added codespace ui

case-study-memory/memo_1/chat_history.json

@@ -0,0 +1,48 @@
+{
+  "timestamp": "2026-02-24T11:15:18.711819",
+  "workflow_type": "full",
+  "metadata": {
+    "workflow_type": "full",
+    "query": "Use the Kepler Exoplanet Dataset to train and iteratively refine a set of machine learning models that detects exoplanet transit signals from stellar light curves, autonomously generating and testing feature hypotheses (e.g., periodic dips, depth, duration, signal-to-noise ratio) to maximize classification performance.",
+    "path": null,
+    "data_path": "/private/var/folders/xm/j_y2w6zd2tnch_l4t705nly80000gn/T/scider_uploads/tmp2m5qv268/extracted",
+    "run_data": true,
+    "run_exp": true
+  },
+  "messages": [
+    {
+      "role": "assistant",
+      "content": "Hello. I can run ideation, data analysis, experiments, or a full workflow.\n\nPlease select a workflow type below to get started."
+    },
+    {
+      "role": "user",
+      "content": "Full Workflow: Use the Kepler Exoplanet Dataset to train and iteratively refine a set of machine learning models that detects exoplanet transit signals from stellar light curves, autonomously generating and testing feature hypotheses (e.g., periodic dips, depth, duration, signal-to-noise ratio) to maximize classification performance. (Data: /private/var/folders/xm/j_y2w6zd2tnch_l4t705nly80000gn/T/scider_uploads/tmp2m5qv268/extracted) [Data Analysis] [Experiment]"
+    },
+    {
+      "role": "assistant",
+      "content": "# Workflow Failed\n\nError: DataAgent failed: litellm.ServiceUnavailableError: GeminiException - {\n  \"error\": {\n    \"code\": 503,\n    \"message\": \"This model is currently experiencing high demand. Spikes in demand are usually temporary. Please try again later.\",\n    \"status\": \"UNAVAILABLE\"\n  }\n}\n",
+      "intermediate_state": [
+        {
+          "node_name": "ideation/literature_search",
+          "output": "Found 15 papers for query: 'Use the Kepler Exoplanet Dataset to train and iteratively refine a set of machine learning models that detects exoplanet transit signals from stellar light curves, autonomously generating and testing feature hypotheses (e.g., periodic dips, depth, duration, signal-to-noise ratio) to maximize classification performance.'\n\n1. Empirical Study of Observable Sets in Multiclass Quantum Classification - Paul San Sebastian, Mikel Cañizo, Roman Orus\n2. Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification - Fabian C. Peña, Steffen Herbold\n3. The Maximal Overlap Discrete Wavelet Scattering Transform and Its Application in Classification Tasks - Leonardo Fonseca Larrubia, Pedro Alberto Morettin, Chang Chiann\n4. New Directions in Text Classification Research: Maximizing The Performance of Sentiment Classification from Limited Data - Surya Agustian, Muhammad Irfan Syah, Nurul Fatiara\n5. Performance Improvement in Multi-class Classification via Automated Hierarchy Generation and Exploitation through Extended LCPN Schemes - Celal Alagoz\n6. Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities - Tianbao Yang\n7. Deep Dive into Semi-Supervised ELBO for Improving Classification Performance - Fahim Faisal Niloy, M. Ashraful Amin, AKM Mahbubur Rahman\n8. Performance landscape of resource-constrained platforms targeting DNNs - Panagiotis Miliadis, Christos-Savvas Bouganis, Dionisios Pnevmatikatos\n9. Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification - Kunal Pattanayak, Vikram Krishnamurthy\n10. Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification - Zhuoning Yuan, Yan Yan, Milan Sonka"
+        },
+        {
+          "node_name": "ideation/analyze_papers",
