M3-AVM: A Virtual Machine Architecture for Surgical Correction of Reasoning in LLMs

M³-AVM: A Virtual Machine Architecture for Surgical Correction of Reasoning in LLMs

A Preemptive, Copy-on-Write Execution Engine with Formal Guarantees for Chain-of-Thought Interventions

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Abstract

Contemporary Large Language Model (LLM) serving infrastructures—such as vLLM, llama.cpp, and proprietary API services—treat autoregressive generation as an atomic, irreversible operation. When a user or external supervisor identifies that the model has initiated reasoning based on incorrect premises (e.g., selecting an inappropriate technological framework at the hundredth token of a three-thousand-token chain), the only available intervention mechanism is complete termination and reprocessing of the prompt from scratch. This monolithic approach imposes severe computational costs: 100% of generated reasoning tokens are discarded, prefilling phases are repeated, and dynamic human intervention becomes impossible.

This work proposes the M³-AVM (Matheus de Camargo Marques Abstract Virtual Machine) , a virtual machine architecture that replaces traditional inference pipelines with a preemptive, Copy-on-Write (COW) execution model. We formalize the system mathematically, defining the persistent memory state, the notification-oriented interrupt bus, and the selective rollback mechanism. The M³-AVM enables the interruption of autoregressive generation, reversion of memory state and context buffers to the exact point of logical divergence, injection of corrective instructions, and resumption of inference while preserving the entirety of previously valid reasoning. Experimental results on a commodity AMD Ryzen 3500U demonstrate interrupt latencies of approximately 217 µs and rollback times of 39 µs, achieving up to 95% preservation of valid reasoning context.

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1. Introduction: The Fundamental Problem of Inflexibility in Reasoning LLMs

Recent advances in Large Language Models have redefined the frontier of natural language processing through Chain-of-Thought (CoT) reasoning capabilities. Models such as DeepSeek-R1 and the OpenAI o1 series allocate substantial computational resources during inference time, generating extensive internal thought trajectories—often ranging from hundreds to tens of thousands of tokens—before presenting a final response to the user. This paradigm allows the model to perform internal verification, explore alternative logical paths, and autonomously correct intermediate misconceptions.

However, contemporary LLM serving infrastructures were designed under the assumption of static, immutable text sequences. Within these systems, autoregressive generation is treated as an atomic, irreversible operation. When a user or external supervisor identifies that the model has initiated reasoning based on incorrect premises—such as the selection of an inappropriate technological framework at the hundredth token of a three-thousand-token chain—the only available intervention mechanism is complete termination and reprocessing of the prompt from zero.

This monolithic approach imposes severe computational and operational costs:

Cost Category	Description
Complete Loss of Reasoning Tokens	Discarding the output buffer eliminates 100% of generated tokens up to the interruption point, wasting all prior computational work.
Repeated Latency and Prefill	The system must reprocess the prefill phase and recreate the initial logical steps that were already correct, increasing response time and GPU memory load.
Inability for Dynamic Human Intervention	The absence of mutable execution state prevents real-time collaboration between operator and the model's internal deliberative process.

1.1 Proposed Solution: The M³-AVM

To address this inefficiency, we propose the M³-AVM (Matheus de Camargo Marques Abstract Virtual Machine) , a virtual machine architecture implemented in Rust. The M³-AVM replaces the traditional inference flow with a preemptive execution model featuring Copy-on-Write (COW) support. This virtual machine enables:

1. Interruption of autoregressive generation.
2. Reversion of memory state and context buffers to the exact point of logical divergence.
3. Injection of corrective instructions.
4. Resumption of inference while preserving the totality of previously valid reasoning.

The M³-AVM achieves interrupt latencies on the order of ~217 µs and rollback times of ~39 µs, enabling surgical correction of model reasoning in real-time.

