All papers

The paper introduces a post-training quantization framework for autoregressive visual generation models to retain image quality under low precision.

The paper introduces CHEAR, an activity recognition framework that pre-organizes confusion hierarchies offline to reduce inference costs.

The paper introduces SuperAI, a framework to merge complementary capabilities across heterogeneous foundation models using low-dimensional subspace alignment.

The paper introduces a rule-based curriculum training system that improves reinforcement learning performance on Craftax without language models.

The paper introduces a zeroth-order influence function framework to enable memory-efficient training data selection using only forward passes.

The paper introduces MultiFlow, a generative framework that models distributions of fMRI neural responses using stimulus-informed flow matching.

The paper presents StimFlow, using conditional flow matching to synthesize fMRI brain activity directly in voxel space from visual stimuli.

The paper presents CodeWalk, a method to create challenging coding benchmarks at low cost by programmatically composing verified atomic problems.

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 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 empirical study evaluates how compact versus full state representations affect the ability of language model agents to learn long-horizon workflows.

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 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 an offline compiler that detects conflicts and merges multiple agent skills into a single coherent capability.

This paper investigates catastrophic forgetting in continual agent skill learning and proposes a factorized memory architecture to preserve procedural execution.

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 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 introduces an agentic system that reconstructs multi-layer 3D garment sewing patterns from a single image using simulation feedback.

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 presents a training-free memory selection method that progressively composes non-redundant evidence sets for long-term agents.

The paper introduces a conflict-clustering solver with lightweight learning to coordinate distributed agents solving constraint satisfaction problems.

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

The paper presents an automated framework that jointly designs reinforcement learning observation spaces, reward programs, algorithms, and training configurations.

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 presents a self-improvement method where coding agents refine natural-language behavioral specifications using execution-verified tests.

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 demonstrates that language model optimizers frequently re-propose previously rejected candidates during search trajectories.

The paper presents Google Earth AI's geospatial reasoning agent along with evaluation benchmarks for complex multi-step geospatial workflows.

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

The paper introduces a benchmark measuring how software interface versions affect the task performance of desktop language model agents.

The paper presents a hierarchical agent combining vision-language models for task planning with deterministic control programs for micromanipulation.

The paper introduces a framework to model evolving social dynamics and update personas in multi-agent language model teams.

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 uses in-context language model evaluations as state value functions to improve credit assignment in agentic reinforcement learning.

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

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.