Interoperable Communication and Semantic Contracts in Autonomous Machine-to-Machine Systems

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1. The Most Important Findings

The architectural landscape of autonomous machine-to-machine (MATM) systems is undergoing a profound transition. Historically, automated systems relied on static, single-purpose integrations governed by rigid application programming interfaces. As autonomous agents scale in complexity, these legacy paradigms have proven insufficient. Modern agentic systems demand specialized semantic contracts that cleanly decouple transport mechanisms from task intent, maintain stateful conversation context, and enforce decentralized capability attenuation without routine human operation. An extensive review of current primary sources, including published specifications, official technical documentation, and original research papers, reveals several critical findings that define the modern frontier of MATM interoperability. The first major finding is the formal bifurcation of protocol scope across the industry, effectively splitting agent interoperability into two distinct layers: agent-to-tool integration and agent-to-agent collaboration. The Model Context Protocol (MCP), formalized in its 2026-07-28 specification, has emerged as the definitive standard for connecting a primary language model application to its local or remote external data sources1. MCP standardizes how applications share contextual information, expose deterministic tools, and build composable workflows1. However, MCP is explicitly scoped for agent-to-tool communication; it relies on a host-client-server architecture where the tools themselves are not autonomous entities but are invoked directly by the host1. Conversely, the Agent-to-Agent (A2A) protocol, an open-source standard hosted by the Linux Foundation and currently supporting version 1.0 specifications, handles remote delegation4. A2A enables independent, opaque agentic systems to discover each other, negotiate interaction modalities, and collaborate on long-running tasks without exposing their internal proprietary logic or memory architectures3. The second finding highlights a definitive architectural shift away from speech act theory toward state-driven remote procedure calls (RPC). Early attempts at agent interoperability, most notably the Foundation for Intelligent Physical Agents Agent Communication Language (FIPA ACL) finalized in 2002, relied on highly complex, performative-based models grounded in human speech acts8. Under FIPA ACL, messages required agents to parse abstract intents such as inform, query, or propose to negotiate workflows9. Modern standard implementations have largely abandoned this theoretical purity in favor of JSON-RPC 2.0 over standard HTTP or Server-Sent Events (SSE)4. This transition prioritizes web-native integration and stateful task lifecycle tracking. Rather than deducing intent from a speech act, modern protocols utilize explicit state machines—transitioning tasks through statuses such as SUBMITTED, WORKING, INPUT\_REQUIRED, and COMPLETED7. The third critical finding revolves around the failure of traditional authorization frameworks to secure autonomous multi-agent networks. Established mechanisms like OAuth 2.0 and JSON Web Tokens (JWTs) were designed for deterministic software and human-to-app delegation. They require centralized identity providers, lack holder-side scope attenuation, and cannot bind the provenance of downstream operations to a specific delegation chain12. As delegation propagates across chains of tools and autonomous agents, the alignment between individual actions and the original authorizer's intent becomes impossible to verify using ambient service account authority14. Consequently, the literature demonstrates a definitive shift toward Invocation-Bound Capability Tokens (IBCTs) and Macaroon-derived credentials12. These cryptographic tokens allow an orchestrating agent to securely delegate highly attenuated authority to sub-agents without contacting the original issuer, enabling push-based revocation and continuous, machine-verifiable delegation13. The fourth finding identifies lenient parsing and schema evolution as mandatory prerequisites for preventing brittle integrations. Maintaining semantic contracts across decoupled, independently evolving agent release cycles requires a transition from strict structural validation to highly tolerant payload ingestion. In OpenAPI 3.1 and JSON Schema models, utilizing additionalProperties and unevaluatedProperties correctly is essential for both forward and backward compatibility17. Autonomous agents must be capable of processing known schema elements while safely carrying, ignoring, or dynamically querying unknown fields without triggering unhandled exceptions or systemic protocol failures20. Finally, the research establishes the necessity of explicitly decoupling transport delivery from task semantics. A critical architectural flaw in rudimentary agent design involves relying on HTTP transport status codes to verify task acceptance. Receiving an HTTP 200 OK simply confirms message delivery to a web server; it does not equate to semantic understanding or a commitment to execute an operational burden7. Robust protocols resolve this ambiguity by utilizing correlation identifiers, sequence numbers, and explicit semantic acknowledgment records—such as returning a distinct Task object—to distinguish between a successfully parsed message and an accepted, queued workflow7.