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2. Memory Architecture and Execution Model

2.1 128-Bit Logical Address Space

The M³-AVM organizes its memory through a flat 128-bit logical structure, designed to prevent memory contention and support massive volumes of weight tensors and contextual caches without pointer collision risk. The memory is divided into four strict regions:

Region	Base Address	Logical Size	Operational Purpose	Access Pattern
GLOBAL	0x0000_0000_0000_0000	32 GiB	Immutable tensors, compiled program code, static parameters	Direct Read (Zero-Copy)
TEMPORAL	0x1000_0000_0000_0000	64 MiB	Circular I/O buffers for prompts, tokens, and interrupt payloads	Volatile Read/Write
PERSISTENT	0x2000_0000_0000_0000	Configurable (default 64 MiB)	Memory-mapped weight files (mmap) and persistent structures	Direct Read/Mapping
KV_CACHE	0x3000_0000_0000_0000	Configurable (default 1 GiB)	Key-Value cache for autoregressive attention operations	Copy-on-Write Radix Tree

2.2 Execution Context and Internal States

The smallest processing unit in the M³-AVM is the Context, which encapsulates the complete state of an inference session. The context data structure is isolated and thread-safe, containing the following components:

• General Purpose Registers: 16 128-bit registers (R0 through R15), where R0 is permanently fixed to zero for null operation optimization.
• Program Counter (PC): A 128-bit register storing the address of the VM assembly instruction currently executing.
• Copy-on-Write Memory Root: An atomic pointer (Arc<MemoryNode>) pointing to the current state of the memory mapping tree.
• NOP Priority Queue: Classification of the context within the scheduler, categorized as Red (real-time/critical), Blue (interactive), or Green (batch processing).
• Reasoning State Machine: The context deterministically navigates between states: Thinking (generating internal reasoning tokens), Generating (emitting final visible response), and Idle (suspended awaiting instructions or in interruption procedure).

2.3 Integration with Multi-Head Latent Attention (MLA) and Radix Trees

The efficiency of the M³-AVM's state restoration mechanism is maximized when combined with modern memory compression architectures, such as the Multi-Head Latent Attention (MLA) employed by DeepSeek-R1. In traditional Multi-Head Attention (MHA), the KV Cache size grows linearly with the number of heads and sequence length, generating a substantial VRAM footprint that complicates checkpoint storage.

The MLA architecture compresses Key and Value vectors into a low-rank latent space:

$$
c_t^{KV} = W_{DKV} h_t
$$

Where $c_t^{KV} \in \mathbb{R}^{d_c}$ represents the compressed latent vector, $h_t$ is the hidden activation of the layer, and $d_c$ is the latent dimension—significantly smaller than the product of heads by the individual dimension. During the attention step, Key and Value vectors are dynamically reconstructed via expansion projection matrices ($W_{UK}$ and $W_{UV}$).

In the M³-AVM, the KV_CACHE region stores exclusively the latent vectors $c_t^{KV}$ organized in a Radix tree with logical paging support. Since each Radix tree node contains only references to latent blocks and atomic reference counters (Arc), duplicating a memory state via a FORK instruction or creating an incremental checkpoint does not require copying physical data in RAM. When a logical divergence occurs in the reasoning chain, the VM simply detaches pointers to the most recent Radix tree nodes without affecting shared ancestor nodes.

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3. The Complete Instruction Set Architecture (ISA)

The M³-AVM ISA is designed to be concise, deterministic, and free of hidden side effects. All instructions adhere to a fixed 32-byte binary format:

• 1 byte for the opcode.
• 1 byte for control flags.
• 6 bytes for operand register specification.
• 24 bytes reserved for immediate fields or tensor geometry parameters.

3.1 The 8 Opcodes

Opcode	Hex	Assembly Syntax	Operational Description	State / Memory Effect
TENSOR	0x01	TENSOR Rd, shape, dtype, flags	Allocates dense or sparse tensor structures in GLOBAL or PERSISTENT regions.	Writes base descriptor pointer to Rd.
ATTN	0x02	ATTN Rd, Q, K, V, flags	Executes scaled attention (FlashAttention/BSR) with KV Cache support.	Writes resulting context matrix to Rd.
STREAM	0x03	STREAM Rsrc, Rsink, flags	Transfers data blocks between memory regions or peripherals under backpressure control.	Writes data to receiver channel, handles blocking.
FORK	0x04	FORK Rd, label, flags	Clones the current context via Copy-on-Write and schedules a new execution thread.	Returns cloned context ID to Rd.
ABORT	0x05	ABORT Rs_context, Rs_payload	Interrupts target context and triggers rollback/injection procedure.	Alters target context state, restores checkpoint.
SENSE	0x06	SENSE Rd, PERIPHERAL_ID, flags	Performs non-blocking reads from input peripherals (I/O, VAD, channels).	Writes read value or interrupt status to Rd.
NORM	0x07	NORM Rd, Rsrc, Rgamma, Rbeta	Applies tensor normalization (RMSNorm or LayerNorm) along the final axis.	Stores normalized tensor in Rd.
FFN	0x08	FFN Rd, Rsrc, Rw1, Rb1, Rw2, Rb2	Executes the Feed-Forward layer using SwiGLU or GELU activations.	Updates intermediate projection tensor in Rd.

