This module is trained in an adversarial learning manner [5]. The Selectionmodule selects high possibility candidates from proposals and combines to produce the final prediction, according to the criteria of better position matching.

We conduct a systematic comparison of methods in a variety of domains, varying thenumber oflabeled instances available inthetargetdomain (k), as well as the number of target-domain classes.

Toeasethelearning, wedemonstrate thatitisbeneficial toadoptacurriculum learning strategy [23], where harder negatives are introduced after an initial stage of learning on easiernegatives.

While many techniques for detecting these attacks have been proposed, theyareeasily bypassed when theadversary hasfullknowledge of the detection mechanism and adapts the attack strategy accordingly. In this paper,we adopt anovel perspectiveand regard the omnipresence of adversarial perturbations asastrength rather thanaweakness.

Neural Information Processing Systems http://nips.cc/

Large language models (LLMs) can generate structured artifacts, but using them as dependable optimizers for scientific design requires a mechanism for iterative improvement under black-box evaluation. Here, we cast quantum circuit synthesis as a closed-loop, test-time optimization problem: an LLM proposes edits to a fixed-length gate list, and an external simulator evaluates the resulting state with the Meyer-Wallach (MW) global entanglement measure. We introduce a lightweight test-time learning recipe that can reuse prior high-performing candidates as an explicit memory trace, augments prompts with a score-difference feedback, and applies restart-from-the-best sampling to escape potential plateaus. Across fixed 20-qubit settings, the loop without feedback and restart-from-the-best improves random initial circuits over a range of gate budgets. To lift up this performance and success rate, we use the full learning strategy. For the 25-qubit, it mitigates a pronounced performance plateau when naive querying is used. Beyond raw scores, we analyze the structure of synthesized states and find that high MW solutions can correspond to stabilizer or graph-state-like constructions, but full connectivity is not guaranteed due to the metric property and prompt design. These results illustrate both the promise and the pitfalls of memory evaluator-guided LLM optimization for circuit synthesis, highlighting the critical role of prior human-made theoretical theorems to optimally design a custom tool in support of research.

This paper aims to tackle the challenging problem of one-shot object detection.

Neural Information Processing Systems http://nips.cc/