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请输入英文单字，中文词皆可：

slinky    音标拼音: [sl'ɪŋki]
a. 鬼鬼祟祟的,苗条的,美观的

鬼鬼祟祟的,苗条的,美观的

Slinky \Slink"y\, a.
Thin; lank. [Prov. Eng. & U. S.]
[1913 Webster]

79 Moby Thesaurus words for "slinky":
airy, attenuate, attenuated, back-door, backstairs, boyish,
clandestine, covert, cowering, cringing, delicate, diaphanous,
diluted, ethereal, feline, fine, fine-drawn, finespun, flimsy,
frail, furtive, gauzy, girlish, gossamer, gracile, hidlings,
hole-and-corner, hugger-mugger, insubstantial, lacy, light, misty,
papery, privy, quailing, quiet, rare, rarefied, shifty, skulking,
slender, slenderish, slight, slight-made, slim, slimmish, slinking,
sly, small, sneaking, sneaky, stealthy, subtle, surreptitious,
svelte, sylphlike, tenuous, thin, thin-bodied, thin-set, thin-spun,
thinnish, threadlike, under-the-counter, under-the-table,
undercover, underground, underhand, underhanded, unobtrusive,
vague, wasp-waisted, watered, watered-down, watery, weak, willowy,
wiredrawn, wispy


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  • GitHub - Visual-Agent DeepEyes
    The capability of DeepEyes to think with images is learned via end-to-end reinforcement learning It is directly guided by outcome reward signals, requires no cold-start or supervised fine-tuning, and does not rely on specialized external model
  • [2505. 14362] DeepEyes: Incentivizing Thinking with Images via . . .
    To address this, we introduce DeepEyes, a model that learns to "think with images", trained end-to-end with reinforcement learning without requiring pre-collected reasoning data for cold-start supervised fine-tuning (SFT)
  • DeepEyes后日谈：我们用端到端RL复刻了o3的部分thinking . . .
    肝了几个月的工作DeepEyes终于正式release了，DeepEyes是一个具备像o3一样“边看图边思考”的能力的模型，我们基于Qwen2 5-VL-7B-Instruct的原生能力，没有SFT冷启，也不依赖外部专家模型，完全端到端RL训练，用ou…
  • DeepEyes – 小红书联合西安交大推出的多模态深度思考模型
    DeepEyes 是小红书团队和西安交通大学联合推出的多模态深度思考模型。 基于端到端强化学习，实现类似 OpenAI o3 的“用图思考”能力，无需依赖监督微调（SFT）。 DeepEyes 在推理过程中动态调用图像工具，如裁剪和缩放，增强对细节的感知与理解。
  • DeepEyes - visual-agent. github. io
    Thus, in this paper, we explore the interleaved multimodal reasoning paradigm and introduce DeepEyes, a model with "thinking with images" capabilities incentivized through end-to-end reinforcement learning without the need for cold-start SFT
  • 程序员必看！DeepEyes V2让AI从“工具狂魔“变“智能决策者“
    DeepEyesV2 通过 “冷启动微调 + 强化学习” 两阶段训练，整合代码执行、网页搜索等工具，实现了感知、搜索、推理的跨能力协同，在真实世界理解、数学推理、搜索密集型任务中表现远超现有模型，为智能体化多模态模型的构建提供了完整方案。 _deepeyes
  • DeepEyes-端到端RL复刻o3的Thinking With Images能力
    (已授权) 肝了几个月的工作DeepEyes终于正式release了，DeepEyes是一个具备像o3一样“边看图边思考”的能力的模型，基于Qwen2 5-VL-7B-Instruct的原生能力，没有SFT冷启，也不依赖外部专家模型，完全端到端RL训练，用outcome reward signal来激发VLM图像思考能力
  • DeepEyes：无需SFT，端到端RL复刻o3看图思考能力 - 知乎
    DeepEyes框架流程 DeepEyes的核心是通过 强化学习（RL）训练模型自主决定何时调用视觉工具（如图像缩放），并将视觉信息动态融入推理链中。 其流程如下： 1 输入：用户问题 + 原始图像 I_0。 2 推理循环：模型在 每一步可生成文本推理或调用图像缩放工具。
  • DeepEyes: Incentivizing Thinking with Images via Reinforcement . . .
    The paper introduces DeepEyes, a vision-language model trained end-to-end with reinforcement learning to “think with images” by interleaving textual chain-of-thought with active visual perception
  • DeepEyes: Incentivizing “Thinking with Images” via Reinforcement Learning
    DeepEyes achieves significant performance gains on general perception and reasoning benchmarks and also demonstrates improvement in grounding, hallucination, and mathematical reasoning tasks





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