Fathom-DeepResearch: Unlocking Long Horizon Information Retrieval and Synthesis for SLMs
Authors: Shreyas Singh
Kunal Singh
Pradeep Moturi
Venue: ICLR 2026 Poster (2026)
Keywords: DeepResearch
Reasoning
agentic reasoning
Primary area: reinforcement learning

Abstract: Tool-integrated reasoning has emerged as a key focus for enabling agentic applications. Among these, DeepResearch Agents have gained significant attention for their strong performance on complex, open-ended information-seeking tasks. We introduce Fathom-DeepResearch, an agentic system composed of two specialized models. The first is Fathom-Search-4B, a DeepSearch model trained from Qwen3-4B and optimized for evidence-based investigation through live web search and targeted webpage querying. Its training combines three advances: (i) DUETQA, a ∼5K-sample dataset generated via multi-agent self-play that enforces strict web-search dependence and heterogeneous source grounding; (ii) RAPO, a zero-overhead extension of GRPO that stabilizes multi-turn Reinforcement Learning with Verifiable Rewards through curriculum pruning, reward-aware advantage scaling, and per-prompt replay buffers; and (iii) a steerable step-level reward that classifies each tool call by cognitive behavior and marginal utility, enabling explicit control over search trajectory breadth, depth, and horizon. These improvements enable reliable extension of tool-calling beyond 20 calls when warranted. The second is Fathom-Synthesizer-4B, trained from Qwen3-4B, which converts multi-turn DeepSearch traces into structured, citation-dense DeepResearch Reports for comprehensive synthesis. Evaluated on DeepSearch benchmarks (SimpleQA, FRAMES, WebWalker, Seal0, MuSiQue) and DeepResearch-Bench, the system achieves state-of-the-art performance in the open-weights category while closely rivaling proprietary
closed systems, while also demonstrating strong performance in general reasoning benchmarks: HLE, AIME-25, GPQA-Diamond, and MedQA.

Preprint
FATHOM-DEEPRESEARCH:: UNLOCKING LONG HORI- ZON INFORMATION RETRIEVAL AND SYNTHESIS FOR
SLMS
Shreyas Singh*
Kunal Singh
Fractal Al Research
Fractal Al Research
shreyas. singhhfractal
kunal. .
Pradeep Moturi
Fractal AI Research
pradeep gradeep.moturiin
ABSTRACT
Tool-integrated reasoning has emerged as a key focus for enabling agentic appli- cations. Among these, DeepResearch Agents have gained significant attention for their strong performance on complex, open-ended information-ssckingg tasks. We introduce Fathom-Dees Deeppeeeeee,,, an agentic system composed of two spe- cialized models. The first is from Qwen3-4B and optimized for evidence-based investigation through live web search and targeted webpage querying. Its training combines three advances: (i) DUETQA, 5ample dataset generated via multi-agent self-play that enforces strict web-search dependence and heterogeneous source grounding: (ii) RAPO, zero-overhead extension GRPO that stabilizes multi-turn Reinforcement Learn- ing with Verifiable Rewards through curriculum pruning, reward-aware advantage scaling, and per-prompt replay buffers; and (iii) a steerable step-level reward that classifies each tool call by cognitive behavior and marginal utility, enabling explicit control over search trajectory breadth, depth, and horizon. These improve- ments enable reliable extension of tool-calling beyond 20 calls when warranted.
DeepSearch model trained
The second is Fathom-Syntie Syrtoom--yyntesiie--BB trained from Qwen3-4B, which converts multi-turn DeepSearch traces into structured, citation-dense DeepResearch Reports for comprehensive synthesis. Evaluated on DeepSearch benchmarks (SimpleQA, FRAMES, Welkal,er, SealO, MuSiQue) and DeepResearch- Bench, the system achieves state-of-the-arr performance in the open-weights category while closely rivaling proprietary closed systems, while also demonstrating strong performance in general reasoning benchmarks: HLE, AIME-25, GPQA-Diamona and MedQA.
INTRODUCTION
Recent advancements in reasoning capabilities of Large Language Models (LLMs) have enabled significant performance advancement across diverse set of tasks, such as mathematical reasoning. code generation (Jain et al. 20244) DeepSeck-Al et al.) 2025 Singh et al.) 2025b a). We are not only witnessing expert level performance on academic benchmarks, but are perceiving a paradigm shift towards agentic intelligence. Owing to tool-integrater reasoning, these models can now autonomously observe, reason and interact with complex and dynamic environments. Contemporary state-of- the-art tool-augmented AI systems like DeepResearch (OpenAl 2025b] Team 2025alc). have exhibited super-human performance highly sophisticated long-horizon, deep-information retrieval and synthesis tasks. These agents transcend the limitations of static parametric knowledge by
"Equal contribution.
Projject lead.

---

Preprint
Accuracy vo Average Tool Calls
Accuracy vs Average Response Length
Fathorm-Searhh Starchhhrchhhheee
Pathom- Search Stagel
Pathom Stagch
Issearch-1a
I-search-48
3.40
leh-neno-120k
janinane 28k
0.50
jat-nano-333 32K
Webfaller-38
Wibballer-33
swrosaarch77
Seanch-sl(Quen2-41)
serossarch-78
Fearch-a1lQmen2-400
Qwen3-45
@Qwenn-3k 48
R1-searcher-77
R1-searcher-77
39
32.5
150
375
25:0
254.0
17.03
Avor Average Cals (per question)
Average Resporce Length
Figure I: Accuracy vs Response length & Accuracy vs Avg. calls plot comparing spen-source DeepSearch models, clearly demonstrates the higher accuracy and efficient long horizon tool interaction ability of Fathom-Search models compared to its contemporaries.
implementing dynamic reasoning frameworks that autonomously partition multifaceted queries, coordinate multiple tool interactions, and integrate heterogeneous information sources into unified, evidence-supporten conclusions.
However, a substantial performance gap remains between (OpenAl 2025b Team 2025a c and
open-source (Team] 2025b; AIl (025), making the development of robust DeepResearce architectures a critical challenge. Current open-source frameworks suffer from two fundamental limitations. First, they lack proficiency in sustained tool usage required for high-uncertainty reasoning and synthesis tasks (Wu al., 2025) Pham et al., 2025 Trivedi et al., 2022; Krishna et al. 2024 Weil itt al., 2024). Efforts to scale DeepResearch capabilities are constrained by (i) the absence of high-quality, verifiable, and scalable dataset creation pipeline, (ii) algorithmic instability in multi-turn reinforcement learning (RL) with tools, and (iii) inefficient tool-calling behavior that undermines deep information exploration and retrieval. Second, there is an overemphasis on dlosed-form problem solving, which comes the expense of the information synthesis capabilities essential for tackling open-ended investigative queries. In the next section we discuss the aforementioned issues further.
1.1 MOTIVATION
(1) Training instability GRPO in multi-turn tool interaction: RLVR (Reinforcement Learning with verifiable rewards) with GRPO (Shao et al.) 2024 has demonstrated early promise in aligning LLMs with sparse reward signals for single-turn reasoning tasks, particularly in structured domains like Math/STEM et al. (2024); Yang et al. (2024). However, GRPO struggles scale to multi- turn -og-auume environments, because external tool interaction responses induce distribution shift in the policy model from its set token generation patterns, this leads to decoding instability and malformed generations. This cascading of errors causes group-relative advantages to saturate, leading to extremely unstable gradient updates that breaks the entire training process. (Xue al. 2025).
(2) Reward hacking and inefficient tool calling (a) Correctness-only sparse rewards do not scale to Long-horizon tool calling. When training with only single end of episode correctness signal, the agent shows early improvements achieving format adherence and basic tool-calling competence in the beginning, however, as training progresses, tool usage increases sharply while both training reward and validation performance deteriorate (Nguyen et al. 2025). This degradation stems from reward hacking: the agent collapses into repetitive, identical tool calls because the vanilla RLVR objective provides no incentive for efficiency or diversity in tool use. (b) RI. amplifies SFT priors, limiting control over the cognitive behaviors developed by the policy (Gandhi et al.) 2025): Tool-use RL typically relics an SFT cold start elicit basic tool competence (Li et al. 2025at; (Dong ct al. 2025) RL then amplifies pre-existing cognitive behaviors seeded by SFT. Standard RLVR affords limited control over the exploration and verification strategies developed by the policy model, consequently the quality of cold-start trajectories disproportionately shape the policy model's 's tool-use behavior and provides steerability.
(3) Limited training data characterized by high and hard-to-reduce intrinsic information uncertainty: Training datasets such TriviaQA (Joshi et al. 2017), and multi-hop variants like
2

