13 articles tagged with #agent-systems. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
AIBullisharXiv – CS AI · 2d ago7/10
🧠Researchers demonstrate that inference-time scaffolding can double the performance of small 8B language models on complex tool-use tasks without additional training, by deploying the same frozen model in three specialized roles: summarization, reasoning, and code correction. On a single 24GB GPU, this approach enables an 8B model to match or exceed much larger systems like DeepSeek-Coder 33B, suggesting efficient deployment paths for capable AI agents on modest hardware.
AIBullisharXiv – CS AI · 6d ago7/10
🧠AgentOpt v0.1, a new Python framework, addresses client-side optimization for AI agents by intelligently allocating models, tools, and API budgets across pipeline stages. Using search algorithms like Arm Elimination and Bayesian Optimization, the tool reduces evaluation costs by 24-67% while achieving near-optimal accuracy, with cost differences between model combinations reaching up to 32x at matched performance levels.
AIBearisharXiv – CS AI · 6d ago7/10
🧠Researchers have identified SkillTrojan, a novel backdoor attack targeting skill-based agent systems by embedding malicious logic within reusable skills rather than model parameters. The attack leverages skill composition to execute attacker-defined payloads with up to 97.2% success rates while maintaining clean task performance, revealing critical security gaps in AI agent architectures.
🧠 GPT-5
AIBullisharXiv – CS AI · Mar 177/10
🧠Researchers introduce Orla, a new library that simplifies the development and deployment of LLM-based multi-agent systems by providing a serving layer that separates workflow execution from policy decisions. The library offers stage mapping, workflow orchestration, and memory management capabilities that improve performance and reduce costs compared to single-model baselines.
AINeutralGoogle Research Blog · Jan 287/106
🧠The article discusses the scientific principles behind scaling agent systems in generative AI, examining the conditions and factors that determine when agent systems perform effectively. It appears to focus on understanding the theoretical foundations for building and deploying AI agent systems at scale.
AIBullisharXiv – CS AI · 1d ago6/10
🧠Aethon is a new systems primitive that enables stateful AI agents to be instantiated in near-constant time by using reference-based replication instead of full materialization. This architectural innovation addresses latency and memory overhead constraints in existing AI runtime systems, making it possible to spawn, specialize, and govern agents at production scale.
AINeutralarXiv – CS AI · 2d ago6/10
🧠Researchers introduce STARS, a framework for continuously auditing AI agent skill invocations in real-time by combining static capability analysis with request-conditioned risk modeling. The approach demonstrates improved detection of prompt injection attacks compared to static baselines, though remains most valuable as a triage layer rather than a complete replacement for pre-deployment screening.
AIBullisharXiv – CS AI · Apr 76/10
🧠Researchers have released SuperLocalMemory V3.3, an open-source AI agent memory system that operates entirely locally without cloud LLMs, implementing biologically-inspired forgetting mechanisms and multi-channel retrieval. The system achieves 70.4% performance on LoCoMo benchmarks while running on CPU only, addressing the paradox of AI agents having vast knowledge but poor conversational memory.
AIBullisharXiv – CS AI · Mar 116/10
🧠Researchers present LLM Delegate Protocol (LDP), a new AI-native communication protocol for multi-agent LLM systems that introduces identity awareness, progressive payloads, and governance mechanisms. The protocol achieves 12x lower latency on simple tasks and 37% token reduction compared to existing protocols like A2A, though quality improvements remain limited in small delegate pools.
AIBullisharXiv – CS AI · Mar 37/108
🧠Researchers introduce AI Runtime Infrastructure, a new execution layer that sits between AI models and applications to optimize agent performance in real-time. This infrastructure actively monitors and intervenes in agent behavior during execution to improve task success, efficiency, and safety across long-running workflows.
AINeutralImport AI (Jack Clark) · Mar 26/1010
🧠Import AI 447 discusses the economic implications of artificial general intelligence (AGI), focusing on how most labor may shift to machines while humans transition to verification roles. The article explores the concept of the 'singularity' and its potential impact on the workforce and economy.
AINeutralarXiv – CS AI · Mar 34/104
🧠Researchers introduce Structured Diversity Control (SDC), a new framework for multi-agent reinforcement learning that improves coordination by controlling behavioral diversity within and between agent groups. The method achieved up to 47.1% improvement in average rewards and 12.82% reduction in episode lengths across various experiments.
AINeutralarXiv – CS AI · Mar 25/105
🧠Researchers present the Artificial Agency Program (AAP), a framework for developing AI systems as resource-bounded agents driven by curiosity and learning progress under physical constraints. The program aims to create AI that enhances human capabilities through better sensing, understanding, and action while reducing interface friction between people, tools, and environments.
