Full-text deep reads of Agent and LLM-inference papers: mechanism, verified numbers, what the abstract leaves out.
Problem: Long-horizon coding agents accumulate millions of tokens of context; how to compact it without losing what matters and without re-prefilling (and paying) on every step.
The mechanism is sound and worth borrowing, but headline numbers are oversold: cost savings are solid, 'quality improves' sits within seed noise, and 'matches Opus 4.7' / '25% over specialized methods' are cross-harness / cross-model comparisons.
Problem: Local serving stacks silently confound tool-use evaluation by rejecting or altering requests before they reach the model, leading evaluators to misattribute infrastructure failures to model incompetence.
The paper demonstrates that 0% tool-use rates are often artifacts of the serving stack rather than model inability. However, the measurements suffer from extremely small denominators and high variance, meaning the numbers highlight the serving confound but cannot reliably rank model capabilities.
Problem: Accelerating LLM inference without sacrificing accuracy by resolving the conflicting quantization requirements of the compute-bound prefill phase and the memory-bound decode phase.
Shows that decoupling quantization formats for prefill (compute-native) and decode (weight-only) significantly improves accuracy and speed, but the SSD streaming optimization (ODP) is ineffective for MoE architectures and short context prompts.
Problem: How to enable omni models to perform long-horizon multimodal productivity tasks, such as video editing and analysis, without incurring prohibitive token costs from processing full-length videos.
Demonstrates that wrapping an omni-model in a native agent harness drastically reduces token consumption for long videos while improving reasoning, though the model still lags behind Gemini 3.8 Flash on several agentic and video reasoning tasks, and some core multilingual translation capabilities regress slightly.
Problem: The task is to allocate test-time compute for LLMs to effectively discover verifiable algorithmic and mathematical solutions, questioning how much complex evolutionary machinery is actually necessary.
It shows that token sampling with a simple greedy update is a stronger and cheaper exploration mechanism than perturbing weights or maintaining complex populations, though its headline state-of-the-art results rely on the single best seed in float-sensitive environments and struggle without domain-specific prompts.