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.
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%.
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.
CircuitLasso enables scalable circuit learning in large language models by using sparse linear regression. It recovers circuits with structural accuracy matching state-of-the-art methods at significantly lower computational cost, and demonstrates human-interpretable semantic propagation through model components. The learned circuits achieve comparable performance on a domain-generalization task with reduced cost.
A model-agnostic auditing framework detects and distinguishes true and phantom disclosures in synthetic data. It uses only synthetic outputs and a held-out control set to perform statistical testing, offering tighter privacy leakage bounds than prior methods without requiring model access or additional training.
A residual learning approach using directional task-error supervision achieves stable five-ball juggling on real robots, converging from the second attempt. The system outperforms human practice timelines and relies on both directional feedback and an informative prior, with a fixed-Jacobian Newton update proving most reliable.
A measurement study finds that 26 semantic post-hoc operators do not improve held-out accuracy over Best-of-N in frozen small code models. While some operators reduce compute usage or recover correct programs, none outperform BoN in accuracy, due to systemic limitations like coverage walls and consensus traps. An expression-layer recovery (M1) improves performance on HumanEval+ by 12 tasks, with no harm or leakage, and shows consistent results across model cells.
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 stabilize prompt prefixes and manage context segments efficiently.
KVEraser enables efficient localized context erasing in large language models by replacing only the KV cache states of an erased span with learned steering states. It achieves near-full-recomputation performance on in-domain tasks and offers a 24% latency increase versus a 17.6x increase for full recomputation, with up to 3--4x speedup on long-document QA tasks.
A new attack, RING, exploits differential privacy in federated learning to conceal backdoor signals while maximizing impact. It achieves 90.3% attack success against state-of-the-art defenses, up to 26.08x over baseline methods, and reveals a critical security gap in DP-FL due to inherent masking of malicious updates.
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.
The paper derives the exact posterior score in closed form for linear Gaussian inverse problems, enabling efficient posterior sampling via denoising. It introduces Exact Posterior Score (EPS), a training objective that preserves pretraining structure and achieves superior performance on fidelity, perceptual, and distributional metrics with fewer denoiser evaluations than gradient-based methods.
vLLM has introduced a new streaming parser for Qwen3+ available in its nightly build, addressing issues like mid-turn stopping and failed streaming tool calls due to chunk boundaries. The update reportedly resolves these problems in limited testing, improving reliability for agentic workflows.
The study introduces PReLU-IPM, a new integral probability metric based on a neural network discriminator with a single node. The resulting PReLU-TST test is nonparametric, consistent, and asymptotically equivalent to standard IPM-based tests, showing higher power or competitive performance on simulated and real datasets.
A convolutional variational autoencoder trained on 6,034 Binance Options surfaces for BTC and ETH achieves 0.94-1.56 vol-point RMSE under 10-50% masking. The hybrid predictor reduces error from 7.00 to 0.83 vol points at 50% masking, outperforming parametric re-fit in structured hole patterns and detecting abnormal market events without supervision.
A new method decouples ML inference from state persistence in streaming systems using probabilistic thinning. It selectively triggers durable state updates based on event informativeness, reducing persistence path overhead by up to 90% without compromising downstream utility or introducing systemic errors.
Dynestyx is a probabilistic programming library that provides first-class support for state-space models. It enables users to specify arbitrary priors for discrete- or continuous-time dynamical systems, perform inference on mixed-effect data, and obtain state and parameter estimates with principled uncertainty quantification.
A compact spectral representation using Betti numbers, spectral gap, and analytic torsion distills persistent Laplacians into three mathematically grounded invariants. This approach captures essential predictive signals from the full spectrum, outperforms it in some cases, and reduces computational overhead on datasets like MNIST, QM-3D, and SKEMPI WT.