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language-model agents

topic4 events
papersTODAY 04:00 UTC

Paper combines process supervision with outcome-based credit for agent RL

A new arXiv preprint addresses a weakness in outcome-based reinforcement learning for language-model agents: because the whole trajectory receives a single advantage signal, individual decisions get only coarse credit over long interaction sequences. The authors propose reconciling process supervision with outcome-based credit, drawing on on-policy self-distillation to produce finer-grained guidance. The work is presented as a revised submission and targets long-horizon agent training.

agents/reinforcement-learning/training/arxiv
papersTODAY 04:00 UTC

Study questions realism of language-model agents in farming decision simulations

A new arXiv paper examines whether language-model agents can credibly stand in for human respondents in surveys and social simulations. The authors argue that judging realism from population averages or distributional similarity can be misleading, an effect they call the "average-farmer illusion." Their experiments test what such aggregate evidence actually demonstrates about individual-level behavior.

agents/simulation/evaluation/research
papersSEP 12 04:00 UTC

arXiv paper introduces GitSkills, a dataset of agent skills collected from GitHub

A new arXiv paper presents GitSkills, a dataset built from GitHub repositories that package agent skills as folders containing a SKILL.md instruction file, sometimes with helper scripts and reference material. The work focuses on skills that language-model agents load when they decide a task matches a skill's description. It is a replacement submission (v2) in the cs.AI category.

agents/datasets/github/research
papersSEP 10 04:00 UTC

Paper Compares Subagents and Agent Skills for Long-Horizon Agentic Tasks

A new arXiv paper investigates how language model agents can draw on libraries of reusable knowledge when tackling long-horizon tasks. It contrasts two approaches—subagents and agent skills, where skills are packaged as multi-file bundles—and examines which executes such knowledge more effectively. The study was announced in the cs.AI category and cross-listed in cs.CL and cs.LG.

agents/llm/skills/long-horizon