Biases in the Blind Spot: Detecting What LLMs Fail to Mention
Automated black-box pipeline detects unverbalized biases in LLM reasoning where models hide internal biases in plausible-sounding chain-of-thought explanations.
Automated black-box pipeline detects unverbalized biases in LLM reasoning where models hide internal biases in plausible-sounding chain-of-thought explanations.
LoRA-Squeeze compresses LoRA modules through post-tuning and in-tuning methods to simplify rank selection and improve deployment efficiency for fine-tuning LLMs.
MolmoSpaces is an open ecosystem for robot navigation and manipulation with diverse benchmarks for evaluating generalization in real-world robotic tasks.
SCOPE framework for calibrated pairwise LLM judging with statistical guarantees, reducing miscalibration and systematic biases in evaluations.
Graph-based frequency spatio-temporal network for cellular traffic prediction capturing complex temporal dynamics and spatial correlations.
VisPhyWorld evaluation framework tests whether MLLMs reason about physical dynamics through code-driven video reconstruction tasks.
Diary study examining how multimodal LLMs assist blind and low vision users accessing visual information through conversational interfaces.
CT-Bench benchmark dataset with 20K+ lesion annotations from CT studies for multimodal lesion understanding and report generation.
Optimization method for dataset distillation using exploration-exploitation to compress large datasets while retaining model performance.
Proves logit distance bounds representational similarity for discriminative models including autoregressive language models.
Automated ML pipeline for vessel segmentation and classification in intracoronary OCT images using preprocessing and artifact removal.
Addresses temporal leakage in clinical NLP models for hospital discharge planning, ensuring safe deployment with realistic performance estimates.
Resp-Agent system uses active adversarial curriculum learning for multimodal respiratory sound generation and disease diagnosis.
Large-scale intracranial EEG dataset and benchmark for epilepsy research enabling automated seizure localization and clinical workflow support.
Theoretical framework using convex conjugate duality to characterize trainability and generalization properties of deep neural networks.
Evaluates LLMs as zero-shot annotators for Bangla hate speech detection, examining reliability and bias in low-resource language settings.
RoboGene uses agentic framework to automatically generate diverse robotic manipulation tasks, addressing data scarcity in VLA pre-training.
Framework for LLM agents to reason about cost-uncertainty tradeoffs when deciding whether to explore environments before committing to answers.
PCAS system enforces deterministic authorization policies in LLM agents for customer service, workflows, and compliance without relying on prompts.
Few-shot LLM classification framework predicts electricity market price spikes using natural language prompts with system state features.
ML framework for predicting secondary traffic crashes using real-time data features excluding post-crash information.
Studies stability of transformer attention-head circuits across model instances to determine if interpretability findings are universal or idiosyncratic.
DeepVision-103K dataset of 103K visually diverse mathematical problems for multimodal LLM reinforcement learning with verifiable rewards.
PETS framework for principled trajectory allocation in test-time self-consistency scaling with sample efficiency optimization.
Geometric analysis of grokking in transformers showing low-dimensional optimization dynamics with PCA of attention trajectories.
LiveClin live benchmark for clinical LLM evaluation using contemporary peer-reviewed cases updated biannually to prevent contamination.
Attention-based weighting mechanism for improving Wi-Fi signal router aggregation in indoor localization.
ML method for correcting LEO satellite orbit propagation and uncertainty quantification under atmospheric drag mismodeling.
Analysis of how distribution shifts in language models relate to omitted variable bias and mitigation approaches.
Method improving LLM causal reasoning on counterfactual questions via double counterfactual consistency learning.
Efficient inference pipeline achieving IMO-level math reasoning with off-the-shelf models at reduced computational cost.
Fine-tuning diffusion and flow models for tail-aware generative optimization with control over reward distribution.
Physics-guided neural network for air quality prediction using topography and wind direction.
Two-timescale analysis showing feedback-truth gap governs learning under noisy supervision across neural networks and human studies.
Verbalized Action Masking method for controllable exploration in RL post-training of LLMs with chess case study.
Residual-aware theoretical analysis explaining position bias in transformer attention mechanisms and cumulative attention rollout.
Progressive Thought Encoding method for efficient training of large reasoning models via parameter-efficient RL fine-tuning.
Theoretical analysis of data-driven newsvendor problem with censored demand data and regret bounds.
Mechanistic analysis of how two-layer networks learn Fourier features for modular addition with theoretical training dynamics explanation.
Framework for detecting and reducing ballast information across structured, semi-structured, and unstructured multimodal datasets.
Dementia classification model for Brazilian adults using variable selection and multivariable analysis from ELSI-Brazil dataset.
Mixed-integer programming framework providing sound and complete certification guarantees for worst-case data poisoning attacks.
Symbolic alternative to GNNs with improved expressivity beyond 1-Weisfeiler-Lehman barrier and fine-grained interpretability.
NSGGM: neuro-symbolic framework for molecule generation combining neural proposals with symbolic constraints for controllability.
Communication-free decentralized multi-agent bandit protocol with Lipschitz-structured action spaces and hard collision constraints.
Unified framework for exploiting locality in scalable multi-agent RL with relaxed conditions on exponential decay property.
Studies grokking transition via loss-landscape geometry on sequence-learning tasks SCAN and Dyck-1 using commutator defect metrics.
Fail-closed alignment design principle for robust LLM safety through redundant refusal mechanisms across latent features.
UniLeak: mechanistic interpretability framework identifying universal activation directions that trigger PII leakage in language models.
Dynamic Delayed Tree Expansion improves multi-path speculative decoding for faster LLM token sampling verification.