Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States¶

L1 core mechanism diagnostic. Belief updates and stagnation detection run. No external tool environment is called.

Paper information¶

Field	Details
Paper	arXiv v1
Institution	The first page does not list the first author's affiliation.
First public release	2026-10-01 (arXiv v1)
Original source code	No public author implementation found as of 2026-10-03.
Adapter	`belief-state-pos`
Local reproduction code	`src/auto_research/agent_research/latest_20261003.py`

Original paper summary¶

Background and main changes¶

Explicitly maintain current world facts and unresolved task requirements as a belief. Repeated actions that do not reduce unresolved requirements indicate Belief Trapping and trigger targeted recovery.

flowchart LR
  I[Input and current state] --> M[Belief updates and stagnation detection]
  M --> A[Auditable intermediates]
  A --> O[Output or state update]

Key figure from the paper¶

Figure 2: Overall design and components. From the paper; copyright belongs to the authors. Click for the source.

Core equations¶

\(b_t=(facts_t, unresolved_t)\); \(\Delta|unresolved|\ge0\) marks stagnation.

Offline and online results¶

The paper reports improved long-horizon agent success on four execution and diagnostic benchmarks.

Local reproduction¶

Comparison protocol: Disabled or default mechanism versus its defining operator. L1 does not report formal relative gains; percentages are not applicable.

Three-seed diagnostic: metrics/mechanism-seeds42-44.json.
diagnostic_only=true; excluded from formal capability rankings.

Scope¶

Belief updates and stagnation detection run without any external tool environment.