E-TTS: Embodied test-time scaling framework for robotic manipulation with history-aware iterative refinement
Researchers introduce E-TTS, a modular test-time scaling framework for robotic manipulation that unifies reasoning and action scaling via history-aware iterative refinement and vision-language verifiers. The framework addresses two gaps in prior work: underexplored reasoning scaling mechanisms and inadequate use of historical context in long-horizon sequential tasks. Evaluated across 4 benchmarks, 6 environments, 3 embodiments, and 4 base vision-language-action models, E-TTS achieves up to 33.14% improvement in simulation and 26.62% in real-world scenarios without additional expert data or retraining.
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TREAD: VLM-based re-labelling framework improves robot policy generalization via dataset augmentation
TREAD (Task Robustness via Re-Labelling Vision-Action Robot Data) is a scalable framework that uses pretrained Vision-Language Models to augment existing robotics datasets without new data collection. The approach decomposes demonstrations into sub-tasks, segments videos accordingly, and generates linguistically diverse instruction labels, enriching language-action pair diversity. Evaluations on the LIBERO benchmark show improved generalization to novel tasks and goals, addressing a key limitation of current robot learning policies.
EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL
EnvFactory is a fully automated framework for training tool-use LLM agents via Agentic Reinforcement Learning, addressing two key bottlenecks: scalable execution environments and realistic multi-turn training data. It autonomously constructs stateful, executable tool environments from authentic resources and synthesizes natural trajectories with implicit human intents via topology-aware sampling. Using only 85 verified environments across 7 domains, it generates 2,575 SFT and RL trajectories and improves Qwen3-series models by up to +15% on BFCLv3, +8.6% on MCP-Atlas, and +6% on conversational benchmarks, outperforming prior approaches that use 5x more environments.
DIRECT: Adaptive test-time compute routing for embodied VLM planners
Researchers introduce DIRECT, a routing framework that dynamically allocates test-time compute for Vision-Language Models acting as embodied planners, using multimodal scene context to decide per-prompt how much compute to spend. Experiments on VLABench and RoboMME benchmarks show that different scaling axes (chain-of-thought depth, model size, memory history) yield qualitatively distinct gains, and that naive uniform scaling is wasteful. On a physical Franka arm, DIRECT matches or exceeds a stronger model's success rate at up to 65% lower average latency, improving the success-cost Pareto frontier.
Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning
This paper introduces Equilibrium Reasoners (EqR), a framework that formalizes test-time compute scaling through learned task-conditioned attractors in latent space, where stable fixed points correspond to valid solutions. EqR scales along two axes—depth (more iterations) and breadth (aggregating stochastic trajectories)—without requiring external verifiers or task-specific priors. On Sudoku-Extreme, unrolling up to 40,000 equivalent layers boosts accuracy from 2.6% (feedforward baseline) to over 99%. The work provides a mechanistic lens for understanding why iterative latent models generalize beyond memorized patterns.
VeriEvol: Verified data construction pipeline for scaling multimodal mathematical reasoning
VeriEvol is a new framework for scaling reinforcement learning on visual mathematical reasoning by decoupling prompt difficulty expansion from answer reliability verification. It uses a type-aware evolution module to generate harder image-grounded prompts and an HTV-Agent verifier that rejects answers only after failing to find counter-evidence. Scaling SFT data from 10K to 250K samples raises mean accuracy from 35.42 to 54.73 across five visual-math benchmarks, with an additional +3.88 cumulative gain over an un-evolved RL baseline when combined with GRPO-style training. The authors release prompts, data, models, code, and full verifier traces.
TempoVLA: Speed-Controllable Vision-Language-Action Policy for Robot Manipulation
Researchers introduce TempoVLA, a Vision-Language-Action model that enables explicit speed control during robot manipulation by conditioning on a speed signal rather than inheriting a fixed speed from training data. The system pairs Variable-Speed Trajectory Augmentation (VSTA), which re-times demonstrations by merging or splitting actions, with a model-side conditioning mechanism. Experiments in simulation and real-world tasks show flexible bidirectional speed control, with dynamic adaptation—accelerating in low-risk transit phases and decelerating for high-risk contact stages—achieved by coupling with a large multimodal model.
STT-Arena: Benchmark for Adaptive Replanning Under Spatio-Temporal Dynamics in Tool-Using LLMs
STT-Arena is a new benchmark of 227 interactive tasks designed to evaluate LLMs' ability to detect mid-task disruptions and replan under spatio-temporal dynamics, covering nine conflict types and four solvability levels. Evaluation of frontier models including Claude-4.6-Opus shows less than 40% overall accuracy, revealing fundamental limitations in dynamic reasoning. The authors identify three recurring failure modes—Stale-State Execution, Misdiagnosis of Dynamic Triggers, and Missing Post-Adaptation Verification—and propose an iterative trajectory refinement technique combined with online RL to train STT-Agent-4B, a 4B-parameter model that outperforms frontier LLMs on the benchmark.
AHA-WAM: Asynchronous world-action modeling with temporal decoupling for robot manipulation
AHA-WAM introduces a dual Diffusion Transformer architecture that decouples world prediction (low-frequency) from action execution (high-frequency) in robot manipulation policies, addressing the inefficiency of existing world-action models that force both branches to operate at the same temporal resolution. The system uses a rolling key-value memory video DiT as a long-horizon scene planner and a fast action DiT that queries layerwise latent context via joint attention, with Observation-Guided Video-Context Routing enabling asynchronous execution. On RoboTwin benchmarks, AHA-WAM achieves 92.80% average success and 78.3% on real-world tasks at 24.17 Hz, a 4.59x speedup over Fast-WAM, without robot-data pretraining.