SeCo performs semantic-driven context compression for LLMs by anchoring on query-relevant semantic centers and applying consistency-weighted token merging, yielding better downstream performance, lower latency, and stronger out-of-domain robustness than position-based methods across 14 benchmarks.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
dataset 2
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
dataset 2polarities
use dataset 2representative citing papers
ModelLens learns a performance-aware latent space from 1.62M leaderboard records to rank unseen models on unseen datasets without forward passes on the target.
SHADE adaptively combines coverage and spectral signals to estimate semantic alphabet size from few LLM samples, yielding better performance than baselines in low-sample regimes for alphabet estimation and QA error detection.
citing papers explorer
-
Beyond Position Bias: Shifting Context Compression from Position-Driven to Semantic-Driven
SeCo performs semantic-driven context compression for LLMs by anchoring on query-relevant semantic centers and applying consistency-weighted token merging, yielding better downstream performance, lower latency, and stronger out-of-domain robustness than position-based methods across 14 benchmarks.
-
ModelLens: Finding the Best for Your Task from Myriads of Models
ModelLens learns a performance-aware latent space from 1.62M leaderboard records to rank unseen models on unseen datasets without forward passes on the target.
-
Mind the Unseen Mass: Unmasking LLM Hallucinations via Soft-Hybrid Alphabet Estimation
SHADE adaptively combines coverage and spectral signals to estimate semantic alphabet size from few LLM samples, yielding better performance than baselines in low-sample regimes for alphabet estimation and QA error detection.