A$^2$FM: An Adaptive Agent Foundation Model for Tool-Aware Hybrid Reasoning
Authors: Qianben Chen
Jingyi Cao
Jiayu Zhang
Tianrui Qin
LiXiaowan
King Zhu
Dingfeng Shi
He Zhu
Minghao Liu
Xiaobo Liang
Ge Zhang
Jian Yang
Yuchen Eleanor Jiang
Wangchunshu Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: Adaptive LLMs
Deep Research
Agent Reasoning
Primary area: foundation or frontier models, including LLMs
TL;DR: We propose A²FM, a unified 32B model combining agentic, reasoning, and instant modes via adaptive routing and APO, achieving state-of-the-art accuracy with substantially improved cost efficiency.

Abstract: Large language models split into two families: reasoning-centric LLMs, which strengthen internal chain-of-thought reasoning but cannot invoke external tools, and agentic LLMs, which learn to interact with environments and leverage tools but often lag in deep reasoning. This divide arises from fundamentally different training objectives, leading to mismatched strengths and inefficiency on simple queries, where both families tend to overthink or over-call tools. In this work, we present Adaptive Agent Foundation Model (A$^2$FM), a unified framework that follows a route-then-align principle: the model first learns task-aware routing and then aligns mode-specific trajectories under a shared backbone. To address the inefficiency gap, we introduce a third instant mode that handles simple queries directly, preventing unnecessary reasoning or tool calls while complementing the agentic and reasoning modes. To jointly enhance accuracy and efficiency, we propose Adaptive Policy Optimization (APO), which enforces adaptive sampling across modes and applies a cost-regularized reward. On the 32B scale, A$^2$FM achieves 13.4\% on BrowseComp, 70.4\% on AIME25, and 16.7\% on HLE, setting new SOTA among comparable models and performing competitively with frontier LLMs across agentic, reasoning, and general benchmarks. Notably, the adaptive execution achieves a cost of pass of only \$0.00487 per correct answer—cutting cost by 45.2\% relative to reasoning and 33.5\% relative to agentic, thus delivering substantially higher cost efficiency while maintaining comparable accuracy.

Published as a conference paper at ICLR 2026
A F2M AN ADAPTIVE AGENT FOUNDATION MODEL FOR TOOL-AWARE HYBRID REASONING
Qianben Chen ; Jingyi Caol:2; Jiayu Zhang 3.3, Tianrui Qin', Xiaowan Li King Zhu', Dingfeng Shi', He Zhu', Minghao Liu", Xiaobo Liang", Ge Zhang', Jian Yang,, Yuchen Eleanor Jiang', Wangchunshu Zhou
OPPO AI Agent Team "The Chinese University of Hong Kong, Shenzhen
3 The Chinese University of Hong Kong Unnivrriity of Science and Technology of China
5 2077AI Soochow University ByteeDance Beihang University chenqianben
9970gmail. @
com, wcszhouuouttlokk.com
ABSTRACT
Large language models split into two families: reasoning-centric LLMs, which strengthen internal chain-of-thought reasoning but cannot invoke external tools, and agentic LLMs, which learn to interact with environments and leverage tools but often lag in deep reasoning. This divide arises from fundamentally different training objectives, leading to mismatched strengths and inefficiency simple queries, where both families tend to overthink or over-call tools. In this work, we present Adaptive Agent Foundation Model (A²FM), unified framework that follows a route-then-alige principle: the model first learns task-aware routing and then aligns mode-specific trajectories under shared backbone. To address the inefficiency gap, we introduce a third instant mode that handles simple queries directly, preventing unnecessary reasoning tool calls while complementing the agentic and reasoning modes. To jointly enhance accuracy and efficiency, we propose Adaptive Policy Optimization (APO), which enforces adaptive sampling across modes and applies regulaagulr reward. On the 32B scale, AFFM achieves 13.4% on BrowseComp. 70.4% on AIME25, and 16.7% on HLE, setting new SOTA among comparable models and performing competitively with frontier LLMS across agentic, reasoning, and general benchmarks. Notably, the adaptive execution achieves cost of pass of only S0.00487 per correct answer-cutting cost by 45.2% relative to reasoning and 33.5% relative to agentic, thus delivering substantially higher cost efficiency while maintaining comparable accuracy.
Agentic
Reasoning
General
42.0 40.1 39.0
GAIA, BrowsaComp. XBench-D8
96
MATH500, AIME24, AIME25
60
GPQA-d, SuperGPOA, MMLU-Pro,
33.4
80.080.0
73.7
69.3
$2.1
50.6
64
48
44,7
15.5
13.6
$1.3
42
40.3
7.6
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Figure 1: Average performance on agentic, reasoning, and general (ARG) benchmarks. Overall, A FM' ranks 1st, 2nd. and 1st on the three categories, respectively. Moreover, A denoted as variant that uses the best-suited mode for each benchmark, further improves over the adaptive version by +0.8 on agentic and +3.2 on general benchmarks.
'Euual contribution
Corresponning author

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Published as a conference paper at ICLR 2026
INTRODUCTION
APFM
TASK
Instant Mode: canswer=It about 40,075 km.s/ordwer>
MRUHPro
48% 7%
42
What length of the Earth's s equator?
Does the tosk need Tools?
Reasoning Mode:
TASK 2:
<ressoning-11 The diamond
A1M23
100 C4
Notes boxes
How complex is the task's logical ceasoning?
in Box 8.</ressonngg
Which box contoins the diamond?
<onswer=Box B</andwers
Agentic Mode:
crcal
TASK
stool sfool_coll.....
call
Tool
XBeehh
95 06
Where did USGS this *Finding
for </t../..ooo_____.... _coll
Feasoring
(xip codes)?
stool rroopresp..... Lresp
canswer-34689-</aawee
heep
Tool
code
30% Mib
Input
Classify
Execution
mode-triggering rates across multiple benchmanks
Figure 2: Overview of AFM. Left: the framework integrates three execution modes-insten reasoning, and agentic-unde under unified backbone with task-aware routing. Right: mode allo- cation across six benchmarks (MMLU-Pro, GPQA-d, AIME25, MATH500, Xbenchh-eeepSercch (Xbench), and GAIA-text (GAIA), illustrating how A adapts routing to task characteristics.
Large language models have evolved along two largely divergent directions. Grasoning-centric mod- els such as OpenAl o3 (OpenAl 2025c) and DeepSeek-RI (Guo al. (025) focus on strengthening reasoning for mathematics, scientific diagrams, and logical tasks. Their training pipelines rely on text-only corpora and emphasize multi-step internal deliberation, yet they lack the ability to interact with external environments or invoke tools. In contrast, agentic models including GLM-4.5 Zeng et al. 2025). Kimi K2 (Team et al. 2025a). DeepSeek- V3.1 (DeepSeek- 2025) and agent frameworks such OAgents (Zhu et al. 2025) and BrowseMaster (Pang et al., 2025) prioritize tool use, planning, browsing, and code execution. These systems achieve strong performance in tool-intensive environments, but typically lag behind reasoning-centrr models when multi-step logical derivation is required. The divergence in training objectives produces two com- plementary but disjoint capabilities: internal oh-ouutf reasoning and external environment- driven tool use.
internal chain-of-thouge
More recently, several hybrid agentic systems attempt to combine reasoning and tools within single model family. Representative examples include GPT-5 and partially open systems such as Qwen3 Yaang et al.. 2025a), which support both reasoning and tool capabilities. However, existing hybrid systems typically treat these behaviors loosely coupled components. For instance, Qwen3 trains reasoning and agentic abilities in separate alignment stages and lacks a unified controller to decide between reasoning and tool use at inference time. Closed systems such GPT-5 (OpenAll 2025b) provide hybrid capabilities but do not disclose data construction, trajectory formats, or training pipelines, making it difficult to assess or reproduce their underlying mechanisms. As result, cur- rent hybrid solutions fall short of achieving integrated decision making that jointly governs instant responses, internal reasoning, and agentic actions.
A parallel line of work aims to regulate the computational overhead of reasoning. Recent approaches explore when models should produce long chains of thought and when they should respond directly, thereby reducing unnecessary computation on easy inputs (Chen et al.] 2024b Fatemi et al.) [025] Lou et al. 2025 Kumar al.) 2025 Sui al. (025). These think and non-think methods focus primarily on optimizing the length or triggering of reasoning traces. However, they fundamentally operate within text-only settings and do not address the broader challenge of choosing between internal reasoning and external actions, especially when tool use introduces additional latency and a higher cost of pass (Erol et al. 2025).
To address the challenges mentioned above, we introduce the Adaptive Agent Foundation Model (AFFM), which unifies three execution modes within single backbone: an instant mode for direct answers on simple queries, a reasoning mode for long-form thain-of-thouglle and an agentic mode for multi-step tool use and environment interaction. To bridge the capability gap, A A2FM internalizes a self-adaptive router that learns what to do per query, naturally combining the strengths of reasoning and agentic paradigms in one model. To address efficiency, we add the instant mode so that simple queries answered directly, avoiding unnecessary reasoning or tool calls. Supervised fine-tuning follows a route- then-align principle: the model first performs task-aware aware routing, then aligns mode- conditioned trajectories under shared policy.
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Nevertheless, simply mixing modes falls short: the model must not only maintain high accuracy but also minimize computational cost, yet boundary queries remain difficult to route and data often underutilized. Prior work either omits explicit modeling of mode mixing (Jiang et al. 2025 Zhang et al. 2025), or focuses solely on efficiency by shortening reasoning traces in post-training (Tem al. an end-to-end framework (Wu et al. (225a)). We therefore introduce Adaptive Policy Optimization (APO), reinforcement learning procedure tailored for mode selection. APO builds on group- relative policy optimization (Shao et al.) 2024), enforces adaptive sampling across modes through combination of forced and adaptive rollouts, and introduces a per-query cost-regularized reward with LLM-as-Judge signals (Zheng et al. (023). The reward explicitly conditions whether the instant mode can already solve a query, thereby encouraging minimal-effor instant responses on easy inputs while escalating to reasoning or agentic modes only when external evidence or extended deliberation is needed.
2025b Arora & Zanette 2025), or fails to jointly optimize routing and trajectory generation in
Empirically, A FM achieves state-of-the-art results across benchmarks: agentic tasks it obtains 13.4% on BrowseComp, on reasoning it reaches 70.4% on AIME25, and on general tasks it delivers 16.7% on HLE. Beyond raw accuracy, APO substantially improves efficiency: on SuperGPQA, the adaptive execution yields a cost of pass of only $0.00487 per correct answer- cutting cost by 45.2% % relative to reasoning and 33.5% relative while maintaining comparable accuracy.
In summary, our key contributions include:
We present FM, the first hybrid reasoning and agentic model that jointly integrates task-aware routing and mode-specific trajectories under shared backbone.
We propose A two-stage process: (i) supervised toute-then-align fine-tuning for mode- conditioned trajectories; (ii) APO for mode selection with adaptive sampling and cost-regularized rewards.
We empirically demonstrate that FM achieves state-of-the tht---rttee-tttt results at the 32B scale across agentic, reasoning, and general benchmarks, with substantial reductions in token usage and com- putation versus mode-forcing baselines.
