Adapting the Interface, Not the Model: Runtime Harness Adaptation for Deterministic LLM Agents
- URL: http://arxiv.org/abs/2605.22166v2
- Date: Wed, 27 May 2026 04:34:36 GMT
- Title: Adapting the Interface, Not the Model: Runtime Harness Adaptation for Deterministic LLM Agents
- Abstract summary: We propose Life-Harness, a lifecycle-aware runtime harness that improves frozen LLM agents without changing model weights or evaluation environments.<n>Life-Harness evolves from training trajectories by converting recurring interaction failures into reusable interventions across environment contracts, procedural skills, action realization, and trajectory regulation.<n>Life-Harness improves 116 out of 126 model--environment settings across 18 model backbones, with an average relative improvement of 88.5%.
- Score: 4.765206163164323
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: LLM agents are shaped not only by their language models, but also by the runtime harness that mediates observation, tool use, action execution, feedback interpretation, and trajectory control. While existing agent adaptation methods mainly update model parameters, many failures in deterministic, rule-governed domains stem from mismatches at the model--environment interface. We propose Life-Harness, a lifecycle-aware runtime harness that improves frozen LLM agents without changing model weights or evaluation environments. Life-Harness evolves from training trajectories by converting recurring interaction failures into reusable interventions across environment contracts, procedural skills, action realization, and trajectory regulation, and remains fixed for evaluation on unseen tasks. On seven deterministic environments from $τ$-bench, $τ^2$-bench, and AgentBench, Life-Harness improves 116 out of 126 model--environment settings across 18 model backbones, with an average relative improvement of 88.5%. Harnesses evolved only from Qwen3-4B-Instruct trajectories transfer to 17 other models, showing that Life-Harness captures reusable environment-side structure rather than model-specific behavior. These results position runtime interface adaptation as a complementary alternative to model-centric agent training. Code is available at https://github.com/Tianshi-Xu/Life-Harness.
Related papers
- Amortized Low-Rank Adaptation for Model-Based Reinforcement Learning [49.3087425299849]
World models let agents plan by predicting the consequences of their actions, but changes in the environment can make them inaccurate.<n>Existing approaches trade off computational cost against expressivity, i.e., the range of models a method can produce.<n>We present CLAW, which addresses this tradeoff by using a hypernetwork to generate adapters at test time.
arXiv Detail & Related papers (2026-09-10T23:16:40Z) - Self-Evolving Embodied Agents via Skill-Harness Evolution [53.307052568223945]
Embodied agents are increasingly built as systems around foundation models.<n>While supervised fine-tuning and reinforcement learning can adapt agents to new environments, they require additional data, rewards, and training runs.<n>We propose SHAPER, a self-evolving framework for train-free embodied adaptation that keeps model parameters frozen.
arXiv Detail & Related papers (2026-08-11T18:55:58Z) - Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents [37.91666123749406]
Co-Harness is a framework that jointly optimize the agent harness and model parameters during post-training.<n>An autonomous case study shows that Co-Harness can recover from system crashes, improve inference efficiency, and discover ensemble strategies without human intervention.
arXiv Detail & Related papers (2026-07-17T02:39:57Z) - TTHE: Test-Time Harness Evolution [50.26245555541721]
Existing approaches optimize such harnesses before deployment, searching training or development data for a fixed agent workflow that is then frozen at test time.<n>We ask whether the harness can instead be optimized during evaluation itself, using only the unlabeled execution traces the agent produces on the test inputs.<n>During evaluation, TTHE maintains a population of candidate harnesses and refines them through an agentic proposer that reasons over their execution traces.<n>A judge then commits an improved harness from execution-derived proxy signals, and the selected program persists to govern subsequent inputs.
arXiv Detail & Related papers (2026-07-09T05:53:39Z) - Inference-Time Machine Unlearning via Gated Activation Redirection [1.1699531043716684]
We introduce Inference-Time Unlearning via Gated Activation Redirection (GUARD-IT)<n>GUARD-IT unlearns via input-dependent activation steering at inference time, leaving model weights untouched.<n>Experiments on TOFU and MUSE show that GUARD-IT matches or exceeds 12 gradient-based baselines across three model scales.
