https://survivingthesingularity.com/signals Algorithmically swept arXiv research ranked by singularity relevance. Not human-curated. en-us Sat, 27 Jun 2026 21:39:22 +0000 https://arxiv.org/abs/2606.25532 https://arxiv.org/abs/2606.25532 Wed, 24 Jun 2026 00:00:00 +0000 Artificial intelligence increasingly drives automated scientific discovery, yet contemporary generalist agents lack physical grounding, frequently hallucinating hardware-incompatible designs. Here, we present a physically grounded, multi-agent discovery engine that autonomously architects hardware-c... [Score: 9.5 | Flagged for: foundation model, agent, multi-agent, agentic, autonomous] Authors: Jiangwei Zhang, Wen Sun, et al. https://arxiv.org/abs/2606.27154 https://arxiv.org/abs/2606.27154 Thu, 25 Jun 2026 00:00:00 +0000 Root cause analysis (RCA) poses a holistic test of LLM agentic capabilities, such as long-context understanding, multi-step reasoning, and tool use. However, existing datasets suffer from a fundamental gap: they label only the root cause, not the propagation path connecting it to the observed sympto... [Score: 9.0 | Flagged for: agent, agentic, tool use, reasoning, llm] Authors: Aoyang Fang, Yifan Yang, et al. https://arxiv.org/abs/2606.26575 https://arxiv.org/abs/2606.26575 Thu, 25 Jun 2026 00:00:00 +0000 Complex multi-agent control tasks remain challenging for traditional rule-based and model-based approaches, motivating the adoption of learning-based methods. However, learning-based methods often struggle with sim-to-real transfer because they rely on accurate dynamics modeling or system identifica... [Score: 9.0 | Flagged for: agi, agent, multi-agent, alignment, rag] Authors: Chenlong Liu, Zhuohui Zhang, et al. https://arxiv.org/abs/2606.25358 https://arxiv.org/abs/2606.25358 Wed, 24 Jun 2026 00:00:00 +0000 Assessing financial literacy during gameplay without disrupting the learning experience remains a key challenge in serious games for education. We present the Agentic BKT pipeline, a multi-agent large language model architecture for stealth assessment of financial competencies from open-ended gamepl... [Score: 8.5 | Flagged for: agent, multi-agent, agentic, llm, language model] Authors: Gabriel Santos, Rita Julia, et al. https://arxiv.org/abs/2606.26294 https://arxiv.org/abs/2606.26294 Wed, 24 Jun 2026 00:00:00 +0000 Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains. However, their search methods generally assume a stationary evaluation criterion: a fixed verifier, benchmark, or labeled dataset that remains valid as the agent improve... [Score: 8.0 | Flagged for: recursive self-improvement, self-improving, agent, agentic] Authors: Alex Iacob, Andrej Jovanović, et al. https://arxiv.org/abs/2606.25899 https://arxiv.org/abs/2606.25899 Wed, 24 Jun 2026 00:00:00 +0000 We evaluate manipulative behavior in six frontier language models across six environments, ranging from negotiation tasks to agentic workflows, resulting in 13{,}590 individual scenarios. Manipulation rates are measured across three axes: framing (mandate honesty or permit manipulation), incentive s... [Score: 7.5 | Flagged for: agent, agentic, llm, language model, rag] Authors: Adeeb Zaman, Erik Nordby, et al. https://arxiv.org/abs/2606.23991 https://arxiv.org/abs/2606.23991 Mon, 22 Jun 2026 00:00:00 +0000 What is an agent? What constitutes agency? With the rise of Large Language Model (LLM) systems marketed as ``coding agents'', ``AI co-scientists'', and other ``agentic" tools that promise to drive up productivity, and at the same time, ``existential" concerns such as AI escaping human control with d... [Score: 7.5 | Flagged for: agent, agentic, llm, language model, automation] Authors: Eric Xing, Mingkai Deng, et al. https://arxiv.org/abs/2606.23664 https://arxiv.org/abs/2606.23664 Mon, 22 Jun 2026 00:00:00 +0000 