CORE: Coarse-to-Fine Search with Context Management for Deep Research
arXiv:2611.07859v1 Announce Type: cross Abstract: Deep research agents answer complex questions through repeated search. They rely on a harness that runs their …
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arXiv:2611.07859v1 Announce Type: cross Abstract: Deep research agents answer complex questions through repeated search. They rely on a harness that runs their …
arXiv:2611.07486v1 Announce Type: cross Abstract: Mapping behavioral intelligence in malware analysis reports to ATT&CK techniques supports threat analysis and …
arXiv:2611.07174v1 Announce Type: cross Abstract: MapHub, the MIDAS arXiv Preprint Hub, assesses each day's new arXiv submissions for every participant in a res…
arXiv:2611.08228v1 Announce Type: new Abstract: Information Retrieval (IR) is much more than ad hoc web search: a large portion of the information seeking activ…
arXiv:2611.08014v1 Announce Type: new Abstract: Dense similarity supplies reusable corpus representations, while probabilistic relevance decisions offer query-d…
arXiv:2611.07631v1 Announce Type: new Abstract: Long-horizon LLM agents require memory systems that preserve useful information across sessions and retrieve it …
arXiv:2611.07591v1 Announce Type: new Abstract: Re-ranking aims to present users with an ordered list that maximizes sequence-level utility. Industrial deployme…
arXiv:2611.07588v1 Announce Type: new Abstract: Collaborative-filtering (CF) scores provide graded, personalized rewards for training large language model (LLM)…
arXiv:2611.07523v1 Announce Type: new Abstract: Semantic information can enrich the identifiers and structured fields used by recommender systems. Semantic toke…
arXiv:2611.07425v1 Announce Type: new Abstract: Generative recommendation retrieves items by autoregressively generating Semantic IDs(SIDs), where an RQ-VAE qua…
arXiv:2611.07393v1 Announce Type: new Abstract: Recent work in machine learning has demonstrated that transformer-based language model agents can communicate st…
arXiv:2611.07298v1 Announce Type: new Abstract: Embedding models compress variable-length text into fixed-size vector representations, but this compression can …
arXiv:2611.07951v1 Announce Type: new Abstract: Governments increasingly use machine learning (ML) to handle data volumes beyond their analytical capacity, yet …
arXiv:2611.07887v1 Announce Type: new Abstract: Maintainers sometimes cite a low token-similarity score as evidence that an LLM-assisted rewrite of a copyleft p…
arXiv:2611.07882v1 Announce Type: new Abstract: Loop invariants are essential for deductive program verification, yet automatically inferring them for {multipha…
arXiv:2611.07876v1 Announce Type: new Abstract: Large language models increasingly judge code, yet without external feedback such judgments are unreliable, and …
arXiv:2611.07772v1 Announce Type: new Abstract: External validity is a central concern in empirical software engineering, yet the debate has largely focused on …
arXiv:2611.07760v1 Announce Type: new Abstract: Anomaly detection on software logs ensures system reliability through failure identification, root cause analysi…
arXiv:2611.07759v1 Announce Type: new Abstract: LLM-based code assistants are increasingly used in iterative repository-level development, where effective assis…
arXiv:2611.07754v1 Announce Type: new Abstract: Graph databases are becoming foundational infrastructure for connected data, and a growing body of automated tes…