Model Detail
Prism-Qwen3.5-Reranker-0.8B
—Prism-Qwen3.5-Reranker-0.8B is a large language model with 0.8B parameters released by infgrad. The model is registered under the text-ranking pipeline tag on Hugging Face, distributed under the permissive mit license.
Prism-Qwen3.5-Reranker-0.8B ships with 0.8B parameters. The mit license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Prism-Qwen3.5-Reranker-0.8B is best fit for general-purpose chat and instruction-following workloads. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Prism-1: Position- and Rival-Independent Scoring for System One Models
arXiv:2610.22066v1 Announce Type: new Abstract: Given a state and a set of options, such as intents, tools or interface elements, System One decision models return a distribution over the options in one forward pass, without generated reasoning. Web systems increasingly build them by reading decisio
PRISM: PRIvileged Sibling-guided Multi-Agent Topology Design
arXiv:2610.20628v1 Announce Type: new Abstract: Multi-agent systems (MAS) based on large language models (LLMs) have become a powerful way to address complex problems, and their effectiveness depends critically on the collaboration topology that decides which agents participate and how they communic
PRISM: Transfer and Limits of Activation Interventions in Tool-Calling Language Models
arXiv:2610.20808v1 Announce Type: new Abstract: Recent literature shows that unsafe tool calls can often accompany textual refusals in language models. To investigate this hidden vulnerability, we introduce PRISM, a framework for evaluating the transfer of activation interventions across precision,
PRISM: Calibrating Black-Box Judges with Counterfactual Probes
arXiv:2610.18389v1 Announce Type: cross Abstract: Large language models are increasingly used as judges to reduce reliance on costly human evaluation, yet their scores can systematically disagree with human judgments. We ask whether responses to an explicitly imposed evaluative bias can help correct
A prism hierarchy of learning regimes in large linear autoencoders
arXiv:2606.05335v2 Announce Type: replace Abstract: Theoretical studies of machine learning models commonly consider different limiting regimes in which the learning dynamics of gradient descent becomes theoretically tractable. It is, however, desirable to have a systematically obtained picture of q
PRISM: A Category-Theoretic Framework for Measuring and Refining Multimodal Analogies
arXiv:2610.01383v1 Announce Type: new Abstract: Analogical reasoning involves identifying and preserving relational structures across domains. However, existing approaches to AI-driven multimodal analogy generation lack an interpretable measure of whether this structure is understood and maintained
