KinemaForge jointly infers part-level shape, joint topology, and parameters from RGB-D sequences using a kinematic constraint graph and differentiable screw-axis solver. It validates results with an energy-consistent verifier, reducing joint-axis error and simulation drift while improving closed-loop manipulation success by 14.6 percentage points over Ditto.
A meta-feedback semi-supervised learning framework enables scaling of automatic emergency braking using massive unlabeled fleet data. The stabilized approach reduces pseudo-label errors through noise-aware decoupling and kinematics-gated pseudo-labeling, improving safety with a 100:1 positive-to-false activation ratio and 35% more accident-free driving mileage compared to rule-based systems.
Xcientist introduces a research harness that externalizes and makes visible the reasoning processes in AI scientists. It preserves traceable, contract-governed research artifacts from problem formulation to validation, addressing claim drift and ensuring scientific accountability.
A domain-shift aware neural network is proposed for estimating unbalance masses in rotating systems under varying conditions. The model uses maximum mean discrepancy to align feature representations across different operating domains, improving prediction accuracy when system behaviors differ from training conditions. Results show its effectiveness in structural health monitoring applications.
BeliefDiffusion combines diffusion models for multimodal belief representation with Model Predictive Control for long-term navigation planning. It outperforms model-free reinforcement learning and other generative methods in navigation success and path efficiency in partially observable environments.
SGCD introduces an iterative framework to improve GUI agents by addressing supervision gaps in off-trajectory states. It extracts skills from both successful and failed rollouts, using them to guide policy continuations that are mixed with expert trajectories. On OSWorld-Verified, SGCD boosts success rates of three base models from low-30\% to over 50\%.
SAERec constructs fine-grained, interpretable intent priors from textual corpora using sparse autoencoders to disentangle intent-related semantics. It retrieves both personal and public intents for users, guiding recommendations with human-understandable explanations and outperforms state-of-the-art models on public datasets.
A study finds that large language models struggle to interpret negation in figurative language. Performance varies significantly based on prompt style, highlighting a key limitation in current models' understanding of complex linguistic structures.
TransitNet, a compact attention-augmented deep learning framework, achieves 95.2% accuracy in low-SNR transit blind searches, outperforming TLS and BLS in ROC-AUC and PR-AP values. It recovers 93.0% of injected Earth- and sub-Earth-size transits, with 97.4% of injected transits fully covered by estimated transit windows, and successfully recovers all 34 confirmed Kepler planets with a mean midpoint error of 1.24 hours.
SciRisk-Bench introduces a benchmark to evaluate AI4Science safety by assessing models across 7 disciplines, 31 subdisciplines, and 10 risk dimensions. It evaluates both mainstream and science-oriented LLMs to identify specific gaps in risk recognition and avoidance within high-stakes scientific contexts.
Decoupled Search Grounding (DSG) separates search functionality from reasoning models, enabling vendor-agnostic, tunable, and reusable search grounding. DSG achieves near-native accuracy on SimpleQA with 91% lower search cost and 99.4% warm-cache hit rate, while reducing latency by 68% and preserving concise output contracts.
RTSGameBench addresses limitations in existing RTS benchmarks by offering diverse gameplay, targeted competency diagnosis, and self-evolving scenario generation. It evaluates vision-language models in strategic reasoning under uncertainty, revealing that state-of-the-art models struggle with multiagent coordination and large-scale tasks.
A controlled benchmark found no significant performance gain from quantum generative models in brain MRI augmentation. Synthetic samples produced by quantum and classical GANs were statistically indistinguishable, with both showing mode collapse and off-distribution samples, especially at low data fractions. The study concludes that quantum augmentation does not provide meaningful data expansion and acts more as regularization.
CAPRA is a multi-agent LLM system that generates personalized, template-compliant LaTeX feedback on software architecture deliverables. It uses specialized agents, PyMuPDF, and gpt-4o to extract and analyze text and UML diagrams, with evidence anchoring and consistency management to ensure reliability. A preliminary evaluation of 10 student reports shows CAPRA met 88.8% of eight criteria and achieved moderate inter-rater agreement (kappa = 0.582), with each report processed in under 4 minutes.
CADE introduces direct timestep embedding and contrastive alignment to preserve metric structure in time-series data. By mapping each timestep directly into LLM embedding space, it avoids tokenization bottlenecks and outperforms existing LLM baselines on six TSQA tasks.
ThinkDeception introduces a progressive reinforcement learning framework that enables interpretable multimodal deception detection. It leverages a step-by-step annotated Chain of Thought dataset and proposes Visual-Audio Consistency Group Relative Policy Optimization with a dynamic curriculum, enhancing reasoning quality and outperforming existing methods on mainstream benchmarks.
G-IdiomAlign introduces a gloss-pivoted benchmark using English glosses from Wiktionary to anchor idioms. It includes controlled multiple-choice equivalence and gloss-contrastive generation protocols, showing that glosses improve performance in embedding-based semantic alignment, though results remain modest, indicating significant potential for improvement in cross-lingual idiom generation.
TRAP evaluates how well models complete tasks using private data without leaking it. Across 22 models, all show non-trivial privacy leakage, with instruction-following ability linked to higher leakage. Structural private field isolation prevents leakage by replacing private fields with hash keys, maintaining task accuracy without sacrificing privacy.
Spotlight enables DiT RL post-training by leveraging idle spot GPUs, reducing costs by 1.4-6.4× while achieving superior image quality. It uses stale model weights in exploration and reconfigures sequence parallelism in real time, allowing efficient GPU utilization without breaking training pipelines.
FoMoE introduces a system that partitions expert layers across workers to avoid full model replicas, reducing communication costs by up to 1.42x over efficient baselines and 45.44x over DDP. It achieves up to 1.4x throughput speedups via a skip-token mechanism and demonstrates stable routing, with projected benefits extending to 100B-scale models through system modeling.