---

Preprint
HH et al. 2020). and HotpotQA YYang et al., 2018) represent problems where solutions can often be found through minimal set queries or even from a model's parametric knowledge alone. These datasets do not expose models to the real-world retrieval challenges posed by noisy. heterogeneous data sources on the internet. Recent synthetic efforts (Sun et al. 2025a Li et al.
2025a [Snn al. 2025b) attempt bridge this gap by simulating realistic search behavior. For instance, WebSailor's s(i et al. 2025a) SailorFog- QA constructs ambiguous queries using obfuscated subgraphs of entity graphs, while SimpleDeepResearchee tool calls over raw HTML. Despite their innovation, these pipelines remain expensive, brittle, and time-consuming. They rely on handcrafted heuristies, graph expansion. or multi-stage LLM orchestration, limiting scalability, topical diversity, and adaptability to new domains.
(Sun et al., 2025b) issues multi-stage
(4) Challenges in handling open-ended queries Recent work largely targets closed-ended queries with well-defined objectives (Dao & Vu, 2025 Internet 2025 Li et al., 2025a). In contrast, many real- world tasks are open-ended: they lack a single definitive answer and require multi-turn exploration, retrieval of diverse perspectives, and synthesis of wiidence-grundde conclusions. Addressing these challenges, demands strong cnermmation-yyttheee and verification capabilities, gaps that current open-source approaches largely leave unfilled.
1.2 OUR CONTRIBUTIONS
To this end, we present an end-to-end DeepSearch system centered on Sachom-SSeach-44 (search en- abled reasoning) and Fathom--Sytthesizer--2 (synthesis & report-generation). Our key contributions:
RL Zero framework for DeepSearch training. We present novel two-stage RL-Zero framework that helps to steer cognitive behaviors developed by the policy model like exploration & verification during the training.
RAPO: Reward Aware Policy Optimization. We introduce zero-ovethead modification GRPO with dataset pruning, advantage scaling, and replay buffers, and steerable step-level reward that stabilizes multi-turn RL and enables Long-horizon tool use.
DUETQA. We make open-source release of 5K sample dataset created through our novel multi-agent self-play pipeline, which has verifiable question-answer pairs, impossible to answer without live web search from heterogeneous sources, unconstrained on any specific domains (e.g. wikipedia) for DeepSearch model training.
DEEPRESEARCH-S. -FF. A synthetic SFT corpus for converting downstream search/investi- gation traces of DeepSearch enabled models into comprehensive citation-backed DeepRe- search reports via an explicit plan then write protocol.
FATHOM-4B. We opensource Fathom-Search-4B, trained using RAPO policy on RL-Zero framework with DuetQA dataset, optimized for long-horizon, evidence-seeking through live web search. Fathom-Syntin Synthesizer-44. finetuned with DeepResearch-SFF dataset designed for open-ended synthesis and report generation.
2 FATHOM-SEARCH
We describe the methodology underlying Fathom-Search-4B, tool-using LLM that leverages live web-search capabilities to do evidence based reasoning in a multi-turn tool interaction setting, unlocking long-horizon tool use (> 20 calls) ability. These capabilities arise from combined approach of: (i) a curated synthetic data pipeline tailored to search-tool augmented reasoning, (ii) targeted upgrades to GRPO to effectively adapt it to multi-curn tool interaction, and (iii) two-stage training regimen with reward shaping to expand the tool-use horizon in steerable manner.
2.1 DUETQA: A DEEPSEARCH DATASET, GENERATED VIA MULTI-AGENT SELF PLAY
To address the aforementioned dataset challenges in (Sec. I.I we develop a self-supervised dataset construction framework designed yield verifiable, search-dependens multi-how hoop QA pairs. This pipeline serves as the basis for generating DUETQA, a dataset tailored for training agentic deepsearch models. The design goals are: Live web-search dependency: for each QA pair (g, a), the question
3

---

Preprint
Search-Tool Calls Distribution (Gorrect Answers)
Ours
FRAMES
Musique
SimpleQA
3333
OF
Figure 2: Distribution of number of search-calls issued by O3(nnl 2025a) over correctly answered questions comparing DuetQA to other prominent benchmarks. DuetQA shows strict live-web-search dependence and multi-hop reasoning as evident from the long-tailed distribution (unlike simpleQA (Wei et al. (2024) and > search call(s) required to answer all DuetQA questions correctly (unlike FRAMES
Krishna et al. 2024). Musique(Trived) et al. 2022
is unanswerable without search by enforcing that at least one hop contains information post-2024 (i.e., for a model M, P(a g. Mao-sahhh)) < (a q. g.eMca) )); Diverse source domains: questions require querying hetrogeneous web-sources beyond Wikipedia: and Steerable theme control: each example is grounded in & E [5, 7] sampled themes from T, a manually curated taxonomy of 200 + themes covering broad range of topics. We generate questions using two frontier web search enabled LRMs, M₁ (O3) and M2 (04-mini) (OpenAl 2025a), acting proxy web-crawling agents that produce QA pairs and independent verifiers that ensure question solvability: third model, M3 (GPT-4o), is a non-search model used for controlled paraphrasing/obfusct
of questions and as baseline verifier without search, Refer to (Appendix A.2 for more details on the dataset)
Data Generation. We adopt two strategies to synthesize multi-hop, search-dependent question- answer pairs. In both, we sample a set of themes Tample Uniform(T) with |Tsamplel k. k {5, 6, 7}. In the Mixture of Themes setting, for each / E TTample. the generator (Mi or M2) issues live queries to retrieve recent and/or obscure facts, and composes a multi-hop pair (q, a) by chaining subset of them into coherent reasoning path. In the Seeded Question setting, maintain a seed bank of 100 questions. (50 % manually curated and 50% sampled from BrowseComp (Wei er al. 2025); given a seed qu. the generator rewrites it into new question q by integrating more sampled facts while preserving the multi-hop scaffold of go- In both settings, we enforce that at least incorporated fact references information after 2024. Refer (Fig. 8 & Fig. (9) in Appendix for examples.
Data obfuscation. To remove surface cues that enable shorf-circuiting the multi-hop reasoning, run an obfuscation pass, using M3 (GPT-4o) with in-context examples, we paraphrase questions, masking intermediate hops. Specifically, M3 attenuates exact anchors by (i) coarsening dates (c.g.. "March 2025" - "carly 2025"), (ii) mapping precise numerics to qualitative magnitudes (e.g.. "1%" + "negligible"), (iii) replacing named entities with indirect descriptors (e.g., "University of Florida" + "a major southeastern university").
Multi-agent Verification. We retain a candidate pair (g. a) only if two independent search-enables LRMs Mi and M2 produce the same correct answer while strong non-search baseline M3 fails. This filter enforces correctness through cross-model agreement and certifies that web retrieval indispensable, ensuring the non-triviality of the question while guarding against overlap of information with the model's 's parametric knowledge.
2.2 AGENTIC REINFORCEMENT LEARNING
In this section, we formulate multi-turn, tool-augmented RL with LLM policies. Let E X' be an input from distribution D and T the set of available tools. The policy na generates a reasoning trajectory R interleaved with tool feedback, followed by a final textual answer y. A reference policy Tref is used for KL regularization, and a verifiable reward function ra (LLM-as (LLM-as-judge) provides supervision. The joint rollout can be written as:
4

