agentic-predictor/index.html at main · DeepAuto-AI/agentic-predictor · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
<!DOCTYPE html>
<html>
<head>
  <meta charset="utf-8">
  <!-- Meta tags for social media banners, these should be filled in appropriatly as they are your "business card" -->
  <!-- Replace the content tag with appropriate information -->
  <meta name="description" content="DESCRIPTION META TAG">
  <meta property="og:title" content="SOCIAL MEDIA TITLE TAG"/>
  <meta property="og:description" content="SOCIAL MEDIA DESCRIPTION TAG TAG"/>
  <meta property="og:url" content="URL OF THE WEBSITE"/>
  <!-- Path to banner image, should be in the path listed below. Optimal dimenssions are 1200X630-->
  <meta property="og:image" content="static/image/your_banner_image.png" />
  <meta property="og:image:width" content="1200"/>
  <meta property="og:image:height" content="630"/>


  <meta name="twitter:title" content="TWITTER BANNER TITLE META TAG">
  <meta name="twitter:description" content="TWITTER BANNER DESCRIPTION META TAG">
  <!-- Path to banner image, should be in the path listed below. Optimal dimenssions are 1200X600-->
  <meta name="twitter:image" content="static/images/your_twitter_banner_image.png">
  <meta name="twitter:card" content="summary_large_image">
  <!-- Keywords for your paper to be indexed by-->
  <meta name="keywords" content="KEYWORDS SHOULD BE PLACED HERE">
  <meta name="viewport" content="width=device-width, initial-scale=1">


  <title>Agentic Predictor: Performance Prediction for Agentic Workflows via Multi-View Encoding</title>
  <link rel="icon" type="image/x-icon" href="static/images/favicon.ico">
  <link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
  rel="stylesheet">

  <link rel="stylesheet" href="static/css/bulma.min.css">
  <link rel="stylesheet" href="static/css/bulma-carousel.min.css">
  <link rel="stylesheet" href="static/css/bulma-slider.min.css">
  <link rel="stylesheet" href="static/css/fontawesome.all.min.css">
  <link rel="stylesheet"
  href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css">
  <link rel="stylesheet" href="static/css/index.css">

  <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
  <script src="https://documentcloud.adobe.com/view-sdk/main.js"></script>
  <script defer src="static/js/fontawesome.all.min.js"></script>
  <script src="static/js/bulma-carousel.min.js"></script>
  <script src="static/js/bulma-slider.min.js"></script>
  <script src="static/js/index.js"></script>
</head>
<body>


  <section class="hero">
    <div class="hero-body">
      <div class="container is-max-desktop">
        <div class="columns is-centered">
          <div class="column has-text-centered">
            <h1 class="title is-1 publication-title">Agentic Predictor<br>Multi-View Encoders for Performance Prediction in LLM-Based Agentic Workflows</h1>
            <div class="is-size-5 publication-authors">
              <!-- Paper authors -->
              <span class="author-block">
                <a href="https://itouchz.github.io" target="_blank">Patara Trirat</a><sup>1</sup>,</span>
                <span class="author-block">
                  <a href="https://wyjeong.notion.site/Wonyong-Jeong-53f439978dfe438e95736d60f0055f7f" target="_blank">Wonyong Jeong</a><sup>1</sup>,</span>
                  <span class="author-block">
                    <a href="http://www.sungjuhwang.com/" target="_blank">Sung Ju Hwang</a><sup>1,2</sup>
                  </span>
                  </div>

                  <div class="is-size-5 publication-authors">
                    <span class="author-block"><sup>1</sup>DeepAuto.ai, <sup>2</sup>KAIST<br>Seoul, South Korea<br>MAS @ ICML 2025 <strong>(Outstanding Paper 🏆)</strong><br><strong style="color: darkred">ICLR 2026</strong></span>
<!--                      <span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span> -->
                  </div>

                  <div class="column has-text-centered">
                    <div class="publication-links">
                         <!-- Arxiv PDF link -->
                      <span class="link-block">
                        <a href="https://openreview.net/forum?id=7oeKDZsmWp" target="_blank"
                        class="external-link button is-normal is-rounded is-dark">
                        <span class="icon">
                          <i class="fas fa-file-pdf"></i>
                        </span>
                        <span>Paper</span>
                      </a>
                    </span>

