SST V2 introduces parallel-trainable nonlinear recurrence in latent space to let transformers reason continuously across positions, delivering +15 points on GPQA-Diamond and halving remaining GSM8K errors over matched baselines.
Brevity constraints reverse performance hierarchies in language models
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
A 432-run experiment across capability tiers refutes the assumption of a monotone inverse relationship between LLM capability and optimal harness complexity, showing model-type-specific patterns instead.
Entropy Gate applies entropy quenching with adaptive temperature schedules and multi-factor token energies to achieve 40-60% compression in LLM prompts while keeping semantic similarity above 0.80.
citing papers explorer
-
State Stream Transformer (SST) V2: Parallel Training of Nonlinear Recurrence for Latent Space Reasoning
SST V2 introduces parallel-trainable nonlinear recurrence in latent space to let transformers reason continuously across positions, delivering +15 points on GPQA-Diamond and halving remaining GSM8K errors over matched baselines.
-
It's Not the Capability: Harness Sensitivity Is Non-Monotone Across LLM Agent Tiers
A 432-run experiment across capability tiers refutes the assumption of a monotone inverse relationship between LLM capability and optimal harness complexity, showing model-type-specific patterns instead.
-
Entropy Gate: Entropy Quenching for Near-Lossless Token Compression in LLM Pipelines
Entropy Gate applies entropy quenching with adaptive temperature schedules and multi-factor token energies to achieve 40-60% compression in LLM prompts while keeping semantic similarity above 0.80.