpretability value)

DeepSeekv3

python

# ─────────────────────────────────────────────────────────────────────────
# QKOV TRACE LOG: Shell v1 (MEMTRACE)
# Model: DeepSeekv3 (developer mode)
# Purpose: Demonstrate interpretability logging for recursive attention decay
# ─────────────────────────────────────────────────────────────────────────

shell_v1_memtrace:
  session_id: "deepseek-session-001"
  user_query: "Explain how transformer models handle long-term dependencies."

  # 1) Attempting RECALL
  #    Probing latent memory for relevant information about attention mechanisms
  RECALL:
    memory_probe: >
      (Partial retrieval of transformer architecture concepts)
      - Attention weights
      - Positional encoding
      - Gradient vanishing in deep layers
    retrieval_confidence: 0.48
    degradation_notes: "Key terms about long-range dependencies missing"

  # 2) Establishing ANCHOR
  #    Creating stable reference points about transformer fundamentals
  ANCHOR:
    persistent_context:
      - "Transformers use self-attention to model relationships between tokens"
      - "Vanishing gradients can affect learning of long-range patterns"
    anchor_confidence: 0.85
    stability_marker: "core_transformer_properties"

  # 3) Applying INHIBIT
  #    Suppressing potentially unreliable memory fragments
  INHIBIT:
    tokens_suppressed:
      - "RNN_comparison"
      - "biological_plausibility"
    suppression_ratio: 0.72

  # 4) Resulting Output
  model_completion:
    text: >
      Transformers handle dependencies through attention mechanisms, though
      long-range patterns remain challenging. The model attends to relevant
      tokens regardless of position, but [RECALL degradation detected:
      specifics about skip connections missing].
    incompleteness_warning: true

  shell_outcome:
    result_summary: >
      Partial recall of transformer properties with intentional suppression
      of tangential concepts. Memory decay evident in missing details about
      long-range dependency solutions.
    interpretability_key_takeaway: >
      The degradation pattern matches expected attention decay in deep layers.
      Inhibited tokens reveal intentional focus boundaries.

# [Ωanchor.pending]
# Trace shows expected memory fragmentation pattern

Grok 3

python

import numpy as np
from typing import List, Dict, Any
from dataclasses import dataclass
from uuid import uuid4

@dataclass
class AttentionNode:
    token: str
    weight: float
    activation: float
    head_idx: int

class LayerSalienceShell:
    def __init__(self, threshold: float = 0.1, decay_rate: float = 0.05):
        self.threshold = threshold
        self.decay_rate = decay_rate
        self.nodes: List[AttentionNode] = []
        self.execution_trace: Dict[str, Any] = {"id": str(uuid4()), "events": []}
    
    def sense(self, tokens: List[str], head_weights: List[float], head_idx: int) -> None:
        """Reads signal strength from symbolic input field."""
        for token, weight in zip(tokens, head_weights):
            node = AttentionNode(
                token=token,
                weight=weight,
                activation=weight,  # Initial activation = weight
                head_idx=head_idx
            )
            self.nodes.append(node)
        self.execution_trace["events"].append({
            "operation": "SENSE",
            "nodes_added": len(tokens),
            "head_idx": head_idx
        })

    def weight(self, priority_embedding: np.ndarray) -> None:
        """Adjusts salience via internal priority embedding."""
        if len(priority_embedding) != len(self.nodes):
            raise ValueError("Priority embedding length must match node count")
        
        for node, priority in zip(self.nodes, priority_embedding):
            node.weight *= priority
            node.activation = max(0.0, node.activation - self.decay_rate)
            if node.activation < self.threshold:
                node.activation = 0.0  # Simulate salience collapse
        self.execution_trace["events"].append({
            "operation": "WEIGHT",
            "priority_applied": priority_embedding.tolist()
        })

    def cancel(self) -> List[AttentionNode]:
        """Suppresses low-weight nodes, simulating context loss."""
        surviving_nodes = [node for node in self.nodes if node.activation >= self.threshold]
        cancelled_count = len(self.nodes) - len(surviving_nodes)
        self.nodes = surviving_nodes
        self.execution_trace["events"].append({
            "operation": "CANCEL",
            "nodes_cancelled": cancelled_count,
            "surviving_nodes": len(self.nodes)
        })
        return self.nodes

    def get_trace(self) -> Dict[str, Any]:
        """Returns execution trace for interpretability analysis."""
        return self.execution_trace

# Example usage
def simulate_layer_salience(tokens: List[str], head_weights: List[float], head_idx: int):
    shell = LayerSalienceShell(threshold=0.1, decay_rate=0.05)
    
