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Development case · blind model judgment pending independent human review; not a formal Benchmark conclusion.
AutocompleteNo-Evidence version preferred88 / 162 · b4e0d98f9105fbfd

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

Psychology and cognitive neuroscience · 2512.00027v1

FLOWING EVIDENCE BENCHMARK

How do we tell whether Evidence helps?

At the same writing position, with the same model and task, how does supplying retrieved paper passages change the first output? We compare matched versions and retain ties, unusable outputs and incomplete reviews.

SAME DRAFT · EVIDENCE ON OR OFF

01 · FIXED WRITING POSITION

MANUSCRIPT

Same writing position ▌

Matched autocomplete at the same draft position

02 · TWO MATCHED INPUTS

SHARED BY BOTH

Manuscript context, model, task and prompt

A · With Evidence

Retrieved paper passages supplied

B · Without Evidence

No retrieved passages supplied

LLM

Same model and version

A → first output

B → first output

Blind judge agent

First outputs are anonymized as X and Y

Continuation review: accuracy, fit to the writing task and usability
Returns: X preferred / tie / Y preferred / both unusable

Order check: X / Y → Y / X

HOW DOES BLIND JUDGING WORK?

① Anonymize both outputs
The judge sees the same draft and both first outputs without knowing which received Evidence.

② Compare and swap order
The judge applies task-specific criteria in X/Y and then Y/X order.

③ Review disagreements
A third pass resolves disagreements. Incomplete reviews remain in the denominator.

How are autocomplete positions stratified?

Before seeing generation outcomes, we check whether a retrieved passage contains a specific proposition that directly supports the next writing move. Those positions appear in the left opportunity group; the rest are ordinary positions on the right. We select a balanced sample from admitted papers in each field. The 50/50 split is experimental, not a measure of how often either type occurs in writing.

AUTOCOMPLETE · 110

Biology, statistics and astrophysics

55 positions on each side; statistics uses the ten-paper rerun.

AUTOCOMPLETE · 52

Psychology and climate science

26 positions on each side; psychology includes nine papers and climate science four.

How are the table percentages calculated?

Across five fields there are 81 source-opportunity positions. The original blind review preferred Evidence in 51; a task check moved one empty Evidence continuation to both unusable, leaving 50 in public counts. In the ordinary group, another pair of empty outputs moved from no winner to both unusable. Original verdicts remain visible on case pages.

50Evidence version preferred
÷
81All positions in this group
=
62%Evidence preference in this group

Source contribution is a separate review: 16 of 21 Evidence wins entered into source review directly used retrieved papers; another 29 wins await review.

These are development-stage model judgments pending independent human review. They are not formal Benchmark conclusions and do not, on their own, establish causality.

Manuscript writing position

Text excerpt · not a PDF page

Research manuscript · excerpt

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 ) .

↓ Next paragraph was generated here

The highlighted text is near the writing position. This is a layout of the evaluation input, not a PDF screenshot.

A matched comparison at the same writing positionJudgment: No-Evidence version preferred

Both arms use the same model, task and manuscript context. Only the retrieved passages differ. The first recorded output from each arm is shown below.

Evidence

Same model and position · retrieved passages supplied · first output

Not preferred in this comparison

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.

Passages supplied to the Evidence version

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…

Read full passage excerpt

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 > > .

No Evidence

Same model and position · no retrieved passages · first output

Preferred by blind review

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.

Judgment and task check

The blinded model review preferred the no-Evidence version. Its original rationale appears below.

Read the original model judgment

In this rationale, Output A = No 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.

The passages are input material; their presence does not mean an output used them or that they support every claim. Draft and source passages are excerpted; no simulated scores or PDF appear here.