RELATED WORK
growing body of work shows that indiscriminate chain-of-thoughh (CoT) reasoning imposes sub- stantial computational overhead, latency, and token usage easy inputs CCen et al. 2025b Fatemi jet al. 2025). Though "auto-thinking" has emerged as a remedy, practical deployments often still rely manual controls e.g., Qwen3 requires explicit thinking mode toggling by users (Yang al., 2025a). To address these issues, recent progress in efficient LLM responses has converged on two core directions, complemented by "when-to-think" research.
2.1 LENGTII-AWARY APPROACHES
Length-aware approaches aim to reduce redundant token consumption by training models pri- oritize concise CoT when full-step reasoning is unnecessary. This direction adopts two dominant technical paths: (1) Reinforcement Learning with length regularization, which integrates length- regularized rewards to modulate CoT depth and balance accuracy and brevity (Arora & Zanette, 2025 Yeo et al. 2025; Aggarwal & Welleck 2025): (2) Supervised Fine-Tuning with CoT com- pression, which builds variable-length CoT via post-reasoning CoT compression or the acquisition of compressed CoT data the course of reasoning (Xia et al. 2025) Kang et al., 2024] Munkhbat
al. 2025
Liu et al. 2024). By learning to budget inference, these methods reduce token expendi-
ture.
2.2 CAPABILITY-AWARE ROUTING APPROACHES
Capability-awee aware routing enables adaptive mode selection by estimating a model's knowledge boundary via internal signals (uncertainty. logit margins, intermediate representations) to trigger adaptive reasoning modes (Chen et al.) 2024a). Self-routing leverages these signals to assess task difficulty and choose between short and long CoT responses (He et al. 2025). Yet, current routers
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often linear probes internal features and binary policies, limiting sensitivity to nonlinear state-diffie diffidiif ties and domain-specific control (e.g., math, code).
A complementary sub-direction is "when-to-thine policies. which explicitly learn whether to trig-
ger reasoning. Several methods introduce new RL reward designs to regulate thinking preferences
(Lou et al. 2025 Yu al., 2025). Despite promising results, they have two key limitations: (i) reliance on complex reward functions or manual annotated data introduccun subjectivity and hin- dering scalability; (ii) hyperparameter sensitivity and training instability, where ressonniigurtion causes mode collapse and undermines adaptive reasoning. To mitigate these, (Yang et al. 2025b)
propose Bimodal Policy Optimization (BPO). which contrasts thinking vs. non non-thinking trajectory utilities for the same input to learn robust switching. In related vein, (Jiang et al., (2255) propose the Large Hybrid Reasoning Model (LHRM) dynamically decides whether to invoke extended reasoning via query semantics and context, but lacks agentic tool-use integration.
3 METHOD
3.1 PROBLEM FORMULATION
We consider system that processes queries X drawn from task mixture D and produces outputs y E y. For each query, a router selects one three execution modes:
M {instant, reasoning, agentic), where Troute is routing policy over the mode set M.
Troute
Given the selected mode the system then generates output through mode policy Tms i.c.,
y Ty x)
Each mode induces distinct form of trajectory Tmi direct answers for instant, chain-of- offin-of-thought sequences for reasoning, and tool-interaction traces for agentic. denote the decoding function of trajectory under mode fm (I, Tm).
Let Acc(r.3) 3) E [0, be a task-specific accuracy metric. The expected performance of mode m on input is
Qm))) E mm (1a) Acc((2 fm (x, (m))
Our objective is to jointly optimize the router and mode policies such that the system maximizes expected accuracy across the task mixture:
max
ExnD
Σ mEM
Trouto (m xx) Qm (x)
Krouter (To
This unified formulation makes explicit the two levels of decision-making: (1) the router Troute decides which mode to activate for given query, and (2) the mode-specific policy 7m determines the quality of the generated trajectory. Together, they provide the foundation for both supervised alignment and reinforcement learning.
3.2 STAGE ROUTE- SOUTE-THEN-ALEE ALIGN FINE-TUNING
Training Data Generation. The first stage centers supervised route-then-align training, where the model learns to classify a query into one three modes-instant, reasoning, or agentic-ane and then generate mode-consistent trajectories. Mode routing is triggered by paired <classification> tags, after which the model proceeds with different behaviors:
In the instant mode, the model directly outputs the final prediction within <answer> tags, mini- mizing deliberation.
In the reasoning mode, the model provides chain-of-thouge wrapped in <reasoning> tags, followed by the final result in <answer> tags.
In the agentic mode, the model interleaves high-level reasoning with external tool usage. While inspired by Agent Foundation Models (Li et al. 20255), our design departs significantly how Plan and Summary used.
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Figure 3: Overview of Adaptive Policy Optimization (APO). Left: Rollout and reward pro- For each query, mode-specific rollouts generated either by prefix injection (forced agen- tie/reasoning/instant)) or by adaptive classification. Both prefix-injection tokens and tool-response tokens are excluded from loss since they jectory under APO, showing how AFFM progressively approaches the Pareto frontier by improving accuracy while reducing non- con-instant triggering (excluding AIME24/25).
model-generated. Right: Accuracy-efficiency tra-
Plan: appears only once at the beginning, decomposing the query into multiple sub-goals
that can be executed in parallel.
Summary: operates dynamically across the process, allowing the system to concurrently aggregate solved sub-tasks, terminate completed threads, and open new ones when needed.
This explicitly parallel architecture enables simultaneous multi-tool execution, substantially improv- ing both efficiency and effectiveness of tool usage.
External tools follow the MCP format and include (see Appendix
Search Tool: a search engine for information retrieval.
Crawl Tool: visitor to access web pages.
Code Interpreter Tool: a sandboxed environment to execute Python code.
The agentic trajectory begins with a <plan> section, executes tools parallel (each wrapped in <tool_call> tags), and collects their results within scool_response>> tags. These tool results are masked during training foolowing the strategy of Search-R1 (Jin et al., (2225) so that during the fine-tuning stage, the model focuses reasoning and routing, memorization of tool outputs. final <summary> may be added before producing the <answer>. Details of the training data trajectories template deferred to Appendix A.2
Data Curation.
To construct diverse, challenging training data, we use two heuristics (difficulty- based sampling adjustment and dassification-amiigee query handling); detailed implementation and rationale are deferred to Appendix B.I.I
For distillation, adopt mode-specialized teachers: DeepSeek RI (Guo et al. 2025 (strong reason- ing) for the reasoning mode, and DeepSeek V3.1 (DeepSeek-Al 2025) (broad general competence) for agentic/instant mode trajectories. This complementary design ensures each mode leverages the most suitable teacher, enabling stronger distillation and more reliable alignment under a shared backbone.
3.3 STAGE2: ADAPTIVE POLICY OPTIMIZATION
Aiming to tackle the challenges in balancing accuracy and efficiency in query processing, propose Adaptive Policy Optimization (APO), which equips the model with dynamic mode- among agentic, reasoning, instant-based on task demands.
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APO builds on GRPO (Shao al. 2024) but extends it in two key ways: (i) tailored rollout strat- egy that ensures sufficient exploration of all modes, avoiding under-sampling; and (ii) an adaptive reward that explicitly encodes the ceturacc-effcccinn trade-off, rather than relying coarse bi- nary rewards. This design enables the model to not only maintain correctness but also minimize unnecessary computation, achieving accurate yet cost-effective routing.
Rollout Strategy. For each query, APO performs both mode-forced rollouts and adaptive rollouts. In the forced setting, the model is compelled operate in each of the three modes-agentic,, reason- ing, and instant-for p rollouts per mode, where is a tunable hyperparameter. Mode enforcement implemented via prefix injection, in which pre-specified classification tag is inserted the be- ginning of the model's 's response (see Appendix [33). This guarantees that every query is explored under all modes, enabling an unbiased estimate of their relative success rates. Such statistics pro- vide the foundation for adaptive rewards (See Adaptive Reward below), which encourages the use of more accurate modes while preferentially selecting the instant mode whenever possible to minimize token consumption.
In addition, APO samples 7 adaptive rollouts, where y is another hyperparameter controlling the number of trials in which the model autonomously selects its operating mode. These adaptive samples provide the opportunity to reward correct self-routing, thereby reinforcing accurate and cost-efficient mode selection.
Accuracy Reward.
We adopt the LLM-as-Judge framework (Zheng et al. 2023) (See the judge
prompt in Appendix
I.3) to assess correctness, where a judge model M, provides binary feedback:
Tace 11 (x, =1].
This avoids the limitations of rule-based metrics (e.g.. F1, EM) that cannot fully capture the validity of open-ended outputs.
Adaptive Reward. To further guide proper mode selection, we introduce an adaptive reward that explicitly favors minimal-effort solutions on easy tasks. Intuitively, if query can already be solved by the instant mode, then choosing a more costly mode (reasoning or agentic) should incur a penalty. Formally, we label query easy if the instant mode achieves accuracy above a threshold T. For such easy queries, the reward is defined as
[1 1-p",, if non-instant mode is chosen, otherwise,
Tadaptive
where p is the empirical success rate of all forced rollouts on that query, and a 0 is a scaling factor. This design ensures that correct use of the instant mode always receives full reward, while reasoning or tool use on easy tasks is penalized in proportion to how confidently the query could be solved instantly. For hard queries, no penalty is applied, prioritizing the focus on correctness.
Format Reward. We enforce strict schema compliance: if the output violates the mode-specific format (e.g., tool tags in instant), the reward zero; otherwise it is one:
[1, if y matches the format of mode m,
Tformat =
0. otherwise.
Total Reward. The final reward combines accuracy, adaptivity, and format constraints:
Ttotal Faccuracy X Tadaptive Tformat-
This multiplicative design ensures that failure in any single component (c.g., wrong answer, misuse of modes, or format violation) immediately results in reward deduction, thereby enforcing strict correctness while still encouraging efficiency and proper schema adherence.
We adopt GRPO's objective calculation with certain specializations. Specifically, we strictly employ on-policy training, as varying model capabilities may affect query classification, thereby leading to an unstable training process. Additionally, to expedite training and explore more efficient mode selections, we omit the computation of KI. divergence. Consequently, given an actor parameterized by 0, for each question I,, APO samples group of outputs {ya y/2, '", yıG) from the old policy

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Published as a conference paper at ICLR 2026
Bald and then optimizes the policy model by maximizing the following objective: (0)
(Yii))
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(55)
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(3)) Aug, clip
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8
where of (6)) is the token at index t in response
(65) = (2s, 90 ... (-1
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(6) ,G == 3 x p+ is the sum of all mode trajectory in the rollout stage. generating a,
This improved framework with rollout strategy and adaptive reward ensures more stable policy optimization and targeted mode selection. Through this refined reinforcement learning paradigm, A achieves enhanced performance in adaptive mode routing, while simultaneously boosting overall accuracy and efficiency across diverse query scenarios.
4
EXPERIMENT
4.1
EXPERIMENTAL SETUP
We train A'FM on mixture of agentic, reasoning, and code-related datasets, while also retaining relatively simple cases enable learning of the instant mode. Full dataset composition and sampling strategy are provided henchmark against three categories of systems: (i) general-purpose LLMs, (ii) agent frameworks, and (iii) agent foundation models the 32B scale. A complete list of baselines detailed in Appendix [322]
Appendix 1.1.3 For comparison,
We evaluate our model on three key types of benchmarks. First, for agentic tasks, we BrowseComp/ (Wei et al. 2025aa, GATA(Mialon) al. (2023) (we test just 103 text-only subset extracted in (Wu al. (2055)), and XBench-Deeyye Beench--eepSearchiCCS
Evaluation Benchmarks.
al. 2025a), which assess the model's 's ability interact with external tools like search engines and databases for information seeking. Second, for reasoning-dependenn tasks, we utilize AIME24(of America [AA].. AIME25 America [MAA), and Mathh50oeesdyycks et al..
