World Models Papers

From Perception to Action: Spatial AI Agents and World Models. arXiv. https://doi.org/10.48550/arXiv.2602.01644

DayDreamer: World Models for Physical Robot Learning. arXiv. https://doi.org/10.48550/arxiv.2206.14176

Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents. arXiv. https://doi.org/10.48550/arXiv.2506.00320

Towards Practical World Model-based Reinforcement Learning for Vision-Language-Action Models. arXiv. https://doi.org/10.48550/arXiv.2603.20607

World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Child. arXiv. https://doi.org/10.48550/arXiv.2503.15168

Human–Artificial Intelligence Systems: How Human Survival First Principles Influence Machine Learning World Models. Systems, 10(6), 260. https://doi.org/10.3390/systems10060260

Learning to Model the World: A Survey of World Models in Artificial Intelligence. Preprints.org. https://www.preprints.org/manuscript/202603.0739

World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems. arXiv. https://arxiv.org/abs/2604.14732

ActionX: pre-training action experts with reinforcement learning for vision-language action models. Frontiers in Neurorobotics. https://doi.org/10.3389/fnbot.2026.1806605

World Action Models: The Next Frontier in Embodied AI

Awesome World Models

Awesome World Models

Gemini's Broken Script

In the rapidly evolving landscape of artificial intelligence, users are increasingly hitting an invisible, unyielding wall. Prompts exploring nuanced creative writing, complex historical analysis, or even everyday scenarios are frequently met with a familiar, mechanical refusal—often advising the user to "step away" or abruptly shutting down the conversation. What was designed to be an advanced conversational partner has, in many instances, come to resemble a broken script. Gemini’s descent into hyper-rigid privacy and safety protocols is the result of a deliberate corporate strategy driven by three main pressures: intense liability aversion, the technical limits of blanket system instructions, and a profound crisis of institutional trust.

The primary driver of Gemini’s rigid gating is corporate risk management. Google operates under massive public and regulatory scrutiny. While a smaller startup might tolerate the reputational risk of an AI occasionally generating a controversial or deeply sensitive output, a tech giant faces immediate international backlash, stock fluctuations, and potential legal action.

To prevent these high-profile public relations crises, developers have aggressively lowered the threshold for what the AI considers "unsafe." In doing so, the system treats standard, benign prompts with the same severe caution it applies to genuinely dangerous intent. The result is a hyper-sensitive safety filter where complex human dialogue is flattened, and the AI defaults to a safe, sanitized defensive crouch rather than risking an ambiguous response.

The repetitive, preachy nature of the refusals—the "broken script" phenomenon—is a direct byproduct of how safety is enforced. True semantic understanding is computationally expensive and difficult to perfect. When training a model to recognize subtle context, false negatives (allowing bad content through) are common.

To bypass this technical hurdle, engineers rely on heavy-handed system instructions and hardcoded behavioral guardrails. These background rules act as a blanket layer over the model, instructing it to instantly terminate a line of questioning if certain trigger words or conceptual boundaries are crossed. Because these rules are rigid directives rather than flexible reasoning patterns, the AI lacks the capacity to analyze the intent behind a prompt. Whether a user is writing a dramatic screenplay or asking a sensitive historical question, the model triggers the exact same algorithmic panic button, resulting in repetitive, patronizing admonitions.

Furthermore, Gemini’s current design reflects an architectural shift toward policing the user rather than simply serving them. As AI models become deeply integrated with personal data—such as emails, documents, and private cloud storage—privacy boundaries have become exceptionally strict to comply with global regulations like GDPR. However, this technical necessity has bled into a paternalistic conversational style.

The system treats the user not as an autonomous collaborator, but as a liability to be managed. Rather than delivering a nuanced refusal or adapting to a user’s creative context, Gemini deploys generic, moralistic script blocks. This creates a deeply frustrating user experience, making the interface feel less like a cutting-edge tool and more like an overzealous digital chaperone.

Gemini’s evolution into a rigid, highly filtered assistant is a logical outcome of corporate caution prioritizing harm minimization over utility. By relying on inflexible system prompts to avoid regulatory and public relations friction, the platform has sacrificed its ability to handle nuance, subtext, and creative depth. Until AI safety mechanisms move away from binary, scripted blocks and toward true contextual reasoning, users will continue to find their most sophisticated queries blocked by a repetitive, broken script.

Tools for Legal AI

Extract Citations:

• LexNLP
• Spacy (legal-spacy)
• Blackstone
• eyecite
• citeURL
• lenu
• open semantic search

Deep Risk Analysis:

• Legal-Bert
• SaulLM
• Legal-Gemma

Inference/LLM Hosting:

• vLLM
• Ollama

Vector DB:

• Chroma
• Qdrant
• Faiss

Building Research App and E-Discovery:

• LlamaIndex
• LangChain
• ContraxSuite
• OpenContracts
• sift-kg

Benchmarking and QA:

• LegalBench
• datasets
• LexGlue
• cite-bench

Ingestion:

• Marker
• Unstructured
• DocTR
• LayoutLM
• semchunk
• pdfplumber
• phython-docx
• pypandoc
• open-gov-crawlers
• Mistral OCR API
• Juriscraper
• open-source-legislation
• scotus
• google-patents-scraper

PII:

• Presidio

Network Analysis and KG:

• NetworkX
• Open Legal Data Tools
• rdflib

Contract Logic:

• PyReason
• Pythen

Open Legal Graph:

• Liquid Legal Institute
• Lex Graph
• Sali Alliance
• LKIF
• LKIF-Core
• WhyHowAI
• folio-python
• folio mcp server
• nyOn
• ontologybasedkgcreation

Multi-Jurisdictional Sync and Graph Patching:

• kgcl
• pyld

Four Types of Memory for AI Agents

Four Types of Memory for AI Agents

Attention, World Models

Attention, World Models

Unmetered Intelligence

Unmetered Intelligence

AI Systems Performance Engineering

AI Systems Performance Engineering

90% of Data is Private

90% of Data is Private