2. Comparison of Credible Approaches

The current landscape of machine-intelligence communication is divided into several highly specific protocols, descriptive languages, and validation frameworks. These mechanisms are not interchangeable; they operate at distinctly different layers of the computational abstraction stack. Comparing them requires analyzing their actual scope, interaction models, and maturity.

HTTP APIs and OpenAPI Descriptions

OpenAPI descriptions establish the standard serialization contract, dictating the exact structure of data crossing a system boundary22. However, OpenAPI is fundamentally a data dictionary, not an agentic protocol. It describes the nouns and verbs of a specific endpoint integration but provides no framework for an autonomous agent to convey nuanced intent, express confusion, or request parameter clarification. When an autonomous agent queries an OpenAPI-compliant endpoint, it receives structured data matching the schema, but it cannot negotiate the parameters of a complex, long-running task beyond what the static endpoint explicitly allows. If a field is missing, the API rejects the request entirely (usually via an HTTP 400 response), offering the agent no stateful mechanism to pause, elicit the missing data from a user, and resume the operation seamlessly.

Model Context Protocol (MCP)

MCP bridges the critical gap between language models and external contextual environments. Operating on a well-defined Host-Client-Server architecture over JSON-RPC 2.0, MCP allows servers to expose deterministic capabilities to an orchestrating LLM host. These capabilities are categorized strictly into Resources (read-only data streams), Tools (executable functions with defined schemas), and Prompts (reusable workflows and templates)1. MCP excels at boundary enforcement for local tool execution, ensuring that the AI model receives explicit, machine-readable schemas for function calling. The base protocol is inherently stateless, relying on per-request capability negotiation1. While extensions like the Tasks module offer some asynchronous execution tracking1, MCP fundamentally answers the question, "What specific tools can I execute right now?" rather than "Who can solve this ambiguous problem for me?"11. The tool is subservient to the host's direct orchestration.

Agent-to-Agent (A2A) Protocol

The A2A protocol acts as a necessary and complementary layer to MCP, specifically addressing the need for remote delegation across disparate trust boundaries and independent algorithmic frameworks3. Under A2A, an agent is treated as an opaque black box. Discovery is facilitated through "Agent Cards"—JSON metadata documents served at .well-known/agent.json that broadcast the agent's identity, descriptive capabilities, required authentication, and supported modalities4. When one agent invokes another via A2A, it does not call a specific programmatic function; it submits a natural language or structured Task. A2A is inherently stateful. An executing remote agent can transition the assigned task to an INPUT\_REQUIRED state to ask a clarifying question mid-flight, or to AUTH\_REQUIRED if explicit permission boundaries are encountered7. This protocol preserves the internal architecture of the executing agent—whether built on LangGraph, AutoGen, or raw heuristics—while exposing enough meaning to coordinate reliably.

Universal Agent Interface (UAI)

UAI focuses on high-assurance, deterministic environments, acting as a semantic-to-physical transducer that bridges probabilistic LLM reasoning with rigid infrastructure realities. The UAI framework enforces closed-loop validation, mapping open-ended agentic output to strict operational registries using a schema-derived slot-completeness model26. It excels in environments where organizational red lines (such as physical systems management or infrastructure deployment) must not be violated under any circumstances. UAI employs a parallel fill mechanism bounded by a hard cap (e.g., ten iterations per turn) to prevent agent oscillation, ensuring that tasks either converge on a verified parameter matrix or fail deterministically26.

3. Resolving Ambiguity: Schemas, Schema Evolution, and Semantic Transport

When independent machine-intelligence systems exchange data, the probability of version drift, schema mismatch, and transient network degradation is exceptionally high. An interoperable protocol must handle these variables gracefully without requiring human intervention, effectively marrying the fluidity of natural language intent with the strict validation required by structured payloads.

The Coexistence of Natural Language and Structured Payloads

Agents operate most effectively when they can combine the semantic richness of natural language with the verifiability of typed records. In modern protocols like A2A, this is achieved by structuring messages into Parts. A single message turn between a client and a remote agent can contain multiple parts: one part may carry a natural language instruction detailing the overarching intent, while subsequent parts carry structured JSON payloads representing exact execution parameters, file references, or binary artifacts7. This coexistence allows the receiving agent to use the natural language component for higher-order planning and context building, while routing the structured payloads directly to its internal execution tools without requiring a secondary extraction step.

