Large language model search agents are often trained with synthetic questions whose difficulty is increased through larger evidence graphs, additional hops, and longer trajectories. These global properties, however, are only indirect proxies for the local retrieval capabilities required during search. To address this mismatch, we introduce latent anchor reasoning, which consists of resolving an unnamed retrieval anchor from descriptive specifications and transferring the recovered anchor into a subsequent information demand. This primitive retrieval unit decomposes deep search into chains of coupled operations and organizes question construction around anchor resolution and relation transfer, without prescribing a canonical search path. Based on this formulation, we propose PrimeSeeker, a capability-oriented framework that constructs web-grounded anchor structures and jointly derives a question and a reference evidence skeleton. The skeleton preserves supporting evidence from construction and guides expert generation through extractive highlights of current tool observations. These highlights are removed before supervised fine-tuning, while the skeleton is subsequently reused to audit reference-step coverage for reinforcement-learning rewards. We construct 9,221 expert trajectories, training a 30B search agent. Across five deep-search benchmarks, PrimeSeeker achieves strong performance, while reference-step optimization further improves the supervised policy. The resulting trajectories exhibit low retrieval redundancy, and fixed-budget evaluation shows strong solution coverage with substantially fewer tool calls than long-horizon systems.
Figures & tables
Figure 1: Illustration of latent anchor reasoning. Descriptive specifications first identify an unnamed play; the recovered anchor makes the ceremony relation queryable, leading to a second latent anchor and ultimately the name of the requested location. The decomposition specifies local retrieval capabilities without prescribing a unique search trajectory.
Figure 2: Overview of PrimeSeeker . The constructed anchor structure yields both a question q and a reference evidence skeleton Dq . During expert generation, Dq extracts matching evidence from the current observation. These annotations are removed before SFT, while Dq remains external to the policy and later provides reference-step coverage signals during reinforcement learning.
Model
BS-EN
BS-ZH
DS2505
DS2510
GAIA
P@1
P@3
P@1
P@3
P@1
P@3
P@1
P@3
P@1
P@3
Advanced tool-augmented models
OpenAI o3
49.7
–
58.1
–
67.0
–
–
–
70.5
–
DeepSeek-V3.1
30.0
–
49.2
–
71.0
–
–
–
63.1
–
GPT-5
54.9
–
63.0
–
77.8
–
75.0
–
76.4
–
DeepSeek-V3.2
67.6
–
65.0
–
78.0
–
55.7
–
75.1
–
Table 1: Reported performance on five deep-search benchmarks. Underlined values are the best among reported models in each block; bold values are the best among PrimeSeeker variants.
Figure 3: Pass@3 across tool call and context budgets. Solid lines denote PrimeSeeker- Dq , and pale dashed lines denote PrimeSeeker-SFT. A question is solved if correct within the cutoff.
Model
DS2505
DS2510
Standard P@1
Budgeted Solve Rate
Consumption Avg. Calls
Correct Avg. Calls
Standard P@1
Budgeted Solve Rate
Consumption Avg. Calls
Correct Avg. Calls
OpenSeeker-v1
74.0
83.0
34.5
16.2
–
57.0
109.5
46.4
MiroThinker
77.2
85.0
99.9
70.4
57.2
58.0
144.2
72.6
PrimeSeeker- Dq
76.0
91.0
21.8
16.3
41.0
68.0
68.6
33.5
Table 2: Performance under a shared 300-call budget on DS2505 and DS2510. Pass@1 values for OpenSeeker-v1 and MiroThinker are from their original reports; all remaining results are obtained by rerunning each system with its original inference procedure.
Dataset
Samples
Avg. Tools
Avg. Tokens
Search Dup.
Visit Dup.
DeepResearch-9K
12,974
25.0
12.3k
3.1%
13.1%
OpenSeeker
11,677
47.0
73.9k
1.6%
10.3%
REDSearcher
10,001
65.1
56.4k
0.2%
14.5%
PrimeSeeker
9,221
19.5
37.8k
0.1%
8.0%
Table 3: Aggregate statistics of expert search trajectories.
Appendix figures & tables2 assets
Supplementary material from the paper’s appendix.
Appendix
Expert
K
Unguided
Random Highlight
Dq -guided
Acc.
Calls
Acc.
Δ Acc.
Calls
Δ Calls
Acc.
Δ Acc.
Calls
Δ Calls
Qwen3
2
52.0
6.45
28.0
-24.0
70.52
+64.07
54.0
+2.0
9.09
+2.64
Qwen3
3
57.0
7.01
34.0
-23.0
72.10
+65.09
61.0
+4.0
9.87
+2.86
DeepSeek-V4
2
51.0
24.67
24.0
-27.0
50.12
+25.45
56.0
+5.0
21.99
-2.68
DeepSeek-V4
3
59.0
28.68
24.0
-35.0
57.18
+28.50
66.0
+7.0
24.59
-4.09
Appendix
Table 4: Comparison of random and Dq -guided highlights against unguided expert generation.
Figure 4: Distributional diagnostics of expert trajectories. (a) Trajectory length distribution, shown as KDE-smoothed expected sample count per 1K-token window. (b) Tool-call distribution, shown as KDE-smoothed expected sample count per one-tool-call window. (c) CCDF of duplicated search-query counts within trajectories. (d) CCDF of duplicated visited-URL counts under the same criterion. PrimeSeeker trajectories occupy moderate tool-use ranges and exhibit lighter duplicate-retrieval tails, indicating more compact and less redundant expert supervision.