NeuroGenesysThe research we build on.
1,514 papers by other researchers that our work cites, from continual learning and verification to language models, agents and hardware. Each entry links to the paper on arXiv and to where we use it. 301 are cited on published pages of this site; the rest in the research notes behind our studies. Our own papers are in the archive.
- 2026-08arXiv 2608.30661SwarmBench: Can Large Language Models Act as Agent Swarm Orchestrators?
Jinshan Gao, Zhuoran Jin, Tianyi Men et al. · Language models & NLP
Large language model-based multi-agent systems are evolving from fixed interaction topologies toward dynamically orchestrated Agent Swarms.
- 2026-06arXiv 2606.31174ClawArena-Team: Benchmarking Subagent Orchestration and Dynamic Workflows in Language-Model Agents
Kaiwen Xiong, Haonian Ji, Shi Qiu et al. · AI & agents
Production large language-model (LLM) agents are increasingly deployed not as lone problem-solvers but as managers: a main model creates specialized subagents, delegates work, and orchestrates their parallel, asynchronous returns through dynamic workflows.
- 2026-06arXiv 2609.04217At Equal Inference Cost, Multi-Agent Structure Does Not Beat a Single Frozen Agent
David Dylan, Aoife Brennan, Cian Murphy et al. · AI & agents
Multi-agent LLM pipelines, such as Planner-Executor-Critic teams, often report gains over single agents, but these gains usually come with higher inference cost because the team makes multiple model calls per environment step.
- 2026-05arXiv 2606.00820Not All Flips Are Conformity: Decomposing Stance Convergence in Multi-Agent LLM Debate
Xiqi Hao, Zengqing Wu, Yu-Xuan Qiu et al. · Language models & NLP
Multi-agent debate (MAD) among large language models (LLMs) is often judged by answer changes and convergence, yet these outcomes alone cannot establish that peers improved the answer.
- 2026-05arXiv 2605.29800Nine Judges, Two Effective Votes: Correlated Errors Undermine LLM Evaluation Panels
Guneet Kohli · Language models & NLP
LLM-as-a-judge panels aggregate votes from multiple models, with the expectation that diverse models yield more reliable evaluations. We develop a framework to measure the true informational value of such panels and quantify how far their reliability falls short of the independent-voting ideal.
- 2026-05arXiv 2605.22863Latent Cache Flow: Model-to-Model Communication Without Text
Maximillian Rossi, Prajwal Raghunath, Eugene Wu · Machine learning
LLM agents today communicate via text, which incurs considerable latency and information loss due to the need to autoregressively decode the sharer model's state and encode at the receiver model.
- 2026-05arXiv 2605.19193Sequential Consensus for Multi-Agent LLM Debates: A Wald-SPRT compute governor with calibration-based failure detection
Andrea Morandi · Machine learning
Multi-agent LLM debate improves factuality and reasoning, but most recipes pick a fixed round count, over-spending on easy items and under-spending on hard ones.
- 2026-05arXiv 2605.20247CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
Yang Liu, Toan Nguyen, Flora D. Salim · Machine learning
Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs).
- 2026-04arXiv 2605.00914The Cost of Consensus: Isolated Self-Correction Prevails Over Unguided Homogeneous Multi-Agent Debate
Blaž Bertalanič, Carolina Fortuna · AI & agents
Multi-agent debate, where teams of LLMs iteratively exchange rationales and vote on answers, is widely deployed under the assumption that peer review filters hallucinations.
- 2026-04arXiv 2604.14525Quantifying Cross-Query Contradictions in Multi-Query LLM Reasoning
Rohit Kumar Salla, Ramya Manasa Amancherla, Manoj Saravanan · AI & agents
Large language models frequently produce mutually inconsistent answers when reasoning over multiple related queries. We study case-file logical consistency: maintaining a globally satisfiable belief state across interdependent queries.
- 2026-04arXiv 2604.02460Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets
Dat Tran, Douwe Kiela · Language models & NLP
Recent work reports strong performance from multi-agent LLM systems (MAS), but these gains are often confounded by increased test-time computation.
- 2026-03arXiv 2603.18073Continually self-improving AI
Zitong Yang · AI & agents
Modern language model-based AI systems are remarkably powerful, yet their capabilities remain fundamentally capped by their human creators in three key ways.
- 2026-02arXiv 2603.06612Consensus is Not Verification: Why Crowd Wisdom Strategies Fail for LLM Truthfulness
Yegor Denisov-Blanch, Joshua Kazdan, Jessica Chudnovsky et al. · Machine learning
Pass@k and other methods of scaling inference compute can improve language model performance in domains with external verifiers, including mathematics and code, where incorrect candidates can be filtered reliably.
- 2026-01arXiv 2601.21795Effective LoRA Adapter Routing using Task Representations
Akash Dhasade, Anne-Marie Kermarrec, Igor Pavlovic et al. · Machine learning
Low-rank adaptation (LoRA) enables parameter efficient specialization of large language models (LLMs) through modular adapters, resulting in rapidly growing public adapter pools spanning diverse tasks.