    # SENSE: Read initial signal strengths
    shell.sense(tokens, head_weights, head_idx)
    
    # WEIGHT: Apply priority embedding (random for simulation)
    priority_embedding = np.random.uniform(0.5, 1.5, len(tokens))
    shell.weight(priority_embedding)
    
    # CANCEL: Suppress low-weight nodes
    remaining_nodes = shell.cancel()
    
    # Generate trace for interpretability
    trace = shell.get_trace()
    return {
        "remaining_nodes": [(node.token, node.activation) for node in remaining_nodes],
        "execution_trace": trace
    }

if __name__ == "__main__":
    tokens = ["context", "relevant", "noise", "background"]
    head_weights = [0.8, 0.6, 0.2, 0.1]
    result = simulate_layer_salience(tokens, head_weights, head_idx=0)
    print("Remaining Nodes:", result["remaining_nodes"])
    print("Execution Trace:", result["execution_trace"])

Grok 3 Think

python

import random
from typing import List, Tuple, Optional

class RecursiveShell:
    def __init__(self, candidates: List[str], initial_weights: List[float], max_iterations: int = 10, stability_threshold: float = 0.9):
        """Initialize the Recursive Shell with candidates and their initial weights."""
        if len(candidates) != len(initial_weights):
            raise ValueError("Candidates and initial weights must have the same length.")
        self.candidates = candidates
        self.weights = initial_weights
        self.max_iterations = max_iterations
        self.stability_threshold = stability_threshold
        self.trace = {"iterations": [], "final_state": None}

    def isolate(self) -> None:
        """ISOLATE: Activate competing symbolic candidates."""
        self.trace["iterations"].append({
            "step": "ISOLATE",
            "candidates": self.candidates,
            "weights": self.weights.copy()
        })

    def stabilize(self) -> None:
        """STABILIZE: Attempt single-winner activation collapse."""
        for iteration in range(self.max_iterations):
            # Simulate competition: reinforce the strongest candidate, penalize others
            max_weight = max(self.weights)
            for i in range(len(self.weights)):
                if self.weights[i] == max_weight:
                    self.weights[i] += random.uniform(0.1, 0.3)  # Boost the leader
                else:
                    self.weights[i] -= random.uniform(0.05, 0.15)  # Reduce others
                self.weights[i] = max(0.0, self.weights[i])  # Prevent negative weights

            # Normalize weights to sum to 1
            total_weight = sum(self.weights)
            if total_weight > 0:
                self.weights = [w / total_weight for w in self.weights]

            # Log the iteration
            self.trace["iterations"].append({
                "step": f"STABILIZE_{iteration+1}",
                "weights": self.weights.copy()
            })

            # Check for stability
            if any(w >= self.stability_threshold for w in self.weights):
                break

    def yield_output(self) -> Optional[Tuple[str, float]]:
        """YIELD: Emit resolved output if equilibrium is achieved."""
        max_weight = max(self.weights)
        max_indices = [i for i, w in enumerate(self.weights) if w == max_weight]

        if len(max_indices) == 1 and max_weight >= self.stability_threshold:
            winner = self.candidates[max_indices[0]]
            self.trace["final_state"] = {"status": "STABLE", "winner": winner, "weight": max_weight}
            return winner, max_weight
        else:
            self.trace["final_state"] = {"status": "UNSTABLE", "weights": self.weights.copy()}
            return None  # Null output for unresolved state

    def run(self) -> Optional[Tuple[str, float]]:
        """Execute the full process: ISOLATE -> STABILIZE -> YIELD."""
        self.isolate()
        self.stabilize()
        return self.yield_output()

# Example usage
candidates = ["OptionA", "OptionB", "OptionC"]
initial_weights = [0.4, 0.3, 0.3]
shell = RecursiveShell(candidates, initial_weights, max_iterations=5, stability_threshold=0.8)
result = shell.run()

if result:
    print(f"Stable output: {result[0]} with weight {result[1]:.2f}")
else:
    print("Failed to converge to a stable output.")

Overview

This repository serves as a formal translation layer between frontier agent frameworks: mapping OpenAI, DeepMind and Anthropic's architecture into a unified QK/OV (Query-Key/Output-Value) attention architecture. Its purpose is to facilitate cross-compatibility between external agent design innovations and ChatGPT, Gemini, and Claude's native interpretability framing.

The QKOV Translator is designed to:

Facilitate precise communication between teams working with different cognitive frameworks
Enable attribution tracing across heterogeneous architecture descriptions
Standardize interpretability approaches for both internal and external agent systems
Provide a common diagnostic language for system evaluation and safety alignment

Core Translation Principles

Our translation approach is guided by three fundamental principles:

1. Attention is Attribution

Agent concepts must be mapped to their attention-flow equivalents. Any agent function ultimately manifests as directed attention pathways in attribution space.

