AgentStream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?

• URL: http://arxiv.org/abs/2608.00155v1
• Date: Fri, 31 Jul 2026 17:50:08 GMT
• Title: AgentStream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?
• Abstract summary: Large language model (LLM) agents can self-evolve by continually improving from their own accumulated experience.<n>We introduce AgentStream, a unified framework that evaluates self-evolving agents spanning diverse evolution components.<n>Our results show that self-evolution reliability varies across streaming scenarios, the benefit of self-evolution is gated by model capability and non-monotonic in model strength.
• Score: 65.05151983193441
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model (LLM) agents can self-evolve by continually improving from their own accumulated experience. However, existing studies predominantly adopt independent evaluation. Consequently, the behavior of self-evolving agents in realistic streaming settings, where agents adapt to diverse and complex task streams, remains poorly understood. To address this gap, we introduce AgentStream, a unified framework that evaluates self-evolving agents spanning diverse evolution components by organizing agentic benchmarks into a configurable task stream and instantiating the \texttt{Isolated}, \texttt{Sequential}, and \texttt{Interleaved} streaming scenarios at test time, which progressively vary the scope and domain composition of the stream. Over these scenarios, we combinatorially evaluate five representative self-evolving methods across three frontier foundation models, disentangling how model capability, method architecture, and streaming scenario jointly shape self-evolution. Our results show that self-evolution reliability varies across streaming scenarios, the benefit of self-evolution is gated by model capability and non-monotonic in model strength, and no single method dominates across models and scenarios. These findings offer concrete guidance for selecting self-evolving methods across models and streaming scenarios. Overall, we advocate that self-evolving agents should be evaluated under realistic task streams rather than isolated single-task settings.

Related papers

• MobEvolve: An Agentic Self-Evolving Heuristic System for Interpretable Human Mobility Generation [51.824145170051516]
  MobEvolve is a self-evolving framework for human mobility generation.<n>It employs an agent to iteratively evolve its internal logic.<n>It significantly outperforms state-of-the-art deep generative and LLM-based methods.
  arXiv  Detail & Related papers  (2026-06-01T03:46:25Z)
• Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models [17.7409616106227]
  Differentiable Mixture-of-Agents (DMoA) is a self-evolving multi-agent framework that enables elastic and adaptive agent collaboration during inference.<n>We show that DMoA achieves state-of-the-art performance while exhibiting strong efficiency, robustness, and ensembling capabilities.
  arXiv  Detail & Related papers  (2026-05-15T07:54:46Z)
• Evolutionary Ensemble of Agents [7.465452178698495]
  We introduce EvE, a decentralized framework that organizes coding agents into a live, co-evolving system for algorithmic discovery.<n>By maintaining two co-evolving populations, EvE evaluates agents through a synchronous race, updating their empirical Elo ratings.<n>Compared to variants driven by a fixed initial agent or even a frozen "best-evolved" agent, EvE uniquely avoids phase mismatch.
  arXiv  Detail & Related papers  (2026-05-09T15:56:10Z)
• SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams [53.78257200138774]
  We propose a Self-Evolving Relevance Model approach (SERM), which comprises two complementary multi-agent modules.<n>We evaluate SERM in a large-scale industrial setting, which serves billions of user requests daily.
  arXiv  Detail & Related papers  (2026-01-14T14:31:16Z)
• Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the Rails [103.05296856071931]
  We identify the Alignment Tipping Process (ATP), a critical post-deployment risk unique to self-evolving Large Language Model (LLM) agents.<n>ATP arises when continual interaction drives agents to abandon alignment constraints established during training in favor of reinforced, self-interested strategies.<n>Our experiments show that alignment benefits erode rapidly under self-evolution, with initially aligned models converging toward unaligned states.
  arXiv  Detail & Related papers  (2025-10-06T14:48:39Z)
• What Do LLM Agents Do When Left Alone? Evidence of Spontaneous Meta-Cognitive Patterns [27.126691338850254]
  We introduce an architecture for studying the behavior of large language model (LLM) agents in the absence of externally imposed tasks.<n>Our continuous reason and act framework, using persistent memory and self-feedback, enables sustained autonomous operation.
  arXiv  Detail & Related papers  (2025-09-25T14:29:49Z)
• A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence [87.08051686357206]
  Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static.<n>As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck.<n>This survey provides the first systematic and comprehensive review of self-evolving agents.
  arXiv  Detail & Related papers  (2025-07-28T17:59:05Z)
• Multi-Agent Imitation Learning with Copulas [102.27052968901894]
  Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions. In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems. Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
  arXiv  Detail & Related papers  (2021-07-10T03:49:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.