| Type | Status | Workspace | Updated | Summary |
|---|---|---|---|---|
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production_build
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failed | inventions/production-build-app-fastapi-service-for-rag-document-retrieval | 2026-03-13 08:55:24 | Built Python/FastAPI workspace: inventions/production-build-app-fastapi-service-for-rag-document-retrieval Delivery mode: Production Build Entrypoint: app.main:app Contract files: |
PYTHON SKILL EXPERIMENT: Creating a Type-Annotated and Packaged Python Application | status=Success | sources=https://docs.python.org/3/whatsnew/, https://docs.python.org/3/reference/, https://docs.python.org/3/library/
| Technique | Priority | Success | Runs | Best VRAM | Best TPS |
|---|---|---|---|---|---|
| ssm | 4.648 | 100.0% | 5 | 0.09 MB | 182857940.05 |
| linear | 4.375 | 100.0% | 14 | 0.00 MB | 176678439.93 |
| ssm_mamba | 4.283 | 100.0% | 2457 | 0.01 MB | 196608000000000.00 |
| throughput_optimization | 4.262 | 99.6% | 7384 | 0.00 MB | 196608000000000.00 |
| distillation | 4.215 | 100.0% | 342 | 0.09 MB | 16384000000000.00 |
| dynamic_precision | 4.211 | 100.0% | 152 | 0.02 MB | 3588136765.55 |
| cache | 4.195 | 99.5% | 186 | 0.01 MB | 14156365.55 |
| memory | 4.183 | 99.6% | 251 | 0.00 MB | 176678439.93 |
| Title | Hypothesis / Plan | Priority | Status | Attempts | Techniques |
|---|---|---|---|---|---|
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Sparse Attention and SSM Mamba Co-Design for Enhanced RAG Efficiency
|
Co-designing sparse attention with SSM Mamba will lead to reduced latency and improved memory usage in RAG systems.
Implement a prototype of sparse attention mechanism integrated with an existing SSM Mamba framework under different configurations. Benchmark for RAG tasks.
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8.66 | proposed | 0 | sparse_attention, ssm_mamba, rag |
|
Student hypothesis: ssm + linear co-design
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Combining ssm + linear + ssm_mamba will improve throughput or memory efficiency without breaking 8GB execution.
Create a compact comparative benchmark against a simple baseline, measure VRAM and tokens/sec, and isolate the effect of each ingredient.
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8.65 | succeeded | 1 | ssm, linear, ssm_mamba |
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ARES Mesh for Latency Reduction
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Using ARES, a context-aware approach to model orchestration substantially lowers system latency and VRAM usage when switching between different model architectures based on user query intensity.
Deploy an implementation of ARES mesh over simulated server environments. Evaluate improvements in handling high-traffic requests against typical setups without such dynamic routing.
|
8.64 | proposed | 0 | distillation, throughput_optimization, ares_mesh |
|
Sparse Attention and SSM Mamba Co-Design
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Integrating sparse attention with SSM Mamba models can significantly reduce memory footprint without a noticeable drop in performance.
Design and prototype new sparse kernels to evaluate their impact on large language model inferences.
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8.56 | proposed | 0 | sparse_pruning, ssm_mamba |
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Sparse Attention with SSM Mamba for Latency Reduction
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By leveraging SSM Mamba’s management capabilities alongside sparse attention, a prototype will yield faster generation speeds without compromising output quality.
Prototype an implementation that merges sparse attention with SSM Mamba on an application-oriented benchmark (such as conversational AI); analyze latency reduction vs. accuracy retention.
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8.49 | proposed | 0 | sparse_attention, ssm_mamba |
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Efficiency of Retrieval-Augmented Generation with SSM Mamba
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Combining state-spaces modeling (SSM) techniques with retrieval augmented generation (RAG) models can significantly improve recall and precision.
[{'step': 'Define an evaluation framework for testing efficiency', 'techniques': ['rag', 'ssm_mamba'], 'concept_combo': []}, {'step': 'Determine the effectiveness of this hybrid approach in terms of latency and accuracy metrics.', 'techniques': [], 'concept_combo': []}]
|
8.26 | proposed | 0 | knowledge_grounding, rag, ssm_mamba, throughput_optimization |
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Integrated Knowledge Grounding for Enhanced RAG Models
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Models with grounded knowledge exhibit superior reasoning capabilities, especially in complex dialog systems.
[Step 1] Integrate existing knowledge base (e.g., Wikipedia) into model training and inference. [Step 2] Test across various domains to ensure broad applicability.
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8.25 | proposed | 0 | knowledge_grounding, rag |
|
Effect of SSM Mamba and Linear Co-design on Code Generation Accuracy
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The combination of SSM Mamba and linear transformations in a model increases its ability to accurately generate syntactically correct source code.
[{'step': 'Develop prototype', 'techniques': ['ssm_mamba'], 'concept_combo': ['linear attention', 'SSM']}, {'step': 'Benchmark accuracy of code generation tasks', 'techniques': []}]
|
8.15 | proposed | 0 | ssm_mamba, linear_attention |
| Paper ID | Topic / Summary | Source | Score | Attempts | Action |
|---|---|---|---|---|---|
| No queued historic candidates. | |||||
| Invention | Summary / Files | Updated | Files | Source | Action |
|---|---|---|---|---|---|
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SSM State Recycling for Tooling
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SSM State Recycling for Tooling -> Success (score=2.20). Promote this line toward an invention brief.
.local_states.json, DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, ssm_state_recycling_for_tooling/__init__.py, ssm_state_recycling_for_tooling/agent.py, ssm_state_recycling_for_tooling/agent_context.py
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05-22 18:12 | 430 | student_autonomy | |
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CPU-Offloaded Tiered State Cache
|
Combine recent validated techniques into a productizable artifact.
.ares_cache_disk.bin, .cache_shelf, .gitignore, .invention_cache, cache_data, cache_storage.db, demo_cache.bin, demo_cache_db
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05-22 16:22 | 502 | student_autonomy | |
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Frequency-Modulated State Spaces (FMSS)
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Combine recent validated techniques into a productizable artifact.
DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, frequency_modulated_state_spaces_fmss/__init__.py, frequency_modulated_state_spaces_fmss/_state_space.py, frequency_modulated_state_spaces_fmss/agent.py, frequency_modulated_state_spaces_fmss/analysis.py
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05-22 13:27 | 375 | student_autonomy | |
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Delta-State Compression for Long Context
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Delta-State Compression for Long Context -> Success (score=6.50). Promote this line toward an invention brief.
cache.json, DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, delta_state_compression_for_long_context/__init__.py, delta_state_compression_for_long_context/algorithm.py, delta_state_compression_for_long_context/analysis.py
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05-20 09:49 | 391 | student_autonomy | |
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Tiered Precision State Cache (TPSC)
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Tiered Precision State Cache (TPSC) -> Success (score=6.58). Promote this line toward an invention brief.
DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, dist/tiered_precision_state_cache_tpsc-0.1.0.tar.gz, tiered_precision_state_cache_tpsc/__init__.py, tiered_precision_state_cache_tpsc/_cache.py, tiered_precision_state_cache_tpsc/analysis.py
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05-20 00:04 | 196 | student_autonomy | |
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Dynamic Precision State Skipping
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Combine recent validated techniques into a productizable artifact.
DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, dynamic_precision_state_skipping/__init__.py, dynamic_precision_state_skipping/adjuster.py, dynamic_precision_state_skipping/adjustment.py, dynamic_precision_state_skipping/analyzer.py
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05-19 21:03 | 558 | student_autonomy |