ActiveSAM is a training-free, zero-shot framework that enhances SAM 3 for open-vocabulary semantic segmentation by identifying an image-conditioned active class set. It improves speed-accuracy tradeoff, outperforming SegEarth-OV3 by +1.4 mIoU on average and running up to 5.5x faster on large-vocabulary datasets, with strong robustness under image corruption.
We introduce a method to identify agents by their procedural behavior fingerprints, achieving 85.7% accuracy in attributing unseen trajectories to correct agents. Using ProcGrep, we analyze coding agent behavior in SWE-Bench, finding that models from similar release periods or distilled from each other exhibit closer behavioral similarity, with a Jensen-Shannon divergence of 0.25.
ActiveSAM is a training-free, zero-shot framework that enhances SAM 3 for open-vocabulary semantic segmentation by identifying an image-conditioned active class set. It improves speed-accuracy tradeoff, outperforming SegEarth-OV3 by +1.4 mIoU on average and running up to 5.5x faster on large-vocabulary datasets, with strong robustness to image corruption.
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.
ExpRL introduces a novel mid-training approach for LLMs using human-written question-answer data as reward scaffolds. Instead of imitating reference solutions, it constructs problem-specific grading rubrics to reward intermediate reasoning steps, enabling better initialization for sparse-reward RL and outperforming SFT, sparse-reward GRPO, and self-distillation on math reasoning tasks.
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%.
The Geometric Action Model (GAM) enables robot policies to reason about 3D physical interactions by repurposing a pretrained geometric foundation model. GAM splits the GFM to serve as both an observation encoder and a causal future predictor, then routes predicted future geometry and actions through the same backbone, achieving accurate, robust, and efficient manipulation performance in simulation and real-robot benchmarks.
HalBench evaluates 29 open-source LLMs on a custom benchmark for sycophancy and hallucination. Qwen 3.6 and Gemma 4 outperform larger models, with Qwen 3.6 achieving 36.6% pushback—higher than GPT-5.4 and Gemini 3.1 Pro. Model size does not correlate with honest responses, indicating that architecture and training data matter more than parameters.
Analysis of 56,800 AI conference papers shows documentation practices improved from 2014 to 2024. Papers sharing both code and data increased from 11% to 64%, and estimated reproducibility rose from 28% to 64%. These improvements predate formal reproducibility checklists, indicating a broader shift toward open science.
A consensus-based agentic large language model framework is proposed for accurate 10-digit Harmonized Tariff Schedule code classification in Canadian maritime logistics. Evaluated on 3,300 expert-labeled product records, the framework shows that fine-grained HTS classification remains challenging for advanced LLMs, highlighting the need for evidence-grounded, uncertainty-aware, and human-in-the-loop workflows.
PACT combines a reactive RL policy with a 2B-parameter Small Language Model to generate and validate action plans. The SLM plan is executed directly if verified as safe, feasible, and complete, bypassing the RL policy. PACT outperforms baselines on three increasingly difficult FrozenLake environments.
Public AI evaluation archives show that a single terminal result can arise from two distinct pre-terminal histories, with estimated times to reach 95% of performance ceilings at 23.03 or 75.13. A candidate selection-aware frontier model fails synthetic recovery and uncertainty calibration, and is rejected by fixed audit gates. An archive-and-adjudication protocol verifies timing boundaries and falsifies unsupported frontier claims.
TuneJury is an open, instance-level pairwise reward model that predicts music preference scores from text prompts and audio clips. It is trained on diverse human-preference data and demonstrates strong generalization, with anchor calibration enabling efficient post-hoc alignment for music generation systems.
TokenPilot reduces inference costs by 61% to 87% in both isolated and continuous modes, outperforming prior systems in cost efficiency while maintaining competitive performance. It uses ingestion-aware compaction and lifecycle-aware eviction to preserve prompt cache continuity and minimize token footprint without introducing prefix mismatches.
FusionRS introduces the first large-scale RGB-infrared-text dataset for remote sensing vision-language modeling. It aligns RGB and infrared images with IR-aware captions, enabling dual-modal vision-language foundation models. Experiments show improved RGB-IR alignment, retrieval, and captioning, with ablation studies confirming the critical role of modality-specific textual supervision.
Image classifiers like PRISM2D, GFNet, and ViT-B/16 show that phase, not magnitude, drives predictions in hidden layers. ResNet-50 reveals a latent sign code in late blocks, indicating phase/sign identity exists across architectures, though expressed differently due to activation and readout mechanisms.
Microsoft CEO Satya Nadella introduces 'Loopcraft' as a new theory of the firm, emphasizing that the real opportunity in AI lies not in selecting the best model, but in building learning loops that compound human and token capital. He asserts that the priority must be creating frontier ecosystems where every organization can own and grow its institutional knowledge, enabling broad value flow across industries and countries.
Hyperball is a simple optimizer wrapper that sets fixed Frobenius norms for weight matrices and their updates. It improves training speed and learning rate transfer in large models, achieving 20--30% token equivalent speedup over weight decay baselines on up to 1.2B parameter models.
Factorized Neural Operators (FaNO) decompose spectral representations into equivariant dynamic and invariant persistent responses. This factorized structure enables better interpretability, generalization, and consistent predictions across scales, domains, and physical regimes.
CEAP, a new circuit discovery method, substantially reduces resampling variance compared to EAP-IG. The paper shows that rephrasing variance arises from prompt templates activating different circuits, suggesting LLMs are inherently hard to steer across diverse inputs. Sample-wise variance is largely benign, as poor unfaithfulness scores result from selective contribution scaling, not circuit defects.