---

Preprint
tR II Po Ree, T)
Iv II
Po a
Polue Uea, T. R₁ = (pe,, 0z),
where Pr is latent "think" segment, E T tool call (with arguments), and the tool response, all expressed in ReAct-style template. We optimize the policy model with a token-level clipped loss defined as follows:
G
T min rs Ases clip(r - e, + Au. Than (ote Hz-1
To(or, x. H2--1)
LGRPO = G
T
(2)
The trajectory-level scalar reward combines a format score and an answer score
Li et al.
2025a):
0.1 R, Rofomat 0.9 R, Ranswer
Here, Rffommat verifies that rollout follows the ReAct template (i.e., all steps are correctly wrapped in <think>, <tool_call>> <tool_response> tags). Meanwhile, Rqquewer 1 pred agth.
(6)
where correctness of the final answer is judged by an LLM-as-judge against the ground truth.
For a group of G sampled rollouts with scalar rewards {ri}, group-relative advantages defined as:
G
Fi
PR
Σ f=1
(ry kR)
G
0
OR
Ø=1
2.3 RAPO: REWARD-AWARE POLICY OPTIMIZATION
RAPO is lightweight extension of GRPO designed stabilize multi-turn, tool-augmented training by addressing the issues outlined in Sec. L In GRPO, the per-prompt (group) reward variance oR (Eq. 4) determines the strength of the adaatte signal. Let R denote the sithin-group reward standard deviation; a group is Good if o > O and Bad if on = O. Bad groups arise under both prompt saturation (all rollouts succeed) and cascading errors (all fail). In both cases, no advantage signal is produced, shrinking gradient norms and destabilizing updates. (Fig. [1]in Appendix). RAPO counters these effects through three modifications applied on top GRPO, all at zero additional rollout cost:
Dataset pruning. We prune prompts solved at epoch end using SolveRate(g) = = Σ 1R: > 0] and drop q when SolveRate(q) 0.9. This prevents training batches from being dominated by saturated groups that provide negligible variance, while implicitly yielding a curriculum in which the active set concentrates harder prompts.
Advantage scaling. To counter gradient dilution when only a few groups in a batch are informative, we rescale token-level advantages for Good groups inversely with their batch frequency Å = G At, with Ggoud = # groups with on > 0}. This adjustment preserves effective gradient Good magnitude without requiring costly re-sampling as in DAPO Yuu et al. 2025), ensuring that updates remain stable even when informative groups are sparse.
Replay buffer. We maintain per-prompt buffer 5 containing the most recent successful trajectory o" with R(g. o*) 0..5. all rollouts for prompt fail in the current epoch, one trajectory randomly replaced with from B. This reintroduces variance (0R30) into otherwise collapsed groups, restores group-relative advantages, and anchors updates to a high-quality, low-entropy reference that curbs uncontrolled trajectory growth.
2.4 STEERABLE STEP-LEVEI REWARD DESIGN FOR SEARCH TooLS
We design novel Steerable Step-Level Reward that alleviates the reward-hacking challenge faced by RLVR training in the multi-tumn cool-interaction setting using vanilla reward (Eq. B as described in (Sec. L.I. Our reward function enables to steer (i) how much the agent uses tools and (ii) how it allocates cognition to exploration and verification. Starting from the vanilla RLVR objective (Eq. make the correctness branch Ranswer depend cognitive behaviors and marginal utility aware
5

---

Preprint
labels assigned to each call e in the rollout R {(pt, ct, of) by GPT-4.1 LLM-as-jun judge as follows. Refer to (Appendix. A.I) for exact details on the search & browsing tool implementation.
search urls { UNIQUESEARCH: (semantically new query about previously unseen entities/factss) REDUNDANTŠEARCH (Highly similar to prior query; overlapping results))
query_url E
EXPLORATION: (first query of a new URL),
VERIFICATION: (cross-source check new URL for existing query; allowed B, times), REDUNDANTQUERY: (further checks for query/fact on new URLs beyond B,)}
From the LLM-as- Juddge tool call classification we form tallies and define the following aggregates:
RredS + Peed
Runigs - TredS:
= AunqqQ - Reedl()
(5)
T
Using these aggregates define Steerable Step-Level Reward as:
0.1 Pformaa + max( (1 pp), 0.5),
if "prod = Agt.
(8)
ri
0.1 Pformat + c1 * min(1, 23) + C2 min(1, ca):
if a pred # agt.
(6)
(6)
Here, P penalizes redundant tool calls when the rollout is correct, pushing for efficiency; whereas Ag and Aq provide credit to incorrect rollouts that exhibit genuine, non-redundant exploration & infor- mation seeking behavior.
waaeessearchhccc unique query redundant, query
Monotonocity.. We set c = C2 = 0.2, to ensure any incorrect rollout has r; S 0.5, while any correct rollout has r, 2 0.5, which ensures incorrect trajec- tories never get rewarded more than the correct ones. =2 also ensures equal weight to bearch_url- (As) and query_ur! (AQ).
Figure 3: Evolution of unique/redundant tool-calls during Stage-2 training using our Steerable Step-level Reward (Eq(o)
Steerability. We expose three primary knobs: (i) Css and (ii) Co set the saturation thresholds for creditable novelty in search_urt and query_u _u,,, respectively. Increasing Cs and/or Cq raises the novelty caps, enabling more steps to earn credit when they introduce genuinely new evidence; decreasing them compresses trajectories. (iii) The per-claim verification budget B. controls verification depth: higher B, permits multiple creditable cross-checks per claim, promoting verification. For our experiments we set B. allowing cross-check per claim, additionally set Cs and Cq 16.
2.5 TRAINING RECIPE
We build our reinforcement learning with verifiable rewards (RLVR) framework on top of RE- CALL (Chen al. 2025). For web search, we use the Serper API (Serper.dev), and implement retrieval toolchain leveraging Jina-Al together with open-source components such as TRAFILTURA and CRAWL4AI. Training is carried out two stages. Stage We train with RAPO for 10 epochs on our curated DUETQA dataset, comprising 4,889 high-quality QA instances. The setup uses constant learning rate of x 10-6 with the Adam optimizer (3, = 0.9, 33 = 0.95), batch size 32, mini-batch size 16, 5 rollouts per group, and top-p 1.0 sampling. Each rollout is capped at 32 tool-interaction
steps, with each step limited to 8,192 output tokens. The vanilla reward (Eq. 3 with a = 0.1 used to instill correct tool-calling behavior and strict format adherence. Stage 2. We continue RLVR training for an additional epochs under the same hyperparameter settings. For Stage 2, we construct mixed dataset by combining DUETQA, with math data from S1 dataset (Muennighoffl
et al. 2025). , and the training split of MUSIQUE (Trieedi et al. 2022) and medical reasoning data from
Tuniig ++ # UNIQUESEARCH calls; Aheeds ++ # REDUNDANTSEAACH calls; calls; Tiverity ++ VERIFICATION calls; PunigQ
Dexplore #
nesplore + Tiveriy;
Deed REDUNDANTTUERYY calls;

