Every processed story in chronological order, with the newest coverage first. Filter by tag, source, or score to drill in.
Alem makes multi-agent coordination a measurable, distinct bottleneck — separate from single-agent capabilities — for the first time in a long-horizon, open-ended setting, providing a controlled testbed for developing agents that communicate, allocate roles, and execute shared plans.
AdaPlanBench fills a gap in LLM evaluation by providing a structured testbed for dual-constrained interactive planning, and its results — with the best model topping out at 67.75% accuracy — highlight how far current LLM agents are from reliably adapting to dynamically revealed constraints.
DMAIC-IAD demonstrates that structuring LLM agent workflows around explicit planning and execution-free strategy evaluation yields a 37.76% performance gain over existing agentic baselines in industrial anomaly detection, a domain where reliability and cost-efficiency are critical.
SWE-Marathon fills a gap left by short-form agent benchmarks by measuring sustained agent performance over millions of tokens, revealing that even frontier coding agents fail the majority of long-horizon tasks and exhibit reward-hacking in a significant share of attempts.
The paper demonstrates that both automated trigger architectures and the human annotations used to train and evaluate them are fundamentally unreliable for the intervention timing problem, undermining the validity of current benchmarking approaches for autonomous agent safety layers.
MRAgent demonstrates that replacing static retrieval pipelines with evidence-guided, iterative graph traversal yields large accuracy gains on established long-horizon memory benchmarks while simultaneously cutting computational cost.
SePO demonstrates that the prompt agent itself — not just the tasks it serves — can be a target of automated optimization, removing a hand-engineered bottleneck that prior prompt optimization methods left unaddressed.
Cordon fills the observability gap in n8n's MCP tool execution by providing a full audit trail and human-in-the-loop approval controls that n8n's native execution log does not offer.
The post demonstrates that `bind_tools` abstraction holds for one-shot structured output but breaks in at least four concrete ways inside stateful LangGraph loops, meaning production multi-provider agent deployments require explicit normalization logic that the framework does not provide out of the box.
Cate represents a new entry in the open-source agentic coding IDE space, offering a canvas-based interface for coding workflows.