Find a story
Search Spins
Search titles, summaries, and missing voices across published articles — press releases, announcements, and media coverage.
6 results for “KV cache”
KV cache as an agent runtime [R]
A Yandex research team proposes reframing the KV cache—the intermediate state in LLM inference—as an active, modifiable runtime environment for AI agents, enabling more responsive and interactive agent behavior without full model retraining or architectural overhaul.
Published Sep 7, 2026 · Analyzed Sep 10, 2026
Is KV Cache in a high dimensional vector space? [D]
A Reddit user proposes reframing the KV cache in transformer models as a navigable geometric search space rather than a flat memory array, suggesting indexing and localized attention could improve inference efficiency.
Aug 21, 2026
CommitKV: Lifecycle-Aware KV Cache Compression via Commit Transitions for Multi-Turn Agents
CommitKV is a new KV cache compression method for multi-turn ReAct agents that identifies and removes only truly completed information—distinguishing it from temporarily dormant but future-relevant data—thereby reducing memory use, speeding inference, and improving accuracy.
Aug 11, 2026
A requiem for Optane, Intel's KV cache killer that could have eased the RAM price crunch - The Register
Intel discontinued its Optane memory technology, a high-performance persistent memory and key-value cache solution that had potential to alleviate RAM supply constraints and pricing pressures but failed to achieve market traction.
Published Jul 28, 2026 · Analyzed Aug 1, 2026
SpecLA: Efficient Speculative Decoding for Linear-Attention Models
SpecLA is a new speculative decoding runtime designed specifically for linear-attention models, enabling up to 1.70x end-to-end speedup by addressing recurrent-state verification challenges that existing speculative systems ignore.
Jul 21, 2026
Kara: Efficient Reasoning LLM Serving via Sliding-Window KV Cache Compression
Kara is a new sliding-window KV cache compression method for reasoning LLMs that improves decoding throughput and reduces memory overhead by selectively preserving flexible-sized semantic chunks of the key-value cache during inference.
Published Jul 3, 2026 · Analyzed Jul 6, 2026