All papers

The paper introduces a benchmark to measure demographic robustness, stereotyping, and intentional personalization in speech language models.

The paper analyzes the runtime performance differences of test-passing issue resolution patches generated by language models on SWE-bench.

The paper demonstrates that machine-generated text detectors rely heavily on document formatting and PDF extraction artifacts rather than text quality.

The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference.

The paper presents a multilingual benchmark of minimal pairs to evaluate language model sensitivity to negative polarity items across 33 languages.

The paper trains conversational user simulators equipped with latent evaluation thoughts and scalar ratings to provide intrinsic feedback.

The paper presents an importance-weighted downsampling protocol to lower evaluation costs on transaction graph datasets without distorting precision metrics.

The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions.

The paper evaluates linguistic obfuscation patterns and reality-monitoring mechanisms across fraudulent and genuine scientific publications.

The paper evaluates knowledge-graph-informed chunking and retrieval strategies over German legal code to support verifiable retrieval-augmented generation.

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

The paper presents a benchmark evaluating how effectively vision-language models recover fully occluded objects using remaining visual cues.

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

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

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.

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.

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.

Pairs attack success rate with operative understanding rate to assess whether models recognize harmful intent during safety evaluations.

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.

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.

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

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

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.

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.

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

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

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.

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.

Proposes a benchmark and probing methodology based on attribution theory to examine how language models assign responsibility and blame.

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.

Evaluates how schema ordering impacts LLM performance under structured output constraints, showing reasoning-first designs prevent accuracy drops.

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

Audits the reliability of LLM judges across multiple prompt formats, temperatures, and orderings, introducing a trustworthy verdict metric.

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