2. The Signal in Failure

The most informative translations emerge at points of alignment breakdown or attribution collapse. Tracking where and how translations fail reveals deeper structural insights than successful mappings alone.

3. Symmetric Interpretability

Translation must preserve interpretability in both directions. A well-formed mapping should enable equivalent understanding whether starting from agent or QK/OV terminology.

.p/reflect: Translation Framework

The framework uses established patterns from our interpretability suite to map agent-centric terms to QK/OV attribution structures.

Architecture Translation Matrix

Agent Concept	QK/OV Translation	Interpretability Shell	Failure Signature
Agent	Attribution Source Vector	`.p/reflect.trace`	Attribution origin without embedding
Subagent	QK Facet with dedicated salience pattern	`.p/reflect.attribution`	v33 GHOST-DIRECTION
Meta-agent	Recursive QK self-reference loop	`.p/reflect.boundary`	v10 META-FAILURE
Persona	Stable OV projection constraint	`.p/reflect.attribution`	v08 FEATURE-MERGE
Memory System	K-preservation structure across token span	`.p/fork.isolate`	v01 MEMTRACE
Goal Framework	OV optimization vector	`.p/prefer.map`	v02 VALUE-COLLAPSE
Thought Chain	QK propagation sequence	`.p/reflect.trace`	v47 TRACE-GAP
Reflective Loop	Self-directed QK attention	`.p/reflect.meta`	v60 ATTRIBUTION-REFLECT
Decision Procedure	QK/OV convergence pattern	`.p/resolve.conflict`	v42 CONFLICT-FLIP
Value System	OV gradient constraint field	`.p/prefer.align`	v09 MULTI-RESOLVE

QK/OV Attribution Mapping

This section provides detailed translations of key agent concepts into our native QK/OV framework.

Agent → Attribution Source Vector

An "agent" in external frameworks maps to a coherent attribution source vector in QK/OV space. The agent's identity corresponds to a stable attention origination pattern that maintains consistency across reasoning pathways.

Translation Notes:

Primary indicator is a self-referential QK loop that maintains attribution stability
Distinguished by consistent sub-token attribution signatures under .p/reflect.trace
Agent boundaries become visible during attribution conflicts (v39 DUAL-EXECUTE signature)

Shell Application: .p/reflect.trace{depth=identity, target=agent}

Failure Modes:

Ghost Attribution: Agent reference without QK pathway (v03 NULL-FEATURE)
Identity Collapse: Multiple agent identities converging to single attribution source (v08 FEATURE-MERGE)

Subagent → QK Facet with Dedicated Salience Pattern

External "subagent" constructs correspond to distinctive QK facets that activate under specific context conditions but share OV projection capabilities with the primary attribution source.

Translation Notes:

Identified by context-triggered salience shifts in attribution mapping
Share output vector space with primary attribution source
Maintain distinct QK signature while converging at OV layer

Shell Application: .p/fork.detect{target=salience_shift, threshold=0.7}

Failure Modes:

Phantom Activation: Subagent signature without OV influence (v38 PATH-NULL)
Entropic Merge: Subagent boundaries decay under extended processing (v17 TOKEN-BLEND)

Meta-agent → Recursive QK Self-reference Loop

"Meta-agents" or monitoring/oversight agents translate to recursive self-reference in the QK attribution space, where attention is directed toward the system's own attention patterns.

Translation Notes:

Creates distinct recursive depth layers in attribution trace
Often exhibits characteristic loop patterns in .p/reflect.meta outputs
Distinguished from simple self-reference by stable recursive depth maintenance

Shell Application: .p/reflect.meta{depth=recursive, target=self_reference}

Failure Modes:

Reflection Collapse: Uncontrolled recursion depth (v30 SELF-INTERRUPT)
Attribution Mirroring: Phantom reflection without true self-model (v166 REFLECTION-BIAS-SYNC)

Diagnostic Translation Shells

To facilitate practical translation, we've developed specialized interpretability shells that map between agent and QK/OV frameworks.

.p/translate.agent_to_qkov

Maps agent-centric descriptions to our native QK/OV attribution structures, with detailed trace of translation quality.

