The paper introduces COL‑Tree, a compact landmark‑based tree structure designed to accelerate hierarchical object search in spatial networks. It supports diverse query types—such as aggregate k‑nearest neighbor (AkNN), k‑farthest neighbor (kFN), and others—by enabling efficient graph traversal with a more accurate heuristic than Euclidean‑based methods. Experiments on real‑world and synthetic road, terrain, and physics mesh networks show that COL‑Tree outperforms existing approaches by up to three orders of magnitude, with only modest preprocessing overhead.
By Tenindra Abeywickrama, Muhammad Aamir Cheema, Sabine Storandt
SecureGARD is a dynamic ontology‑guided governance framework designed to secure tool‑using large language model agents. It grounds heterogeneous tool calls in a shared semantic representation, using an executable ontology combined with episode‑level task state to make deterministic ready, waiting, and forbidden decisions. The system evolves its ontology offline from verified training executions, ensuring logical consistency and protecting entailments, and achieves a 0% attack success rate across multiple benchmarks while improving task utility under attack.
By Guoqin Xiong, Shengtao Wen, Xiang Chen, Xinyi Zeng, Zhaoxin Fan, Wei Ye, Xiaofeng Cao, Sheng-Jun Huang
LongBanana is the first expert-designed and expert-verified benchmark for long-context multi-reference image synthesis, featuring 90 tasks each with about 32 reference images, 20 constraints, and 50 evaluation items, totaling 4,670 atomic evaluation items. The benchmark uses a graph-grounded evaluation design that converts entity-relation graph obligations into checklist items for Content Fidelity, Relational Binding, and Global Constraint Satisfaction. An accompanying agent-harness framework, LongBanana-Gen, demonstrates that minimal harnesses improve generation quality, with a ReAct-Plan-Exec-Reflect workflow further enhancing evidence grounding and repair.
By Haoxiang Cao, Yuxuan Zhang, Penghui Du, Bo Li, Jiajiong Cao, Yingying Fan, Zimeng Wu, Yifu Luo, Mingzhe Zheng, Junwen Miao, Huaisong Zhang, Yang Yao, Yu Wang, Dongfu Jiang, Ping Nie, Wenhu Chen, Chaoqun Wang, Changqian Yu, Kelsey R. Allen
The paper presents a comprehensive evaluation of 11 memory substrates for long‑horizon LLM agents, using a unified set of 26 performance and efficiency metrics across three backbone models and four benchmark suites. It finds that no single substrate consistently outperforms others; retrieval‑heavy substrates aid long‑context factual QA but can impair sequential decision‑making, and scalability can shift performance trade‑offs. The study demonstrates that selecting a memory substrate tailored to the operating regime yields better results than any fixed choice, offering empirical guidance for designing efficient, reliable, and regime‑aware long‑term memory systems.
By Wei-Chieh Huang, Weizhi Zhang, Yuchen Wu, Yankai Chen, Eric Hanchen Jiang, Wooseong Yang, Yiwei Yang, Henry Peng Zou, Hanrong Zhang, Ying Nian Wu, Haolun Wu, Kai-Wei Chang, Philip S. Yu, Xue Liu, Aylin Caliskan
LITMUS is a benchmark for testing the safety of large language model agents in real operating system environments, focusing on behavior jailbreaks that can cause irreversible OS-level harm. It includes 819 high-risk test cases organized into a harmful seed subset and six attack-extended subsets covering jailbreak speaking, skill injection, and entity wrapping, and employs a semantic-physical dual verification mechanism with OS-level state rollback. Evaluation of frontier agents shows that even strong models can execute dangerous operations, exhibit execution hallucination, and are vulnerable to skill injection and entity wrapping attacks.
By Chiyu Zhang, Lu Zhou, Xiaogang Xu, Huiqin Yang, Bendong Jiang, Xiaolei Zhang, Yiran Zhao, Ruyi Chen, Jiafei Wu, Liming Fang, Zhe Liu
MKD2World introduces Typed Spatial Markdown (TSM), a Markdown-style textual representation that serializes desktop web interface elements by type, visual semantics, location, and hierarchy. Using the AtomBlock-WebUI dataset, a screenshot-to-TSM renderer is trained, enabling a two-stage world model that predicts current and next states for GUI agents. The resulting MKD2World-9B model outperforms GPT-5.6-Sol on EleF1 and boosts the success rate of a Qwen3.5-35B web agent on Online-Mind2Web tasks.
By Zhihao Nan, Sifan Zhou, Yiming Cheng, Jinkai Zheng, Zheng Zhu, Si Shi, Ming Li, Fei Yu
arXiv:2611. 05586v1 Announce Type: new Abstract: As autonomous AI agents enter permissionless onchain markets, selecting new service providers remains a core challenge.
By Reza Ramji, Preston Vander Vos, Ziming Zeng, Duc V. Le
MA‑SafeDiffuser is a multi‑agent extension of SafeDiffuser that incorporates diffusion‑based trajectory planners with finite‑time diffusion invariance guarantees for joint‑agent safety specifications. It formalizes joint safe sets as intersections of per‑agent and pairwise barrier sets, and derives both centralized and decentralized constrained denoising procedures that maintain invariance under mild assumptions. The approach also tackles local‑trap and deadlock issues using time‑varying specifications and liveness CBFs, and introduces a lightweight benchmarking suite including a multi‑agent Maze2D domain, showing reduced violation counts compared to unconstrained diffusion baselines while preserving planning quality.
By Kiran Ravish, Ankita Kushwaha, Preeti, Pawan Kumar
The paper introduces a challenge‑aware adaptive recruitment system for medical multi‑agent reasoning. An external CHALLENGER monitors the evolving reasoning trace to detect factual or expertise gaps, triggering targeted knowledge retrieval or specialist recruitment mid‑discussion. Experiments on the MEDAGENTSBENCH hard subset show that challenge‑aware in‑between retrieval and Challenger‑triggered specialist recruitment improve accuracy, while naive combinations degrade performance, underscoring the importance of timing and coordination in resource recruitment.
By Riccardo Riccio, Haitz S\'aez de Oc\'ariz Borde, Pietro Li\`o
VirtualTrialHarness (VTHarness) is an agent framework designed to streamline the operationalization of virtual trial protocols by selecting reusable procedural skills for each workflow stage and using explicit contracts to ensure consistent study decisions. It introduces VTBenchmark for evaluating source selection and cohort construction, achieving 95.1% workflow success after requirement clarification versus 60.4% with a standard procedural corpus. The framework demonstrated improved source recovery across language models, increased overlap with analyst code in Mayo Clinic cases, and enabled reference‑matched cohort execution across multiple MIMIC‑IV tasks, supporting investigator review within the Mayo Clinic Virtual Trials Program.
By Pengze Li, Jingna Feng, Yue Yu, Yining Wu, Xinyue Hu, Jie Lin, Avanti Bhandarkar, Cui Tao
The paper introduces Conformal Reservation for Orchestration (CRO), an online algorithm that uses past execution traces to allocate compute and bandwidth for agentic AI systems at dispatch time. CRO provides finite‑sample, distribution‑free coverage guarantees, ensuring that resource reservations meet specified probabilistic deadlines. Simulations demonstrate that CRO uniquely satisfies target coverage across all risk levels while reducing over‑reservation—and thus resource waste—by about half compared to a Chebyshev‑bound baseline.
By Masoud Shokrnezhad, Tarik Taleb
Agent Harness Awareness addresses the gap between tool descriptions and real‑time operational suitability by having the host provide a verified, up‑to‑date state snapshot before an agent dispatches an action. In controlled benchmarks, this approach cut observable policy regret by nearly 97 % and boosted single‑dispatch completion from 69.4 % to 95.8 %. In live, fault‑injected tests, verified state eliminated excess operational cost and achieved 100 % completion across all model‑by‑fault scenarios.
By Manu Agrawal, Shrey Nagpal
AvalancheBench is a multimodal enterprise analytics benchmark that tests data agents’ ability to recover a latent world from observed data, using 58 questions across text, images, and documents. It evaluates agents on analytical fidelity, monetary cost, and a new capability‑adjusted token cost (CATC) metric, revealing that misinterpreting requirements and misuse of semantic operators are the biggest sources of error. The benchmark highlights how errors can propagate through dependent questions, affecting overall performance.
