All papers

Argues that inferential accuracy fails to evaluate agent coordination safely because correlated errors can compound into costly team actions.

Introduces a prompt optimization framework that monitors item-level regressions and recoveries rather than relying solely on aggregate accuracy.

Analyzes how language models develop in-context learning by separating simple demonstration copying from multi-token rule composition across training checkpoints.

The paper proposes a training-free structured pruning method that trims multilayer perceptron layers while preserving attention mechanisms for reasoning tasks.

The paper presents a system using technical language processing to transform industrial maintenance work order records into an ontology.

The paper proposes confining feature distillation loss to language-routed adapters to balance performance in compact multilingual speech recognition models.

The paper introduces a benchmark assessing language models on operational planning from incomplete, complex, and contradictory military document sets.

The paper introduces a benchmark to evaluate whether frontier language model agents can execute open scientific research and produce follow-up papers.

The paper introduces an evergreen benchmark to evaluate the ability of language models to retrieve and format multi-element factual lists.

The paper presents a method to retrofit attention models with hierarchical test-time neural memory modules to improve long-context inference efficiency.

The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions.

The paper proposes adding an explicit copy-paste prediction head to language models to improve in-context retrieval and copying efficiency.

The paper introduces a framework to audit cultural, geographic, and user-representation biases embedded within popular machine learning benchmarks.

The paper details the development, training data criteria, and performance of the open Japanese sovereign models, LLM-jp-4.

The paper introduces a test showing that steering vectors designed for competing ethical frameworks behave interchangeably in open-weight models.

The paper analyzes Best-of-N sampling as an extreme-value competition between tails of correct and incorrect responses to explain reward hacking.

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

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

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

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

The paper compares typed probabilistic decision models with large language models on tabular data classification and format robustness.

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

The paper introduces a benchmark to measure how tool-calling agents perform when interacting with emotionally varied, impatient users.

The paper introduces a training-free controller that uses token entropy to target and regenerate faulty reasoning steps at test time.

The paper proposes a likelihood-based attribution method to identify the source model of machine-generated text under distribution shifts.

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

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

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

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

This paper investigates using Classical Chinese as a compact intermediate reasoning register to reduce token length in Chinese language models.

This paper proposes a multi-agent system designed to reduce clinical medication errors through dynamic collaborative coordination.

This paper trains language models using reinforcement learning to generate explicit perception-to-action rules for theory-of-mind reasoning tasks.

This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales.

The paper proposes weighting majority voting responses using a length-conditioned prior adapted at test time without model fine-tuning.

The paper investigates whether machine translation systems preserve or neutralize media bias across languages.

The paper proposes a tree search method that optimizes agent skills through contrastive atomic rule editing and execution memory.

The paper evaluates harnesses for legislative document conversion, showing structured multi-agent pipelines narrow performance gaps between models.

The paper identifies an activation steering vector that controls whether language models rely on internal memory or retrieved external context.

The paper proposes a synthetic data pipeline that extracts atomic facts from source texts to guide factual pretraining for language models.

The paper describes an automated system using language models to verify corporate loan applications against internal bank lending policies.

The paper introduces a benchmark to evaluate conversational floor control, turn completion, and interruptions in multi-party spoken dialogue systems.

The paper uses in-context neurofeedback sessions to evaluate whether language models can intentionally control their internal activations.

The paper presents a benchmark evaluating whether conversational agents agree on verifiable plans that satisfy both parties' private conditions.

The paper introduces a reasoning-guided translation framework to scale multilingual safety and reasoning benchmarks while preserving original task meanings.

The paper introduces a benchmark and evaluation framework assessing context-sensitive code-switching by language models in creative writing tasks.

The paper proposes a framework where an agent policy navigates a global search tree by alternating between reflection and execution roles.

The paper presents a method to consolidate context gains into parametric model weights using prospective queries and low-rank feed-forward updates.

The paper evaluates failure modes and confidence calibration across frontier and lightweight language models during entity extraction from cyber threat intelligence reports.

The paper analyzes the performance of retrieval-augmented generation architectures on open-domain question answering tasks involving culturally grounded South Asian narratives.

The paper presents a recursive self-improvement framework using scientific agent swarms that mutate hypotheses and recombine code based on experimental feedback.