arXiv AI
Jun 24
Entity Resolution via Batched Oracle Queries
arXiv:2606. 24407v1 Announce Type: cross Abstract: We consider an oracle that processes a limited batch of records at a time and clusters those that refer to the same real-world entity.
Entity Resolution in Practice: Lessons from a Self-Serve Pipeline
arXiv:2607. 26298v1 Announce Type: new Abstract: We built and evaluated a self-serve entity resolution (ER) system on six benchmarks spanning 864 to 5M records, and three lessons emerged that are absent from existing ER literature.
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Hugging Face Trending Papers
Jun 23
Entity Resolution via Batched Oracle Queries
We consider an oracle that processes a limited batch of records at a time and clusters those that refer to the same real-world entity. We study how to interrogate such an oracle to resolve entities in a dataset whose size is far larger than a single batch, and where no batch is guaranteed to contain all records of any given entity.
arXiv Machine Learning
Jul 13
Forget Narrowly, Retain Broadly: Unlearning as an Asymmetric Generalization Problem
arXiv:2607. 09236v1 Announce Type: new Abstract: Machine unlearning in LLMs is the targeted removal of specific knowledge while preserving all other capabilities, critical for privacy and safety.
arXiv AI
Jul 16
OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets
arXiv:2607. 13037v1 Announce Type: new Abstract: 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.
arXiv AI
Jul 21
Accurate and Efficient Long-Term Memory for LLM Agents
arXiv:2607. 16211v1 Announce Type: new Abstract: LLM agents augmented with persistent memory can recall past interactions, but existing systems suffer from two limitations: flat, unstructured storage loses relational context needed for multi-hop and temporal reasoning, and reliance on expensive LLM-based classification makes them impractical for latency-sensitive deployment.
arXiv AI
Jul 7
IRC-Bench: Recognizing Entities from Contextual Cues in First-Person Reminiscences
arXiv:2605. 06142v2 Announce Type: replace-cross Abstract: When people recount personal memories, they often refer to people, places, and events indirectly, relying on con-textual cues rather than explicit names.
arXiv AI
Jun 26
Understanding Domain-Aware Distribution Alignment in Budgeted Entity Matching
arXiv:2606. 27342v1 Announce Type: cross Abstract: Entity Matching (EM) is a core operation in the data integration pipeline, where records from different sources are compared to determine whether they refer to the same real-world entity.
arXiv AI
Jun 8
Standard vs. Modular Sampling: Best Practices for Reliable LLM Unlearning
arXiv:2509. 05316v2 Announce Type: replace-cross Abstract: A conventional LLM Unlearning setting consists of two subsets -"forget" and "retain", with the objectives of removing the undesired knowledge from the forget set while preserving the remaining knowledge from the retain.
arXiv AI
Jun 17
Scaling Enterprise Agent Routing: Degradation, Diagnosis, and Recovery
arXiv:2606. 17519v1 Announce Type: cross Abstract: Production LLM assistants route user requests to growing libraries of specialized tools, but how does routing accuracy degrade as the catalog scales?
arXiv AI
Jul 1
RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora
arXiv:2604. 19047v2 Announce Type: replace-cross Abstract: Existing QA benchmarks typically assume distinct documents with minimal overlap, yet real-world retrieval-augmented generation (RAG) systems operate on corpora such as financial reports, legal codes, and patents, where information is highly redundant and documents exhibit strong inter-document similarity.
arXiv AI
Jul 8
StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems
arXiv:2607. 05844v1 Announce Type: new Abstract: Agent systems accumulate conflicting observations across branches, retries, and replicas, yet many practical memory layers still collapse disagreement behind overwrite rules that are difficult to inspect or correct.
arXiv AI
Jul 22
BatchDAG: LLM-Planned Execution Graphs for Scalable Ad-Hoc Analysis Over Enterprise Data
arXiv:2607. 18241v1 Announce Type: new Abstract: Large language models (LLMs) excel at analyzing individual documents but break down on exhaustive, cross-entity analytical questions over enterprise-scale datasets due to context overflow, loss of per-entity attribution, and linear latency from sequential tool calls.