Recursive self-improvement (RSI) envisions agent systems that autonomously improve their own models and harnesses, with each updated system continuing to improve itself. Existing approaches typically restrict optimization objectives, editable components, or evaluation procedures, limiting open-ended exploration and continuity across generations. We introduce RSI-Env, a persistent environment for coordinated model-harness improvement, supporting autonomous training, harness modification, and retention of research state. An independent Host Controller manages services, evaluation, and versioning. Under Self, each accepted model-harness version takes over the next improvement round. We define three settings that vary goal specificity and access to benchmark feedback: Open-General, Capability-Directed, and Benchmark-Guided. Across these three settings, we compare Self with two fixed external Teachers on seven tasks, starting from Qwen3.8-27B with a minimal harness. Without benchmark feedback, Open-General Self raises GPQA Diamond accuracy from 65.2% to 73.2% and AIME 2026 accuracy from 53.3% to 56.7%. All three Capability-Directed Self runs achieve target-aligned gains. Knowledge Self demonstrates continued improvement across system updates, with SimpleQA accuracy rising from 82.0% to 85.0% and then 88.0%. Benchmark guidance and Teacher benefits vary by task. These results demonstrate RSI-Env's support for open-ended exploration and sustained recursive improvement within the studied runs, while highlighting challenges in preserving gains across capabilities.
We present Xiaomi Auto World Model 2.0, a unified streaming causal world model for Physical AI. It models and generates causal physical evolution in the real world, and unifies heterogeneous conditioning inputs for view-consistent generation. The architecture integrates View-Temporal Attention (VTA) to capture cross-view spatial dependencies and temporal dynamics, together with Multi-view Rotary Position Encoding (RoPE) to encode positional relationships across views and time. WorldRec 2.0 reconstructs static and dynamic 3D Gaussians from multi-view, multi-temporal observations in a single forward pass and uses their renderings as explicit scene context to preserve geometry and appearance during long-horizon generation. Conditioning inputs are fused through VAE latent concatenation in a shared representation space. We incorporate Gated Linear Attention (GLA) into the temporal layers of VTA for efficient streaming inference, reducing computation and KV-cache memory during multi-view rollouts. A physical-interaction data pipeline covers diverse interactions and long-tailed events. From Pre-train onward, these data are combined with large-scale videos and heterogeneous visual modalities to learn visual, geometric, and dynamic priors. During Mid-train, training across diverse view configurations further improves the model's ability to capture how actions and object states shape subsequent scene evolution. After distillation-based Post-train, we apply the model to closed-loop simulation, closed-loop training, and World Action Model (WAM) applications. These applications generate physically consistent future states and support world-state prediction, policy learning, and action generation for Physical AI.
Accurate, robust, and adaptive localization is essential for various robotic operations. This paper proposes a parametric message passing (MP) algorithm for distributed collaborative localization, unifying Gaussian belief propagation (GBP) for robot state estimation and mean-field (MF) approximation for noise inference. Unlike conventional MP algorithms, the proposed formulation directly optimizes the natural parameters of beliefs, establishing a principled connection between MP and gradient estimators for handling non-conjugate terms arising from nonlinear measurement models. Beyond linearization and sampling, we further design a normalizing flow (NF)-based gradient estimator, enabling learnable sampling. End-to-end training tunes NF parameters according to the behavior of MP, improving estimation accuracy while reducing the required number of iterations. To support robot states involving rotations, the method is extended to Lie group spaces. Finally, the method is applied to a multirobot localization task fusing odometry, global navigation satellite system (GNSS) measurements, and inter-robot ultra wideband (UWB) ranging. Extensive simulations and experiments on autonomous surface vehicles (ASVs) demonstrate superior state estimation accuracy, enhanced robustness and adaptability under ranging outliers and varying noise statistics, as well as good generalization across different operating conditions.
Existing robot navigation methods primarily focus on isolated tasks, overlooking the need to accumulate knowledge and experience across repeated navigation episodes. Although recent lifelong navigation methods have explored cross-task knowledge accumulation, they mainly focus on scene knowledge while neglecting historical navigation experience and its connection to scene evidence, limiting the ability to reflect on past mistakes and improve future decisions. To address this limitation, this paper presents RetroNav, a training-free self-evolving navigation framework that integrates navigation, memory accumulation, and failure reflection. Specifically, we develop a unified memory system that connects geometric and semantic scene knowledge with historical navigation experience, distinguishing objective observations from task-dependent interpretations. After each navigation attempt, an independent evaluator determines whether a retry is necessary. If so, a Retrospection Agent revisits historical decisions using accumulated scene knowledge and navigation records, generating an evidence-grounded retry brief to guide the next attempt. RetroNav achieves state-of-the-art performance across five navigation benchmarks. Our method will be open-sourced at https://github.com/3dv-casia/RetroNav .
Kai Wei, Yuwen Cui, Kehan Shen University of South Florida, Hua Wei Arizona State University, Guangjing Wang University of South Florida
Terminal simulation, framed as a terminal command-level Turing test, is a long-standing problem of symbolic language generation in dialogue and interactive systems. Prior scripted simulators lack the flexibility needed for complex, multi-turn interactions, while LLM-based approaches often misinterpret commands, break output formats, drift from system state, and remain vulnerable to prompt injection. In this work, we propose MANTIS, a terminal simulation framework that improves realism, consistency, and robustness in command-language generation. MANTIS integrates a multi-agent architecture with a filter-based routing model that safely dispatches commands to external tools or an LLM-based agent, enabling support for interactive commands while defending against prompt injection attacks. In addition, we design an agentic file system with history pruning to preserve long-term state consistency. We release three datasets: 28,045 real terminal input-output pairs, a 1,000-session multi-turn interaction dataset, and a 25,849-instance labeled classification dataset. MANTIS outperforms state-of-the-art baselines by more than 9%, achieving over 95% accuracy on multi-turn terminal simulation. The dataset and source code are available at https://github.com/kaiwei666a/MANTIS_Terminal_Simulation
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cs.CL · cs.AI · cs.MA
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Yan Wang Yale University, Yitao Xu George Mason University, Nanhan Shen, Jinyan Su, Jimin Huang University of Manchester and The Fin AI, Zining Zhu Stevens Institute of Technology
Mixture of Experts models are widely assumed to achieve domain specialization through sparse routing. In this work, we question this assumption by introducing COMMITTEEAUDIT, a post hoc framework that analyzes routing behavior at the level of expert groups rather than individual experts. Across three representative models and the MMLU benchmark, we uncover a domain invariant Standing Committee. This is a compact coalition of routed experts that consistently captures the majority of routing mass across domains, layers, and routing budgets, even when architectures already include shared experts. Qualitative analysis further shows that Standing Committees anchor reasoning structure and syntax, while peripheral experts handle domain-specific knowledge. These findings reveal a strong structural bias toward centralized computation, suggesting that specialization in Mixture of Experts models is far less pervasive than commonly believed. Crucially, this inherent bias indicates that current training objectives, such as load-balancing losses that enforce uniform expert utilization, may be working against the model’s natural optimization path, thereby limiting training efficiency and performance.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表