Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.11421 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911315762610176 |
|---|---|
| author | Gürsun, Gonca |
| author_facet | Gürsun, Gonca |
| contents | Large Language Models demonstrate strong reasoning and generation abilities, yet their behavior in multi-turn tasks often lacks reliability and verifiability. We present a task completion framework that enables LLM-based agents to act under explicit behavioral guidance in environments described by reinforcement learning formalisms with defined observation, action, and reward signals.
The framework integrates three components: a lightweight task profiler that selects reasoning and generation strategies, a reasoning module that learns verifiable observation - action mappings, and a generation module that enforces constraint-compliant outputs through validation or deterministic synthesis. We show that as the agent interacts with the environment, these components co-evolve, yielding trustworthy behavior. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11421 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Trustworthy Multi-Turn LLM Agents via Behavioral Guidance Gürsun, Gonca Artificial Intelligence Large Language Models demonstrate strong reasoning and generation abilities, yet their behavior in multi-turn tasks often lacks reliability and verifiability. We present a task completion framework that enables LLM-based agents to act under explicit behavioral guidance in environments described by reinforcement learning formalisms with defined observation, action, and reward signals. The framework integrates three components: a lightweight task profiler that selects reasoning and generation strategies, a reasoning module that learns verifiable observation - action mappings, and a generation module that enforces constraint-compliant outputs through validation or deterministic synthesis. We show that as the agent interacts with the environment, these components co-evolve, yielding trustworthy behavior. |
| title | Towards Trustworthy Multi-Turn LLM Agents via Behavioral Guidance |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.11421 |