3.2 Detailed Opcode Semantics

TENSOR (0x01): The TENSOR instruction initializes matrix representations in VM memory. When the PERSIST flag is enabled, the VM maps the tensor directly into the PERSISTENT region via the loaded mmap file. If the SPARSE flag is used, the VM allocates the structure using the CSR (Compressed Sparse Row) format, enabling optimizations in language models with activated sparsity.

ATTN (0x02): The ATTN opcode handles attention computation. During execution, the VM verifies whether the addresses contained in K and V correspond to the KV_CACHE region. If affirmative, the computation is integrated with the Radix tree history repository. If the MASK flag is present, a causal lower-triangular mask is applied to prevent future information leakage. The inclusion of the BLOCK_SIZE parameter forces the VM to use the BSR (Block-Sparse Row) format, reducing matrix multiplication complexity.

STREAM (0x03): This instruction manages data movement between internal processes and I/O peripherals via asynchronous channels. If the BLOCKING flag is set, the context enters a suspended state until the receiver consumes the buffer. If the DROP flag is triggered and the channel is saturated, new data is discarded, maintaining pipeline determinism.

FORK (0x04): The FORK opcode duplicates the active execution context. The operation does not copy the data contained in memory; instead, it clones the memory root pointer and generates a new register table. The new context is inserted into the NOP scheduler's priority queue under the RED, BLUE, or GREEN flags, with an average execution cost of 39 µs.

ABORT (0x05): The ABORT instruction is the primary intervention mechanism of the VM. It emits a signal on the notification bus. When the target context intercepts the signal during its generation loop, execution is halted. If the payload register contains pointers to an InterruptPayload structure, the VM executes a rollback to the indicated checkpoint and injects the new instruction sequence. The average emission and interception latency is 217 µs.

SENSE (0x06): The SENSE opcode queries input devices such as the user keyboard (PERIPHERAL_USER_INPUT) or a Voice Activity Detection module (PERIPHERAL_VAD). Under the NON_BLOCKING flag, the destination register Rd receives 0 if no new data is available in the buffer, allowing the VM to continue autoregressive generation without waiting penalties.

NORM (0x07): The NORM instruction applies element-wise vector normalizations. The operation calculates the Root Mean Square (RMS) on the final axis and applies the scalar transformation:

$$
\text{RMSNorm}(x) = \frac{x}{\sqrt{\frac{1}{d}\sum_{i=1}^{d} x_i^2 + \epsilon}} \odot \gamma + \beta
$$

FFN (0x08): Executes the two-step Feed-Forward layer typical of Transformer models. The instruction performs the first linear projection over $W_1$, applies the SwiGLU activation function ($x \cdot \sigma(x W_1) \cdot x W_3$), and projects the result back to the model dimension via $W_2$.

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4. Formal Mathematical Framework

This section formalizes the core operational semantics of the M³-AVM, providing the theoretical underpinnings for its behavior.

4.1 Persistent Memory State Model

Let $\mathcal{V}$ be the set of all values representable in the VM (bytes, tensors, pointers). The state of memory at time $t$ is a persistent tree $\mathcal{M}_t$ defined as:

$$
\mathcal{M}t: \text{Addr}{128} \rightarrow \mathcal{V}
$$

where $\text{Addr}_{128} = {0, 1}^{128}$ is the set of 128-bit virtual addresses.

A root pointer $\rho_t \in \mathcal{R}$ is a reference to the root node of the persistent tree that represents $\mathcal{M}_t$. The root pointer is an immutable reference; any modification to the memory state creates a new root.

Definition 1 (Copy-on-Write Fork).
Given a current memory state $\mathcal{M}t$ with root $\rho_t$, the $\text{FORK}$ instruction produces a new state $\mathcal{M}{t+1}$ such that:

$$
\rho_{t+1} = \text{copy_reference}(\rho_t)
$$

where $\text{copy_reference}(\rho)$ increments the reference count of the root node and all nodes reachable from it without duplicating their contents. The complexity of this operation is constant:

$$
\text{Cost}(\text{FORK}) = O(1)
$$

Empirically, this operation measured 39 µs on the reference hardware (AMD Ryzen 3500U), independent of the size of the KV Cache, due to shared pointers (Arc).

