All papers

The paper proposes a metamorphic testing method with mutation operators to evaluate how retrieval-augmented generation systems handle changing document corpora.

The paper estimates reasoning confidence by segmenting chain-of-thought traces into argument graphs evaluated through natural language inference.

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

The paper studies Byzantine robustness in fair federated learning and proposes an update screening method using client trajectory consistency.

The paper refines a high-level spatial AI orchestration framework into an implementation-focused low-level architecture for governed agent execution.

This paper presents a mid-training method, corpus, and benchmark to help language models understand and reason about time series data.

Combines language model review with mechanical numerical checks to verify mathematical formalizations and catch errors missed by automated judges.

This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions.

The paper introduces a sparse interaction diffusion method that coordinates multi-robot trajectories using a frozen single-robot prior and modular corrections.

The paper introduces an evaluation benchmark comprising four clinical datasets to assess large language model performance in the Swedish medical domain.

The paper presents a benchmark evaluating how reliably language models correct and maintain formatting requirements across changing instructions.

The paper introduces an agentic framework that removes peripheral visual clues to protect geolocation privacy in images without adding visible artifacts.

The paper separates parameter specification, instruction generation, and code synthesis for language-conditioned robotic arm control using reinforcement learning.

The paper presents an empirical framework measuring neural network backdoor stealthiness across input, feature, and parameter spaces.

The paper introduces a zero-shot LLM pipeline to generate counterfactual examples for stress-testing NLP models.

Proposes an optimization method to discover undisclosed or harmful generation concepts embedded within diffusion model adaptation weights.

The paper introduces a compiler framework that transforms defective natural-language protocols into verifiable laboratory execution workflows.

The authors propose KnowGPO, a reinforcement learning framework that assigns credit to deep search agents based on acquired knowledge conditions rather than trajectories.

The paper introduces a diagnostic pipeline to identify low-confidence patient subgroups and detect harmful covariate shifts in clinical prediction models.

The paper proposes FGSS-BO, adding structured hypothesis management to language-model-guided Bayesian optimization.

The authors present IDGuardian, an adversarial perturbation framework designed to defend facial images against unauthorized personalized diffusion models.

The paper introduces an authorization system that reuses validated consequence evidence across agent execution branches while maintaining separate live authority.

The paper examines decentralized multi-agent coordination through sequential market auctions under conditions of imperfect competition.

Introduces a system that generates and compares alternative scene drafts to guide full dramatic script adaptations from prose.

Proposes a decentralized coordination mechanism using commitment tracking to preserve clear accountability across multi-agent delegation chains.

Proposes a lossy compression framework for federated learning that balances user privacy against model utility on resource-constrained devices.

Combines language models with multi-agent auctions to manage resource contention in 5G edge network slicing.

The paper introduces ZoomCap, an adaptive visual zooming and evidence-routing framework to improve fine-grained image captioning supervision.

The authors introduce an embodied reasoning foundation model that unifies perception, planning, and execution using task-conditioned adaptive adapters.

Evaluates the security of streaming image classifiers to edge devices against physical cold-boot memory extraction attacks.

Orchestra decomposes multimodal instructions into subtasks, tracks step progress, and maintains execution context to improve long-horizon robotic manipulation.

This paper presents a benchmark and link inference attack framework to evaluate edge unlearning methods in graph neural networks.

TrueMAD introduces one-to-one peer feedback between debate rounds to help language model agents resolve conflicting reasoning before final revision.

This paper presents a pathwise conformal prediction method that guarantees valid coverage across adaptive early-exit stopping policies without multi-exit score inflation.

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

Introduces a metric to measure how non-linearly features learned by sparse autoencoders interact during downstream model behaviors.

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

Presents an exact machine unlearning method for federated socially fair k-means clustering using separate group-level local summaries.

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

This paper introduces a training-free safety filter that evaluates scene context to constrain generated humanoid motion trajectories.

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

This paper presents a feasibility-aware safety filter that amortizes minimal-intervention constraints using a feedforward neural network in reinforcement learning.

The paper proposes a multi-agent framework to coordinate analytical tools and evaluate multi-factor supply chain risks across diverse locations.

This paper develops a unified Bellman operator to optimize task performance and enforce constraints within safety-critical reinforcement learning.

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

The paper proposes a credal machine learning framework that models epistemic uncertainty with probability distribution sets for risk-averse decision-making.

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

Proposes a multi-route retrosynthesis planner that evaluates synthesizability and cost across chemical reaction options.

The paper argues that uncertainty estimation in smart assistants is necessary to prevent unsafe actions when user preferences remain ambiguous.

The paper argues that full robotic value alignment is impossible and examines how to manage acceptable misalignment in autonomous systems.

The paper presents GENDIS, a multilingual dataset of YouTube comments from India, Kenya, and Nigeria annotated for gender-related discourse and stance.

Uses agentic program search to iteratively write and refine design algorithms for nucleic acid sequences that transfer across independent models.

This paper evaluates whether attention-graph curvature features can help verifiers detect errors in mathematical reasoning steps.

This paper introduces GUIDE, a multi-agent copilot system that assists clinicians in drafting and updating evidence-based medical practice guidelines.

The paper presents a byte-local text watermarking method that scores outputs without invoking tokenizers during detection.

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

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

Studies uncertainty estimation for abstract meaning representation parsers using top-k beam candidates and prediction rejection ratios.

The paper presents an ontology-guided retrieval framework combining population and personalized memory for wearable sensor human activity recognition.