+          "output": "```toon\nresearch_domain: \"Use the Kepler Exoplanet Dataset to train and iteratively refine a set of machine learning models that detects exoplanet transit signals from stellar light curves, autonomously generating and testing feature hypotheses (e.g., periodic dips, depth, duration, signal-to-noise ratio) to maximize classification performance.\"\npapers_analyzed: 15\nsummary: \"Analyzed 15 papers in the domain of Use the Kepler Exoplanet Dataset to train and iteratively refine a set of machine learning models that detects exoplanet transit signals from stellar light curves, autonomously generating and testing feature hypotheses (e.g., periodic dips, depth, duration, signal-to-noise ratio) to maximize classification performance.. Use the LLM to generate detailed research ideas based on these papers.\"\npapers_text: \"Paper 1: Empirical Study of Observable Sets in Multiclass Quantum Classification\\nAuthors: Paul San Sebastian, Mikel Cañizo, Roman Orus\\nPublished: 2026-02-09T10:36:47Z\\nURL: https://arxiv.org/abs/2602.08485v1\\n\\nPaper 2: Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification\\nAuthors: Fabian C. Peña, Steffen Herbold\\nPublished: 2025-06-10T08:51:21Z\\nURL: https://arxiv.org/abs/2506.08581v1\\n\\nPaper 3: The Maximal Overlap Discrete Wavelet Scattering Transform and Its Application in Classification Tasks\\nAuthors: Leonardo Fonseca Larrubia, Pedro Alberto Morettin, Chang Chiann\\nPublished: 2025-05-23T20:45:58Z\\nURL: https://arxiv.org/abs/2506.12039v1\\n\\nPaper 4: New Directions in Text Classification Research: Maximizing The Performance of Sentiment Classification from Limited Data\\nAuthors: Surya Agustian, Muhammad Irfan Syah, Nurul Fatiara, Rahmad Abdillah\\nPublished: 2024-07-08T05:42:29Z\\nURL: https://arxiv.org/abs/2407.05627v1\\n\\nPaper 5: Performance Improvement in Multi-class Classification via Automated Hierarchy Generation and Exploitation through Extended LCPN Schemes\\nAuthors: Celal Alagoz\\nPublished: 2023-10-31T17:11:29Z\\nURL: https://arxiv.org/abs/2310.20641v1\\n\\nPaper 6: Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities\\nAuthors: Tianbao Yang\\nPublished: 2021-11-01T15:31:32Z\\nURL: https://arxiv.org/abs/2111.02400v1\\n\\nPaper 7: Deep Dive into Semi-Supervised ELBO for Improving Classification Performance\\nAuthors: Fahim Faisal Niloy, M. Ashraful Amin, AKM Mahbubur Rahman, Amin Ahsan Ali\\nPublished: 2021-08-29T02:44:03Z\\nURL: https://arxiv.org/abs/2108.12734v2\\n\\nPaper 8: Performance landscape of resource-constrained platforms targeting DNNs\\nAuthors: Panagiotis Miliadis, Christos-Savvas Bouganis, Dionisios Pnevmatikatos\\nPublished: 2021-07-21T12:33:21Z\\nURL: https://arxiv.org/abs/2107.10047v2\\n\\nPaper 9: Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification\\nAuthors: Kunal Pattanayak, Vikram Krishnamurthy\\nPublished: 2021-02-09T01:14:24Z\\nURL: https://arxiv.org/abs/2102.04594v3\\n\\nPaper 10: Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification\\nAuthors: Zhuoning Yuan, Yan Yan, Milan Sonka, Tianbao Yang\\nPublished: 2020-12-06T03:41:51Z\\nURL: https://arxiv.org/abs/2012.03173v2\\n\\nPaper 11: Classification with Costly Features in Hierarchical Deep Sets\\nAuthors: Jaromír Janisch, Tomáš Pevný, Viliam Lisý\\nPublished: 2019-11-20T08:15:09Z\\nURL: https://arxiv.org/abs/1911.08756v6\\n\\nPaper 12: Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification\\nAuthors: Han Bao, Masashi Sugiyama\\nPublished: 2019-05-29T14:59:36Z\\nURL: https://arxiv.org/abs/1905.12511v2\\n\\nPaper 13: Machine learning based localization and classification with atomic magnetometers\\nAuthors: Cameron Deans, Lewis D. Griffin, Luca Marmugi, Ferruccio Renzoni\\nPublished: 2017-10-18T09:08:11Z\\nURL: https://arxiv.org/abs/1710.06629v2\\n\\nPaper 14: A Unified View of Multi-Label Performance Measures\\nAuthors: Xi-Zhu Wu, Zhi-Hua Zhou\\nPublished: 2016-09-01T15:49:43Z\\nURL: https://arxiv.org/abs/1609.00288v2\\n\\nPaper 15: Optimal Decision-Theoretic Classification Using Non-Decomposable Performance Metrics\\nAuthors: Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit S. Dhillon\\nPublished: 2015-05-07T18:19:24Z\\nURL: https://arxiv.org/abs/1505.01802v1\"\n```"
+        },
+        {
+          "node_name": "ideation/generate_ideas",