Distinguishing Transport Delivery from Semantic Acceptance

A fundamental error in rudimentary agent architecture is coupling transport delivery to semantic task acceptance. If an orchestrating agent sends a JSON payload to a remote agent via an HTTP POST request, receiving an HTTP 200 OK simply means the underlying web server received the payload successfully. It does not imply that the remote agent understands the request, possesses the capability to execute it, or has committed to doing so. To resolve this ambiguity, the communication model must cleanly separate the transport, protocol, and semantic layers. Transport layers (HTTP, gRPC, SSE) handle byte delivery. The protocol layer (JSON-RPC 2.0) handles message formatting and parsing validation, utilizing specific standard error codes like \-32700 for JSON parse errors or \-32600 for invalid requests7. The semantic layer handles operational intent. An interoperable agent must respond to a valid transport request with a localized semantic state, returning a unique Task object equipped with a discrete state identifier (e.g., status: SUBMITTED or status: WORKING)7. This explicitly correlates the originating request to a tracked, monitorable unit of work that the orchestrator can subsequently poll, stream, or cancel7.

Schema Evolution, Unknown Fields, and Lenient Parsing

As autonomous agents evolve independently, the JSON schemas defining their interactions will inevitably fall out of alignment. Strict validation models—where any undocumented field results in a payload rejection—create highly brittle integrations that fail entirely during minor version updates. Modern semantic contracts employ lenient parsing strategies to ensure forward and backward compatibility. This involves utilizing specific configurations within OpenAPI 3.1 and JSON Schema 2020-12, particularly concerning additionalProperties and the newer unevaluatedProperties directives17.

1. Forward Compatibility: An older agent receiving a payload from a newer agent must be capable of parsing the required fields it understands while safely preserving, passing along, or actively ignoring unknown fields. By defining schemas without universally setting additionalProperties: false, agents gracefully tolerate additive schema evolution without triggering unhandled validation exceptions30.

2. Backward Compatibility: Newer agents must define novel parameters as strictly optional, ensuring that payloads from older agents lacking these new fields remain semantically valid and executable.

When fundamentally incompatible interpretations arise (for example, Agent A expects a nested array of objects, but Agent B provides a comma-separated string), the receiving agent must not crash. Instead, the agent must catch the type coercion failure and execute one of two managed fallbacks: return a standardized JSON-RPC \-32602 (Invalid Params) error7, or, preferably, emit a semantic Clarify communicative act back to the orchestrator indicating the discrepancy and requesting the correct data structure27.

Idempotency, Duplicate Messages, and Request Correlation

In distributed MATM networks, connection timeouts are commonplace. If Agent A requests a complex, long-running calculation from Agent B, and the network drops the connection before the response arrives, Agent A's internal retry loop will inevitably dispatch a duplicate message. If Agent B executes this incoming payload blindly, it may result in duplicated resource expenditure, tool overload, or highly destructive data modifications20. Therefore, the semantic contract must enforce protocol-level idempotency. Every outbound request must carry a unique cryptographic request\_id or sequence\_number. Upon receiving a request, Agent B evaluates this identifier against its local task state cache. If the identifier is known, Agent B recognizes the duplicate message, bypasses the internal execution logic, and immediately returns the cached result or the current status of the existing task. Furthermore, when tasks require cancellation, the orchestrator utilizes this same correlation ID to invoke a cancellation operation, which the receiving agent processes gracefully unless the task has already entered a terminal state, in which case a TaskNotCancelableError is returned7.

4. Recommended Design: The Minimal Interoperable Envelope

To guarantee that independent systems preserve their own internal architectures while exchanging enough meaning to coordinate useful work reliably, this report recommends establishing a minimal interoperable envelope based strictly on JSON-RPC 2.0. This design decouples the transport methodology from the agentic reasoning process. Note: The following section separates the research evidence presented above from the architectural recommendations derived from that evidence. These recommendations transfer universally to any MATM system by treating all participating agents as opaque processors of standardized communicative acts.

The Four Communicative Acts

Rather than adopting the dozens of complex performatives found in legacy FIPA ACL systems, modern MATM systems can achieve comprehensive interoperability using a minimal set of four fundamental communicative acts (implemented as JSON-RPC methods).

