All papers

The paper evaluates whether providing page images to vision-language models improves the detection of errors in Japanese-English manga translation.

The paper proposes Batch-Paced, a method that dynamically allocates decoding compute across batched requests to improve discrete diffusion language model inference.

The paper introduces Fish Audio Drama 3, an open-source text-to-speech model designed for multi-character dialogue, long-form synthesis, and iterative voice revision.

The paper examines machine grading on undergraduate essays across three universities, finding models compress score distributions and vary across institutions.

The paper introduces BanglaTOD, a tri-lingual benchmark for task-oriented dialogue understanding covering Bangla, English, and romanized Banglish across nine service domains.

The paper probes language model hidden states across generated stories to test whether models anticipate narrative events multiple steps ahead.

The paper evaluates typed decision models against standard language model judges to measure how emotional tone and biases affect their evaluation accuracy.

The paper presents SparPR, a runtime key-value cache recovery mechanism that reduces memory bottlenecks during model inference on consumer hardware.

Evaluates AI text detectors under attacks applied to both human and machine text, revealing high false-positive rates on edited human writing.

Proposes dynamic activation steering using preceding denoising predictions to reduce hallucinations in diffusion language models.

Presents a pragmatic prompting framework to preserve speaker voice, emotion, and tone during speech-to-speech translation.

Surveys methods for compressing large language model benchmarks to reduce evaluation costs while maintaining accurate score estimates.

Presents a multilingual framework and dataset spanning four languages to evaluate clinical pain representations across exams and clinical notes.

Introduces a human-verified benchmark to evaluate how accurately reward models judge language model outputs on legal tasks.

Develops a simulation framework using persona-guided models to optimize comparative doctor summary presentations for medical patients.

Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax.

Demonstrates an attack method that alters embedded multi-bit text watermarks without knowing the secret generation key.

Equips diffusion language models with multiple residual streams and adaptive routing to separate visible context from masked token generation.

Proposes estimating both contextual and unigram surprisal from the same language model to reduce estimation mismatch in reading time studies.

Estimates surprisal directly from continuous speech signals using a speech language model and importance sampling over abstract linguistic units.

Identifies passage copying as a failure mode in long-form retrieval systems and introduces sentence-level credit assignment to encourage genuine grounding.

Evaluates the safety of ChatGPT health advice using real user conversation logs across four Global South countries.

Demonstrates that knowledge distillation transfers safety dispositions and behavioral tendencies from teacher models to student models on unrelated tasks.

Analyzes retrieval divergence across languages in multilingual dense embedders and introduces disagreement-guided steering to align shared-index search results.

Evaluates fast, system-one decision models and language models across fifteen medical datasets, testing their accuracy, format robustness, and abstention awareness.

Examines whether model empathy degrades reliability, showing that warm behavior effects depend on tasks and training approaches rather than an inevitable trade-off.

This paper introduces a visual associative test benchmark to evaluate the lateral reasoning and creative association capabilities of vision-language models.

This paper predicts human annotator disagreement on harmful content across multiple languages by extracting interpretable semantic question features with models.

This paper analyzes dynamic clinical evidence gathering in language models, identifying failures in evidence identification, updating, and diagnostic reasoning.

This paper analyzes multi-person conversational involvement by jointly evaluating speaking floors and spatial formations from multimodal video features.

This paper evaluates pretraining on chemical language models across 64 molecular datasets, finding that gains saturate below gradient-boosted tree baselines.

This paper proposes a Bayesian method that infers task-aligned similarity metrics at test time using weak interactive feedback.

This paper presents a method to detect spoken code-switching directly from speech representations using language-selective hidden dimensions.

This paper uses referential communication games to analyze why natural language autoencoders fail to provide discriminative oversight of model activations.

This paper evaluates language-model-generated disaster situation reports against human-written reports using a domain-specific sixteen-slot information schema.

This paper proposes a rubric-based reward model that derives criteria strictly from instructions and evaluates responses pointwise using tool assistance.

This paper introduces a benchmark to evaluate multimodal models on joint audio-visual temporal grounding, spatial grounding, and question answering.

This paper introduces a learned correction step to reduce error accumulation and the training-inference gap in iterative diffusion model sampling.

This paper presents an ecosystem of benchmark datasets and evaluation frameworks to advance machine learning applications in veterinary medicine.

This paper evaluates whether combining radiomics with deep image representations improves probability calibration and discrimination in renal cancer classification models.

This paper analyzes decision logits in radiomics-image fusion models to distinguish gate weight values from actual feature contributions during cancer classification.

This paper introduces a coarse-to-fine text generation method using discrete code hierarchies to accelerate parallel sampling over diffusion language models.

This paper presents a synthetic multi-panel chart visual question answering dataset to evaluate how multimodal models interpret complex scientific figures.

The paper uses counterfactual interventions to visualize and examine implicit environmental concepts learned inside a reinforcement learning agent's value function.

The authors introduce sparse autoencoders to trace multimodal training influence down to specific image regions and text phrases.

The paper proposes a learnable low-rank polynomial sketch to reduce approximation errors in linear attention mechanisms.

The paper develops and evaluates a multimodal foundation model for interpreting oncologic chest, abdomen, and pelvis CT scans.

The authors develop an error-compensated 4-bit all-reduce algorithm to reduce communication overhead during distributed model training and inference.

The paper studies gate learning in recurrent networks, showing how adding short-dependency samples helps models learn long-range temporal connections.

The authors introduce a framework that uses cross-view depth consistency as a single reward to improve 4D video generation geometry.

The paper proposes a training-free method leveraging pullback directions to increase sample diversity in conditional diffusion generation.

The authors propose decoupled positional encodings enabling full-duplex speech models to run internal thinking streams at hardware-adaptive speeds.

The paper introduces a receding-horizon optimal control algorithm for constrained sampling in flow matching generative models.

The authors present a feed-forward network that aggregates local wall predictions to reconstruct floor plans from sparse unposed panoramas.

The paper presents an elastic vision architecture that dynamically reconfigures width and supports partitionable inference across heterogeneous edge devices.

The paper introduces a post-training residual correction method to predict and correct endpoint errors during low-step generative model sampling.

The paper establishes and validates a set of meta-requirements to guide the design of frameworks for auditing AI system robustness.

The paper benchmarks how caregiver-directed language models compromise medical advice when users express clinically inferior care preferences.

The authors present a foundation controller that converts motion drafts into contact-consistent joint commands for dexterous robotic hands.

The paper reviews deep learning architectures and training dynamics through the lens of continuous-time nonlinear control theory.