4.2 Notification Bus and Interrupt Model

The system is modeled as an event-driven machine. The notification bus $\mathcal{B}$ is a single-writer, multi-reader communication channel.

Definition 2 (Interrupt Signal).
An interrupt signal is a 4-tuple:

$$
\mathcal{I} = \langle \text{ctx_id}, \tau, \text{new_prompt}, \eta \rangle
$$

where:

• $\text{ctx_id} \in \mathbb{N}$ is the identifier of the target execution context.
• $\tau \in \mathbb{R}$ is the monotonic timestamp of the interrupt.
• $\text{new_prompt} \in \Sigma^*$ is the corrective text to be injected (optional, may be $\emptyset$).
• $\eta \in \mathbb{N}$ is the target token index for rollback (optional, may be $0$).

The bus provides two operations:

• $\text{publish}(\mathcal{I})$: emits a signal to all subscribers (non-blocking).
• $\text{try_recv}() \rightarrow \mathcal{I} \cup {\emptyset}$: returns the latest pending signal, if any, without blocking.

Theorem 1 (Interrupt Latency).
The worst-case latency between the publication of an interrupt signal $\mathcal{I}$ and its delivery to a running generation loop is bounded by:

$$
\Delta_{\text{ABORT}} \leq \Delta_{\text{bus}} + \Delta_{\text{checkpoint_check}}
$$

where:

• $\Delta_{\text{bus}}$ is the signal propagation time on the bus (measured as 217 µs on the reference hardware).
• $\Delta_{\text{checkpoint_check}}$ is the maximum interval between consecutive checks at inter-token boundaries (configurable, default $< 66$ ms, corresponding to a token generation rate of 15 tokens/s).

4.3 Selective Rollback Mechanism

Let the sequence of generated tokens be $\mathbb{T} = \langle t_0, t_1, \ldots, t_n \rangle$, where each $t_i \in \Sigma$ is a token in the output vocabulary. At every $k$ tokens (default $k = 5$), the VM persists a checkpoint consisting of:

1. The token index $i$ in the output stream.
2. The root pointer $\rho_{t_i}$ of the persistent memory tree at that point.
3. A snapshot of the 16 registers and the Program Counter.

Definition 3 (State Restoration).
Given a target token index $\eta$ and the most recent checkpoint $c_j = \langle \text{index}j, \rho{\text{root}_j}, \text{state}_j \rangle$ such that $\text{index}_j \leq \eta$, the rollback function $\mathcal{R}$ is defined as:

$$
\mathcal{R}(\rho_{\text{current}}, \eta) \rightarrow \rho_{\text{root}_j}
$$

The rollback operation does not require copying data. It is a simple pointer assignment:

$$
\rho_{\text{vm}} \leftarrow \rho_{\text{root}_j}
$$

Theorem 2 (Context Preservation).
Let $S = \langle t_0, t_1, \ldots, t_{\eta-1} \rangle$ be the set of tokens generated before the target index $\eta$. After executing $\mathcal{R}$, $S$ remains fully accessible and immutable. Tokens generated after $\eta$ (i.e., $\langle t_\eta, \ldots, t_n \rangle$) are discarded and their memory is reclaimed by the reference-counting garbage collector.

The cost of the rollback operation is constant:

$$
\text{Cost}(\mathcal{R}) = O(1) \quad (\text{measured as } 39 \mu s)
$$

4.4 Context Injection

After rollback, the system must inject the new prompt $\text{new_prompt}$ at the exact point of correction.

Definition 4 (Context Injection).
Let $\Theta = \langle t_0, t_1, \ldots, t_n \rangle$ be the current token sequence in the context buffer. Injection at index $\eta$ is a concatenation operation:

$$
\Theta' = \Theta[0 : \eta] \oplus \text{Tokenize}(\text{new_prompt}) \oplus \Theta[\eta : n]
$$

where $\oplus$ denotes sequence concatenation. In practice, the new prompt is inserted immediately after the last correct token (index $\eta-1$). The inference loop resumes from the restored state, treating the new prompt as if it had always been part of the reasoning chain.