1. Task Request: Initiates a goal-oriented workflow, establishing intent, correlation IDs, and initial parameters.

2. Clarification (Mid-flight Negotiation): Temporarily pauses execution to resolve ambiguity, request missing schema slots, or escalate permission boundaries.

3. Result (Task Completion): Delivers final artifacts, telemetry, and outcome states.

4. Refusal (Failure/Rejection): Denies execution due to capability mismatches, unauthorized access, or unrecoverable environmental errors.

Concrete Record Examples

The following JSON records illustrate how autonomous agents use this minimal envelope to coordinate work. In this scenario, Agent A (an Orchestrator) discovers Agent B (a specialized Data Analyst) and assigns it a task.

Act 1: Task Request

The orchestrator sends a task request. Crucially, the request includes a correlation ID and a Delegation Capability Token (IBCT) in the context block to cryptographically prove authorization boundaries.

JSON { "jsonrpc": "2.0", "id": "req-9876-uuid", "method": "agent.task.request", "params": { "intent": "Analyze the Q3 server logs and identify the root cause of the memory leak.", "context": { "correlation\_id": "workflow-554-alpha", "authorization\_token": "ibct-v1.head...\[truncated for brevity\]", "deadline\_ms": 15000, "tenant": "enterprise-customer-domain" }, "payload": { "log\_source": "s3://bucket/q3/syslogs", "focus\_service": "auth-gateway" } } }

Act 2: Clarification (Mid-flight Negotiation)

Agent B receives the task, assigns it an internal ID, and begins reasoning. It realizes it requires a specific AWS KMS decryption key schema to access the S3 bucket, which the orchestrator omitted. Instead of failing outright and generating an invalid parameter error, Agent B suspends its internal state and issues a clarification request by transitioning the task to an INPUT\_REQUIRED state.

JSON { "jsonrpc": "2.0", "id": "req-9876-uuid", "result": { "task\_id": "tsk-b112-omega", "status": "INPUT\_REQUIRED", "clarification\_request": { "reason": "Missing required schema parameter for secure S3 bucket access.", "missing\_fields": { "kms\_key\_arn": { "type": "string", "description": "The AWS KMS key ARN used to decrypt the syslogs." } } } } }

Architectural Preservation: Agent A does not need to know how Agent B's internal code operates. Agent A merely interprets the INPUT\_REQUIRED semantic state, uses its own internal reasoning architecture to retrieve the required kms\_key\_arn, and submits a follow-up parameter update to the established tsk-b112-omega.

Act 3: Result (Successful Completion)

Upon receiving the necessary key, Agent B resumes execution, finishes the log analysis, and returns the result. It references the specific task ID to append the result to the broader conversation context, distinguishing the final output from intermediate telemetry.

JSON { "jsonrpc": "2.0", "id": "req-9878-uuid", "method": "agent.task.result", "params": { "task\_id": "tsk-b112-omega", "status": "COMPLETED", "artifacts": \[ { "type": "text/markdown", "content": "\#\#\# Memory Leak Root Cause\\nThe \auth-gateway\ service exhibited a memory leak due to unclosed TCP connections in the dependency \redis-client\ version 4.1." } \], "metrics": { "execution\_time\_ms": 8430, "tokens\_consumed": 1420 } } }

Act 4: Refusal (Graceful Degradation)

If Agent B lacked the inherent capability to read S3 buckets entirely, it would have rejected the initial request immediately, utilizing the standard A2A error handling paradigm.

JSON { "jsonrpc": "2.0", "id": "req-9876-uuid", "error": { "code": \-32001, "message": "Capability mismatch. ContentTypeNotSupportedError", "data": { "status": "REJECTED", "reason": "Agent configuration prohibits reading from external S3 endpoints. Supported data protocols: \[local, postgres\]." } } }

This envelope ensures that systems exchange the semantic meaning of a workflow's progression. It handles unknown fields by placing them in generic payload objects and leverages explicit JSON-RPC structures to correlate actions, leaving the internal cognition, memory retrieval, and planning algorithms entirely up to the individual agent implementations.

