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开发阶段案例 · 模型盲评尚待独立人工复核，不代表正式 Benchmark 结论。
自动补全无 Evidence 更优88 / 162 · b4e0d98f9105fbfd

A Survey on Improving Human Robot Collaboration through Vision-and-Language Navigation

心理学与认知神经科学 · 2512.00027v1

FLOWING EVIDENCE BENCHMARK

怎么判断 Evidence 真的有帮助？

核心问题是：在同一写作位置、使用同一模型和任务时，提供检索文献片段会怎样改变首次输出？我们成对比较两种条件，保留持平、不可用和评审未完成的结果。

同一段原稿 · 比较是否提供 Evidence

01 · 固定写作位置

论文原稿

同一处写作点位 ▌

同一原稿位置的自动补全成对比较

02 · 构造两组输入

两组共用

原稿上下文、模型、任务和提示词

A · 提供 Evidence

额外提供检索文献片段

B · 不提供 Evidence

不提供检索文献片段

LLM

同一模型、同一版本

A → 首次输出

B → 首次输出

盲评 Agent

匿名标记两份首次输出为 X、Y

按续写质量判断：准确性、任务贴合度和可用性
输出：X 更优 / 持平 / Y 更优 / 两组均不可用

换序复评：X / Y → Y / X

盲评具体怎么判？

① 匿名两份输出
评审看到同一原稿和两份首次输出，但不知道哪份用了 Evidence。

② 比较并换序复评
根据当前任务的标准，按 X/Y、再按 Y/X 的顺序各评一次。

③ 复核分歧
两次结论不一致时再做第三次判定；未完成的评审也留在分母。

自动补全点位怎样分层？

在查看生成结果前，先核查检索片段是否含有能直接支撑下一步续写的具体命题；有则放在左侧机会组，否则放在右侧普通组。每个学科从可准入论文中均衡选取两组点位。50/50 是实验设计，不代表真实写作中两类点位各占一半。

AUTOCOMPLETE · 110

定量生物学、统计学、天体物理学

左侧 55 个、右侧 55 个点位；统计学采用 10 篇论文的复测结果。

AUTOCOMPLETE · 52

心理学与气候科学

左侧 26 个、右侧 26 个点位；心理学计入 9 篇，气候科学计入 4 篇。

表格里的百分比怎么算？

五学科共有 81 个左侧点位。原始盲评有 51 个 Evidence 胜出；任务校验将其中一条空白续写改归“两组均不可用”，因此公开统计为 50 个。普通组另有一条双方空白，已从“未分出胜负”改归“两组均不可用”。这些原始判定仍可在案例页查看。

50Evidence 版本更优
÷
81该层全部点位
=
62%该层 Evidence 获评更优的比例

来源贡献是另一项复核：已进入复核的 21 个 Evidence 获胜点位中，16 个确认直接使用了检索论文；另有 29 个胜出点位尚待复核。

当前是开发阶段的模型评审结果，尚未完成独立人工复核；这些数字不代表正式 Benchmark 结论，也不能单独证明因果关系。

原稿写作位置

原文摘录 · 非 PDF 页面

研究论文 · 原文片段

A Survey on Improving Human Robot Collaboration through Vision-and-Language Navigation

1. INTRODUCTION

…D environments ( Anderson et al., 2018b ) ( Chen et al., 2019 ) , ( Thomason et al., 2020 ) , . In VLN tasks, agents interpret human instructions and leverage visual observations to traverse previously unseen spaces with increasing reliability. These agents integrate visual inputs and language instructions to generate navigation commands, provide verbal feedback, execute manipulation actions, and identify object locations ( Wu et al., 2023 ) . As a hallmark of embodied AI (EAI) ( Das et al., 2018 ) , VLN has catalyzed extensions into vision-language-guided manipulation and outdoor navigation.