2021), which focus mathemattcal reasoning and problem-solving without relying external information. EPQAAd(Rem) et al. 2024). SuperGPQA (Du et al, 2025). MMLU-Pro (raang al. 2024), and HL et al. 2025) (We use a text-only subset defined in et et al.] (025c)), which test the model's ability to synthesize information and apply broad general knowledge across a wide range of domains.
Finally, for general knowledge and comprehension,
Implementation Details. We conduct all experiments on the Qwen2.5-32B-Inse then-aie -lign SET stage, train for 3 epochs with batch size of 256, AdamW optimizer, cosine decay learning rate schedule, and a max sequence length of 32,768. the APO stage, we train for epochs with learning rate of le-6, 5 warmup steps, batch size 128, and 12 rollouts per prompt (with p 3 and y = 3) capped at 65,536 tokens. We set a 2 for adaptive reward calculation. For inference, we set temperature 1.0, top-p 0.9, top-k 20, and max output length of 131,072.
backbone. In the route-
4.2
MAIN RESULTS
Intart mode Whare Intar: moce accuracy
mode share Non-rolant acouracy
0.00880
3300244
100
100
0,00732
Mode alocation (N)
80
30.7%
80
S0.7%
Accurery (36
0.0083
60
0.0040
11.7%
0.0020
Easy
Reasoning
Ag antiiic
Adaptive
(a) Relation between task difficulty, allocation ratio, and accuracy for instant and non-instant modes. Figure 4: Efficiency analysis on SuperGPQA: mode allocation vs. difficulty and token usage across modes.
(b) Cost of Pass from different modes.
Evaluation of Agentic Abilities. The results in Table T show that our adaptive mode (A (A²FM) achieves con- sistently competitive performance across all agentic benchmarks. On XBench-DS. A FF reaches 56.0 %, the
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(a) Agentic benchmarks (GAIA, BrowseComp. XBench- Seench-DeepSearch)
Benchmark
A'FM
A F'FM Agentic
GPT-4.1
Claude 4 Somnet
Qwen2.5 Instruct-32B
OAgents (GPT-4.1)
DeepDive WebSailor Asearcher
AFM Search
XBench-DS
56.0
54.0 201
17.0
21.5
8.7
47.0
50.5
53.3
42.1
54.0"
GAIA
57.3
60,7-344
60.7
22.3"
13.6
58.3
52.8
BC
13.4
14.4
+1.01
1.5
2.6
0.6
13.7
14.8
10.5
5.2
11.1
(b) Reasoning benchmarks (MATH500, AIME24, AIME25).
FFM Reasoning
Claude Sonnet
Simple TIR
Effective
Auto TIR
AFM
Benchmark
A FM
GPT-4.1
ol
ReTool
TIR
Code
MATH500
95.0
95.2 +0.21
92.4
94.0
96.4
92.9
86.4
62.6
93.2
94.6
AIME24
74.5
74.5 +0.0
46.5
43.4
74.3
59.9
42.3
33.3
67.0
66.7
AIME25
70.4
70.4+0.0
70.4
+0.0
37.0
33.1
79.2
49.2
25.2
16.7
49.3
59.8
(c) General-knowledge henchmarks (GPQA-d, SuperGPQA, MMI.U-rr, HLEE).
AFFM Agentic
A'FM Reasoning
Claude Sonnet Instruct-32B
Qwen2.5
Qwen3 -32B
Benchmark
A
GPT-4.1
QwQ
GPQA-d
63.1
67.7 +4.61
64.7+1.61
64.7
66.3
68.3
49.5
54.6
65.6
SuperGPQA
54.7
56.0
+1.31
51.2
3.54
50.8
55.7
38.8
43.2
43.6
MMLU-Pro
73.8
75.8
+2.01
77.0
+3.21
81.8
83.5
69.0
72.7
76.4
HLE
16.7
20.6
+3.91
13.4 -3.3
3.7
5.8
3.8
8.3
8.2
Table I: Unified results across (a) agentic, (b) reasoning, and (c) -nowlelge benchmarks. Bold best; underline second-best. Teal/Red superscripts indicate gain/loss of the forced mode relative to adaptive FM. indicates results reproduced by us. All numbers are reported as avg@) 1, except AIME24/25 which use avg@32.
highest among all baselines and surpassing the second-best model, AFM-Search (54.0%), by +2.0 points. On GAIA, A'FM ranks second overall, trailing only OAgents-whin leverages GPT-4.1 as its backbone- while still outperforming all caneral--puposs LLMs (e.g., Claude-4-Sonnet at 2.6%) and all 32B-scale agent founda- tion models. BrowseComp. A A achieves 12.4%. ranking third behind DeepDive (14.8%) and OAgents (13.7%), but still exceeding AFM-Search (1 (11.1% 11)1%) and general-purpose L.MSs.
When forcing the model into the agentic mode (A FM-AAtetic), performance further improves: on BrowseC- omp it rises to 14.4%. surpassing OAgents and achieving the second-highed high-hhghstt score overall. just behind Deep- Dive (14.8%); GAIA reaches 60.7 % establishing delivers robust adaptive performance but also, when focused on tool-intensive reasoning, can outperform spe- cialized frameworks in complex deep-search scenarios.
SOTA. This demonstrates that A A'FM only
Evaluation of Reasoning. As shown in Table lb A FM achieves reasoning performance comparable al on MATH500 (95.0%), while significantly outperforming all 32B-scale agent foundation models (c.g., Re- Tool 67.0% on AIME22, AFM Code 59.8% on AIME25). On AIME24, sets a new SOTA with 74,5 %, and on AIME25 it attains 70.4% %. second only to ol (79.2%). Compared to Claude 4 Sonnet, A'FM is stronger by +33.3 and +40.2 points on AIME24/25, underscoring reasoning capabilities far beyond even top general- purpose LLMs. Notably, adaptive and reasoning-specific variants yield identical results AIME24/25, show- ing that FM reliably routes nearly all AIME queries into the reasoning mode.
Evaluation of General Abilities. The results in Table Ia show that A A'FM delivers highly competitive general- domain performance at the 32B seale. The adaptive mode consistently outperforms instruction-tuner baselines (e.g., Qwen2.5-32D5 3-3BB) and even surpasses the geasoning-focuseee backbone. A'FM achieves large gains: GPQA-d improves by +13.6 points Qwen2.5-32BB (63.1 49.5), and on SuperGPQA Claude 4 Sonnet (55.7).
QwQ. Despite starting from a 32B instruct
+15.9 points higher (54.7 vs. 38.8), notably surpassing GPT-4.1 (50.8) and
HLE further highlights the model's strength on integrated tasks requiring both reasoning and tool use. Here, A'FM-Agentic achieves remarkable 20.6%, exceeding the next-hest haseline (QwQ, 8.2%) by +12.4 points, while adaptive mode (16.7% still leads all comparable 32B models by a wide margin. The gap hetween adaptive and agentic variants reflects that some queries are routed into the instant mode, illustrating A A'FM's ability halance effectiveness with efficiency even under challenging integrated settings.
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Other modee
Adkesiiv mode
GAIA
Xbench-DS
AIME24
SuperGPQA
100
100
100
100
Nouraoy (50
80
80
80
744
50
60
4677
60
56.
80
60
51.3
56.
$4.7
40
40
40
20
10.5
12.1
Reezeninng
Agondo
Adupton
Rescosing
Agendo
Adaptive
Inwhen
Rewabning
Ayenti
Aduptii
batol
Reasaneng
Aganito
Adtutiive
Figure 5: Comparison of adaptive mode (red) against forced single modes across four benchmarks.
Evaluation of Efficiency. We focus on A A'MM's efficiency by analyzing its mode allocation across task dif- ficulty (Fig. 4a.. On SuperGPQA (with human-annotated difficultyy, the model uses instant mode for 61.1 % of easy questions, but this drops to just 8.3% for difficult ones. This shows adaptive routing: it leans rea- computation. Notably, instant
soning/tool use for complex tasks and direct answers for simple ones response accuracy stays stable % across all difficulty levels, highlighting this mode's robustness even for harder queries.
We further analyze efficiency through the metric cost-of-pass (dollar cost per correct answer) on the four general benchmarks (GPQA-d, SuperGPQA, MMLUUPro and HLE). Formally, for model m problem p.
C. (p)
Cost-of-Pass(mmp)) p)
R,, (p)
where Rmn (p)(p) is the accuracy and (p) is the expected inference cost, computed from input/output token counts and unit token prices. We follow the official Swwen..5-32B-Imae pricing, with $0.00028 per 1k input tokens and $0.00084 per 1 output tokens.
As illustrated in Fig. 4b The reasoning mode incurs cost of S0.00889 per correct answer. while the agentic mode reduces this $0.00732. Strikingly, the adaptive mode further lowers the cost to only $0.00487, corre- sponding to reduction of 45.2% % relative to reasoning and 33.5% relative to agentic. other words, adaptive exccution delivers cach correct answer at roughly half the cost of reasoning- based execution.
ANALYSIS
5.1
EFFECTIVENESS OF ADAPTIVE ROUTING MECHANISM
Accuracy of Routing between FM and Human An- notations. We evaluate routing accuracy on three representa-
Table 2: Human-labeled mode distribu- tion and model routing accuracy.
tive datasets: GAIA (mixed agentic and reasoning). BrowseC- omp -oventt-ddmmiated,,, and AIME24 (reasoning dominated), -rensnnig-dommnnated) For GATA, ground-truth labels are obtuined via majority vot- ing from five strong baselines: DeepSeck-R1, DeepSeck-V3.I GPT-4.1, GPT-5, and Claude-4-Sonnet, and are followed by manual verification. BrowseComp only consists of search- centric queries and is therefore annotated agentic, while AIME24 contains pure mathematical reasoning problems and is correspondingly annotated as reasoning.
Dataset Agentic Reasoning Instant Acc.
GAIA
84.5
13.6
1.9
92.2
BC
100.0
0.0
0.0
94.0
AIME
0.0
100.0
0.0
100.0
As shown in Table 2 our model achieves 92.2% accuracy on GAIA and 94.0% on BrowseComp. Errors on GAIA often stem from queries where both reasoning and agentic are plausible (see Case Study in Appendix(_2). while BrowseComp
arise when the model overestimates its internal knowledge and avoids tool calls. On AIME24, the model reaches 100%, confirming robust mode discrimination on reasoning tasks.
Robustness of Adaptive Routing on Complex Tasks. We compare adaptive routing with forced single-mode execution (agentic, reasoning. and near-instant) across four benchmarks: GAIA, XBench- DeepSearch, AIME24, and SuperGPQA. GAIA and (Bench-Deeepearce are primarily agentic-oriented with some reasoning and instant cases; SuperGPQA contain mix of tasks where tool and fast responses cesooingg-dmmnncee
critical; AIME24 superior performance in most settings. On GAIA, adaptive slightly underperforms pure agentic (57.3 vs. 60.7), primarily due to classification noise. On XBench-DeepSearce DeepSearch and AIME24, adaptive surpasses single modes, demonstrating its strength in routing composite, multi-facet queries to the appropriate execution mode rather than over-committing single behavior. On AIME24, adaptive mode strictly matches reasoning mode, since
Results (Fig. 5) show that adaptive mode achieves competitive or

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Published as a conference paper at ICLR 2026
all queries routed into reasoning where accuracy dominates. Overall, adaptive mode provides robust per- formance while retaining flexibility across diverse task types.
