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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.
The pipeline replaces prohibitively expensive manual architectural labeling with a scalable agentic approach, enabling fine-tuned models to achieve dramatically higher SWE-bench Verified resolved rates than either the base model or unfiltered fine-tuning.
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.
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.
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.
The contrastive context-selection objective demonstrably outperforms simply adding more contrastive data, showing that how the training signal is structured — not just what data is used — drives grounding improvements in both agentic and multimodal LLM settings.
StateGen's backend-is-truth invariant eliminates tool-call hallucinations by construction — a problem the paper identifies as the dominant failure class in tool-augmented LLM training data — while combining capabilities (multi-turn generation, state-grounded tool simulation, hierarchical multi-agent support, and built-in judge scoring) that no single publicly available platform currently offers together.