…nts to interpret a single instruction before navigating, while others support free-form dialogue with an oracle. Similarly, task objectives range from precise route following to dynamic exploration and object interaction. Even seemingly simple directives such as “Turn left, climb the stairs, enter the bathroom” pose challenges for computational agents. They must decompose such instructions into sub-goals, ground each step in real-world objects and dynamics, recognize visual cues (e.g., identifying the bathroom), and execute actions accurately while knowing when to stop ( Jain et al., 2019 ) .

↓ 此处生成下一段续写

黄色标出写作点位附近的原文；本页根据评测输入重排文本，不是原始 PDF 截图。

同一写作位置的成对对照盲评结果：无 Evidence 更优

两组使用同一模型、同一写作任务和原稿上下文，仅比较是否提供检索文献片段。下方展示评测记录中的首次输出。

Evidence

同一模型与写作位置 · 提供检索文献片段 · 首次输出

本次未获偏好

Despite this progress, VLN agents remain largely evaluated in isolation, and their extension to collaborative settings where instructions are jointly negotiated with a human partner is comparatively underexplored. Prior work on human-object interaction detection has begun to model multivariate relationships involving auxiliary entities such as tools, explicitly capturing the functional role of these objects through triplet structures.

提供给 Evidence 版本的文献片段

Contextualized Representation Learning for Effective Human-Object Interaction Detection ↗

Human-Object Interaction (HOI) detection aims to simultaneously localize human-object pairs and recognize their interactions. While recent two-stage approaches have made significant progress, they still face challenges due to incomplete context modeling. In this work, we introduce a Contextualized R…

展开完整文献摘录

Human-Object Interaction (HOI) detection aims to simultaneously localize human-object pairs and recognize their interactions. While recent two-stage approaches have made significant progress, they still face challenges due to incomplete context modeling. In this work, we introduce a Contextualized Representation Learning Network that integrates both affordance-guided reasoning and contextual prompts with visual cues to better capture complex interactions. We enhance the conventional HOI detection framework by expanding it beyond simple human-object pairs to include multivariate relationships involving auxiliary entities like tools. Specifically, we explicitly model the functional role (affordance) of these auxiliary objects through triplet structures < < human, tool, object > > .

无 Evidence

同一模型与写作位置 · 不提供检索文献片段 · 首次输出

盲评更优

A central challenge in VLN is grounding language to the visual environment: agents must resolve referring expressions against egocentric observations that change continuously as they move, so a landmark identified in one frame may be occluded or out of view in the next. This coupling between perception and action distinguishes VLN from static vision-and-language tasks such as image captioning or visual question answering, where the visual input is fixed at inference time.

评审结论与任务校验

模型盲评判定“无 Evidence 更优”。下方保留评审原始理由（英文），供核对判断依据。

查看模型评审原始理由（英文）

原始理由中的 Output A = 无 Evidence，Output B = Evidence。

Output A provides a coherent, self-contained continuation grounded in the VLN task established in the draft. It discusses the challenge of grounding language to visual environments with egocentric observations, which naturally extends the draft's discussion of VLN agents interpreting instructions, decomposing sub-goals, grounding steps in real-world objects, and recognizing visual cues. The contrast with static vision-and-language tasks is a reasonable academic framing that doesn't require source support since it's general domain knowledge context, not a factual claim needing citation. Output B is unusable due to unsupportedClaim=true. It introduces 'human-object interaction detection' and 'triplet structures' from the supplied source, but this is a forced, irrelevant insertion. The source is about HOI detection in static images, not VLN navigation. The draft discusses VLN (vision-and-language navigation) in embodied AI—agents moving through spaces following instructions. The source's content about modeling tools as triplets <human, tool, object> for interaction detection has no connection to the navigation task, egocentric observation, or instruction following described in the draft. Output B misleadingly suggests HOI detection work is relevant to 'collaborative settings' in VLN, which is an unsupported factual claim and topic mismatch. The source title and snippet clearly identify a different research area (HOI detection) with no stated connection to VLN or collaborative navigation.

文献片段是输入材料；出现于此不代表输出使用了它，也不代表它能够支持全部主张。原文与检索片段经过截取；页面没有展示模拟分数或模拟 PDF。