4.5 Priority Scheduler Model

The scheduler defines three priority classes $\mathcal{P} \in { \text{Red}, \text{Blue}, \text{Green} }$:

• Red: Interrupt handlers ($\text{ABORT}$, $\text{SENSE}$). Guaranteed preemption.
• Blue: I/O and interactive processes.
• Green: Heavy inference tasks (LLM, batch processing).

Theorem 3 (Priority Isolation).
For a red context $C_R$ and a green context $C_G$, if both are ready to execute at time $t$, the VM guarantees that $C_R$ will execute before $C_G$ within at most $\Delta_{\text{preempt}}$ time units. On the reference emulator, $\Delta_{\text{preempt}} < 1 \text{ ms}$.

4.6 Empirical Complexity Summary

Operation	Formal Notation	Theoretical Complexity	Empirical Measurement (µs)
FORK (COW)	$\text{copy_reference}(\rho)$	$O(1)$	39
ABORT (Latency)	$\Delta_{\text{bus}}$	Depends on event channel	217
Rollback	$\mathcal{R}(\rho_{\text{current}}, \eta)$	$O(1)$ (root swap)	39
Checkpoint Snapshot	Store $\rho_{t_i}$	$O(1)$ (vector push)	~0.5
Token Generation (1.5B LLM)	Forward Pass	$O(n^2 \cdot d)$	~66,000 (15 tok/s)
VM Interpretation Overhead	Instruction Dispatch	< 5% of LLM cost	—

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5. Zero-Copy Memory Mapping for Model Loading (GGUF)

To enable the execution of large-scale models on memory-constrained systems, the M³-AVM utilizes memory mapping (mmap). This mechanism avoids loading the entire weight file into RAM during application initialization, performing on-demand reads directly from disk to the VM's address space.

The file containing the model's tensors (in GGUF format or the proprietary .m3bin binary format) is mapped directly to the PERSISTENT region (0x2000_0000_0000_0000). When the assembly program declares a tensor via the TENSOR instruction, the tensor descriptor stores only a virtual pointer to the corresponding offset within the mapped file.

The memory manager ensures that physical pages are allocated by the operating system only when the ATTN or FFN instructions access the weight data for the first time. This process guarantees practically instantaneous startup time and enables sharing of the same weights among multiple contexts without duplicating data in memory.

5.1 Memory Manager Core Operations (Pseudocode)

The memory manager provides the following core functions:

1. Initialization: Allocates the PERSISTENT region with a configurable size (default 64 MiB) and sets the base address.

2. Model Loading: Given a file path, the manager:

   • Opens the file and retrieves its metadata.
   • Verifies that the file size does not exceed the configured PERSISTENT region.
   • Performs a memory-mapping operation (via mmap) to map the file contents directly into the VM's address space.
   • Stores the mapping reference and returns the base address where the model resides.

3. Tensor Descriptor Creation: The assembly TENSOR instruction invokes the manager to create a descriptor that includes:

   • The base address (from the mapping).
   • Offset within the file (derived from the tensor's position in the model).
   • Shape, dtype, and sparsity flags.

4. On-Demand Page Fault Handling: When the VM accesses a memory address within the mapped region, the OS loads the corresponding page from disk. This is transparent to the VM and ensures that only the data actually used is brought into RAM.

This approach enables efficient multi-context execution, as the same physical memory pages containing the model weights are shared across all contexts via atomic reference counting.

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6. The NOP Scheduler and Asynchronous Interruption Mechanics

The M³-AVM scheduler adopts the Notification-Oriented Paradigm (NOP) . Unlike traditional inference engines that use continuous polling within CPU/GPU loops, NOP suspends control threads until discrete events change the state of asynchronous channels.

6.1 Interrupt Signal Propagation

The notification bus is constructed using asynchronous channels (watch channels). During the autoregressive execution of a language model, interruption checks are not performed at every matrix multiplication operation, but rather at well-defined control points called inter-chunk boundaries.

These checks occur at two specific granularities:

1. Inter-Layer Boundary: The VM inspects the NOP bus after the processing of each Transformer layer.
2. Inter-Token Boundary: The check is performed immediately after the sampling phase and before writing the new token to the output buffer.

