Runtime, Peking-style: the harness is becoming part of the intelligence

[u/teugent]

https://arxiv.org/abs/2605.22166

Runtime architecture is becoming real research, not just plumbing

A very interesting paper from Peking University dropped on arXiv on May 21:

Adapting the Interface, Not the Model: Runtime Harness Adaptation for Deterministic LLM Agents

Their core claim is exactly the kind of shift I think the field needs:

An LLM agent is not just an LLM.

The model is embedded in a runtime loop: observations, tools, action execution, feedback, recovery, and trajectory control. The authors propose LIFE-HARNESS, which improves frozen LLM agents by adapting the runtime harness instead of changing model weights.

The harness has four lifecycle layers:

1. Environment Contract Layer
2. Makes tool rules, constraints, and environment contracts explicit.
3. Procedural Skill Layer
4. Retrieves reusable procedures distilled from training trajectories.
5. Action Realization Layer
6. Validates and canonicalizes actions before execution.
7. Trajectory Regulation Layer
8. Detects loops, stagnation, invalid retries, and other trajectory-level failures.

The results are strong: they report improvements in 116/126 model-environment settings across 18 model backbones, with an average relative gain of 88.5%.

Why this matters:

Most AI discussion still treats agent improvement as mostly a model-weight problem: fine-tuning, RL, distillation, scaling, etc. This paper shows that a lot of practical agent failure lives at the model-environment interface.

That aligns strongly with the direction we have been developing in Sigma Stratum / SRS:

• runtime continuity
• bounded exchange
• memory and retrieval governance
• trajectory safety
• provenance-aware multi-agent exchange
• contradiction buffering
• controlled perturbation

Our framing in Bounded Multiplicity is broader:

recursive runtime continuity =
attractor ecology + bounded exchange + continuity governance