+          "output": "## Research Title: Autonomous Feature Engineering and Model Refinement for Exoplanet Transit Detection in Kepler Data\n\n### Literature Review Summary\n- Number of papers reviewed: 15\n- Key themes identified:\n    - Maximizing classification performance (Papers 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15)\n    - Automated feature generation/selection (Papers 2, 5)\n    - Deep learning and AUC maximization (Papers 6, 10)\n    - Semi-supervised learning (Paper 7)\n    - Costly features and hierarchical classification (Paper 11)\n    - Non-decomposable performance metrics (Paper 15)\n    - Wavelet transforms for feature extraction (Paper 3)\n- Current state of research: The reviewed literature highlights a strong emphasis on improving classification performance across various domains, with several papers focusing on automated aspects of feature engineering and the use of advanced metrics. While papers touch upon classification performance and, to a lesser extent, automated feature generation (e.g., automated hierarchy generation in Paper 5, or Sentence-BERT for code comments in Paper 2), there's no direct exploration of *autonomously generating and testing feature hypotheses specifically for exoplanet transit detection in time-series light curve data*. The Kepler dataset, a rich source of such data, is not explicitly mentioned in relation to these automated feature engineering approaches.\n\n### Research Gaps Identified\n1.  **Lack of Domain-Specific Autonomous Feature Hypothesis Generation**: Most papers focus on general classification performance or automated features in specific domains (text, medical images, quantum). There's a gap in approaches that autonomously generate *physically meaningful feature hypotheses* relevant to astronomical time-series data, like exoplanet transits (e.g., transit shape, periodicity, out-of-transit behavior variations).\n2.  **Iterative Model Refinement Coupled with Feature Hypothesis Generation**: While some papers discuss improving classification, the concept of an iterative loop where the model's performance directly guides the generation and testing of *new* feature hypotheses (beyond predefined sets) for exoplanet detection is underexplored. This is crucial for complex datasets where subtle signals might be missed by initial feature sets.\n3.  **Integration of Diverse Feature Engineering Techniques for Exoplanet Transits**: The literature shows various feature extraction methods (e.g., wavelet transforms in Paper 3, deep learning embeddings in Papers 6, 10, 7). A gap exists in an integrated framework that can autonomously explore and combine different feature engineering strategies (traditional time-series features, signal processing techniques, learned features) to optimize exoplanet detection.\n4.  **Performance Metrics Tailored for Exoplanet Discovery**: While papers discuss AUC maximization (Papers 6, 10) and non-decomposable metrics (Paper 15), there's an opportunity to define and optimize for metrics specifically relevant to exoplanet discovery, considering the rarity of true positives and the cost of false positives in large surveys.\n\n### Proposed Research Ideas\n\n```json\n[\n  {\n    \"title\": \"Automated Transit Feature Hypothesis Generation for Exoplanet Detection\",\n    \"description\": \"Develop a framework that uses a meta-learning or reinforcement learning approach to autonomously generate novel feature hypotheses for exoplanet transit detection from Kepler light curves. The system would propose candidate features based on observed patterns (e.g., dips of varying duration and depth, periodicities, out-of-transit variations) and evaluate their predictive power. This would go beyond predefined features like 'dip duration' by exploring more complex, potentially non-linear combinations or transformations of the light curve data.\",\n    \"rationale\": \"Current exoplanet detection often relies on manually engineered features or standard feature sets. This idea addresses the gap in autonomously discovering domain-specific features that could capture subtle or unusual transit signatures missed by conventional methods. The Kepler dataset provides a large and well-characterized testbed.\",\n    \"potential_impact\": \"Enhanced accuracy and sensitivity in exoplanet detection, potentially leading to the discovery of previously missed exoplanets, and a novel methodology for feature engineering in time-series analysis.