WebAI/Scientific research
WEBAI KNOWLEDGE ATLAS · EDITORIAL PREVIEW

Scientific research⌁

Foundational work and evaluation methods with links to original papers.

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20 results

Annotated bibliography: distinguish preprints, reviewed publications and experimental projects. Results describe specific setups, not necessarily current tool capabilities.

01Research resource

Transformers paper

Foundational paper on Transformer architecture.

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02Research resource

RAG paper

Research paper on retrieval-augmented generation.

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03Research resource

WebArena paper

Evaluation of agents on realistic web tasks.

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04Research resource

VisualWebArena paper

Evaluation of agents using visually grounded interfaces.

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05Research resource

OSWorld paper

Evaluation environment for computer-using agents.

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06Research resource

GEO paper

Paper on optimization for generative search engines.

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08Research resource

Crossref REST API

Research metadata and identifier API.

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09Research resource

Web.dev AI

Responsible AI design and agent-friendly Web.

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10Research resource

RAG foundation paper

Foundational paper by Lewis et al. (2020).

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11Preprint

ReAct — Reasoning and Acting (2022)

Study of combining reasoning traces and actions involving sources or environments. Useful for explaining tool-using agents without establishing general robustness.

Method and study limitations

Method to examine : Compare reported reasoning steps and external interactions, then inspect successes and failures on the evaluated tasks.

Caution : Results depend on particular environments and models, not a guarantee of safety or reliability for current agents.

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12Research publication

Toolformer — Language Models Using Tools (2023)

NeurIPS work on learning to call external tools from language models. Experiments focus on selected APIs and tasks.

Method and study limitations

Method to examine : Study tool selection, arguments and outputs within a response, then measure incorrect choices.

Caution : Model tool use alone does not establish authorization, data quality or deployed-system compliance.

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13Research publication

Lost in the Middle — Long Context (2024)

Published TACL study showing that position of relevant information in long contexts can affect model use. Important for RAG design and testing retrieved passages.

Method and study limitations

Method to examine : Compare tasks with key evidence at the beginning, middle and end while holding other variables constant.

Caution : Effects vary with model, task and context length; do not mechanically generalize to every system.

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14Preprint

WebArena — Realistic Web Agents (2023)

Self-hostable research environment with realistic Web tasks for evaluating agents’ ability to complete actions. Findings depend on benchmark sites and scenarios.

Method and study limitations

Method to examine : Use reproducible tasks and check the website’s final state rather than scoring generated prose alone.

Caution : Benchmarks cannot capture every public-site permission, ambiguity or real deployment cost.

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15Preprint

VisualWebArena — Visual Web Tasks (2024)

Web benchmark where visual perception complements text interpretation, supporting evaluation of agents navigating human-facing interfaces.

Method and study limitations

Method to examine : Compare multimodal agent success on tasks requiring visual information not easily available from text alone.

Caution : Results do not automatically establish accessibility for disabled people or performance in production.

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16Preprint

OSWorld — Computer Use Benchmark (2024)

Benchmark of computer tasks spanning applications and system states, illustrating the difficulty of reliably completing real operations.

Method and study limitations

Method to examine : Compare environment versions, success criteria and final-state checks without presenting historical scores as current results.

Caution : The benchmark evolves; results from different versions or task sets may not be directly comparable.

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17Preprint

RAGAS — RAG Evaluation (2023)

Research on evaluating RAG systems along dimensions including passage retrieval and generated-answer faithfulness. Automated metrics require validation.

Method and study limitations

Method to examine : Build an evaluation sheet of context relevance, faithfulness and answer quality on annotated examples.

Caution : Automated judges may be inconsistent or miss substantive errors; comparisons benefit from human checks.

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18Preprint

GEO — Generative Engine Optimization (2023)

Study of strategies and visibility metrics in generative search engines. Results apply to experimental settings, not guaranteed universal rankings.

Method and study limitations

Method to examine : Compare the authors’ metrics with actual observations for a given website, accounting for domain, language and interface.

Caution : Experimental effects do not prove llms.txt or a particular technique is required by current search engines.

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19Research publication

SWE-bench — Software Agents (2024)

ICLR benchmark of code repair tasks derived from real repository issues, using executable checks rather than prose evaluation alone.

Method and study limitations

Method to examine : Inspect tests failing before a patch and passing afterward, while checking regressions and code rights.

Caution : Historical scores do not represent all assistants or deployed software maintainability.

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20Research resource

AgentDojo — Prompt Injection Evaluation

Research environment for assessing prompt injections and defenses in tool-using agents. It separates simulated attacks from observed incidents.

Method and study limitations

Method to examine : Build controlled tests with harmless actions, limited tool permissions and logs of both attack successes and incorrect refusals.

Caution : Passing a set of attack cases does not establish immunity to new variants or different systems.

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