---

Preprint
Table Accuracy(%) of Fathom-Seaun Search-4B on DeepSearch benchmarks SimpleQA, FRAMES, WebWalker, SealO, Musique and general reasoning benchmarks HLE, AIME-25, GPQA-D, MedQA. 'Agg' is the unweighted mean within each block. Bold/italics denote best/second per benchmark.
DeepSearch Benchmarks
General Reasoning Benchmarks
Model
SimpleQA
FRAMES WebWalker Sealo Musique
Avg
HLE AIME-25
GPQA-D
MedQA
Avg
Closed-Seee Seneed-Suureeaae Models
GPT-4o (without search)
34.7
52.4
3.2
7.2
34.0
26.3
2.3
71.0
53.0
88.2
53.6
(without search)
72.5
GPT-4o (with scarch)
84.4
63.7
31.6
15.3
37.5
46,5
4.3
21.0
53.0
88.2
54,1
o3 (with search)
96.0
86.8
57.0
49.5
51.2
68.1
27.4
88.9
85.4
95.4
74.3
Open-Source Models
Owen Qwen-2.5.7B 2.5.7B
4,0
16.5
2.1
1.4
6.2
6.0
1.2
33.0
61.2
24.7
Qwen-2.5-7B Search
50.8
23.3
10.1
3.0
13.6
20.2
2.4
33.5
62.0
25.3
Owen3-4B
3.8
14.7
2.6
2.1
9.0
6.4
4.2
65.0
55.1
71.0
48.8
Qwen3-4B Search
67.7
27.2
17.5
6.2
18.7
27.5
55.9
72.0
49.8
ZervSeurch-33 3B
$1.9
11.3
8.7
7.1
13.8
18.6
3.4
10.0
14.6
51.0
17.3
ZeroScareh-7B
75.3
30.0
6.2
20.6
30.1
4.2
29.3
57.5
22.8
R1-Searcher-76
58.8
37.0
1.8
1.4
19.1
23.6
2.1
10.0
33.3
36.5
25.5
seurch-ol (Qwen3-4B)
57.5
26.8
10.8
5.5
15.3
23.2
3.4
40.0
30.5
53.7
31,9
WebSailor-BB
87.1
44.4
52,2
9.0
27.4
44.0
7.4
40.0
45.5
51.3
36,0
Jan-Nano-32K
80.7
36,1
25.0
6.2
21.4
33.9
5.5
60.0
37.4
66.0
42.2
Jan-Nano-128K
33.7
6.2
23.9
38.1
51.0
II ie-Seacch44B
88.2
58.7
40.8
17.1
31.8
47.3
7.4
60.0
$1.5
72.1
47.8
Fathom (Sugee)
19.8
Selbom--Saacher (Stage-2)
90.0
64.8
50.0
22.5
33.2
52.1
9.5
70.0
60.1
75.4
53.8
Table 2: Accuracy( ()) of various Open/Closed-sourceed DeepResearch-Agents and Search
Augmented LLMs on DeepResearch- Bench. Bold/italics denote best/second-ber. per category.
RACE
FACT
Model
Overall
Comp.
Depth
Inst.
Read.
Acc. E. Cit.
Closed Source LLM with Search Tools
Claude-3.7 3.7-Sonnet w/Search
40.67
38.99
37.66
45.77
41.46
93.68
32.48
Perplexity-Sonee Son---nnnnoaninngee... (high)
40.22
37.38
36.11
45.66
44.74
39.36
Saaiii---.---P--rrrnndddd TTT-4--Searc-PPrviiee (high)
35.12
34.06
29.79
41.67
37.16
81.81
32,88
35.10
31.99
27.57
43.17
41.23
88.41
4.79
GPT-4.1 w/Search (high)
33.46
29.42
25.38
42.33
40.77
87.83
4.42
Closed Source Deep Research Agent
Grok Deeper Search
40.24
37.97
35.37
46.30
44.05
83.59
8.15
42,25
40.69
39.39
46.40
44.28
90.24
31.26
Gemini-2.5-PP Pro DeepResearch OpenAl--eepRescarch
48.88
48.53
48.50
49, 18
49.44
81.44
111.21
46.98
46.87
45.25
49.27
47.14
77.96
40.79
Open Source Deep Research Agent
Kimi-Rescarcchr Muubao-DeepReseee
44.64
44.96
41.97
47.14
45.59
44.34
44.84
40.56
47.95
44.69
52.86
52.62
LangChain Open-Deen DeepResearch
43.44
42.97
39.17
48.09
45.22
Fathom DeepResearch
45.47
42.98
45.14
48.25
46.12
56.1
38.3
MedQA (Jin al., 202T) train split. This combined pool is adversarially filtered against the Stage- checkpoint, yielding 5,471 instances. From MUSIQUE, we retain only questions requiring at least three reasoning hops to ensure sufficient compositional depth. For this stage, we adopt the Steerable Step-Level Reward (Eq. 6 to extend the tool-use horizon beyond 20 calls in stable manner. We use the Qwen3-4B model Yang et al., (025) as the base, which supports a maximum context length of 40,960 tokens; utilize the full window during training. We use GPT-4. 1-7T-4.1miii(111 1-mmiii(Temeeattte Temperature=O.) as the query LLM for training and evaluation unless stated otherwsie. A higher sampling temperature of 1.4 is applied Qwen3 models, consistent with prior findings (An al. 2025 All experiments conducted on a single node with H100 GPUs.
7

---

Preprint
3 FATHOM-SYNTHESIZERRBB
Fathom-Synin Byhhom--ytthhesiee--BB is planning and synthesis model built on Qwen3-4B via supervised fine- tuning (SFT). It converts multi-hop DeepSearch traces from Fathom-Search-48 into secision-grade citation-dense DeepResearch Reports. Following Plan-then-Wiin protocol, the model first de- composes the question into sub-goals, defines the report structure, maps evidence to sections, and specifies strategies for insight generation; only then does it produce the public report with citations drawn strictly from URLs explored by alignment, strengthens citation accuracy through section-level constraints, and provides structured supervision during SFT, enhancing the distillation process.
This explicit planning improves question
3.1 DEEPRESBARCH-SHE
2025a) to train Fathom- Synthesizer-4B, it provides supervision along three complementary axes: (i) Question decomposition. Each input question q is decomposed into ordered sub-questions π deccpmp (S)...., Sa), which form the report scaffold and ensure coverage of all facets, (ii) Section mapping. Every piece of evidence recovered during search (URLs, quoted passages, tables, figures) is grounded to one or more sections via mapping Tmp this aligns each explored URL to the most relevant S,, enhancing citation accuracy and preventing omissions/dupllcationn (iii) Planning for insights. The model specifies an analysis strategy inisihht how the gathered evidence should be synthesized into higher-level insights. Formally, given question y and trajectory = (R1,... ... RT), the teacher outputs P Plan and Report. The plan π = (eeeecom,, x xuu π ( appears in a private <think> block, followed by the public report The training target
DEEPRESEARCH-SEE is a synthetic dataset distilled from GPT-5 (OpenAl
<think> π </think>
Report structure. The public-facing report r follows fixed, inline-cittiion-drien format: an Executive Summary followed by a Main Body organized exactly by the sections (S....., S,,) from deccomp, where each section weaves the mapped evidence from xh using the analysis strategy in inniight Sections are citation-dense: every pivotal or non-obvious claim carries inline citations drawn only from URLs explored in т, with section-level citations restricted to items mapped to that section in map, The report concludes with deduplicated Sources used list of the cited URLs.
Thematic diversity & scale. Training questions are generated via the Seeded Question mode (Sec 2.1., starting from 100 open-ended real-world questions spanning law, business, technology, science, and policy. Rewritten across sampled themes, this yields 2,500 questions for training. Refer to (Appendix A.2 for more details on the dataset)
3.2 TRAINING RECIPE
We fine-tune Qwen3-4B on DEEPRESEARCH SFT, training for epochs on the sample split using single node of HxH0 GPUs We use bfl6. FlashAttention-2,, a 65,536-token context, gradient accumulation of 8, cosine LR with peak 5.0 10-5, Adam (3,=0.9, 0.000.9..20099 =0.95), and sequence parallel size 4. Context extension. Our DeepSearch traces exhaust Qwen3-4B's ' native 40,9 00.9k6-token context window, so we extend the effective context during SFT using YaRN RoPE scaling: rope_scall w.sssclinggg ing:: (type-yarn, factor=2... )). This increases the usable positional range to 65,536 tokens, allowing the synthesizer to ingest the full investigation trace and generate high-quality synthesis while preserving section aligmment and citation locality.
BASELINES, BENCHMARKS & METRICS
Baselines. Open-source Seaphe agents: with public checkpoints: Jan-Nano DDa & Vu 2025
I1-Search-4B (Internet 2025), Qwen3-4B (Yang et al. 2222), ZeroSearch (Sun al. 2025a). Search- ol (Li et al. 2025b). R -Seearrherr (Song et al. 2025).
WebSailor (Li et al. 2025a). Closed-source:
comparators: o3 (OpenAl 2025a). GPT-4o (OpenAl
2024).
Benchmarks (9), DeepSearch (5): SimpleQA (Wei et al. 2024). FRAMES (Krishna et al.
2024).
W (Wu et al. (2225), Seal0 (Pham | 2025), ) MuSiQue (Trivedi et al. (022). General
reasoning (4): HLE (Phan et al. 2025), AIME-25 (AMME 2025), GPOA-Diamond (Rein et al. 2024).
MedQA (Jin et al. 2021). Metric: Pass @ using GPT-4.1min LLM as Judge (Temperature=0)
8

