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#auditability

4 curated events
papersTODAY 04:00 UTC

Deletion Certificates for Support-Vector Memory: What They Cover and When They Expire

This paper examines how to verifiably delete stored items from a memory that places a support-vector boundary around keys and weights their values by the resulting coefficients. It shows that when a coefficient is exactly zero, the key can be removed without altering the current normal, and identifies the conditions under which such deletion guarantees stop holding. The work aims toward auditable removal in memory-based learning systems.

machine-learning/deletion/auditability/arxiv
papersTODAY 04:00 UTC

arXiv Paper Proposes Making LLM Agent Actions Auditable

A new arXiv paper argues that as LLM agents gain the ability to call tools, query databases, delegate work and cause external side effects, the focus should shift from merely blocking harmful actions to keeping those actions answerable and reviewable. The work frames auditability as a core requirement for deployed agent systems rather than a secondary add-on. It appears as a replacement submission on arXiv cs.AI.

agents/safety/research/auditability
papersSEP 10 04:00 UTC

Evidence-Grounded Text Evaluation with LLM Judges Aims to Make Rubric Scoring Reliable

A research paper on arXiv introduces a method for scoring text against evaluation rubrics using large language models, addressing how black-box judge models can apply identical criteria in inconsistent ways. The approach ties each score to concrete evidence drawn from the evaluated text, making the reasoning behind judgments easier to audit and reproduce. The work is cross-listed under arXiv categories for artificial intelligence, computational linguistics, and machine learning.

evaluation/llm-judges/rubrics/auditability
papersSEP 12 04:00 UTC

Survey Maps Evidence Tracing and Provenance Methods for LLM Agents

A new arXiv survey examines how evidence tracing and execution provenance can be applied to LLM-based agents that plan, call tools, retrieve information, and collaborate across multiple agents. The authors frame provenance tracking as a way to make agent behavior auditable and to build trust as these systems take on more autonomous tasks. The paper is a revision of an earlier preprint.

agents/research/trust/auditability