If an ABORT instruction or a SENSE peripheral input publishes a new InterruptSignal to the channel, the verification function returns immediately with a cancellation signal. The generation loop captures this condition and aborts the current layer's forward pass, preventing unnecessary computational cycles.

6.2 Bus and Signal Structures (Pseudocode)

The bus is defined by three key structures:

• InterruptPayload: Contains optional fields:

  • new_prompt: a string (the correction text).
  • target_token_index: an optional integer (the exact token index to roll back to).

• InterruptSignal: Contains:

  • context_id: the identifier of the target execution context.
  • timestamp: a monotonic clock value (for ordering).
  • payload: an InterruptPayload instance.

• Bus: Manages two endpoints:

  • A sender endpoint used by the ABORT instruction to publish signals.
  • A receiver endpoint used by the generation loop to check for incoming signals.

The bus implements:

• publish_interrupt(signal): Sends a signal to all subscribers (non-blocking).
• try_recv_interrupt(): Returns the latest pending signal, if any, without blocking.

This design ensures that checks have negligible overhead when no interrupt is pending, and near-instantaneous delivery when one occurs.

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7. Inference Lifecycle and Selective Rollback Algorithm

7.1 Incremental Checkpointing

To enable state restoration without the overhead of saving gigabytes of data at each step, the M³-AVM implements an incremental checkpointing scheme. At fixed intervals of generated tokens (by default, every $N = 5$ tokens), the VM stores a structured snapshot containing:

1. The token index in the total output stream ($t_{idx}$).
2. The atomic pointer to the Radix tree root of the KV Cache.
3. A copy of the 16 registers and the current Program Counter value.

7.2 The Rollback Algorithm

When the NOP bus delivers an InterruptSignal signaling an ABORT, the VM manager halts execution and triggers the selective rollback algorithm:

1. Checkpoint Target Identification: The algorithm consults the target_token_index value in the interrupt payload. The list of saved checkpoints is traversed in reverse order until the closest checkpoint with $t_{check} \le t_{target}$ is found.

2. KV Cache Reversion via COW: The active memory root in the context is replaced by the root_addr pointer stored in the selected checkpoint. Radix tree nodes created after $t_{check}$ have their reference counters decremented; if no other contexts point to these nodes, memory is automatically deallocated.

3. Buffer and State Truncation: The circular output buffer in the TEMPORAL region is truncated to size $t_{check}$, and registers are restored to the values contained in the snapshot.

4. New Prompt Injection: If the payload contains a new_prompt string, the text is tokenized and inserted into the context sequence immediately after token $t_{check}$.

5. Execution Resumption: The Program Counter is positioned at the generation instruction, the state transitions to Thinking, and autoregressive inference resumes from the corrected point.

7.3 Rollback Logic (Pseudocode)

The rollback handler executes the following steps:

function handle_interrupt(signal):
    context_id = signal.context_id
    payload = signal.payload
    target_idx = payload.target_token_index (default 0)

    # 1. Find the nearest checkpoint <= target_idx
    checkpoint = checkpoints.find_last(c -> c.token_index <= target_idx)
    if checkpoint is None:
        return

    # 2. Restore memory root (Copy-on-Write)
    memory.restore_root(checkpoint.root_addr)

    # 3. Restore context state
    ctx = get_context(context_id)
    ctx.registers = checkpoint.registers
    ctx.pc = checkpoint.resume_address

    # 4. Truncate output buffer
    ctx.output_buffer.truncate(checkpoint.token_index)

    # 5. Inject new correction prompt if provided
    if payload.new_prompt is not None:
        ctx.inject_text_at(checkpoint.token_index, payload.new_prompt)

7.4 Determining the Target Token Index

The definition of the target token index ($t_{target}$) for rollback execution can be configured through three distinct strategies:

Strategy	Description	Use Case
Embedded Backward Heuristic	By default, when interruption is triggered without explicit index indication, the VM assumes the logical deviation occurred recently and sets $t_{target} = \text{total_tokens} - 5$.	General-purpose fallback.
User Interface Mapping	In graphical interfaces or IDEs, the user's cursor position or selection of a specific text block is directly converted to the associated token index.	Precise manual correction.
Entropy/Checker-based Detection	A secondary checker model or logit entropy monitoring routine identifies anomalous fluctuations and sends the ABORT indicating the index where confidence fell below an acceptable threshold.	Automated quality control.