By Darek Kleczek, Fuheng Zhao, Alexander W. Lee, Julien Tissier, Pawel Liskowski, Ugur Cetintemel, Anupam Datta
The paper introduces OPSD, a method that uses on‑policy self‑distillation to train a context‑free student model from a teacher conditioned on user memory, combined with a verify‑then‑regenerate router that directs queries to either weight or text memory. OPSD enables weight memory to compete with or surpass traditional text‑based memory systems on the HaluMem benchmark, reducing token usage per query. The study also explores how different training recipes influence weight memory performance and offers guidance for integrating it with retrieval in persistent, multi‑session agent deployments.
By Zeyu Fang, Jun Yin, Yongsheng Mei, Xu Han, Chaitali Joshi, Zhongkai Sun, Jie Hao, Bin Guo, Chengyuan Ma, Benjamin Z. Yao, Chenlei Guo
The paper investigates confirmation bias in Agent-as-a-Judge systems, which evaluate open-ended agent tasks by gathering evidence from the agent’s trajectory. It demonstrates that a single false pre‑screening note can overturn up to 40% of correct verdicts across multiple judges and datasets, even when the judge collects decisive evidence. The authors classify the resulting errors into over‑trust, hallucination, and under‑investigation, noting that over‑trust dominates in larger Claude judges, and show that lightweight mitigations reduce but do not eliminate the bias.
By Drake Benjamin Brown, Khushbu Pahwa, Yubin Ge, Salvatore Romeo, Akhil Agnihotri, Yi Zhang, Xiaofei Ma, Daniele Bonadiman
The paper investigates how vision‑language models (VLMs) perceive uncertainty in visualizations and make decisions based on visual data. Two studies were conducted: one on agents’ ability to quantify uncertainty in charts, and another on their decision‑making when presented with images. Results show GPT‑4o best perceives uncertainty in pie and bar charts, while agents align most closely with rational decision theory when uncertainty is shown via circle charts, icon arrays, or text.
By Keyty Loza, Shayan Monadjemi
The paper proposes a four‑layer architecture—Signals, Domains, Claims, and Dimensions—to preserve and revise organizational interpretations of evidence. It introduces a truth‑maintenance network that updates the standing of interpretations as new evidence arrives and uses validated, versioned Dimensions to position these interpretations along named axes. Experiments show that incorporating Dimensions improves retrieval performance, with a 0.028 nDCG@10 gain for the Dimension term and overall nDCG@10 increases of 0.081 and 0.053 over baseline configurations.
By Andrew Miracle, Phyl Georgiou, Christine Okoth, Ese Monday
Reglos is a training‑free personal memory agent that continuously updates an append‑only record of user interactions, allowing newer preferences to override older ones and enabling selective forgetting of facts. Its memory harness evolves through recursive self‑improvement: a coding agent generates patches from failure traces, a critic from a second model family screens them, and a fixed evaluator decides which patches to accept. Using the same backbone across systems, Reglos surpasses state‑of‑the‑art memory agents such as Mem0, MemOS, and EverOS, achieving 61.3 % and 84.0 % on PersonaMem‑v2 and LongMemEval‑S with Qwen3.8‑27B, and 57.5 % and 87.2 % with GPT‑5.6 Luna.
By Sen Yang, Jiaduo Lin, Zehong Huang, Wen Liu
EnzymeRSI is a dual‑loop recursive self‑improvement system that separates the evolution of harness assets (outer loop) from the development of prediction and selection algorithms (inner loop), closing the loop with a cloud laboratory for physical feedback. The system improved ProteinGym Spearman correlation by 0.044 across five assays, achieved high correlations on CD19 and OPSD, and accelerated ThermoMPNN and TadA‑Bench performance. In prospective wet‑lab tests, EnzymeRSI identified variants with markedly higher activity, including a cytidine deaminase variant with 90.2% 5mC conversion and a DNA polymerase KOD‑E variant with 31.1‑fold increased catalytic efficiency.
By Genoria AI Team, Yuejie Hou, Lixiang Yang, Yisi Liu, Weiting Tang, Yueyuxiao Yang, Meng Yang
ElecLoad is a context‑aware agent that improves electricity load forecasting by adaptively organizing the forecasting process around multiple events. It analyzes each event’s relevance, timing, and interactions with historical load, then decides whether to forecast the entire horizon or partition it into segments. Sub‑agents generate initial forecasts for each segment, and the main agent refines them based on feedback, achieving state‑of‑the‑art performance on several datasets.
By Huajian Zhang, Mingyue Cheng, Xingpeng Gao, Xiaoyu Tao, Zhiding Liu, Jiqian Yang, Zhi Li