All papers

Proposes MedEvo, an iterative training framework that repairs multimodal medical reasoning trajectories using minimal hints and taxonomy-guided feedback.

Introduces the PocketWatch benchmark and zPocket, an approach training pocket-prediction heads on frozen representations from protein co-folding models.

Studies the trade-off between spatial smoothness and gene-level fidelity when mapping single-cell RNA sequencing profiles to images for neural networks.

Introduces a graph embedding and Procrustes alignment framework to provide a bounded metric for evaluating discovered process models against ground truth.

Proposes a training-free framework that mitigates vision-language model hallucinations by admitting claims only when supported relative to a visual-null input.

Develops SCRIM, a method quantifying state criticality in imitation learning by measuring downstream value degradation under bounded action perturbations.

Introduces an adaptive-group process reward framework that partitions GUI trajectories into subgoals to provide intermediate credit assignment for reinforcement learning.

Proposes a recursive self-improvement framework for electrocardiogram analysis models combining verified synthetic waveform edits with targeted intervention selection.

Presents PolyGraph, a method storing provenance receipts during knowledge graph construction to enable precise updates when source documents are retracted.

Analyzes how multi-agent workflow architecture affects the propagation and attenuation of coordinated stance manipulation across sequential agent chains.

This paper proposes a training framework that triggers supervised fine-tuning specifically on failure states during reinforcement learning to prevent agent forgetting.

This paper presents a compact decision model designed to screen malicious agent tool skills with high recall directly from specification files.

This paper proposes a credit-assignment method that uses rubric evaluation outcomes to allocate token-level advantages during reinforcement learning.

This paper presents a reinforcement learning approach that uses in-context dynamic memory during rollouts and self-distillation to accelerate reasoning exploration.

This paper introduces a posterior learning approach that improves multi-sample reasoning coverage by updating models using single posterior draws.

This paper proposes an end-to-end framework integrating nonlinear dimension reduction with longitudinal counterfactual prediction for high-dimensional observational data.

This paper presents a coupled evolution framework that co-adapts an executable tool library and a model's policy for scientific equation discovery.

This paper presents a system combining interactive human corrections with iterative policy retraining for long-horizon whole-body mobile robot manipulation.

This paper introduces a framework that quantifies delegation flexibility to analyze the spectrum between centralized and decentralized multi-agent coordination.

This paper proposes Gaussian process bandit algorithms with selective verification audits to optimize AI system configurations under noisy evaluations.

This paper presents an autonomous system combining LiDAR SLAM, visual imagery, and thermal sensing to inspect under-panel components of solar farms.

This paper presents an optimization method that trains models to decide when to invoke reasoning tokens, reducing unnecessary thinking overhead.

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

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.

Presents ADMA, an adaptive deliberation controller that dynamically adjusts multi-agent critique and coordination to improve reasoning accuracy.

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.

Presents ECMR, a dynamic evidence-aware controller that guides multi-agent deliberation steps to prevent false consensus and reduce redundant reasoning.

Shows that evil persona vectors extracted from low-resource languages transfer poorly and fail to steer model behavior effectively across languages.

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

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.

Finds that framing malicious coding prompts as engineering specifications systematically reduces language model refusal rates across multiple models.

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.

The paper introduces local contract-conditioned rewriting to prevent sensitive data leakage in email-generating language model agents cost-effectively.

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.