All papers

Introduces an immutable, provenance-aware state representation framework with deterministic replay and audit logging for robotic systems.

Proposes a hierarchical evolution framework that allows individual agent software harnesses in multi-agent systems to adapt using feedback.

Proposes a conformal prediction framework that learns stochastic groupings to improve subgroup coverage reliability without inflating overall prediction sets.

Uses multi-objective evolutionary algorithms to optimize agent execution harnesses across competing metrics like accuracy and operating cost.

Tests whether priming annotators with scenario vignettes and micro-narratives improves the operationalization of lived experience in subjective annotation.

The paper demonstrates an on-chip power side-channel attack reconstructing color ImageNet inputs from machine learning accelerators on shared multi-tenant FPGAs.

This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature.

The paper introduces a black-box method to extract training sequences from genomic language models using output sampling and sequence alignment.

This paper presents an experience-based learning framework that uses trajectory quality signals rather than final task outcomes to guide agent improvement.

This paper presents an interface framework applying visual transformations and adversarial attacks to create assignment text resistant to machine extraction.

This paper presents an on-device communication agent that uses small language models to generate low-latency mobile network configuration rules.

This paper proposes an aggregator-agnostic watermarking scheme to embed persistent watermarks into models during federated learning despite robust aggregators.

This position paper outlines a conceptual operating model and set of principles for integrating persistent AI agents into product management.

This paper introduces a server-side defense against active label inference attacks in vertical federated learning using embedding asymmetry signals.

The paper introduces a reinforcement learning approach for multi-turn agents that identifies recurring decision patterns to improve intermediate step credit assignment.

This paper proposes an external memory-based framework to unlearn specific identities from trained models when training data is absent.

The paper proposes a structured chain-of-thought grounding method to track visual evidence across video frames for goal-oriented embodied navigation.

This paper presents a multi-agent framework that translates natural language into verified, executable data analytical programs using schema-grounded refinement.

The paper proposes certified adversarial detection using Lipschitz-margin analysis to detect perturbations rather than forcing invariant predictions.

This paper proposes a counterfactual certification framework to bound performance loss during sequential machine unlearning requests.

This paper proposes a critic-free reinforcement learning algorithm using prefix trees to assign step-level credit for interactive multi-step tasks.

The authors analyze robustly reliable learning for regression under data corruptions and propose an algorithm to compute optimal prediction intervals for Lipschitz predictors.

This paper presents a framework that uses history tracking and problem-specific tools to help language agents solve complex combinatorial optimization tasks.

The paper proposes a fine-tuning method that trains language model agents using textual expert critiques alongside corrected actions.

This paper presents a psychometric framework to test whether automated sentiment classification remains equivalent across different social media platforms.

Presents an automated framework using language models to manage storytelling, rules, and combat in tabletop role-playing games.

This paper measures whether ecological alignment in large language models survives under pressure using a calibrated pretraining ladder.

The paper introduces a hierarchical memory agent framework for multi-center medication recommendation that accounts for hospital differences and drug safety constraints.

This paper introduces a data-flow graph representation and mutation testing technique to uncover bugs in deep learning frameworks.

The paper compares three textual interface designs for optimizing prompts in multimodal audio-visual question answering systems.

This paper proposes a hierarchy of sparse second-order cone programming relaxations for certified neural network robustness.

The paper proposes a vision-language-action model that dynamically crops high-resolution regions to manipulate objects without using a wrist camera.

The paper presents a framework using random noise and Sigmoid function analysis to detect directional adversarial attacks on medical imaging models.

The paper develops methods to evaluate and recalibrate binary predictors using noisy, multi-annotator labels without requiring clean ground truth.

The paper proposes a progressive validation framework for zero-shot tool selection that evaluates functional compatibility and contextual relevance.

The paper proposes a sequential conformal prediction algorithm that groups calibration scores by state similarity rather than pure temporal recency.

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.