---

Preprint
Table Ablation on using RAPO as the policy optimization algorithm for Stage-1 training compared to GRPO. Both trainings done on top of Qwen3-4B model.
Algorithm
SimpleQA
FRAMES
WebWalker
Seal0
Avg. Tokems
GRPO
87.8
55.2
33.8
14.4
9,000
RAPO
88.1
57.2
39.0
19.8
5,000
Table Ablation on using our steerable step-level reward compared to the vanilla trajectory-level RLVR reward for Stage-2 training. Trained on top of Fathom-Search-4B (Stage-1) 1) using RAPO.
Reward
SimpleQA
FRAMES
WebWalker
Sealo
Avg. Tokens
Vanilla Reward (Eq.
88
58.2
43.2
21.6
5,500
Steerable Step-Level keward (Eq.
64,8
50
22.5
14,500
DeepResearch (1): DeepResearch-Bencc (Du et al. 2025). Metrics: RACE (reference-based adaptive criteia-driven evaluation of comprehensiveness, depth, sssruction-folloiing readability) and FACT (factuality via citation accuracy and effective citation count) for open-ended citation driven report generation.
5 DISCUSSION
Strong performance rivaling closed-source proprictary models Sethoo--DeeResccarce estab- lishes itself as a clear state-of-the-an by achieving large, non-incremental gains on the most chal- lenging DeepSearch tasks like FRAMES, WebWalker, & Seato, (Table. while also showing strong generalization to broader reasoning benchmarks like (GPQA-Diaman
and Humanity's 's Last Exam). Unlike many search-augmented systems that falter outside their training domain, it consistently out- performs both its base model and other open-source systems, and even surpasses larger closed-source models such GPT-4o with notable margins. On open-ended benehmark: DeepResearch Bench, outperforms most proprietary closed-source systems (including Claude, Grok, and Perplexity Deep Research) (Table. 2 underscoring its competitiveness in and-to-end deep research tasks.
On policy optimization: RAPO vs. GRPO. Ta-
ble. 3 contrasts RAPO and GRPO as the policy- optimization algorithm with the Stage-1 setup fixed. RAPO consistently outperforms GRPO across DeepSearch benchmarks, This shows that RAPO pro- vides a more stable and effective optimization signal. As shown in Fig. 4 GRPO expands response length as training progresses, but this growth does not trans- late into higher accuracy because the model collapses into redundant tool-call spamming behavior. RAPO, in contrast, achieves stronger tool-calling efficiency and more accurate results.
16000
14000
Steerable Step Level Reward
12000
10000
8000
Vanilla Reward
6000
Stop-
50
100
150
200
10000
8000
On the Steerable Step-Level reward. As shown in Fig. 3 the steerable step-level reward provides a uner-grained training signal that steers the tool calling behavior of the model. By directly shaping the utility of each intermediate step, it encourages controlled growth in response length without inflating reasoning traces with redundant tool call spam, thereby yielding both efficiency and stability in multi-step reasoning, outperforming the vanilla RLVR reward function on all DeepSearch tasks shown in Table.
GRPO
RAPO
4000
Step
100
Figure 4: Response length evolution during (i) top. Stage-2 training (Steerable Step Level Reward vs. Vanilla Reward) & (ii) bottom. Stage-1 training (GRPO vs RAPO)
Limitations. While RAPO is effective for stabiliz- ing multi-tum RL training, it shows limited test-time scaling. As illustrated in Fig. 4 RAPO with vanilla rewards during Stage-2 training saturates before 6,000 tokens and yields only marginal accuracy

---

Preprint
gains (Table 4 as question difficulty increases, when the steerable step-level reward is absent. This trade-off arises from its reliance on trajectory replacement in the replay buffer, which anchors learn- ing to low-entropy traces which prevents training collapse but also hinders adaptation extended reasoning horizons. More broadly, our current system depends on synchronous training pipelines that, although simple to implement, remain inefficient and brittle at scale. Transitioning asynchronous frameworks presents natural next step for improving efficiency and robustness.
6 CONCLUSION
We present Fathom- Deepoee-eeeees..chh..... agentic system that addresses critical gaps in open-source deep research capabilities through two specialized 4B models: -eaaoo--ee for multi-turn web search and reasoning, and Fathom- Synthssiiernes for structured report synthesis. Our key contributions include DuetQA, a multi-agent self- dataset that ensures search dependency; RAPO, stabilized extension of GRPO that enables reliable tool use beyond 20 calls through curriculum pruning, advantage scaling, and replay buffers; steerable step-level reward system that mitigates reward hacking while providing explicit control over exploration and verification behaviors; and DeepResearch-SFF. a synthetic corpus that enables comprehensive information synthesis through explicit plan-then-we supervision.
REFERENCES
Moonshot AL
Kimi researcher, 2025.
URL httpss//moonhhottt..gtttuu.
Kimi Reseiceerer
AIME Aime problems and solutions, 2025, 2025. UKLLhtppss/arrtofrrrollemssss/// Problems and Solutions
Chenxin An, Zhihui Xie, Xiaonan Li, Lei Li, Jun Zhang, Shansan Gong, Ming Zhong, Jingjing Xu, Xipeng Qiu, Mingxuan Wang, and Lingpeng Kong. Polaris: A post-training recipe for scaling reinforcement learning on advanced reasoning models, 2025. URL hattlttts:///t //hkunlp
Mingyang Chen, Tianpeng Li, Haoze Sun, Yijie Zhou, Chenzheng Zhu, Haofen Wang, Jeff Z. Pan, Wen Zhang, Huajun Chen, Fan Yang. Zenan Zhou, and Weipeng Chen. Research: Learning to reason with search for Ilms via reinforcement learning, 2025. URL /tttpp:://axiv.ooggg.
3bs/2500.1941
Alan Dao and Dinh Bach Vu. Jan-nano technical report, 2025. URL
abs/ Abs/2506.2211
DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, 7. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei Feng. Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Deli Chen, Dongjie Ji. Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Qu, Hui Li, Jianzhong Guo, Jiashi Li, Jiawei Wang, Jingchang Chen, Jingyang Yuan, Junjie Qiu, Junlong Li, J. L. Cai, Jiaqi Ni, Jian Liang, Jin Chen, Kai Dong, Kai Hu, Kaige Gao, Kang Guan, Kexin Huang, Kuai Yu, Lean Wang, Lecong Zhang, Liang Zhao, Litong Wang. Liyue Zhang, Lei Xu, Leyi Xia, Mingchuan Zhang, Minghua Zhang, Minghui Tang. Meng Li, Miaojun Wang, Mingming Li. Ning Tian, Panpan Huang, Peng Zhang, Qiancheng Wang. Qinyu Chen, Qiushi Du, Ruiqi Ge, Ruisong Zhang, Ruizhe Pan, Runji Wang. R. Chen, R. Jin, Ruyi Chen, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shengfeng Ye, Shiyu Wang, Shuiping Yu, Shunfeng Zhou, Shuting Pan, S. S. Li, Shuang Zhou, Shaoqing Wu, Shengfeng Ye, Tao Yun, Tian Pei, Tianyu Sun, T. Wang, Wangding Zeng, Wanjia Zhao, Wen Liu, Wenfeng Liang, Wenjun Gao, Wengin Yu, Wentao Zhang, W. L. Xiao, Wei An, Xiaodong Liu, Xiaohan Wang, Xiaokang Chen, Xiaotao Nie, Xin Cheng, Xin Liu, Xin Xie, Xingchao Liu, Xinyu Yang, Xinyuan Li, Xuecheng Su. Xuheng Lin, X. Q. Li. Xiangyue Jin, Xiaojin Shen, Xiaosha Chen, Xiaowen Sun, Xiaoxiang Wang, Xinnan Song, Xinyi Zhou, Xianzu Wang, Xinxia Shan, Y. K. Li,
10

