Adaptive-Computation

TL;DR for operators A retrieved prompt can contain one useful sentence or evidence distributed across several passages. Giving both requests the same compressed-context budget assumes that the amount of information worth preserving is constant. The evidence in ATACompressor1 suggests that assumption is costly: on MSMARCO with LLaMA-2-7B, removing adaptive allocation reduced F1 from 50.06 to 47.52 and compression ratio from 27.36x to 19.06x; removing selective compression reduced F1 further to 40.83. ...

TL;DR for operators A synthetic training video can look coherent at its beginning and end while losing the target identity somewhere in the middle. The paper proposes changing the synthetic-data factory so that every generated frame is checked against a real target-identity reference, the weakest eligible frame receives an additional identity anchor, and only then is the affected span regenerated. ...

Most companies do not actually want an AI system that “thinks longer.” They want one that knows when extra thinking is worth the bill. That distinction is becoming more important. Reasoning models are moving from demo-stage math puzzles into document review, financial research, compliance analysis, customer support escalation, and agentic workflows. In these settings, reasoning has three costs: latency, compute, and misplaced confidence. A model that spends 30 seconds producing an elegant wrong answer has not reasoned. It has performed expensive theatre. Very fluent theatre, admittedly. ...