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13 papers matching “retrieval” and 1 domain

1. Retrieval-augmented generationWeek 43Top 3Raise with clients

   Retrieval Augmented Generation Compositionality

   RAG pipeline components do not reliably compound their isolated performance gains when combined, and the best-performing pipeline composition shifts as document corpus size expands.

   • Surprising result
   • Reframes the problem
2. Retrieval-augmented generationWeek 43Skim it

   Toward Agentic Retrieval-Augmented Generation for Enterprise Retail Workflows over Heterogeneous Data Sources

   Chaining evidence across disparate structured and unstructured sources is the primary performance bottleneck in enterprise retail workflows, which model scaling alone fails to solve.

   • New benchmark
3. Retrieval-augmented generationWeek 42Raise with clients

   CoverSynth: Coverage-Guided Generation of Retrieval Evaluation Sets

   It introduces CoverSynth, an automated framework that generates enterprise-grade retrieval evaluation sets from unannotated corpora to uncover failure modes and differentiate retrieval systems far more effectively than human-authored sets.

   • Reframes the problem
4. Retrieval-augmented generationWeek 42Read in full

   Beyond Retrieval Failure: Understanding Why LLMs Miss Evidence in Multi-Hop RAG

   The study demonstrates that apparent multi-hop retrieval failures often stem from search drift rather than retriever inadequacy, though recovering missing evidence primarily shifts errors to premature resolutions and abstentions.

   • Surprising result
   • Reframes the problem
5. Retrieval-augmented generationWeek 41Read in full

   UNREAL: Unifying Retrieval and Long-Context with a Single Model

   It introduces a single model-internal selection mechanism adding under 500K parameters to a frozen LLM that improves both multi-hop corpus retrieval recall and long-context inference speed.

6. Retrieval-augmented generationWeek 41Read in full

   RIT-RAG: Navigating Document Corpora with Retrieval-Induced Trees

   It improves agentic RAG accuracy on large enterprise documentation by dynamically constructing multi-document sub-trees from initial retrieval results to guide LLM exploration.

   • New benchmark
7. Retrieval-augmented generationWeek 40Raise with clients

   AGO AI Quality Gate: Evidence-First Release Decisions for Retrieval-Augmented Generation

   It introduces an evidence-first RAG release framework that reduces unsafe production deployments by treating LLM judges as fallible and quantifying regression risk probabilistically.

   • Reframes the problem
8. Retrieval-augmented generationWeek 40Skim it

   Re-ranking and Late Interaction Drive Retrieval Quality: A Controlled Comparison of RAG Strategies for Scientific Question Answering

   • New benchmark
9. Retrieval-augmented generationWeek 43Top 3Read in full

   MMEnterprise-Bench: Evaluating Enterprise Agents over Evolving Multimodal Records

   Higher retrieval recall achieved through larger models or budgets fails to consistently improve answer accuracy when enterprise workplace data contains temporal and cross-source conflicts.

   • New benchmark
10. Retrieval-augmented generationWeek 43Read in full

    Is Complex Agentic Memory Always Needed? Naive RAG Is Stronger Than We Thought

    A naive dense retrieval and reranking baseline operating directly over raw conversational history outperformed complex agentic memory architectures across multiple long-term conversation benchmarks.

    • Surprising result
11. Retrieval-augmented generationWeek 41Top 3Raise with clients

    Inverting Multi-Vector Visual Document Indices

    It demonstrates that multi-vector visual document indices stored in vector databases can be inverted to reconstruct document images, exposing 45% of sensitive tokens.

    • Surprising result
12. Retrieval-augmented generationWeek 40Top 3Read in full

    Equal Ranking Quality, Different Decisions: Measuring and Reducing Order Dependence in LLM Scorers

    It demonstrates that LLM rerankers with equivalent ranking metrics produce widely inconsistent downstream decisions due to candidate ordering, and introduces order-consistency fine-tuning to resolve the issue.

    • Surprising result
    • Reframes the problem
13. AI agentsWeek 41Read in full

    Accurate but Not Humble: Evaluating Epistemic Humility in LLM Agents under Knowledge Conflict

    Higher agent task accuracy does not correlate with epistemic humility, as high-performing models often detect knowledge conflicts during execution but fail to escalate or report unresolved uncertainty in incorrect answers.

    • New benchmark
    • Surprising result