Data Flow Control formalizes data safety as aggregate predicates over provenance monomials and implements enforcement via the Passant query rewriting layer achieving near-zero overhead across five DBMS engines.
Gonzalez, and Aditya G
10 Pith papers cite this work. Polarity classification is still indexing.
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AgileLog introduces forkable shared logs with cheap forking and isolation to support AI agents on data streams.
New Text-to-Big SQL metrics show that LLM agents must balance accuracy with cost and speed at scale, where GPT-4o trades some accuracy for up to 12x speedup and GPT-5.2 proves more cost-effective than Gemini 3 Pro on large inputs.
Interviews with 22 participants identify a typology of deception on Chinese gay dating apps and document probabilistic trust strategies and community-shared risk knowledge.
Automatically optimizing agent skill files on a branching lakehouse improved held-out validation accuracy by 31.9% on 25 synthetic-but-trace-anchored tasks.
GitLake lifts Iceberg snapshots into lakehouse-wide commits, branches, and merges so agents develop in isolation and multi-table pipelines publish atomically via temporary-branch merges.
Interviews reveal interpretive misalignment in EM systems where supervised individuals and authorities reason differently about the same data streams due to asymmetric access.
Sophrosyne augments fine-grained data APIs with directives to curb over-exploration by Text2SQL agents, reducing it 4.6x and improving accuracy up to 4 points.
Hyperparam supplies under-70KB JS libraries (Hyparquet, Squirreling, Icebird) for async-native SQL over Parquet/Iceberg with per-cell LLM UDFs, claiming 300x speedup versus DuckDB-WASM on filter queries and two-thirds lower cost on agent analyst tasks.
BranchBench shows that existing branchable DBMSes face severe trade-offs between branching speed and read/write performance, with no system supporting representative agentic workloads at scale.
citing papers explorer
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Data Flow Control: Data Safety Policies for AI Agents
Data Flow Control formalizes data safety as aggregate predicates over provenance monomials and implements enforcement via the Passant query rewriting layer achieving near-zero overhead across five DBMS engines.
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AgileLog: A Forkable Shared Log for Agents on Data Streams
AgileLog introduces forkable shared logs with cheap forking and isolation to support AI agents on data streams.
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Both Ends Count! Just How Good are LLM Agents at "Text-to-Big SQL"?
New Text-to-Big SQL metrics show that LLM agents must balance accuracy with cost and speed at scale, where GPT-4o trades some accuracy for up to 12x speedup and GPT-5.2 proves more cost-effective than Gemini 3 Pro on large inputs.
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"Everyone Says Them": Deception Typologies, Probabilistic Trust, and Grassroots Safety Knowledge Among Gay Dating App Users in China
Interviews with 22 participants identify a typology of deception on Chinese gay dating apps and document probabilistic trust strategies and community-shared risk knowledge.
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"Skill Issues'': Data-Centric Optimization of Lakehouse Agents
Automatically optimizing agent skill files on a branching lakehouse improved held-out validation accuracy by 31.9% on 25 synthetic-but-trace-anchored tasks.
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GitLake: Git-for-data for the agentic lakehouse
GitLake lifts Iceberg snapshots into lakehouse-wide commits, branches, and merges so agents develop in isolation and multi-table pipelines publish atomically via temporary-branch merges.
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Reading the Same Data Differently: Interpretive Labor Across System Boundaries in Electronic Monitoring
Interviews reveal interpretive misalignment in EM systems where supervised individuals and authorities reason differently about the same data streams due to asymmetric access.
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Sophrosyne: Agentic Exploration of Relational Data Systems Needs Moderation
Sophrosyne augments fine-grained data APIs with directives to curb over-exploration by Text2SQL agents, reducing it 4.6x and improving accuracy up to 4 points.
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A Query Engine for the Agents
Hyperparam supplies under-70KB JS libraries (Hyparquet, Squirreling, Icebird) for async-native SQL over Parquet/Iceberg with per-cell LLM UDFs, claiming 300x speedup versus DuckDB-WASM on filter queries and two-thirds lower cost on agent analyst tasks.
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BranchBench: Aligning Database Branching with Agentic Demands
BranchBench shows that existing branchable DBMSes face severe trade-offs between branching speed and read/write performance, with no system supporting representative agentic workloads at scale.