All papers

The paper proposes multi-task pre-training guided by knowledge graphs to improve zero-shot legal reasoning in language models.

The paper studies language model sensitivity to discourse particles such as 'just' and 'only' in natural conversations.

The paper introduces a transformer architecture incorporating semantic role labeling into attention mechanisms to detect sentiment, emotion, politeness, and formality.

The paper analyzes phoneme-level error patterns from audio and text data augmentation techniques in low-resource automatic speech recognition.

The paper proposes a training-free query expansion method that filters hallucinated terms using corpus-conditioned cross-context reproducibility.

The paper studies policy learning and answer-selection mechanisms to help language models decide when to abstain on factual questions.

The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment.

The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing.

The paper introduces a domain-general framework using predictive world models to guide action selection and policy improvement in recursive self-improvement settings.

The paper extends the Graph Model for Conflict Resolution by incorporating epistemic states and state-preserving actions into its decision-making framework.

Studies how the non-generative decision model Jev can serve as a prior or policy component within reinforcement learning frameworks.

Introduces a unified benchmark and pretrained encoder-decoder model for evaluating enzymatic reaction prediction and enzyme commission classifications.

Presents a mathematical audit on how intervention anchors and coordinate transport affect stability in synthetic vascular predictive representations.

Introduces a step-level memory reuse framework using transition graphs to adapt relevant prior mobile graphical interface execution trajectories.

Presents a hypernetwork that maps document contexts directly into low-rank adapter weights for large language models.

Presents a multi-dimensional data pruning framework that filters code repositories by syntax, diversity, and representation for efficient retriever fine-tuning.

Presents a portfolio management agent that uses fractional factorial counterfactual evaluation and Banzhaf values to isolate memory utility from market movements.

Introduces a multivariate time series foundation model pretrained using synthetic temporal and relational pattern primitives for zero-shot forecasting.

Describes an onboard satellite pipeline combining segmentation and self-supervised feature encoding for real-time marine anomaly detection.

Analyzes collider bias in Bayesian physics-informed neural networks and introduces a hierarchical generative chain model to resolve parameter estimation drift.

Develops a low-rank generalized Laplace approximation over active curvature directions for scalable neural network uncertainty quantification.

Designs a most-significant-digit-first neural network accelerator supporting early exits and digit-level pruning for energy-efficient medical image segmentation.

Introduces an exact level-parallel tree verification procedure for speculative decoding derived from a canonical exit law of target-model probabilities.

Proposes a narrow-and-deep ontology tower to structure equipment knowledge for language model agents operating industrial air-handling systems.

Proposes an interpretability framework using sparse autoencoders to explain mixture of experts routing based on superposed fine-grained features.

Presents a system combining representation learning and music alignment to generate editable aesthetic stage lighting cues for live performances.

Proposes an external memory framework allowing frozen language model agents to learn and iteratively refine natural-language world models compiled into executable code.

Evaluates eight lightweight open-source vision-language models on optical character recognition and schema-compliant JSON extraction across heritage document collections.

Introduces a training-free inference method using Gaussian optimal transport to reduce object hallucination in vision-language models without suppressing valid representations.

Analyzes modular addition learning in two-layer neural networks using probability signatures to explain how gradient training selects Fourier-structured representations.

Presents a rollout selection strategy that drops high-probability positive-advantage trajectories to prevent entropy collapse during language model reinforcement learning.

Proposes a method linking execution traces to specific skill segments to guide automated revision of software engineering agent skills.

Introduces an AND/OR graph proof search algorithm optimizing customizable objectives like length and compute cost during automated formal theorem proving.

Surveys and taxonomizes recurring architectural patterns across language-model-integrated software applications, ranging from chatbots to autonomous agent systems.

Proposes an adaptive multi-source steering decoding method to mitigate hallucinations in vision-language models by balancing visual and textual context contributions.

Formulates neural decoding as Bayesian cognitive inference to extract stable meta-neural semantic representations across varying neural recording modalities.

Presents a program-search method using a curated elite solution table to guide language models in synthesizing executable control policies.

Proposes a conformal prediction framework that learns stochastic groupings to improve subgroup coverage reliability without inflating overall prediction sets.

Presents a bilevel optimization framework for on-policy distillation that meta-learns dynamic token weights based on student validation loss.

Presents a bound-propagation verification framework to bound worst-case reconstruction errors for deep joint source-channel coding decoders under noise.

Proposes a policy mechanism enabling language models to decide when, how, and which test-time training procedures to execute.

Uses Monte Carlo tree search and structural experience distillation to identify interpretable symbolic expressions for partial differential equation solutions.

Introduces a neural network architecture enforcing layer-wise polytopal constraints to improve latent representation interpretability without degrading task performance.

Proposes a rolling-denoising framework for vision-language-action models to improve robot control reactivity without sacrificing long-horizon execution consistency.

Analyzes the computational complexity of grounded and preferred semantics decision problems within computably finitary abstract argumentation frameworks.

Develops a representation learning approach for photoplethysmography data that enforces strict causal information boundaries during future-beat forecasting.

Introduces a world action model for humanoid robots that jointly models robot internal states and scene evolution to close the execution gap.

Introduces an immutable, provenance-aware state representation framework with deterministic replay and audit logging for robotic systems.

Presents an explainer for molecular graph neural networks combining factual, counterfactual, and exemplar graph perturbations.

Proposes a training method to adaptively skip turn-by-turn agent reasoning when previous thoughts remain sufficient for future actions.

Combines motif priors with search-tree diffusion to generate 2D and 3D molecular structures from spectroscopic data.

Analyzes training data extraction risks in language models when prompts include contextual conditioning such as retrieved documents.

Proposes a self-evolving agent that allocates evaluation resources dynamically across candidate programs during iterative code evolution.

Introduces a framework that dynamically retrieves and recomposes prior latent reasoning steps to guide vision-language-action robot policies.

Transfers editing capabilities from image pairs to video diffusion models using in-context visual demonstrations across decomposed sub-tasks.

Applies zonotope geometry to adaptively scale critic pessimism and disagreement handling in soft actor-critic locomotion policies.

Presents a parameter-free distillation framework transferring knowledge between language models via an iteratively updated natural language guide.

Applies Weisfeiler-Leman graph kernel features to improve automated algorithm selection for constraint optimization problems without training GNNs.

Introduces a hierarchical memory architecture that consolidates nested agent task intents into reusable knowledge across long horizons.

Synthesizes novel views by reusing nearby rendered RGB-D images and applying pose-guided warping with residual refinement.