All papers

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

The paper introduces a benchmark framework evaluating LLM-generated quantum software unit tests against independent ground-truth checkers.

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 evaluates vision-language models' spatial representations using causally controlled perturbations, showing spatial relations degrade compared to lexical features.

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.

Proposes a three-dimensional framework evaluating decision-making artificial intelligence methods across sequential formalisms, autonomy levels, and computational cost.

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.

Introduces a Minecraft diagnostic benchmark testing whether multiplayer world models maintain consistent and persistent representations across independently controlled views.

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.

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 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.

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 benchmarks, metrics, and experimental practices used to evaluate automated research systems across ideation, coding, writing, and peer review.

Evaluates language models on predicting program outputs and intermediate loop checkpoint states directly from source code and inputs.

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

Introduces a dataset and benchmark to model multimodal emotion understanding through cognitive appraisal reasoning rather than superficial affective cues.

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.

Presents an execution-based evaluation pipeline using browser agents to verify language model solutions for no-code software bug reports.

Proposes a framework modeling research ideation as modular thinking flows sampled dynamically from a probabilistic supernet.

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

Introduces a benchmark evaluating multimodal language models on extracting data, tracking narrative flows, and answering questions from animated data videos.

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.

Presents a simulated participant environment to evaluate multi-turn AI interviewers across market research and social science scenarios.

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

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.

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.

Introduces an evidence-grounded multi-agent framework combining multimodal medical data and clinical literature for knee osteoarthritis progression prediction.

Develops an abstraction approach for hierarchical reinforcement learning to balance temporal option boundaries with state-dependent value representation.