All papers

Introduces a benchmark and environment for evaluating coding agents on text-conditioned LEGO design with physical and discrete assembly constraints.

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

The paper analyzes forty legal instruments to examine how copyright governance protects scholarly authors whose works train generative AI models.

Presents a top-down skill refinement system for robot self-improvement through structured execution feedback and reusable modular skills.

Surveys dataset design, construction methods, and evaluation protocols for improving spatial reasoning in multimodal language models.

The paper formalizes transparency as role-dependent answerability using information flow networks to evaluate AI-supported decision systems.

Introduces an evidence-grounded multi-agent framework combining multimodal medical data and clinical literature for knee osteoarthritis progression prediction.

Evaluates closed and open models on English and Bengali abstractive summarization using lexical metrics, factuality checks, and automated judges.

The paper proposes an unsupervised drift monitor using internal embedding distances to flag unfamiliar inputs in deployed deepfake speech detectors.

Introduces a hierarchical memory architecture that consolidates nested agent task intents into reusable knowledge across long horizons.

The paper presents a Korean dataset extracted from corporate sustainability reports to support information extraction on environmental, social, and governance topics.

This paper presents an imitation framework that enables agents to identify violated contracts, extract skills, and repair execution failures.

Proposes a self-evolving agent that allocates evaluation resources dynamically across candidate programs during iterative code evolution.

Measures demographic bias related to age, gender, and race based on first names across local language models.

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

Proposes a framework modeling research ideation as modular thinking flows sampled dynamically from a probabilistic supernet.

Reviews and formalizes data selection, weighting, and scheduling policies across the pretraining and post-training lifecycle of language models.

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

Presents a program-search method using a curated elite solution table to guide language models in synthesizing executable control policies.

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

The report presents an omni-modal model family trained by scaling reinforcement learning throughput, environments, and agentic reward compute.

The paper develops an attack framework to generate physical adversarial textures that deceive video models across varying trajectories and viewpoints.

The paper proposes an event-centric memory framework that converts dialogue history into query-relevant graph working memory for conversational agents.

This paper proposes a watermarking method embedded during speech synthesis to trace and verify generated audio.

The paper introduces a dual-path parametric memory method that combines pooled history representations with sequential adapter updates.

The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas.

This paper fine-tunes language models to selectively correct speech recognition transcripts using curated candidate pools.

This paper analyzes prediction errors in spectral saliency when deleting singular components to selectively edit language model capabilities.

This paper combines language models with a differentiable traffic equilibrium layer to help non-experts optimize transportation planning problems.

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.

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

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

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.

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.