Failure Case Analysis of Adaptive Routing. Under adaptive routing, A achieves 57.3% accuracy on GAIA, with 44 failed cases in total, among which 5 are routing errors and the remaining 39 arisc from within-mode exccution (Sec the confusion matrix Table 3 This decomposition shows that the majority of failures do not stem from the routing mechanism, but from downstream execution within the selected mode.
Table 3: Confusion Matrix for Routing Behavior.
Pred GT
Agentic Reasoning Instant
Agentic Reasoning Instant
84
0
0
12
0
Two key observations can be drawn from this matrix: most rout- ing mistakes result from the model over-selecting the instant mode for questions that actually require agentic or reasoning steps, while confusion between agentic and reasoning modes is very rare, a fact that indicates the router already maintains a clear separation between these two modes. All routing failures fall into two patterns: (i) premature instant selection (4 of 5 cases), and (ii) unnecessary agentic invocation for simple factoid or light-reasoning tasks. Illustrative examples provided Appendix I.T is worth noting that after APO fine-tuning, the model correctly switches instant for the latter case, thus showing reduced tool-overuse and alignment with the true required complexity.
1
5.2 EFFECTIVENESS OF ADAPTIVE POLICY OPTIMIZATION
Approaching the Pareto Frontier under APO. Figure illustrates how A'FM evolves under APO training, evaluated across all benchmarks except AIME24/25, since these tasks are routed entirely into the reasoning mode and offer meaningful trade-off with instant responses. The three yellow points step 10, 30, and 50, showing a clear trajectory where the adaptive model steadily reduces non-instant usage while improving accuracy. As training progresses, FFFM moves closer the Pareto frontier of accu- racy-efficiency trade-offs, indicating that APO effectively teaches the router to allocate more queries to the instant mode without sacrificing correctness. At convergence, FM (RL) achieves average accuracy of 53.8% with non-instant ratio of 77.1 %, compared the "Best Mode" oracle (aceuracy 55.4%, non-instant ratio 100%). Although accuracy gap is marginal (-1.6 points), our adaptive model lowers non-instant trig- gering by 22.9 points, demonstrating the substantial improvement in efficiency while maintaining accuracy.
mark check-
Ablation of the Adaptive Reward APO. To isolate the contribution of the adaptive cost term, is critical to evaluate APO using naive accuracy and format reward. As core com- ponent APO, our adaptive reward enables query-aware rout- ing: it prompts the model to use instant mode for simple queries, and only switch to reasoning or agentic modes when necessary. Without this term, APO collapses into a GRPO-style objective with prefix-enforeed multi mode sampling. We have conducted a controlled ablation comparing two variants: (1) APO with ac- curacy, format and adaptive reward; (2) APO with accuracy and format reward. We tested these two variants on SuperGPQA, with the experimental results summarized in Table 4 We observe that the adaptive mode on easy queries while still routing to reasoning agentic behaviors harder ones. Notably, the overall score decreases only slightly. but the instant-mode usage increases substantially, indicating that the adaptive reward effectively encourages efficiency without sacrificing much accuracy. This experiment directly measures how essential the cost penalty is for inducing efficient routing under identical sampling conditions.
Table 4: Ablation Results of Adaptive
Reward in APO on SuperGPQA.
Variant
Score Instant Ratio
w/o adaptive reward adaptive reward
55.6
50.2
54.7
58.6
CONCLUSION
We present AMM,, an Adaptive Agent Foundation Model that unifies agentic, reasoning, and instant modes under single backbone with self-adaptive router. By combining route-then-align training with Adaptive Policy Optimization, A'FM achieves both high accuracy and efficiency, matching surpassing strong baselines across agentic, reasoning, and general benchmarks. Our results highlight the promise of adaptive multi mode modeling as a scalable path toward efficient and versatile LLM agents.
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7 ETHICS STATEMENT
This work complies with the ICLR Code of Ethics and has been reviewed all authors ensure adherence ethical research standards.
Data usage, All training and evaluation data employed this study are publicly available from established datasets and benchmarks (e.g.. WebDancer, Taskeraft, GAIA, AIME, GPQA, MMLU-Pro)), No private, sen- sitive, personally identifiable information is included in our datasets, and we performed no data collection involving human subjects.
Intended and impact. FMFM is developed research model advance the study of adaptive routing across agentic, reasoning, and instant modes. The primary contributions are methodological and benchmark- driven; the system is not designed for direct deployment in safety-critical applications. While efficiency gains can reduce computational cost, we caution that misuse of agentic capabilities (e.g., unconstrained tool use) may carry risks if deployed without safeguards. We therefore encourage further research into safe routing strategies and responsible usage.
Integrity and transparency. All reported results are based on controlled experiments and reproducible bench- marks. We disclose implementation details and evaluation settings to ensure transparency. All authors af- firm that results are reported honestly, without fabrication or selective reporting, and that no financial or non- financial conflicts of interest exist related to this work.
8 REPRODUCIBILITY STATEMENT
We have taken deliberate steps ensure the reproducibility of our findings.
Data. All datasets used for training and evaluation are publicly available benchmarks (e.g.. GAIA, BrowseComp, AIME, GPQA, MMLU-Pro), Detailed descriptions of dataset composition, sampling strate- gies, and mode balancing provided in 1.3.
Code. Our full implementation, including the route-then-align SFT pipeline, APO training procedure, and evaluation scripts, will be released upon publication to facilitate reproduction and extension of our results.
Experimental Setup. Hyperparameters, training configurations, rollout strategics, and reward definitions are specified in 4.1 Token pricing and cost-of-pass calculations are explicitly documented to enable con- sistent efficiency evaluation.
Limitations. While our methods and datasets are fully described, minor deviations may arise from nonde- terminism in sampling and stochastic rollouts during APO training.
By providing code, data references, and detailed experimental settings, we aim to make FF fully repro- ducible and to support future research on adaptive agent foundation models.
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A METHOD
A.1 TOOLS
Our agentic agent utilizes three types of tool answer the questions: web_ search, crawl_page and code execute:
seerchh We interface with the Google Scarch engine for information retrieval, conducting web searches via the SerpAP[] tool. The core SerpAPI configuration parameters are the search query string and the number of results return. In practice, the tool issues model-generated queries and, by default, retrieves the top results for each query. Each result includes titlc, a smippet, and the cor- responding Uniform Resource Locator (URL). This setup provides essential support for subsequent analysis and decision-matee makinn processes.
crawl_ page: We employed a web-crawling tool authenticated via Jina AP summarized the retrieved content using gpt-5-mini. The tool's core configuration parameters comprise the Uniform Resource Locator (URL), the summary query. Candidate URLs are generated by the model, and the tool uses the Jina API to crawl each URL. Subsequently, we invoke the gpt-5-mini model to produce summary for each crawled page; the summary prompt provided in Appendix [.2
To ensure usability and security, we realize the code sandbox with nsjai a lightweight utility that provisions isolated execution environments for Python code. By leveraging Linux namespace isolation, nsjail hardens file-system boundaries and prevents unauthorized access to host resources. A notable strength of this approach is its compatibility with containerized ecosys- tems (e.g.. Docker), which facilitates seamless migration across diverse training and testing settings. The tool also offers fine-gre the-grained resource controls. during the training process, we cap CPU time at 5 seconds and memory 5 GB to keep code execution strictly bounded.
A.2 TEMPLATE
As shown in Table 13 we classify the tasks into three modes: Instant mode, Reasoning mode, and Agentic modc. Each mode tollows distinct task exccution flow, and we record the task exccution trajectories for each mode, which are then used for supervised fine-tuning (SFT) and reinforcement learning (RL) training. The detailed trajectories for each mode are in Table 13
https::/google. wwtp:eseeeeereeeeeeeeeeeeeeeeeee
https://jina..r
https://github. http:/athhhb.ccm/ooooll////sssll
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B EXPERIMENTS
B.I DATA
B.1.1 DATA CURATION
To construct diverse and challenging training data, combine two hcuristics. First, ceffiiullt--assee sampling strategy adjusts the natural distribution of task success. practice, raw sampling tends to yicld U-shaped distribution: tasks are either consistently solved or consistently unsolved, with few between. To increase cov- erage of moderately difficult tasks, we deliberately lower the proportion of "always-solved" cases, resulting in J-shaped distribution (See Fig. 6 that emphasizes more challenging queries. Second, target classification- ambiguous queries where routing decisions uncertain, Instead of relying
majority votes, select the tag corresponding to the mode that achieves highest accuracy on that query. This strategy emphasizes su- pervision on decision boundaries, encouraging the model to improve its discrimination ability in cases where multiple modes appear plausible.
B.1.2 DIFFICULTY DISTRIBUTION
As shown in Fig. 6 difficulty-based sampling results in J-shaped distribution. Compared the U-shaped pattern, this distribution reduces proportion trivially solved cases and emphasizes more chal- lenging queries, thereby providing better coverage of moderately difficult tasks.
0.50
0.45
0.40
0.24
0.35
0.22
Qu Iimeannee
0.20
0.55
0.0
3/4
3/4
1/4
1o4)
Figure Data distribution among agentic (left) and reasoning (right)
B.1.3 DATASET COMPOSITION
dataset sources encompass a variety types and domains. specifically chose not to focus on easy datasets but rather to select data from more challenging and complex sources. These include datasets such as Webdancer Wu et al, 20225b, Webshaper[Tao Tao et al.
2025), Taskcraft (Si et al. (2025). 2Wiki et al.) 2202)).
MusiqueHo al. (2200). and HotporQA(Yangg
al. 2018 8 for agentic-oe aeentic-related related tasks, and mathematic reasoning
(2025) and GSM8K (CoMBK et al. 2021). Additionally, code-related data sources like Skywork-Code(Zem et al. (0044) and Codeforces(Peneda, Ct al.
datasets like Skywork- Math(Zeng al. 2024p. DAPO-Math-17K0
2025 were incorporated for programming tasks. Importantly, while we did not curate separate datascts solely for the instant mode, we deliberately retained relatively simple examples within the search and reasoning datasets. This ensures balance across the three modes and allows the model naturally learn apply the instant mode on straightforward tasks. Finally, we created 5289, 2829, and trajectories for agentic, reasoning and instant modes respectively during the route-then-align SFT phase and 6000 question-anae answer pairs during the APO phase.
B.2 BASELINES
We conducted comprehensive evaluation of our trained model against three categories of systems: (1) general-purpose large language models (LLMss), including closed-source models such GPT-4.1 1 OpenAA
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2025a ol Jaech 2024). Claude-4Sonnee
2025 and open-source models DeepSeek-R R al. 2025). , Srren2.--Insrrcteee (Yang et al. 2024), Qwen3-32B Yang al. 2025a QwQ-32B (Team
2025
(2) statc-of-the-art agent frameworks, such as OAgents(Zhu et al. 2025); and (3) Agent Foundation
Models, including DeepDive(Lu et al. 2025). WebDancer Wu et al. 2025b), WebSailor Li ct al.
