Artem KK

Most agents don't fail because the model is dumb. They fail because the loop has no boundaries, memory is a mess, and nobody defined "done." Wrote about where agent architecture actually breaks. @kazkozdev/agent-anatomy-where-the-loop-breaks-5bc0184da906" target="_blank" rel="nofollow noopener">medium.com/@kazkozdev/age… #AIAgents #LLM #AIEngineering

Tested a customer support system prompt against 300+ adversarial attacks. 50% of authority bypasses worked. Base64 beat content filters. A simple roleplay prompt overrode every safety rule. Open-sourced my benchmark: github.com/KazKozDev/syst… #LLMSecurity #PromptInjection

LLMFlow Search — open-source deep research that runs on your machine. 9 sources, zero API keys, fully local via Ollama. Cloud version also available — deployed on AWS. github.com/KazKozDev/llmf… ⭐ MIT licensed. #OpenSource #LLM #AI #LocalAI

New approach to train LLM agents using interactive ML tasks with online reinforcement learning, featuring exploration-enhanced fine-tuning, step-by-step RL, and an agent-specific reward module. arxiv.org/abs/2505.23723 #LLMTraining #ReinforcementLearning #AgentML

AlphaEvolve from Google DeepMind's automatically makes computer programs better by evolving them like species in nature. It doesn't just write code from scratch—it takes existing programs and evolves them into better versions. arxiv.org/pdf/2506.13131 #GoogleDeepnd #AlphaEvolve

Reducing AI hallucinations: "TruthPrInt" proposes a latent truthful-guided pre-intervention approach to mitigate object hallucination in Large Vision-Language Models. Building on insights from LLMs where internal states encode truthfulness, this method addresses a major challenge in multimodal AI trustworthiness. buff.ly/gYhzBOr #AITrust #MultimodalAI #HallucinationPrevention

Memory efficiency innovation: "KV-Distill" introduces a nearly lossless learnable context compression framework for LLMs. During generation, KV cache accounts for significant GPU memory usage. This solution distills representations to dramatically reduce memory requirements while maintaining performance on long contexts. buff.ly/dA6W6u7 #AIEfficiency #ModelCompression #LLMOptimization

Cognitive science meets AI: "LLM Agents Display Human Biases but Exhibit Distinct Learning Patterns" investigates how LLMs handle Decisions from Experience tasks. While showing similar biases to humans (underweighting rare events), LLMs demonstrate fundamentally different learning trajectories, revealing insights about their decision processes. buff.ly/f8OlBkb #CognitiveAI #DecisionMaking #AIBehavior

"Copyright Law vs. AI: A Conceptual Conflict". Professor Craig identifies a fundamental contradiction between generative AI and copyright legislation. The research demonstrates that applying the concept of "authorship" to AI is erroneous, as authorship is inseparable from human intentionality. The author warns of reciprocal risks: AI may undermine copyright foundations, while strict regulation could impede AI development. This work advocates for creating a new legal paradigm to govern AI-generated content. buff.ly/ouusTmU #AILaw #Copyright #GenerativeAI

Mixtral LLMs Deliver Human-Comparable Evaluation for Question Answering Systems. Research reveals that Mixtral language models can effectively evaluate reading comprehension tasks through zero-shot prompting, outperforming traditional metrics and closely matching human judgment across multiple datasets. This approach requires minimal training data while providing accurate assessment for both Likert scale scoring and binary correctness evaluation, potentially eliminating costly human annotation processes in NLP development pipelines. buff.ly/ALaJgzV #LLMEvaluation #ReadingComprehension #NLPResearch

Healthcare AI research: "LLMs in Disease Diagnosis" compares DeepSeek-R1 and O3 Mini across chronic health conditions. The study evaluates predictive accuracy at both disease and category levels, showing how these models are revolutionizing medical diagnostics through enhanced classification and clinical decision support. buff.ly/iX5vIDm #MedicalAI #HealthTech #LLMDiagnostics

Advancing multilingual evaluation: "MMLU-ProX" introduces a comprehensive benchmark covering 13 typologically diverse languages with ~11,829 questions per language. Building on MMLU-Pro's reasoning-focused design, this framework addresses the gap in evaluating sophisticated language models across culturally diverse contexts. buff.ly/Tpn64u5 #NLP #MultililingualAI #AIBenchmarks

Research from Brown University demonstrates that retrieval model optimization can significantly enhance RAG systems, often outperforming improvements achieved by using larger language models. Their novel approach, OpenRAG, leverages in-context retrieval learning to optimize the entire RAG pipeline end-to-end. Key findings show that in-domain retriever training is critical, and that optimized retrieval can yield performance gains comparable to or exceeding those achieved by scaling up to larger LLMs—a more cost-effective approach for many applications. The research opens new pathways for efficient RAG systems that rely on improved retrieval rather than exclusively focusing on scaling language models. buff.ly/0uLwA6J #RAG #RetrievalAugmentedGeneration #LLM

Google's whitepaper presents a strategic approach to implementing large language models in cybersecurity operations. The framework addresses critical industry challenges: security talent shortages, manual task burden, and data overload. Their multi-layered methodology integrates specialized AI models with existing security infrastructure and authoritative data sources, enabling more efficient threat detection and response capabilities across security roles. This approach promises significant improvements in organizational security posture through faster response times and reduced operational overhead. buff.ly/2mJ0zjn #CybersecurityAI #GenAI #ThreatIntelligence

The Chinese startup Butterfly Effect just launched Manus - the world's first general AI agent that's taking the tech world by storm! What makes it exceptional is its ability to use multiple AI models (including Claude 3.5 Sonnet and fine-tuned Qwen) to work autonomously across diverse tasks. Its "Manus's Computer" window offers unprecedented transparency, allowing users to observe and intervene in real-time. Although still in limited release (<1% of waitlist), it's already demonstrating impressive research capabilities and adaptability. Early testing shows it performs better than ChatGPT DeepResearch on certain tasks at just 1/10th the cost! #ManusAI #AIagent #TechInnovation #AIbreakthrough #FutureOfWork #ChineseTech

Researchers at Carnegie Mellon University have developed LCPO, a method that enables AI models to reason effectively within specified token limits. A 1.5B parameter model outperformed GPT-4o at equal reasoning chain lengths. This technology paves the way for more cost-efficient AI scaling while maintaining high accuracy. buff.ly/HQxkaL4 #AI #LLM #MachineLearning

Simple Attack Method Breaks Top AI Vision Models with 90%+ Success Rate. Researchers at MBZUAI reveal a surprisingly effective method to fool advanced AI vision systems like GPT-4o. Their "M-Attack" injects semantic information into images that completely misleads these systems while appearing normal to humans. This research exposes critical security vulnerabilities in the latest vision-language models from major labs. Read the paper: buff.ly/lm3G4fF #AIvulnerability #MachineLearning #ComputerVision

Work with Apps on macOS is now available to everyone, including Enterprise, Edu, and Free users. ChatGPT can read and edit content in your coding apps, bringing you smarter answers tailored to your work and helping you stay in flow. But don't forget to update the app!

New research "Siege" introduces an innovative approach to LLM safety testing through multi-turn conversations rather than isolated prompts. The system employs breadth-first search to explore various interaction strategies and tracks "partial compliance" with safety policies, revealing hidden vulnerabilities missed by traditional methods. A significant advancement in AI protection. #LLMSafety #RedTeaming arxiv.org/abs/2503.10619…