When do compressed vectors make the same decisions?
Research at a glance
When Does Low-Bit Quantization Preserve the Decisions of Vector Search?
Places quantization error inside the ranking and graph-pruning decisions that vector search actually makes, relating reliability to comparison margins, correlated residuals, and execution traces.
Why does the value of the same quantization move change?
Research at a glance
Contextual Utility of Quantization Moves in Extreme Low-Bit LLMs
Studies the real utility of a discrete move in extreme low-bit LLMs by evaluating the move along its own path and combining decisions in an evolving model state.
Covariance Structure and Coordinate Heterogeneity Govern Binary Quantization of Contrastive Embeddings
Explains low-bit ranking behavior through covariance structure and coordinate heterogeneity, including why an extra magnitude bit and random rotation help different representations in different ways.
QuIVer: building the search graph directly in binary space
Research at a glance
QuIVer: Rethinking ANN Graph Topology via Training-Free Binary Quantization
Builds, prunes, and navigates an ANN graph in a training-free two-bit space, reading full vectors only for final reranking and exposing the data regimes where compact topology works.
An open-source runtime for AI companions, connecting long-term memory, context, and tools while powering persistent conversation and desktop interaction.
An open-source workspace for AI agents. Models, tools, files, and projects meet in one place, with a native Rust runtime and clients for desktop, web, and mobile.
Astr evokes the distant sky; Mira is a real variable star; Mirage is an imagined horizon still to be reached. These images come together in our Chinese name, 幻梦星芒.
Mira changes in brightness and leaves a long ultraviolet tail. It reminds us to preserve imagination around the unknown and leave a trail that others can continue.