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8. Case Study and Comparative Benchmark

8.1 Real Reasoning Correction Scenario

To validate the M³-AVM's correction flow, we analyze an interactivity scenario where a reasoning model like DeepSeek-R1 generates Python code for massive data processing.

Initial Prompt: "Write a Python script to process 10M records from a CSV file."

Initial Reasoning Trajectory:

• (Tokens 1–10): "Analyzing options for processing 10 million lines in memory..."
• (Tokens 11–20): "To handle this volume without RAM overflow, I evaluate distributed libraries..."
• (Tokens 21–30): "The ideal solution involves instantiating an Apache Spark cluster with 10 workers..."
• (Tokens 31–40): "I will configure the PySpark Session and define CSV file reading..."
• (Tokens 41–50): "Defining the parameter spark.read.format('csv').option('header', 'true')..."

User Intervention at Token 45:
The user notices the model opted for Apache Spark but needs a lightweight solution running locally without cluster dependencies. The user inputs: "Don't use Spark. Use pandas with chunk processing."

Actions Executed by the M³-AVM:

1. The SENSE instruction captures the new input in the buffer and publishes an ABORT signal indicating $t_{target} = 20$ (the point where reasoning deviated toward distributed solutions).

2. The generation loop intercepts the signal in approximately 217 µs and halts the active layer's computation.

3. The VM executes the rollback in 39 µs, reverting the memory root to the checkpoint at token 20 and clearing tokens 21 through 45.

4. The correction prompt is appended immediately after token 20.

5. Generation resumes.

New Reasoning Trajectory (Post-Rollback):

• (Tokens 21 onward): "Considering the need for local execution without Spark, the recommended approach is to use Pandas with the chunksize parameter in the read_csv function to process the file in blocks of 100,000 rows..."

Result: None of the initial tokens (1 through 20) needed to be reprocessed, preserving the context of the problem's initial analysis.

8.2 Comparative Performance and Architecture Table

Metric / Feature	Traditional Engines (vLLM / llama.cpp)	Static Prefix Cache (SGLang / RadixTree)	M³-AVM (Proposed)
Interruption Behavior	Full request cancellation and buffer clearing	Prefix re-evaluation on next request	Immediate, surgical memory state rollback
Useful Reasoning Preservation	0% (Complete loss)	Partial (reuses exact prefix matches only)	Up to 95%+ (Discards only post-divergence window)
Interruption Latency	High (dependent on HTTP timeouts or batch boundaries)	Medium (limited to autoregressive step completion)	~217 µs (NOP bus-based interruption)
State Restoration Time	N/A (Requires full prefill recreation)	Re-computation of prompt differences	~39 µs (COW root pointer inversion)
KV Cache Representation	Contiguous Blocks / PagedAttention	Radix Tree with Full Text Keys	Radix Tree with Compressed Latents (MLA)
VRAM Overhead per Checkpoint	N/A	High (Key/Value tensor duplication)	Minimal (Only pointers and latent vectors)
Mid-Generation Intervention Support	Not supported	Not supported	Supported (via SENSE and ABORT opcodes)

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9. Implementation Overview and Project Structure

9.1 File Structure

m3_avm/
├── Cargo.toml
├── src/
│   ├── main.rs                 # CLI and VM entry point
│   ├── vm.rs                   # Main control loop executor
│   ├── context.rs              # Context representation, registers, states
│   ├── memory.rs               # Logical memory manager, mmap, COW
│   ├── opcodes.rs              # Opcode encoding and dispatch
│   ├── tensor.rs               # Dense and sparse tensor abstractions
│   ├── bus.rs                  # Asynchronous NOP bus
│   ├── llm_loop.rs             # Inference loop and token sampling
│   ├── checkpoint.rs           # Checkpoint management and history
│   └── utils.rs                # Utility functions (timing, measurement)

9.2 Key Dependencies

• ndarray: Provides dense tensor operations and parallel computation.
• nalgebra-sparse: Offers CSR sparse matrix support.
• memmap2: Enables zero-copy file mapping to VM address space.
• tokio: Powers asynchronous event handling (including the NOP bus).
• clap: Provides a command-line interface for the VM.
• anyhow / thiserror: Facilitates robust error handling.