arXiv Detail & Related papers (2026-05-12T21:26:25Z) - Pioneer Agent: Continual Improvement of Small Language Models in Production [6.452260317191361]
Small language models are attractive for production deployment due to their low cost, fast inference, and ease of specialization.<n>We present Pioneer Agent, a closed-loop system that automates the process of adapting small language models to a specific task.
arXiv Detail & Related papers (2026-04-10T18:13:09Z) - AdaptEvolve: Improving Efficiency of Evolutionary AI Agents through Adaptive Model Selection [14.17960333915609]
Evolutionary agentic systems intensify the trade-off between computational efficiency and reasoning capability.<n>We introduce AdaptEvolve: Adaptive Selection for Multi-LLM Evolutionary Refinement.
arXiv Detail & Related papers (2026-02-12T13:26:56Z) - ProAct: Agentic Lookahead in Interactive Environments [56.50613398808361]
ProAct is a framework that enables agents to internalize accurate lookahead reasoning through a two-stage training paradigm.<n>We introduce Grounded LookAhead Distillation (GLAD), where the agent undergoes supervised fine-tuning on trajectories derived from environment-based search.<n>We also propose the Monte-Carlo Critic (MC-Critic), a plug-and-play auxiliary value estimator designed to enhance policy-gradient algorithms.
arXiv Detail & Related papers (2026-02-05T05:45:16Z) - ET-Agent: Incentivizing Effective Tool-Integrated Reasoning Agent via Behavior Calibration [68.89572566071575]
ETAgent is a training framework for calibrating agent's tool-use behavior.<n>It is designed to progressively calibrate erroneous behavioral patterns to optimal behaviors.
arXiv Detail & Related papers (2026-01-11T11:05:26Z) - Sample-Efficient Online Learning in LM Agents via Hindsight Trajectory Rewriting [92.57796055887995]
We introduce ECHO, a prompting framework that adapts hindsight experience replay from reinforcement learning for language model agents.<n> ECHO generates optimized trajectories for alternative goals that could have been achieved during failed attempts.<n>We evaluate ECHO on stateful versions of XMiniGrid, a text-based navigation and planning benchmark, and PeopleJoinQA, a collaborative information-gathering enterprise simulation.
arXiv Detail & Related papers (2025-10-11T18:11:09Z) - GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs [56.93583799109029]
GrAInS is an inference-time steering approach that operates across both language-only and vision-language models and tasks.<n>During inference, GrAInS hidden activations at transformer layers guided by token-level attribution signals, and normalizes activations to preserve representational scale.<n>It consistently outperforms both fine-tuning and existing steering baselines.
arXiv Detail & Related papers (2025-07-24T02:34:13Z) - ChameleonLLM: Batch-Aware Dynamic Low-Rank Adaptation via Inference-Time Clusters [3.729242965449096]
This paper introduces ChameleonLLM, a novel framework that enables inference-time adaptation of large language models.<n>Unlike traditional fine-tuning approaches such as Low-Rank Adaptation (LoRA), our method dynamically generates adaptive modifications to the decoder weights.<n>By intelligently grouping similar inputs and computing context-aware low-rank updates via a hyper-network, ChameleonLLM achieves significant performance gains.
arXiv Detail & Related papers (2025-02-06T18:57:06Z) - STORM: Efficient Stochastic Transformer based World Models for
Reinforcement Learning [82.03481509373037]
Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy in visual input environments.
We introduce Transformer-based wORld Model (STORM), an efficient world model architecture that combines strong modeling and generation capabilities.
Storm achieves a mean human performance of $126.7%$ on the Atari $100$k benchmark, setting a new record among state-of-the-art methods.
arXiv Detail & Related papers (2023-10-14T16:42:02Z) - Mismatched No More: Joint Model-Policy Optimization for Model-Based RL [172.37829823752364]
We propose a single objective for jointly training the model and the policy, such that updates to either component increases a lower bound on expected return.
Our objective is a global lower bound on expected return, and this bound becomes tight under certain assumptions.
The resulting algorithm (MnM) is conceptually similar to a GAN.
arXiv Detail & Related papers (2021-10-06T13:43:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.