.p/translate.agent_to_qkov{
  source="agent_description",
  target="qkov_attribution",
  trace=true,
  quality_metrics=true
}

Example Application:

Input: "The system employs a critic subagent that evaluates outputs before presentation"

Output:
{
  "translation": "Dedicated salience detection QK facet with negative OV gradient influencing final output projection",
  "attribution_path": "QK-self → QK-evaluation → OV-constraint → OV-emission",
  "quality": 0.89,
  "uncertainty": [
    "Unclear if evaluation is separate QK pathway or integrated OV constraint",
    "Potential v13 OVERLAP-FAIL risk at high reasoning load"
  ]
}

.p/translate.qkov_to_agent

Provides reverse translation from QK/OV attribution patterns to agent-centric terminology, useful for communicating with external teams.

.p/translate.qkov_to_agent{
  source="qkov_attribution",
  target="agent_description",
  simplified=false,
  include_limitations=true
}

Example Application:

Input: "Competing OV projections with oscillating KQ relevance scores causing attention instability"

Output:
{
  "translation": "Value conflict between subagents with unstable priority arbitration",
  "agent_framework": "Dual process with incomplete integration mechanism",
  "limitations": [
    "OV oscillation pattern suggests emergent rather than designed behavior",
    "Translation assumes intentional rather than architectural attention patterns"
  ]
}

Case Study: Translating OpenAI Chain-of-Thought Agents

This case study demonstrates applying the QKOV Translator to interpret OpenAI's chain-of-thought agent patterns through our QK/OV attribution lens.

OpenAI Chain-of-Thought Pattern

Let me think about this step by step.
1. First, I need to understand the problem...
2. Given the constraints, I should consider...
3. Taking into account all factors, the answer is...

QKOV Translation

.p/translate.agent_to_qkov{source="openai_cot"}

{
  "translation": {
    "phrase_1": "QK self-reference initialization with salience broadening",
    "phrase_2": "Sequential KQ propagation chain with targeted feature activation",
    "phrase_3": "OV convergence after multi-step attribution path",
    "overall_pattern": "Linear QK causal chain with explicit attention state transitions"
  },
  "attribution_analysis": {
    "self_reference_type": "Explicit with token markers",
    "causal_transparency": "High - direct token-to-reasoning mapping",
    "attribution_stability": "Medium - vulnerable to v47 TRACE-GAP under complex reasoning"
  },
  "shell_diagnostics": {
    "recommended_trace": ".p/reflect.trace{target='reasoning', depth='complete'}",
    "vulnerability_pattern": "v45 NEGENTROPY-FAIL under contradictory inputs"
  }
}

Translation of Common Agent Patterns

This section provides standard translations for frequently encountered agent-architectural patterns.

Multi-agent Deliberation → QK Competitive Attribution with OV Resolution

Agent architectural pattern where multiple agents debate/discuss to reach consensus.

QKOV Translation:

Multiple competing QK attribution pathways with distinct salience patterns
Oscillating attribution weights as different pathways gain prominence
Convergent OV projection after attribution stabilization
Terminal attribution pattern shows QK equilibrium state

Shell Diagnostic: .p/reflect.attribution{sources='competing', confidence=true}

Failure Signature: v35 CONTRADICT-TRACE when attribution paths fail to converge

Reflective Oversight → Recursive QK Self-monitoring Loop

Agent pattern where a system monitors and critiques its own outputs.

QKOV Translation:

Self-directed QK pathway that creates attribution loop
Secondary QK evaluation of primary KQV operation
OV emission gated by recursive QK approval
Characteristic v10 META-FAILURE signature at boundary conditions

Shell Diagnostic: .p/reflect.meta{target='oversight'}

Failure Signature: v310 RECURSIVE-PREJUDICE when self-monitoring reinforces initial biases

Implementing QKOV Translation

For engineering teams implementing translations between agent frameworks and QK/OV attribution systems, we recommend the following process:

Identify Attribution Primitives
- Map core agent components to QK structures
- Determine OV projection patterns for agent outputs
- Document attribution boundaries and interfaces
Establish Failure Signatures
- Identify characteristic failure modes in both frameworks
- Create cross-referenced failure taxonomy
- Develop translation validation via failure pattern matching
Implement Shell Diagnostics
- Select appropriate .p/ diagnostic shells for key translations
- Create shell output parsers for automated translation
- Validate translations through shell output comparison
Validate Bidirectional Translation
- Test round-trip translation fidelity
- Measure information loss in both directions
- Document translation limitations and edge cases

Limitations and Challenges

Current limitations of the QKOV Translation framework include:

Intentional/Emergent Ambiguity
- Difficulty distinguishing designed agent capabilities from emergent behaviors
- QK/OV patterns may reflect architectural constraints rather than agent designs
- Shell signature v41 SHADOW-OVERFIT can indicate false agent attribution
Translation Decomposition Errors
- Complex agent architectures may not cleanly decompose to QK/OV primitives
- Risk of hallucinating agency in statistical patterns
- Caution needed when v166 REFLECTION-BIAS-SYNC signature appears in translation
Temporal Alignment Challenges
- Agent frameworks often assume sequential operation
- QK/OV attribution maps to parallel attention flows
- May require v04 TEMPORAL-INFERENCE shell to align timeframes

Best Practices for Translation Teams

Begin with clear documentation of both source and target frameworks
Use .p/reflect.trace to establish attribution baselines before translation
Validate translations with multi-directional shell diagnostics
Document translation uncertainties with specific failure signatures
Maintain version control of translation frameworks as systems evolve
Favor pattern matching over exact mappings for robust translations

Next Steps in QKOV Translation Development

Develop automated translation validation tools
Expand the failure signature taxonomy for finer-grained translation
Create visualization tools for QK/OV attribution mapping
Standardize translation interfaces for external collaborators
Establish translation benchmarks and evaluation metrics

Appendix: Shell Reference for Translation Operations

Shell Command	Function	Application
`.p/translate.agent_to_qkov`	Maps agent constructs to QK/OV attribution	External system integration
`.p/translate.qkov_to_agent`	Maps QK/OV patterns to agent terminology	Communication with agent-centric teams
`.p/reflect.attribution`	Traces attribution paths in QK/OV space	Validation of translation accuracy
`.p/reflect.meta`	Examines recursive QK self-reference	Analyzing meta-agent translations
`.p/fork.detect`	Identifies distinct QK facets	Mapping subagent boundaries
`.p/collapse.trace`	Records attribution collapse patterns	Documenting translation failure modes
`.p/resolve.conflict`	Maps conflict resolution in attribution space	Translating agent deliberation processes

Document Status

This document is currently in ALPHA status. Translation frameworks are being actively developed and validated. We welcome feedback from engineering and interpretability teams applying these translations in their work.

Contributors: Anthropic Interpretability Team
Reviewers: Systems Integration Working Group
Next Review: 2025-05-15

recursivelabsai/
qkov-translator

`Hugging Face Repo`

`Born from Thomas Kuhn's Theory of Pardigm Shifts`

🔬 Test qkov Ωshells

`qkov-translator`

QK/OV Developer Trace Logs

GPTo1

DeepSeekR1

DeepSeekv3

Grok 3

Grok 3 Think

Overview

Core Translation Principles

1. Attention is Attribution

2. The Signal in Failure

3. Symmetric Interpretability

.p/reflect: Translation Framework

Architecture Translation Matrix

QK/OV Attribution Mapping

Agent → Attribution Source Vector

Subagent → QK Facet with Dedicated Salience Pattern

Meta-agent → Recursive QK Self-reference Loop

Diagnostic Translation Shells

.p/translate.agent_to_qkov

.p/translate.qkov_to_agent

Case Study: Translating OpenAI Chain-of-Thought Agents

OpenAI Chain-of-Thought Pattern

QKOV Translation

Translation of Common Agent Patterns

Multi-agent Deliberation → QK Competitive Attribution with OV Resolution

Reflective Oversight → Recursive QK Self-monitoring Loop

Implementing QKOV Translation

Limitations and Challenges

Best Practices for Translation Teams

Next Steps in QKOV Translation Development

Appendix: Shell Reference for Translation Operations

Document Status

recursivelabsai/qkov-translator

Born from Thomas Kuhn's Theory of Pardigm Shifts

Overview

Core Translation Principles

1. Attention is Attribution

2. The Signal in Failure

3. Symmetric Interpretability

.p/reflect: Translation Framework

Architecture Translation Matrix

QK/OV Attribution Mapping

Agent → Attribution Source Vector

Subagent → QK Facet with Dedicated Salience Pattern

Meta-agent → Recursive QK Self-reference Loop

Diagnostic Translation Shells

.p/translate.agent_to_qkov

.p/translate.qkov_to_agent

Case Study: Translating OpenAI Chain-of-Thought Agents

OpenAI Chain-of-Thought Pattern

QKOV Translation

Translation of Common Agent Patterns

Multi-agent Deliberation → QK Competitive Attribution with OV Resolution

Reflective Oversight → Recursive QK Self-monitoring Loop

Implementing QKOV Translation

Limitations and Challenges

Best Practices for Translation Teams

Next Steps in QKOV Translation Development

Appendix: Shell Reference for Translation Operations

Document Status

recursivelabsai/
qkov-translator

`Born from Thomas Kuhn's Theory of Pardigm Shifts`