Aditi Khare — AI Product & Research Leader
Writing on AI research, product thinking, and system architecture
🌐 Website: aditikhare.com
🔗 GitHub Repository: Github
🤗 Live Demo: View on Hugging Face
💼 LinkedIn: Aditi Khare
Research Interests
🔷 Generative AI · 🔷 Agentic AI
| Paper | Focus | Link | Category |
|---|---|---|---|
| Multi-Sourced Multi-Agent Evidence Retrieval for Fact-Checking | Multi-agent reasoning + retrieval pipelines | https://arxiv.org/abs/2603.00267 | Agentic AI |
| TraderBench: Robustness of AI Agents in Adversarial Markets | Evaluating agent robustness in real-world environments | https://arxiv.org/abs/2603.00285 | Agentic AI |
| DIG: Scaling Agent Collaboration via Explainable Decision Paths | Multi-agent coordination + explainability | https://arxiv.org/abs/2603.00309 | Agentic AI |
| ARC-AGI-3 Benchmark | Benchmark for general agentic intelligence | https://arxiv.org/abs/2603.24621 | Agentic AI |
| Generative AI for Quantum Circuits & Code | GenAI for program synthesis + evaluation gaps | https://arxiv.org/abs/2603.16216 | Generative AI |
| Attack & Defense Landscape of Agentic AI (Survey) | Security risks, threats, guardrails in agents | https://arxiv.org/abs/2603.11088 | Agentic AI |
| Paper | Focus | Link | Category |
|---|---|---|---|
| The Auton Agentic AI Framework | Standardized architecture for agent systems (memory, MCP, POMDP) | https://arxiv.org/abs/2602.23720 | Agentic AI |
| Supporting Software Engineering Tasks with Agentic AI | Multi-agent systems for coding, retrieval & QA | https://arxiv.org/abs/2602.04726 | Agentic AI |
| The 2025 AI Agent Index | Benchmarking deployed agentic systems & safety | https://arxiv.org/abs/2602.17753 | Agentic AI |
| Paper | Focus | Link | Category |
|---|---|---|---|
| Toward Agentic AI: Task-Oriented Communication for Hierarchical Planning of Long-Horizon Tasks | Hierarchical planning & communication in agents | https://arxiv.org/abs/2601.13685 | Agentic AI |
| Generative Intent Prediction Agentic AI for Edge Orchestration | Generative intent modeling + proactive agents | https://arxiv.org/abs/2601.13694 | Agentic AI |
| The Path Ahead for Agentic AI: Challenges and Opportunities | Evolution from GenAI → autonomous agents | https://arxiv.org/abs/2601.02749 | Agentic AI |
| Paper | Focus | Link | Category |
|---|---|---|---|
| Copyright Detection in LLMs | Memorization risks | https://arxiv.org/abs/2511.20623 | Generative AI |
| Beyond Automation – Governance in GenAI | Governance & work | https://arxiv.org/abs/2512.11893 | Generative AI |
| AgentEval | Evaluating agents | https://arxiv.org/abs/2512.08273 | Agentic AI |
| Paper | Focus | Link | Category |
|---|---|---|---|
| GenAI × Extended Reality | XR + GenAI | https://arxiv.org/abs/2511.03282 | Generative AI |
| GenAI in Qualitative Research | Methods & risks | https://arxiv.org/abs/2511.08461 | Generative AI |
| Safety Guardrails | Alignment & safety | https://arxiv.org/abs/2511.15732 | Generative AI |
| Paper | Focus | Link | Category |
|---|---|---|---|
| Generative AI – Deep Survey | Models & use cases | https://arxiv.org/pdf/2510.21887 | Generative AI |
| GenAI & Scientific Writing | Empirical study | https://arxiv.org/abs/2510.17882 | Generative AI |
| Chronologically Consistent GenAI | Temporal consistency | https://arxiv.org/abs/2510.11677 | Generative AI |
| Paper / Model | Focus | Link | Category |
|---|---|---|---|
| DeepSeek-V3 | Open LLM | https://github.com/deepseek-ai/DeepSeek-V3 | Generative AI |
| Inference-Time Self-Improvement | Self-refining LLMs | https://arxiv.org/pdf/2412.14352 | Generative AI |
| Modern BERT | NLP advances | https://arxiv.org/abs/2412.13663 | NLP |
| Paper / Tool | Focus | Link | Category |
|---|---|---|---|
| OpenAI Swarm | Multi-agent workflows | https://github.com/openai/swarm | Agentic AI |
| Claude 3.5 | Reasoning & multimodality | https://www.anthropic.com/news/3-5-models-and-computer-use | Generative AI |
| Paper / Tool | Focus | Link | Category |
|---|---|---|---|
| Llama 3.2 | Edge AI & vision | https://www.llama.com | Edge AI |
| Self-Correction via RL | Reasoning | https://arxiv.org/abs/2409.12917 | Generative AI |
| Iteration of Thought | Inner dialogue | https://arxiv.org/abs/2409.12618 | Generative AI |
| OpenAI o1 | Reasoning models | https://openai.com/index/introducing-openai-o1-preview | Generative AI |
| AutoGen Studio | Agent orchestration | https://github.com/microsoft/autogen | Agentic AI |
| Strategic CoT | Advanced reasoning | https://arxiv.org/abs/2409.03271 | Generative AI |
| RAG Noise | Retrieval robustness | https://arxiv.org/abs/2408.13533 | Generative AI |
| GameGAN | Simulated worlds | https://github.com/nv-tlabs/GameGAN_code | Generative AI |
| Agentic RAG | Time-series RAG | https://arxiv.org/abs/2408.14484 | Generative AI |
| Paper | Focus | Link | Category |
|---|---|---|---|
| The AI Scientist | Automated discovery | https://paperswithcode.com/paper/the-ai-scientist-towards-fully-automated-open | Generative AI |
| ControlNeXt | Video & image control | https://arxiv.org/pdf/2408.06070v2 | Computer Vision |
| RAG-Checker | RAG diagnostics | https://arxiv.org/abs/2408.08067 | Generative AI |
| Paper | Focus | Link | Category |
|---|---|---|---|
| Microsoft SAMBA | Efficient LLMs | https://arxiv.org/pdf/2406.07522 | Generative AI |
| Quantinuum Quixer | Quantum transformers | https://openai.com/index/extracting-concepts-from-gpt-4 | Quantum AI |
| No Language Left Behind | Multilingual translation | https://github.com/facebookresearch/fairseq/tree/nllb | Generative AI |
| Paper / Model | Focus | Link | Category |
|---|---|---|---|
| Meta Llama-3 | Long-context LLMs | https://huggingface.co/papers/2404.19553 | Generative AI |
| Multi-Token Prediction | Faster inference | https://arxiv.org/abs/2404.19737 | Generative AI |
| Phi-3 | Small efficient LLMs | https://arxiv.org/pdf/2404.14219 | Generative AI |
| FrugalGPT | Cost-aware LLM usage | https://portkey.ai/blog/implementing-frugalgpt-smarter-llm-usage-for-lower-costs | Generative AI |
| GNN-RAG | Graph-based RAG | https://github.com/cmavro/GNN-RAG | Generative AI |
- Track AI research trends
- Discover models worth experimenting with
- Understand trade-offs before building
- Reference system & architecture decisions
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