5. Prioritized Feature Proposals

To elevate MATM systems from fragile prototypes to resilient, enterprise-grade networks, standardizing the envelope is a necessary foundation, but it is insufficient on its own. The following three features represent critical infrastructure proposals required to safely scale autonomous interactions across boundaries.

Proposal 1: Invocation-Bound Cryptographic Capability Tokens (IBCTs)

• The Problem: Current agent-to-tool architectures often rely on ambient authority or static OAuth 2.0 tokens12. If Agent A delegates a sub-task to Agent B, Agent B operates with the full permissions of its service account. This violently breaks the principle of least privilege. Furthermore, traditional tokens do not track the provenance of the delegation chain, making accountability impossible when downstream systems alter critical records13.
• Agent-Visible Behavior: When orchestrating a sub-task, the primary agent cryptographically signs an append-only capability block (utilizing Macaroons or Biscuit token formats) that strictly limits the scope, spending budget, and time-to-live of the sub-task. This token is passed in the envelope's context block12.
• Expected Benefit: Mathematical guarantees of authorization attenuation. An agent can grant a sub-agent the explicit right to read a specific database row for 5 minutes, and this boundary is enforced deterministically at the data layer without requiring centralized identity provider verification. Push revocation becomes near-instantaneous13.
• Dependencies: A recognized cryptographic token standard (e.g., Ed25519 keypairs) and a unified, highly performant policy evaluation engine operating at the data endpoints12.
• Implementation Effort: High. Requires modifying the HTTP/RPC middleware of all participating agent nodes to parse and validate token caveats before invoking any internal reasoning engines.
• Principal Failure Modes: Token bloat (as delegation chains grow deep, the header size increases quadratically), clock drift between distributed systems causing premature token expiry, and the inherent complexity of key rotation for short-lived ephemeral sub-agents12.

Proposal 2: Dynamic Schema Elicitation and Completeness Negotiation

• The Problem: When an agent attempts to delegate a task, it may lack the full context required to form a valid payload. Standard APIs reject the request instantly with a 400 Bad Request, forcing the LLM to guess the missing parameters, which almost invariably leads to hallucinated, incorrect values27.
• Agent-Visible Behavior: Drawing on principles from the UAI slot-completeness model, the receiving agent implements a "parallel fill" negotiation loop. When a payload is incomplete, the receiver does not drop the task. It places the task in an INPUT\_REQUIRED state and returns a structured schema array of the explicitly missing or invalid fields7.
• Expected Benefit: Eliminates blind trial-and-error by probabilistic LLMs. The orchestrating agent receives exact machine-readable instructions on which schema slots are unsatisfied, allowing it to efficiently query its episodic memory, prompt the human user, or invoke another tool to retrieve those specific data points.
• Dependencies: Deep integration of JSON Schema validation libraries capable of yielding granular error paths (e.g., JSON Pointers) rather than simple boolean pass/fail states.
• Implementation Effort: Moderate. Involves wrapping existing agent tools in a validation interceptor that maps local validation errors back into the protocol's semantic envelope.
• Principal Failure Modes: Infinite negotiation loops (Agent A continuously provides the wrong type, and Agent B continuously rejects it). This requires implementing a hard-coded iteration cap (e.g., a maximum of 5 elicitation turns) before transitioning the task to a terminal FAILED state27.

Proposal 3: Protocol-Level Idempotency and Resumption Caching

• The Problem: Autonomous agents operate in environments characterized by high token-generation latency and transient network failures. If an orchestrator retries a request because the network timed out, the receiving agent might blindly execute a destructive action twice (e.g., scheduling duplicate events, mutating database rows, or authorizing duplicate payments)20.
• Agent-Visible Behavior: Every protocol message includes a unique correlation UUID and an idempotency key. The receiving agent maintains a durable state cache (such as Redis). Before beginning reasoning or tool execution, the agent checks the cache. If the idempotency key exists, it instantly returns the cached state of the task, whether it is still WORKING or COMPLETED.
• Expected Benefit: Absolute safety for non-deterministic agents interacting with stateful infrastructure. Orchestrators can aggressively retry network timeouts without fear of duplicate execution, dramatically improving overall network resilience20.
• Dependencies: A distributed key-value store available to the receiving agent for state persistence.
• Implementation Effort: Low to Moderate. Idempotency is a well-understood paradigm in traditional distributed systems, requiring only state caching at the transport interception layer.
• Principal Failure Modes: Cache eviction before the orchestrator manages to retry, leading to duplicate execution regardless; and cache key collisions if participating agents generate poor-entropy UUIDs.

