All papers

This paper introduces a framework combining language model agents with deterministic software controls for reliable sensor-based industrial fault diagnosis.

This paper proposes a flow-matching method that averages Bellman target directions under a posterior distribution to improve reinforcement learning critics.

This paper introduces a benchmark to evaluate whether forecasting models can incorporate forward-looking contextual information into accurate time-series predictions.

This paper introduces a framework that enables language models to optimize physical structural designs through simulated fracture experience and hypothesis revision.

This paper presents a benchmark to evaluate performance, resource usage, and privacy trade-offs when language model agents collaborate across edge and cloud environments.

Introduces QK-Edit, a training-free query-key attention manipulation method for text-guided image and video editing in multimodal diffusion transformers.

Proposes Diff Diffusion, a diffusion-based model that predicts localized code edit sequences rather than regenerating entire source files.

Introduces Agentic-MatriBO, a modular Bayesian optimization framework using LLM agents and deterministic compilation for material and chemical experiment design.

Provides a tutorial analyzing theoretical expressiveness gains and parameter tradeoffs when re-uploading data in quantum amplitude embedding architectures.

Introduces FASHION-RQ, an automated framework deriving hierarchical evaluation benchmarks for fashion retrieval directly from catalog structures.

Releases MMTA, a multimodal benchmark testing the ability of language models to jointly analyze time series, text, and visual data.

Introduces Socratic Skill Learning, interleaving expert guidance with student practice to produce reusable and transferable agent skills across tasks.

Proposes TStruct, a network that captures recurring temporal relationship structures across variables to improve long-term time-series forecasting.

Presents RT-J, a relational transformer pretrained on diverse databases to achieve strong in-context table predictions with few sample examples.

Proposes CoSIR to train multi-agent collaboration policies using information-theoretic surprisal rewards that assess the helpfulness of communication.

Presents Interaction-as-a-Judge, using computer-use agents to evaluate interactive web and desktop software applications through direct dynamic interaction.

Introduces PaceReasoner, using long reasoning outputs during test-time reinforcement learning to boost the accuracy of brief, low-latency model answers.

Introduces DriveWLA, a mixture-of-transformers architecture unifying scene semantics, future dynamic prediction, and vehicle trajectory planning.

Proposes LinMorph, a learnable weight transformation method to initialize linear attention models from pretrained video diffusion transformers efficiently.

Introduces CineMem, an attention-aligned memory retrieval framework using clustered KV caches for autoregressive multi-shot long video generation.

Presents a memory decomposition framework separating spatio-temporal beliefs to support autonomous robot navigation under evolving online goals.

Introduces SwitchMem, a routing system that dynamically selects between text and visual memory representations for vision-language GUI agents.

Proposes MERIT, a framework using visual table reasoning and adapted encoders to extract information directly from table images.

The clinical study compares hyperspectral and RGB imaging for intraoperative detection of low-grade gliomas during brain tumor surgery.

HaloBlock allocates adaptive context halo regions across blocks to smooth context transitions and improve diffusion language model decoding.

FETALFusion combines CNN and Mamba architectures to segment fetal heads accurately across different ultrasound acquisition methods.

Virtual Head Attention uses two linear operations to compress key-value cache overhead without increasing training compute or sacrificing output quality.

The paper proposes a shopping basket choice model using specialized negative sampling to capture product interactions from retail transactions.

BELT compresses key-value caches using attention-distribution alignment and learned global rank allocation across model heads.

The paper analyzes how vision-language models favor 2D image cues over internal 3D depth representations when judging relative distances.

The paper presents a saliency-guided patch replay method to mitigate catastrophic forgetting in spiking neural networks under tight memory budgets.

The paper proposes predictability-aware supervised fine-tuning to resolve training and inference order mismatches in diffusion language models.

SWEPatchTest provides a benchmark to evaluate whether agent-generated test suites correctly accept valid patches and reject invalid ones.

The study shows that training on-policy distillation exclusively on student errors degrades accuracy rather than improving it.

LatentRemedy uses task-conditioned latent tokens to allow safe key-value cache reuse across conflicting tasks in multi-agent systems.

SHELDON introduces a dual-search reasoning framework that independently explores hypotheses and observation spaces to avoid confirmation bias.

ChampionKV adaptively allocates key-value cache memory budgets across attention heads using observation-window concentration proxies.

The authors propose methods to train machine learning potentials that transfer across quantum-chemical methods to exceed single-method training accuracy.

The paper introduces a training-free token pruning and merging framework with new robustness metrics for vision transformers.

The paper proposes a neural operator and state-space framework for high-fidelity 3D turbulent flow reconstruction from coarse data.

The paper investigates transforming text embeddings into visual sketches to improve model explainability and evaluate their utility in text retrieval.

The authors prove and demonstrate empirically that a two-layer attention Transformer can learn and implement in-context Q-learning algorithms.

WorkRover introduces a framework to train long-horizon cowork agents by recursively evolving contexts, tasks, and verifiers based on execution feedback.

This paper introduces an agent coordination framework for scaling recursive self-improvement research teams without updating model weights.

This paper introduces an evaluation framework grounded in published papers to assess continuous multi-stage execution in automated research agents.

This paper introduces a benchmark with expert-written rubrics to evaluate deep research agents on real-world life and productivity tasks.

This paper introduces an agentic post-training framework that coordinates multiple agents to evaluate and adaptively train text-to-image models.

This paper formalizes graph-structured missing features where missingness spreads across edges, testing existing graph propagation methods under this setting.

This paper presents a generative compression framework using implicit randomized Hadamard dictionaries to speed up diffusion-based image and video coding.

This paper presents an interpretability protocol to monitor latent reasoning dynamics and stability across multiple looped language model families.

This paper introduces a scheduler that decouples prefix matching from state readiness to eliminate execution bubbles in hybrid LLM serving.

This paper defines conditions under which learned multidimensional representation blocks preserve causal abstractions and high-level concept interventions.

This paper shows data ownership determines whether agents should broadcast or relay table queries, and proposes ownership-aware routing.

This paper introduces an order-flow allocation framework and evaluation protocol to analyze how intervention rules influence adaptive trading dealers.

This paper introduces a self-distillation method that searches over repeated layer execution orders to create stronger teacher guidance.

This paper establishes functional convergence bounds for wide neural networks approximating Gaussian processes on arbitrary domains.

This paper proposes predicting the final state first and using diffusion to interpolate long-horizon physical particle trajectories.

This paper proposes a recurrent architecture that adds rotational degrees of freedom to prevent oscillatory bottlenecks in looped reasoning.

This paper presents a neuro-symbolic graph transformer with first-order logic loss to recognize hierarchical human intentions from multimodal activity sequences.

QORH models multi-relation rules as query-specific hypergraphs with message passing to improve inductive link prediction on unseen knowledge graph entities.