WorldEvolver: Self-Evolving World Models for LLM Agent Planning via Test-Time Memory Revision
Researchers introduce WorldEvolver, a framework that equips LLM agents with self-improving world models that revise their context at deployment time without updating model parameters. The system combines episodic memory (retrieval-based simulation), semantic memory (heuristic rule extraction from prediction errors), and selective foresight (confidence-based filtering). Evaluated on ALFWorld and ScienceWorld benchmarks, WorldEvolver achieves state-of-the-art world model prediction accuracy and improved downstream agent success rates across three backbone models. The work addresses a key challenge in long-horizon agent planning: unreliable foresight that can degrade rather than improve decision-making.
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EvoArena benchmark and EvoMem memory paradigm for LLM agents in dynamic environments
Researchers introduce EvoArena, a benchmark suite that evaluates LLM agents in dynamic environments by modeling changes as progressive update sequences across terminal, software, and social domains. Alongside it, they propose EvoMem, a patch-based memory paradigm that records memory evolution as structured update histories to help agents reason about environmental change. Current agents score only 39.6% average accuracy on EvoArena, while EvoMem yields consistent gains on EvoArena and also improves performance on GAIA and LoCoMo benchmarks. The work highlights a significant gap between static-benchmark performance and real-world dynamic deployment requirements.
Qwen-AgentWorld: Language world models for general agent simulation and planning
Alibaba's Qwen team introduces Qwen-AgentWorld, a pair of language world models (35B-A3B and 397B-A17B) trained to simulate agentic environments across 7 domains using over 10M interaction trajectories. The models are trained via a three-stage pipeline (CPT, SFT, RL) and evaluated on AgentWorldBench, a new benchmark constructed from 5 frontier models across 9 established benchmarks. Beyond simulation, the work demonstrates two downstream use cases: using the world model as a decoupled RL training environment and as a warm-up for agent foundation models, both yielding gains over baselines.
MLEvolve: Self-evolving multi-agent framework for automated ML algorithm discovery
MLEvolve is a new LLM-based multi-agent framework for end-to-end machine learning algorithm discovery, addressing limitations of existing MLE agents including information isolation and memoryless search. The system introduces Progressive MCGS (a graph-extended tree search), Retrospective Memory for experience accumulation, and decoupled strategic planning from code generation. Evaluated on MLE-Bench, it achieves state-of-the-art medal and valid submission rates within a 12-hour budget, and also outperforms AlphaEvolve on mathematical algorithm optimization tasks.
Role-Agent: Bootstrapping LLM Agents via Dual-Role Evolution
Role-Agent is a new framework that uses a single LLM simultaneously as both agent and environment, enabling self-bootstrapped co-evolution without external environment feedback. The system has two components: World-In-Agent (WIA), which uses predicted vs. actual state alignment as a process reward, and Agent-In-World (AIW), which reshapes training data by retrieving tasks with similar failure patterns. Experiments across multiple benchmarks show an average performance gain of over 4% over strong baselines. The approach addresses key limitations in LLM agent training: inefficient feedback and static environments.
RePro: Retrospective Progress-Aware Self-Refinement for LLM Agent Training
Researchers introduce RePro (Retrospective Progress-Aware Training), a framework addressing the gap between step-wise RL optimization and metacognitive task-progress awareness in LLM agents. The approach uses a forward-then-reflect rollout paradigm where agents execute actions online and then retrospectively assess step-wise progress given the completed trajectory and known outcome. Evaluated on WebShop, ALFWorld, and Sokoban, RePro achieves up to 12% absolute success rate gains over baseline Qwen-family models without requiring continuous external supervision.
ManimAgent: Self-evolving multimodal agent with cross-task episodic memory for code generation
ManimAgent is a multimodal agent system that accumulates reflection experience across tasks via a dual-channel Episodic Memory Bank, without weight updates or human-curated seeds. The agent generates Python/Manim animations from scientific paper sections, and a vision-language model scores rendered keyframes to populate positive (success rationales) and negative (failure patterns) memory channels. On a fixed-probe evaluation, Pass@1 improves and reflection rounds decrease as memory grows, outperforming no-memory, RAG, and shuffled-memory baselines. The work addresses a known limitation of single-episode reflection in LLM agents by enabling persistent, self-generated learning across task boundaries.
EEVEE: Multi-dataset test-time prompt learning framework for self-improving LLM agents
EEVEE is a new framework enabling LLM agents to perform test-time prompt learning across heterogeneous multi-dataset task streams, addressing a gap where prior methods only handled single-dataset settings. The system uses a router to partition inputs into task clusters and assigns them to suitable prompt configurations, optimized via a router-prompt co-evolution strategy. Experiments show improvements of 10.38 and 24.32 average points over Qwen3-4B-Instruct and DeepSeek-V3.2 respectively, outperforming prior SOTA methods GEPA and ACE by up to 48.2%.
Looped World Models introduce iterative latent depth as a new scaling axis for world simulation
A new arXiv preprint introduces Looped World Models (LoopWM), a parameter-shared transformer architecture that iteratively refines latent environment states to achieve up to 100x parameter efficiency over conventional world models. The approach uses adaptive computation to scale depth dynamically per prediction step, addressing the tension between long-horizon simulation fidelity and deployment cost. The authors position iterative latent depth as a new scaling axis orthogonal to model size and training data.