Multi-agent systems (MAS) offer a scalable path forward for agentic AI, comprising multiple LLM-based agents, each assigned a system prompt and a position within a workflow that governs inter-agent coordination and output aggregation. System prompts thus form a critical and accessible optimization s... [Score: 7.0 | Flagged for: agent, multi-agent, agentic, llm] Authors: Juyang Bai, Laixi Shi https://arxiv.org/abs/2606.22844 https://arxiv.org/abs/2606.22844 Mon, 22 Jun 2026 00:00:00 +0000 Long-term memory has become increasingly important for LLM agents that operate across extended interactions and evolving task contexts. Recent memory systems have made past experiences more persistent, compact, and retrievable, but retrieval alone does not ensure that a memory provides valid evidenc... [Score: 7.0 | Flagged for: agent, agentic, llm, retrieval, rag] Authors: Wei Yang, Bryce Kan, et al. https://arxiv.org/abs/2606.27233 https://arxiv.org/abs/2606.27233 Thu, 25 Jun 2026 00:00:00 +0000 We present a conceptual framework for analyzing dialogue in collaborative problem-solving contexts, with an emphasis on the emerging dynamics of human-AI and multi-agent collaboration. As intelligent systems become active agents capable of autonomous reasoning and strategic cooperation, understandin... [Score: 6.5 | Flagged for: agent, multi-agent, autonomous, reasoning] Authors: Zhengyuan Liu, Stella Xin Yin, et al. https://arxiv.org/abs/2606.27009 https://arxiv.org/abs/2606.27009 Thu, 25 Jun 2026 00:00:00 +0000 Multi-agent large language model (LLM) loops, for example a Writer that drafts and a Critic that revises, are almost always terminated by a fixed iteration cap (max_iterations). This is a syntactic kill-switch: it is blind to whether the answer is still improving, so it over-spends tokens on easy in... [Score: 6.5 | Flagged for: agent, multi-agent, llm, language model] Authors: Sahil Shrivastava https://arxiv.org/abs/2606.23797 https://arxiv.org/abs/2606.23797 Mon, 22 Jun 2026 00:00:00 +0000 Graph and multi-agent orchestration frameworks make production large language model (LLM) workflows practical, but they do not by themselves solve conversational continuity when users maintain several interdependent objectives. This conceptual systems paper focuses on the high-complexity end of that... [Score: 6.5 | Flagged for: agent, multi-agent, llm, language model] Authors: Mariano Garralda-Barrio https://arxiv.org/abs/2606.27251 https://arxiv.org/abs/2606.27251 Thu, 25 Jun 2026 00:00:00 +0000 Building persistent embodied agents in unstructured environments demands unified orchestration of heterogeneous tools spanning both cyber (APIs, IoT) and physical (manipulation, navigation) domains, coupled with autonomous recovery from physical failures that inevitably arise over extended operation... [Score: 6.0 | Flagged for: agent, autonomous, autonomy, embodied] Authors: Junhao Shi, Zezheng Huai, et al. https://arxiv.org/abs/2606.27128 https://arxiv.org/abs/2606.27128 Thu, 25 Jun 2026 00:00:00 +0000 Wildfire monitoring from UAVs requires reliable reasoning over complex aerial scenes, where smoke, scale variation, and occlusions often limit RGB-only interpretation. We introduce FlameVQA, a multiple-choice visual question answering benchmark for UAV-based wildfire intelligence built on FLAME 3, l... [Score: 6.0 | Flagged for: agi, reasoning, interpret, rag] Authors: Mobin Habibpour, John Spodnik, et al. https://arxiv.org/abs/2606.27123 https://arxiv.org/abs/2606.27123 Thu, 25 Jun 2026 00:00:00 +0000 Closed-loop traffic simulation remains challenging because it must generate interactive multi-agent behaviors that are scene-consistent and controllable throughout rollout. Prior diffusion-based approaches achieve strong realism, but their computational cost can hinder deployment in time-constrained... [Score: 