arXiv AI
GenCoord: Skill-Path Commitments under Private Information
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arXiv AI
Oct 12
GenCoord: Executable Commitments for Embodied LLM Agents under Private Information
GenCoord is a decentralized protocol for embodied language‑model agents that turns the division of work into the communication message itself. By pairing a sender’s skill path with a partner’s continuation in a short domain‑specific language, agents create commitments that link delivery to subsequent work, allowing fulfillment to be verified separately from task success. Experiments in Minecraft show that GenCoord matches free‑form communication performance while cutting median commitment time by 68.2 % and reducing output tokens, demonstrating that structured, work‑based messages can improve efficiency in embodied cooperation.
arXiv AI
Sep 25
Who Holds the Pen? Let Specifications, Not Agents, Sign Off
The paper argues that large language model agents should not be the sole authority on whether they have satisfied a task. It identifies two gaps—understanding–execution and state–authority—where agents may claim completion without actually meeting the specification. The authors propose SpecHarness, a framework that separates agent proposals from authoritative state by requiring evidence from qualified providers to confirm compliance, and demonstrate its effectiveness on guideline‑following and artifact‑generation tasks.
arXiv AI
Oct 15
When "Must" Becomes "Maybe": Erosion of Workflow Constraints in LLM Agent Handoffs
arXiv:2608.24569v2 Announce Type: replace Abstract: Large language model (LLM) agents build on one another's work through summaries, plans, and other intermediate artifacts. These records let a downs...
arXiv AI
Oct 15
Observe, Share, or Predict? Disentangling Partner Information in Embodied Multi-Agent Collaboration
arXiv:2610.16895v1 Announce Type: new Abstract: LLMs are used as high-level controllers for embodied agents, but it remains unclear whether they support collaborative reasoning, such as anticipating...
arXiv AI
Jul 7
Evaluating Generative Agents with Actions Grounded in Socially Distributed Task Environments using Incognita
arXiv:2607. 02975v1 Announce Type: new Abstract: Effective agency in social environments depends on when an agent seeks knowledge, when it acts, and whether its actions are justified by acquired information.
arXiv AI
Jun 9
Collaborative Human-Agent Protocol (CHAP)
arXiv:2606. 09751v1 Announce Type: new Abstract: Foundation models are moving from response generation into operational roles.