- 2026-01arXiv 2601.19659KeepLoRA: Continual Learning with Residual Gradient Adaptation
Mao-Lin Luo, Zi-Hao Zhou, Yi-Lin Zhang et al. · Vision
Continual learning for pre-trained vision-language models requires balancing three competing objectives: retaining pre-trained knowledge, preserving knowledge from a sequence of learned tasks, and maintaining the plasticity to acquire new knowledge.
- 2026-01arXiv 2601.17311Phase Transition for Budgeted Multi-Agent Synergy
Bang Liu, Linglong Kong, Jian Pei · AI & agents
Multi-agent systems can improve reliability, yet under a fixed inference budget they often help, saturate, or even collapse.
- 2026-01arXiv 2601.07206LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing
Hao Li, Yiqun Zhang, Zhaoyan Guo et al. · AI & agents
Large language model (LLM) routing assigns each query to the most suitable model from an ensemble. We introduce LLMRouterBench, a large-scale benchmark and unified framework for LLM routing.
- 2025-12arXiv 2512.08296Towards a Science of Scaling Agent Systems
Yubin Kim, Ken Gu, Chanwoo Park et al. · AI & agents
Agents, language model-based systems capable of reasoning, planning, and acting are widely adopted in real-world tasks, yet how their performance changes as these systems scale across key dimensions remains underexplored.
- 2025-11arXiv 2511.20639Latent Collaboration in Multi-Agent Systems
Jiaru Zou, Ruizhong Qiu, Gaotang Li et al. · Language models & NLP
Multi-agent systems (MAS) extend large language models (LLMs) from independent single-model reasoning to coordinative system-level intelligence.
- 2025-10arXiv 2510.15416Adaptive Minds: Empowering Agents with LoRA-as-Tools
Pavan C Shekar, Aswanth Krishnan · AI & agents
We investigate a framework in which LoRA adapters are treated as callable tools that a base language model can dynamically select and invoke.
- 2025-10arXiv 2510.12266HiLoRA: Adaptive Hierarchical LoRA Routing for Training-Free Domain Generalization
Ziyi Han, Huanyu Wang, Zeyu Zhang et al. · Machine learning
Low-Rank Adaptation (LoRA) has emerged as a widely used technique for adapting large language models (LLMs) to new domains, due to its modular design and broad availability on platforms such as HuggingFace.
- 2025-10arXiv 2510.12697Multi-Agent Debate for LLM Judges with Adaptive Stability Detection
Tianyu Hu, Zhen Tan, Song Wang et al. · AI & agents
With advancements in reasoning capabilities, Large Language Models (LLMs) are increasingly employed for automated judgment tasks., majority voting), which can fail even when individual agents provide correct answers.
- 2025-10arXiv 2510.03418LegalWiz: A Multi-Agent Generation Framework for Contradiction Detection in Legal Documents
Ananya Mantravadi, Shivali Dalmia, Olga Pospelova et al. · AI & agents
Retrieval-Augmented Generation (RAG) integrates large language models (LLMs) with external sources, but unresolved contradictions in retrieved evidence often lead to hallucinations and legally unsound outputs.
- 2025-10arXiv 2510.01879REPAIR: Robust Editing via Progressive Adaptive Intervention and Reintegration
Yisu Wang, Ming Wang, Haoyuan Song et al. · Language models & NLP
Post-training for large language models (LLMs) is constrained by the high cost of acquiring new knowledge or correcting errors and by the unintended side effects that frequently arise from retraining.
- 2025-09arXiv 2509.23055Peacemaker or Troublemaker: How Sycophancy Shapes Multi-Agent Debate
Binwei Yao, Chao Shang, Wanyu Du et al. · Language models & NLP
Large language models (LLMs) often display sycophancy, a tendency toward excessive agreeability. This behavior poses significant challenges for multi-agent debating systems (MADS) that rely on productive disagreement to refine arguments and foster innovative thinking.
- 2025-09arXiv 2509.18137LoRALib: A Standardized Benchmark for Evaluating LoRA-MoE Methods
Shaoheng Wang, Yao Lu, Yuqi Li et al. · Machine learning
As a parameter efficient fine-tuning (PEFT) method, low-rank adaptation (LoRA) can save significant costs in storage and computing, but its strong adaptability to a single task is often accompanied by insufficient cross-task generalization capabilities.
- 2025-09arXiv 2509.05396Talk Isn't Always Cheap: Understanding Failure Modes in Multi-Agent Debate
Andrea Wynn, Harsh Satija, Gillian Hadfield · Language models & NLP
While multi-agent debate has been proposed as a promising strategy for improving AI reasoning ability, we find that debate can sometimes be harmful rather than helpful.
- 2025-08arXiv 2508.17536Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models?
Hyeong Kyu Choi, Xiaojin Zhu, Sharon Li · Language models & NLP
Multi-Agent Debate~(MAD) has emerged as a promising paradigm for improving the performance of large language models through collaborative reasoning. Despite recent advances, the key factors driving MAD's effectiveness remain unclear.
