Deep research agents answer complex questions through repeated search. They rely on a harness that runs their tool calls and returns evidence to their context. This creates a trade-off: Search works best over long passages, which give enough context for reliable semantic matching. But the returned evidence should be short, because every observation stays in the agent's context for the rest of the trajectory. We introduce CORE, a training-free harness that combines coarse-to-fine search with context management. CORE retrieves passages with a dense retriever. It then reuses the token-level similarities from a late-interaction reranker to return only the most relevant snippet from each passage. Context management lets the agent expand the context around sources it has already retrieved and suppresses repeated snippets. In controlled closed-search comparisons with the Tongyi DeepResearch and OpenResearcher harnesses, CORE achieves state-of-the-art answer accuracy and evidence recall across three agent models and three benchmarks (BrowseComp-Plus, BrowseComp-Wikipedia and BrowseComp-Plus-CM), on corpora containing up to 553M documents.
Figures & tables
Figure 1: Overview of CORE. Coarse retrieval and fine-grained reranking select a broad selection of passages and fine-grained evidence as passage snippets within such selection. The agent can request more thorough context via context expansion, while cross-search deduplication replaces repeated snippets with compact references. The gathered context allows the agent to decide, whether to continue the search, or to finalize the answer to the original question. The current sub-query can also be input directly into the context expansion to find more information in the previously visited passages without triggering the search tool.
Agent model
Harness
Answer accuracy ↑
Evidence recall ↑
Peak context ↓
Tokens/search ↓
Search calls
(a) BrowseComp-Plus
GPT-5.4
Tongyi DeepResearch
84.70
81.53
93,500
26,316
4.5
OpenResearcher
91.45
89.55
35,323
3,690
12.1
CORE (ours)
94.94
94.47
42,732
3,365
16.8
Tongyi-DeepResearch- 30B-A3B
Tongyi DeepResearch
65.66
68.74
80,771
15,259
5.2
OpenResearcher
79.88
83.99
41,912
1,447
20.6
Table 1: Main comparison results. Answer accuracy and evidence recall are in percent. Peak context is the median, over questions, of the maximum context length in tokens. Tokens/search is the mean number of tokens returned per search call, summed over all subqueries. Search calls is the mean number of search calls per question. Token counts are comparable only within the same agent model. Arrows show the preferred direction, and bold marks the best value in each of these columns within each model–benchmark group. We find that Tongyi-DeepResearch-30B-A3B, using the original Tongyi DeepResearch harness with document-level retrieval based on AgentIR-4B, achieves 63.13% on BrowseComp-Plus, which is lower than the 65.66% achieved by our current passage-level retrieval setup. On BrowseComp-Plus, Jiang et al. (2026) report an evidence recall of 38.1% for Tongyi-DeepResearch-30B-A3B, whereas their Harness-1 (20B) reported the evidence recall at 58.4%.
Configuration
Answer accuracy (%) ↑
No tools
4.58
Coarse retrieval only
62.61
Coarse-to-fine search tool only
70.84
Full CORE
80.12
Table 2: Component analysis on BrowseComp-Plus with Tongyi-DeepResearch-30B-A3B. All tool-enabled configurations share the AgentIR-4B passage index, inference settings, and context budget.
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
Agent model
Answer accuracy (%) ↑
Tongyi-DeepResearch-30B-A3B
80.12
OpenResearcher-30B-A3B
62.41
Appendix
Table 3: Deep research agents evaluated with CORE on BrowseComp-Plus. Accuracy is computed over all 830 questions; empty answers receive no credit. Bold marks the higher accuracy.
Deep research agents have achieved remarkable progress on complex information seeking tasks. Even long ReAct style rollouts explore only a single trajectory, while recent state of the art systems scale inference time compute via parallel search and aggregation. Yet deep research answers are composed of complementary pieces of evidence, which parallel rollouts often duplicate rather than complete, yielding diminishing returns while pushing the aggregation context toward the model's limit. We propose Argus, an agentic system in which a Searcher and a Navigator cooperate to treat deep research as assembling a jigsaw from complementary evidence pieces, rather than brute forcing the whole answer in parallel. The Searcher collects evidence traces for a given sub-query through ReAct-style interaction. The Navigator maintains a shared evidence graph, verifying which pieces are still missing, dispatching Searchers to gather them, and reasoning over the completed graph to produce a source-traced final answer. We train the Navigator with reinforcement learning to verify, dispatch, and synthesize, while independently training the Searcher to remain a standard ReAct agent. The resulting Navigator supports rollouts with a single Searcher or many in parallel without retraining. With both Searcher and Navigator built on a 35B-A3B MoE backbone, Argus gains 5.5 points with a single Searcher and 12.7 points with 8 parallel Searchers, averaged over eight benchmarks. With 64 Searchers it reaches 86.2 on BrowseComp, surpassing every proprietary agent we benchmark, while the Navigator's reasoning context stays under 21.5K tokens.
Deep Research agents are rapidly emerging as primary consumers of modern retrieval systems. Unlike human users who issue and refine queries without documenting their intermediate thought processes, Deep Research agents generate explicit natural language reasoning before each search call, revealing rich intent and contextual information that existing retrievers entirely ignore. To exploit this overlooked signal, we introduce: (1) Reasoning-Aware Retrieval, a retrieval paradigm that jointly embeds the agent's reasoning trace alongside its query; and (2) DR-Synth, a data synthesis method that generates Deep Research retriever training data from standard QA datasets. We demonstrate that both components are independently effective, and their combination yields a trained embedding model, AgentIR-4B, with substantial gains. On the challenging BrowseComp-Plus benchmark, AgentIR-4B achieves 68% accuracy with the open-weight agent Tongyi-DeepResearch, compared to 50% with conventional embedding models twice its size, and 37% with BM25. Code and data are available at: https://texttron.github.io/AgentIR/.
Zijian Chen, Xueguang Ma, Shengyao Zhuang +3
University of Waterloo · University of Queensland · Carnegie Mellon University
DeepSearch LLM agents repeatedly search and carry retrieved sources into subsequent reasoning steps. This expanding context increases token consumption and latency, although many results do not contribute to later reasoning. We propose Query-Relevant Attention Entropy (QRAE), an online context compression method that uses sparse evidence-assessment heads to decide which intact retrieved chunks should persist across reasoning steps. It scores bounded candidate batches through paired prefills: a query-free control suppresses shared attention responses, while normalized entropy measures the breadth of the positive response within each chunk. High-scoring chunks enter a persistent evidence whitelist and are included in the reasoning context. Uncertain chunks remain outside the reasoning context but are retained in a candidate cache, where they can be retrieved for later reevaluation. QRAE requires neither an additional generative compression pass nor modification of the attention operator. Across multiple DeepSearch benchmarks and reasoning models, QRAE improves task success by an average of 2.35 percentage points and reduces end-to-end latency and reported token usage by an average of 30% and 45%, respectively. These benefits generalize to a model with nearly 100B parameters.