Search papers, labs, and topics across Lattice.
LLMs struggle with structured 2D tasks when inputs are serialized into 1D, revealing a surprising performance gap compared to vision-augmented models that directly process the 2D layout.
CroSearch-R1 reveals that integrating cross-lingual knowledge through a dynamic retrieval strategy can substantially enhance the performance of Retrieval-Augmented Generation systems.
ManifoldRank reveals that treating fairness as a taxation cost can significantly enhance the effectiveness of online fair re-ranking algorithms.
LLMs re-rank documents better when you learn to route each query to the specific attention heads that matter, instead of relying on static subsets or everything at once.
Continuously nudging LLM activations during generation can effectively correct misalignment without sacrificing coherence, offering a lightweight runtime defense against adversarial prompts and other triggers.
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.
One in four initial posts on a major cybercrime forum contain explicit crime-related content, revealing a surprisingly high baseline of open criminal activity.
Diagonal SSMs, despite their empirical success, provably fail to track states of non-Abelian groups, revealing fundamental limitations in their expressive power.
Forget full fine-tuning: this dynamic routing strategy lets you adapt dense retrieval to new domains while using just 2% of the parameters.
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.
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.
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.
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.
Dramatically improve protein language models by simply post-training them to align with protein graphs, yielding a 59% increase in contact prediction accuracy.
