SGKR uses function-call dependency graphs to retrieve structured code knowledge, improving LLM correctness on multi-step data reasoning benchmarks over similarity baselines.
Llm/agent-as-data-analyst: A survey
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K-Token Merging compresses LLM inputs by merging blocks of K token embeddings in latent space, achieving up to 75% length reduction with minimal performance drop on reasoning, classification, and code tasks.
A literature survey that taxonomizes methods, datasets, and evaluation practices for natural language interfaces to geospatial and temporal databases while identifying recurring trends and future directions.
citing papers explorer
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Structure-Grounded Knowledge Retrieval via Code Dependencies for Multi-Step Data Reasoning
SGKR uses function-call dependency graphs to retrieve structured code knowledge, improving LLM correctness on multi-step data reasoning benchmarks over similarity baselines.
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Compressing Sequences in the Latent Embedding Space: $K$-Token Merging for Large Language Models
K-Token Merging compresses LLM inputs by merging blocks of K token embeddings in latent space, achieving up to 75% length reduction with minimal performance drop on reasoning, classification, and code tasks.
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Natural Language Interfaces for Spatial and Temporal Databases: A Comprehensive Overview of Methods, Taxonomy, and Future Directions
A literature survey that taxonomizes methods, datasets, and evaluation practices for natural language interfaces to geospatial and temporal databases while identifying recurring trends and future directions.