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Research2026-06-05 · source-backed
The serving system targets the practical inference-cost wall you hit when agents produce very long outputs and dense attention becomes the bottleneck (arXiv). As agent runs get longer, this is the kind of infra that decides whether serving them at scale is affordable. Directly relevant if you're hosting agentic LLMs yourself.
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ArXiv paper 2603.14805 argues the primary bottleneck in scaling agentic development is knowledge architecture — "skills" (composable, governance-aware units encoding institutional knowledge) are the right primitive for agents, not raw docs or in-context retrieval. Directly rel...
This comparison ran the baseline the retrofitted-linear-attention literature skipped. Across multiple LLMs and downstream tasks SWA with sinks matches or beats post-trained linear attention, and on Needle-in-a-Haystack and BABILong it scores 2 to 10 times higher. The recommend...
Studdiford and Lupyan tested human participants and 25 LLMs on common-sense causal reasoning and found shared, predictable error patterns triggered by irrelevant prompt details (arXiv). They localized the attention heads driving it. The uncomfortable implication: the gap betwe...
Multi-turn serving hurts because the KV cache grows linearly with conversation length, choking GPU memory and bandwidth. Tangram spends memory unevenly across the cache instead of treating all tokens equally, cutting the footprint of long sessions. Source: arXiv Directly usefu...
Addresses the fundamental privacy dilemma: cloud models need data access but enterprises can't share sensitive information. Splits execution between enterprise-side privacy agents and cloud-side capability agents. Directly relevant to AWS AgentCore and enterprise adoption. arX...
Introduces temporal causal diagnostics to distinguish legitimate task execution from injected manipulation in multi-turn agent interactions, plus context purification to neutralize poisoned content. Directly applicable to anyone building agents that call external tools. arXiv...
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