VCT: A Verifiable Transcript System for LLM Conversations

• URL: http://arxiv.org/abs/2606.23003v1
• Date: Mon, 22 Jun 2026 08:17:39 GMT
• Title: VCT: A Verifiable Transcript System for LLM Conversations
• Abstract summary: Verifiable Conversation Transcript builds non-linear LLM semantic operations into account-level authenticated state transitions.<n>Security analysis demonstrates that, under standard cryptographic assumptions, VCT guarantees the integrity, consistency, verifiable shareability, and non-repudiation of account-level conversation records.
• Score: 5.3181399750078535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model (LLM) interaction records are increasingly vital in digital forensics and compliance auditing. However, traditional linear tamper-evident logs fail to capture the inherent non-linear evolution of LLM conversations, such as re-prompting based on historical queries, response regeneration, session deletion, multi-device concurrency, and selective sharing. To address this issue, this paper proposes Verifiable Conversation Transcript (VCT), which abstracts complex non-linear LLM semantic operations into account-level authenticated state transitions. VCT constructs a three-tier cryptographic data structure: atomic Q&A pairs form branch-level hash chains, branch tails aggregate into session-level Merkle roots, and all session roots are further aggregated into an account-level Merkle root anchored by joint signatures from both the user and the server. VCT introduces a serialized state transition protocol with deletion barriers to eliminate conflicts between deletion and modification, complemented by a deterministic state-merge protocol to preserve concurrent non-deletion incremental operations. Furthermore, incremental denial checks and a gossip protocol enable asynchronous user devices to autonomously detect view forks caused by malicious servers and generate non-repudiable forensic evidence. Security analysis demonstrates that, under standard cryptographic assumptions, VCT guarantees the integrity, consistency, verifiable shareability, and non-repudiation of account-level conversation records. Evaluation of a Python prototype shows that the cryptographic latency of core operations is within sub-millisecond to low-millisecond ranges. Under a realistic configuration with 21 KB of text, security metadata introduces a negligible storage overhead of only 0.9%, validating the deployment feasibility of VCT for high-stakes forensic review on production-grade LLM platforms.

