Beaver is an agent harness that extracts structured information from scientific papers by integrating multimodal evidence tooling, task scaffolding, and artifact-grounded autoresearch. It achieves 81.0 on the Gold-Referenced Attribute Score, outperforming frontier agents by over 23 points, with key gains on high-value attributes requiring cross-modal reasoning.
H-RePlan introduces a hierarchical replanning framework that separates device-local strategy recovery from global orchestrator replanning. It outperforms existing baselines by achieving higher completion and instruction adherence, with reduced token cost, through scope-aware recovery in multi-device agent systems.
UAV-VLN-FOV isolates the see-and-reach stage for precise evaluation of UAV navigation. 3DG-VLN enhances visual grounding and spatial alignment using dynamic 3D direction cues, achieving a 13.82% success rate improvement over baselines and validated in real-world trials.
STARE addresses policy entropy collapse in GRPO-based reinforcement learning by identifying entropy-critical token subsets via surprisal quantiles and reweighting their advantages. It maintains stable policy entropy across model scales and tasks, outperforming DAPO and other baselines by 4%-8% on AIME24 and AIME25, with consistent exploration-exploitation balance.
OneCanvas enables 3D scene understanding in Vision-Language Models by aggregating patch features onto a single panoramic canvas using 3D world coordinates. It achieves state-of-the-art performance on SQA3D and VSI-Bench, and generalizes to out-of-distribution data on SPBench, using significantly less training compute than existing methods.
STARE addresses policy entropy collapse in GRPO-based reinforcement learning by identifying entropy-critical token subsets via surprisal quantiles and reweighting their advantages. It maintains stable policy entropy across model scales and tasks, outperforming DAPO and other baselines by 4%-8% on AIME24 and AIME25, with consistent exploration-exploitation balance.
Rubric-Conditioned Self-Distillation introduces a framework that uses structured rubrics to provide fine-grained, token-level feedback during self-distillation of reasoning language models. By conditioning teacher models on rubric-level criteria, it enables more precise credit assignment than scalar rewards, outperforming GRPO and OPSD by 1.0 and 0.9 points on average across science reasoning benchmarks.
STARE addresses policy entropy collapse in GRPO-based reinforcement learning by identifying entropy-critical token subsets via surprisal quantiles and reweighting their advantages. It maintains stable policy entropy across model scales and tasks, outperforming DAPO and other baselines by 4%-8% on AIME24 and AIME25, with consistent exploration-exploitation balance.
Rubric-Conditioned Self-Distillation introduces a framework that uses structured rubrics to provide fine-grained, token-level feedback during self-distillation of reasoning language models. By conditioning teacher models on rubric-level criteria, it enables more precise credit assignment than scalar rewards, outperforming GRPO and OPSD by 1.0 and 0.9 points on average across science reasoning benchmarks.
ROVE enables humanoid Vision-Language-Action models to learn effective manipulation behaviors using imperfect human interventions. It combines a human-in-the-loop data collection pipeline with Optimistic Value Estimation and cross-embodiment supervision to prioritize high-value actions and improve robustness. ROVE outperforms baseline methods on real-world, contact-rich manipulation tasks through iterative rollout and intervention cycles.
Hierarchical Advantage-Weighted Behavior Cloning (HABC) enhances online RL fine-tuning of vision-language agents by using separate critic heads for viability and efficiency. It combines their outputs via a state-adaptive gate and applies per-transition weights, while intervention-aware credit assignment prevents supervision leakage. In real-robot experiments, HABC boosts success rates to 92%, 88%, and 38% on three bimanual tasks, surpassing SFT baselines of 36%, 44%, and 12%.
ARCO introduces a rubric framework that enables step-level credit assignment for multi-step LLM agents. It jointly updates a shared model with generation and scoring heads, allowing the rubric content and scoring function to co-evolve via on-policy data, improving performance and interpretability across benchmarks.
Tmax-27B is a terminal agent based on Qwen3.6-27B, trained with DPPO (RL), achieving 43% on Terminal Bench 2.0 and 69% on TB Lite. To run on consumer GPUs, it is quantized using importance-matrix-calibrated GGUF models from 2 to 5 bits per weight, with a grafted MTP head enabling speculative decoding. IQ2_XS at 8.5 GiB achieves 70% pass rate in agentic coding tasks, outperforming plain quantization and demonstrating stable tool-call generation.
ToolGraph enhances multi-turn tool-using agents by integrating schema topology, transition weights, and history-aware controls. Training with DPO on 161 divergence-point preference pairs improves performance: ToolGraph+DPO achieves a 16.8% relative reward gain over baseline, especially in airline and retail tasks, with reward positivity emerging as the key diagnostic signal.
A study using Olmo 3 and Olmo Hybrid open weights finds hybrid models outperform transformers on open-class content words and opening delimiters. The gains are less consistent for closed-class function words and closing delimiters, with hybrids excelling in semantic state tasks like pronoun memory and entity tracking, while transformers perform better on bracket-matching tasks. These results suggest recurrent layers enhance state-aware predictions, while attention supports n-gram and syntactic pattern recognition.
ARCO introduces a rubric framework that enables step-level credit assignment for multi-step LLM agents. It jointly updates a shared model with generation and scoring heads, allowing the rubric content and scoring function to co-evolve via on-policy data, improving performance and interpretability across benchmarks.
TMax introduces TMax-15k, a dataset of 14,600 RL environments, over 2.5× larger than the next-largest open terminal dataset. It also presents a simple RL recipe that trains open models from 2B to 27B parameters, with TMax-9B achieving 27.2% on Terminal Bench 2.0 and TMax-27B reaching 42.7%.
AllenAI has released two MolmoMotion models that predict 3D point trajectories based on short video histories and natural-language instructions. One model uses a three-frame history, the other a one-frame history, enabling future motion forecasting for objects in 3D space.
A novel variable-length tokenizer uses learnable global merging to enable cross-length representation alignment in diffusion models. This data-independent approach overcomes position-dependent semantics and improves the quality-compute trade-off on ImageNet 256×25-6 generation compared to prior methods.
Visual tokens enter large language models as raw, unstructured signals. Their internal transformation and integration depend on architecture—either as in-context prompts or injected into intermediate layers—leading to distinct evolution paths in visual representation and frequency characteristics. We find that attention alone is insufficient; performance is driven by the quality of visual representations at each layer across different integration paradigms.