---

Preprint
Y. Q. Wang. Y. X. Wei, Yang Zhang, Yanhong Xu, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Wang, Yi Yu, Yichao Zhang, Yifan Shi, Yiliang Xiong, Ying He, Yishi Piao, Yisong Wang, Yixuan Tan, Yiyang Ma, Yiyuan Liu, Yongqiang Guo, Yuan Ou, Yuduan Wang, Yue Gong, Yuheng Zou, Yujia He, Yunfan Xiong, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuyang Zhou, Y. X. Zhu, Yanhong Xu, Yanping Huang, Yaohui Li, Yi Zheng, Yuchen Zhu, Yunxian Ma, Ying Tang, Yukun Zha, Yuting Yan, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhenda Xie. Zhengyan Zhang, Zhewen Hao, Zhicheng Ma. Zhigang Yan, Zhiyu Wu, Zihui Gu, Zijia Zhu, Zijun Liu, Zilin Li, Ziwei Xie, Ziyang Song, Zizheng Pan, Zhen Huang. Zhipeng Xu, Zhongyu Zhang, and Zhen Zhang. Deepseek-rl: Incentivizing reasoning capability in llms via reinforcement learning, 2025.
URL httth:::/aaaat
org/ abs/ /20s2/55000 2501 12948
Guanting Dong, Hangyu Mao, Kai Ma, Licheng Bao, Yifei Chen, Zhongyuan Wang, Zhongxia Chen, Jiazhen Du, Huiyang Wang, Fuzheng Zhang, Guorui Zhou, Yutao Zhu, Ji-Rong Wen, and Zhicheng Dou. Agentic reinforced policy optimization, 2025. URL pattps://arxier wwg. abs 2507
19849
Mingxuan Du, Benfeng Xu, Chiwei Zhu, Xiaorui Wang, and Zhendong Mao. Deepresearch bench: A comprehensive benchmark for deep research agents. arXiv preprint 1 2025.
Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh, Nathan Lile, and Noah D. Goodman. Cognitive behaviors that enable self-improving reasoners, or, four habits of highly effective stars, 2025. URL
01307
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. Constructing a multi- hop QA dataset for comprehensive evaluation of reasoning steps. In Proceedings of the 28th International Conference on Computational Linguistics, pp. 6609-6625, Barcelona, Spain (Online), December 2020. International Committee on Computational Linguistics. URL httth:ts////// /www aclweb. wwl.ebb.rr//mmthcooo////// .eel0oooolnnggg laiingmmiin.. 580
Intelligent Internet. Ii-search-4b: Information seeking and web-integrated reasoning llm. https Searhhar--aea----- 2025.
Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica. Livecodebench: Holistic and contamination free evaluation of large language models for code. arXiv preprint arXiv:2403.cm 07977, 2024.
Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. What disease does this patient have? large-scale open domain question answering dataset from medical exams. Applied Sciences, 11(1)):642,, 2021.
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. triviaqa: Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension. arXiv e-prints, art. arXiv: M.Kiv11755.035511 2017.
Satyapriya Krishna, Kalpesh Krishna, Anhad Mohananey, Steven Schwarcz, Adam Stambler, Shyam Upadhyay, and Manaal Faruqui. Fact, fetch, and reason: A unified evaluation of retrieval- augmented generation, 2024. URLLhttss://arxlv..o obs//or/////////o/////o///aeeeee abs/://////4999 /209 12941
Kuan Li, Zhongwang Zhang, Huifeng Yin, Liwen Zhang, Litu Ou, Jialong Wu, Wenbiao Yin, Baixuan Li, Zhengwei Tao, Xinyu Wang, Weizhou Shen, Junkai Zhang, Dingchu Zhang, Xixi Wu. Yong Jiang, Ming Yan, Pengjun Xie, Fei Huang. and Jingren Zhou. Websailor: Navigating super-human reasoning for web agent, 2025a.
http:///xxivv..oo aba /ttp:///r......ttaa///55....... 02592.
Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, and Zhicheng Dou. Search-ol: Agentic search-enhanced large reasoning models. CoRR, 034/2501.05366 2025b. doi: A4AS550/ARXI..5500005022. URL httphhtt////aee /tttt::://oi..r//10)) org/10
48550/arXiv.250 2553000000.
Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannanch Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. s1: Simple test-time scaling, 2025. URL httpp:://arxi...rrrr
19393

---

Preprint
Xuan-Phi Nguyen, Shrey Pandit, Revanth Gangi Reddy, Austin Xu, Silvio Savarese, Caiming Xiong, and Shafiq Joty. Sfr-deepresearch: Towards effective reinforcement learning for autonomously reasoning single agents, arXiv preprint arXIv:2509.06288, 2025.
OpenAl. Hello gpt-4o, 2024. URL httpp///cp////ppppaacacc////////
OpenAl. Introducing openai o3 and 04-mini, 2025a. URL https:///penaa..cmm
OpenAL. Introducing deep research, 2025b.
URI.
introducinn--eee--ess--
Thinh Pham. Nguyen Nguyen, Pratibha Zunjare, Weiyuan Chen, Yu-Min Tseng, and Tu Vu. Sealqa: Raising the bar for reasoning in search-augmented language models. arXiv preprint arXiv:2506601066. 2025.
Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, Josephina Hu, et al. Humanity's 's last exam, 2025.
URL httthtt/////xxiitt org/ 2tt.//511..444
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R. Bowman. GPQA: A graduate- google-proof q&a benchmark. In First Conference on Language Modeling, 2024. URL /BLLsattp///ppeeeeivie
Serper.dev. Serper.dev -alee-aippppoe ai-powered search api. URL
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang. Y. K. Li, Y. Wu, and Daya Guo. Deepseekmath: Pushing the limits of mathematical reasoning in open language models, 2024. httpp:/:t//////
2402. 03300
Kunal Singh. Sayandeep Bhowmick, Pradeep Moturi. and Siva Kishore Gollapalli. NO STRESS NO GAIN: STRESS TESTING BASED CONSSSSONSSTNNC FOR OLYMPIAD PROGRAM- MING. In ICLR 2025 Workshop: VerifAl: AI Verification in the Wild, 2025a. URL https
// /openreview net/ d-t.p//wwwee/ooommdddd////// 7S
Kunal Singh, Ankan Biswas, Sayandeep Bhowmick, Pradcep Moturi, and Siva Kishore Gollapalli. Sbsc: Step-by-step coding for improving mathematical olympiad performance, 2025b. URL
atttss://arxt orgo halgg//a//// 2aaas///52.. 2502. 11622.6666 16666
Huatong Song, Jinhao Jiang, Yingqian Min, Jie Chen, Zhipeng Chen, Wayne Xin Zhao, Lei Fang, and Ji-Rong Wen. R1-searcher: Incentivizing the search capability in Ilms via reinforcement learning. 2025. URL wttps: /wwp///aavvvvv/a////i//aa./aaaaa org. abs 22.0255535555
Hao Sun, Zile Qiao, Jiayan Guo, Xuanbo Fan, Yingyan Hou, Yong Jiang, Pengjun Xie, Yan Zhang, Fei Huang, and Jingren Zhou. Zerosearch: Incentivize the search capability of Ilms without searching, 2025a. URL. htt.httt////axivvoort abs absaab/saaa 22055 04588
Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Fei Bai, Jia Deng, Wayne Xin Zhao, Zheng Liu, et al. Simpledeepssaarchr: Deep information seeking via web-powered reasoning trajectory synthesis. arXiv preprint 2684,55584,,, 2025b.
Gemini Team. Gemini deep research, 2025a. URL
Seep-researchh
Langchain Team. Langchain open deep research, 2025b. URL https://github.or
langchain-at/open, beapbee__ddepreeeeereeee
Perplexity Team. Introducing perplexity deep research, 2025c. URL. httpp:://ithub.. com/
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. Musique Multihop questions via single-hop question composition, 2022. URL www.wxiog..w...///o/ooooooa.aaar
2108. 00573
12

