All papers

Introduces a benchmark evaluating language models on eliciting geospatial data requirements and constraints from natural language requests.

Trains an unsupervised vision-language reward model using intermediate layer representations and caption perturbations without human preference labels.

Proposes an inference-time unlearning method that detects leaks and injects corrective embeddings into large reasoning model traces.

Develops a framework for evolving deterministic formal policies to serve as runtime guardrails for large language model agents.

Introduces a counterfactual-guided few-shot distillation framework to train smaller question-answering language models using limited training samples.

Proposes matrix-entropy and kernel alignment criteria to select optimal layer subsets of hidden states for speculative decoding.

Introduces a multi-turn conversational benchmark to evaluate sales agents against resistant, psychologically grounded buyer personas.

Introduces an information-theoretic framework to reweight supervised fine-tuning data based on conditional information gain.

Compares hyperbolic and Euclidean embeddings for unsupervised patent representation to improve cross-category prior-art discovery.

Presents a simulation benchmark evaluating how embodied agents continually acquire basic skills and recombine them for complex tasks.

Proposes spatial-semantic token compression and layer routing to accelerate 3D vision-language model inference across multi-view inputs.

Presents a conversational benchmark testing whether models can edit text under counterfactual changes while preserving unaffected factual details.

Introduces a framework allowing agents to rewind long-horizon environments to earlier states while retaining learned exploratory knowledge.

Designs a modular multi-agent pipeline separating search, verification, and reading for multimodal document question answering.

Proposes JANUS, using a co-evolving world model to predict, verify, and guide exploration paths in autonomous scientific research.

Studies uncertainty estimation for abstract meaning representation parsers using top-k beam candidates and prediction rejection ratios.

Proposes BoundaryEvo, dynamically adapting training data around a student model's evolving capability boundary during long-chain reasoning distillation.

Evaluates a label-free rubric clarification method, finding it increases consensus among LLM judges without improving agreement with human evaluations.

Proposes PRISM, a multimodal clinical prognostic model combining patient records with retrieved population-level embedding memory banks.

Introduces UPTURN, a framework that trains a small classifier to inject redirection prompts as new user turns during reasoning failures.

Benchmarks combinations of language models, communication protocols, and agent frameworks on web interaction tasks.

Combines load-adaptive hierarchical key-value caching with speculative decoding to accelerate long-context language model serving.

Examines error localization and credit assignment failures in critique-conditioned on-policy self-distillation for mathematical reasoning.

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

Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale.

Investigates how changing tasks redistributes attention head influence and alters internal activations in open-weight large language models.