The Flow has not summarised this story yet — read it at arXiv Computation and Language.
arXiv:2606. 26185v1 Announce Type: new Abstract: LLM-as-judge ("grader") components are now standard in evaluation harnesses, including safety evaluations where a pass/fail verdict may gate downstream deployment decisions.
arXiv:2609.37647v1 Announce Type: cross Abstract: Jev is a commercial System One model from TypeSafe AI that does not generate text: given a state and typed questions, it returns a choice from fixed...
JevAdvBench introduces the first adversarial benchmark for reinforcement‑learning‑based calibrated decision (RLCD) models, providing 812 typed questions across 66 scenarios and a black‑box attack suite of 9,744 single‑edit variants. The benchmark evaluates attacks by comparing each perturbed decision to the model’s own clean decision and to an identical re‑run, revealing that rewording changes decisions by only 1.2 percentage points while certain injected opinions can flip 12.1% of decisions and lower confidence below 0.8 in 38% of cases. These findings demonstrate that RLCD models can be significantly misled by seemingly innocuous input edits, underscoring the need to treat the state as untrusted in applications.
Almost every paper on LLM jailbreaks and prompt injection reports an attack-success rate (ASR), and that number is assigned not by people but by an automated judge: either a safety classifier trained for the task, or a general chat model prompted to grade. The judge is rarely checked.
arXiv:2606. 25487v1 Announce Type: cross Abstract: Almost every paper on LLM jailbreaks and prompt injection reports an attack-success rate (ASR), and that number is assigned not by people but by an automated judge: either a safety classifier trained for the task, or a general chat model prompted to grade.
arXiv:2609.38612v1 Announce Type: new Abstract: As natural language drives more applications, language models increasingly run inside programs as decision components: the program sends them the curre...