arXiv (All)
open access
GACC-Bench: Benchmarking Goal-Adaptive Context Compression Ability across Large Language Models
Context compression in long-running applications of large language models (LLMs) must preserve goal-relevant information, often before specific downstream questions are known. We introduce GACC-Bench, a benchmark for Goal-Adaptive Context Compression that evaluates how reliably LLMs adapt informatio
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- zai-org/LongBench#786833
- ContextWeave: A Real-World Workflow Benchmark#429407
- Message Passing Does More with Less for In-Context Learning on Graphs#1132258
- Scope Before You Persist: Preventing Cross-Family Interference in Agent Memory#1077073
- Towards Reliable Long-Context Reasoning: Detecting Unanswerable Questions via FactGuard#1355891
Record · ID 1357548
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive