Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2609.26368v1 Announce Type: new Abstract: Long-horizon and multi-turn agents typically generate short actions and process long observations from tools and environments. This growing context dem...
arXiv:2609.26610v1 Announce Type: new Abstract: Despite their outstanding performance on many NLP tasks, LLMs face serious challenges related to semantic abstraction. In this study, we are interested...
arXiv:2609.25176v1 Announce Type: cross Abstract: Real-time voice assistants must reason over evolving requests, execute actions, and follow conversational rules. Qwen-Audio-3.1-Realtime brings these...
arXiv:2609.26693v1 Announce Type: new Abstract: A coding agent must emit a valid tool call--a parseable invocation of a tool in the provided schema--before the harness can execute its chosen action....
arXiv:2609.26048v1 Announce Type: cross Abstract: Language-model agents often reach a working solution and then fail to consistently deliver it. We study runtime policies: targeted natural-language i...
arXiv:2508.15526v2 Announce Type: replace Abstract: The rapid proliferation of large language models (LLMs) has intensified the requirement for reliable safety evaluation to uncover model vulnerabili...
arXiv:2604.13706v2 Announce Type: replace Abstract: Professional fact-checkers rely on domain knowledge and deep contextual understanding to verify claims. Large language models (LLMs) and large reas...
arXiv:2605.25200v3 Announce Type: replace Abstract: Travel planning in the real world is overwhelmingly a \textit{group} activity, yet existing LLM travel-planning benchmarks reduce it to a single us...
arXiv:2508.07292v4 Announce Type: replace-cross Abstract: Endoscopic diagnosis is an iterative process in which clinicians acquire, compare, and verify local visual evidence before reaching a conclus...
arXiv:2609.25652v1 Announce Type: new Abstract: Recent game world models support realistic visual simulation and interactive gameplay based on player inputs. However, they typically learn environment...
arXiv:2609.25831v1 Announce Type: cross Abstract: Recent VLM-based autonomous driving planners adopt GRPO-style reinforcement learning to optimize driving performance. However, existing GRPO recipes...
arXiv:2605.25059v4 Announce Type: replace Abstract: Crucial for autonomous exploration, online 3D occupancy prediction and mapping incrementally construct dense spatial representations on the fly. Em...
The paper discusses the trustworthiness of agentic AI systems built on large language models, highlighting new security and operational risks such as indirect prompt injection, memory contamination, and cross‑session data leakage. It categorizes failure modes, reviews mitigation strategies—including instruction hierarchies, context isolation, and constrained tool use—and introduces the Trustworthy Agent Development Lifecycle (TADL), a six‑phase framework for specification, design, training, evaluation, deployment, and monitoring. The authors note that TADL has not yet been empirically validated but offers a structured foundation for developing more secure and accountable agentic systems, and they call for improved benchmarks and future research priorities.
The paper introduces a method for creating reactive character behaviors in continuous games as compact, human‑readable programs. It searches over a domain‑specific language that uses reactive geometric decisions and higher‑order constructs to discretize continuous behavior space, while eliminating redundant program forms through synthesis antipatterns. The approach, called agentic sketching, combines bottom‑up symbolic enumeration with top‑down guidance from a coding agent, and outperforms either technique alone on a benchmark of 14 continuous games.
EDGEGEN is a synthetic task generation framework that extracts compliance rules from a tool‑calling agent’s specification to create database‑grounded edge‑case tasks that violate those rules. By combining EdgeGen with existing synthetic data generation methods, it forms a fully automated closed‑loop system that requires no human annotation. Experiments show that finetuning on EdgeGen data improves performance by 2–42 % on the tau2bench airline domain, while harness optimization yields 10–30 % gains over human‑curated and base harnesses for the Gemma‑4‑e4b model.
DUMA-Bench is a new benchmark that evaluates the security of large language model agents in dual‑control settings, where both the agent and the user can modify the shared environment. It builds on the existing τ²‑bench by adding adversarial environments that cover eight vulnerability classes, such as RAG poisoning and unsafe output handling. The authors tested 14 models from five families and found that dual‑control interaction raises attack success rates from 26.9% to 41.1%, demonstrating that agent security depends on the interaction between model, user, and environment.
SemDHT introduces a certified semantic index for discovering agent-accessible capabilities over exact-key distributed hash tables (DHTs). It uses a two-layer semantic sketch—coarse cells for grouping descriptors and residual codes for refining candidate selection—allowing providers to publish at a bounded set of derived keys while requesters probe precision keys first. Anchor committees certify descriptor-to-key consistency, enabling efficient, consistent discovery with fewer lookups and reduced publication fan‑out, as demonstrated by high recall and significant speedups over locality‑sensitive hashing in real‑world experiments.
ActGov is a runtime enforcement framework that validates each action proposed by a large language model (LLM) agent before it interacts with external tools, ensuring that actions stay within task‑scoped authorization boundaries and comply with dynamically constructed policies. It builds policies from tool specifications, benign tasks, and failure traces, verifying updates via SMT‑based counterexample checking. In evaluations on AgentDojo and AgentDyn benchmarks, ActGov consistently reduces indirect prompt‑injection attack success while maintaining task utility, outperforming existing defenses.
Agent0 is a fully autonomous framework that enables large language model agents to evolve without external data by using a multi‑step co‑evolution process. It pits a curriculum agent against an executor agent, both derived from the same base LLM, where the curriculum agent creates increasingly challenging tasks and the executor learns to solve them. By integrating external tools into the executor’s workflow, the system creates a self‑reinforcing cycle that continuously generates high‑quality curricula, leading to significant gains in reasoning performance—an 18% improvement on mathematical reasoning and 24% on general reasoning for the Qwen3‑8B‑Base model.
The ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains introduced a new Visual Question Answering benchmark that tests reasoning over documents from eight distinct domains such as business reports, scientific papers, and engineering drawings. Twenty valid submissions from eight teams were evaluated, featuring approaches ranging from zero‑shot vision‑language models to multi‑agent ensembles and fine‑tuned multimodal systems. Results indicate that the most effective systems employ structured evidence extraction, retrieval, verification, and orchestration across multiple components rather than single‑pass prompting.