Question directory
A growing directory of research works, organized by task. Each entry links to original papers, code, datasets, or project records so readers can check authorship, versions, and scope at the source.
When to use this work: Use this source for its attention-based multiscale fusion method; distinguish it from the later edge-enhanced extension. Research image-fusion results do not establish clinical benefit.
When to use this work: Cite this dataset when using or comparing time-aligned multimodal embodied-agent data. Consult the source for collection protocol, synchronization and permitted use.
When to use this work: Use this benchmark when discussing evaluation of deep-research reports and their semantic quality, topical focus and retrieval trustworthiness.
When to use this work: Cite this work for the problem and approach of surfacing knowledge gaps between English Wikipedia and other language editions, with its defined scope of epistemic equity.
Language models produce structured artifacts such as JSON, HTML and SVG. A valid format can still encode the wrong content or render incorrectly. StructEval evaluates generation and conversion tasks across text and visual structures.
When to use this work: Use this benchmark when evaluating structural output generation or conversion across textual and visually rendered formats. Syntax validity alone does not establish content correctness.
Academic writing requires coherent text and relevant references. Generating a plausible citation string does not establish that a paper exists or supports a claim. ScholarCopilot combines scholarly writing with learned retrieval of references.
When to use this work: Cite this work when discussing joint academic text generation and citation retrieval, or when using its released writing model and retrieval setup.
When to use this work: Use this work when comparing evaluation of generated-video quality and reasoning-based scoring. Compare dimensions and protocols rather than combining scores from different benchmarks.
When to use this work: Use this 2025 record for its theoretical and empirical treatment of distributional matching in vector quantization. The related 2026 record has a separate identifier and overlapping material.
When to use this work: Use this source for the edge-enhanced dilated residual attention fusion extension. Keep the original medical-fusion paper and this extension separately identified.
When to use this work: Cite this benchmark when examining whether an agent's self-testing and self-judging lead to measurable improvement through interaction.
When to use this work: Cite the appropriate version when discussing learning human preferences for image editing. The homepage publication title and the saved preprint BibTeX may differ; both are exposed explicitly.
When to use this work: Cite this method when discussing token-level compression and decoding-free scoring for LLM reranking. Check the reported retrieval tasks and cost measurements in the source.
When to use this work: Use this work for structured feedback about where a defect is, what it is, why it matters and its importance; consult the source for the grounding and feedback protocol.
When to use this work: Cite this benchmark when evaluating creation or evolution of agent execution infrastructure, keeping harness changes distinct from model-weight updates.
When to use this work: Use this work when discussing rubric anchoring and source-aware selection of mid-training data; refer to the paper for the precise selection procedure.
When to use this work: Use this work when discussing browser execution and external feedback in web-code improvement, including the limitations of judging changes by visual plausibility.
When to use this work: Cite this work when studying agent adaptation without assuming a curated learning loop or ready-made successful trajectories.
Video question answering should depend on what the frames show. Language-only shortcuts can make benchmark accuracy look stronger than visual understanding. This study investigates visually grounded post-training and the role of data selection.
When to use this work: Cite this study when discussing linguistic shortcuts in video question answering or selecting post-training data for visual dependence. Check the paper's experimental scope before generalizing.
When to use this work: Use this method when discussing mid-training for integrating external tool returns into coding-agent reasoning, and consult the released data and model descriptions.
When to use this work: Cite this research method for long-horizon video reasoning, planning and evidence retrieval. It is not evidence of clinical deployment or patient benefit; public resource availability must be checked separately.
When to use this work: Reference the public project article or code for the disclosed work. No public manuscript is linked in this catalog, so this page does not present it as a published paper.
When to use this work: Use this work when discussing application of on-policy self-distillation to diffusion language models and its proposed self-future learning approach.
When to use this work: Use this 2026 record for the unified framework described in its current version. The related 2025 paper must not be counted as independent evidence without checking overlap.
When to use this work: Cite this benchmark when investigating visual reasoning through video generation; distinguish its task-based evaluation from generic video appearance or quality scoring.
When to use this work: Reference this system when discussing integrated research workspaces for coding agents. System availability and workflow capabilities should be checked against the current release.
When to use this work: Use this benchmark when evaluating agents completing everyday online tasks on websites. Report the task setup and scoring protocol rather than equating a benchmark score with general autonomy.
When to use this work: Cite this dataset when using its hairstyle-recommendation annotations or task formulation. Consult the original work and dataset terms before reuse.