All papers

Identifies internal attention heads, neurons, and sparse autoencoder features that causally influence narrative representation in language models.

Formalizes language model creativity evaluation as long-tail input-output distribution performance and tests it across multiple benchmarks and models.

Proposes a co-evolution framework that coordinates agent harness prompt modifications with model weight updates using self-generated experience.

Analyzes quantization error propagation in recurrent-depth language models and proposes adaptive mixed-precision schedules for faster prefill inference.

Evaluates speech self-supervised learning models on Finnish broadcast audio using ASR confidence filtering and controlled architectural comparisons.

Presents a multi-dimensional annotated corpus of Bengali political YouTube discussions to support research on contextual cyberbullying detection.

Introduces an end-to-end open pipeline, pretraining corpus, and family of decoder-only models trained from scratch for Romanian.

Constructs a temporal and multilayer social dialogue network dataset based on the film adaptation of Les Misérables.

Introduces a synthetic data generation framework to produce long-horizon task trajectories for terminal agents.

Presents a predictive subtree prefetching method to accelerate power sampling for test-time language model reasoning.

Introduces speculative decoding using self-sparse attention to speed up rollout generation during reinforcement learning.

Proposes a prompt-guided text augmentation framework to assist fake news detection in low-resource settings.

Designs diagnostic probes to determine whether audio-language evaluation models judge music based on audio or prompt content.

Tests whether self-supervised vision transformers represent semantic meanings of rendered words without paired text data.

Proposes a chunk-level credit assignment method for on-policy distillation across language models with different tokenizers.

Evaluates whether automated prompt optimizers inadvertently cause regressions in unscored prompt constraints and requirements.

Studies host-level budget allocation and crawling policies to collect pretraining web data for language models.

Develops a GPU-accelerated tokenizer using CUDA graphs to reduce time-to-first-token in agentic language model serving.

Analyzes the emergence and behavior of attention sinks in text-to-image diffusion and autoregressive Transformers.

Proposes category-conditioned expert routing biases to mitigate data imbalance in mixture-of-experts language models.

Analyzes rubric construction mechanisms and introduces a tree-search method to refine rubrics for evaluating language model outputs.

Presents a modular prompt optimization framework and benchmark targeting stance detection in text.

Surveys twenty-two years of Tibetan natural language processing literature across corpora, speech processing, and language models.

Investigates continual learning regularization techniques for speech recognition during domain adaptation on financial earnings calls.

Evaluates DistilHuBERT-based accent classification under speaker-disjoint and cross-corpus conditions to measure generalization to unseen speakers.

Implements open-vocabulary speech recognition on a low-cost ESP32-S3 microcontroller without dedicated neural accelerators.

Proposes a workflow to enrich design-time cyber-physical system models using empirical evidence collected from simulations and operational runs.

Analyzes representational similarity metrics and demonstrates that input category selection has a larger impact on comparisons than the choice of metric.

Introduces POrchestra, an agentic orchestration framework enabling sub-agents to proactively communicate with the orchestrator during dynamic execution.

The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations.

The paper introduces a benchmark to measure demographic robustness, stereotyping, and intentional personalization in speech language models.

The paper analyzes the runtime performance differences of test-passing issue resolution patches generated by language models on SWE-bench.

The paper demonstrates that machine-generated text detectors rely heavily on document formatting and PDF extraction artifacts rather than text quality.

The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference.

The paper presents a multilingual benchmark of minimal pairs to evaluate language model sensitivity to negative polarity items across 33 languages.

The paper trains conversational user simulators equipped with latent evaluation thoughts and scalar ratings to provide intrinsic feedback.

The paper introduces a benchmark evaluating agent susceptibility to prompt and script injections across multi-tool shared workspace environments.

The paper presents an importance-weighted downsampling protocol to lower evaluation costs on transaction graph datasets without distorting precision metrics.

The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions.

The paper evaluates linguistic obfuscation patterns and reality-monitoring mechanisms across fraudulent and genuine scientific publications.

The paper proposes multi-task pre-training guided by knowledge graphs to improve zero-shot legal reasoning in language models.

The paper presents a benchmark evaluating how effectively vision-language models recover fully occluded objects using remaining visual cues.

The paper studies language model sensitivity to discourse particles such as 'just' and 'only' in natural conversations.

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 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 evaluates vision-language models' spatial representations using causally controlled perturbations, showing spatial relations degrade compared to lexical features.

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

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.

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

Introduces a benchmark evaluating multimodal language models on extracting data, tracking narrative flows, and answering questions from animated data videos.

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.