New paper frames agent context acquisition as active inference over latent task state
omarsar0 · x · 2026-08-23
@omarsar0 highlights a fascinating new paper on AI agents. Core thesis: many current agent problems — hallucination, cost inefficiency, unreliable tool calls — stem from wrong assumptions the LLM makes.
- Root cause: users leave out constraints when prompting, so the agent must guess defaults or burn tokens on clarifying questions, retrieval calls, tool calls, or prompt trials. Context acquisition is treated as an afterthought — and it shouldn't be.
- The paper gives this an objective function: context acquisition becomes active inference over a latent task state — an inner step updates beliefs, an outer step picks the next context action, task action, or stop action to minimize overall cost.
The author argues solving this would significantly improve performance and efficiency even with current LLMs.
More from coding & agent
- Team of agents solves engineering problems end-to-end, manufacturing real objects — ProfBuehlerMIT · 2026-08-23
- Bezalel gives your AI agent memory, email, a computer and money behind one MCP URL — Rasmic · 2026-08-23
- Multi-Model Agent Workflow: GPT Writes, Claude Reviews, Auto-Generates PR — Saboo_Shubham_ · 2026-08-23
- Open Source Multi-Host Management for AI Jobs with Agent Orchestration — ii_social · 2026-08-23
- Agensis update auto-adds tasks from long-running requests — jasonkneen · 2026-08-23
- Open Source Windows MCP Connector Adds Sub-Agents and Computer Control to ChatGPT — Present-Boat-2053 · 2026-08-23