All papers

The paper introduces a brain-inspired reflection framework that diagnoses failure points to guide self-evolving agents in embodied spatial tasks.

Proposes an interactive agent framework that utilizes physics simulation and spatial exploration for complex 3D object assembly and composition.

Proposes a multimodal reasoning framework to avoid biased role attributions in underspecified or ambiguous social memes.

Presents a graph-based framework with decision certificates to determine when multimodal agents should acquire visual evidence under cost constraints.

The paper analyzes physical recursive self-improvement in robotics, proposing a multi-level evaluation framework and reviewing existing adaptation methods.

The paper introduces a method that selectively adjusts positive credit for undesirable agent actions using the policy's confidence during multi-turn reinforcement learning.

The authors analyze readout scaling in sparse autoencoders and propose canonicalized coordinates to improve the recovery of underlying generative factors.

The paper proposes a decision-guided co-training method that selectively filters observation units to improve world modeling for language model agents.

This paper investigates theoretical limits and conditions for verifying reachability policies in unknown Markov decision processes using sample access.

The paper proposes a contextual parameterization framework that dynamically adjusts robot policy parameters as interaction contexts change.

The paper presents a coding method that embeds hidden communication into an agent's policy randomness without altering its observable action distribution.

The paper presents a multi-agent system designed to automate advertising operations and coordinate tasks like bidding and budget allocation.

This paper models preference model alignment as a Stackelberg game to identify comparisons where evaluators are prone to error.

The paper introduces a framework for recursively composing, verifying, and persistently reusing modular agent capabilities across diverse problem domains.

The paper analyzes theoretical computational limits of sequential sampling circuits in autoregressive language model token generation.

The paper presents an autonomous agent platform designed to monitor surveillance data and generate early warnings for infectious disease outbreaks.

The paper specifies a verifier-governed protocol for panvascular research and benchmarks a baseline model on intervention imaging tasks.

The paper introduces an operation-level graph framework that helps interactive agents retrieve and compose reusable fragments from past task experience.

This paper establishes minimax rates for estimating language model semantic entropy using verdicts from weak pairwise equivalence judges.

The paper introduces an experience-graph framework enabling heterogeneous agents to share and reuse execution history across diverse tasks.

The paper introduces a neurosymbolic world model that learns interpretable concepts from temporal logic constraints for agent planning.

Proposes input loss curvature as a sampling-free metric to detect the onset of training data memorization in diffusion models.

The paper proposes a reinforcement learning framework that decides when and what external knowledge to retrieve for machine translation.

Introduces a welfare-aware objective to allocate limited human feedback across diverse individuals when training pluralistic alignment models.

The paper proposes an in-context reinforcement learning method enabling vision-language-action policies to adapt during robot deployment.

This paper introduces a multi-hop reasoning benchmark to evaluate fine-grained, explainable hate speech analysis in Persian.

The paper introduces a distillation framework that triggers teacher takeovers when student policies diverge during long-horizon reasoning.

The paper proposes a hierarchical two-loop optimization framework that uses residual errors to trigger active evidence acquisition during inference.

The paper presents a multimodal agent designed to trace the origin, spread, and potential safety risks of online memes.

The paper presents an online Thompson Sampling bandit policy to select classifiers that balance predictive accuracy and subgroup fairness under delayed feedback.

Presents an end-to-end distributed system orchestrating coding agents to execute computational research experiments alongside human scientists.

This paper establishes theoretical limits on what forms of annotator disagreement can be identified from pairwise preference feedback data.

The paper presents a graph-based framework using control nodes and behavior trees to coordinate heterogeneous artificial intelligence models.

Proposes conformal calibration to weight criterion-level uncertainty when using rubric-based language model judges for reinforcement learning.

The paper introduces a hierarchical task network framework that separates multi-agent planning from execution scheduling.

The paper introduces a parameter decomposition method to interpret the mechanistic subcomponents of language model weights.

The paper uses language model subtask decomposition and dense progress tracking to enhance reinforcement learning on long-horizon tasks.

The paper examines AI-generated text detection in scientific papers by combining visual formatting, PDF text, and LaTeX representations.

The authors present an agent loop that closes functional test coverage in hardware design using independent simulation scoreboards.

The paper derives mathematical bounds to evaluate the calibration of query-based probabilistic models using logical consistency without ground-truth labels.

Develops a recursive self-improvement framework where an evolving meta-agent adapts agent workflows based on execution trajectories and feedback.

The paper investigates data poisoning attacks targeting pre-training and retrieval corpora of protein language models and generative design systems.

Develops a domain harness and policy learning pipeline to improve language model agent execution in computer-aided design environments.

Improves deep search agents by refining search queries and filtering evidence to align retrieval with multi-step reasoning goals.

Presents a bound-propagation verification framework to bound worst-case reconstruction errors for deep joint source-channel coding decoders under noise.

Combines language model dialogue with workflow automation to process satellite imagery for internal solitary wave detection and forecasting.

This paper investigates internal model representations that separate assistant actions from externally provided text across conversational layers.

Uses tool-augmented language models to extract clinical variables from patient notes and reliably execute deterministic medical calculation formulas.

Introduces a Ukrainian meme dataset and evaluates vision-language models on detecting persuasive and propaganda techniques.

Analyzes how prompt variations redistribute internal feature activations in sparse autoencoders and traces their impact on downstream model decisions.

Proposes a training framework enabling aerial vision-language agents to navigate in full action chunks and stop reliably at destinations.

Integrates reinforcement learning and tabular model guidance to help language models select features and predict emergency department clinical risk.

The paper evaluates the impact of sarcasm on Turkish hate speech detection models using a targeted external test dataset.

The paper presents an agent system that generates, edits, and renders interactive virtual worlds from text using executable code rules.

Develops a closed-loop distillation pipeline where student feedback refines teacher models to improve adversarial robustness.

The paper presents a metric and benchmark measuring how quickly environment dynamics can change before agent decision-making fails.

Proposes a training-free structural lexical retriever to select relevant agent cards without overloading model context windows.

Uses reinforcement learning to train an agent module that selects, sizes, and orders retrieved memories for downstream tasks.

The paper proposes an algorithm using betting-based sequential tests to monitor supermartingale certificate violations during runtime execution of stochastic systems.

Presents a multi-agent system for paper screening and data extraction in systematic literature reviews following standard guidelines.