Mila

Sparse autoencoders' failure to generalize compositionally isn't due to amortized inference, but because they learn lousy dictionaries in the first place.

Mimicking human cognition, FLAIR lets dialogue models "think while listening," boosting performance without adding latency.

Ditch the cross-world counterfactuals: Sequential Transport offers a DAG-aware, optimal transport approach to causal mediation analysis, providing deterministic counterfactual mediators and fine-grained attribution.

Forget exotic attention mechanisms – MobileLLM-Flash achieves up to 1.8x faster LLM prefill on mobile CPUs by smartly pruning and adapting existing architectures for on-device use.

Tactile sensing can be efficiently injected into vision-language-action models via feature-wise linear modulation, boosting robot manipulation performance without the computational overhead of large-scale pretraining.

Forget contrastive learning: LLM2Vec-Gen learns text embeddings by representing the *response* an LLM would generate, unlocking safety and reasoning abilities for embedding tasks.

Democratized LLM pre-training is now a reality: Covenant-72B proves you can train a competitive 72B model with untrusted peers over the internet, opening the door to broader participation and reduced costs.

Diagonal SSMs, despite their empirical success, provably fail to track states of non-Abelian groups, revealing fundamental limitations in their expressive power.

One in four initial posts on a major cybercrime forum contain explicit crime-related content, revealing a surprisingly high baseline of open criminal activity.

Forget full fine-tuning: this dynamic routing strategy lets you adapt dense retrieval to new domains while using just 2% of the parameters.

Takeuchi's Information Criterion (TIC) accurately predicts DNN generalization gaps, but only when models operate near the Neural Tangent Kernel (NTK) regime.

Quantum computers could compromise nuclear power plants' safety systems and create unsolvable forensic paradoxes, with current defenses showing alarming vulnerability.

A global consensus on AI safety risks and capabilities has emerged from a panel of 100+ independent experts, representing a landmark effort in international collaboration.

Attention-based re-ranking gets a boost: ReAttn's post-hoc re-weighting tames over-concentration and lexical bias, leading to more accurate and interpretable results without extra training.

Achieve state-of-the-art dynamic graph anomaly detection with limited labels by learning a robust decision boundary around normal data, outperforming methods that overfit to scarce anomalies.

LLMs struggle to balance rational financial decisions with mimicking noisy user behavior, often overfitting to short-term market trends instead of aligning with long-term investment goals.

Coreference benchmarks may be overstating language models' NLU abilities, as even small changes to evaluation contexts reveal a failure to generalize.

Forget fixed teams: this new reinforcement learning framework lets agents spawn new teammates on the fly, unlocking dynamic strategies previously impossible.

Cybercriminals are actively exploring AI's potential for both enhancing existing attacks and creating novel illicit tools, but harbor significant doubts about its real-world effectiveness and impact on their operations.

Boost macrocycle generation rates from 1% to 99% by guiding diffusion models with persistent homology, opening new avenues for drug discovery.

Freehand sketches can now drive photorealistic image generation, even without paired training data, thanks to a novel loss that prioritizes semantic understanding over pixel-perfect alignment.

Current multimodal retrieval systems fall flat when faced with realistic visual streams where context is distributed across time, motivating a new agentic paradigm for context-aware image retrieval.

Mismatched standard deviations in multi-objective RL advantage estimation can completely break constrained learning, but a simple scalarization fixes it.

This study establishes SSL as a promising paradigm for ECG analysis, particularly in settings with limited annotated data, enhancing accessibility, generalizability, and fairness in AI-driven cardiac diagnostics across diverse clinical environments and questions.

Even when trained on suboptimal data, a Bayesian in-context RL agent can achieve near-optimal decisions on unseen tasks by fusing a learned Q-value prior with in-context information and employing an upper-confidence bound for exploration.

GPT-5's real-time router learns to route queries to specialized models, making it faster and more useful than its predecessors.

This work integrates small-molecule high-throughput screening with a deep-learning-based virtual screening approach to uncover new antibacterial compounds, illustrating a 90-fold improved hit rate over the high-throughput screening experiment used for training.

By recursively aggregating reasoning chains, even smaller LLMs can now achieve performance competitive with much larger models, challenging the assumption that scale is the only path to improved reasoning.

Dramatically improve protein language models by simply post-training them to align with protein graphs, yielding a 59% increase in contact prediction accuracy.

Command A shows how to build an enterprise-grade LLM that balances performance, efficiency, and multilingual capabilities using decentralized training and model merging.

Forget retraining from scratch: port fine-tuning updates between LLM versions and get up to 47% performance boost on tasks like instruction following, even surpassing fully fine-tuned models.

Self-supervised learning beats supervised learning for ECG interpretation when labeled data is scarce, unlocking more robust and generalizable AI-driven cardiac diagnostics.