All papers

The paper investigates when language models learn capabilities by tracking unembedding matrix dynamics during pretraining using parameter-trajectory crosscoding.

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

Combines language models and lightweight decision models to fact-check statements in peer reviews against original manuscripts.

Develops an open agent environment for evaluating how autonomously language agents can conduct post-training machine learning experiments.

Applies trajectory Bellman residual minimization to turn sparse rewards into dense token signals for arithmetic reasoning.

Presents a multi-turn benchmark measuring language agent capabilities on complex, long-horizon bioinformatics workflows.

Introduces a benchmark evaluating whether language agents can make correct scientific judgment calls derived from human interventions.

Introduces an equivariant self-supervised learning framework that avoids topological collapse in 3D point cloud masked autoencoding.

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

Measures tool-agent performance failures under controlled harness limits such as completion caps and tool menu visibility.

Analyzes trade-offs between aggressive and conservative planning horizons in language models operating under partial observability.

Introduces a dataset benchmarking human-agent interaction and collaboration across long-horizon professional workflows.

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

Presents a decomposed network architecture that applies separate graph and convolution modules to trend and seasonal time series components.

Presents an opinion article on wearable on-body language models based on an industrial panel talk.

Investigates cost-aware policies for adaptively switching between language models of varying fidelity in stochastic optimization pipelines.

Presents a framework that decouples speech generation and synthesis token spaces to speed up spoken dialogue model inference.

Introduces a reinforcement learning framework that evaluates memory operations in agents using a shared state and process rewards.

Develops a structured explainable reasoning architecture to extract sensitive self-disclosures from cybersecurity interviews using local small language models.

Proposes a graph-guided synthetic data generation framework with controlled difficulty levels for training multi-hop research agents.

Introduces a localized code repair framework that evolves language model memory architectures using fine-grained execution feedback.

Analyzes working memory error patterns in language models to show that failures stem from readout interference rather than target retrieval.

Investigates whether continuous latent reasoning tokens genuinely compute answers or merely settle early on predetermined conclusions.

Presents a test-time method that extracts structured schemas from corpora to guide generative models toward more diverse outputs.

Introduces a condition-contrastive distillation approach to prevent student language models from learning erroneous teacher continuations.

Examines how extended reasoning steps lead language models to interpret everyday logical terms strictly literally instead of inferring pragmatic meaning.

Presents an autonomous supervisor agent that iteratively tunes an agent's code, harness, and prompt on few examples.

Introduces a two-stage model merging method to enable dynamic multi-objective recommendation trade-offs at inference time without retraining.

Introduces a benchmark evaluating single-forward-pass discrete decision models versus generative reasoning models across diverse closed-loop tasks.

Proposes a fractional power-of-two quantization grid to enable efficient multiplier-free integer inference for large language models.

Introduces a multimodal harness and benchmark pairing continuous video with clinical records to assist patient monitoring in hospitals.

Proposes a trajectory-aware retrieval method that indexes tool sequences in a trie to cut token costs and improve selection accuracy.

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

The paper explores whether measuring information density across reasoning steps can predict and guide answer correctness in large language models.

The paper applies heterogeneous graph neural networks directly to bank relational schemas to detect money-mule accounts prior to flag dates.

The paper examines how increasing visual token budgets and cropping key evidence improves document retrieval-augmented generation in vision-language models.

The paper evaluates a constrained multi-agent reinforcement learning method that learns policies inside world models to reduce environment interaction costs.

The paper presents an agent orchestration framework that trains smaller language models to navigate, inspect, and reason over long multimodal documents.

The paper introduces a method that accelerates on-policy knowledge distillation by truncating student rollouts and applying inverse-probability weighting.

The paper compares quantum and classical machine learning models on multi-cohort fMRI datasets to test their utility for neuropsychiatric classification.

The paper introduces an agenda-driven neuro-symbolic framework to improve the reliability of user simulation when testing interactive conversational agents.

The paper proposes a federated learning framework that uses version control operations instead of full document aggregation to evolve agent skills.

The paper introduces a hierarchical graph convolutional network and dataset to detect internal inconsistencies and headline mismatches in news documents.

The paper introduces an in-context memory method storing latent transitions between visual observations to assist robot manipulation without extra annotation.

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

The paper introduces a system that retrieves document information into a fixed set of prompt embeddings for frozen language models.

The paper proposes an error-correction method to prioritize and transmit key-value cache tokens across edge-deployed multi-agent systems under latency deadlines.

The paper introduces a reinforcement learning method that uses internal certainty profiles and selective filtering to train models without external verifiers.

The paper presents a self-guided reasoning framework where language models generate code to detect and diagnose errors in vehicle routing problem instances.

The paper studies combining classical and quantum representations to determine when multi-view fusion improves machine learning predictions over single representations.

The paper introduces a benchmark to evaluate how language models perform counterfactual reasoning regarding AI data center infrastructure and siting commitments.

The paper proposes a framework that replaces generative model calls with lightweight decision models for routing and reviewing in multi-agent workflows.

The paper presents a benchmark and harness to delegate routine tool choices from large language models to cheaper decision models.

Presents methods for query-guided video grounding across object localization, relationship detection, and temporal moment identification.

Creates a structured schema that converts research papers into connected scientific entities to improve retrieval-augmented generation for autonomous agents.

Introduces a distillation method that identifies critical sub-spans in student agent trajectories for teacher revision and continual learning.

Provides theoretical convergence guarantees and finite-sample bounds for advantage-based value sharing in reinforcement learning algorithms.

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

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

Presents a full-duplex conversational model that generates real-time speech alongside synchronized facial expressions and body gestures.