Beyond Multi-Token Prediction: Pretraining LLMs with Future Summaries
Proposes future summary prediction as alternative to next-token prediction during LLM pretraining, improving long-horizon reasoning and planning capabilities.
Proposes future summary prediction as alternative to next-token prediction during LLM pretraining, improving long-horizon reasoning and planning capabilities.
OffSim: Model-based offline inverse reinforcement learning framework that learns environment dynamics and reward functions from offline data without manual specification.
Multilingual LLM watermarking robustness study showing current methods fail on low-resource languages, proposes back-translation approach for 100+ language coverage.
QUARK: FPGA acceleration framework leveraging quantization and common patterns in nonlinear operations to accelerate transformer inference.
Curiosity-driven quantized Mixture-of-Experts framework using Bayesian uncertainty routing for accurate inference on resource-constrained devices.
ContagionRL: Gymnasium-compatible RL platform for reward engineering in spatial epidemic simulations, enabling systematic evaluation of behavioral learning strategies.
Unified distillation and adaptation framework for diffusion models enabling fast, high-quality image generation in novel domains with single-stage pipeline.
Vision-Language-Action models enhanced via Tweedie discrete diffusion for improved generalization and fine-grained control in robotic manipulation tasks.
Proposes goal-oriented multi-agent semantic networking architecture for 6G services integrating AI-native communication with network-level intelligence.
Biomedical vision-language pretraining approach that captures fine-grained correspondences in scientific figures and text, improving domain-specific representations.
Proposes adaptive frame selection method for long-form video understanding with large multimodal models, reducing computational overhead while maintaining query awareness.
Research on LLM-based agents for decision support, proposing collaborative sensemaking approach where agents act as partners rather than answer engines to improve human-AI complementarity.
ODMA proposes on-demand memory allocation strategy for efficient LLM serving on low-bandwidth accelerators, addressing limitations of static pre-allocation and fine-grained paging.
Probability-guided token selection for SFT to address overfitting to single reference answers. Leverages multiple references while managing data costs.
100M high-quality Chinese image-text dataset for vision-language pre-training. Addresses bottleneck in Chinese VLP model development.
Scalable compliance evaluation framework for multi-policy AI governance. Integrates comprehensive model-card format and streamlines policy compliance burden.
Reference-free hallucination detection for LLM-generated code review comments. Identifies context misalignment without ground truth, enabling practical adoption in code review automation.
Framework addressing sycophancy in LLM decision support systems through premise governance. Proposes structured verification for deep-uncertainty decisions.
Self-distillation approach for machine unlearning in text-to-image diffusion models. Balances effective forgetting with retention of unrelated concepts.
Statistical analysis of variance in agentic system evaluations. Shows single-run pass@1 scores on SWE-Bench vary substantially (2.2-6.0%), calling for improved evaluation methodology.
Hierarchical framework for log anomaly detection that preserves component execution structure. Addresses spurious correlations in flat-sequence approaches.
AceGRPO combines adaptive curriculum learning with GRPO for autonomous ML engineering agents. Addresses behavioral stagnation and data inefficiency in long-horizon optimization tasks.
Heterogeneous multi-agent framework treating diverse LLM models as specialized tools. Introduces orchestrator calibration for efficient test-time scaling through coordinated tool calling.
Smooth gate functions for stabilizing GRPO LLM training. Replaces hard clipping with sigmoid-based gating to improve optimization stability in reasoning tasks.
Theoretical analysis of offline reinforcement learning with general function approximation and parametric policies, extending beyond finite action spaces.
Open-source framework for deploying DARPA AIxCC cyber reasoning systems locally. Makes competition CRSs usable outside original infrastructure with improved accessibility.
Evaluation framework for persona-adaptive LLM-powered agents in multi-modal settings, addressing user-aware behavior in customer experience management.
Red-teaming Vision-Language-Action models through quality diversity prompt generation to improve robot policy robustness.
AgentDrift: reveals safety risks in LLM agent recommendations when tools are corrupted, hidden by standard metrics.
Framework for improving VideoLLM understanding of camera motion through benchmarking, diagnosis, and explicit geometry injection.
Visual state representations for robotic agents using what-is-where composition for dynamic scene understanding.
FedPBS: federated learning algorithm for personalized training on non-IID data with improved robustness.
Sample-efficient hypergradient estimation for decentralized bi-level reinforcement learning in strategic decision-making.
Proxy models reduce cost and latency of AI queries in SQL databases by 100x through approximation techniques.
Domain-grounded tiered retrieval architecture to reduce LLM hallucinations through retrieval-based verification.
Evolutionarily Stable Stackelberg Equilibrium: game theory solution concept for asymmetric leader-follower games.
Ontology-Guided Diffusion for zero-shot sim2real transfer using neuro-symbolic approach to bridge simulation-reality gap.
Agent Control Protocol: formal specification for admission control governance of autonomous agents with cryptographic identity and policy compliance.
Multi-agent AI system with six specialized agents for automated NIST CSF-aligned cybersecurity risk assessments for small organizations.
Study showing finetuning bypasses LLM safety mechanisms and triggers verbatim recall of copyrighted training data.
Explainable DRL framework for autonomous APT defense using provenance-based graphs and stage-aware modeling.
LLM-based workflow system for multidisciplinary software development coordinating domain experts and developers in automotive.
PRISM photonic accelerator approach reducing KV cache memory bandwidth from O(n) to O(1) for long-context LLM inference.
mSFT algorithm for optimizing heterogeneous multi-task SFT data mixtures by dynamically adjusting compute per sub-dataset.
Weather prediction combining radar observations with foundation model priors for extended nowcasting horizons.
Sim-to-real transfer for humanoid robot control using state-dependent joint torque perturbations instead of domain randomization.
Inference-time scaling with lightweight latent verifiers instead of MLLMs to reduce computational cost in verification.
Method using causal interventions and Vision-Language Models to explain sparse autoencoder features in vision models.
Interpretable evaluation combining symbolic rules with mechanistic interpretability to detect memorization vs genuine generalization.
ITPO framework for optimizing multi-turn human-LLM interactions via RL despite sparse rewards and user stochasticity.