Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2610.28763v1 Announce Type: cross Abstract: Frontier coding agents are increasingly framed as end-to-end automated researchers, and ML for science offers a demanding test of that promise: impro...
arXiv:2610.29812v1 Announce Type: cross Abstract: Literature-searching language agents are becoming a standard component of scientific assistants for the life sciences, yet there is little controlled...
ToM2Vec introduces continuous theory-of-mind tokens that enable large language model agents to reason about multiple possible partner states in multi‑agent settings. By training these tokens to abstract partner thoughts, the model generates latent representations that condition agents’ thoughts and actions, improving performance across collaborative, competitive, and mixed‑motive environments. The approach demonstrates that latent reasoning outperforms text‑based reasoning and generalizes to unseen partner models, with tokens encoding partner beliefs, desires, and next actions.
arXiv:2610.28818v1 Announce Type: cross Abstract: As systems engineering (SE) shifts from monolithic systems to Systems of Systems, Mission Engineering (ME) has emerged as a new line of thinking in S...
VeriEvolve is an evaluator-first evolution framework that first builds an executable evaluator from task instructions, then uses this evaluator as a frozen fitness signal while evolving agents. The system generates challenge states and counterfactual test cases to refine the evaluator across rounds, producing 30 evaluators for 30 MCPMark Filesystem tasks with perfect agreement on generated suites and high agreement on held-out states. Evaluator-guided evolution significantly improves externally verified success rates for GLM-5.2 and Claude, and cross-environment studies reveal complementary limits from observability, state representation, and search/execution capacity.
arXiv:2610.29249v1 Announce Type: new Abstract: Omni-modal assistants must remember not only what users say, but also what they show and convey. Existing long-term memory benchmarks are mostly text-a...
arXiv:2610.29413v1 Announce Type: new Abstract: Recently, we see a rise in conversational agents as a form of social and emotional support, where disclosure will unfold over time. Yet, current evalua...
arXiv:2610.29722v1 Announce Type: new Abstract: Retrieval Augmented Generation (RAG) grounds large language models in external knowledge. Modern pipelines combine embedding, chunking, retrieval, rera...
arXiv:2610.29823v1 Announce Type: new Abstract: Agent memory systems such as Mem0 and Zep decide what to keep before any question arrives; coding agents keep the raw history and let the model search...
arXiv:2610.30014v1 Announce Type: new Abstract: LLM agents with similar success rates can follow different action sequences. An Agent Behavior Map represents LLM-harness combinations using centered a...
arXiv:2607.20491v4 Announce Type: replace-cross Abstract: When a financial agent repeats its decision, how much does that tell us about the actions behind it? DFAH-Bench examines this question throug...
The paper introduces Software World, a framework that simulates multi‑agent collaboration within realistic software ecosystems. It generates environments of interdependent Python packages and lets agents maintain and improve them, measuring collective speedup in hidden dependent packages. Experiments across three worlds and eight models show that performance improvement scales with inference‑time compute, with the best model boosting downstream efficiency by up to 31%.
arXiv:2610.24899v2 Announce Type: replace-cross Abstract: While LLM agents are increasingly capable of gathering and synthesizing evidence from a single homogeneous source, such as structured tables...
arXiv:2605.11388v2 Announce Type: replace Abstract: Humans intuitively solve complex problems by flexibly shifting among reasoning modes: they plan, execute, revise intermediate goals, resolve ambigu...
arXiv:2602.16429v2 Announce Type: replace Abstract: Agentic systems, AI architectures that autonomously execute multi-step workflows to achieve complex goals, are often built using repeated large lan...
arXiv:2610.29784v1 Announce Type: new Abstract: Most contemporary artificial intelligence (AI) systems are released with frozen parameters and improve through external updates, whereas an ideal syste...
Wrist photoplethysmography (PPG) heart rate estimates are vulnerable to motion artifacts, and corrections that reduce mean error can worsen individual windows. We propose an agent architecture with to...
Vision-and-Language Navigation (VLN) requires agents to follow instructions and navigate to target locations. However, current VLN agents often suffer severe performance degradation in unseen environm...
Unmanned aerial vehicles (UAVs) have recently emerged as important remote sensing platforms, and UAV navigation has shown great potential in remote sensing applications. However, existing methods do n...
AI agents increasingly act as analysts whose decision can often be cast as hypothesis tests. Classical tests control the Type I error rate, the probability of rejecting a true null hypothesis, for fix...