\",\n    \"related_papers\": [\"Paper 3\", \"Paper 5\"]\n  },\n  {\n    \"title\": \"Iterative Refinement of Transit Detection Models through Active Feature Hypothesis Testing\",\n    \"description\": \"Design an iterative learning system where a machine learning model (e.g., a deep neural network or an ensemble) is trained on Kepler light curves. Based on the model's performance and uncertainty, the system actively selects and tests new, autonomously generated feature hypotheses. If a new feature significantly improves performance (e.g., reduces false positives or increases true positives for specific types of signals), it is incorporated into the model. This creates a feedback loop for continuous model improvement.\",\n    \"rationale\": \"This addresses the gap of coupling model performance directly with feature hypothesis generation for iterative refinement. It moves beyond static feature sets to a dynamic system that adapts its feature space based on learning progress, crucial for challenging detection tasks like exoplanet transits.\",\n    \"potential_impact\": \"A more robust and adaptive exoplanet detection system, capable of handling diverse and complex light curve signals, and a new paradigm for building scientific detection models.\",\n    \"related_papers\": [\"Paper 4\", \"Paper 7\", \"Paper 15\"]\n  },\n  {\n    \"title\": \"Multi-Modal Feature Fusion and Optimization for Exoplanet Transit Classification\",\n    \"description\": \"Explore the autonomous generation and testing of feature hypotheses that combine signals from different representations of the light curve. This could include: 1) traditional time-series features, 2) features derived from signal processing techniques (e.g., wavelet scattering transforms as in Paper 3), and 3) learned features from deep neural networks (similar to Papers 6, 10). The system would learn to weigh and combine these diverse feature types optimally to maximize a relevant exoplanet discovery metric.\",\n    \"rationale\": \"This idea tackles the gap in integrating diverse feature engineering strategies. By autonomously exploring the 'feature space' across different methodologies, the system can identify synergistic combinations that outperform any single approach, leading to more comprehensive exoplanet detection.\",\n    \"potential_impact\": \"A more powerful and versatile exoplanet detection pipeline that can leverage the strengths of various feature extraction and representation techniques, leading to improved detection rates and characterization.\",\n    \"related_papers\": [\"Paper 2\", \"Paper 3\", \"Paper 6\", \"Paper 10\", \"Paper 11\"]\n  },\n  {\n    \"title\": \"Maximizing Exoplanet Discovery Metrics via Automated Feature Engineering in Kepler Data\",\n    \"description\": \"Focus on defining and optimizing for non-standard performance metrics relevant to exoplanet discovery (e.g., prioritizing the detection of Earth-like planets, minimizing false positives that require extensive follow-up, or optimizing for signal-to-noise ratio in candidate detection). Develop an automated feature engineering framework that specifically targets maximizing these defined metrics, potentially using techniques like surrogate loss optimization (Paper 12, 10).\",\n    \"rationale\": \"The 'Maximizing performance' theme is strong, but it needs to be tailored to the specific goals and challenges of exoplanet science. This idea addresses the gap by aligning automated feature engineering with discovery-oriented metrics, moving beyond generic accuracy or AUC.\",\n    \"potential_impact\": \"A more efficient exoplanet discovery process, reducing the burden of false positives and increasing the rate of scientifically valuable discoveries. It also contributes to the development of specialized performance metrics in astrophysical data analysis.\",\n    \"related_papers\": [\"Paper 6\", \"Paper 10\", \"Paper 12\", \"Paper 15\"]\n  }\n]\n```\n\n### Next Steps\n- **Papers to read in detail:**\n    - Papers focusing on automated feature engineering or selection (e.g., Paper 5).\n    - Papers discussing advanced performance metrics and optimization (e.g., Papers 6, 10, 12, 15).