Transformers paper
Foundational paper on Transformer architecture.
Read original ↗Foundational work and evaluation methods with links to original papers.
Annotated bibliography: distinguish preprints, reviewed publications and experimental projects. Results describe specific setups, not necessarily current tool capabilities.
Foundational paper on Transformer architecture.
Read original ↗Research paper on retrieval-augmented generation.
Read original ↗Evaluation of agents on realistic web tasks.
Read original ↗Evaluation of agents using visually grounded interfaces.
Read original ↗Evaluation environment for computer-using agents.
Read original ↗Paper on optimization for generative search engines.
Read original ↗Open research metadata index.
Read original ↗Research metadata and identifier API.
Read original ↗Responsible AI design and agent-friendly Web.
Read original ↗Foundational paper by Lewis et al. (2020).
Read original ↗Study of combining reasoning traces and actions involving sources or environments. Useful for explaining tool-using agents without establishing general robustness.
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.
NeurIPS work on learning to call external tools from language models. Experiments focus on selected APIs and tasks.
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.
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 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.
Self-hostable research environment with realistic Web tasks for evaluating agents’ ability to complete actions. Findings depend on benchmark sites and scenarios.
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.
Web benchmark where visual perception complements text interpretation, supporting evaluation of agents navigating human-facing interfaces.
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.
Benchmark of computer tasks spanning applications and system states, illustrating the difficulty of reliably completing real operations.
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.
Research on evaluating RAG systems along dimensions including passage retrieval and generated-answer faithfulness. Automated metrics require validation.
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.
Study of strategies and visibility metrics in generative search engines. Results apply to experimental settings, not guaranteed universal rankings.
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.
ICLR benchmark of code repair tasks derived from real repository issues, using executable checks rather than prose evaluation alone.
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.
Research environment for assessing prompt injections and defenses in tool-using agents. It separates simulated attacks from observed incidents.
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.