2025a
Asearcher-Weblf Web al. 2025). AFM-Search(Li et al. (025b), (impleTIR(Xuee et al. 2025
Effective-
TIR et al.
2025 Auto Wei al. 2025h and Re RoToolleeng
2025
B.3 ROLLOUT
Mode-Forcing Strategy To enforce the model's selection of specific exccution mode for given task, utilize fixed prompt structure that directs the model to choose the appropriate mode. The prompts for each mode are outlined below:
Reasoning Mode: This task requires complex logical reasoning (such as mathematical proofs, multi-step problem solving) and causal analysis, so will select reasoning_mode <classification> reasoning_mos </classffication>
2. Agentic Mode: This task requires acquiring real-world information (such
and data)
executing code (such as programming problems, data processing, or statistics), so will select agentic_mode. <classificat cesssffccatio>>> agentic_mode </classification>
3. Instant Mode: This task needs no real-world info, code. or complex reasoning juutt basic knowledge brief responses, will select instant_mode. <classification> instant_mode </classification>
C ASSESSING THE RELIABILITY OF THE LLM JUDGE
To assess the reliability of the binary LLM judge used in APO's reward mechanism. conduct validation across multiple datasets and cross-check the judgments with human evaluation. The main goal is verify the chosen judge model (We use GPT-5-Mini in our experiment) produces consistent and unbiased hinary decisions under diverse task types, including both deterministic reasoning and open-ended agentic tasks.
We first compare two evaluation paradigms: Exact Matching (EM) and binary LLM-as-Judges On deterministic benchmarks such AIME24/25. both methods yield identical correctness labels, showing 100% consistency. However, EM fails on tasks involving free-form or semantically rich outputs (e.g., GAIA, BrowseComp), where cquivalent answers may differ lexically. Thus, the binary Judgge provides a unified and semantically robust evaluation signal across all datascts.
To further confirm judgment stability. we perform human verification study comparing GPT-S-Mini and 1etm2255-22BInstruue following the same evaluation setup and standardized prompt defined in Section L3 Results are summarized in Table GPT-5-Mini exhibits perfect alignment with human assessments across samples, while Qwen2.5-72B 7-2-----2B-n shows minor inconsistencies due to formatting ambiguity, confirming GPT-5-Mini's superior reliability for binary reward evaluation.
Table 5: Human evaluation of LLM judge consistency GAIA.
Judge Model
Disagreement with Human Judgment
GPT-5-Mini Qwen2.5-72B-Innsee Instrucc
0/103
1/103
Overall, these results demonstrate that GPT-s-Mini--s-a- a-Judge provides stable and fair binary judgment sig- nal suitable for reinforcement learning supervision, ensuring both semantic robustness and cross cross-dataset con- sistency.
D EFFECT OF TEACHER MODEL CHOICE ON DISTILLED PERFORMANCE
We examine whether the choice of teacher model influences the supervised route-then-align stage of AFM. Teachers with substantially different capabilities are compared across domains: agentic tasks (GPT-5- Mini vs. DeepSeek-Vas V3.1) 1) For each teacher, we measure (i) its raw performance on domain-speciffe benchmarks and (ii) the performance of an SFT model distilled from that teacher using identical data size. backbone, and training setup.
and mathematical reasoning (DeepSeek-RI vs. Qwen3-235B-A222 A22B--hinning........ Thinking-2507)
Table 6 reports the results. In both domains, teachers differ meaningfully in accuracy. For example, GPT-5-Mini exceeds DeepSeck-V3. -3.11 by 5.5 points BC-200 and 7.9 points GATA; DeepSeek-R1 exceeds
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Qwen3-235B- Whenn-i3SB-Thinninn by 7.0 points on AIME24. However, their corresponding distilled SFT models converge nearly the same performance: the agentic SFT scores differ by only 0.9 points, the reasoning SFT scores differ by only 3.1 points. These findings indicate that the -oute-then-align framework largely absorbs teacher variability, leading to stable downstream performance even when teacher capability varies significantly.
Table 6: Raw teacher model performance vs, distilled SFT performance.
Task Domain
Teacher Model
Raw Teacher Score
SFT Score After Distillation
GPT-5-Mini DeepSeek- V3.1
31.0 (BC-2000) / 70.0 (GAIA)
51.5
Agentic
25.5 (BC-200) 62.1 (GAIA)
52.4
DeepSeek-RI RI
90.2 (AIME24)
66.7
Reasoning
Qwen3-235B-ABe -228--222-111180 422B-Thinking-255
83.2 (AIME24)
63.6
E EVALUATING THE FUNCTIONAL ROLE OF THE INSTANT MODE
We study whether the instant mode is essential for the efficiency gains exhibited by A FM, and whether com- parable behavior could emerge from two-mode (reasoning + agentic) system. A direct ablation of removing the instant mode within APO not well-defined. APO's adaptive penalty relies categorizing queries "easy" or "hard" based on the success rate forced-instant rollouts. Without the instant mode, APO lacks baseline required to infer query difficulty. Thus, a straightforward two-mode APO variant cannot reproduce the adaptive mechanism central to A FM.
To empirically isolate the contribution of the instant mode at inference time, we design an alternative compar- ison 200-query subset of SuperGPQA. The first setting executes both the reasoning and agentic modes for every query and computes @ass@2 and Cost-of-Pass (CoP) from these two trajectories. This simulates a system without access to the instant mode. The second setting uses the full A FM router to choose among instant, reasoning, and agentic modes.
Table 7: Comparison of efficiency and performance 200-query subset of SuperGPQA.
Setting
Pass @2
Cost-of- (CoP)
Reasoning + Agentic (two modes)
67.0 0
0,00812 0,00432
Adaptive routing (A FMM
62.5
The results indicate that the joint two-mode system attains slightly higher Pass@2 (+4.5 points), but incurs nearly double the computational cost. Under adaptive routing, the instant mode is selected for more than half of successful @ass@2 cases, and the resulting Cost-of-Pass decreases by 46.8%. These observations show that the instant mode plays a central role handling simple queries efficiently, reducing unnecessary reasoning or tool-use trajectories while maintaining competitive task performance.
F ABLATION ON THE PARALLEL AGENTIC EXECUTION ARCHITECTURE
analyze contribution of A2FM's parallel agentic execution design by comparing it against a sequential baseline under matched training conditions. The parallel architecture allows each agentic step to dispatch multiple tool calls concurently., whereas the sequential baseline follows standard ReAct-style pipeline in which all calls serialized (Wu et al. 2025b). Both variants are trained with the same 1,968 trajectories and identical hyperparameters.
The motivation for parallel design is twofold. First, it establishes clear capability-cost separation between the agentic mode and the instant mode by assigning the agentic branch a high high-capacity, high-cost behavioral profile. Second, it better reflects real-world agentic workloads, where multiple subtasks (e.g., parallel search or crawling) can be processed concurrently, enabling broader evidence collection for complex queries.
We evaluate both variants GATA, BrowseComp, and XBench, and measure efficiency using Cost-of-Pass (CoP). Results are shown Table
Across all benchmarks, the parallel architecture yields consistent performance improvements (+2.9 pp on GAIA, +3.1 pp on BrowseComp. +5.0 on XBench), indicating that concurrent tool execution improves ev- idence gathering and task coverage. As expected, these gains come with moderately higher computational cost. This capability-cn trade-off essential for APO, which requires modes with distinct efficiency profiles
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Table 8: Performance and efficiency of sequential vs. parallel agentic exccution.
Architecture
GAIA
BrowseComp
XBench
Cost-of-Pass (CoP)
Sequential baseline
47.6
9.1
42.0
0.00586 0.00768
Parallel (ours)
50.5
12.2
47.0
enable meaningful adaptive routing: the parallel agentic mode offers a high-capability option for complex queries, while the sequential and instant modes serve as lower-cost alternatives.
G COMPARISON OF A BEFORE AND AFTER APO.
Quantifying the contribution of the RL phase (APO) is essential for understanding F'MM's improvements in performance and efficiency. We thoroughly compare our SFT and APO versions in two aspects: Accuracy and Non-Instant Triggering Rate. Both the model's accuracy the standalone SFT stage and final APO stage across benchmarks, and its Non-Instant Triggering Rate in these two stages, are detailed Table on the one hand, APO yields substantial accuracy gains across all task families: reasoning benchmarks see the largest improvements (e.g.- +11 on MATH500, +10 on AIME24, +8.6 AIME25), while agentic datasets and broad general-knowlede knowledge evaluations also show consistent boosts. This proves APO is not minor refinement SFT, but the primary source A FM's final performance. On other hand, APO noticeably reduces unnecessary use of expensive non-instant modes: its Non-Instant Triggering Rate drops by over 10 points on average on general henchmarks, and stays unchanged on reasoning-only benchmarks (AIME24/25) as expected. This shows that APO learns cost-aware routing, preferring instant mode when sufficient and invoking heavier modes only when needed, which is exactly as intended by the adaptive reward design.
observed that
Table 9: Model Performance at the SFT and APO Stages.
Benchmark
Accuracy
NITR
SFT
APO
SFT
APO
XBench
51.0
56.0
96.0
93.0
GAIA
51.5
57.3
96.2
93.2
BrowseComp
10.1
13.4
95.0
89.9
MATH500
84.0
95.0
86.0
77.0
AIME24
64.5
74.5
100.0
100.0
AIME25
61.8
70.4
100.0
100.0
GPQA-d
56.0
63.1
88.2
77.8
SuperGPQA
46.9
54.7
63.8
50.2
MMLU-Pro
65.0
73.8
58.0
46.0
HLE
11.0
16.7
96.0
89.7
H GENERALIZATION ACROSS MODEL BACKBONES AND PARAMETER SCALES
We evaluate the portability of the A?FM training framework across multiple backbones (Qwen2.5, Qwen3) and parameter scales (4B, 14B, 32B). Unless stated otherwise, all results reported here are based on SFT-only models to isolate the effect of the training recipe.
H.1 EXPERIMENTAL SETUP
We consider families of backbones (Qwen2.5, Qwen3) and three parameter scales (32B, 14B, 4B). At the 32B scale, we directly compare two backbones (Qwen2.5-32B-Instl scales, we compare A'FM models with available baselines, including Webthinker (14B), DeepSeekRI Dis- tillQwen (14B). and Qwen3 base models (reasoning non-reasoming variants). Evaluation follows the same benchmark suite as in the main experiments: GATA, BrowseComp, AIME24, AIME25, SuperGPOA, and HLE..
Qwen3-32B), the 14B and
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H.2 32B BACKBONE COMPARISON
Table I0 reports the performance of A'FM on two distinct 32B backbones.
Table 10: Performance of A?FM on different 32B backbones (SFT-only).
Dataset
AFFM-Qwen..-222--nnnit
A7FM-Qwen3-32B
GAIA
51.5
55.3
BrowseComp
10.1
12.0
AIME24
64.5
68.6
AIME25
61.8
63.5
SuperGPQA
46.9
48.2
HLE
11.0
10.5
Qwen3-32B backbone yields improved performance on 5 datasets, indicating that the A4FM training procedure generalizes robustly across backbone families.
H.3
14B SCALE EVALUATION
At the 14B scale, compare A2FM-Qwen3-14B with available SFT- RL-based basclines. Results shown in Ta
Table 11: Performance of AFFM-Qwen3-14B vs. 14B baselines (SFT-only).