                    <!-- Supplementary PDF link -->
                    <span class="link-block">
                      <a href="static/pdfs/poster.pdf" target="_blank"
                      class="external-link button is-normal is-rounded is-dark">
                      <span class="icon">
                        <i class="fas fa-file-image"></i>
                      </span>
                      <span>Poster</span>
                    </a>
                  </span>

                    <span class="link-block">
                      <a href="static/pdfs/slides.pdf" target="_blank"
                      class="external-link button is-normal is-rounded is-dark">
                      <span class="icon">
                        <i class="fas fa-person-chalkboard"></i>
                      </span>
                      <span>Slides</span>
                    </a>
                  </span>

                  <!-- Github link -->
                  <span class="link-block">
                    <a href="https://github.com/DeepAuto-AI/agentic-predictor" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                    <span class="icon">
                      <i class="fab fa-github"></i>
                    </span>
                    <span>Code</span>
                  </a>
                </span>

                <!-- ArXiv abstract Link -->
                <span class="link-block">
                  <a href="https://arxiv.org/abs/2505.19764" target="_blank"
                  class="external-link button is-normal is-rounded is-dark">
                  <span class="icon">
                    <i class="ai ai-arxiv"></i>
                  </span>
                  <span>arXiv</span>
                </a>
              </span>
            </div>
          </div>
        </div>
      </div>
    </div>
  </div>
</section>


<!-- Paper abstract -->
<section class="section hero is-light">
  <div class="container is-max-desktop">
    <div class="columns is-centered has-text-centered">
      <div class="column is-four-fifths">
        <h2 class="title is-3">Abstract</h2>
        <div class="content has-text-justified">
          <p>
            Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but optimizing LLM-based agentic systems remains challenging due to the vast search space of agent configurations, prompting strategies, and communication patterns. Existing approaches often rely on heuristic-based tuning or exhaustive evaluation, which can be computationally expensive and suboptimal. This paper proposes Agentic Predictor, a lightweight predictor for efficient agentic workflow evaluation. Agentic Predictor is equipped with a multi-view workflow encoding technique that leverages multi-view representation learning of agentic systems by incorporating code architecture, textual prompts, and interaction graph features. To achieve high predictive accuracy while significantly reducing the number of required workflow evaluations for training a predictor, Agentic Predictor employs cross-domain unsupervised pretraining. By learning to approximate task success rates, Agentic Predictor enables fast and accurate selection of optimal agentic workflow configurations for a given task, significantly reducing the need for expensive trial-and-error evaluations. Experiments on a carefully curated benchmark spanning three domains show that our predictor outperforms state-of-the-art methods in both predictive accuracy and workflow utility, highlighting the potential of performance predictors in streamlining the design of LLM-based agentic workflows.
          </p>
        </div>
      </div>
    </div>
  </div>
</section>
<!-- End paper abstract -->


<!-- Paper poster -->
<section class="hero is-small is-light">
  <div class="hero-body">
    <div class="container">
      <h2 class="title">Poster</h2>

      <iframe  src="static/pdfs/poster.pdf" width="100%" height="550"></iframe>

      </div>
    </div>
  </section>
<!--End paper poster -->


<!--BibTex citation -->
  <section class="section" id="BibTeX">
    <div class="container is-max-desktop content">
      <h2 class="title">BibTeX</h2>
      <pre><code>
        @inproceedings{
trirat2026multiview,
title={Multi-View Encoders for Performance Prediction in {LLM}-Based Agentic Workflows},
author={Patara Trirat and Wonyong Jeong and Sung Ju Hwang},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=7oeKDZsmWp}
}
        </code></pre>
    </div>
</section>
<!--End BibTex citation -->


  <footer class="footer">
  <div class="container">
    <div class="columns is-centered">
      <div class="column is-8">
        <div class="content">

          <p>
            This page was built using the <a href="https://github.com/eliahuhorwitz/Academic-project-page-template" target="_blank">Academic Project Page Template</a> which was adopted from the <a href="https://nerfies.github.io" target="_blank">Nerfies</a> project page.
            You are free to borrow the source code of this website, we just ask that you link back to this page in the footer. <br> This website is licensed under a <a rel="license"  href="http://creativecommons.org/licenses/by-sa/4.0/" target="_blank">Creative
            Commons Attribution-ShareAlike 4.0 International License</a>.
          </p>

        </div>
      </div>
    </div>
  </div>
</footer>

<!-- Statcounter tracking code -->

<!-- You can add a tracker to track page visits by creating an account at statcounter.com -->

    <!-- End of Statcounter Code -->

  </body>
  </html>