A Symbolic Neural CPU for Quantization-Simulated Writeback and Interpretable Program Execution

• URL: http://arxiv.org/abs/2607.10021v1
• Date: Fri, 10 Jul 2026 22:55:23 GMT
• Title: A Symbolic Neural CPU for Quantization-Simulated Writeback and Interpretable Program Execution
• Abstract summary: We introduce a trace-supervised symbolic neural CPU that combines recurrent control, an explicit operation router and trajectory supervision.<n>The model exposes the selected operation, source and destination registers, register trajectory, memory signals and writeback semantics.<n>Together, these results establish a trace-verifiable framework for interpretable, low-precision and controllable neural execution.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural networks can learn algorithmic input-output mappings, but trusting a learned executor requires more than a correct final answer because the state transitions that produce it are usually hidden. To make those transitions visible, we introduce a trace-supervised symbolic neural CPU, a factorized learned execution architecture that combines recurrent control, an explicit operation router over a fixed differentiable arithmetic-logic unit bank, destination-masked register writeback, complete trajectory supervision and matched fixed-point replay. The model exposes the selected operation, source and destination registers, register trajectory, memory signals and writeback semantics at every step. On the principal 16-wide benchmark, the non-quantized executor reproduces reference execution exactly, while the eight-bit quantization-simulated executor preserves the symbolic operation path through programs of 1,000 instructions. When the same execution is evaluated against a matched fixed-point replay, the residual numerical drift disappears, showing that it comes from a mismatch between continuous and low-precision reference semantics rather than from execution failure. We compare recurrent, Transformer, temporal-convolution, temporal graph-inspired and state-space controllers, and the ablations show that operation-gate supervision is necessary for an inspectable execution path. Hidden-opcode memory-pressure tasks expose the remaining limits in delayed state use and temporal binding. We also extend the interface with ValueMemory, hybrid adaptive leaky integrate-and-fire controllers, candidate-constrained symbolic control trained through behaviour cloning and actor-critic reinforcement learning, and an RV32I base-integer semantic bridge. Together, these results establish a trace-verifiable framework for interpretable, low-precision and controllable neural execution.

