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            <h1 class="title is-1 publication-title" style="margin: 0 0 0 20px;"><img src="./static/images/principles.png" style="height: 60px; vertical-align: middle; margin-right: 5px; margin-bottom: 10px;">PRINCIPLES: Synthetic Strategy Memory for Proactive Dialogue Agents</h1>
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          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a href="#">Namyoung Kim,</a>
            </span>
            <span class="author-block">
              <a href="#">Kai Tzu-iunn Ong,</a>
            </span>
            <span class="author-block">
              <a href="#">Yeonjun Hwang</a>
            </span>
            <span class="author-block">
              <a href="#">Minseok Kang</a>
            </span>
            <br>
            <span class="author-block">
              <a href="#">Iiseo Jihn,</a>
            </span>
            <span class="author-block">
              <a href="#">Gayoung Kim,</a>
            </span>
            <span class="author-block">
              <a href="#">Minju Kim,</a>
            </span>
            <span class="author-block">
              <a href="#">Jinyoung Yeo</a>
            </span>
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          <div class="is-size-5 publication-authors">
            <span class="author-block">Department of Artificial Intelligence, Yonsei University</span>
          </div>

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        <h2 class="title is-3">Abstract</h2>
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          <p>
            Dialogue agents based on large language models (LLMs) have shown promising performance in proactive dialogue, which requires effective strategy planning. 
            However, existing approaches to strategy planning for proactive dialogue face several limitations: 
            limited strategy coverage, preference bias in planning, and reliance on costly additional training. 
            To address these, we propose <img src="static/images/principles.png" style="height: 1em; vertical-align: middle;"><b>PRINCIPLES</b>: a synthetic strategy memory for proactive dialogue agents. 
            PRINCIPLES is derived through offline self-play simulations and serves as reusable knowledge that guides strategy planning during inference, eliminating the need for additional training and data annotation. 
            We evaluate PRINCIPLES in both emotional support and persuasion domains, demonstrating consistent improvements over strong baselines. 
            Furthermore, PRINCIPLES maintains its robustness across extended and more diverse evaluation settings.
          </p>
          <p>
            <b>🏆 Accepted at EMNLP 2025 Findings</b>
          </p>
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</section>



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        <h2 class="title is-2">Methodology</h2>
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          <ul>
            <li id="step1-tab" class="is-active"><a href="#" onclick="showStep(1); return false;">Phase I: Principles Construction</a></li>
            <li id="step2-tab"><a href="#" onclick="showStep(2); return false;">Phase II: Principles-driven Strategy Planning</a></li>
          </ul>
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        <div id="step1-content" class="content step-content">
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            <img src="static/images/main_1.png" alt="Principles Construction">
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          <h3 class="title is-4">Step I: Success and Failure Detection</h3>
          <p>
            At each turn \( t \), the agent and the user simulator generate their responses, and a critic model assigns a scalar reward \( r_t \).  
            We determine the <code>status</code> as either success or failure by evaluating whether the reward is higher than the previous turn:
            \begin{equation}
\text{status}(s_t, a_t, u_t) = 
\begin{cases}
\text{1} & \text{if } r_t > r_{t-1} \\
\text{0} & \text{otherwise}
\end{cases}
\end{equation}
          </p>
          
          <h3 class="title is-4">Step II: Strategy Revision</h3>
          <p>
            Upon detecting a failure, the simulation invokes a revision step to refine the previously failed strategic decision.  
It then generates a revised strategy \(\sigma_t^{\prime}\) to re-simulate from the failure point, leveraging prior failed attempts at turn \(t\). Formally, the revised strategy is generated as:
\begin{equation}
\sigma_t^{\prime} = \texttt{LLM}_{\theta}(\rho_{r}; s_t, \mathcal{F}_t)
\end{equation}
where \(\rho_{r}\) is the revision prompt and \(\mathcal{F}_t\) denotes the set of previously failed trials at turn \(t\), defined as \( \mathcal{F}_t = \{ (\sigma_t^{1}, a_t^{1}, u_t^{1}), \dots, (\sigma_t^{n}, a_t^{n}, u_t^{n}) \} \)
where \(n\) is the maximum number of failed attempts. This failure history guides the model to avoid previously ineffective strategies.
          </p>

