All papers

The paper introduces a transformer architecture incorporating semantic role labeling into attention mechanisms to detect sentiment, emotion, politeness, and formality.

The paper analyzes phoneme-level error patterns from audio and text data augmentation techniques in low-resource automatic speech recognition.

The paper proposes a training-free query expansion method that filters hallucinated terms using corpus-conditioned cross-context reproducibility.

The paper introduces a benchmark framework evaluating LLM-generated quantum software unit tests against independent ground-truth checkers.

The paper studies policy learning and answer-selection mechanisms to help language models decide when to abstain on factual questions.

The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment.

The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing.

The paper introduces a domain-general framework using predictive world models to guide action selection and policy improvement in recursive self-improvement settings.

Presents a hypernetwork that maps document contexts directly into low-rank adapter weights for large language models.

Describes an onboard satellite pipeline combining segmentation and self-supervised feature encoding for real-time marine anomaly detection.

Pairs attack success rate with operative understanding rate to assess whether models recognize harmful intent during safety evaluations.

Proposes an interpretability framework using sparse autoencoders to explain mixture of experts routing based on superposed fine-grained features.

Proposes an external memory framework allowing frozen language model agents to learn and iteratively refine natural-language world models compiled into executable code.

Presents a rollout selection strategy that drops high-probability positive-advantage trajectories to prevent entropy collapse during language model reinforcement learning.

Surveys and taxonomizes recurring architectural patterns across language-model-integrated software applications, ranging from chatbots to autonomous agent systems.

Proposes a framework modeling research ideation as modular thinking flows sampled dynamically from a probabilistic supernet.

Proposes a policy mechanism enabling language models to decide when, how, and which test-time training procedures to execute.

Proposes a benchmark and probing methodology based on attribution theory to examine how language models assign responsibility and blame.

Presents a simulated participant environment to evaluate multi-turn AI interviewers across market research and social science scenarios.

Proposes a training method to adaptively skip turn-by-turn agent reasoning when previous thoughts remain sufficient for future actions.

Audits the reliability of LLM judges across multiple prompt formats, temperatures, and orderings, introducing a trustworthy verdict metric.

Synthesizes novel views by reusing nearby rendered RGB-D images and applying pose-guided warping with residual refinement.

Deploys a quantized YOLOX-S detector on embedded hardware for onboard maritime satellite imagery selection and data reduction.

Evaluates whether LLM compliance systems adjust verdicts when governing rules are perturbed, finding many verdicts are insensitive to rule changes.

Presents RiCo, a neural physics simulation method that models rigid-body interactions via sparse local contact surface reasoning.

Proposes ContiLNN, which uses closed-form continuous-time modules to improve cross-slice continuity and restoration in 2D medical imaging.

Introduces AAArena, a benchmark evaluating how AI agents revise executable code policies across 12 adversarial games under fixed budgets.

Analyzes long-tail concept learning during supervised fine-tuning using a theoretical prior barrier metric to guide data needs.

Proposes an evaluation framework to measure how LLM agents handle uncertainty and resolve conflicting knowledge during execution.

Audits legal chain-of-thought faithfulness by substituting cited authorities and observing whether model verdicts causally change.

Proposes a streaming optimal transport method to monitor LLM agent trajectories in real time against reference runs.

Introduces a training-free 3D generation pipeline enabling local geometric and appearance control using 3D primitives.

Presents a bidirectional JEPA world action model with diffusion-steering model predictive control for robot state tracking and planning.

Evaluates whether internal model representations can predict how fine-tuning on one value generalizes across other held-out values.

Factorizes scene-aware human-object interaction generation into motion-affordance prediction and affordance-conditioned motion synthesis.

Introduces a benchmark and environment for evaluating coding agents on text-conditioned LEGO design with physical and discrete assembly constraints.

Introduces a generative model that allows flexible post-hoc text editing through learned insertion, substitution, and deletion operations.

Presents a benchmark evaluating agentic large language models on long-horizon strategic reasoning in the game Slay the Spire.

Investigates self-preservation in language models by using task banks to steer synthetic replay during continual instruction tuning.

Demonstrates a backdoor attack where malicious reasoning degradation is triggered incrementally using invisible prompt tokens and stage tracking.

Proposes a recurrent harness framework that uses streaming feedback to revise textual memory for continual agent learning.

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

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

Introduces an evolutionary optimization method using multi-turn reflective mutators to improve production data agents and pipelines.

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.

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.

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.

Introduces ProgramGym, a framework providing verifiable sandbox environments and black-box test feedback for training long-horizon program reproduction agents.

Proposes a model-agnostic method to integrate biological domain knowledge into machine learning models trained on weakly annotated bioimaging.

Presents a multi-agent LLM framework that automatically tunes specific internal algorithm components while preserving overall neural network structures.