Related papers

• Penelope: Localized Latent Recurrence for Efficient Structured Reasoning [26.549641831847982]
  Penelope is an efficient latent-reasoning framework for pretrained decoder-only Transformers.<n>It localizes recurrent computation to a selected decoder interval.<n>It attains competitive accuracy relative to established latent-reasoning models.
  arXiv  Detail & Related papers  (2026-07-28T16:06:46Z)
• PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution [49.776611937968]
  We present PhyAgentOS, a foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services.<n>Its Session-Centered treats a session, not an action, as the minimum unit of scheduling, compatibility, supervised execution, evidence collection, and acceptance.<n>SessionVerifier distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan.<n> Benchmarking reuses the deployment session and verification path, so results trace to real execution.
  arXiv  Detail & Related papers  (2026-07-18T04:46:53Z)
• MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations [50.24315431387575]
  We introduce MemOps, a benchmark that reformulates conversational memory as a sequence of lifecycle operations.<n>MemOps represents each memory event with a structured trace specifying its trigger, target, scope, state transition, and supporting evidence.<n>Across long-context, retrieval-based, parametric and managed-memory systems, MemOps disentangles failure modes that final-answer accuracy alone conceals.
  arXiv  Detail & Related papers  (2026-07-14T15:33:44Z)
• From Open Loop to Closed Loop: A Test-Time Iterative Optimization Framework for Reference-Consistent Image Generation [108.02425846269234]
  controllable image generation has made significant strides by incorporating visual reference conditions.<n>Existing methods predominantly operate as open-loop systems.<n>We propose a novel test-time iterative optimization framework that reformulates reference-consistent generation as a closed-loop dynamic tracking problem.
  arXiv  Detail & Related papers  (2026-07-06T05:36:30Z)
• Stalls and Spequlation: Pipelined Execution for Fault Tolerant Quantum Computation [1.6724455800127804]
  Current scheduling models treat logical operations as atomic, hiding the fact that these subsystems operate sequentially and spend significant time idle.<n>We present a pipelined execution framework that decomposes each logical operation into its component stages.<n>We show that pipelining with speculation reduces total pipeline steps by 20-40% compared to a no-speculation baseline.
  arXiv  Detail & Related papers  (2026-06-17T20:47:45Z)
• Graph Grounded Cross Attention Transformer Neural Network for Structurally Constrained Full Event Sequence Generation in Predictive Process Monitoring [3.793186450307008]
  This paper proposes the Graph Grounded Cross Attention Transformer Neural Network.<n>GGATN generates activities, timestamps, length, and event level and sequence level attributes in a single pass.<n>Experiments on six benchmark event logs show more reliable generation quality than local instruction.
  arXiv  Detail & Related papers  (2026-06-17T06:10:27Z)
• Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior [107.2098567818173]
  Latent Recurrent Transformer (LRT) is a lightweight augmentation of autoregressive transformers.<n>LRT reuses a high-level source-layer hidden state from the previous token as recurrent memory for the next token.
  arXiv  Detail & Related papers  (2026-05-26T10:10:26Z)
• The Path Matters: Learning a Token-Commitment Policy for Diffusion Language Models [52.93186090124315]
  We argue that token commitment can be learned as a reusable trace-state policy.<n>We introduce TraceLock, a lightweight plug-in controller that instantiates this policy for a frozen diffusion language model.
  arXiv  Detail & Related papers  (2026-05-23T18:23:46Z)
• A Physical Agentic Loop for Language-Guided Grasping with Execution-State Monitoring [5.437966695589128]
  We reformulate language-guided grasping as a bounded embodied agent operating over grounded execution states.<n>We introduce a physical agentic loop that wraps an unmodified learned manipulation primitive.<n>We validate the resulting loop on a mobile manipulator with an eye-in-hand D405 camera.
  arXiv  Detail & Related papers  (2026-04-08T08:01:35Z)
• DynamicGate MLP Conditional Computation via Learned Structural Dropout and Input Dependent Gating for Functional Plasticity [0.0]
  Dropout is a representative regularization technique that deactivates hidden units during training to mitigate overfitting.<n>Standard inference executes the full network with dense computation, so its goal and mechanism differ from conditional computation.<n>This paper organizes DynamicGate-MLP into a single framework that simultaneously satisfies both the regularization view and the conditional-computation view.
  arXiv  Detail & Related papers  (2026-03-17T10:54:16Z)
• TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs [8.818252253980985]
  TempoNet is a reinforcement learning scheduler that pairs a permutation-invariant Transformer with a deep Q-approximation.<n>A latency-aware sparse attention stack with blockwise top-k selection and locality-sensitive chunking enables global reasoning over unordered task sets.
  arXiv  Detail & Related papers  (2026-02-20T09:56:23Z)
• Fast SAM2 with Text-Driven Token Pruning [52.8350457627401]
  Segment Anything Model 2 (SAM2), a vision computation model has significantly advanced in prompt-driven video object segmentation.<n>SAM2 pipelines propagate all visual tokens produced by the image encoder through downstream temporal reasoning modules, regardless of their relevance to the target object.<n>We introduce a text-guided token pruning framework that improves inference efficiency by selectively reducing token density prior to temporal propagation.
  arXiv  Detail & Related papers  (2025-12-24T18:59:05Z)
• Taming Imperfect Process Verifiers: A Sampling Perspective on Backtracking [54.43083499412643]
  Test-time algorithms that combine the generative power of language models with process verifiers offer a promising lever for eliciting new reasoning capabilities.<n>We introduce a new process-guided test-time sampling algorithm, VGB, which uses theoretically grounded backtracking to achieve provably better robustness to verifier errors.
  arXiv  Detail & Related papers  (2025-10-03T16:21:14Z)
• Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration [54.897493351694195]
  We propose a novel parallel decoding approach, namely textithidden transfer, which decodes multiple successive tokens simultaneously in a single forward pass. In terms of acceleration metrics, we outperform all the single-model acceleration techniques, including Medusa and Self-Speculative decoding.
  arXiv  Detail & Related papers  (2024-04-18T09:17:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.