AI Core

The authors built WorldAuditBench, a benchmark of 213 tasks across 13 Unreal Engine 5 and Three.js environments and five anomaly families for interactive 3D world auditing, and compared a single-model VLM agent with a two-stage VLA–VLM auditor, finding VLM agents reach 28.2%–42.3% success versus 6.6%–17.4% for two-stage auditors, both far below the 83.4% human rate.

DAGent introduces Evaluate-then-Grow incremental planning, in which an Orchestrator grows the task DAG one batch at a time conditioned on confidence and uncertainty signals from completed nodes, paired with a hierarchical context layer that propagates compact QueryDocs by default while preserving full execution traces for on-demand recall, and with DAGRPO, a DAG-conditioned RL adaptation combining topology-conditioned credit with structural compliance regularization; it surpasses the strongest open-source baseline by 5.3/5.8/2.0 points on BrowseComp-Plus, GAIA and xbench-DeepSearch at the Qwen3-235B-A22B scale, improves over a same-budget outcome-only GRPO baseline by 3.

The work introduces Loop Scaling Laws, the first scaling law that jointly models recurrence and MoE sparsity alongside model size and training data, using a bounded, sparsity-conditional recurrence mapping to characterize the effective-parameter gain from looping and its saturation; it predicts held-out loss more accurately than linear and power-law mappings, guides the choice of recurrence and expert count under compute and memory budgets, and at trillion-token scale lets a 0.3B-active/1.3B-total looped MoE match a roughly 2x larger non-looped MoE on reasoning benchmarks.

DyRAD represents dynamic driving scenes as static background point reflectors plus motion-tracked dynamic point reflectors and renders complete range-azimuth-Doppler (RAD) tensors through a fixed analytic point-spread function derived from the radar signal-processing chain, with Doppler serving both as a rendered output and as supervision for object tracks, evaluated on RADIal, Boreas, and a synthetic benchmark at both on-path poses and displaced viewpoints, raising radar detection recovery on RADIal from 26.9% for the strongest baseline to 90.7% of reference-detected objects.

The work introduces Imagine3D-LLM: a small set of learnable Gaussian summary tokens is appended after the image tokens, decoded into a compact 3D Gaussian Splatting representation, and trained jointly with a photometric reconstruction loss and standard next-token prediction, so that a multi-view multimodal LLM assembles a coarse 3D scene representation before answering; it consistently outperforms prior approaches across seven spatial reasoning and 3D scene understanding benchmarks, reaching 68.5 average on SPAR-Bench and surpassing the strongest spatially-aware baseline 3DThinker-7B by 5.2 points.

The work introduces Reward-Weighted Transport Distillation (RWTD), a post-training method for one-step generators that needs only generated samples and scalar reward evaluations, builds an adaptive target mixing separately reward-tilted current and reference distributions, realizes it through feature-space optimal transport and fixed-point regression, and raises the one-step SANA Sprint 1.6B GenEval score from 0.73 to 0.80 while showing cross-reward generalization in preference alignment experiments.

Tacit-TTS, distilled from IndexTTS2, replaces autoregressive text-to-semantic decoding with masked non-autoregressive generation, adds training-free acoustic length estimation, and accelerates the flow-matching renderer via ReFlow distillation, achieving competitive zero-shot cloning quality on two English and two Mandarin datasets while generating speech over 10x faster than IndexTTS2 for utterances longer than 5 seconds and supporting cross-lingual and non-lexical references without a reference transcript.

The work introduces OSWorld-Science, a benchmark and evaluation environment combining expert-defined scientific tasks, artifact-based execution graders, and a shared agent harness, and evaluates 12 vision-language models across 7 scientific domains, 17 software packages, and 3 languages, finding that the strongest model, Claude Fable 5.1, averages 73.7% and about 40% on the hardest task set, with failures concentrated in the interaction layer and result verification rather than in scientific reasoning itself.

This work systematically analyzes how reinforcement learning (RL) reshapes flow-based vision-language-action (VLA) models, finding that across π0.5 and GR00T N1.5/N1.6 on LIBERO, ManiSkill, MetaWorld, and CALVIN, RL induces low-rank parameter updates highly concentrated in the action expert's Timestep Modules, which hold 14.83%–27.58% of the parameters yet capture a disproportionate share of the RL gain, with shift-vector update directions predicting task success at up to 99.6% ROC-AUC and steering along them improving policies without additional RL training.