All papers

The paper releases a studio-quality audio dataset of Korean traditional singing with dense annotations for vocal expressions and melodic contour.

The paper introduces an explainable Siamese deep learning framework combining synchronized EEG and eye-tracking signals for early ALS detection.

The paper proposes certified adversarial detection using Lipschitz-margin analysis to detect perturbations rather than forcing invariant predictions.

The paper introduces TRACE to monitor harmful response risks during generation, edit token activations selectively, and maintain auditable decision traces.

The paper presents a hybrid language model adapted for reasoning, instruction following, and tool use across English and eight Indian languages.

This paper shows that running multiple parallel attempts with a smaller model can match the coding accuracy of much larger models.

This paper details vocabulary expansion and continual pretraining techniques to adapt an open language model across eight Indic languages.

This paper analyzes the computational dynamics of biologically structured modular reservoir networks using a comparative dynamic similarity framework.

This paper presents a lattice vector quantization method that compresses attention key-value caches to two bits while preserving model quality.

This study analyzes a decade of crowdsourced disaster photos to evaluate vision-language models against human geographic reasoning for location estimation.

This paper introduces a synthetic data generation and assessment framework designed to produce comprehensive evaluation benchmarks for enterprise retrieval systems.

This paper proposes a speculative decoding tree selection strategy that accounts for host-to-device transfer costs during offloaded mixture-of-experts inference.

This paper proposes a pessimistic offline meta-reinforcement learning method that generates synthetic off-support latents to improve out-of-distribution task transfer.

This paper introduces a world-modeling framework that uses kernel similarity to control which physical properties are preserved in learned embeddings.

This paper presents an adversarial self-play framework using multi-perceptual views to improve language model detection of obfuscated harmful text.

This paper proposes an optimal-transport bridge framework to translate audio representations directly between mismatched neural codecs at low bitrates.

This paper develops an online tree search method providing deterministic anytime bounds for robust planning under uncertain transition dynamics.

This paper introduces a retrieval-augmented reasoning framework that extracts recurring patterns to detect harmful multimodal social media memes.

This systematic review analyzes security vulnerabilities, benchmark shortcomings, and developer oversight challenges in code generated by language models.

This paper examines how persistent digital records and AI influence individual futures, proposing legal and conceptual limits on the eternal use of criminal data.

This paper adapts language models into single-pass decision-makers that output explicit probability distributions over task labels specified at run time.

This article analyzes current legal and technical frameworks for data portability, arguing for a human-centric approach to personal data governance in AI.

This paper introduces a method to translate key-value caches between different language models, enabling context handoffs without reprocessing input tokens.

This paper proposes a black-box evolutionary prompt optimization framework using a small open-weight model to discover adversarial jailbreaks against language models.

This paper presents a structured pruning framework that decouples data-free neuron selection from localized weight reconstruction to reduce model memory requirements.

The paper proposes a flow matching framework for probabilistic global ocean modeling to generate calibrated conditional ensembles.

The paper establishes improved approximation bounds and polynomial-time algorithms for allocating indivisible chores fairly among weighted agents.

The paper proposes a training-free method establishing entity identity before final rendering to maintain visual consistency across long storyboards.

The paper introduces an internal feature alignment loss for diffusion models to improve background music generation from video inputs.

The paper presents an inorganic materials synthesis framework that incorporates condition prediction as context to guide reaction route planning.

The paper proposes an iterative reasoning framework that models intermediate multimodal reasoning steps as dynamic state transitions.

The paper introduces a benchmark measuring whether language models resist sycophancy when presented with invalid user arguments versus valid critiques.

The paper examines how hypernetwork adapters resolve knowledge conflicts and compose information during continual learning in language models.

The paper presents a dataset and framework mapping sketches and text to executable parametric computer-aided design code.

The paper proposes an evaluation technique that embeds behavioral traces into score-only feedback to trace distilled student models.

The paper presents an in-context diffusion model for generating, completing, and synthesizing unseen tabular data records jointly.

The paper introduces a weakly supervised learning framework and dataset for identifying acoustic evidence in respiratory sound classification.

The paper introduces a synthetic parallel translation dataset covering 22 scheduled Indic languages aligned via English pivot sentences.

The paper evaluates whether genomic foundation models offer predictive value over simple sequence frequency baselines across classification benchmarks.

The paper proposes a training-free episodic memory mechanism that extends linear attention language models to long-context tasks.

The paper integrates quality-diversity optimization with diffusion models to generate diverse sets of high-performing outputs.

The paper proposes a hyperbolic time series forecasting framework that maps multiscale frequency hierarchies onto the Poincaré ball.

The paper presents a Nastaliq Urdu comment corpus and evaluates transformer models with word-level attributions for offensive content detection.

The paper examines interpretive lock-in where planning agents generate code passing self-generated tests while missing the actual specification requirements.

The paper proposes a reasoning framework that discretizes visual states into tokens to jointly optimize visual computation and chain-of-thought steps.

The paper analyzes visual attention sinks in vision-language models and shows they stem from alignment with language text sinks.

The paper compares single-head autoregressive beam search with parallel multi-token prediction for generating semantic IDs in recommendation systems.

The paper studies whether language models can track temporal validity changes in parametric memory via adapter conditioning and clocks.

The paper examines how trigger-wrapped text can be made completely invisible to language model activations and downstream tasks.

The paper compares canvas-based and coordinate-based neural network surrogates for predicting indoor airflow and temperature fields under varying room dimensions.

The paper proposes variance-driven state resampling to optimize the state sampling distribution for reinforcement learning with verifiable rewards.

The paper explores transferring frozen video representations across heterogeneous sensor subsets to classify breathing activity and pose.

The paper introduces a learned search policy that predicts required repair steps to prioritize code edits before generating and testing patches.

The paper shows that LLM review pipelines rarely outperform the stronger model alone due to confirmation bias rather than true verification.

The paper proposes an image-to-video framework combining three coordinate spaces to enable camera-controllable video generation.

The paper presents a phase-annotated synthetic conversational corpus generated using a retrieval-augmented pipeline to study online grooming of minors.

The paper presents an unsupervised pretraining method that uses the multiscale structure of jet formation for high-energy physics data.

The paper presents a LightGBM architecture for multi-horizon queue forecasting and resource-aware decision support in border control systems.

The paper presents a diffusion-based framework that uses reweighted score matching to compute Nash equilibria in continuous multi-agent action spaces.

The paper tests what context judge models require, showing that omitting source constraints degrades the quality of iterative feedback loops.