Think Big, Search Small: Where Capacity Matters in Hierarchical Search Agents?
Empirical study on capacity allocation across hierarchical search agent roles (delegation, execution, generation) in multi-agent LLM systems.
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Empirical study on capacity allocation across hierarchical search agent roles (delegation, execution, generation) in multi-agent LLM systems.
There are a lot of fast-growing AI startups, but some are growing even faster, they say.
Bounds on failure region probability for Langevin dynamics training, proving exponential suppression of unsafe states with dimension.
Mahalanobis distance-based unified detection framework for LLM-generated text, hallucinations, watermarks, and adversarial examples.
Analysis of human-AI collaboration in structural design showing friction from constraints can drive creative exploration rather than final-answer generation.
GRiLS, a gradient-free Riemannian MCMC sampler for multimodal distributions, improves mode mixing without density gradient evaluations.
Theoretical proof that data augmentation induces graph-Laplacian regularization, achieving O(1/n_L) label-sample efficiency versus O(1/√n_L) supervised.
Analysis of permutation-equivariance and stability in continuous flow models parameterized by GNNs for graph signal generation.
Asynchronous single-rollout RL for agentic LLM post-training addresses training stability and efficiency limits of batch-synchronous GRPO.
HIVE benchmark systematically evaluates how hallucinated content in vision-language models influences downstream reasoning, moving beyond detection to understanding post-hallucination inference.
Empirical study compares LLM-generated versus expert-written skill modules for agentic data science tasks, finding trade-offs in automation cost versus performance across SQL, cleaning, and statistical testing.
Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are... Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier…
TimEE is a 4.5M-parameter foundation model enabling end-to-end time series classification via in-context learning, eliminating per-dataset retraining.
RAID applies iterative reinforcement learning to automatically discover exploits in EA Sports NHL 26's goalie AI, reducing manual playtesting effort.
GIFT proposes geometry-informed gradient quantization for low-precision communication during LLM pretraining, addressing scaling bottlenecks in distributed training.
Pyligent training framework treats reasoning as validated search with backtracking, enabling models to correct mid-inference and recover from failed branches.
FourierQK applies FFT-based spectral preprocessing to query-key projections in transformer attention, achieving 79% perplexity reduction on character-level language modeling.
Action-graded severity scale for agent red-teaming replaces binary attack-success metrics with 7-level ordinal harm rubric, enabling nuanced risk assessment of tool-using AI compromise.
First systematic evaluation of fairness interventions under differential privacy constraints on synthetic tabular data, revealing tensions between DP and fair-ML objectives.
SynthAVE uses LLM-based synthetic labeling with arena validation for e-commerce attribute extraction at scale, covering 12,726 products across 229 categories and 12 languages.
Statistical theory for sparse function recovery from noisy indirect observations via ℓ1-regularization and empirical risk minimization.
SpaCellAgent: LLM-based multi-agent framework automating trajectory inference for spatial and single-cell transcriptomics analysis.
Self-evolving LLM agents with biased reward signals fail to retire bad skills, disabling safety constraints in skill libraries.
RLVP: Reward function design for real-world agents requiring path constraints and outcome-neutral safety rules beyond reward maximization.
Empirical study of biologically-informed neural network architecture and optimization for reliable mechanistic operator recovery from sparse data.
PAC learning theory: sample complexity of Chain-of-Thought reasoning bounds by local next-token classification dimension.
InductWave: Wavelet-based inductive embedding for logical multi-hop query answering over knowledge graphs with unseen entities.
DeLS-Spec: Decoupled context speculative decoding for parallel LLM token drafting without retraining draft models.
SAMPA: Whisper-based speech segmentation model for prosodic boundary detection in Brazilian Portuguese.
Tool-using LLM agents silently violate deployed policies via well-formed tool calls that bypass domain constraints; 78% of failures undetected.