          <h3 class="title is-4">Step III: Re-simulation via Backtracking</h3>
          <p> 
            After generating a revised strategy \(\sigma_t^{\prime}\), the simulation backtracks to the original state \(s_t\) preceding the failure and re-simulates turn \(t\) using \(\sigma_t^{\prime}\). The agent generates a revised response \(a_t^{\prime}\), and the user simulator produces a new reply \(u_t^{\prime}\) based on the updated context.
            \begin{equation}
            a_t^{\prime} = \texttt{LLM}_{\theta}(\rho_{a}; s_t, \sigma_t^{\prime}) 
            \end{equation}
            \begin{equation}
            u_t^{\prime} = \texttt{LLM}_{\theta}(\rho_{u}; s_t, a_t^{\prime})
            \end{equation}

          </p>
          <h3 class="title is-4">Step IV: Principle Derivation</h3>
          <p> 
            If the corrected turn is re-evaluated as successful (<code>status</code> == 1), indicating a transition from failure to success, we derive a principle \( \tilde{p_t} \) as a result of overcoming the failure:
            \begin{equation}
            \tilde{p_t} = \texttt{LLM}_{\theta}(\rho_{\psi}; s_{t}, \mathcal{T}_t^{*}, \mathcal{F}_{t})
            \end{equation}
            where \(\rho_{\psi}\) is a prompt designed to extract a principle from failure, and the successful revised interaction is denoted as \(\mathcal{T}_t^{*} = (\sigma_t^{*}, a_t^{*}, u_t^{*})\). The extracted principle is then added to the principle set \(\mathcal{P}\):
            \begin{equation}
            \mathcal{P} \leftarrow \mathcal{P} \cup \{ \tilde{p_t} \}
            \end{equation}

          </p>
        </div>

        <div id="step2-content" class="content step-content" style="display: none;">
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            <img src="static/images/main_2.png" alt="Principles-driven Strategy Planning">
          </div>
          <p>
            To apply the extracted PRINCIPLES at inference time, we first identify candidate principles that closely match the current context. Since the <code>When</code> clause captures the core situation, we retrieve relevant top-\(k\) principles by comparing the current state \(s_t\) and the <code>When</code> clause using L2 distance between embedding vectors.
Only the <code>When</code> component of each principle is used to compute similarity, allowing us to identify contextually analogous dialogue situations across diverse scenarios. We denote the set of top-\(k\) retrieved principles as \( \Sigma_t = \{\sigma_1, \dots, \sigma_k\} \subset \mathcal{P} \). Since even within the same domain, retrieved principles may not directly align with the dialogue context, we perform a reinterpretation step. Formally, the reinterpreted principles \(\tilde{\Sigma}_t\) are generated as:
\begin{equation}
\tilde{\Sigma}_t = \texttt{LLM}_{\theta}(\rho_{\nu}; s_t, \Sigma_t)
\end{equation}
where \(\rho_{\nu}\) is a reinterpretation prompt designed to adapt retrieved principles \(\Sigma_t\) to the current context. This aligns each principle with the context.
          </p>


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        <h2 class="title is-2">Qualitative Example</h2>
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          <p>
            Qualitative example comparing AnE, PPDPP, and our approach based on PRINCIPLES. our
            approach tended to combine logical coherence and emotional empathy (i.e., Balanced Support).
          <div class="content has-text-centered">
            <img src="static/images/figure_case.png" alt="Qualitative example" style="width: 100%;">
          </div>
          </p>
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    <!-- </div> -->
    <!--/ Abstract. -->
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</section>


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        <h2 class="title is-2">Main Results</h2>
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          <p>
            We investigate our method’s effectiveness in addressing three key challenges in strategy planning: coverage, bias, and training. For more details, please refer to our paper.
          <div class="content has-text-centered">
            <img src="static/images/main_results.png" alt="Main results" style="width: 100%;">
          </div>
          </p>
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    <!-- </div> -->
    <!--/ Abstract. -->
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<!-- <section class="section" id="BibTeX">
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    <h2 class="title">BibTeX</h2>
    <pre><code>@article{park2021nerfies,
  author    = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
  title     = {Nerfies: Deformable Neural Radiance Fields},
  journal   = {<EMNLP>},
  year      = {2025},
}</code></pre>
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</section> -->