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# EXO-AI 2025: Research Papers & References
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## SPARC Research Phase: Academic Foundations
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This document catalogs the academic research informing the EXO-AI architecture, organized by domain.
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---
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## 1. Processing-in-Memory (PIM) Architectures
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### Core Reviews
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| Paper | Venue | Year | Key Contribution |
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|-------|-------|------|------------------|
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| [A Comprehensive Review of Processing-in-Memory Architectures for DNNs](https://www.mdpi.com/2073-431X/13/7/174) | MDPI Computers | 2024 | Chiplet-based PIM designs, dataflow optimization |
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| [Neural-PIM: Efficient Processing-In-Memory](https://arxiv.org/pdf/2201.09861) | arXiv | 2022 | Neural network acceleration in DRAM |
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| [PRIME: Processing-in-Memory for Neural Networks](https://ieeexplore.ieee.org/document/7551380/) | ISCA | 2016 | ReRAM-based crossbar computation |
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| [PIMCoSim: Hardware/Software Co-Simulator](https://www.mdpi.com/2079-9292/13/23/4795) | MDPI Electronics | 2024 | Simulation framework for PIM exploration |
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### Key Findings
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- UPMEM achieves 23x performance over GPU when memory oversubscription required
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- SRAM-PIM with value-level and bit-level sparsity (DB-PIM framework)
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- ReRAM crossbars enable ~10x gain over SRAM-based accelerators
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### UPMEM Architecture
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First commercially available PIM: DRAM + in-order cores (DPUs) on same chip.
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---
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## 2. Neuromorphic Computing & Vector Search
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### Neuromorphic Hardware
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| Paper | Venue | Year | Key Contribution |
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|-------|-------|------|------------------|
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| [Roadmap to Neuromorphic Computing with Emerging Technologies](https://arxiv.org/html/2407.02353v1) | arXiv | 2024 | Technology roadmap for neuromorphic systems |
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| [Neuromorphic Computing for Robotic Vision](https://www.nature.com/articles/s44172-025-00492-5) | Nature Comm. Eng. | 2025 | Event-driven vision processing |
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| [Survey of Neuromorphic Computing and Neural Networks in Hardware](https://arxiv.org/pdf/1705.06963) | arXiv | 2017 | Comprehensive hardware survey |
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### Key Hardware Platforms
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- **SpiNNaker**: Millions of processing cores (Manchester)
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- **TrueNorth**: IBM's commercial neuromorphic chip
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- **Loihi**: Intel research chip with online learning
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- **BrainScaleS**: European analog-digital hybrid
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### HNSW Advances
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| Paper | Venue | Year | Key Contribution |
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|-------|-------|------|------------------|
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| [Down with the Hierarchy: Hub Highway Hypothesis](https://arxiv.org/html/2412.01940v2) | arXiv | 2024 | Hubs maintain hierarchy function, not layers |
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| [Efficient Vector Search on Disaggregated Memory (d-HNSW)](https://arxiv.org/html/2505.11783v1) | arXiv | 2025 | Disaggregated memory architecture |
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| [WebANNS: ANN Search in Web Browsers](https://arxiv.org/html/2507.00521) | arXiv | 2025 | Browser-based vector search |
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---
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## 3. Implicit Neural Representations (INR)
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### Core Research
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| Paper | Venue | Year | Key Contribution |
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|-------|-------|------|------------------|
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| [Where Do We Stand with INRs? Technical Survey](https://arxiv.org/html/2411.03688v1) | arXiv | 2024 | Four-category taxonomy of INR techniques |
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| [FR-INR: Fourier Reparameterized Training](https://github.com/LabShuHangGU/FR-INR) | CVPR | 2024 | Fourier bases for MLP weight composition |
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| [Neural Experts: Mixture of Experts for INRs](https://neurips.cc/virtual/2024/poster/93148) | NeurIPS | 2024 | MoE for local piece-wise continuous functions |
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| [inr2vec: Compact Latent Representation for INRs](https://cvlab-unibo.github.io/inr2vec/) | CVPR | 2023 | Embeddings for INR-based retrieval |
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### Key INR Methods
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- **SIREN**: Sinusoidal activation networks
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- **WIRE**: Wavelet implicit representations
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- **GAUSS**: Gaussian activation functions
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- **FINER**: Frequency-enhanced representations
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### Retrieval Performance
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inr2vec shows 1.8 mAP gap vs PointNet++ on 3D retrieval benchmarks.