6.0 | Flagged for: agent, multi-agent, autonomous, planning] Authors: Shubham Vaijanath Phoolari, Aleyna Kara, et al. https://arxiv.org/abs/2606.25334 https://arxiv.org/abs/2606.25334 Wed, 24 Jun 2026 00:00:00 +0000 Post-discharge clinical follow-up is critical for maintaining continuity of care and mitigating long-term health risks. However, traditional follow-up paradigms suffer from shortage of health workforce, fragmented patient histories, and information silos across clinical departments. While large lang... [Score: 6.0 | Flagged for: agent, multi-agent, language model, rag] Authors: Runwei Guan, Yi Zhou, et al. https://arxiv.org/abs/2606.23585 https://arxiv.org/abs/2606.23585 Mon, 22 Jun 2026 00:00:00 +0000 As autonomous aircraft are introduced at scale and traffic density increases, centralized management becomes insufficient to coordinate the large numbers of crewed and uncrewed aircraft. Dedicated Advanced Air Mobility (AAM) corridors have therefore been proposed for organizing high-density autonomo... [Score: 6.0 | Flagged for: agent, multi-agent, autonomous, planning] Authors: Jasmine Jerry Aloor, Aadarsh Govada, et al. https://arxiv.org/abs/2606.23764 https://arxiv.org/abs/2606.23764 Mon, 22 Jun 2026 00:00:00 +0000 Fei Xiaotong's Differential Order Pattern characterizes rural society as egocentric and relationally graded, with cooperation attenuating over social distance. Although often treated as culturally specific, its mechanistic basis remains under-operationalized, and prior LLM-based simulations have mai... [Score: 6.0 | Flagged for: agent, multi-agent, llm, emergent] Authors: Zhiyuan Ji, Xinyu Chen, et al. https://arxiv.org/abs/2606.27330 https://arxiv.org/abs/2606.27330 Thu, 25 Jun 2026 00:00:00 +0000 Multimodal web agents can assist humans in operating repetitive GUI tasks, where effective task planning is essential for decomposing complex tasks into executable actions. While small open source MLLMs are cost efficient and privacy preserving compared with commercial large models, they suffer from... [Score: 5.5 | Flagged for: agent, autonomous, planning, llm] Authors: Tianyi Men, Zhuoran Jin, et al. https://arxiv.org/abs/2606.27288 https://arxiv.org/abs/2606.27288 Thu, 25 Jun 2026 00:00:00 +0000 Multi-model LLM systems such as routing, voting, cascades, fusion, and mixture-of-agents are used to beat single-model accuracy. We show that their gain is capped by a quantity the field rarely reports. For any policy whose output is one member model answer, accuracy cannot exceed one minus beta, wh... [Score: 5.5 | Flagged for: agent, llm, language model, rag] Authors: Josef Chen https://arxiv.org/abs/2606.27136 https://arxiv.org/abs/2606.27136 Thu, 25 Jun 2026 00:00:00 +0000 For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience. Existing work has typically separated two uses of such experience: keeping it outside the model as natural-language rules for later prompting, or using trajectories ... [Score: 5.5 | Flagged for: agent, interpret, llm, language model] Authors: Shicheng Ye, Chao Yu https://arxiv.org/abs/2606.26964 https://arxiv.org/abs/2606.26964 Thu, 25 Jun 2026 00:00:00 +0000 As embodied AI and world models increasingly operate in dynamic 3D environments, visual perception must move beyond passively interpreting given observations toward actively deciding what to observe. We study this problem through camera planning in dynamic 3D story worlds, where the camera must not ... [Score: 5.5 | Flagged for: world model, planning, interpret, embodied] Authors: Jiaming Bian, Bingliang Li, et al. https://arxiv.org/abs/2606.27103 https://arxiv.org/abs/2606.27103 Thu, 25 Jun 2026 00:00:00 +0000 Humans flexibly adapt their reasoning strategies to the requirements of a given problem. Large language models (LLMs) have performed well on many cognitive