Evidence Blindness in Direct Corpus Interaction: Persistent Navigation with AtlasNav

• URL: http://arxiv.org/abs/2608.24764v1
• Date: Tue, 25 Aug 2026 16:03:02 GMT
• Title: Evidence Blindness in Direct Corpus Interaction: Persistent Navigation with AtlasNav
• Abstract summary: We call this progressive silent loss Evidence Blindness and quantify it through stage-wise evidence realization.<n>We formulate large-scale agentic search as finite-budget navigation over reusable corpus structure.<n>We introduce AtlasNav, a persistent multi-view corpus-navigation framework that retains direct corpus interaction but organizes the corpus once into a Corpus Atlas.
• Score: 23.473106381426405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model agents are moving beyond conventional retrieval-augmented generation toward direct interaction with external corpora. Direct Corpus Interaction (DCI) keeps the full corpus accessible, yet reachable evidence can remain unusable under finite interaction budgets. Required evidence may fail to surface, a surfaced supporting document may remain unopened, or an opened document may fail to expose its decisive fragment. We call this progressive silent loss Evidence Blindness and quantify it through stage-wise evidence realization. Within the DCI paradigm, raw interaction adds little reusable corpus organization, while dynamic-workspace methods reconstruct a query-conditioned interaction space from each query and trajectory. In both cases, useful structure is recovered largely online. We instead formulate large-scale agentic search as finite-budget navigation over reusable corpus structure. We introduce AtlasNav, a persistent multi-view corpus-navigation framework that retains direct corpus interaction but organizes the corpus once into a Corpus Atlas, allowing each query to navigate adaptively rather than reconstruct shared structure. On BrowseComp-Plus, AtlasNav achieves 92.05% strict accuracy while reducing recorded online inference cost by 30.21% relative to the prior dynamic-workspace state of the art. Under matched budgets, it realizes the complete required evidence earlier and approaches the same model's evidence-supplied empirical reference more rapidly. The same representation principle remains effective under PhantomWiki's distinct corpus organization and controlled 10K-1M scaling, and transfers competitively to heterogeneous enterprise knowledge. These results show that agentic search depends not only on accessible evidence, but also on how the corpus is represented so that limited interaction becomes effective navigation.

Related papers

• A New Role for Relevance: Guiding Corpus Interaction in Agentic Search [45.65771530564621]
  Relevance is a query-dependent estimate of whether a document or excerpt contains useful evidence.<n>We introduce the Relevance-Aware RipGrep Search Agent (RARG), which turns relevance into an execution prior to interaction.
  arXiv  Detail & Related papers  (2026-07-27T09:56:52Z)
• Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion [76.02214947070392]
  DR-DCI is a retriever-steered DCI framework that treats retrieval as an agent-callable action for expanding a local workspace.<n>We show that DR-DCI is both effective and efficient across scales.<n>In corpus-scaling experiments, DR-DCI remains effective from 100K to 10M documents, whereas raw DCI becomes unstable and BM25 performs substantially worse.
  arXiv  Detail & Related papers  (2026-06-12T18:46:18Z)
• Rethinking Embodied Navigation via Relational Inductive Bias [53.72276435022479]
  We propose DB-Nav, a framework that reshapes the search space via dual relational biases.<n>It factorizes target-centric relations into an Activation Bias and an Inhibition Bias.<n>It significantly outperforms existing methods in success rate (SR) and Success weighted by Path Length (SPL)
  arXiv  Detail & Related papers  (2026-06-09T02:57:34Z)
• Towards Retrieving Interaction Spaces for Agentic Search [62.666902610940525]
  Recent direct corpus (DCI) work shows that agents can interact with the raw corpus through shell tools such as 5.4 interaction and file reads.<n>We argue that the role of retrieval for agentic search is not just to select documents that fit in the LLM context window, but to construct an interaction space.
  arXiv  Detail & Related papers  (2026-06-05T03:47:40Z)
• GrepSeek: Training Search Agents for Direct Corpus Interaction [66.69568141699311]
  GrepSeek is an optimized direct corpus interaction (DCI) search agent that trains a compact search agent to find, filter, and compose evidence from large text corpora.<n>To make DCI practical at scale, we further use a semantics-preserving sharded-parallel execution engine that accelerates shell-based retrieval by up to $7.6times$ while preserving byte-exact equivalence with sequential execution of the shell command.
  arXiv  Detail & Related papers  (2026-05-28T03:37:33Z)
• Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction [127.64173950476702]
  We study direct corpus interaction (DCI), where an agent searches the raw corpus directly with general-purpose terminal tools.<n>This approach requires no offline indexing and adapts naturally to evolving local corpora.<n>Across IR benchmarks and end-to-end agentic search tasks, this simple setup substantially outperforms strong sparse, dense, and reranking baselines.
  arXiv  Detail & Related papers  (2026-05-03T19:13:11Z)
• Agent-Driven Corpus Linguistics: A Framework for Autonomous Linguistic Discovery [1.954006174763915]
  Corpus linguistics has traditionally relied on human researchers to formulate hypotheses, construct queries, and interpret results.<n>We propose Agent-Driven Corpus Linguistics, an approach in which a large language model (LLM) connected to a corpus query engine via a structured tool-use interface.<n>Unlike unconstrained LLM generation, every finding is anchored in verifiable corpus evidence.
  arXiv  Detail & Related papers  (2026-04-08T15:14:22Z)
• WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research [73.58638285105971]
  This paper tackles textbfopen-ended deep research (OEDR), a complex challenge where AI agents must synthesize vast web-scale information into insightful reports.<n>We introduce textbfWebWeaver, a novel dual-agent framework that emulates the human research process.<n>Our framework establishes a new state-of-the-art across major OEDR benchmarks, including DeepResearch Bench, DeepConsult, and DeepResearchGym.
  arXiv  Detail & Related papers  (2025-09-16T17:57:21Z)

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.