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OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets

06:00 · July 16, 2026 · arXiv cs.AI RSS

OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets

When a data contributor requests removal, model trainers face a practical gap: unlearning algorithms require a forget set, yet no tool can locate which training records belong to a given author. Existing provenance systems operate at file or dataset level, forcing catastrophic over-deletion. We present ob, a record- and token-level data provenance system that propagates author identity through data processing pipelines and resolves revocation requests into precise forget sets via deterministic queries. Evaluation on 219,555 Wikipedia pages demonstrates that record-level provenance eliminates dataset-level over-deletion (from 101x to 1.3x), while integration adds 1.3-4.0% throughput overhead (HuggingFace) and 2.1-19.0% (Datatrove) on wiki data. On a 1.7B model, provenance-based forget sets improve unlearning by 42% over random baselines.

Summary

OriginBlame addresses a practical disconnect in machine unlearning workflows: when an author requests data removal, trainers lack a reliable way to identify the exact records that originated from that contributor. Existing data version control and provenance systems track files or entire datasets, which forces either wholesale deletion or reliance on imprecise post-hoc inference. The system propagates author identity already present in source environments—such as revision histories—through tokenization and packing stages, storing metadata in plain JSONL files under a .ob directory.

Its architecture uses three content-addressable layers linked by SHA-256 hashes. An authors layer records identities and revocation status, a sections layer captures source-file copyright details, and a document-index layer maps individual output records to their contributing sections. An optional token-index layer extends attribution to token granularity. Integration requires a single track call per record, after which deterministic queries can produce forget sets at either record or token level without model access or gradient computations.

Evaluation on 219,555 Wikipedia pages shows that record-level provenance reduces over-deletion from a factor of 101× to 1.3× relative to dataset-level approaches. Integration overhead remains modest—between 1.3 % and 4.0 % on Hugging Face pipelines and 2.1 % to 19.0 % on Datatrove—while provenance-derived forget sets improve unlearning effectiveness by 42 % over random baselines on a 1.7 B parameter model. The design avoids machine-learning dependencies and central databases, relying instead on sharded hash buckets for efficient lookup and auditability.

Why it matters

Directly supports EU GDPR and AI Act compliance needs for data removal and transparency, actionable for Dutch AI teams building ethical pipelines; high technical depth and reproducibility for researchers.

More in this beat
data-provenancedata-security-governanceexperimental-benchmarkshugging-facemachine-unlearningnovel-methodologiesOriginBlame
How Far Can Root Cause Analysis Go on Real-World Telemetry Data?

06:00 · July 16, 2026

How Far Can Root Cause Analysis Go on Real-World Telemetry Data?

This research is highly relevant for AI researchers and AIOps practitioners in the Netherlands managing complex cloud-native environments. It provides actionable insights into improving LLM-based multi-agent systems for automated diagnostics, a critical area for Dutch tech enterprises and infrastructure providers.

Relevance 85 · Audience 95

GES-TSP: Graph Edge Sparsification for TSP

06:00 · July 14, 2026

GES-TSP: Graph Edge Sparsification for TSP

This research is highly relevant for AI researchers and operations research practitioners in the Netherlands, particularly those optimizing logistics, supply chain, and routing systems. The integration of Graph Neural Networks with classical combinatorial optimization offers actionable, scalable methodologies for Dutch enterprises dealing with complex transportation networks.

Relevance 75 · Audience 90

ARCANA: A Reflective Multi-Agent Program Synthesis Framework for ARC-AGI-2 Reasoning

06:00 · July 13, 2026

ARCANA: A Reflective Multi-Agent Program Synthesis Framework for ARC-AGI-2 Reasoning

This highly technical paper is directly relevant to AI researchers and advanced practitioners in the Netherlands working on AGI, multi-agent systems, and abstract reasoning. Its focus on achieving state-of-the-art results under strict hardware constraints makes it highly actionable for Dutch research labs and AI-driven SMEs looking to deploy efficient reasoning models.

Relevance 85 · Audience 95

Agentic Neural Architecture Search

06:00 · July 11, 2026

Agentic Neural Architecture Search

High technical depth, novelty in bridging open-ended LLM generation with combinatorial NAS, full reproducibility via public code, and direct applicability for Dutch researchers advancing AutoML and agentic systems.

Relevance 75 · Audience 90

Memory in the Loop: In-Process Retrieval as ExtendedWorking Memory for Language Agents

06:00 · July 8, 2026

Memory in the Loop: In-Process Retrieval as ExtendedWorking Memory for Language Agents

This research is highly relevant for Dutch AI researchers and engineers developing autonomous language agents, offering a practical architectural shift to drastically reduce latency and improve agent reasoning. It provides deep technical insights into optimizing memory loops, which is crucial for building efficient, scalable AI software in the Netherlands.

Relevance 85 · Audience 95

Controlling Tool Use with Heading-Specific Activation Steering

06:00 · July 8, 2026

Controlling Tool Use with Heading-Specific Activation Steering

This research provides advanced techniques for controlling LLM agent behavior, which is crucial for Dutch AI researchers developing reliable and efficient AI systems. Understanding and steering tool use aligns with the EU's push for transparent and predictable AI deployments.

Relevance 85 · Audience 95

A Sliding-Window-Based Reinforcement Learning for Dynamic Assembly Flow Shop Scheduling with Multi-Product Delivery

06:00 · July 7, 2026

A Sliding-Window-Based Reinforcement Learning for Dynamic Assembly Flow Shop Scheduling with Multi-Product Delivery

The research provides advanced reinforcement learning methodologies for dynamic scheduling, which is highly applicable to the Netherlands' robust high-tech manufacturing and logistics sectors (e.g., Brainport region). AI researchers and practitioners can leverage these graph-based MDP techniques to optimize complex assembly lines and supply chains.

Relevance 75 · Audience 90

The Wiola Architecture for Efficient Small Language Models

06:00 · July 3, 2026

The Wiola Architecture for Efficient Small Language Models

This research is highly relevant for Dutch AI researchers and SMEs as it provides a novel, efficient, and open-source Small Language Model architecture. SLMs align perfectly with the Netherlands' focus on sustainable, cost-effective, and transparent AI solutions that can be easily deployed by local enterprises without massive compute resources.

Relevance 85 · Audience 95