tasks, however, it is unclear whether this accuracy is a result of pattern matching from training data or flexible reasoning. Here, we introduce... [Score: 5.5 | Flagged for: reasoning, interpret, llm, language model] Authors: Bella Fascendini, Kathryn McGregor, et al. https://arxiv.org/abs/2606.25978 https://arxiv.org/abs/2606.25978 Wed, 24 Jun 2026 00:00:00 +0000 Multi-agent goal recognition asks an observer to jointly infer which agents act together and what each team is trying to achieve, so the hypothesis space grows combinatorially with the number of team partitions and goals per team. Real applications such as drone surveillance and collaborative roboti... [Score: 5.5 | Flagged for: agent, multi-agent, robot, robotics] Authors: Thiago Thomas, Gabriel de Oliveira Ramos, et al. https://arxiv.org/abs/2606.25073 https://arxiv.org/abs/2606.25073 Tue, 23 Jun 2026 00:00:00 +0000 In cooperative multi-agent reinforcement learning (MARL), from a deployment perspective, it is challenging and expensive to train agents from scratch for each new environment or task. In this work, we propose GCT-MARL, a transfer learning framework that builds on the multi-view graph contrastive bac... [Score: 5.5 | Flagged for: agent, multi-agent, alignment] Authors: Animesh Animesh, Satheesh K Perepu, et al. https://arxiv.org/abs/2606.23760 https://arxiv.org/abs/2606.23760 Mon, 22 Jun 2026 00:00:00 +0000 Engineering reliable autonomous systems is an important and growing topic in computer science. As autonomous systems become more prevalent, easy-to-use techniques for building them reliably are increasingly important. This workshop report captures and expands on the discussions at the Lorentz Cent... [Score: 5.5 | Flagged for: agent, autonomous, autonomy, robot] Authors: Marie Farrell, Matt Luckcuck, et al. https://arxiv.org/abs/2606.27268 https://arxiv.org/abs/2606.27268 Thu, 25 Jun 2026 00:00:00 +0000 Recently, a few works have made early attempts to study test-time scaling for embodied tasks. However, two major challenges remain unsolved: (1) reasoning can effectively improve the performance of the policy, but its scaling mechanism has seldom been studied; (2) historical information is essential... [Score: 5.0 | Flagged for: reasoning, embodied, robot, scaling] Authors: Wen Ye, Peiyan Li, et al. https://arxiv.org/abs/2606.27188 https://arxiv.org/abs/2606.27188 Thu, 25 Jun 2026 00:00:00 +0000 We introduce the process harness, a new mechanism for uplifting legacy workflows into Agentic Business Process Management (Agentic BPM) without replacing the underlying workflow engine. A process harness places a policy-governed agentic layer around a deterministic workflow engine, intercepting desi... [Score: 5.0 | Flagged for: agent, agentic, reasoning] Authors: Fabiana Fournier, Lior Limonad https://arxiv.org/abs/2606.26969 https://arxiv.org/abs/2606.26969 Thu, 25 Jun 2026 00:00:00 +0000 Does intelligence require the ability to reason about phenomena beyond direct experience? It is natural to suspect that some complex thought cannot be captured through language alone. However, of particular concern to this work, is whether visualising counterfactual events can complement language as... [Score: 5.0 | Flagged for: world model, reasoning, llm] Authors: Munachiso Samuel Nwadike, Zangir Iklassov, et al. https://arxiv.org/abs/2606.27025 https://arxiv.org/abs/2606.27025 Thu, 25 Jun 2026 00:00:00 +0000 Building general-purpose role-playing agents that faithfully portray any character from a natural-language profile remains challenging. The dominant paradigm -- supervised fine-tuning -- encourages behavioral mimicry without deep, human-like internal thought processes, resulting in poor out-of-distr... [Score: 5.0 | Flagged for: agent, reasoning, chain-of-thought, rag] Authors: Zhenhua Xu, Dongsheng Chen, et al.