---

Preprint
Jason Wei, Nguyen Karina, Hyung Won Chung, Yunxin Joy Jiao, Spencer Papay, Amelia Glaese, John Schulman, and William Fedus. Measuring short-form factuality large language models. arXiv preprint arXhv:2411.03368, 2024.
Jason Wei, Zhiqing Sun, Spencer Papay, Scott McKinney, Jeffrey Han, Isa Fulford, Hyung Won Chung, Alex Tachard Passos, William Fedus, and Amelia Glaese. Browsecomp: A simple yet challenging benchmark for browsing agents, 2025. URL https:tttatxi...ottot wwtp:/iwwwggga///////aa
12516
Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, and Fei Huang. Webwalker: Benchmarking ilms in web traversal, 2025. URL
org htlbb///501.075777
Zhenghai Xue, Longtao Zheng, Qian Liu, Yingru Li, Xiaosen Zheng, Zejun Ma, and Bo An. Sim- pletir: End-to-end reinforcement learning for multi-turn tool-integrated reasoning. arXiv preprint arXiv:2509.02479, 2025.
An Yang, Beichen Zhang, Binyuan Hui, Bofei Gao, Bowen Yu, Chengpeng Li, Dayiheng Liu, Jianhong Tu, Jingren Zhou, Junyang Lin, Keming Lu, Mingfeng Xue, Runji Lin, Tianyu Liu, Xingzhang Ren, and Zhenru Zhang. Swen2.5-math technical report: Toward mathematical expert model via self- ieff-improveeett, 2024. URL https::: //wpxaaaavvvv//aa////i//aaaaaava swxivv.ooo org. abs/ 2409 12122,
An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, et al. Qwen3 technical report, 2025. URL
09388
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Conference on Empirical Methods in Natural Language Processing (EMNLP). 2018.
Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Weinan Dai, Tiantian Fan, Gaohong Liu, Lingjun Liu. Xin Liu, Haibin Lin, Zhiqi Lin, Bole Ma, Guangming Sheng, Yuxuan Tong. Chi Zhang, Mofan Zhang, Wang Zhang, Hang Zhu, Jinhua Zhu, Jiaze Chen, Jiangjie Chen, Chengyi Wang, Hongli Yu, Yuxuan Song, Xiangpeng Wei, Hao Zhou, Jingjing Liu, Wei- Ying Ma, Ya-Qin Zhang, Lin Yan, Mu Qiao, Yonghui Wu, and Mingxuan Wang. Dapo: open-source Ilm reinforcement learning system at scale, 2025. httpt:tttps/ttttt/ 0bs/2500.14477
A APPENDIX
A.1 AGENTIC TOOL DESIGN
We provide our policy model access to two tools:
search urls (web search). The tool takes as input a natural language query & and returns ranked list of triples (u, title, snippet) using a live search engine. The policy model uses this to identify promising sources and optionally select URL u for opening in the next step. The tool is invoked as follows: <tool_call 1> (name: search_urls, args: {query: q} } //t////tollsss
query_ue url (goal-conditioned page reading). Given goal g and URL the tool leverages a query LLM to return targeted evidence-backed response that address g. This tool enables precise grounding of facts and targeted querying of web- pages. Compared to the injestion of entire web-page into the policy model's trajec- tory, this tool minimizes noise and increases recall. The tool is invoked follows: @ccol_calll call_aallllaaaas_ (name: query_uri, args: (goal: g, url: u/}</tool_call>
A.2 DATASETS
Datasets provided in the supplementary material:
Stage-1 DeepSearch Training (DuetQA): QA pairs (search-essential, multi-hop).
13

---

Preprint
G3PD SMPC: Calle Accuracy Exelition
144
Limo le enyy
Figure 5: GRPO vs RAPO: Tool call and accuracy evolution
Figure 6: Steerable Reward vs Vanilla: Tool call and accuracy evolution
Stage-2 DeepSearch + General Reasoning Training: mixed QA pairs spanning DeepSearch and general reasoning tasks from SI (maths)(Muennighofff et al., 2025). Musique (Trivedi et al. (muliihuh)hh and MheeQAA(ive-eeb--eerr versely filtered against Pathom-Search-Staee checkpoint.
MedQA(Jin et al. 202 (medical-reasoneeeee ad-
DeepResearch-SFT. Contains synthetically generated open-ended questions, their DeepSearch the per-example planning used for report synthesis, and the final DeepResearch reports generated by GPT-5 from the corresponding search traces.
traces from
A.3
TRAINING
From 5
GRPO shows rising tool use without accuracy gains (inefficient exploration), whereas RAPO
achieves higher accuracy with fewer calls, indicating productive long-horizon reasoning.
From 6 With Steerable reward, accuracy increases monotonically with tool-call depth, indicating Steerable reward leads to productive exploration, where increasing number tool calls translate to higher accuracy, a signature of genuine long-horizon scaling absent in the vanilla reward.
From 7 Test-time scaling plot for checkpoint, and WebSailor-3B (Li et al., (2025a). It clearly demonstrates that WebSailor-3B and GRPO saturate early 15 calls) as their performance does not increase with further tool calling while models continue improving as the call budget increases upto 30 tool calls, demonstrating superior scaling under long-horizon budget.
G GRPO-trained checkpoint, RAPO-trained
A.4 FAILURE MODE ANALYSIS
To contextualize when long-horizon tool use required and when models fail, we follow the task- uncertainty taxonomy introduced by WebSailor (I.i et al. 2025a)
Level-1 tasks exhibit low uncertainty.
Level-2 tasks (e.g., multi-hop QA) begin with higher uncertainty but follow a deterministic, well-structured reasoning path in which uncertainty monotonically contracts the model links successive entities; these questions can solved with < searches.
Level-3 DeepSearch tasks: the most challenging category combine high uncertainty each step with no predefined reasoning path. Several intermediate hypotheses remain viable for many hops, and only cross-verification across distant, independent clues collapses the ambiguity to a correct answer. These questions require up to 100 searches.
Level-1 tasks are oftens solved by model's pretrained knowledge or 1-2 searches.
Level-2 tasks (e.g., multi-hop QA) begin with higher uncertainty but follow deterministic. well- structured reasoning path which uncertainty monotonically contracts as the model links successive entities; these questions can be solved with < 25 searches.
14