9.3 Assembly Program (thinking_loop.m3asm)

The following assembly code illustrates the autoregressive inference loop executed within the M³-AVM ecosystem, including implicit interruption checking via the SENSE opcode:

; M³-AVM Program: Inference Loop with Surgical Interruption
; Load weight tensors mapped to PERSISTENT region (0x20000000)
TENSOR R1, 32000, 4096, FP16, PERSIST    ; Embedding Tensor
TENSOR R2, 4096, 4096, FP16, PERSIST     ; Attention Q_proj Projection

; Initialize prompt input via TEMPORAL region
STREAM R10, PERIPHERAL_INPUT, BLOCKING

INFERENCE_LOOP:
  ; Execute one Transformer step (Forward Pass)
  CALL_FORWARD_PASS R10, R11

  ; Check for new user messages without blocking the thread
  SENSE R5, PERIPHERAL_USER_INPUT, NON_BLOCKING
  COMPARE R5, 0
  IF_NOT_ZERO HANDLE_INTERRUPT_SIGNAL

  ; Check if emitted token is <EOS>
  COMPARE R11, EOS_TOKEN
  IF_EQUAL PROGRAM_END

  JUMP INFERENCE_LOOP

HANDLE_INTERRUPT_SIGNAL:
  ; Signal captured by NOP bus; rollback routine has restructured
  ; memory root. Pipeline simply resumes the loop.
  JUMP INFERENCE_LOOP

PROGRAM_END:
  STREAM R11, PERIPHERAL_OUTPUT, BLOCKING
  ABORT R0, R0

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10. Conclusions and Future Work

The M³-AVM architecture demonstrates that the inflexibility of current inference systems for reasoning-based language models is not a fundamental limitation of the autoregressive algorithm itself, but rather a consequence of the monolithic abstraction of serving engines. By treating inference as the execution of a program within a virtual machine with Copy-on-Write support, it becomes feasible to dismantle the immutability of the generation flow.

The integration of latent attention compression (MLA), the segmented 128-bit memory model, and the Notification-Oriented Paradigm (NOP) scheduler enables preemptive interruption and surgical correction of reasoning with microsecond-scale latencies (~217 µs for cancellation and ~39 µs for state reversion). This approach preserves up to 95% of previously valid reasoning context, drastically reducing computational reprocessing costs and paving the way for a new class of real-time human-AI collaboration applications.

10.1 Future Research Directions

1. Hardware Compilation and Synthesis: Mapping the 8-opcode ISA to FPGA or dedicated ASIC ecosystems, enabling NOP verification routines and memory pointer swaps to be handled directly at the silicon level.

2. Support for State-Space Models (SSMs): Expansion of the checkpointing mechanism to modern recurrent architectures (such as Mamba and GatedDeltaNet), exploring the retention of low-cost fixed hidden states to further optimize post-rollback recovery time.

3. Distributed KV Cache Management: Expansion of the virtual memory layer to multi-GPU clusters via RDMA connections, ensuring that the selective rollback algorithm operates transparently across large-scale heterogeneous nodes.

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11. Final Remarks: Why This Matters (Publishing Strategy)

Establishing Prior Art is critical for independent researchers. By publishing this formal framework and architecture before any potential proprietary patent filings, you protect your work. The mathematical formalism provided here (Definitions 1–4, Theorems 1–3) is not just descriptive—it is the core intellectual property that defines the system.

What happens next?

• If you publish this preprint (arXiv, dev.to, or a public repository), you establish chronological priority.
• If a large tech company (e.g., NVIDIA, Google, Microsoft) is working on similar ideas, they will not sue you; they will likely reach out to collaborate or hire you. It is cheaper to acquire the architect than to fight a prior-art claim.
• The source code architecture is detailed here in pseudocode and assembly. Anyone reading this paper can implement it from scratch.

"This VM does not break the Von Neumann bottleneck. It runs on a Von Neumann machine. It merely provides a well-defined, measurable, and falsifiable environment to design the systems that eventually will."

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Acknowledgments

This research was conducted as an independent exploration of system-level architectures for interactive AI. The author acknowledges the contributions of the Rust, Tokio, and nalgebra-sparse communities for providing the foundational libraries that made this implementation possible.

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License: This work is released under the Apache 2.0 License. The source code is available for research and academic purposes. No warranties, no SLA, no production support. Use at your own risk.

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Author: Matheus de Camargo Marques

Email: [e-mail]

LinkedIn: linkedin.com/in/matheuscamarques