All papers

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

The paper evaluates whether language models genuinely benefit from compressed memory by comparing them against memory-free baseline models.

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

The paper presents techniques to adapt and quantize a small language model for on-device deployment in phone call agent services.

The paper introduces a structured experience graph memory that organizes and maintains agent workflows across hierarchical abstraction levels.

The paper introduces a speech dataset spanning 675 underrepresented languages to support speech recognition and text-to-speech technologies.

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

The paper proposes an agentic reinforcement learning method that uses executable oracle continuations to guide exploration during long-horizon tasks.

The paper introduces a benchmark to evaluate how well language models recognize, infer, and coin newly emerging Korean terms.

The paper presents a systematic framework for selecting optimal parallelism strategies to balance latency and throughput during distributed language model serving.

The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence.

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

The paper presents a method that uses agent failure patterns to generate tailored simulation environments for embodied navigation learning.

The paper proposes a hybrid projection and simulation method to automatically configure distributed training parallelisms across GPU clusters.

The paper examines failure modes in recursively self-improving execution harnesses for agentic video understanding models.

The paper presents an evaluation framework to test the interactive agentic capabilities of base language models before post-training.

The paper introduces a benchmark to evaluate how well language models understand legal philosophy and apply it to legal reasoning.

The paper introduces a multilingual benchmark to evaluate how language models identify financial concepts and extract supporting numerical evidence.

The paper introduces an evaluation framework that diagnoses bottlenecks and dynamically regenerates benchmark tasks for language model agents.

The paper compares physics simulations and chain-of-thought language reasoning for physical problem solving in multimodal language models.

The paper proposes a graph-based method to generate explainable recommendations by identifying influential user peers in graph neural networks.

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

The paper proposes marginal inverse probability scaling to reduce mode collapse in reinforcement learning with large outcome spaces.

The paper introduces a discrete codebook representation that pairs associative indexing with vector search for long-term agent memory.

The paper introduces an event ledger and executable aggregation framework to help agents accurately query and summarize trajectory history.

The paper introduces a multi-task benchmark and a masked autoencoder to evaluate transfer learning for wrist-worn inertial measurement units.

The paper combines camera video and insole sensor signals to improve real-time monocular human pose estimation under visual occlusion.

The paper introduces a document-level data augmentation method using discourse structure parsing to preserve logical flow during text generation.

The paper learns 3D shape representations by mapping text hierarchies into hyperbolic space without relying on synthetic 2D renderings.

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

The paper presents an automated framework to predict suspected drug overdose deaths from investigation reports using expert-guided rationales.

The paper presents a contrastive learning method for time series that unifies morphological similarity and temporal proximity.

The paper introduces a spatial reasoning benchmark to evaluate how models handle varying reference frames and altered visual evidence.

The paper proposes divergence-routed on-policy distillation to target critical token positions where students diverge from teacher language models.

The paper studies transferring learned predictive structure between continuous-time dynamic graphs and temporal knowledge graphs across distinct representations.

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

The paper introduces a horse-racing decision benchmark to evaluate whether agents can extract profitable signals from noisy historical data.

The paper evaluates delegating code-repair decisions to low-cost probabilistic judges to reduce token costs without losing accuracy.

The paper proposes a Bayesian formulation of self-attention to address non-stationary temporal dependencies in long-term time series forecasting.

The paper learns trajectory embeddings to predict the success or failure of language model agents early during execution.

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

The paper proposes a Transformer architecture that plans dynamic cross-layer connections globally across the model based on input tokens.

The paper combines a vision-language model with a Transformer-VAE to generate expressive, human-like gaze dynamics for humanoid robots.

The paper uses sparse autoencoders to establish a shared coordinate system for matching symbols across heterogeneous neuro-symbolic systems.

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

The paper presents a human-in-the-loop framework providing role-bounded explanations of cyber-physical system resilience graphs using Formal Concept Analysis.

The paper simulates student behavior by modeling individual learner history and constraining simulated actions via a third-person prediction harness.

The paper measures whether requester users can understand multi-step agent replies and evaluates how explicit constraints improve reply usability.

The paper proposes a graph-based task synthesis framework to help long-horizon agents satisfy inter-dependent constraints and achieve complete task outcomes.

The paper introduces a single-policy optimization framework combining privileged adaptation with stored historical next-token distributions for language model reasoning.

The paper proves computational complexity results for contiguous envy-free cake-cutting and indivisible goods allocations under additive valuations.

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 paper introduces a multi-domain benchmark to evaluate language model agents on identifying, understanding, and applying domain-specific rules across complex files.

The paper proposes a 3D-anchored latent space representation for simultaneous scene reconstruction and view generation.

The paper analyzes model swapping in multi-session agents and proposes a history summarization method to preserve strong reasoning behaviors.

The paper introduces an uncertainty-driven self-play method where exploring agents target policy weaknesses to learn robust autonomous driving policies.

The paper models individual scientific taste using criterion assessments and introduces privileged hindsight distillation to align proposals with researcher preferences.

The paper introduces prompt optimization and recovery measurement tools to evaluate the limits of transmitting concepts between scaling language models.

This paper introduces a large-scale underwater acoustic recording dataset paired with vessel tracking data for ship recognition tasks.

This paper introduces a task and benchmark for tracking candidate sets of referent objects across ambiguous interactive dialogue turns.