\n    - Papers on feature extraction for time-series data, particularly signal processing techniques (e.g., Paper 3).\n    - Research on meta-learning or reinforcement learning for feature generation.\n- **Experiments or analyses to validate ideas:**\n    - Implement an initial automated feature generation module (e.g., based on permutations, simple transformations, or signal characteristics).\n    - Integrate this module with a baseline exoplanet transit classifier.\n    - Develop an iterative loop where model performance guides feature generation.\n    - Experiment with different performance metrics tailored for exoplanet discovery.\n    - Benchmark the performance of the proposed autonomous system against traditional feature engineering approaches on the Kepler dataset.\n- **Potential collaborators or resources needed:**\n    - Domain experts in exoplanet detection and astrophysics.\n    - Expertise in machine learning, particularly meta-learning, reinforcement learning, and time-series analysis.\n    - Access to computational resources for training models and performing extensive hyperparameter tuning/feature exploration.\n    - A comprehensive Kepler light curve dataset pre-processed for transit detection."
+        },
+        {
+          "node_name": "ideation/novelty_check",
+          "output": "```json\n{\n  \"novelty_score\": 7.5,\n  \"breakdown\": {\n    \"uniqueness\": 2.5,\n    \"innovation\": 2.5,\n    \"gap_addressing\": 2.0,\n    \"potential_impact\": 3.0\n  },\n  \"feedback\": \"The proposed research ideas represent a solid advancement over the existing literature, particularly by focusing on the application of automated feature engineering and iterative model refinement to the specific domain of exoplanet transit detection using the Kepler dataset. While several papers discuss maximizing classification performance (Papers 1, 2, 4, 6, 10, 12, 15) and some explore automated feature aspects (Paper 5, Paper 2), none directly integrate these concepts with the autonomous generation of *physically meaningful feature hypotheses* tailored for astronomical time-series data like exoplanet transits. The iterative refinement aspect, where model performance directly drives the generation and testing of new features, is also a key innovative element not explicitly found in the reviewed works. The focus on domain-specific metrics (Idea 4) and multi-modal fusion (Idea 3) further enhances the novelty. The potential impact is high, as successfully automating feature engineering for exoplanet detection could significantly accelerate discovery and improve our understanding of planetary systems. The main area for potential enhancement could be in the novelty of the *specific methods* for autonomous generation (e.g., are the meta-learning/RL approaches truly novel, or standard applications?), but the *combination and application* are highly novel.\",\n  \"comparison_with_literature\": \"The reviewed literature provides a strong foundation in classification performance maximization and some initial steps towards automated feature engineering. Papers like 'The Maximal Overlap Discrete Wavelet Scattering Transform and Its Application in Classification Tasks' (Paper 3) introduce advanced feature extraction, and 'Performance Improvement in Multi-class Classification via Automated Hierarchy Generation...' (Paper 5) touches on automated aspects. However, these papers do not specifically address the problem of autonomously generating *hypotheses about physical transit characteristics* (e.g., dip morphology beyond simple shapes, periodicity over long baselines, variations in transit depth/duration) as features for exoplanet detection from light curves. The proposed ideas integrate these disparate elements (classification optimization, automated feature ideas, time-series analysis) into a cohesive framework specifically for exoplanet discovery using Kepler data, which is a significant step beyond the existing work. The focus on *iterative refinement driven by performance* and *domain-specific feature hypothesis generation* is a clear departure from the more static or general approaches found in the reviewed literature.\"\n}\n```"