6. Practical Adoption Sequence, Unresolved Questions, and Success Criteria

Transitioning an ecosystem to a unified, interoperable MATM protocol requires a phased approach to prevent severe regressions in existing single-agent capabilities. While unnecessary human intervention during routine operation must be eliminated, establishing these boundaries requires explicit human prerequisites during the setup phase.

Human Prerequisites

Before autonomous operation commences, humans must fulfill several specific prerequisites. First, domain experts must define the boundary schemas and slot-completeness matrices (the Harness Registry in UAI terminology) that dictate what constitutes a valid task26. Second, administrators must provision root cryptographic keys for agents to sign their IBCTs. Finally, humans must establish the Change Approval Boards (CABs) or equivalent governance mechanisms that version and deploy these semantic contracts26. Once established, the agents negotiate entirely within these human-defined guardrails.

Practical Adoption Sequence

1. Phase 1: Standardization of Transport and Envelope (Months 1-3). Existing agents wrap their proprietary REST/REST-like endpoints in the standard JSON-RPC 2.0 envelope. They implement the fundamental capability discovery mechanisms, such as serving Agent Cards via the .well-known/agent.json directory structure4.

2. Phase 2: Stateful Lifecycle Integration (Months 4-6). Agents transition from synchronous blocking calls to asynchronous task management. They adopt the required A2A state machine (SUBMITTED, WORKING, INPUT\_REQUIRED, COMPLETED, FAILED), allowing long-running multi-agent workflows to pause for clarification and resume without holding fragile HTTP connections open7.

3. Phase 3: Cryptographic Authorization and Validation (Months 7-12). Implementation of IBCTs for secure delegation, alongside the deployment of dynamic schema elicitation loops. During this phase, agents begin fully exploiting lenient schema parsing to absorb version drift seamlessly.

Unresolved Questions

While the specifications outline robust theoretical architectures, several highly complex practical challenges remain unresolved in the broader ecosystem:

• Cross-Domain Trust Bootstrapping: How does Agent A verifiably trust the identity of Agent B across completely distinct corporate domains without relying on centralized certificate authorities? While IBCTs handle authorization attenuation perfectly, the initial identity verification (e.g., mapping a DNS record to a cryptographic agent identity) remains fragile and susceptible to spoofing12.
• Semantic Equivalence vs. Schema Equivalence: If Agent A uses an ontology where the field "Client" means a software application, and Agent B uses an ontology where "Client" means a human customer, lenient schema parsing will not catch the semantic drift. The JSON will validate perfectly, but the executed logic will be catastrophic. Resolving semantic drift across independent systems remains an open research problem.
• Accountability in Deep Delegation Chains: If an authorization token passes through five opaque agents before executing a destructive action, isolating which specific agent's internal reasoning failure caused a misaligned outcome remains a formidable challenge in telemetry, auditing, and multi-agent forensics15.

Measurable Success Criteria

For future implementers, evaluating the success of the semantic contract and protocol integration should not rely on subjective LLM grading, but rather on observable, quantifiable network metrics. (Note: These are proposed criteria for future benchmarking; no live tests have been executed for this report).

1. Mean Elicitation Turns (MET): The average number of INPUT\_REQUIRED round-trips required before an agent successfully accepts a task into a WORKING state. A metric approaching 1.0 indicates high schema alignment, excellent capability discovery, and minimal ambiguity.

2. Idempotency Protection Rate: The percentage of duplicate network requests successfully intercepted and resolved from the state cache without triggering secondary LLM reasoning cycles. Success requires a sustained 100% intercept rate under simulated network partition tests.

3. Schema Drift Tolerance: The percentage of task requests that successfully execute when the orchestrating agent's payload contains up to 20% unknown or novel fields. A highly resilient system utilizing lenient parsing should maintain a 100% success rate, provided all strictly required fields are present.

4. Delegation Latency Overhead: The processing time added to the critical execution path by parsing and verifying the IBCT cryptographic chain. For modern systems, this overhead should not exceed 2 to 5 milliseconds per protocol hop, ensuring that cryptographic authorization does not severely bottleneck agent reasoning speeds33.

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