Every processed story in chronological order, with the newest coverage first. Filter by tag, source, or score to drill in.
ACCORD demonstrates that a training-free grounding layer can close a substantial portion of the task-completion gap in LLM agents across both digital and embodied benchmarks, without modifying the underlying model.
Strands Evals provides structured, automated root cause analysis for AI agent failures — including confidence scores, causal chains, and targeted fix recommendations — replacing ad-hoc manual debugging in evaluation pipelines.
The work shows that real hardware feedback is the critical missing ingredient for LLM agents to autonomously replace expert-driven MCU optimization, turning a previously manual, multidimensional process into a closed-loop pipeline that outperforms human experts within seven iterations.
An open-source coding agent from Xiaomi claiming to outperform Claude Code on long-horizon tasks is a notable development in the agentic coding tooling space.
AgentFairBench shifts fairness measurement from LLM text outputs to agent decisions in consequential domains, and its arity-matched null methodology corrects a ~2.4× overstatement of disparity that prior comparison approaches produce.
ASSAY demonstrates that matching skills to tasks at inference time — rather than global library curation — is the key bottleneck for experience-based agent improvement, achieving state-of-the-art results on two benchmarks without any weight updates.
DeepRoot is the first system to simultaneously achieve low hallucination rates (7–10%) and high reasoning coherence on historical medical text, demonstrating a viable path for converting pre-ontological archives into verifiable drug-discovery leads at scale.
RetailBench exposes that current LLMs cannot sustain coherent long-horizon decision-making in economically grounded environments, with most models failing to complete even a 180-day simulation and all falling substantially short of an oracle policy on net worth and sales.
Open-SWE-Traces provides a large-scale, permissively licensed, multilingual trajectory dataset that enables fine-tuning of open-source LLMs for autonomous software engineering — directly addressing the data scarcity the paper identifies as the primary bottleneck on this path.
LatentGym fills a gap left by existing frameworks by providing the first controllable latent structure and disentangled exploration/exploitation metrics for measuring cross-task experiential learning in LLM agents.