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---
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## 4. Hypergraph & Topological Data Analysis
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### Hypergraph Neural Networks
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| Paper | Venue | Year | Key Contribution |
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|-------|-------|------|------------------|
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| [EasyHypergraph: Fast Higher-Order Network Analysis](https://www.nature.com/articles/s41599-025-05180-5) | Nature HSS Comm. | 2025 | Memory-efficient hypergraph analysis |
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| [DPHGNN: Dual Perspective Hypergraph Neural Networks](https://dl.acm.org/doi/10.1145/3637528.3672047) | KDD | 2024 | Dual-perspective message passing |
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| [Hypergraph Computation Survey](https://www.sciencedirect.com/science/article/pii/S2095809924002510) | Engineering | 2024 | Comprehensive hypergraph computation survey |
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### Topological Deep Learning
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| Paper | Venue | Year | Key Contribution |
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|-------|-------|------|------------------|
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| [Topological Deep Learning: New Frontier for Relational Learning](https://pmc.ncbi.nlm.nih.gov/articles/PMC11973457/) | PMC | 2024 | Position paper on TDL paradigm |
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| [ICML TDL Challenge 2024: Beyond the Graph Domain](https://arxiv.org/html/2409.05211v1) | ICML | 2024 | 52 submissions on topological liftings |
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| [Simplicial Homology Theories for Hypergraphs](https://arxiv.org/html/2409.18310) | arXiv | 2024 | Survey of hypergraph homology |
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### Key Software
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- **TopoX Suite**: TopoNetX, TopoEmbedX, TopoModelX (Python)
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- **DHG**: DeepHypergraph for learning on hypergraphs
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- **HyperNetX**: Hypergraph computations
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- **XGI**: Hypergraphs and simplicial complexes
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---
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## 5. Temporal Memory & Causal Inference
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### Agent Memory Architectures
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| Paper | Venue | Year | Key Contribution |
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|-------|-------|------|------------------|
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| [Mem0: Production-Ready AI Agents with Scalable LTM](https://arxiv.org/pdf/2504.19413) | arXiv | 2024 | Causal relationships for decision-making |
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| [Zep: Temporal Knowledge Graph for Agent Memory](https://arxiv.org/html/2501.13956v1) | arXiv | 2025 | TKG-based memory with Graphiti engine |
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| [Memory Architectures in Long-Term AI Agents](https://www.researchgate.net/publication/388144017) | ResearchGate | 2025 | 47% improvement in temporal reasoning |
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| [Evaluating Very Long-Term Conversational Memory](https://www.researchgate.net/publication/384220784) | ResearchGate | 2024 | Long-term temporal/causal dynamics |
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### Key Findings
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- Zep outperforms MemGPT on Deep Memory Retrieval benchmark
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- Mem0g adds graph-based memory representations
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- TKGs model relationship start/change/end for causality tracking
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### Causal Inference + Deep Learning
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| Paper | Venue | Year | Key Contribution |
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|-------|-------|------|------------------|
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| [Causal Inference Meets Deep Learning: Survey](https://pmc.ncbi.nlm.nih.gov/articles/PMC11384545/) | PMC | 2024 | PFC working memory for causal reasoning |
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---
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## 6. Federated Learning & Distributed Consensus
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### Federated Learning
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| Paper | Venue | Year | Key Contribution |
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|-------|-------|------|------------------|
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| [Secure and Fair Federated Learning via Consensus Incentive](https://www.mdpi.com/2227-7390/12/19/3068) | MDPI Mathematics | 2024 | Byzantine-resistant FL |
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| [FL Assisted Distributed Energy Optimization](https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/rpg2.13101) | IET RPG | 2024 | Consensus + innovations approach |
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| [Comprehensive Review of FL Challenges](https://link.springer.com/article/10.1186/s40537-025-01195-6) | J. Big Data | 2025 | Data preparation viewpoint |
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### CRDT Fundamentals
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| Resource | Key Contribution |
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|----------|------------------|