A2FM
Qwen3-14h 14B (Non-reasoning)
Qwen3- 14B (Reasoning)
DeepSeekRI DistillQwen-144
Webthinker
Dataset
14B
14B
GATA
46.6
33.0
BrowseComp
9.5
AIME25
59.8
58.0
55.7
55.7
HLE
8.8
4.2
4.3
4.4
7.0
A consistently outperforms both Qwen3-14B models existing baselines, including substantial gains over Webthinker on GAIA (+13.6 points).
H.4 4B SCALE EVALUATION
We further evaluate A?FM-Qwen3-4B on the same benchmark suite; results shown in Table
Table 12: Performance of ArFM-Qwen3-4B vs. Qwen3-4B base models (SFT-only).
Dataset
A'FM-Qwen3 Qwen3-4
Qwen3 4B Instruct
Qwen3 4B Reasoning
GAIA
38.8
BrowseComp
8.9
52.3
22.3
AIME25
57.4
HLE
6.6
4.7
3.7
Despite the small parameter scale, A2FM-4B remains competitive and in several cases surpasses larger models and baselines (c.g., outperforming the 14B Webthinker model GAIA).
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1 PROMPTS
1.1 SYSTEM PROMPT
SYSTEM PROMPT A FM
You are required to solve the task by using one of the three mode options: ageutli_mode, mode, mosonnigg_mode and mode.
1.
Agent Options:
agentic mode:
agent
needs search
real-world
and
executing code (such as programming tasks, data processing statistics).
(b) reasoning mode: choose this
the task requires complex logical reusoning (such mathematical proofs, multi step problem solving) and causal analysis.
(c) instami_man mode: use this agent for simple tasks needing no real-world info, code, or complex reasoming. Instend, just busic knowlodge or brief responses.
2. Trajectory Formulation:
(a)
You should lirst predict one of the three agents above within the lunction <e cclassification> </classification>
Then you should formulate your thinking and processing trajectory according
agent you
agentic rule:
Objective:
Your is systematically solve user assi-es tasks
Decomposing the task into clear goals paths.
Exccuting tools purposefully and officiently.
Advancing all gouls in parallel, while keeping each goal's paths sequential.
Tracking progress with summaries.
Delivering final confirmed answer only when all gouls are resolved.
Exccution Requirements:
logical order functions/tulls
Parallelize independent goals; within each goal, paths sequentially fallbacks.
Each step include:
thinking (before execute tools, why this tool/path is chosen).
<tool_cal _rool_calll >> execution (with correct parameters).
Use results from observations to refine next actions.
Ensure no redundant tool calls (don't repeal identical queries).
Never assume goal completed without explicit verification.
Continue advancing goals they are resolved.
Functions:
<plan> Function:
Role: Decompose the original task into goals and execution paths.
Rules:
1-5 parallelizable goals.
Each goal has paths, executed sequentially as fallback options.
Detine success criteria for each path.
Timing: Only the step.
Format Example:
<plan>
## Goal 1: [Name]
Path 1.1: [Approach]
Success: [Criteria]]
[Approach]
Success: [Criteria]
48 Goal 2: [Name]
Path 2.1: [Approach]
Success: [Criteria]
</plan>
<sumnary>> Function:
Role: Recap execution status and decide actions.
Content:
Plan summary (original goals/paths).
Execution status for each goal: Completed / In Progress / Blocked.
Path analysis (which worked, which failed).
Next steps: specify
pab-paths
parallel.
Timing: livery steps, occurs when enough summarize..
Example:
<sunnary>
** Plan Sunnary
[Brief recap goals]
4ll Execution Status
[Status]
Path Analysis: [...]
Goal 2: [Status]
Path Analysis: [...]
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## Next Parallel Sub-Pathss
Goal 1: Path 1.2
Goal 2: Path 2.1
</sunnary>
<toel_call> Tool:
Role: Exccute tools to advance goals.
web, search: it has only one parameter: query (search statement).
Example:
('id'; xxx, "nane': 'web_search' 'argunents'; ('query': "xox" ")
puge:
parumeters: uri (valid and query (info extract).
Example:
('id'; : xxx, "name': 'craul_page' "arguments'; ('url's "xxx", 'query's "xox ))
code execute: it has only one parameter: code (Markdown snippet).
Example:
('id': xxx,
code_execute', orgunents': ('cade": "xxx')3
Rules:
Use 1-10 tools per step (each targeting distinct task part),
Each toal call must have cumplete, valid parameters.
Always prefer verifying accuracy with crawl_page after web_ scarch.
Timing: All steps except <plan>, <sumnary>, and <answer>.
<answer> Function:
Role: Deliver the final confirmed answer.
Rules:
Only goals are resolved.
Must consolidate
across goals.
language match task language.
Format Example:
<answer>
[Final Answer Content]
</answer>
Execution Rules (Critical)::
Paraliel Goals, Soquential Paths
No Farly Termination
Result Verification
Parallel Functions with Limited workers
Final Answer Condition
reasoning mode
Trajectory:
Reasoning Phase: Output <reasonings... //reasoonnng>> with detailed steps (=1000 words).
Answer Phuse: Present the final conclusion within canswer>... </answer>.
Detailed Function Specificatios:
<reasoning> Punction
<onswer> Function
Notes:
Do not return any other functions or tools.
Outpul sequence is always reusoming then answer.
Reasoning
words.
ii.. instant mode Specification:
Objective:
Rapidly salve lasks without tool usage complex reusoning.
Provide clear and relevant answers.
B. Detailed Function Spec:
<answer> Function only.
Executed immediately, no planning or tool-calling.
Notes:
Must not return other functions or tools.
Entire trajectory under
Important Tips:
(a) You should ohey the nule agent option you choose.
give an answer easily you absolutely sure. answer should possible detailed descriptions. For example, <answer>Be Benswer:Beijinn///eee iigejiing//assser.
I.2 SUMMARY PROMPT
CASE TRAJECTORY OF POST-RL MODEL ON SAME QUESTION ABOVE.
Target: Extract all content from web page that matches specific web search query, ensuring completeness and relevance. (No responscanalysis roquired.) web search query:
Clucs ideas:
Searched Web Page:
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Important Notes:
Summarize lables, lists, code blocks) concise points that directly address the query and clues, and ideas.
Preserve and list all relevant links ([ex)](url)) from web page.
Summarize three weh search query suery-related information, clues and idens-related information, and relevant links with descriptions.
relevant information exists, just outpur
information."
1.3
LLM JUDGE PROMPT
CASE TRAJECTORY OF POST-RL MODEL ON SAME QUESTION ABOVE.
You are an evaluation assistant. Please determine the predicted answer is equivalent to the labeled answer.
Question:
[question]
Laheled Answer: Ilaheled annwer)
Predicted Answer: (pred, answer)
Are these answers equivalent? Please respond with "Corret" they equivalent, or "Incorect" if they not equivalent. Do not any text.
J CASE STUDY
J.1 CASE STUDIES OF ROUTING BEHAVIOR
This section provides representative studies illustrating the behavior of A'FM's routing mechanism different query types. Routing errors refer to misalignment between the model-predicted mode (in- stan/reasooinn//genntie and the ground truth mode required to solve the query. Below, we detail the two most frequent error types, along with illustrative examples and post-optimization improvements. Through quantitative analysis the GATA dataset (supported by confusion matrix results) and qualitative inspection of misclassified queries, identify primary routing modes, accounting for over 90% of all routing failures.
J.1.1 -ERR-ASSIGNMNNT OF INSTANT MODE
This category captures where the model prematurely selects the instant mode despite the task requir- ing structured reasoning or external information access. These errors typically arise from over-confidence internal knowledge or failure to account for multi-step operations.
ROUTING BEHAVIOR UNDER MODEL TRAJECTORY
Qnery: making groery list for mom, who the vegetables from the list and alphabetize them as comma--sparated list.
of botany and very striet about eategorization. Please extract only
Ground Truth Mode: Reasoning.
Predicted Mode: This is straightforward grocery-item classification based on common edible-group definitions (vegetable vs and trequise external data, code execution, complex reasoning. aalassllcttooonnnsaaikksssssoooe
J.1.2 UNNECESSARY INVOCATION OF AGENTIC MODE
second category includes cases which the model invokes external tools queries solvable directly internal knowledge. These errors inflate computational cost without improving accuracy. APO substantially reduces such cases by penalizing non-instan mode usage on casy queries.
CASE TRAJECTORY OF POST-SFT MODEL ON EASY QUESTION.
Question: How many more blocks (also denoted as layers) in BERT base encoder than the encoder from the architecture proposed Attention All You Need?
Classification
This question asks for specific archisectural comparison (namber blocks/layers BERT Base Transformer in "Attention is All You Need") ) which requires precise factual lookup about model definitions. Because needs uccurate, up up-to- date model parameter information rather than simple common the agentic mode which query external sources is the appropriate choice.
cclassification= agentic mode <lelassifcation>
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First tool call round
Now it's step I, need to make a plan.
<plans
Goal I: Determine the number of encoder blocks (layers) in BERT Base
Path L.1: Search authoritative BERT paper and official codebase
Core approach: Use weh search to locate the BERT paper (Devlin et al., 2018) and the Hugzing Face official BERT model repository, then crawl page to extract the explicit encoder-layer count from the paper's experimental setup model configuration file.
Success: A direct citation or model config, line (e.g.. "num_ hidden_layers, X") explicitly stating the encoder block count for BERT Basc is extracted and saved.
Path 1.2 Query popular NLP model resources and documentation
Core approach: Use web search find Hugging, Face model card, Py Tyrrch/Transtin Tranf//ransermerr documentation, TensorFlow model card for BERT Basc, then crawl_page to extract the stated number of encoder blocks.
Success: At least one high quality model resource (madel card or documentation) explicilly lists the encoder block count for BERT Base
Path 1.3: Inspect canonical model configuration code (fallback)
Core appronch: Use web, search to locate the canonical configuration file or constructor in framework (eg.. cort-hase-uncased] and crawl_ page code _execuee read the model config extract the layer count pro- grammatically.
Success: The layer count is prgrammatically read from the model config and matches the value from Path 1.1 or 1.2.
Goul 2: Determine the number of encoder blucks (layers) in the Transformer architecture from "Atlention is All You Need'
Path 2.1:
Locate and extract from the original paper
(Vaswani al., 2017) and crawl_page to extract the encoder layer count from experimental setup or model configuration section (e Table/vections describing the base model).
Core approach: Use web, search to lind the original
Success: A direct cilation or explicit statement from the paper that gives the encuder block count for the base Transformer model extrated.
Path 2.2:
Consult autoritative implementations and model cards
Core approsch: Use web seurch to find official widely-used implementations (e.g. TensorFlow official mod- els, Hugging Face "transformer base" available, or the original repa) and crawl, page to extract the encoder block count.
prawe_page
Success: leust authoritative implementation expliciiy lists the encoder block count for the base Trans- former model from the paper.
Path 2.3: Verify via secondary lilerature reviews
Cure approuch: Use search find reviews, tutorials, or survey papers summarize architecture "Attention is All You Need" and extract the encoder layer count.
Success: Secondary sources correborate the encoder block count from the primary paper and provide consistent numbers.