+        },
+        {
+          "node_name": "ideation/ideation_report",
+          "output": "# Research Ideation Report\n\n## Research Topic\nUse the Kepler Exoplanet Dataset to train and iteratively refine a set of machine learning models that detects exoplanet transit signals from stellar light curves, autonomously generating and testing feature hypotheses (e.g., periodic dips, depth, duration, signal-to-noise ratio) to maximize classification performance.\n## Literature Review\n- Papers reviewed: 15\n- Papers analyzed: 0\n\n## Generated Research Ideas\n\n```json\n{\n  \"novelty_score\": 7.5,\n  \"breakdown\": {\n    \"uniqueness\": 2.5,\n    \"innovation\": 2.5,\n    \"gap_addressing\": 2.0,\n    \"potential_impact\": 3.0\n  },\n  \"feedback\": \"The proposed research ideas represent a solid advancement over the existing literature, particularly by focusing on the application of automated feature engineering and iterative model refinement to the specific domain of exoplanet transit detection using the Kepler dataset. While several papers discuss maximizing classification performance (Papers 1, 2, 4, 6, 10, 12, 15) and some explore automated feature aspects (Paper 5, Paper 2), none directly integrate these concepts with the autonomous generation of *physically meaningful feature hypotheses* tailored for astronomical time-series data like exoplanet transits. The iterative refinement aspect, where model performance directly drives the generation and testing of new features, is also a key innovative element not explicitly found in the reviewed works. The focus on domain-specific metrics (Idea 4) and multi-modal fusion (Idea 3) further enhances the novelty. The potential impact is high, as successfully automating feature engineering for exoplanet detection could significantly accelerate discovery and improve our understanding of planetary systems. The main area for potential enhancement could be in the novelty of the *specific methods* for autonomous generation (e.g., are the meta-learning/RL approaches truly novel, or standard applications?), but the *combination and application* are highly novel.\",\n  \"comparison_with_literature\": \"The reviewed literature provides a strong foundation in classification performance maximization and some initial steps towards automated feature engineering. Papers like 'The Maximal Overlap Discrete Wavelet Scattering Transform and Its Application in Classification Tasks' (Paper 3) introduce advanced feature extraction, and 'Performance Improvement in Multi-class Classification via Automated Hierarchy Generation...' (Paper 5) touches on automated aspects. However, these papers do not specifically address the problem of autonomously generating *hypotheses about physical transit characteristics* (e.g., dip morphology beyond simple shapes, periodicity over long baselines, variations in transit depth/duration) as features for exoplanet detection from light curves. The proposed ideas integrate these disparate elements (classification optimization, automated feature ideas, time-series analysis) into a cohesive framework specifically for exoplanet discovery using Kepler data, which is a significant step beyond the existing work. The focus on *iterative refinement driven by performance* and *domain-specific feature hypothesis generation* is a clear departure from the more static or general approaches found in the reviewed literature.\"\n}\n```\n\n## Novelty Assessment\n\n**Novelty Score: 7.50/10**\n\nThe proposed research ideas represent a solid advancement over the existing literature, particularly by focusing on the application of automated feature engineering and iterative model refinement to the specific domain of exoplanet transit detection using the Kepler dataset. While several papers discuss maximizing classification performance (Papers 1, 2, 4, 6, 10, 12, 15) and some explore automated feature aspects (Paper 5, Paper 2), none directly integrate these concepts with the autonomous generation of *physically meaningful feature hypotheses* tailored for astronomical