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We’re opening a research preview of the Model Hardware Standard (MHS), a shared specification for AI agents to safely operate physical devices, to a first group of scientific research labs and advanced manufacturers.
CritICL uses small-model failure patterns as inference-time guidance to improve LLM reasoning without external verification.
WikiSkill co-evolves agent skills with persistent knowledge bases, enabling systematic skill reuse across agent interactions.
SWE-Prime uses multi-granularity data selection on software trajectories to filter noisy supervision and improve model performance.
TTPO enables test-time policy optimization for math reasoning using asymmetric rollout analysis without ground-truth labels.
MCR-Bench benchmarks LLMs on iterative, multi-round code review tasks capturing real-world defect-state interactions.
RedEvoAgent evolves jailbreak skills for agent security testing, addressing retrieval bias and interpretability in red-teaming.
MAELLE models chemical reactions as discrete flow matching on electron occupation graphs rather than molecular topology.
Transduced language models improve finite-state transducer composition via stochastic estimation of source-model prefix probabilities.
Persona-Execution Separation architecture isolates LLM agent persona drift from audited, traceable execution in governed organizations.
Static scanners are increasingly used to identify executable or otherwise unsafe content in machine- learning artifacts, yet conventional evaluation metrics characterize only cases where a scanner yields a usable security judgment. We evaluate ModelScan, ModelAudit, and Fickling using a controlled, artifact-backed benchmark on a synthetic corpus of 170 Pickle and PyTorch focused artifacts across 145 specimen families, 135 of which have binary security ground truth and 10 of which are intentionally malformed without labels. We explicitly distinguish non-N/A coverage, analysis completion, defin...
Retrospective study learns continuous sepsis severity score from patient trajectories across two hospital systems, replacing decades-old fixed indices.
Method uses weak-model guidance to preserve generative diversity in LLM reasoning during RLVR training, addressing entropy collapse.
Some of the world's largest tech companies and AI startups have come together to decry the current state of cybersecurity and to advertise a new solution that they say can ward off a new generation of cyber threats.
Production GNN ranking system for social graph friend recommendation using multi-hash embeddings and temporal neighbor sampling at scale.
Comparative study of three RLVR fusion paradigms (Merge, Mix, MOPD) for consolidating domain-specific capabilities across LLM tasks.
CLAP trains action-conditioned video models across human and robotic embodiments on internet-scale data for zero-shot physics simulation.
Linear probe analysis of how open-weight LLMs organize Moral Foundations Theory categories in representation space.
CAST framework uses Sparse Autoencoders to expose and suppress note-specific artifacts in clinical LMs for auditable predictions.
LeVJEPA: first video encoder using LeJEPA collapse-free objective without architectural asymmetries, achieving efficient self-supervised pretraining.
RATIO benchmark for evaluating literature retrieval across three ideation operations (Address, Broaden, Zoom) in scientific contexts.
Study of property-specific recoverability from contact PPG to camera rPPG using CHROM pathway on 655 video physiology recordings.
CorporateBench: 230k-document Q&A benchmark for evaluating LLMs on enterprise document retrieval and knowledge base queries.
LAMA: token-level advertising mechanism embedding advertiser influence into generative AI outputs via mixture auctions.
D2C-Routing detects mixed-origin AI/human text by separately attributing content and expression origin before composition.
Theoretical analysis of sharp phase transitions in ellipsoid fitting for random subgaussian vectors.
Puro-2B: open-source 1.5B LM trained on RTX 5090 for <$5k, targeting cost-accessible pretraining.
SCG: adaptive Vision Transformer width/depth expansion for JEPA world models, starting minimal and growing task-driven.
eBay's XGBoost-based signal recommendation system achieves 0.08% GMB lift via contextual item information placement.
KnockGS: infers material properties (elasticity, density) of 3D Gaussian objects from dynamics under applied forces.