Cited inCoherence is selected, anchored, then pruned: a literature synthesis on logical coherence in LLM world modelsStable wrong beliefs evade every self-check: a literature synthesis on model self-knowledge and confident errorsStudy: Logical coherence of LLM world modelsStudy: Model self knowledge and confident errorsStudy: Orchestrated specialists and bridge synthesis - 2025-08arXiv 2508.05078From Isolation to Alignment: Unified LoRA for Efficient Multi-Task Learning
Jinda Liu, Yi Chang, Yuan Wu · Language models & NLP
Parameter-Efficient Fine-Tuning (PEFT) is essential for adapting Large Language Models (LLMs) to multi-task scenarios.
- 2025-07arXiv 2507.07140Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts
Samin Yeasar Arnob, Zhan Su, Minseon Kim et al. · Machine learning
Merging parameter-efficient task experts has recently gained growing attention as a way to build modular architectures that can be rapidly adapted on the fly for specific downstream tasks, without requiring additional fine-tuning.
- 2025-06arXiv 2506.21035Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts
Haodong Lu, Chongyang Zhao, Minhui Xue et al. · Machine learning
Continual learning (CL) with large pre-trained models aims to incrementally acquire knowledge without catastrophic forgetting.
- 2025-06arXiv 2506.18203Shrinking the Generation-Verification Gap with Weak Verifiers
Jon Saad-Falcon, E. Kelly Buchanan, Mayee F. Chen et al. · Language models & NLP
Verifiers can improve language model capabilities by scoring and ranking responses from generated candidates., tools like Lean).
- 2025-06arXiv 2506.12384Model Merging for Knowledge Editing
Zichuan Fu, Xian Wu, Guojing Li et al. · ACL 2025 (Industry) Oral · AI & agents
Large Language Models (LLMs) require continuous updates to maintain accurate and current knowledge as the world evolves.
- 2025-06arXiv 2506.07962Correlated Errors in Large Language Models
Elliot Kim, Avi Garg, Kenny Peng et al. · Language models & NLP
Diversity in training data, architecture, and providers is assumed to mitigate homogeneity in LLMs. However, we lack empirical evidence on whether different LLMs differ meaningfully.
- 2025-06arXiv 2506.04595Hierarchical-Task-Aware Multi-modal Mixture of Incremental LoRA Experts for Embodied Continual Learning
Ziqi Jia, Anmin Wang, Xiaoyang Qu et al. · Vision
Previous continual learning setups for embodied intelligence focused on executing low-level actions based on human commands, neglecting the ability to learn high-level planning and multi-level knowledge.
- 2025-05arXiv 2505.23295How Does Response Length Affect Long-Form Factuality
James Xu Zhao, Jimmy Z. J. Liu, Bryan Hooi et al. · Language models & NLP
Large language models (LLMs) are widely used for long-form text generation. However, factual errors in the responses would undermine their reliability. Despite growing attention to LLM factuality, the effect of response length on factuality remains underexplored.
- 2025-05arXiv 2505.22370SplitLoRA: Balancing Stability and Plasticity in Continual Learning Through Gradient Space Splitting
Haomiao Qiu, Miao Zhang, Ziyue Qiao et al. · Machine learning
Continual Learning requires a model to learn multiple tasks in sequence while maintaining both stability:preserving knowledge from previously learned tasks, and plasticity:effectively learning new tasks.
- 2025-05arXiv 2505.15424Gated Integration of Low-Rank Adaptation for Continual Learning of Large Language Models
Yan-Shuo Liang, Jia-Rui Chen, Wu-Jun Li · Language models & NLP
Continual learning (CL), which requires the model to learn multiple tasks sequentially, is crucial for large language models (LLMs). Recently, low-rank adaptation~(LoRA), one of the most representative parameter-efficient fine-tuning (PEFT) methods, has gained increasing attention in CL of LLMs.
- 2025-05arXiv 2505.03275RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation
Tiantian Gan, Qiyao Sun · AI & agents
Large language models (LLMs) struggle to effectively utilize a growing number of external tools, such as those defined by the Model Context Protocol (MCP)\cite{IntroducingMCP}, due to prompt bloat and selection complexity.
- 2025-04arXiv 2504.18530Scaling Laws For Scalable Oversight
Joshua Engels, David D. Baek, Subhash Kantamneni et al. · NeurIPS 2025 (Spotlight) · AI & agents
Scalable oversight, the process by which weaker AI systems supervise stronger ones, has been proposed as a key strategy to control future superintelligent systems.
Built from every arXiv paper cited in our papers, posts, topic hubs, reports and research notes, with titles, authors, dates and abstracts from the arXiv API (updated 2026-11-12). Summaries are the opening sentences of each paper's own abstract, not our assessment; a citation means we used or checked the work, not that we endorse it. Fields follow each paper's primary arXiv category. Papers cited only outside arXiv (journals, proceedings) are not listed yet. Entries are generated from our citations, so a mistyped ID in a note would list the wrong paper; we remove those as we find them. Report a wrong entry.