Related papers

• SCOUT: Symmetric Consensus Outlier Detection for Failure Localization in LLM Pre-Training [7.018753691717986]
  In pre-training, synchronization propagates rank-local stalls, slowdowns, and numerical errors into job-wide symptoms, obscuring their origin.<n>We present SCOUT, a unified runtime failure-localization framework built on one design principle: identify outliers through strict-majority consensus among equivalent replicas.<n>SCOUT integrates with PyTorch, TorchTitan, Megatron-Core, and DeepSpeed without training-loop or framework-source modifications.
  arXiv  Detail & Related papers  (2026-08-11T15:12:14Z)
• TELLER: Non-intrusive Cross-Layer Root-Cause Analysis for LLM Inference [47.62941623025392]
  We present TELLER, a non-intrusive and Log-aware LLM inference Root-cause analysis framework.<n>TELLER collects NVTX/CUPTI traces and service logs without modifying model binaries.<n>It reconstructs per-request call-chain trees and aligns log lines with corresponding execution steps.<n>On top of these representations, TELLER combines numeric candidate localization with a multimodal root-cause model.
  arXiv  Detail & Related papers  (2026-08-03T09:39:11Z)
• 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)
• Efficient and Trainable Language Model Test-Time Scaling via Local Branch Routing [65.59464371673623]
  Local Branch Routing (LBR) is a token-level test-time scaling framework.<n>It expands a small local lookahead tree, forwards all sampled branches through the language model, and uses a lightweight router to select the depth-1 subtree to commit.<n>LBR allows each token decision to use evidence beyond the root next-token distribution while avoiding full solution-level search.
  arXiv  Detail & Related papers  (2026-06-24T03:42:44Z)
• Governed Shared Memory for Multi-Agent LLM Systems [1.2178992475191557]
  This paper formalizes the fleet-memory problem and identifies four foundational failure modes.<n>We define explicit systems-level primitives: scoped retrieval, temporal supersession, provenance tracking, and policy-governed memory propagation.<n>These primitives are implemented in MemClaw, a production multi-tenant memory service, and evaluated via ArgusFleet.
  arXiv  Detail & Related papers  (2026-06-23T13:04:14Z)
• AuditWeave: A Tamper-Evident, Auditor-Navigable Evidence Layer for AI-Assisted and Data-Transformation Workflows [0.0]
  AuditWeave is a lightweight Python library that records the steps of AI-assisted and data-transformation into a single hash-chained ledger.<n>We describe the design and evaluate recording overhead, scalability, and tamper-detection correctness on the reference implementation.
  arXiv  Detail & Related papers  (2026-06-14T21:46:04Z)
• NLLog: Lightweight, Explainable SOC Anomaly Detection via Log-to-Language Rewriting [3.6732711233211663]
  NLLog is a lightweight pipeline that deterministically rewrites parsed templates into WHO-WHAT-SEVERITY sentences.<n>It pools them with term-frequency-inverse-document-frequency weighting, classifies sessions with tree ensembles, and back-projects evidence.<n>It sustains low false-positive rates with commodity- hardware latency suitable for security operations center triage.
  arXiv  Detail & Related papers  (2026-06-03T14:45:29Z)
• PRIMA: Operational Patterns for Resilient Multi-Agent Research with Verifiable Identity and Convergent Feedback [0.0]
  PRIMA operates as coordinated multi-agent research systems over multi-hour runs.<n>Main contributions are three operational patterns for surviving failure modes.<n>A Graph Isomorphism case study grounds the architectural claims in a generated artifact.
  arXiv  Detail & Related papers  (2026-05-23T23:27:46Z)
• Advancing Narrative Long Video Generation via Training-Free Identity-Aware Memory [79.01059178883817]
  IAMFlow is a training-free identity-aware memory framework that explicitly models and tracks persistent entity identities.<n>A VLM asynchronously verifies and refines attributes from rendered frames, enabling explicit entity tracking in place of implicit similarity-based matching.<n>NarraStream-Bench is a benchmark for narrative streaming video generation that features 324 multi-prompt scripts spanning six dimensions.
  arXiv  Detail & Related papers  (2026-05-18T17:54:34Z)
• Large Language Models as Explainable Cyberattack Detectors for Energy Industrial Control Systems [1.784933900656067]
  We study whether an off-the-shelf large language model (LLM) can serve as a complementary, human-in-the-loop layer for ICS detectors.<n>We cast this as a binary network-side normal/critical decision task on two public ICS datasets.<n>LLM produces a normal/critical alert together with a concise, token-grounded incident record for analyst review.
  arXiv  Detail & Related papers  (2026-04-28T19:44:38Z)
• GoAT-X: A Graph of Auditing Thoughts for Securing Token Transactions in Cross-Chain Contracts [52.51342355102833]
  Cross-chain bridges, the critical infrastructure of the multi-chain ecosystem, have become a primary target for attackers.<n>Existing defenses, such as bytecode-level static analysis, are ill-equipped to handle the semantic complexity of cross-chain interactions.<n>We propose GoAT-X, a framework that shifts automated cross-chain smart contracts auditing from pattern matching toward systematic first-principles verification.
  arXiv  Detail & Related papers  (2026-04-27T11:34:21Z)
• When Only the Final Text Survives: Implicit Execution Tracing for Multi-Agent Attribution [10.973058523304042]
  We introduce IET (Implicit Execution Tracing), a metadata-independent framework that enables token-level attribution directly from generated text.<n>During generation, agent-specific keyed signals are embedded into the token distribution, transforming the text into a self-describing execution trace detectable only with a secret key.<n>At detection time, a transition-aware scoring method identifies agent handover points and reconstructs the interaction graph.
  arXiv  Detail & Related papers  (2026-03-18T07:34:51Z)
• Beyond Short-Horizon: VQ-Memory for Robust Long-Horizon Manipulation in Non-Markovian Simulation Benchmarks [96.60530830276281]
  RuleSafe is a new articulated manipulation benchmark built upon a scalable LLM-aided simulation framework.<n>VQ-Memory is a compact and structured temporal representation that uses vector-quantized variational autoencoders.
  arXiv  Detail & Related papers  (2026-03-10T11:13:54Z)
• Verbatim Data Transcription Failures in LLM Code Generation: A State-Tracking Stress Test [1.8875967655304022]
  Many real-world software tasks require exact transcription of provided data into code.<n>Small omissions or alterations can remain silent while producing syntactically valid programs.<n>This paper introduces a deliberately minimal transcription-to-code benchmark to isolate this reliability concern.
  arXiv  Detail & Related papers  (2026-01-07T06:38:34Z)
• Localizing Factual Inconsistencies in Attributable Text Generation [74.11403803488643]
  We introduce QASemConsistency, a new formalism for localizing factual inconsistencies in attributable text generation.<n>We show that QASemConsistency yields factual consistency scores that correlate well with human judgments.
  arXiv  Detail & Related papers  (2024-10-09T22:53:48Z)
• Triplet Contrastive Learning for Unsupervised Vehicle Re-identification [55.445358749042384]
  Part feature learning is a critical technology for fine semantic understanding in vehicle re-identification. We propose a novel Triplet Contrastive Learning framework (TCL) which leverages cluster features to bridge the part features and global features.
  arXiv  Detail & Related papers  (2023-01-23T15:52:12Z)

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.