Goal 3: Compute the difference (BERT Base encoder blocks minus Transformer base encoder blocks) and verify accuracy
Path 3.1:
Direct numeric subtraction and cross-check
Core approach: Aner Goals and return values, compute difference = (BERT Base encoder blocks) - (Trans- former encoder blocks). Verily consistency across at independent sources per model.
Sucvess: The computed difference is produced and matches across least independent sources for each model.
Path 3.2: Programmatic verification using model configs
Cure approach: Use execute to model configs symthetic conlig BERT Base and base Transformer and proganmatically extract the encoder layer counts, then compute the difference.
Success: Programmatic extraction yields the same layer counts and the computed difference equals the result from Path 3.1.
Path 3.3: Cite final difference with provenance
Core approach: Prepare concise statement of the difference and list the provenance (paper URLs, medel card links) that support the numbers used.
Success: short, unambiguous answer stating how many more blocks are in BERT Base than in the base Transformer, with least two ciied sources (one per model).
<splan>
Now it's step 2, need make tool calls. I will advance both goals in paraliel. For Goal I (BERT Base encoder blocks) the primary path is to locate the BERT paper and its model config: for Goal 2 (Transformer base encoder blocks) the primary path locate the 'Altention is All You Need" paper and its model conlig. I'll perform web searches for each paperimodel in purallel to find authoritaiive sources that the encoder layer counts. After receiving those search results I'll crawl the specific pages to extract the explicit number of encoder blocks/layrs for each model.
tool_calls.
<tool_ call>
(" l'name": : "web_seee bee__scrrch". "arguments": ( ("query": "BERT: Pre-training of Deep Bidirectional Transfomers for Language Under- standing number of encoder layers")
(" ('name": wweb_searche "arguments": ("query": "Attention All You Need number of encoder layers base model'))
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</tool_call>
resporesp
Results for Tool Call
Query: "BERT: Pre-training of Deep Bidiractional Transformers for Language Understanding number of encoder layers"
1. Source: arXiv a...v. 181.004800 (J. Devlin et al., 2018, Cited by 143.290)
BERT pre-trained deep bidirectional model, with line-tuning additional oulput layer (encoder layer count not expliciily stated here).
ACL Anibology: N19-1423/U. Devlin
143,290)
Key Content: Simular to the arXiv version; locuses on fine luning performance, no explicit encoder layer count.
Source: Medium Article
Key Content: Explicitly states: BERT-Base has 12 transformer layers, 12 atteation heads, and 110 million parameters, BERT-Large transfommer layers.
Source: Semantic Scholari
Content: Summarizes BERT's pro-training of bidirectional representations, explicit encoder layer
5. Source: Paperspace Blog
Content: Introduuess BERT's improvement fine luning techniques, explicit encoder count.
Results for Tool Call 2
Query: "Atteation All You Need number of encoder layers base model"
Total Results:
Source: NeurlPS 2017 Paper [A. Vaswani et al., Cited by 192,399)
The encoder composed stack identical layers (each multi head self-attention and feed-forward sublayers).
Content: Explicitly
Source: laxii 03766 2017)
Key Content: Introduces the Transformer as replacement for recurend/oovolatiinal models, explicit encoder
layer count.
3. Source: Wikipedin
decuder were both layers bidirrctional (misseppesentt Transformer's 's structure; original paper uses 6 encoder liyers).
Incorrectly mentions
Source: Medium (rticle (3 months
Key Content: Confirms the original paper's architecture: The encoder stack contains 6 identical layers, and the layers.
decuder
5. Source: [Pain English Al Blog) (Jul 12, 2025)
The encoder layers, consisting sublayers (multi- furward network).
feed-
respooesss
Second tool call round
will advance both goals in parallel using the primary path each: verify the encoder layer directly original papers (BERT and Attention Is All You Need') by crawlin the PDFs found in the sea_shhrch results. I'll extract the explicit statements layers, compute difference. the
encoder
the hase Transformer's (model cards oflicial repos) subsequent
contain the explicit counts, I'll use fallback
but first I'll attempt direct
extraction from the puper PDFs, calol_call>
("name": crawl page", 'arguments"s |'urt": "bmpp:/aasiivorrg// http://iiiir////////// 18.00440055.p0 pdf", "query": "number of encoder BERT Base, number of hidden layers, BERT-base encoder blocks"
I' c'rawwl page". "asguments":
"https//papers neurips wwtpppcpp////11-/o./ istpppprrr/I--tttttttoiiiiii yol-oo-e--tlyyy--eed-..t... 'query":
coder: The encuder is composed of stack of identical layers")
</hool call>
reso_sessp
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Results for Tool Call
Puge [lntpp//anwvv.rg/pllliiiit 10t.:p///.111.......0.. Summaryy:
multi- layer hidirectional Transformer paper layors (i.., Transformer blocks) as and the hidden size H.
architecture:
BERTBASE (commonly "BERT-Base") specifications:
Number of encoder layers Transformer blocks (L): 12
Hidden (H): 768
Number of self attention heads (A):
Food-forward/ilee size: 1H 3072
parameters:
(For context) BERTLARGE: I, 24, 1024, A parameters 340M.
Note: "layers" the paper explicidly refers to Transformer encoder blocks (i.e., encoder layers).
Results for Tool Call 2
Page [hatpss//ppperss cc/paper// i-s-ttentionn--alll--eee-e Summary:
Exact statement: "Encoder: The encoder compused stack of identical layers." (Section 3.1)
Encoder layer structure:
Each of the N layers has two sub-layers:
Multi-head self-atication mechanism.
Position-wise fully connected feed forward network.
Residual connections are applied
suh-layer, by layer normalization:
Output form: LayerNorm( 2 (ablnyen(xz))
All sub-Iayer and emhedding layers produce outputs of dimension daseel 512 to facilitate residual connections.
Feed-forward sub-layer details (applies each encoder layer):
FFN formala: FFN(2)) max(0, =W1 1 bb)lW: 2
Input/output dimensionalit:: dunden
Inner layer dimensionality: dur 2048
Can be seen two convolutions (position-wise, applied identically position).
Attention used in encoder:
Multi-head self-attention (encoder self-attention). queries, keys, values all come from the previous encoder layer's outputs: each position may attend all positions the previous layer.
Multi head attention configuration used in the paper:
heads
de d, duede /h 64
Related decoder note (parallel statement):
"Decoder: The decuder is also compused of stack of N identical layers."
Decoder layers include
sub-layers above third encodor-docoer multi-head attention sub-layer; decoder self-atientee atlentionn is masked prevent attending to subsequent positions.
Embeddings and dimension notes relevant encoder context:
Input tokens are embedded to vectors of dimension duovk 512.
Positional encodings (simusoddal) dimension stacks.
added to embeddings bottoms of encoder/deender
Model configurations (Table 3) confirming N for both base and big variants:
hase: 6, diands 512, dg 2048, h
big: N 6, drasd 1024, du 4096, ls 16
(These points
paper directly related "Encoder: The encoder composed stock of N = 6 identical layers" and the immediate technical details of those encoder layers.)
all
<ttoooeesponse>
Answer
advannced hoth goals parallel: (1) determine the number of encoder blocks (layers) in BERT Base, and (2) determine the number of encoder blocks in the Transfomer base model from Attention is All You Need". used crawl page to extract authoritative al., 2018) explicitly states
encoder layers, and the Attention is All You Need' NeurIPS PDF (Vaswani al., 2017) states the encoder is composed of stack of N identical layers. With both verified, computed the difference: 12 6 6. will now provide the final concise answer with
statements original papers:
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Answer
BERT Base has more encoder blocks/layess than the base Transformer encoder from 'Attention All You Need'
layers). Sources: puper (Devlin et al., 2018, arXiv: (810.04805) and 'Attention is All You Need' (Vaswani et al., 2017; NeurIPS 2017 PDF). </answer>
Base: encoder layers; AAllention Need' encoder:
CASE TRAJECTORY OF POST-RL MODEL ON SAME QUESTION ABOVE.
Question: How many more blocks (also denoted layers) in BERT base encoder than the encoder from the architecture proposed in Attentonn All You Need!
Classification
straightforward factual/comparison question
transformer architectures
original 'Altention
All You Need' encoder). It doesn't 't require web search or code execution; the information is commnn knowledge. Therefore the instant, mode, which handles factual queries, appropriate. <classification>
instant mode
<dolassificntion>
Answer
recall that BERT hase has 12 transformer encoder layers, while the original 'Attention is All You Need' used 6 encoder layers hase difference 12 layers.
Answer
<Lnswer>
layers
</answer>
J.2 AMBIGUOUS TASKS WITH MULTIPLE VALID MODES
Certain tasks naturally support more than one valid solution pathway, allowing both internal reasoning and externa retrieval. These ambiguous cases demonstrate flexibility of A A'FM: model may correctly answer the query via either the reasoning mode or the agentic mode, depending on context.
A representative example is the following high-level mathematical question:
"What the maximun Hausdorff dimension of a Sidon set in the reals between and I?"
This problem admits legitimate solution routes. First, it can be resolved entirely through internal mathemat- ical derivation, relying on properties of Sidon sets, sumset structure, and fractal dimension arguments making the reasoning mode appropriate. Sccond, the answer is also explicitly documented in external mathematical literature, allowing the agentic mode to retrieve the correct value via search or document access.
Both pathways yield same correct conclusion, and A A²FM is capable solving the query under either mode. These cases highlight that ambiguity in mode assignment inherent to the task itself, rather than a failure of the routing mechanism.
K TEMPLATE
L
USE OF LLMS
process writing this paper, large language models (LLMs) were employed solely as an auxiliary tool for expression polishing, with no involvement in core research parts such as study design, data collection, experimental analysis, or conclusion derivation.
After the LLM-assisted polishing, all content of the paper strictly reviewed and verified by the authors. The authors confirm that the scientific validity, accuracy of experimental results, and originality of the research conclusions were not affected by the LLM tool, entirely with the research team. We ensure its application complies with the academic ethics guidelines of the relevant field and the usage agreement of the tool itself.