---

Preprint
Accuracy vs Too/Call Budget (Fathom WebSallo-38)
(RPO Stagel
24
(ArD-Suuge
20
Figure 7: Test-Time Scaling comparison of RAPO GRPO WebSailor-3B
Provide
web searching obility
Hopi-Rtan Wilza
lest sulch played the suse studin where the landmork that
Hephikttulbe
naned ofter coltical who was portroped by Britsh octer octer for brief rale
Green Text
Associatoe with thrre
meckueentory.
akngpide
powered
thematic fact retrieval
astrittd dien worland in the Najj Turtle?
Winss evardd
Idii This solraged Mordals Raspe
Nondels Trepry fourss crignsted 1985
Wake artor
Logical Chaining
Nags- Turrj--Turre
The office 50
Nejo Turtles
Chicket
Selected Themes
tepics)
Epics
Rejected Themes
Figure 8: Sample question from DuetQA, generated using the Misture-of-Themes mode
Our present issues stem not from the method but from the context budget. We train Qwen3-4B (Yang et al., 2025) at a 40,960-token context window (no RoPE/YARN extension) due to compute constraints, which caps feasible trajectories at < 30 tool calls. Level-3 DeepSearch tasks (Wei et al.) 2025) however, routinely require 50-100+ calls and 128K-256K context, making them impossible to model faithfully under current budget.
Equally important, Level-3 problems require fuzzy, high-entropy training data, where multiple intermediate hypotheses remain viable for many hops and uncertainty is intentionally preserved rather than collapsing early. While WebSailor (Li et al.] (225a) reports using such mubiguity-prreervvii supervision, their dataset and generation pipeline uncertainty questions are produced for training. In contrast, we open-source our entire data-generation pipeline and will extend it in the next iteration explicitly produce Level-3-style, multi-hypothesis, long-horizon training data once we can train with 128K-context window.
not open-sourced, it is unclear how these high-
B
ACKNOWLEDGEMENT
B.
USE OF LLMS
We acknowledge the use LLMS for basic writing, grammar and formatting enhancements limited capacity within the paper.
15

---

Preprint
Space
Sports
Napobooni
(200+
A recie developer hes publishe the latest fecturing chaphers. According to 2003 more
privetely develosed luser indder recessed NucoA Tork Onder
named Purk/wwaer and targeting Augent 20zh, inckuee far-
2000
ssse
traffic comes from sadel modia
Seed Questions Bank (100 quastions)
they dicavened
hae descent planea disturt samm poler regairs, What the meeging
they conditiu??
LAM with web searching ability
ked?
Seed Question
Question
Treshfered sementic cues from the seed question to genercted question Quantttarive duess are sumbers codes ( "more than 2000 aeges, % traffic"
Order August
Hidden factual torget: the sought onswer is abscured until the find centerce ( &naiicall condition / comero-payfood name ) Chronilogica anchar: each narrative buids the quechion
dotes tineltne (2523 artice 2024 tuak orde
Figure 9: Sample question from DuetQA, generated using the Seeded Siailed--Dnesttoon-eennrrrrtrrte mode
GradNorm
Entropy
GRPO
RAPO
Figure 10: Comparison of policy entropy and gradient norm during RLVR training. GRPO exhibits
rapid entropy collapse and diminished gradient norms due to sparse rewards, whereas RAPO sustains exploration and stronger updates via targeted updates
16


Reviews:
[{"review_id": "y4VMDx0coO", "reviewer": "Reviewer_nMq5", "summary": "This paper introduces Fathom-DeepResearch, an open-source deep research agent system. The system consists of two coordinated 4B-parameter components: Fathom-Search-4B, a web-search model trained with an improved stabilized reinforcement-learning framework, and Fathom-Synthesizer-4B, a structured report generator trained under a *plan-then-write* protocol. The paper further introduces a dataset (DUETQA ) of 5k self-play-generated, live-search-dependent QA examples, and a synthetic DeepResearch-SFT corpus for training long-form synthesis. Evaluations across benchmarks show competitive performance of the proposed agent.", "questions": "1. A typo in the Table 2: systems such Kimi-Researcher should be closed source agents?\n2. Will all the models and datasets be opensourced?\n3. Broader comparison: \n   1. The authors might consider summarizing how Fathom differs from other recent research agents (in dataset construction, training paradigm, and report synthesis) to contextualize its contributions within the current surge of deep-research systems.\n   2. the same for RL algorithms, such as GSPO which is now widely used by search agents.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The work is well-motivated and technically complete, with reasonable contributions in this crowded space (1) a simple multi-agent framework and models (while most works explore single end-to-end model) (2) proposing a new RL training framework (3) datasets", "weaknesses": "- As this is a quickly evolving and crowded space, it's better to compare with more recent baselines, and more importantly, to compare with more recent related research on data synthesis and recent upgrades to GRPO (such as GSPO which is now widely used in this space).\n- Relatedly, the components in this work, including the data synthesis and minor improvements on the training part, by far, have been largely explored, which undermines the contribution of this work.\n- The RAPO relative to GRPO is still minor update per se, and the improvements in Table 3 is not very substantial.\n- To thoroughly evaluate the \"deep research\" capabilities of the agent, the paper would be benefited from incorporating commonly used browsecomp benchmark. Some RAG benchmarks such as SimpleQA is already relatively too old and too short to be informative in this space.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AEQht7yLvR", "reviewer": "Reviewer_ms6f", "summary": "This paper presents a two-part agentic reasoning system built from Qwen3-4B models: Fathom-Search-4B, which performs multi-turn, evidence-based web investigation, and Fathom-Synthesizer-4B, which transforms search traces into citation-dense reports. The system introduces three contributions — the synthetic DUETQA dataset for training, the RAPO algorithm for stable multi-turn reinforcement learning, and a steerable step-level reward.  This work is evaluated across multiple benchmarks, including DeepSearch and general reasoning benchmarks, on both closed-source and open-source models/agents.", "questions": "1. How long does it take to train the model for different stages?\n2. This work is claimed to build on top of RECALL (Chen et al., 20). Why not treat it as one of the baselines?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. This work provides synthetic training data to facilitate training the deep research agents.\n2. This work proposes reward-aware policy optimization (RAPO), and the experiments show the proposed methods can achieve better performance than vanilla GRPO.\n3. This work provides extensive experiments to verify the effectiveness of the method on multiple benchmarks and compare different closed-source and open-source baselines.", "weaknesses": "1. This work lacks a detailed analysis of whether the final trained model can truly work for long-horizon tool calls. When it succeeds and when it fails?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "MZ3xhsu0RR", "reviewer": "Reviewer_CAQE", "summary": "This paper presents Fathom-DeepResearch, an open-source framework for long-horizon information retrieval and data synthesis for deep research agents.", "questions": "1. What's the meaning of \"SLMs\"? The title mentions SLMs, but the paper does not define this abbreviation.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe motivation is clear and well-grounded, directly addressing key limitations in current DeepResearch models, such as unstable multi-turn reinforcement learning and inefficient tool usage.\n2.\tThe proposed methodologies are sound and well-justified, with each component, RAPO, DUETQA, and the Steerable Step-Level Reward, clearly contributing to overall model stability and performance.\n3.\tThe experiments are comprehensive, spanning both DeepResearch-specific and general reasoning benchmarks, and the results convincingly demonstrate the framework’s effectiveness and competitiveness against baselines.", "weaknesses": "1. The paper points out that GRPO suffers from reward-hacking behaviors, where the model tends to overuse tools without improving reasoning quality. However, it does not clearly demonstrate that the proposed RAPO algorithm effectively mitigates this issue from the perspective of actual tool-call frequency. In Figure 4, only the response length is presented as a proxy for the number of tool calls, which is an indirect measure. A more concrete quantitative analysis or visualization of tool-call usage would strengthen the claim that RAPO truly alleviates reward-hacking behavior.\n2. More challenging  benchmarks, such as BrowseComp, are expected to be included to better demonstrate the model’s deep research capabilities and generalization to new, open-ended evaluation settings.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)