pace
for LLM platform teams, security engineers
Why this scored 6
every term, weightedIts fastest-moving item, measured against the pace of its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities its own items come from
Decays to zero over 14 days, counted from when we first saw it
Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it
Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology
Signal history
7-day window (free)The evidence
The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.
- 119
PACE: Perceived-Latency-Aware Cascading Service Routing and Filler Control for QoE-Efficient Retrieval-Augmented Dialogue Serving
Offers latency-aware cascading routing to boost QoE in retrieval-augmented dialogue services. · for dialogue system engineers, platform ops
arxivSteadyai34d ago - 217
PACE: Provenance-Aware Capability Enforcement for Tool-Using LLM Agents
Adds provenance-aware capability enforcement for tool-using LLM agents. · for LLM platform teams, security engineers
arxivSteadysecurity12d ago - 315
PACE: A Unified Condense-and-Extract Paradigm for Fast VLM Inference
Introduces a unified condense-and-extract method to speed up vision-language model inference. · for ml engineers
arxivSteadyai47d ago