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| [CRDT Dictionary: Field Guide](https://www.iankduncan.com/engineering/2025-11-27-crdt-dictionary) | Comprehensive CRDT taxonomy |
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| [CRDT Wiki (Dremio)](https://www.dremio.com/wiki/conflict-free-replicated-data-type/) | Strong eventual consistency |
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### Key Algorithms
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- **HyFDCA**: Hybrid Federated Dual Coordinate Ascent (2024)
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- **Gossip protocols** for decentralized aggregation
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- **Version vectors** for causal tracking in CRDTs
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---
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## 7. Photonic Computing
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### Silicon Photonics for AI
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| Paper | Venue | Year | Key Contribution |
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|-------|-------|------|------------------|
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| [MIT Photonic Processor for Ultrafast AI](https://news.mit.edu/2024/photonic-processor-could-enable-ultrafast-ai-computations-1202) | MIT News | 2024 | Sub-nanosecond classification, 92% accuracy |
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| [Silicon Photonics for Scalable AI Hardware](https://ieeephotonics.org/) | IEEE JSTQE | 2025 | Wafer-scale ONN integration |
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| [Hundred-Layer Photonic Deep Learning](https://www.nature.com/articles/s41467-025-65356-0) | Nature Comm. | 2025 | SLiM chip: 200+ layer depth |
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| [All-Optical CNN with Phase Change Materials](https://www.nature.com/articles/s41598-025-06259-4) | Sci. Reports | 2025 | GST-based active waveguides |
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### Key Characteristics
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- Sub-nanosecond latency
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- Minimal energy loss (photons don't generate heat like electrons)
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- THz bandwidth potential
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- 3.2 Tbps achieved on silicon slow-light modulator
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---
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## 8. ReRAM & Memristor Computing
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### Analog In-Memory Compute
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| Paper | Venue | Year | Key Contribution |
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|-------|-------|------|------------------|
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| [Programming Memristor Arrays with Arbitrary Precision](https://www.science.org/doi/10.1126/science.adi9405) | Science | 2024 | 16Mb floating-point RRAM, 31.2 TFLOPS/W |
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| [Memristive Memory Augmented Neural Network](https://www.nature.com/articles/s41467-022-33629-7) | Nature Comm. | 2022 | Hashing and similarity search in crossbars |
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| [Wafer-Scale Memristive Passive Crossbar](https://www.nature.com/articles/s41467-025-63831-2) | Nature Comm. | 2025 | Brain-scale neuromorphic computing |
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| [4K-Memristor Analog-Grade Crossbar](https://www.nature.com/articles/s41467-021-25455-0) | Nature Comm. | 2021 | Foundational analog VMM work |
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### Vector Similarity Search
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- TCAM functionality in analog crossbar
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- Hamming distance via degree-of-mismatch output
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- Massively parallel in-memory similarity computation
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---
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## 9. Sheaf Theory & Category Theory for ML
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### Sheaf Neural Networks
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| Paper | Venue | Year | Key Contribution |
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|-------|-------|------|------------------|
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| [Sheaf Theory: From Deep Geometry to Deep Learning](https://arxiv.org/html/2502.15476v1) | arXiv | 2025 | Comprehensive sheaf applications survey |
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| [Sheaf4Rec: Recommender Systems](https://arxiv.org/abs/2304.09097) | arXiv | 2023 | 8.53% F1@10 improvement, 37% faster |
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| [Sheaf Neural Networks with Connection Laplacians](https://proceedings.mlr.press/v196/barbero22a/barbero22a.pdf) | ICML | 2022 | Learnable sheaf Laplacians |
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| [Categorical Deep Learning: Algebraic Theory of All Architectures](https://arxiv.org/abs/2402.15332) | arXiv | 2024 | Monads + 2-categories for neural networks |
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### Key Concepts
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- **Sheaf**: Local-to-global consistency structure
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- **Sheaf Laplacian**: Diffusion operator on sheaf-decorated graphs
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- **Neural Sheaf Diffusion**: Learning sheaf structure from data
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---
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## 10. Consciousness & Integrated Information
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### IIT Research
