← Back to all news
arXiv AI By Iftach Shoham, Yuval David, Offer Akrabi, Erez Bilgory, Ido Levy, Sami Marreed, Sergey Zeltyn, Segev Shlomov

Think Before You Act: Decision Gate for LLM Agents

Read the original on arXiv AI →

The paper introduces JevAct, a runtime verification framework that intercepts selected actions of large language model (LLM) agents before execution. JevAct compiles domain policies into executable state checks and uses a prefill-only decision model to provide probability‑based judgments, enabling feedback‑driven correction without modifying the underlying agent. Evaluations on eleven LLM agents across Airline, Telecom, and BFCL benchmarks show that JevAct improves agent reliability, especially on Telecom and BFCL tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Oct 19

Testing the Planning Claims of LLM-Based Agents against Formal Semantics

The paper evaluates the planning abilities of large language model (LLM) agents by comparing their performance against formal semantics defined in hidden PDDL models. Across five domains, four LLMs, six prompting strategies, and three agent architectures, the best setup solved 78% of solvable tasks offline but only 40% online, with limited recognition of unsolvable tasks and no clear benefit from more elaborate prompting. The study concludes that claims of LLM planning competence require validated action traces rather than end‑to‑end success rates.

By Oriol Miro-Lopez-Feliu, Oscar Mir\'o L\'opez-Feliu, Victor Gimenez-Abalos, Javier Vazquez-Salceda, Victor Carballo Araruna, Sergio Alvarez-Napagao
llmsagents
More like this →
arXiv AI
Sep 17

Symbolic Temporal Supervision of LLM Agents Using Contracts

ContrAgent is a contract‑based framework that provides symbolic temporal supervision for large language model agents. It records an agent’s tool‑call sequence as a trace of checkable predicates and formalizes desired behaviors with assume‑guarantee contracts expressed in linear temporal logic over finite traces (LTLf). Each contract is compiled into a deterministic finite automaton that both gates actions online and evaluates recorded traces offline, enabling deterministic, reproducible verdicts and significantly lower per‑call latency compared to existing LLM‑judge and rule‑based guardrail baselines.

By Yifeng Xiao, Pierluigi Nuzzo
llmsragagentsbenchmarkssafety
More like this →
arXiv AI
Jul 14

AgentAbstain: Do LLM Agents Know When Not to Act?

arXiv:2607. 10059v1 Announce Type: new Abstract: Agent systems based on large language models (LLMs) are increasingly deployed for autonomous tasks, yet existing evaluations mostly focus on task success rather than whether agents know when to abstain.

By Xun Liu, Yi Evie Zhang, Vira Kasprova, Parisa Rabbani, Pardis Sadat Zahraei, Tianyu Zhang, Ali Ebrahimpour-Boroojeny, Varun Chandrasekaran
llmsagentsbenchmarks
More like this →