Proposes Predict-then-Diffuse method to dynamically adjust response length in diffusion-based LLMs, addressing fixed-size constraints while maintaining parallel generation efficiency.
PIQL framework integrates privileged information to accelerate training and improve generalization in tabular foundation models, reducing computational burden.
arXiv paper optimizing vision-language-action models by reducing visual bandwidth in world modules through single token-per-frame representation.
Research paper on Flow-OPD: on-policy distillation technique for multi-task text-to-image flow matching models addressing reward sparsity and gradient interference.
Online auditing system for early failure prediction in multi-agent LLM systems, enabling intervention before trajectory-level failures cascade.
Decentralized framework organizing coding agents into co-evolving system for algorithmic discovery, evolving agent behaviors through cumulative guidance.
Security research on poisoning attacks targeting graph-based memory in LLM agents, introducing ShadowMerge attack via relation-channel conflicts.
Study of FP4 quantization in transformer training, progressively enabling low-precision arithmetic across forward/backward passes for Llama 3.1 pretraining.
Novel technique for improving LLM activation steering by addressing KV-cache contamination issues in multi-turn dialogue via attention-level interventions.
Research on applying FP8 low-precision arithmetic to large recommendation models, addressing numerical sensitivity challenges in GPU training.
Introduces successor-representation diagnostic for multi-agent LLM systems to predict communication topology robustness without post-hoc evaluation.
Proposes training-free method to correct pace and path predictions in Vision-Language-Action models blind to temporal dynamics.
GEAR enables granularity-adaptive credit assignment for LLM agents via self-distillation, improving fine-grained supervision beyond outcome-level rewards.
Audits omni-modal benchmarks for visual bias and studies post-training with visually debiased evaluation to improve audio-visual-language integration.
Introduces TokenRatio for token-level preference optimization in LLMs via ratio matching, improving on sequence-level DPO methods.
Argues sparse-to-dense reward principle is more efficient than GRPO alone for LLM post-training with scarce labeled data.
Proposes dual-learning approach combining parameter updates and in-context learning to enable LLMs to adapt continually while retaining plasticity.
Introduces WriteSAE, sparse autoencoder decomposing matrix cache writes in state-space and recurrent language models like Mamba-2 and RWKV-7.
Equips LLMs with active information-seeking during context training to adapt to tasks requiring newly produced or niche domain knowledge without weight updates.
Demonstrates few-shot spoken word classification scaling to 1000 classes using generative meta-continual learning with only 5 shots per class.
Applies generative meta-learning algorithm to few-shot multilingual spoken word classification.
Argues watermarking in generative models should be treated as monitoring primitive considering internal detection inevitability with per-entity attribution keys.
Addresses over-squashing in Graph Neural Networks for multi-label graphs using information bottleneck approach.
LeanSearch v2 retrieves relevant library lemmas globally for Lean 4 theorem proving, addressing gap between semantic search and step-by-step premise selection.
Demonstrates vulnerability in large reasoning models where incomplete inputs cause excessive reasoning traces, enabling denial-of-service attacks via genetic algorithms.
Proposes test-time self-training method for LLMs that enables parameter updates during inference to adapt to specific queries without correcting model misconceptions.
Studies whether LLMs understand semantic meaning of High-Level Message Sequence Charts used in software architecture design.
Applies sparse autoencoders to interpret internal computations of EEG foundation models for clinical applications.
Addresses out-of-distribution generalization for molecular property prediction in drug discovery using improved data selection and domain adaptation.
TraFL trajectory-balance post-training for diffusion language models addresses trajectory locking and improves solution coverage.
Cross-domain benchmark for federated fine-tuning of LLMs on private data across healthcare, finance and other regulated sectors.
EvolveMem self-evolving memory system for LLM agents that co-evolves stored knowledge and retrieval mechanisms across sessions.
EMA framework for efficient model adaptation in learning-based systems operating in heterogeneous dynamic environments.
Unified geometric framework interprets weighted contrastive learning as Distance Geometry Problems with target geometry specification.
Collider-Bench benchmark evaluates LLM agents on reproducing particle physics analyses from LHC using public papers and software.
End-to-end energy accounting for LLM distillation pipelines analyzing full resource costs including teacher-side workloads.
TabPFN-3 foundation model for tabular data scaled to 1M rows with improved training/inference speed and synthetic pretraining.
HodgeCover uses higher-order topological coverage to compress sparse Mixture-of-Experts layers without retraining.
Analysis of discrete diffusion models showing hierarchy between support and frequency information in denoising objectives.
Dywave dynamic tokenization framework for heterogeneous IoT sensing signals addressing non-stationary multi-scale data.
R2R2 regularization method for reinforcement learning reduces overfitting in self-predictive learning under high update-to-data regimes.
Self-Pruned Key-Value Attention mechanism reduces KV cache size in transformers by predicting future utility for efficient long-sequence generation.
Comparative study of ML approaches for financial distress prediction under class imbalance constraints using classical and neural methods.
SurF generative model for forecasting irregular multivariate time series using Time Rescaling Theorem as learnable bijection.
Study on fairness and calibration in toxicity detection models using training interventions and safety mechanisms.
Research challenging cosine similarity as a metric for assessing layer relevance in LLMs, proposing alternatives for mechanistic interpretability.
Fleet of small foundation models for geospatial hydrologic analysis enabling accessible agentic environmental reasoning systems.
Reinforcement learning approach for tool-calling LLM agents reasoning over healthcare FHIR resource graphs for clinical queries.
Metacognitive framework leveraging LLM self-monitoring signals to control test-time inference and improve problem-solving.
Principled scaling rules for mixture-of-experts architectures analyzing hyperparameter relationships with network and expert dimensions.