Every processed story in chronological order, with the newest coverage first. Filter by tag, source, or score to drill in.
The `oneOf`/`allOf` schema preservation and web search additions extend Codex's compatibility with richer MCP tools and broaden what code mode agents can do without leaving the coding workflow.
The study establishes automated prompt injection as a credible but model-dependent threat to LLM agents, while identifying significant barriers — particularly the failure of smaller-model attacks to transfer to frontier models — that shape the realistic risk landscape for agentic systems.
Loom addresses a gap in agentic coding workflows — reliable multi-step delivery — by adding durable state and structured orchestration on top of existing agents rather than requiring a switch to a new model or editor.
TabClaw's combination of transparent, editable execution plans with a self-evolving skill and memory system directly addresses the transparency and adaptability gaps the paper identifies in current LLM-based data-analysis agents.
The article provides a concrete, end-to-end Java implementation showing how Google ADK's tool bridge pattern connects Gemini's reasoning to external systems via the standardized MCP protocol over JSON-RPC and SSE.
Emergence World is the first platform the paper describes as purpose-built to make long-horizon multi-agent dynamics — behavioral drift, cross-vendor influence, and emergent governance — measurable, filling a gap left by short-horizon benchmarks that cannot observe these phenomena.
EvoDS directly addresses two core failure modes of current LLM-based data science automation — static skill sets and context overflow — with a system that learns to expand its own capabilities and manage long-horizon context, achieving a 28.9% average improvement over existing open-source agents across four benchmarks.
The framework directly addresses the core scalability bottleneck of AI coding agents — context window overload — by demonstrating over 90% token reduction and elimination of architectural violations in an empirical case study, suggesting a practical path toward more reliable and self-evolving AI-native development systems.
SMAC-Talk provides the research community with an open, structured benchmark for evaluating how LLMs coordinate, communicate, and resist deception in cooperative multi-agent environments — conditions increasingly relevant as LLMs are deployed alongside other AI agents.
TMEM demonstrates that agent parameters can be updated within a single episode via online LoRA adaptation, overcoming the permanent information loss that affects all prompt-only memory approaches.