Runtime Observability for Heterogeneous Attention Memory

• URL: http://arxiv.org/abs/2608.05863v1
• Date: Thu, 06 Aug 2026 10:41:50 GMT
• Title: Runtime Observability for Heterogeneous Attention Memory
• Abstract summary: We give a runtime observability contract that covers all four memory classes with three operators.<n>We compose the per-stage stack into an executable request-level risk ledger.
• Score: 20.158719055939383
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern models no longer keep a plain KV cache: latent caches, learned sparse selectors and recurrent states each carry the model's memory in a different form, and each fails differently under compression. We give a runtime observability contract that covers all four memory classes with three operators, instantiate it on six model configurations across five architecture families, and compose the per-stage bounds into an executable request-level risk ledger. Contracts carry their error metric as a type -- composition is only defined when metrics match, and this check rejected our own first composed chain; the repaired chain crosses metrics through two proved bridges, and whatever no formal system can certify is measured instead, dropping the composed tier to empirical automatically: every claim is certified, partially certified, or empirical, composition inherits the weakest tier, and the tier is decided by the machine. Replayed over $12.4$M entry reads and run under eight-way concurrency with per-request budgets and fail-closed identity attribution, the ledger quantifies the honest trade-off on today's witness and holds its risk budget with zero violations. A fused always-on probe observes a declared one-layer subset under CUDA graphs inside the serving noise floor. Applied to a served DeepSeek-V4 stack with a packed compressed-KV prototype, the same machinery localizes a silent corruption to a precise structural boundary -- exact in the eviction-free, identity-isolated regime, with every observed failure in an eviction or slot-reuse regime -- through a machine-adjudicated discrimination campaign whose calculus rejected two of our own confounded inferences along the way. All artifacts, guards, and the Lean development are released at https://github.com/metask-ai/witprobe-attention-memory; every number in this paper regenerates from the shipped artifacts by one command.

Related papers

• Differentiable Interval Bottlenecks for Interpretable Anomaly Detection in Numerical Data [0.5213778368155993]
  DIFFINT is an autoencoder whose latent bottleneck is structured as a set of soft, axis-aligned interval memberships.<n>It is the only interpretable detector in the statistically-tied leading cluster of seven methods.
  arXiv  Detail & Related papers  (2026-09-03T14:05:27Z)
• What Accuracy and Gradient Cosine Miss: Evaluating Feedback Alignment via Scale Stability, Reference Validity, and Depth Utility [48.10132234701036]
  We propose a diagnostic evaluation protocol based on three checks -- scale stability, reference validity, and depth utility -- together with per-layer rather than aggregate cosine reporting.<n>Across multiple architectures and methods, our protocol identifies all failures with wide calibration margins.
  arXiv  Detail & Related papers  (2026-06-19T06:04:17Z)
• Verified Detection and Prevention of Concurrency Anomalies in Multi-Agent Large Language Model Systems [0.0]
  Multi-agent LLM systems state through memory stores, vector indices, and tool registries.<n>We model such sharing as long-running read-generate-write operations under deterministic-generation semantics.<n>We formalize four anomalies in TLA+: stale-generation, phantom-tool, causal-cascade, and tool-effect reordering.
  arXiv  Detail & Related papers  (2026-06-15T18:19:34Z)
• TOKI: A Bitemporal Operator Algebra for Contradiction Resolution in LLM-Agent Persistent Memory [22.85129722207174]
  We show that contradiction resolution is write-time control and make the missing contract explicit.<n> TOKI types the fourjudicats as one family of bitemporal operators over a dual-row schema.<n>A verdict matrix over eight systems localizes the gap: every baseline that keeps a language-model judge on the write path admits at least one of three write-time anomalies.
  arXiv  Detail & Related papers  (2026-06-04T14:46:52Z)
• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• A Universal Cliff and a Design Fingerprint: Cross-Section Defect Detection Under LLM Orchestration [0.0]
  Production language-model systems answer a request byspanning it across an invisible orchestration of worker agents.<n>We ask what this does to a class of defect no single worker can see.<n>We vary only the model -- ten systems across five generations from one developer and five providers from distinct alignment paradigms.
  arXiv  Detail & Related papers  (2026-05-25T05:09:48Z)
• Proof-Carrying Certificates for LLM Pipelines: A Trust-Boundary Architecture [0.0]
  We present a framework for verifying the deterministic structured computations surrounding a large language model.<n>We extend a Lean 4 trust-boundary architecture to the generic interfaces of modern LLM pipelines.
  arXiv  Detail & Related papers  (2026-05-13T12:01:41Z)
• ElephantBroker: A Knowledge-Grounded Cognitive Runtime for Trustworthy AI Agents [45.88028371034407]
  ElephantBroker is an open source cognitive runtime that unifies a Neo4j knowledge graph with a Qdrant vector store.<n>The system implements a complete cognitive loop (store, retrieve, score, compose, protect, learn) comprising a hybrid five source retrieval pipeline.
  arXiv  Detail & Related papers  (2026-03-26T07:03:12Z)
• Authenticated Contradictions from Desynchronized Provenance and Watermarking [48.47756819432157]
  This work formalizes and empirically demonstrates the $textitIntegrity Clash$, a condition in which a digital asset carries a cryptographically valid C2PA manifest asserting human authorship.<n>We propose a cross-layer audit protocol that jointly evaluates provenance metadata and watermark detection status, achieving 100% classification accuracy across 3,500 test images.
  arXiv  Detail & Related papers  (2026-03-02T20:42:12Z)
• To Throw a Stone with Six Birds: On Agents and Agenthood [0.0]
  Six Birds Theory (SBT) treats macroscopic objects as induced closures rather than primitives.<n>We give a type-correct account of agency within SBT.<n>We operationalize this contract in finite controlled systems using four checkable components.
  arXiv  Detail & Related papers  (2026-02-03T10:46:23Z)
• CARE What Fails: Contrastive Anchored-REflection for Verifiable Multimodal [84.71254539482369]
  Group-relative reinforcement learning with verifiable rewards (RLVR) often wastes the most informative data it already has the failures.<n>We present CARE, a failure-centric post-training framework for multimodal reasoning that turns errors into supervision.<n> CARE improves accuracy and training smoothness while explicitly increasing the share of learning signal that comes from failures.
  arXiv  Detail & Related papers  (2025-12-22T16:34:21Z)
• Noise & pattern: identity-anchored Tikhonov regularization for robust structural anomaly detection [58.535473924035365]
  Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns.<n>We tackle structural anomaly detection using a self-supervised autoencoder that learns to repair corrupted inputs.<n>We introduce a corruption model that injects artificial disruptions into training images to mimic structural defects.
  arXiv  Detail & Related papers  (2025-11-10T15:48:50Z)
• Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction [58.51530390018909]
  Large Language Model based multi-agent systems excel at collaborative problem solving but remain brittle to cascading errors.<n>We present MASC, a metacognitive framework that endows MAS with real-time, unsupervised, step-level error detection and self-correction.
  arXiv  Detail & Related papers  (2025-10-16T05:35:37Z)

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.