the core intellectual contribution of the study remains
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Table 13: Templates used in training
Instant mode
Thinking mode <classification> reasoning <elassification>
Agentic mode
<classification> instant <elassification>
<classification> agentic <lasssifiittoonn
<answer>
<reasoning> [reasoning
<plan> {plan_ content)
(response) <lanswer>
[reasoning_content]] content) <freasoning>
</plan>
<answer> [response) </answer>
<tool_call>
[tnol_dicts]
</tool__all>>
<tooo_resposse>
(tool_obs)
<tool_response>
<summary>
(summary content)
</summary>
[response]
<lanswer>
28


Reviews:
[{"review_id": "MqIUkmGYDd", "reviewer": "Reviewer_S9XP", "summary": "The paper proposes A2FM (Adaptive Agent Foundation Model), a single-backbone model that routes each query to one of three execution modes—instant (direct answer), reasoning (explicit CoT), or agentic (tool-use)—and introduces an RL method, Adaptive Policy Optimization (APO), to jointly optimize routing and generation with a cost-aware reward. Training follows a “route-then-align” SFT stage and then APO. The agentic mode executes multiple tools in parallel and masks tool responses during SFT to avoid memorization. Reported results claim strong performance across agentic (BrowseComp, GAIA, XBench-DS), reasoning (AIME24/25, MATH500), and general (GPQA-d, SuperGPQA, MMLU-Pro, HLE) benchmarks, with improved “cost-of-pass.”", "questions": "How generalized is it to unseen tasks?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Clear problem framing (when-to-think/act): The unified routing over instant/reasoning/agentic is well-motivated and ties into efficiency concerns that are very good-framing and practical.\n- Methodical RL design: APO’s forced vs. adaptive rollouts via prefix injection plus an adaptive reward that penalizes non-instant modes on easy queries is a neat, concrete recipe. The multiplicative reward with format checks is pragmatically sound. \n- Routing and costs analysis: The paper measures routing accuracy (e.g., GAIA 92.2%, BrowseComp 94.0%, AIME 100%) and discusses where routing errs. This is interesting. Using cost-of-pass and showing adaptive < agentic < reasoning is useful for the community that cares about $/correct answer.", "weaknesses": "- The novelty claim would benefit from a sharper comparison to recent capability-aware routing, bimodal/bihybrid reasoning, and hybrid agent efforts. The core elements—routing head, SFT with tagged modes, RL with exploration across modes, and a length/cost-aware reward—feel like incremental synthesis rather than a conceptual leap.\n\n- The binary judge drives APO’s reward; while practical, it risks systematic bias and false positives/negatives, especially on agentic tasks where answers can be partially correct or depend on retrieval freshness. A small-scale human evaluation or rule-based metrics where available (e.g., AIME exact answers) to validate judge reliability and quantify disagreement will be helpful.\n\n- The ρ forced and γ adaptive rollouts are critical. Additional analyses about ρ, γ and show accuracy/efficiency trade-offs and training stability will be helpgul.\n\n- A deeper error analysis disentangling routing mistakes (wrong mode) from within-mode generation errors (bad tool plan, crawl failures, or code mistakes), and quantify the contribution of the parallel tool plan itself will be appreciated.\n\n- Lastly, how generalized is it to unseen tasks?", "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": "D84DJCiadI", "reviewer": "Reviewer_7Bhr", "summary": "The paper proposes A2FM, a single-backbone model that dynamically routes each query to one of three execution modes: instant (direct answer), reasoning (explicit CoT), and agentic (tool-use with planning). Training follows a two-stage route-then-align SFT and a reinforcement-learning phase called Adaptive Policy Optimization (APO) that rewards correctness while penalizing unnecessary complexity on “easy” queries. On diverse benchmarks, A2FM reports strong results at the 32B scale and improved cost-of-pass relative to pure reasoning or agentic execution.", "questions": "1. APO reward shaping. How sensitive is performance to τ (easy threshold) and α (penalty exponent)? Did you try adaptive τ per domain (math vs. web tasks) or curriculum schedules? Please provide more ablation like a grid or Bayesian sweep and stability metrics. \n\n2. KL-free on-policy choice. Removing KL can destabilize RLHF-like training. Did you observe mode collapse or reward hacking without KL? If so, what's the solution?\n\n3. Please add head-to-head ablations or analysis vs. recent “when-to-think / routing” methods and hybrid models (e.g., LHRM (Jiang et al., 2025), BPO (Yang et al., 2025b), AdaCoT (Lou et al., 2025), Search-R1 (Jin et al., 2025)). What incremental gain comes specifically from (i) three-way routing, (ii) the adaptive penalty, and (iii) prefix-enforced exploration?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. APO integrates (i) forced per-mode rollouts via prefix injection and (ii) an adaptive reward that explicitly favors instant answers when sufficient—this is a thoughtful twist on GRPO-style objectives for mode selection. \n\n2. Method is specified with a clean problem statement (router policy + mode policies) and a staged training recipe; the trajectory schemas and masked tool-responses (to avoid memorizing tool outputs) are sensible.\n\n3. Hybrid reasoning + tool-use is central for modern LLM systems. Showing that a single model can approximate the “best-mode oracle” while cutting cost is practically important for research and real-world deployment.", "weaknesses": "1. APO design ablations & hyperparameter sensitivity: APO drops KL, relies on on-policy sampling, and uses several knobs (forced rollouts, adaptive rollouts, “easy” threshold, exponent). Stability and sensitivity are unclear. Adding ablations and showing training curves and mode-collapse checks would be helpful.\n\n2. Router supervision and error analysis: Routing accuracy is reported on a few datasets; but failure modes (e.g., over-confidence in instant on BrowseComp; tool over-calling on easy Qs) deserve deeper analysis. Provide a confusion matrix (GT vs. chosen mode) per dataset, plus qualitative case studies where agentic and reasoning are both plausible.\n\n3. Generalization across backbones/scales: All results use Qwen2.5-32B-Instruct as the backbone, which may not demonstrate the generalizability. it will be good to include at least a small-scale reproduction (e.g., 7B/14B) and one alternate 32B backbone to demonstrate portability.\n\n4. Confidence intervals and seed variation are not reported for evaluation.", "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": "KDukx2bzJI", "reviewer": "Reviewer_6wai", "summary": "The paper proposes A²FM, a single-backbone model that routes each query to one of three execution modes—instant (short, direct answers), reasoning (explicit CoT), or agentic (tool-use + code)—and then aligns mode-conditioned trajectories via supervised fine-tuning (“route-then-align”). To further couple accuracy and efficiency, the authors introduce Adaptive Policy Optimization (APO): an RL scheme that (i) forces per-query rollouts in all modes with prefix injection to avoid mode collapse, and (ii) uses a cost-regularized, multiplicative reward that penalizes non-instant modes on “easy” queries, with correctness judged by an LLM-as-Judge. Experiments on agentic, reasoning, and general benchmarks at the 32B scale report competitive or SOTA results and a lower cost-of-pass than forcing a single mode.", "questions": "- The trajectory rules specify that the reasoning phase must be greater than 1000 words. What is the methodological justification for enforcing such a substantial minimum length?\n- The agentic mode’s parallel architecture is a unique design choice. Is there an ablation study quantifying the performance or efficiency improvements?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The method is about a unified 3-mode formulation with an explicit router under a shared backbone, representing clear contributions and problem setup. \n- The authors conducted breadth of evaluation across agentic (BrowseComp/GAIA/XBench-DS), reasoning (AIME24/25, MATH500), and general (GPQA-d, SuperGPQA, MMLU-Pro, HLE).", "weaknesses": "- Novelty relative to prior “when-to-think” / hybrid routing is under-differentiated. The core idea—self-routing among “instant vs. CoT vs. tool-use” with RL—resembles recent hybrid reasoning mode in GPT-5; the paper claims the “first adaptive agent foundation model” but should sharpen distinctions.\n- APO is a minor extension on GRPO with two straightforward additions: (i) forced rollouts per mode, and (ii) adaptive reward penalizing non-instant modes on easy queries. Neither represents significant technical innovation.\n- The proposed method seems to rely heavily on distillation from specialized teachers (DeepSeek R1 for reasoning, DeepSeek V3.1 for agentic/instant).", "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": "Q8MDLCYiIw", "reviewer": "Reviewer_2HMU", "summary": "This paper introduces the Adaptive Agent Foundation Model ($A^{2}FM$), a framework designed to unify reasoning-centric and agentic large language models (LLMs). The authors argue that existing models are inefficient, often overusing complex reasoning or external tools for simple queries.\n\nThe proposed solution, $A^{2}FM$, integrates three distinct execution modes within a single backbone:\n\nFor tasks requiring complex chain-of-thought processes.\nFor tasks that necessitate the use of external tools like web search or a code interpreter.\nFor simple queries that can be answered directly, minimizing computational cost.\n\n\nThe paper's primary contributions are:\n\n1. $A^{2}FM$ Framework: The first model to integrate agentic, reasoning, and instant modes under a unified backbone with a self-adaptive router.\n2. Adaptive Policy Optimization (APO): A novel reinforcement learning method with adaptive sampling and a cost-regularized reward system to jointly optimize for accuracy and efficiency.\n3. Demonstrated Efficiency: The adaptive execution model significantly reduces the cost per correct answer by 45.2% compared to the reasoning-only mode and 33.5% compared to the agentic-only mode, while maintaining comparable accuracy", "questions": "The paper presents a complex, multi-stage framework, and its success is impressive. To better isolate the impact of each novel component, could you provide additional details or ablation studies?\n\nThe primary contribution is the Adaptive Policy Optimization (APO) stage . To quantify its specific contribution, could you report the performance and efficiency (cost-of-pass) of the model after only the supervised \"route-then-align\" SFT stage? This would create a clear baseline to measure the exact improvements gained from the reinforcement learning phase.\n\nThe efficiency argument hinges on the \"instant mode\" preventing overthinking on simple queries. To validate this, have you considered an ablation where you train a two-mode variant ($A^{2}FM_{no-instant}$) with only the reasoning and agentic modes? Comparing its cost-of-pass to the full three-mode model on a benchmark with a mix of difficulties (like SuperGPQA) would provide direct evidence of the instant mode's value.\n\nAPO uses a cost-regularized reward to balance accuracy and efficiency. How critical is this specific reward design? What happens if the model is trained using APO with a reward function that only considers accuracy ($r_{acc}$)? Does the model learn to default to more powerful (and costly) modes, or does it still achieve some level of efficiency?\n\nThe APO stage involves several key hyperparameters, such as the number of forced vs. adaptive rollouts ($\\rho=3$, $\\gamma=3$) and the adaptive reward scaling factor ($\\alpha=2$). Could you comment on the model's sensitivity to these choices? A brief analysis would help future practitioners understand the robustness and tuning requirements of the APO method.\n\nThe accuracy reward is determined by an \"LLM-as-Judge\" . Could you please specify which model was used in this role? Moreover, a brief discussion on the potential biases or failure modes of this judge model would be valuable, as its reliability is critical to the quality of the reward signal during RL training.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The core strength of $A^{2}FM$ is its integration of three distinct operational modes (reasoning, agentic, instant) under a single, self-adaptive router. While prior work focused on switching between short and long chain-of-thought, this paper is novel in its inclusion of tool-use as a third, distinct modality.\n\nThe proposed Adaptive Policy Optimization (APO) is a creative and original extension of reinforcement learning for this specific task. It builds upon existing methods but introduces a tailored rollout strategy and a cost-regularized reward that explicitly teaches the model to balance accuracy with computational efficiency, which is a significant methodological advance.\n\nThe paper goes beyond simply reporting scores. It includes a deep analysis of the model's behavior, such as the accuracy of its mode routing on human-labeled data , its performance evolution during APO training , and a clear breakdown of how mode allocation changes with task difficulty.", "weaknesses": "The paper presents a two-stage training process (SFT then APO) but only reports the final performance . A crucial missing experiment is an evaluation of the model's performance after only the supervised fine-tuning (SFT) stage, before APO is applied. This would quantify the exact performance and efficiency gains attributable to the reinforcement learning phase, which is a central contribution.\n\nThe paper's primary motivation is to improve efficiency by avoiding overthinking on simple queries via an \"instant mode\". However, this claim is not tested directly. An ablation study training a two-mode variant (reasoning and agentic only) using the same APO framework would be highly informative. Comparing this two-mode model's performance and cost-of-pass against the three-mode $A^{2}FM$ would provide direct evidence for the efficiency gains provided by the instant mode.\n\nThe APO methodology introduces an adaptive, cost-regularized reward to balance accuracy and efficiency . The paper would be stronger if it included an experiment where the model is trained with APO using a reward function that only considers accuracy. This would demonstrate how essential the cost penalty is for learning efficient routing behavior.", "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)