arXiv AI June 9, 2026 By Rahul Suresh Babu, Laxmipriya Ganesh Iyer

Contract2Tool: Learning Preconditions and Effects for Reliable Tool-Augmented LLM Agents

arXiv:2606. 07904v1 Announce Type: new Abstract: Tool-augmented large language model agents increasingly rely on external APIs, but standard tool schemas describe how to call a tool, not when the tool is causally appropriate or what task state it produces.

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arXiv AI

Jun 6

ToolChoiceConfusion: Causal Minimal Tool Filtering for Reliable LLM Agents

arXiv:2606. 06284v1 Announce Type: new Abstract: Large language model agents increasingly rely on external tools, but larger tool menus can reduce reliability and efficiency by increasing wrong-tool calls, premature actions, and token cost.

By Rahul Suresh Babu, Laxmipriya Ganesh Iyer

llms agents benchmarks

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

llms rag agents benchmarks safety

Hugging Face Trending Papers

Sep 16

Symbolic Temporal Supervision of LLM Agents Using Contracts

Large language model (LLM) agents augmented by tools can automate complex, multi-step tasks, such as web navigation, code generation, and workflow orchestration, by acting on external systems through...

llms rag agents benchmarks safety

arXiv AI

Aug 12

DOCSCHISEL: Adaptive Tool Documentation Optimization Framework for LLM Agents

arXiv:2608. 10037v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rely on external tools to accomplish complex real-world tasks, making tool documentation a critical grounding resource for LLM agents.

By You Lu, Kun Zhang, Bihuan Chen, Xin Peng

llms agents benchmarks

arXiv AI

Jul 28

CRAFT: Learn the Schema, Execute the Plan

arXiv:2607. 22642v1 Announce Type: new Abstract: Enterprise coding agents translate natural-language analytical requests into executable code over proprietary APIs, schemas, and metric definitions.

By Aakash Kolekar, Sahika Genc, Shahriar Shariat, Bunyamin Sisman, Tibor Mezi, Barbara Poblete, Shree Vandana Kachroo, Calvin Chi, Parth Parmar, Ari Singer, Prayaas Jain, Cindy Barker, Benoit Dumoulin

llms agents reinforcement-learning fine-tuning

arXiv AI

Jun 16

ToolMenuBench: Benchmarking Tool-Menu Filtering Strategies for Reliable and Efficient LLM Agents

arXiv:2606. 15508v1 Announce Type: new Abstract: Tool-augmented large language model agents increasingly operate over large tool libraries, but existing evaluations often focus on whether a model can call a tool correctly rather than how the visible tool menu shapes reliability, efficiency, and safety-relevant risk exposure.

By Rahul Suresh Babu, Laxmipriya Ganesh Iyer

llms agents benchmarks safety

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ToolChoiceConfusion: Causal Minimal Tool Filtering for Reliable LLM Agents

Symbolic Temporal Supervision of LLM Agents Using Contracts

Symbolic Temporal Supervision of LLM Agents Using Contracts

DOCSCHISEL: Adaptive Tool Documentation Optimization Framework for LLM Agents

CRAFT: Learn the Schema, Execute the Plan

ToolMenuBench: Benchmarking Tool-Menu Filtering Strategies for Reliable and Efficient LLM Agents