time-series data like exoplanet transits. The iterative refinement aspect, where model performance directly drives the generation and testing of new features, is also a key innovative element not explicitly found in the reviewed works. The focus on domain-specific metrics (Idea 4) and multi-modal fusion (Idea 3) further enhances the novelty. The potential impact is high, as successfully automating feature engineering for exoplanet detection could significantly accelerate discovery and improve our understanding of planetary systems. The main area for potential enhancement could be in the novelty of the *specific methods* for autonomous generation (e.g., are the meta-learning/RL approaches truly novel, or standard applications?), but the *combination and application* are highly novel.\n\nComparison with Literature: The reviewed literature provides a strong foundation in classification performance maximization and some initial steps towards automated feature engineering. Papers like 'The Maximal Overlap Discrete Wavelet Scattering Transform and Its Application in Classification Tasks' (Paper 3) introduce advanced feature extraction, and 'Performance Improvement in Multi-class Classification via Automated Hierarchy Generation...' (Paper 5) touches on automated aspects. However, these papers do not specifically address the problem of autonomously generating *hypotheses about physical transit characteristics* (e.g., dip morphology beyond simple shapes, periodicity over long baselines, variations in transit depth/duration) as features for exoplanet detection from light curves. The proposed ideas integrate these disparate elements (classification optimization, automated feature ideas, time-series analysis) into a cohesive framework specifically for exoplanet discovery using Kepler data, which is a significant step beyond the existing work. The focus on *iterative refinement driven by performance* and *domain-specific feature hypothesis generation* is a clear departure from the more static or general approaches found in the reviewed literature.\n## Summary\nThis ideation report was generated through literature review of 15 academic papers.\nThe research ideas presented above are based on analysis of current research gaps and opportunities.\n"
+        }
+      ]
+    }
+  ]
+}

pyproject.toml

@@ -32,6 +32,7 @@ dependencies = [
     "rich>=14.2.0",
     "scikit-learn>=1.8.0",
     "streamlit>=1.30.0",
+    "streamlit-file-browser>=3.2.0",
     "watchdog>=3.0.0",
     "tiktoken>=0.12.0",
 ]

requirements-space.txt

@@ -30,5 +30,6 @@ requests-toolbelt==1.0.0
 rich>=14.2.0
 scikit-learn>=1.8.0
 streamlit==1.53.0
+streamlit-file-browser>=3.2.0
 tiktoken>=0.12.0
 watchdog>=3.0.0

streamlit-client/app.py

@@ -26,6 +26,11 @@ from scievo.workflows.data_workflow import DataWorkflow
 from scievo.workflows.experiment_workflow import ExperimentWorkflow
 from scievo.workflows.full_workflow_with_ideation import FullWorkflowWithIdeation
 
+try:
+    from streamlit_file_browser import st_file_browser
+except ImportError:
+    st_file_browser = None
+
 st.set_page_config(page_title="SciDER Chat", layout="centered")
 
 def get_theme_css() -> str:
@@ -712,6 +717,26 @@ with col4:
 
 st.divider()
 
+# Workspace Code File Browser - browse agent-generated files
+if st_file_browser is not None:
+    ws_path = Path(st.session_state.workspace_path).resolve()
+    ws_path.mkdir(parents=True, exist_ok=True)
+    with st.expander("📂 Workspace Files (Agent Code)", expanded=False):
+        st.caption(f"Browsing: `{ws_path}`")
+        _fb_event = st_file_browser(
+            str(ws_path),
+            key="workspace_file_browser",
+            show_choose_file=True,
+            show_download_file=True,
+            show_delete_file=False,
+            show_new_folder=False,
+            show_upload_file=False,
+            show_preview=True,
+        )
+else:
+    with st.expander("📂 Workspace Files (Agent Code)", expanded=False):
+        st.info("Install `streamlit-file-browser` to browse workspace files.")
+
 for m in st.session_state.messages:
     with st.chat_message(m["role"]):
         st.markdown(m["content"])