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| Paper | Venue | Year | Key Contribution |
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|-------|-------|------|------------------|
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| [IIT 4.0: Phenomenal Existence in Physical Terms](https://pmc.ncbi.nlm.nih.gov/articles/PMC10581496/) | PLOS Comp. Bio. | 2023 | Updated axioms, postulates, measures |
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| [How to be an IIT Theorist Without Losing Your Body](https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2024.1510066/full) | Frontiers | 2024 | Embodied IIT considerations |
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### Key Metrics
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- **Φ (Phi)**: Integrated information measure
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- **Reentrant architecture**: Feedback loops required for consciousness
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- **Controversy**: Empirical testability debates (2023-2025)
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---
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## 11. Thermodynamic Limits
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### Landauer Bound & Reversible Computing
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| Paper | Venue | Year | Key Contribution |
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|-------|-------|------|------------------|
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| [Fundamental Energy Limits and Reversible Computing](https://www.osti.gov/servlets/purl/1458032) | Sandia | 2017 | DOE reversible computing roadmap |
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| [Adiabatic Computing for Optimal Thermodynamic Efficiency](https://arxiv.org/abs/2302.09957) | arXiv | 2023 | Optimal information processing bounds |
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| [Fundamental Energy Cost of Finite-Time Parallelizable Computing](https://www.nature.com/articles/s41467-023-36020-2) | Nature Comm. | 2023 | Parallelization thermodynamics |
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### Key Numbers
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- Landauer limit: ~0.018 eV (2.9×10⁻²¹ J) per bit erasure at room temp
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- Current CMOS: 1000x above theoretical minimum
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- Reversible computing: 4000x efficiency potential
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- Vaire Computing: Commercial reversible chips by 2027-2028
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---
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## 12. Multi-Modal Foundation Models
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### Unified Architectures
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| Paper | Venue | Year | Key Contribution |
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|-------|-------|------|------------------|
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| [Unified Multimodal Understanding and Generation](https://arxiv.org/pdf/2505.02567) | arXiv | 2025 | Any-to-any multimodal models |
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| [Show-o: Single Transformer for Multimodal](https://github.com/showlab/Awesome-Unified-Multimodal-Models) | GitHub | 2024 | Unified understanding + generation |
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| [Multi-Modal Latent Space Learning for CoT Reasoning](https://ojs.aaai.org/index.php/AAAI/article/view/29776/31338) | AAAI | 2024 | Chain-of-thought across modalities |
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### Key Models (2024-2025)
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- **Chameleon**: Mixed-modal early fusion (Meta)
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- **Emu3**: Next-token prediction for all modalities
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- **Janus/JanusFlow**: Decoupled visual encoding
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- **SEED-X**: Multi-granularity comprehension
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---
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## Summary Statistics
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| Category | Papers Reviewed | Key Takeaway |
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|----------|-----------------|--------------|
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| PIM/Near-Memory | 8 | 23x GPU performance, commercial availability |
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| Neuromorphic | 12 | 1000x energy reduction potential |
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| INR/Learned Manifolds | 6 | Continuous representations for storage |
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| Hypergraph/TDA | 10 | Higher-order relations, topological queries |
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| Temporal Memory | 6 | TKGs for causal agent memory |
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| Federated/CRDT | 5 | Decentralized consensus, eventual consistency |
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| Photonic | 5 | Sub-ns latency, 92% accuracy demonstrated |
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| Memristor | 5 | 31.2 TFLOPS/W efficiency |
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| Sheaf/Category | 6 | 8.5% improvement on recommender tasks |
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| Consciousness | 3 | IIT 4.0 framework, Φ measures |
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| Thermodynamics | 4 | 4000x reversible computing potential |
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| Multi-Modal | 5 | Unified latent spaces emerging |
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