I'm v curious how you're incorporating this into your workflow. Is it that when you want to implement something you can call out to these books as rigorous reference material for your agents? I'd never thought of doing such a thing and am curious how you're doing it and how it's worked.

You can source it here in the library tool, it connects to a cloud/local LLM for RAG:

https://vektormemory.com/

Extracted whitepapers, should keep you busy:

VL-JEPA: Joint Embedding Predictive Architecture for Vision-language [2512.10942v1] A Multi-Memory Segment System for Generating [2508.15294v3] Adversarial Cues in Decision Models Used [2610.11436v1] AGENTIC BACKDOORS VIA PRETRAINING POISONING [2610.37741v1] Agentic Memory Learning Unified Long-Term [2601.01885v2] Agentic Semi-Structured Memory [2604.14362v1] Agents That Know Too Much [2606.26627v1] AI Agents Enable Adaptive [2606.03811v1] AI Agents That Matter [2407.01502v1] AI Assistants Cloud Versus Local LLM [2604.18566v2] AMA-Bench Evaluating Long-Horizon Memory [2602.22769v2] A-Mem Agentic Memory for LLM Agents [2502.12110v11] APEX-MEM Agentic Semi-Structured Memory [2604.14362v1] ASK ONLY WHEN NEEDED [2604.20572v2] Attention Is All You Need [1706.03762v7] Attention Is Not What You Need [2512.19428v1] Benchmarking Multi-Agent LLM [2603.22651v1] Beyond Nodes vs. Edges [2604.14685v1] Beyond Static Summarization [2601.04463v1] Calibrated Decision Models for Autonomous [2609.28940v1] Character-and-Scene Episodic Memory for Agents [2602.06051v3] CLASP Training-Free LLM-Assisted Source Code Watermarking [2510.11251v2] Cognitive Architectures for Language Agents [2309.02427v3] COMPUTATIONAL COPYRIGHT [2312.06646v5] Constitutional AI [2212.08073v1] Creative Problem Solving in Artificially Intelligent Agents [2204.10358v1] CyberGym [2506.02548v3] Deep image search [2602.10809v1] Developing Surrogates [2602.21588v1] Diffusion-Based Neural Network Weights Generation [2402.18153v2] Dynamic LLM-Powered Agent Network [2310.02170v2] Dynamic Logit-Level Gating [2606.04378v1] EternalMath [2601.01400v1] EverMemOS [2601.02163v2] EvoMemBench Benchmarking Agent Memory [2605.18421v1] ExploitBench [2605.14153v1] FADEMEM BIOLOGICALLY-INSPIRED [2601.18642v2] Finance-Grounded Optimization [2509.04541v2] Formal Algorithms for Transformers [2207.09238v1] Geogalactica [2401.00434v2] HAGE - Harnessing Agentic Memory [2605.09942v1] HaluMem [2511.03506v1] Harnessing the Universal Geometry of Embeddings [2505.12540v4] HIPPOCAMPUS [2602.13594v1] hipporag [2405.14831v3] Hybrid Reasoning Agents [2610.18861v1] Hyper agents [2603.19461v1] HyperMem [2604.08256v2] Interactive Simulacra of Human Behavior [2304.03442v2] Is Complex Agentic Memory Always Needed [2610.30755v1] Language Modeling Is Compression [2309.10668v2] Learn from your own latents [2605.27734v1] Learning on the Job [2607.22157v1] Learning to Memorize at Test Time [2501.00663v1] Learning to Rewrite Tool Descriptions [2602.20426v2] LLM Agents Already Know When to Call Tools [2605.09252v2] LLM-Enabled Frequency-Aware [2602.19522v1] LONGMEMEVAL [2410.10813v2] MAGMA A Multi-Graph based [2601.03236v2] MemGround [2604.14158v1] Memory in the Age of AI Agents [2512.13564v2] Memory Tax [2610.34073v1] Memp - Exploring Agent Procedural Memory [2508.06433v4] Mitigating Provenance-Role [2605.25869v1] MOSS Self-Evolution through Source-Level Rewriting [2605.22794v2] Multi-task DAG Learning [2306.02831v1] Natural Language conductor [2512.04388v5] NeuroGen - Neural Network Parameter Generation [2505.12470v2] Next-Latent Prediction Transformers Learn Compact World Models [2511.05963v4] Novel Memory Forgetting Techniques [2604.02280v1] OMNI-LEAK Orchestrator Multi-Agen [2602.13477v2] On Understanding Extremely Long-Term Video with Adaptive [2411.12593v3] Opal Private Memory [2604.02522v1] Parameter Prediction for Unseen Deep Architectures [2110.13100v1] Prompt Optimization [2602.18449v1] PromptMark [2606.20835v1] RAG-MCP Mitigating Prompt Bloat [2505.03275v1] RAIN-MERGING [2602.22538v1] Recursive Self-Improvement through Multi-Agent [2610.12176v1] Reflection-Based Memory [2506.02158v1] Refusal in Language Models Is Mediated by a Single Direction [2406.11717v3] REMEM REASONING WITH EPISODIC MEMORY [2602.13530v3] Remember When It Matters [2607.08716v1] Remembering More, Risking More [2605.17830v1] Sakana Fugu Technical Report [2606.21228v1] SalesRLAgent [2503.23303v1] Scaling Long-Form Story Generation [2609.35759v1] Search-R1- Training LLM [2503.09516v5] Secure Semantic Communications [2602.22134v2] Selective Memory for Artificial Intelligence [2603.15994v1] Skill.state [2608.26263v2] Smarter Together [2511.08301v1] SmartSearch [2603.15599v1] SOLVING A MILLION-STEP LLM TASK [2511.09030v1] Storage Is Not Memory [2605.04897v1] StructMem Structured Memory [2604.21748v1] Structured Belief State and the First Precision [2605.11325v2] SuperLocalMemory V3.3 [2604.04514v1] SWE-MeM Learning Adaptive Memory [2606.28434v1] Synthius-Mem Brain-Inspired Hallucination [2604.11563v1] The AI Scientist-v2 [2504.08066v1] The Bitter Lesson of Tool Calling [2608.06370v1] The Customer Is the Builder [2610.30307v1] The Gradient of Generative AI Release [2302.04844v1] The Little Book of Generative AI Foundations [2605.29713v2] The Malicious Use [1802.07228v2] The Memory Wars [2508.05867v1] Thinking Fast and Slow in AI [2110.01834v1] Time algo [2610.00577v1] Towards trustworthy agentic AI [2605.23989v1] Transformers learn in-context by gradient descent [2212.07677v2] TReMu Towards Neuro-Symbolic Temporal Reasoning [2502.01630v2] TRINITY AN EVOLVED LLM COORDINATOR [2512.04695v3] VERBALIZED SAMPLING [2510.01171v3] Verifiable Agent-to-Agent [2602.02395v1] Verified Multi-Agent Orchestration [2603.11445v2] WEBCOACH - A MODEL [2511.12997v1] XGrammar-2 Dynamic and Efficient Structured Generation [2601.04426v4] Your Agent’s Memories Are Not Its Own [2607.05029v1] Zep A Temporal Knowledge Graph Architecture [2501.13956v1]